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Journal of
Registry Management
Summer 2013 • Volume 40 • Number 2
Published by the National Cancer Registrars Association • Founded in 1975 as The Abstract
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Summer 2013 • Volume 40 • Number 2
Contents
Original Articles
Descriptive Epidemiology of Malignant Primary Osteosarcoma Using
Population-based Registries, United States, 1999-2008....................................................59
Linh M. Duong, MPH; Lisa C. Richardson, MD, MPH
Validation of a Large Basal Cell Carcinoma Registry......................................................65
Maryam M. Asgari, MD, MPH; Melody J. Eide, MD, MPH; E. Margaret Warton, MPH;
Suzanne W. Fletcher, MD, MSc
Establishing Effective Registration Systems in Resource-Limited Settings:
Cancer Registration in Kumasi, Ghana..............................................................................70
Kieran S. O’Brien, MPH; Amr S. Soliman, MD, PhD; Baffour Awuah, MD;
Evelyn Jiggae, MD; Ernest Osei-Bonsu, MD; Solomon Quayson, MD; Ernest Adjei, MD;
Silpa S. Thaivalappil, MPH; Frank Abantanga, MD; Sofia D. Merajver, MD, PhD
Prostate Cancer Treatment Modalities and Survival Outcomes:
A Comparative Analysis of Falmouth Hospital Versus Massachusetts
and Nationwide Hospitals...................................................................................................78
Sophia Elana Shimer
Treatment Patterns for Cervical Carcinoma In Situ in Michigan, 1998-2003................ 84
Divya A. Patel, PhD; Mona Saraiya, MD; Glenn Copeland, MBA; Michele L. Cote, PhD;
S. Deblina Datta, MD; George F. Sawaya, MD
Feasibility of Matching Vaccine Adverse Event Reporting System
and Poison Center Records..................................................................................................93
Mathias B Forrester, BS
Birth Defect Registries: the Vagaries of Management—The British Columbia
and Alberta Case Histories................................................................................................... 98
R. Brian Lowry; Tanya Bedard
Features and Other Journal Departments:
Raising the Bar: Was Chicken Little a Cancer Registrar?..............................................104
Michele Webb, CTR
On Using Health Informatics to Advance Your Career.................................................106
Herman R. Menck, BS, MBA, CPhil, FACE; Michele Webb, CTR; Kim Watson, CTR;
Sue Koering, MEd, RHIT, CTR; Annette Hurlbut, RHIT, CTR; Suzan Naam, MD, CTR;
Judy Williams, RHIT, CTR
Summer 2013 Continuing Education Quiz......................................................................108
Deborah C. Roberson, MSM, CTR; Denise Harrison, BS, CTR
Call for Papers..................................................................................................................... 111
Information for Authors..................................................................................................... 112
© 2013
National Cancer
Registrars Association
Journal of Registry Management 2013 Volume 40 Number 2
57
Editors
Vicki Nelson, MPH, RHIT, CTR, Editor-in-Chief
Russell S. Kirby, PhD, MS, FACE, Guest Editor
Wendy N. Nembhard, PhD, Guest Editor
Vonetta Williams, MPH, CTR, Associate Editor
Reda J. Wilson, MPH, RHIT, CTR, Editor Emeritus
Ginger Yarger, Copy Editor
Janice Ford, Production Editor
Contributing Editors
Faith G. Davis, PhD—Epidemiology
April Fritz, BA, RHIT, CTR—Cancer Registry
Denise Harrison, BS, CTR—CE Credits
Deborah C. Roberson, MSM, CTR—CE Credits
Michele A. Webb, CTR
NCRA 2012-2013 Board of Directors
President: Shirley Jordan Seay, PhD, OCN, CTR
President Elect/Secretary: Therese M. Richardson, RHIA, CTR
Immediate Past President: Sarah Burton, CTR
Treasurer Senior: Dianna L. Wilson CTR, RHIA
Treasurer Junior: Janet Reynolds, CTR
Educational Director: Paulette Zinkann, CTR
Professional Development Director: Deidre M. Watson, CTR
Public Relations Director & Liaison to JRM: Theresa M. Vallerand,
CTR, BGS
Production and Printing
The Goetz Printing Company
Indexing
The Journal of Registry Management is indexed in the National
Library of Medicine’s MEDLINE database. Citations from
the articles indexed, the indexing terms (key words), and the
English abstract printed in JRM are included and searchable
using PubMed.
For your convenience, the Journal of Registry Management
is indexed in the 4th issue of each year and on the Web
(under “Resources” at http://www.ncra-usa.org/jrm).
The 4th issue indexes all articles for that particular year.
The Web index is a cumulative index of all JRM articles
ever published.
Recruitment & Retention Director: Linda Corrigan, MHE, RHIT, CTR
ATP Director—East: Pamela Moats, RHIT, CTR
ATP Director—Midwest: Mindy Young, CTR
ATP Director—West: Kendra Hayes, RHIA, CTR
2012-2013 JRM Editorial Advisory Board
Tim Aldrich, PhD
Tammy Berryhill, MA, RHIA
Betty Gentry, BS, CTR
Robert German, PhD
Donna Gress, BS, CTR
Alison Hennig, BS, CTR
Amy Kahn, MS, CTR
Leah Kiesow, MBA, CTR
Kathleen Thoburn, CTR
Kevin Ward, PhD, MPH, CTR
Pamela Warren, PhD, CTR
Reda Wilson, MPH, RHIT, CTR (Editor Emeritus)
Vonetta Williams, MPH, CTR (Associate Editor)
58
Journal of Registry Management 2013 Volume 40 Number 2
Original Article
Descriptive Epidemiology of Malignant
Primary Osteosarcoma Using Populationbased Registries, United States, 1999-2008
Linh M. Duong, MPHa; Lisa C. Richardson, MD, MPHb
Abstract: Background: Osteosarcoma is a rare bone tumor that is the most frequently diagnosed among children and adolescents, although this cancer affects people of all ages. This study aims to augment the current literature by examining the
incidence of osteosarcoma by its subsites on a national level. Methods: Data from central cancer registries in the National
Program of Cancer Registries (NPCR) and Surveillance, Epidemiology, and End Results (SEER) programs for diagnosis
years 1999-2008 and covering 90.1% of the US population were analyzed. Analyses included cases of malignant primary
osteosarcomas, which were further segmented by topography, appendicular (C40) and axial (C41), to assess differences
between these sites. Descriptive statistics, including estimated age-adjusted incidence rates standardized to the 2000 US
standard population, were calculated using SEER*Stat 7.0.5 software. Results: Approximately 7,104 cases of malignant
primary osteosarcomas were identified during 1999-2008, of which 5,379 were appendicular and 1,725 were axial. The incidence of malignant primary osteosarcomas differed by age, gender, race, ethnicity, region, grade, and stage. These differences in incidence persisted when malignant primary osteosarcomas were categorized by topography codes. Conclusions:
These analyses provide a better understanding of the incidence of malignant osteosarcoma which cover 90.1% of the US
population from 1999-2008. This study provides a more detailed understanding of age, gender, race, and ethnicity by
primary site for malignant osteosarcoma incidence on a national level in the United States. More importantly, differences
between appendicular and axial sites were observed overall by selected demographic characteristics, in particular regional
variations.
Key words: osteosarcoma, epidemiology, malignant, incidence, primary site
Introduction
Osteosarcoma is a rare bone tumor that is most
frequently diagnosed among children and adolescents,
although this cancer affects people of all ages.1-5 While the
incidence of primary osteosarcoma peaks during adolescence, a second incidence peak occurs among individuals
over age 60, most commonly in the seventh or eighth decades
of life.3,5,6 Among 15-29 year olds, most osteosarcomas
occurred in the metaphyses of long bones and particularly
the distal femur, proximal humerus, and the proximal tibia.1
Among older adults, osteosarcomas are more likely to occur
in the axial skeleton locations and in areas that have been
previously irradiated or have underlying bone abnormalities.7 Males are affected more frequently than females except
during the first decade of life.6
Currently, there is a paucity of information on osteosarcoma in the US population.3,5 In order to understand
the burden of osteosarcoma, this study will analyze the
incidence of osteosarcoma by topography (primary site) and
demographic characteristics, including age groups, race,
ethnicity, and gender.
Materials and Methods
Microscopically confirmed incident cases of invasive
primary osteosarcoma were identified from populationbased cancer registries that participated in the Centers for
Disease Control and Prevention’s (CDC) National Program of
Cancer Registries (NPCR) or the National Cancer Institutes’
(NCI) Surveillance, Epidemiology, and End Results (SEER)
Program. NPCR supports central cancer regis­tries (CCRs)
in 45 states, the District of Columbia, Puerto Rico, and US
Pacific Island jurisdictions, which cover approximately
96% of the US population.8 Together, NPCR and SEER
collect cancer incidence data for the entire US population.8,9
Cancer incidence data for this study included those from the
NPCR-Cancer Surveillance System (NPCR-CSS) November
2010 submission and the November 2010 SEER submission.
The analyses focused on microscopically confirmed incident
malignant primary osteosarcoma cases diagnosed between
1999-2008 from CCRs in 44 states that met case ascertainment and quality criteria for all 10 years.9 These data cover
approximately 90.1% of the US population during these
10 years. Data from 6 states and the District of Columbia
(7 CCRs) were excluded because case ascertainment and
__________
a
Cancer Surveillance Branch, Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion, Centers for
Disease Control and Prevention, Atlanta, Georgia. bDivision of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health
Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Address correspondence to Linh M. Duong, MPH, Centers for Disease Control and Prevention, 4770 Buford Highway NE, Mail Stop F-69, Atlanta, GA 30341.
Telephone: (770) 488-3122. Fax: (770) 488-4759. Email: [email protected].
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control
and Prevention.
Journal of Registry Management 2013 Volume 40 Number 2
59
Table 1. Topography Codes* Used to Define Malignant
Primary Osteosarcomas† by Appendicular and Axial Sites,
United States, 1999-2008
Topography Definitions for Malignant
Primary Osteosarcomas
Appendicular
Topography
Codes
Histology Definitions for Primary
Malignant Osteosarcomas
ICD-O-3 Codes
C40
Osteosarcoma, NOS‡
9180/3
Long bones of upper limb, scapula, and
associated joints
C40.0
Chondroblastic osteosarcoma
9181/3
Fibroblastic osteosarcoma
9182/3
Short bones of upper limb and associated joints
C40.1
Telangiectatic osteosarcoma
9183/3
Long bones of lower limb and associated joints
C40.2
Osteosarcoma in Paget disease
9184/3
Short bones of lower limb and associated joints
C40.3
Small cell osteosarcoma
9185/3
Overlapping lesion of bones, joints, and
articular cartilage of limbs
C40.8
Central osteosarcoma
9186/3
Bone of limb, NOS‡
C40.9
Intraosseous well differentiated osteosarcoma
9187/3
Paraosteal osteosarcoma
9192/3
Periosteal osteosarcoma
9193/3
High grade surface osteosarcoma
9194/3
Intracortical osteosarcoma
9195/3
Malignant osteosarcoma
9200/3
Axial
C41
Bones of skull and face and associated joints
C41.0
Mandible
C41.1
Vertebral column
C41.2
Rib, sternum, clavicle, and associated joints
C41.3
Pelvic bones, sacrum, coccyx, and
associated joints
C41.4
Overlapping lesion of bones, joints, and
articular cartilage
C41.8
Bone, NOS
C41.9
*Topography codes from the American Joint Committee on Cancer
(AJCC) Cancer Staging Handbook, 6th edition.
†The analyses were limited to microscopically confirmed osteosarcomas.
‡NOS, not otherwise specified.
quality criteria standards were not met for the entire time
period.
Tumors were included in the study if they were the only
primary tumor or the first of 2 or more independent primary
tumors for the patient. All malignant primary osteosarcomas
were classified according to the International Classification
of Diseases for Oncology (ICD-O-3).10 Since the International
Classification of Childhood Cancers (ICCC) is similar to
ICD-O-3 for osteosarcomas, it was decided to use the classification schemes from ICD-O-3 only. We included all
malignant primary osteosarcomas (primary site code C40.040.9 and C41.0-41.9 and histology codes 9180/3-9187/3,
9192/3-9195/3, 9200/3) in these analyses. Table 1 shows the
topography codes (primary site) used to define appendicular and axial sites for malignant primary osteosarcomas.11
Osteosarcoma histologic groupings were coded according
to the version of the International Classification of Diseases
for Oncology (ICD-O) used at the time of diagnosis.10,12 The
osteosarcoma cases diagnosed from 1999 to 2000 used the
ICD-O second edition (ICD-O-2), and those from 2001 to
2008 used the ICD-O third edition (ICD-O-3); data from the
1999-2000 diagnosis years were converted to the ICD-O-3
codes. Table 2 shows the histologic codes used to define
malignant primary osteosarcomas in these analyses.
60
Table 2. Histology Categories and ICD-O-3,* Codes Used
to Define Malignant Primary Osteosarcomas,† United
States, 1999-2008
*ICD-O-3 indicates International Classification of Diseases for
Oncology-3rd Edition.
†The analyses were limited to microscopically confirmed osteosarcomas.
‡NOS, not otherwise specified.
Cancer stage was assigned SEER summary stage 1977
(SS77) for cases diagnosed before 2001, SEER summary
stage 2000 (SS2000) for cases diagnosed during 2001-2003,
and derived summary stage using the collaborative stage
algorithm for cases diagnosed in 2004 or later.13 These
data were then aggregated to obtain an overall frequency
count for stage. Stage was categorized as in situ, localized,
regional, distant, or unstaged. Grade was categorized as
I (well differentiated), II (moderately differentiated), III
(poorly differentiated), IV (undifferentiated/anaplastic), or
unknown.
Race was categorized as white, black, American Indian/
Alaska Native (AI/AN), Asian/Pacific Islander (API), or
other/unknown race. To increase the accuracy of the AI/
AN designation, linkages between the CCRs and the Indian
Health Service (IHS) database were completed before data
submission to NPCR and SEER. Ethnicity was categorized
as Hispanic or non-Hispanic based on the North American
Association of Central Cancer Registries’ (NAACCR)
Hispanic/Latino Identification Algorithm (NHIA) which
employs a combination of variables to assign Hispanic/
Latino classification to cancer cases.14 Cases that are not classified as Hispanic by NHIA are classified as non-Hispanic
which leaves no cases with an unknown Hispanic status.15
Nearly all NPCR and SEER registries assign Hispanic
ethnicity through the standardized use of the NHIA.15 Race
and ethnicity are not mutually exclusive.
Frequencies by demographic and tumor characteristics
for all malignant primary osteosarcomas were calculated.
Age-adjusted incidence rates with 95% confidence intervals
Journal of Registry Management 2013 Volume 40 Number 2
(95% CI) for all malignant primary osteosarcomas were
calculated overall, and by gender, race, ethnicity, US Census
regions (Northeast, Midwest, South, and West), grade,
and stage. Rates were expressed per 1,000,000 persons and
age-adjusted using the US 2000 population standard (19
age groups—Census P25-1130). Counts and rates based
on fewer than 16 cases were not presented to ensure rate
stability and confidentiality. All data analyses for this study
were performed using SEER*Stat version 7.0.5.16
Results
Table 3 shows demographic and tumor characteristics
for malignant osteosarcomas by topography (primary site)
for cases diagnosed during the 1999-2008 period. Of the
reported 7,104 malignant osteosarcoma incident cases diagnosed from 1999 to 2008, 5,379 (76%) were appendicular and
1,725 (24%) were axial. The age-adjusted incidence rate for
appendicular malignant osteosarcomas (2.06 per 1,000,000)
was higher than axial (0.65 per 1,000,000).
Rates for appendicular malignant osteosarcoma
peaked for 2 age groups: 10-19 years and 80+ years.
However, for axial malignant osteosarcomas, incidence
rates were much lower and the peaks varied more with
peaks at 10-19 years of age, at 40-49 years of age, and 80+
years. The age-specific incidence rate for appendicular
malignant osteosarcomas was lowest among adults aged
50-59 years (0.78 per 1,000,000) and was highest among
adolescents aged 10-19 years (7.08 per 1,000,000). However,
the age-specific incidence rate for axial malignant osteosarcoma was lowest among children aged less than 10 years
(0.11 per 1,000,000) and highest among adults aged 80+
years (1.17 per 1,000,000).
Moreover, differences in malignant osteosarcomas by
gender were also observed. Among axial malignant osteosarcoma, the frequency was distributed almost equally by
gender, whereas among appendicular malignant osteosarcoma cases, the distribution was slightly higher among
males (57%) compared with females (43%). In addition,
the appendicular malignant osteosarcoma age-adjusted
rates among males (2.32 per 1,000,000) were higher than
those observed among females (1.79 per 1,000,000) but no
difference was seen in axial malignant osteosarcomas ageadjusted incidence rates by gender.
Among appendicular and axial cases, the predominant
race group was whites (approximately 80%) followed by
blacks (16%). Age-adjusted rates for appendicular and
axial malignant osteosarcomas were higher among blacks
than whites, American Indian/Alaska Natives, or Asian/
Pacific Islanders. With respect to ethnicity, non-Hispanics
accounted for approximately 81% of the reported cases
among appendicular malignant osteosarcomas but almost
90% among axial malignant osteosarcomas. Moreover,
Hispanics age-adjusted incidence for appendicular malignant osteosarcomas was higher than non-Hispanics.
Among appendicular and axial cases the geographic
region with the largest proportion of cases (approximately
30%) occurred in the South with about 20%-26% of the
remaining cases distributed in each of the remaining 3
regions (Northeast, Midwest, and West). Appendicular
Journal of Registry Management 2013 Volume 40 Number 2
and axial rates among the 4 geographic regions were not
different.
More than one third of appendicular cases were grade
IV (37%), followed by unknown grade (29%) and grade III
(25%). For the axial site, unknown grade contributed 33% of
cases followed by grade III (27%), and grade IV (25%). The
age-adjusted incidence rates were higher among appendicular cases for grade IV (0.77 per 1,000,000) when compared
with other grades, whereas unknown cases accounted for
the highest incidence rate (0.22 per 1,000,000) for axial cases.
For appendicular osteosarcomas, the highest proportion of cases were localized (41%). Most axial cases were
diagnosed with regional stage (37%). The age-adjusted
rates were higher among appendicular cases for localized
stage (0.84 per 1,000,000), whereas regional stage (0.24
per 1,000,000) was higher for axial cases. Total malignant
osteosarcomas had the highest age-adjusted incidence rates
in localized stage (1.01 per 1,000,000) compared with other
stages at diagnosis. Appendicular osteosarcomas had the
highest age-adjusted incidence rates in the localized stage
(0.84 per 1,000,000) compared with regional stage (0.24 per
1,000,000) among axial osteosarcomas.
Figure 1 presents the incidence of malignant osteosarcomas by primary site and age (5-year intervals). Incidence
rates peaked among 2 age groups: 15-19 year-olds (adolescence) and 80-84 year-olds (elderly).
Discussion
These analyses provide detailed information on the
descriptive epidemiology of malignant osteosarcomas
complementing 2 recent studies published by Mirabello et
al which reported the incidence and survival rates of osteosarcoma from 1973 to 2004 using SEER data in the United
States, as well as the international osteosarcoma incidence
patterns among children and adolescents, and middle-aged
and elderly persons.3,17 The current study covers more
than 90% of the US population. Together, these studies
provide a detailed assessment of the burden of primary
osteosarcomas.
Our results confirms previous findings of a bimodal
frequency distribution for osteosarcoma incidence by age
with the first peak occurring during adolescence and the
second peak among adults over 60 years of age in the
United States.3,6,17 Anfinsen et al reported a bimodal distribution with the highest incidence rate occurring in the
second decade of life, with a decline between 30 and 50
years of age, followed by a second peak among those aged
75-79.6 Mirabello et al study reported an observed bimodal
osteosarcoma incidence pattern.3
Differences in incidence rates by gender were found
overall and by primary site. Among axial osteosarcoma, the
frequency was distributed almost equally by gender, whereas
among total and appendicular malignant osteosarcoma
cases, the distribution was higher among males compared
with females. An international study by Mirabello et al on
osteosarcomas also reported that osteosarcoma was more
common in males than females in most countries.17 Bleyer et
al reported a higher incidence of malignant osteosarcomas
among males than females among the adolescent and
61
Table 3. Description of Primary Osteosarcoma Incidence by Selected Characteristics, United States, 1999-2008
Appendicular (C40)
Count* (%)
Total
5, 379 (100.0%)
Rate† (95% CI)‡
2.06 (2.01, 2.12)
Axial (C41)
Count (%)
Total
Rate (95% CI)
1,725 (100.0%)
0.65 (0.62, 0.69)
Count (%)
7,104 (100.0%)
Rate (95% CI)
2.71 (2.65, 2.78)
Age at diagnosis, years
00-09
526 (9.8%)
1.48 (1.36, 1.61)
40 (2.3%)
0.11 (0.08, 0.15)
566 (8.0%)
1.59 (1.46, 1.73)
10-19
2,655 (49.4%)
7.08 (6.81, 7.35)
316 (18.3%)
0.84 (0.75, 0.94)
2,971 (41.8%)
7.92 (7.64, 8.21)
20-29
780 (14.5%)
2.16 (2.01, 2.31)
261 (15.1%)
0.72 (0.64, 0.82)
1,041 (14.7%)
2.88 (2.71, 3.06)
30-39
403 (7.5%)
1.07 (0.97, 1.18)
198 (11.5%)
0.53 (0.46, 0.61)
601 (8.5%)
1.60 (1.47, 1.73)
40-49
342 (6.4%)
0.86 (0.77, 0.95)
269 (15.6%)
0.68 (0.60, 0.76)
611 (8.6%)
1.54 (1.42, 1.66)
50-59
249 (4.6%)
0.78 (0.69, 0.89)
199 (11.5%)
0.63 (0.54, 0.72)
448 (6.3%)
1.41 (1.28, 1.55)
60-69
187 (3.5%)
0.93 (0.80, 1.07)
173 (10.0%)
0.87 (0.74, 1.01)
360 (5.1%)
1.80 (1.61, 1.99)
70-79
142 (2.6%)
0.97 (0.82, 1.15)
161 (9.3%)
1.10 (0.94, 1.29)
303 (4.3%)
2.08 (1.85, 2.32)
95 (1.8%)
1.03 (0.83, 1.26)
108 (6.3%)
1.17 (0.96, 1.41)
203 (2.9%)
2.20 (1.91, 2.53)
Male
3,057 (56.8%)
2.32 (2.24, 2.41)
885 (51.3%)
0.70 (0.65, 0.74)
3,942 (55.5%)
3.02 (2.93, 3.12)
Female
2,322 (43.2%)
1.79 (1.72, 1.87)
840 (48.7%)
0.62 (0.58, 0.66)
3,162 (44.5%)
2.41 (2.33, 2.50)
White
4,176 (77.6%)
1.99 (1.93, 2.05)
1,387 (80.4%)
0.64 (0.60, 0.67)
5,563 (78.3%)
2.63 (2.56, 2.70)
Black
856 (15.9%)
2.42 (2.26, 2.59)
252 (14.6%)
0.81 (0.71, 0.92)
80+
Gender
Race
§
AI/AN
API
#
1.34 (0.97, 1.83)
#
1,108 (15.6%)
3.23 (3.03, 3.43)
#
54 (0.8%)
1.87 (1.36, 2.51)
220 (4.1%)
1.75 (1.52, 2.00)
53 (3.1%)
0.45 (0.33, 0.59)
273 (3.8%)
2.19 (1.94, 2.48)
Hispanic
1,037 (19.3%)
2.37 (2.22, 2.54)
222 (12.9%)
0.64 (0.55, 0.74)
1,259 (17.7%)
3.01 (2.83, 3.20)
Non-Hispanic
4,342 (80.7%)
2.00 (1.94, 2.06)
1,503 (87.1%)
0.66 (0.62, 0.69)
5,845 (82.3%)
2.66 (2.59, 2.73)
Northeast
1,085 (20.2%)
2.06 (1.94, 2.19)
384 (22.3%)
0.70 (0.63, 0.77)
1,469 (20.7%)
2.76 (2.62, 2.90)
Midwest
1,310 (24.4%)
2.03 (1.92, 2.14)
417 (24.2%)
0.64 (0.58, 0.70)
1,727 (24.3%)
2.67 (2.54, 2.79)
South
1,571 (29.2%)
2.05 (1.95, 2.15)
545 (31.6%)
0.70 (0.65, 0.77)
2,116 (29.8%)
2.75 (2.64, 2.87)
West
1,413 (26.3%)
2.10 (1.99, 2.21)
379 (22.0%)
0.57 (0.52, 0.64)
1,792 (25.2%)
2.68 (2.55, 2.80)
Ethnicity||
Region
young adult population in the United States.1 Anfinsen et al
reported that male predominance was generally apparent at
the older ages and at ages less than 30 years, in contrast to
modest and inconsistent sex differences among the middle
age groups in the United States.6 A possible explanation for
these observed sex differences in osteosarcoma incidence
rates may be attributed to hormonal differences between
genders. The effects of these sex steroids on osteoblasts
have been well-documented both in vivo and in vitro.18-21
However, there is limited research on the possible effects
of sex steroids in human osteosarcomas. Study findings by
Dohi et al, however, suggest that inhibitors of sex steroid
actions could be a potential treatment for osteosarcoma in
the future.22 A study in England by Arora et al reported that
a key factor in osteosarcoma development may be related
to pubertal bone growth due to observed variations in
incidence patterns by age and site.23 However, Arora et al
stated that although pubertal growth may be a biological
plausibility, further research may be required to establish
62
this link.23
Differences were observed in our study by race and
ethnicity separately. Among appendicular and axial cases,
approximately 80% were found among whites followed
by blacks (16%). In contrast, age-adjusted incidence rates
for appendicular and axial malignant osteosarcomas were
higher among blacks compared with other racial groups.
Mirabello et al have reported similar results using SEER
data.3 With respect to ethnicity, non-Hispanics accounted
for approximately 81% of the reported cases among total
and appendicular malignant osteosarcomas but almost 90%
among axial malignant osteosarcomas. Moreover, Hispanics
had a higher age-adjusted incidence rate than non-Hispanics
for appendicular but a similar rate for axial cases. Bleyer et
al reported that Hispanics had the highest incidence of
osteosarcoma among those aged 15-29 in the United States.1
There were no differences observed in the regional
data within subsites we analyzed. The Northeast, Midwest,
South, and West all had similar incidence rates within their
Journal of Registry Management 2013 Volume 40 Number 2
Table 3, cont. Description of Primary Osteosarcoma Incidence by Selected Characteristics, United States, 1999-2008
Appendicular (C40)
Count* (%)
Axial (C41)
Rate† (95% CI)‡
Count (%)
Total
Rate (95% CI)
Count (%)
Rate (95% CI)
Grade
I
220 (4.1%)
0.08 (0.07, 0.09)
95 (5.5%)
0.04 (0.03, 0.04)
315 (4.4%)
0.12 (0.11, 0.13)
II
261 (4.9%)
0.10 (0.09, 0.11)
153 (8.9%)
0.06 (0.05, 0.07)
414 (5.8%)
0.16 (0.14, 0.17)
III
1,350 (25.1%)
0.52 (0.49, 0.54)
462 (26.8%)
0.17 (0.16, 0.19)
1,812 (25.5%)
0.69 (0.66, 0.72)
IV
2,012 (37.4%)
0.77 (0.74, 0.81)
439 (25.4%)
0.17 (0.15, 0.18)
2,451 (34.5%)
0.94 (0.90, 0.98)
1,536 (28.6%)
0.59 (0.56, 0.62)
576 (33.4%)
0.22 (0.20, 0.24)
2,112 (29.7%)
0.81 (0.78, 0.85)
0 (0.0%)
0.00 (0.00, 0.00)
0 (0.0%)
0.00 (0.00, 0.00)
0 (0.0%)
0.00 (0.00, 0.00)
Localized
2,181 (40.5%)
0.84 (0.80, 0.87)
472 (27.4%)
0.18 (0.16, 0.20)
2,653 (37.3%)
1.01 (0.98, 1.05)
Regional
1,837 (34.2%)
0.70 (0.67, 0.74)
630 (36.5%)
0.24 (0.22, 0.26)
2,467 (34.7%)
0.94 (0.91, 0.98)
938 (17.4%)
0.36 (0.34, 0.38)
393 (22.8%)
0.15 (0.13, 0.16)
1,331 (18.7%)
0.51 (0.48, 0.54)
423 (7.9%)
0.16 (0.15, 0.18)
230 (13.3%)
0.09 (0.08, 0.10)
653 (9.2%)
0.25 (0.23, 0.27)
Unknown
Stage at diagnosis
In situ
Distant
Unstaged
¶
*Counts pertain to 90.1% of U.S population covered by eligible cancer registries. Data from 7 central cancer registries are not included.
†Rates are per 1,000,000 persons and age-adjusted to the 2000 US standard population (19 age groups - Census P25-1130) except age-specific rates
which were not age-adjusted.
‡95% CI indicates 95% confidence interval and were calculated using the Tiwari modification.
§Other unspecified/unknown race accounted for 106 (1.5%) cases of primary osteosarcomas.
||Race and ethnicity are not mutually exclusive.
¶SEER summary stage 1977 was used for 1999–2000 cases, SEER summary stage 2000 was used for 2001–2003 cases, and derived summary stage
2000 (also known as collaborative stage) was used for 2004–2008 cases.
#Not presented due to count or rate instability or complementarily suppressed for confidentiality.
AI/AN, American Indian/Alaska Native; API, Asian/Pacific Islander; SEER, National Cancer Institute’s Surveillance, Epidemiology, and End Results
Program.
Percentages may not add to 100% because of rounding. The analyses were limited to microscopically confirmed osteosarcomas.
Figure 1. Incidence of Primary Malignant Osteosarcomas, United States, 1999-2008
Total
Appendicular (C40)
Axial (C41)
9.00
8.00
Rate per million persons
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Age at diagnosis, years
Rates are per million persons and age-adjusted to the 2000 US standard population (19 age groups—Census P25-1130). Rates pertain to 90.1% of the
US population covered by eligible cancer registries. Data from 7 central cancer registries are not included.
Journal of Registry Management 2013 Volume 40 Number 2
63
respective subsites. However, differences were observed
between subsites. The incidence of osteosarcoma in the
appendicular sites tended to be higher (ranged between
2.03-2.10 per 1,000,000) in these regions than axial (ranged
between 0.57-0.70 per 1,000,000). An international study by
Mirabello et al reported minimum variability in osteosarcoma incidence rates between countries in individuals ≤24
years. The country data were from the Cancer Incidence in
Five Continents from the International Agency for Cancer
Research (IARC) database. Overall, they reported that
worldwide osteosarcoma incidence rates varied mostly
among the elderly with little geographic variation among
the younger age group.17
Our findings should be considered in light of several
limitations. In registry data, there is a possibility of misclassification or miscoding especially with rare tumors. In
addition, data were not available for 7 CCR in the 1999-2008
analytic dataset because they did not meet case ascertainment and quality criteria.9 The exclusion of these CCRs may
have influenced the observed osteosarcoma incidence rates.
Although we used data that had been linked to the IHS
database to increase the accuracy of the AI/AN designation, race misclassification of these cases and other cases
may still remain. However, a study by Clegg et al indicated
that this may not be a strong limitation for cancer incidence
any longer.24 Finally, even though the NHIA algorithm was
used to assign ethnicity for Hispanics, ethnicity misclassification may also occur.24 Despite these limitations, our
study is one of the first to report on malignant osteosarcoma
incidence by primary site which covers over 90% of the US
population. In addition, we were among the first studies to
categorize osteosarcoma by appendicular and axial sites.
Differences observed in these analyses between these sites
provide further insight into the incidence of these tumors in
these sites.
These analyses provide a better understanding of
the incidence of malignant osteosarcoma using population-based data covering approximately 90.1% of the US
population during 1999-2008. In addition, these analyses
provide a more detailed understanding of age, gender, race,
and ethnicity by primary site for malignant osteosarcoma
incidence on a national level in the United States. More
importantly, differences between appendicular and axial
sites were observed overall by selected demographic characteristics, in particular regional variations. These analyses
by subsites are a strength of this study.
Acknowledgements
The authors would like to acknowledge the contribution of hospital and central cancer registry staff who
collected and processed the data used in this study.
These data were provided by the statewide central
cancer registries participating in either the National Program
of Cancer Registries (NPCR) and were submitted to CDC in
the November 2010 data submission or to the Surveillance,
Epidemiology, and End Results (SEER) Program in the
November 2010 submission. The dataset includes data for
diagnosis years 1999-2008.
64
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Adolescents and Young Adults 15 to 29 Years of Age, Including SEER
Incidence and Survival: 1975-2000. Bethesda, MD: National Cancer
Institute; 2006. NIH publication 06-5767.
2. Harting MT, Lally KP, Andrassy RJ, et al. Age as a prognostic factor for
patients with osteosarcoma: an analysis of 438 patients. J Cancer Res Clin
Oncol. 2010;136(4):561-570.
3. Mirabello L, Troisi RJ, Savage SA. Osteosarcoma incidence and survival
rates from 1973 to 2004: data from the Surveillance, Epidemiology, and End
Results Program. Cancer. 2009;115(7):1531-1543.
4. Ottaviani G, Jaffe N. The epidemiology of osteosarcoma. Cancer Treat Res.
2009;152:3-13.
5. Savage SA, Mirabello L. Using epidemiology and genomics to understand
osteosarcoma etiology. Sarcoma. 2011.
6. Anfinsen KP, Devesa SS, Bray F, et al. Age-period-cohort analysis of primary
bone cancer incidence rates in the United States (1976-2005). Cancer
Epidemiol Biomarkers Prevent. 2011;20(8):1770-1777.
7. Hayden JB, Hoang BH. Osteosarcoma: basic science and clinical implications. Orthop Clin North Am. 2006;37(1):1-7.
8. Centers for Disease Control and Prevention. National Program of Cancer
Registries: About the Program. Available at: http://www.cdc.gov/cancer/
npcr/about.htm. Accessed March 8, 2012.
9. Centers for Disease Control and Prevention. National Program of Cancer
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Publication Criteria. Available at: http://www.cdc.gov/cancer/npcr/
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10.World Health Organization. International Classification of Diseases for
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Manual - 2000. Bethesda, MD: National Cancer Institute; 2000.
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Guideline for Enhancing Hispanic-Latino Identification: Revised NAACCR
Hispanic/Latino Identification Algorithm [NHIA v2.2]. Available at: http://
www.naaccr.org/LinkClick.aspx?fileticket=i6MN9F8c1eU%3D&tabid=92.
Accessed July 10, 2012.
15.Centers for Disease Control and Prevention. Interpreting the Data: Race and
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uscs/2007/technical_notes/interpreting/race.htm. Accessed July 10, 2012.
16.SEER*Stat [computer program]. Version 7.0.5. Bethesda, Maryland:
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gov/seerstat.
17.Mirabello L, Troisi RJ, Savage SA. International osteosarcoma incidence
patterns in children and adolescents, middle ages and elderly persons. Int J
Cancer. 2009;125(1):229-234.
18.Boden SD, Joyce ME, Oliver BV, Heydemann A, Bolander ME. Estrogen
receptor mRNA expression in callus during fracture healing in the rat. Calcif
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19.Colvard DS, Eriksen EF, Keeting PE, et al. Identification of androgen
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Journal of Registry Management 2013 Volume 40 Number 2
Original Article
Validation of a Large Basal Cell Carcinoma Registry
Maryam M. Asgari, MD, MPHa,b; Melody J. Eide, MD, MPHc; E. Margaret Warton, MPHa;
Suzanne W. Fletcher, MD, MScd
Abstract: Background: The epidemiological study of basal cell carcinomas (BCCs) is difficult because BCCs lack distinct
disease codes and are excluded from most cancer registries. Objective: To develop and validate a large BCC registry based
on electronically assigned Systematized Nomenclature of Medicine (SNOMED) codes and text-string searches of electronic
pathology reports from Kaiser Permanente Northern California. Methods and Materials: Potential BCCs were identified
from electronic pathology reports (n=39,026) in 2005 and were reviewed by a dermatologist who assigned case/non-case
status (gold-standard). A subset of the records (n=9,428) was independently reviewed by a second dermatologist to ascertain reliability of case assignment. In addition, a subset of excluded electronic pathology reports from 2005 (n=2,700) was
reviewed to determine whether inclusion criteria had missed potential BCCs. We calculated the positive predictive value
(PPV) of 3 different algorithms for identifying BCCs from electronic pathology data. Results: BCC-specific SNOMED codes
had the highest PPV for identifying BCCs, 0.992 (95% CI: 0.991-0.993). Inter-rater reliability for case assignment was high
(kappa=0.92, 95% CI: 0.91-0.93). Standardized incidence rates were consistent with previously published rates in the United
States. Conclusions: We created and validated a large BCC registry to serve as a unique resource for studying BCCs.
Key words: registry, skin cancer, basal cell carcinoma, SNOMED, validation
Introduction
Non-melanoma skin cancers (NMSCs), including basal
cell carcinoma (BCC) and squamous cell carcinoma (SCC),
are the most common malignancies in the United States,
afflicting more than 2 million Americans annually.1 Despite
the large population affected, NMSC has proven difficult to
study, due in part to its exclusion from large national cancer
registries such as the Surveillance, Epidemiology and End
Results (SEER) program. Estimates of NMSC incidence and
prevalence are often based on periodic surveys and are
generally outdated, with the last National Cancer Institutefunded survey performed more than 3 decades ago.2 Studies
that rely on disease codes have historically combined
BCCs and SCCs, as they share the same International
Classification of Diseases (ICD) identifiers. Such data limitations hinder the study of BCC prevalence, incidence, and
disease burden. A BCC registry would be an important
resource for studying BCC epidemiology and could help
identify novel risk factors associated with its increased incidence, more accurately capture its disease burden, and help
target screenings and preventative efforts.
We developed and validated a BCC registry at Kaiser
Permanente Northern California (KPNC) based on electronic
pathology reports. All non-Pap smear pathology reports
with relevant Systematized Nomenclature of Medicine
(SNOMED) codes or text-strings for skin tissue specimens
collected during 2005 (n=39,026) were reviewed by a boardcertified dermatologist. Each report was assigned case/
non-case status (“gold standard”), and a subset of records
was independently reviewed by a second board-certified
dermatologist to ascertain intra-rater reliability. In addition,
a random sample of approximately 1% of all non-Pap smear
pathology records obtained in 2005 that did not meet inclusion criteria for possible BCC (n=2,700) were reviewed to
capture BCC specimens potentially missed by the SNOMED
code/text-string inclusion criteria. We calculated the positive and negative predictive value of the SNOMED codes
in identifying BCCs, the incidence of BCC in our total
population by age and gender, and compared standardized
incidence rates to other population-based BCC incidence
estimates.
Materials and Methods
Study Setting
KPNC is a large, integrated health-care delivery system
that provides comprehensive health care and pharmaceutical
benefits to a large and diverse community-based population
of over 3.2 million members residing in Northern California.
KPNC’s membership represents approximately 33% of the
insured population and 28% of the total population in its
service area. Compared with the underlying insured and
uninsured catchment population, the KPNC membership
has similar racial diversity (although with fewer Latinos)
and slightly lower income and education than the underlying population.3
__________
Division of Research, Kaiser Permanente Northern California, Oakland, California. bDepartment of Dermatology, University of California at San Francisco, San
Francisco, California. cDepartments of Dermatology and Public Health Sciences, Henry Ford Hospital, Detroit, Michigan. dDepartment of Population Medicine,
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
a
Address correspondence to Maryam M. Asgari, MD MPH, Kaiser Permanente Northern California, Division of Research, 2000 Broadway, Oakland, CA 94612.
Telephone: (510) 891-3895. Fax: (510) 891-3606. Email: [email protected].
This work was supported in part by the National Cancer Institute (Cancer Research Network Across Health Care Systems, U19 CA 79689).
Journal of Registry Management 2013 Volume 40 Number 2
65
Table 1. SNOMED Codes Used to Identify BCCs
SNOMED ID
SNOMED Name
Table 2. Validity of Algorithms in Identifying Pathologyproven BCCs*
Pathology Review
M809xx (Basal
cell neoplasms)
SNOMED codes
M80901
Basal cell tumor
Algorithm 1
M80902
In situ
Cancer
M80903
Basal cell carcinoma, NOS
No Cancer
M809031
Basal cell carcinoma, NOS, well
differentiated
Algorithm 2
M809032
Basal cell carcinoma, NOS, moderately
differentiated
No Cancer
No Cancer
(95% CI)
†
24,284
203
0.992
782
13,757
(0.991, 0.993)
24,387
347
0.986
679
13,613
(0.984, 0.987)
24,248
324
0.987
818
13,636
(0.985, 0.988)
‡
Cancer
Algorithm 3
Cancer
PPV
§
M809033
Basal cell carcinoma, NOS, poorly
differentiated
M809034
Basal cell carcinoma, NOS, undifferentiated
M80906
Metastatic
M809063
Basal cell carcinoma, NOS, poorly
differentiated, metastatic
M80913
Multicentric basal cell carcinoma
M80922
Basal cell carcinoma, morphea type, in situ
M80923
Basal cell carcinoma, morphea type
M80933
Basal cell carcinoma, fibroepithelial type
M80942
Basosquamous carcinoma, in situ
M80943
Basosquamous carcinoma
M809431
Basosquamous carcinoma, well
differentiated
M809432
Basosquamous carcinoma, moderately
differentiated
M809433
Basosquamous carcinoma, poorly
differentiated
M80946
Basosquamous carcinoma, metastatic
M80953
Metatypical carcinoma
M80960
Intraepidermal epithelioma of Jadassohn
M80003:
neoplasm,
malignant
Neoplasm, malignant
M800031
Neoplasm, malignant, well differentiated
M800032
Neoplasm, malignant, moderately
differentiated
M800033
Neoplasm, malignant, poorly differentiated
M800034
Neoplasm, malignant, undifferentiated
M80103:
carcinoma, NOS;
epithelial tumor,
malignant
Carcinoma, NOS
M801031
Carcinoma, NOS, well differentiated
M801032
Carcinoma, NOS, moderately differentiated
We first identified all electronic pathology reports
of pathology specimens excluding Pap smears collected
between January 1, 2005 and December 31, 2005 (n=351,454).
We then identified reports containing potential BCC diagnoses as follows: reports assigned a SNOMED code of 809xx ,
80003, or 80103 (Table 1) or with pathology report text
containing the following strings in the diagnosis section:
“basal cell carcinoma,” “BCC,” “basosquam,” “basisquam,”
“basal-squam,” “squamous-basal,” or “basal squam”
(n=39,026). To address the possibility that BCC cases existed
among pathology reports not identified by these criteria,
we reviewed a random subset of 2,700 non-Pap smear
pathology reports not included in the possible BCC reports
as identified above.
This study was approved by the Kaiser Foundation
Research Institute Institutional Review Board. The
Declaration of Helsinki protocols were followed and a
waiver of informed consent was obtained due to the nature
of the study.
M801033
Carcinoma, NOS, poorly differentiated
Case Assignment
M801034
Carcinoma, NOS, undifferentiated
The study dermatologist (MA) reviewed each electronic potential BCC pathology report and assigned a case
66
Cancer
No Cancer
*Numbers reported represent unique pathology reports, not individuals.
Some individuals have multiple pathology reports in 2005.
†BCC based on SNOMED code M809xx.
‡BCC based on SNOMED codes M809xx, M80003 or M80103.
§BCC based on SNOMED codes M809xx, M80003 OR M80103
and pathology record text-strings “basal cell carcinoma,” “BCC,”
basosquam,” “basisquam,” “basal-squam,” “squamous-basal,” and “basal
squam.”
In 1997, KPNC implemented a computerized electronic
records system for all pathology specimens received for
examination. Information in the electronic pathology record
includes text fields recording specimen type, anatomic
location, gross and microscopic diagnoses, as well as
assigned SNOMED codes. SNOMED codes are automatically assigned based on a standardized classification of
pathology diagnoses by organ or location (topography
code) and morphological alterations (morphology code).4
Study Population
Journal of Registry Management 2013 Volume 40 Number 2
Table 3. Inter-rater Reliability for Pathology Review
Reviewer 1
Reviewer 2
Cancer
Cancer
No Cancer
5,725
66
285
3,172
No Cancer
Inter-rater Reliability
Kappa Statistic (95% CI)
0.92 (0.91, 0.93)
status (binary variable, yes/no) based on whether the
pathology report definitively identified at least basal cell
carcinoma among the tissue specimens collected (“gold
standard”). Pathology reports with no definitive diagnosis,
with benign lesions included in the pathologist’s differential, or with excision specimens indicating no residual
tumor were not assigned cases status. A subset of potential
BCC pathology reports selected by consecutive member
medical record number was adjudicated by a second dermatologist reviewer to assess inter-rater reliability.
Development of Algorithms
We devised 3 algorithms to identify BCCs. The first and
simplest algorithm was based solely on the BCC-specific
SNOMED codes M809xx. The second, less stringent algorithm, increased the range of SNOMED codes to include
SNOMED codes M80003 or M80103 in addition to M809xx.
The third algorithm searched for relevant text-strings in
addition to the expanded SNOMED code list. We tested
these 3 algorithms to assess how different identification
algorithms impacted positive predictive value (PPV) (Table
2).
Statistical Analysis
We calculated positive predictive values comparing
electronic identification of BCC based on SNOMED codes
and text-strings to pathology record review using the 3
different identification algorithms. We calculated the negative predictive value for the 2,700 reviewed pathology
reports with no SNOMED codes or text strings suggestive
of BCC. The Wilson score interval method was used to
calculate 95% confidence intervals for negative predictive
value (NPV) and PPV. We calculated the simple kappa
coefficient as a measure of inter-rater agreement. We calculated overall incidence rates (counts of BCC events among
KPNC members in 2005) and stratified rates by age and sex.
Finally, we applied our 5-year age and sex stratified rates
to US 2000 Census population estimates and calculated a
directly standardized rate to allow comparison to other
study populations.
Results
A total of 25,066 BCCs were diagnosed among 17,880
KPNC members in 2005, 58.2% of whom were male
(n=10,401). The mean age of the cohort as of January 1,
2005 was 66.4 years (+13.7 years). We found that relevant
SNOMED codes (Table 1) and text-strings had high PPV in
identifying BCCs irrespective of the algorithm used (Table
2). The best identification was achieved with the algorithm
Journal of Registry Management 2013 Volume 40 Number 2
using only SNOMED codes specific to BCCs (M809xx codes)
with no additional text-string requirements resulting in a
PPV of 99.2%. Algorithms using additional information did
not improve PPV. As shown in Table 3, inter-rater reliability
in case assignment was high between the 2 dermatologists
(0.92, 95% CI 0.91-0.93). We observed no BCCs in 2,700
randomly selected non-Pap smear records (of 319,594 potential records) that had been excluded from the initial pool
of potential BCC pathology reports chosen for review. The
resulting NPV of 100% (95% CI: 99.86%, 100.00%) suggests
that our algorithm for selecting pathology records missed
very few BCC pathology specimens.
To further validate our methods of capturing BCCs, we
calculated the incidence of BCCs by age and sex strata in a
total KPNC membership base of 3,094,819 (measured midyear at June 30, 2005), summarized in Table 4. The overall
incidence of BCCs was 5.8 per 1,000 KPNC members. For
females, the overall rate was 4.7/1,000, while for males it
was 6.9/1,000 members. Rates ranged from 0.003/1,000
in those under age 10 to 40.1/1,000 in those 85 and older
(0.003-28.2/1,000 for females; 0.003-61.9/1,000 for males).
Incidence rates of BCCs standardized to the US 2000 population census stratified by age categories and gender are also
shown in Table 4. BCCs were more common in males, and
incidence increased with age across both genders.
Discussion
The study of NMSC is difficult because, unlike most
malignancies, NMSCs are not systematically reported to
national cancer registries, such as SEER. While administrative claims can be used to identify NMSC,5 they cannot be
used to distinguish between BCC and SCC as ICD codes for
NMSC have historically been non-specific. One method of
distinguishing between BCCs and SCCs is use of natural
language processing of the electronic medical record,
which has shown considerable variation in ascertainment
compared to claims-based data.6 Although populationbased BCC registries are available in the Netherlands,7
Denmark,8 and Spain,9 there are no large, validated BCC
registries currently available in the United States.
We developed and validated a large registry of 17,888
individuals with at least one biopsy-proven BCC in 2005
in a health-maintenance organization setting. Our data
show that BCC-specific SNOMED codes are highly accurate
in identifying biopsy-proven BCCs. Inter-rater reliability
of case assignment based on pathology records among 2
board-certified dermatologists was high. To examine the
possibility that SNOMED codes missed some cases of
BCC, we reviewed a random sample of pathology reports
excluded by the initial identification algorithm and found
no additional cases.
Our 2005 standardized BCC incidence rate is consistent with previously reported BCC incidence rates in the
United States,10-12 suggesting accurate and comprehensive
disease capture. Based on our standardized rates of BCC
incidence, we would expect nearly 1.5 million incident
BCCs annually in the United States. The most recently
published estimate of annual NMSC in the United States,
based on claims-based data, approximated the total number
67
Table 4. Incidence of BCCs in 2005 in the KPNC Population and Standardized to the US 2000 Census Population by
Age and Sex*
KPNC
Age
Females
# BCCs
Female
Rate per
1,000
US 2000 Census Population
Males
#
BCCs
Male
Rate per
1,000
Total
Rate per
1,000
Expected
(Female)
Expected
(Male)
Expected
(Total)
Total
Rate by
Age
0-14
288,741
1
0.003
301,142
1
0.003
0.003
92
93
185
0.00
15-19
107,536
6
0.056
111,256
4
0.036
0.046
548
374
922
0.05
20-24
92,415
7
0.076
85,021
12
0.141
0.107
703
1,367
2,070
0.11
25-29
96,909
33
0.341
86,196
17
0.197
0.273
3,263
1,933
5,196
0.27
30-34
105,956
82
0.774
101,483
49
0.483
0.632
7,885
4,984
12,869
0.63
35-39
113,115
159
1.406
110,365
144
1.305
1.356
16,007
14,768
30,776
1.36
40-44
124,592
353
2.833
120,586
271
2.247
2.545
32,052
25,011
57,063
2.54
45-49
128,224
457
3.564
120,537
499
4.140
3.843
36,364
40,941
77,305
3.85
50-54
122,757
598
4.871
111,737
746
6.676
5.731
43,735
57,469
101,203
5.75
55-59
113,807
840
7.381
101,954
1,149
11.270
9.219
51,375
73,352
124,727
9.26
60-64
83,947
784
9.339
74,850
1,288
17.208
13.048
52,942
88,390
141,332
13.08
65-69
63,412
795
12.537
55,404
1,278
23.067
17.447
64,355
101,503
165,858
17.40
70-74
52,951
856
16.166
45,482
1,401
30.803
22.929
80,094
120,223
200,317
22.62
75-79
44,200
912
20.633
34,230
1,510
44.113
30.881
90,196
134,301
224,497
30.27
80-84
33,353
877
26.294
23,230
1,169
50.323
36.159
81,788
92,337
174,125
35.21
85+
25,480
718
28.179
13,951
863
61.859
40.095
84,892
75,901
160,793
37.93
1,597,395
7,478
4.681
1,497,424
10,401
6.946
5.777
646,292
832,946
1,479,238
5.26
Overall
*One case of unknown gender excluded.
of persons treated for NMSC in 2006 at 2,152,500 but could
not differentiate between BCCs and SCCs.1 The most recent
standardized population-based disease estimates reported
specifically for BCCs in the United States, based in northcentral New Mexico in 1998-1999 and standardized to the
2000 US population, showed an overall incidence rate for
those >25 years of age of 812/100,000 people/year (Athas
et al, 2003), nearly identical to our standardized rate for
those >25 years of 811/100,000. Furthermore, the pattern
of BCC incidence by age in our cohort is similar to those
previously reported7,11,12 with the majority of BCCs arising in
members >65 years of age. Although males had an overall
higher incidence rate for BCCs in our cohort and others,10-13
we noted a slightly higher incidence rate in younger (<40
years) women (266/100,000) compared to younger men
(216/100,000), supporting data from a previously published
report indicating higher incidence rates among women
compared to men.13
A unique strength of our study was the leveraging
of electronic data within a large integrated health care
delivery system that included all pathology reports in a
diverse, representative population. Among the limitations
are that data were collected from a single health-care setting
and validation was performed on diagnoses limited to 1
calendar year. Also, inter-rater reliability was performed
on only a subset of patients. Furthermore, it is possible that
BCCs were diagnosed outside of the KPNC setting and
68
thus not captured in the pathology database. However,
seeking care outside of a prepaid health plan and incurring
additional out-of-pocket expense is unlikely. Some BCCs
may be removed without sending a pathology specimen
for definitive diagnosis. However, the KPNC standard of
care is to submit specimens suspected for malignancy for
histopathologic diagnosis, so such events also are likely to
be rare. Finally, BCCs often arise repeatedly and may arise
concurrently in susceptible individuals, and SNOMED
codes do not distinguish between primary, subsequent,
recurrent, or multiple concurrent BCCs, making it difficult
to identify true incident disease.
In conclusion, we found that SNOMED codes from the
KPNC pathology system accurately and reliably identified
BCCs. Having established the validity of SNOMED codes
in identifying BCCs, we have used the codes to develop a
longitudinal registry spanning a 14-year period from 19972010. A total of 136,173 KPNC members had at least 1 BCC
diagnosis during this period. This large registry can serve
as a resource for important scientific questions including
disease incidence and trends over time, patterns of tumor
occurrence (location, subtype), disease burden, risk of
subsequent disease, and risk factors for recurrence. When
used in combination with other readily available KPNC
electronic data sources, the registry can also be used to
study BCC risk in relation to other factors such as comorbidities and disease associations, environmental exposures,
Journal of Registry Management 2013 Volume 40 Number 2
genetic variations, and novel risk factors such as exposure
to certain medication classes. Thus, this registry allows for
studies on the etiology, risk factors, and disease burden in a
population-based setting of the most common cancer in the
United States.
References
1. Rogers HW, Weinstock MA, Harris AR, et al. Incidence estimate of
nonmelanoma skin cancer in the United States, 2006. Arch Dermatol.
2010;146:283-287.
2. Scotto J, Fears TR, Fraumeni JF. Incidence of nonmelanoma skin cancer
in the United States. Washington, DC: United States Department of
Health and Human Services; 1981. NIH publication 82-2433.
3. Similarity of the Adult Kaiser Permanente Membership in Northern
California to the Insured and General Population in Northern California:
Statistics from the 2009 California Health Interview Survey. Available at:
http://www.dor.kaiser.org/external/chis_non_kp_2009/.
4. U.S. National Library of Medicine. Unified Medical Language System
(UMLS). SNOMED Clinical Terms. Available at: http://www.nlm.nih.
gov/research/umls/Snomed/snomed_main.html.
5. Eide MJ, Krajenta R, Johnson D, et al. Identification of patients with
nonmelanoma skin cancer using health maintenance organization
claims data. Am J Epidemiol. 2010;171:123-128.
Journal of Registry Management 2013 Volume 40 Number 2
6. Eide MJ, Tuthill JM, Krajenta RJ, Jacobsen GR, Levine M, Johnson CC.
Validation of claims data algorithms to identify nonmelanoma skin
cancer. J Invest Dermatol. 2012;132:2005-2009.
7. Flohil SC, Koljenovic S, de Haas ER, Overbeek LI, de Vries E, Nijsten T.
Cumulative risks and rates of subsequent basal cell carcinomas in the
Netherlands. Br J Dermatol. 2011;165:874-881.
8. Lamberg AL, Cronin-Fenton D, Olesen AB. Registration in the Danish
Regional Nonmelanoma Skin Cancer Dermatology Database: completeness of registration and accuracy of key variables. Clin Epidemiol.
2010;2:123-136.
9. Bielsa I, Soria X, Esteve M, Ferrandiz C. Population-based incidence of
basal cell carcinoma in a Spanish Mediterranean area. Br J Dermatol.
2009;161:1341-1346.
10.Miller DL, Weinstock MA. Nonmelanoma skin cancer in the United
States: incidence. J Am Acad Dermatol. 1994;30:774-778.
11.Hoy WE. Nonmelanoma skin carcinoma in Albuquerque, New Mexico:
experience of a major health care provider. Cancer. 1996;77:2489-2495.
12.Athas WF, Hunt WC, Key CR. Changes in nonmelanoma skin cancer
incidence between 1977-1978 and 1998-1999 in Northcentral New
Mexico. Cancer Epidemiol Biomarkers Prev. 2003;12:1105-1108.
13.Christenson LJ, Borrowman TA, Vachon CM, et al. Incidence of basal cell
and squamous cell carcinomas in a population younger than 40 years.
JAMA. 2005;294:681-690.
69
Original Article
Establishing Effective Registration Systems in ResourceLimited Settings: Cancer Registration in Kumasi, Ghana
Kieran S. O’Brien, MPHa; Amr S. Soliman, MD, PhDb; Baffour Awuah, MDc; Evelyn Jiggae, MDc,d;
Ernest Osei-Bonsu, MDc; Solomon Quayson, MDc; Ernest Adjei, MDc; Silpa S. Thaivalappil, MPHa;
Frank Abantanga, MDc; Sofia D. Merajver, MD, PhDa,d
Abstract: Cancer control programs are needed worldwide to combat the increases in cancer incidence and mortality predicted for sub-Saharan Africa in the next decades. The effective design, implementation, and evaluation of such programs
require population-based cancer registries. Ghana’s second largest medical center, the Komfo Anokye Teaching Hospital
(KATH) in Kumasi, has made initial progress at developing a cancer registry. This registry, however, is housed in the medical oncology/radiotherapy center at KATH and does not currently include data from other departments that also interact
with cancer patients. The aim of this study was to improve KATH cancer registration by compiling cancer data from other
major departments that see cancer patients. Using recent population estimates, we calculated crude cancer incidence rates
of the “minimally-reported cases” for the Ashanti region. The most common cancers found in this study were breast (12.6
per 100,000), cervix (9.2 per 100,000), and prostate (8.8 per 100,000). These cancers occur at similar crude incidence rates in
other West African countries. Females had overall higher incidence rates than males, which is consistent throughout the
West African region. This study identified a number of methodological challenges facing cancer registries in Ghana that can
be addressed to improve the quality of cancer registries in other resource-limited settings. Such registries should be tailored
to the local health system context. A lack of coordination among the sources reporting cancer cases and a lack of understanding of local health-care systems and payment plans may interfere with the quality, completeness, and comparability
of data from cancer registries in resource-limited settings. Steps, barriers, and solutions for improving cancer registration
in Ghana and countries at similar levels are discussed.
Key words: cancer registration, cancer, epidemiology, Ghana, developing countries
Introduction
More than half of incident cancer cases and two thirds
of cancer deaths already occur in developing countries.1 In
sub-Saharan Africa, cancer incidence is expected to increase
drastically over the next few decades due to multiple factors,
notably increased life expectancy and westernized lifestyles
as well as increased quality of diagnostics.1-4 Despite the
known growing burden of cancer in sub-Saharan Africa,
little is known about the incidence and distribution of the
different tumor types and few resources are allocated to
early detection and disease management in this region.1-3,5
Though cancer registration is indispensable as an accurate
and longitudinal measure of the true burden of disease
and thus to inform national cancer control programs,6,7
only 11% of the population in Africa is covered by a cancer
registry1,8 and only 1% of African populations are covered
by high-quality population-based cancer registries.9 Where
registration systems do exist in these resource-limited
settings, major challenges, including the lack of trained
personnel, insufficient coordination of reporting sources,
and the lack of available census data, threaten the quality
and overall effectiveness of the registry.3,10-12 Though Ghana
currently lacks a fully functioning population-based cancer
registry, there have been earnest attempts to describe the
cancer burden in the country. The 2 largest hospitals that
diagnose and treat cancer patients in Ghana are the Korle
Bu Teaching Hospital (KBTH) in Accra and the Komfo
Anokye Teaching Hospital (KATH) in Kumasi. Limited
frequency data from these hospitals indicate that the most
common cancers are of the cervix, breast, prostate, and
liver, with higher proportions of cancer among women
than men.13-16 In 2008, GLOBOCAN used a mixture of data
from surrounding countries and extrapolations to estimate
an age-standardized national cancer rate of 109.5 cases
per 100,000 persons per year.17 GLOBOCAN also identified the same most frequent cancers that were reported by
the limited patient data available from KBTH and KATH.
Importantly, data from each of 32 sentinel hospitals within
the 10 regions of Ghana indicate that cancer is among the
top 10 causes of death in the country.13,18
Cancer registration at KATH started with the development of a departmental-based registry in 2004.5 This
__________
University of Michigan School of Public Health, Ann Arbor, Michigan. bUniversity of Nebraska Medical Center College of Public Health, Omaha, Nebraska.
Komfo Anokye Teaching Hospital, Kumasi, Ghana. dUniversity of Michigan School of Medicine, Ann Arbor, Michigan.
a
c
Address correspondence to Amr S. Soliman, MD, PhD, Department of Epidemiology, University of Nebraska Medical Center College of Public Health, 984395
Nebraska Medical Center, Omaha, NE, 68198-4395. Telephone: (402) 559-3976. Fax: (402) 559-7259. Email: [email protected].
Kieran O’Brien was supported by the Cancer Epidemiology Education in Special Populations (CEESP) Program of the University of Michigan through funding
from the National Cancer Institute grant (R25 CA112383) and the University of Michigan Center for Global Health. Partial support was also provided by the
Avon Foundation (SDM,AS) and by the Breast Cancer Research Foundation (SDM).
70
Journal of Registry Management 2013 Volume 40 Number 2
registry was developed with the aim of reporting all cancer
cases diagnosed and treated at the medical oncology/
radiotherapy department where most of the patients diagnosed and treated come from the Ashanti region.5 Although
cancer data from other departments exists at KATH, no
previous research has tried to link cancer data from different
diagnostic and treatment sources at this hospital to arrive
at more accurate Ghana-focused estimates of the true incidence of cancer.
Ghana has made initial progress towards the development of cancer registries, yet still lacks the resources
and coordination of reporting sources needed for a fully
functioning registration system. A lack of coordination of
reporting sources within the hospital is a possible source
of underestimation and inaccuracy, as cancer cases are not
reported in every department. To this end, this study aimed
to coordinate cancer data from the surgery, pathology, and
medical oncology/radiotherapy departments to estimate
the cancer burden seen at KATH from 2008-2010, obtain
estimates of cancer incidence for the Ashanti region, and set
the groundwork for the development of an effective cancer
registry in Ghana.
Medical Oncology/Radiotherapy
Data from the medical oncology/radiotherapy department database was obtained for all cases seen during the
same period of 2008-2010. This database contained demographic variables, such as name, age, sex, occupation, and
town/region of residence and clinical information such
as basis of diagnosis, primary site, histology, grade, stage,
and treatment. Individual patient medical records were
reviewed for this period to supplement and validate the data
contained in the database. This process was completed for
the data from the medical oncology/radiotherapy department for 2 main reasons: 1) to supplement the minimal
diagnostic data included in the database with the complete
diagnoses found in the patients’ medical records; 2) to
provide a direct link to cases identified from the pathology
department using the histopathology report number found
in the medical oncology/radiotherapy medical records. A
total of 1,936 cases of cancer were obtained from the medical
oncology/radiotherapy department.
Data Linking/Matching
The study population consisted of all confirmed
or suspected cases of cancer diagnosed in the surgery,
pathology, and medical oncology/radiotherapy departments at KATH from 2008-2010. For each department,
available information on cancer cases was abstracted from
patient logbooks, medical records, or electronic databases
into Excel databases.
Once data from all departments were reviewed for
accuracy, the following variables were used to link cases
between departments: histopathology report number,
patient name, patient age, date of diagnosis, and cancer
site/site of surgical procedure. After linking the data from
the 3 departments and removing duplicates, data was
stripped of all identifiers and the final dataset for analysis
was defined. Approval for this project was obtained from
both the University of Michigan Institutional Review Board
and Komfo Anokye Teaching Hospital/Kwame Nkrumah
University of Science and Technology Committee on Human
Research, Publication, and Ethics.
Surgery
Estimation of Incidence and Statistical Analysis
The electronic database of all 24,889 cases seen for any
surgical procedure in the department of surgery during
the period of January 2008-December 2010 was obtained.
This database contained the following variables: patient
name, age, sex, preoperative diagnosis, and postoperative
diagnosis. Postoperative diagnosis data was missing for
the majority of cases. Based on the available clinical diagnosis reported for each procedure, cases were reviewed
by both Ghanaian and US physicians with more than 30
years of combined expertise in the heterogeneous presentations of cancers. The reviewers categorized the cases into
the following 4 categories: definite cancer, likely cancer,
unlikely cancer, and not cancer. The first 2 groups (cancer
and likely cancer) included a total of 4,304 cases. The judgment call of “likely cancer” or “unlikely cancer” was based
on experience for predicting with over or under 50% probability of cancer.
KATH’s patient population comes largely from the
Ashanti region, in which it is located. With a population
of 4,725,046, Ashanti is the most populous region in Ghana
with one of the fastest growing populations.19 KATH’s total
catchment area covers nearly 50% of the total population
in Ghana and also includes the more rural regions north
of the Ashanti region, namely the Upper East, Upper West,
Northern, and Brong Ahafo regions as well as parts of the
Central and Western regions.19 KATH also sees a majority of
the cancer cases in the Ashanti region. As it is estimated that
75% of KATH’s cancer patients are from the Ashanti region,
and 25% of cancer cases in the Ashanti region are estimated
to receive treatment outside of KATH, 100% of cases from
this project were included for the estimation of incidence.
Population data for the Ashanti region was obtained
from Ghana’s 2010 Population and Housing Census.19
Population estimates for 2008 and 2009 were calculated
using regression estimate data from Ghana’s 2000 and 2010
census, including data on population growth rates. Ageand sex-specific distributions were obtained from the 2008
Demographic and Health Survey in Ghana20 and combined
with the census data to generate the population distribution
for the Ashanti region by 5-year age intervals and sex. KATH
provided statistics on its patient catchment area, which
Materials and Methods
Study Population and Data Sources
Pathology
Logbooks from the pathology department for the
same period of 2008-2010 were obtained. Logbook entries
included information on patient name, age, sex, site of
procedure, and diagnosis. A total of 2,617 cases strongly
suggestive of cancer were identified and abstracted.
Journal of Registry Management 2013 Volume 40 Number 2
71
Figure 1. Unique Cancer Cases by Department, Komfo
Anokye Teaching Hospital, Kumasi, Ghana, 2008-2010
were used to determine the percentage of KATH cancer
patients residing in the Ashanti Region. The combined data
on cancer diagnosis obtained from the 3 departments were
stratified by age groups (5-year intervals), sex, and cancer
site for the primary tumor.
Descriptive statistics and rate analyses were conducted
using SAS (Version 9.2; SAS Institute, Cary, NC). Crude
annual incidence rates for each year were estimated with
the number of cases per year as the numerator and the
respective population estimate for that year as the denominator. Age-, sex-, and site-specific incidence rates were
calculated for each year. Crude incidence rates for the
3-year period were calculated with the total number of
cases identified from 2008-2010 as the numerator, and the
person-time followed for the period as the denominator.
Age-standardized incidence rates were calculated using the
World Health Organization (WHO) world standard population estimates.21 Finally, observational comparisons of crude
and site-specific incidence rates were made with other
West African countries. Côte d’Ivoire, Niger, and Nigeria
were chosen for comparison purposes due to availability of
incidence data,3 similarities in age structure and distribution,20,22-24 and geographic proximity to Ghana. While we
used the term “incidence,” other terms that may reflect the
actual situation in Ghana might be “minimally-reported
cases” or “working estimates.”
Results
A total of 4,012 individual cancer cases were identified from the 3 departments. Figure 1 provides details on
the numbers of unique cases identified in each department
and the overlap of cases between departments. The largest
numbers of cases were seen in the pathology department
only (35.1%), followed by both pathology and oncology
departments (20.1%), and surgery only (18.1%). The majority
of cases (64.9%) were females. The most frequent cancers
identified were breast (22.4%), cervix (16.4%), prostate
(14.7%), and head and neck (10.3%).
72
Table 1. Crude Age-Specific Cancer Incidence Rates (per
100,000) in the Ashanti Region (2008-2010) by Sex
Age
Male
Female
Total
Groups
(males and females)
(n=1408)
(n=2604)
(n=4012)
0-4
(45,39)
4.7
4.4
4.6
5-9
(41,34)
4.0
3.5
3.8
10-14
(33,27)
3.6
3.1
3.3
15-19
(26,37)
3.6
5.4
4.5
20-24
(36,57)
7.1
9.5
8.5
25-29
(54,81)
12.0
13.7
13.0
30-34
(42,139)
11.4
31.5
22.2
35-39 (64,203)
17.3
47.5
33.8
40-44 (69,242)
25.7
73.9
52.3
45-49 (83,319)
30.9
109.3
72.6
50-54
(80,342)
36.1
114.4
82.4
55-59
(98,249)
60.7
129.6
100.3
60-64
(126,203)
81.5
135.8
108.0
65-69
(110,126)
102.3
110.6
106.6
70-74
(221,242)
219.1
200.0
209.1
75-79
(155,125)
288.2
195.1
224.8
80+
206.6
162.7
173.4
Total
21.3
37.0
29.5
(125,139)
The total crude incidence rate for the Ashanti region
over the 2008-2010 period was 29.5 cases per 100,000.
Females had an overall higher total incidence rate (37.0 per
100,000) compared with males (21.3 per 100,000). This was
consistent over each of the 3 years as well as the total for the
3-year period.
The total crude incidence rates per year increased
slightly over time, from 25.5 per 100,000 in 2008, to 30.8 per
100,000 in 2009, and 31.9 per 100,000 in 2010. The incidence
rate per year for females varied similarly to the total, from
32.5 per 100,000 in 2008 to 36.9 per 100,000 in 2009, and
41.5 per 100,000 in 2010. Moreover, the total incidence rate
for males increased from 17.9 per 100,000 in 2008 to 24.3
per 100,000 in 2009, and then decreased slightly to 21.5 per
100,000 in 2010.
Table 1 displays the crude age-specific incidence rates
for the 3-year period by sex. As expected, the incidence rates
increase with increasing age. In this population, however,
the incidence rate begins to increase noticeably around age
40-44 for both sexes. For females, incidence rates begin to
increase substantially at even younger ages, around age
30-34, whereas for males, incidence rates increase notably
starting at older ages, around 55-59 years. Interestingly,
there appear to be significant increases in crude incidence
rates for both sexes at ages older than 70 years compared to
age groups younger than age 70.
Table 2 shows the total incidence rates for the 3-year
Journal of Registry Management 2013 Volume 40 Number 2
Table 2. Crude Cancer Incidence Rates (per 100,000) in
the Ashanti Region (2008-2010) by Site and Sex*
Site
Male
Female
CrudeRate/
100,000
Table 3. Comparison of Crude Total Cancer Incidence
Rates (per 100,000) by Sex among West African
Countries*
Head/neck
Ghana, Ashanti
Region
Cote d’Ivoire
Niger
Nigeria
Head/neck total
3.5
2.5
3.0
Male
21.3
18.7
32.5
32.6
Nasopharynx
0.0
0.0
0.0
Female
37
23.3
48.8
42.6
Other phayrnx
0.0
0.0
0.0
Larynx
0.5
0.1
0.3
Esophagus
0.1
0.0
0.0
Lip/oral cavity
1.2
0.8
1.0
Other head/neck
1.4
1.0
1.2
Stomach
0.7
0.7
0.7
Colon/rectum
1.1
1.1
1.1
Anus
0.1
0.1
0.1
Liver
0.2
0.2
0.2
Gallbladder
0.0
0.0
0.0
Pancreas
0.0
0.0
0.0
Lung
0.1
0.0
0.1
Bone
0.2
0.2
0.2
Skin
0.7
1.2
1.0
Breast
2.0
12.6
12.6
Vulva
–
0.4
0.4
Vagina
–
0.4
0.4
Cervix uteri
–
9.2
9.2
Corpus uteri
–
2.3
2.5
Ovary
–
1.3
1.3
Penis
0.2
–
0.2
Prostate
8.8
–
8.8
Testis
0.2
–
0.2
Kidney
0.5
0.6
0.5
Bladder
1.1
1.2
1.1
Eye
0.7
0.5
0.6
Brain
0.1
0.1
0.1
Thyroid
0.2
0.7
0.5
Hodgkin disease
0.2
0.2
0.2
Non-Hodgkin lymphoma
0.1
0.2
0.1
Lymphoma, unspecified
0.4
0.3
0.4
Sarcoma
0.2
0.1
0.1
Abdomen
0.2
0.1
0.1
Small intestine
0.1
0.1
0.1
Teratoma
0.1
0.1
0.1
Other/unknown site
1.9
1.7
1.8
21.3
37.0
29.5
Total
*n=4012.
Not applicable or not available indicated by dash.
Journal of Registry Management 2013 Volume 40 Number 2
*Crude incidence data from IARC Scientific Publication No. 153,
Cancer in Africa: Epidemiology and Prevention, 2003. Côte d’Ivoire:
Abidjan Cancer Registry, 1995-1997; Niger: Cancer Registry of Niger,
Niamey, 1993-1999; Nigeria: Ibadan Cancer Registry, 1998-1999.
period by cancer site, revealing that the most common
cancers in terms of crude incidence rate were breast, cervix,
prostate, and head and neck cancers. Breast cancer was the
most frequent cancer overall, with an incidence rate of 12.6
per 100,000 for females. The second most common cancer
among females was cervical cancer, with an incidence rate
of 9.2 per 100,000. For males, prostate cancer was the most
common with an incidence rate of 8.8 per 100,000. Head and
neck cancers were common among both sexes. The overall
incidence rate for head and neck cancers was 2.9 per 100,000
for both sexes combined, 3.5 per 100,000 for males, and
2.5 per 100,000 for females. Other cancers that occurred at
low frequencies include cancers of the corpus uteri, ovary,
bladder, colorectum, stomach, thyroid, kidney, skin, and
lymphomas. Due to unavailability of detailed diagnostic
data, many skin cancers and lymphomas were unspecified. Liver cancer is present at low rates (0.20 per 100,000
persons per year for both sexes) and other cancers such as
leukemias and multiple myeloma were not found in these
departments.
Tables 3 and 4 compare the overall crude cancer
incidence rates for males and females and by site for the
Ashanti region with other countries in West Africa (Côte
d’Ivoire, Niger, and Nigeria). As seen in Table 3, the crude
total cancer incidence rates for Ghana as estimated for this
study (female: 37.0 per 100,000; male: 21.3 per 100,000) fall
within ranges seen for the other West African countries.
Table 4 indicates that the higher incidence rates estimated
for females by this study are consistent across other West
African countries. Also, these countries share similar
common cancers; in particular, breast, cervix, and prostate
cancers are common to all 4 of these West African countries.
In contrast, other common cancers seen in Côte d’Ivoire,
Niger, and Nigeria were found at lower frequencies or not at
all in the 3 KATH departments. These include liver cancer,
lung cancer among males, Kaposi sarcoma, non-Hodgkin
lymphoma, leukemia, and multiple myeloma.
The total age-standardized incidence rate was 41.9 per
100,000. For females, the age-standardized incidence rate
was 50.8 per 100,000. Finally, the age-standardized incidence rate for males was 31.7 per 100,000.
Discussion
This study revealed the following interesting findings.
First, this study highlights the importance of developing
strategies tailored to the local context in order to enhance
73
Table 4. Comparison of Crude Cancer Incidence Rates (per 100,000) for the Ashanti Region by Site Compared with Other
West African Countries*
Cancer Site
Ghana, Ashanti Region
Male
Female
Côte d’Ivoire
Male
Mouth
0.9
0.7
0.3
Salivary gland
0.2
0.2
Nasopharynx
0.0
0.0
Other pharynx
0.0
Oesophagus
Niger
Female
Male
Nigeria
Female
Male
Female
0.3
0.5
0.5
0.6
0.1
2.0
–
0.3
0.2
0.3
0.3
0.2
0.0
0.1
0.0
0.8
0.4
0.0
0.2
0.1
0.4
0.1
0.2
0.1
0.1
0.0
0.2
0.0
0.4
0.4
0.5
0.1
Stomach
0.7
0.7
0.8
0.5
0.7
0.8
0.6
0.7
Colon/rectum
1.1
1.1
0.8
0.6
2.0
1.3
2.0
1.6
Liver
0.2
0.2
2.7
1.0
7.3
3.6
3.8
1.3
Gallbladder
0.0
0.0
–
–
0.0
0.1
0.1
0.1
Pancreas
0.0
0.0
0.2
0.1
0.4
0.4
0.4
0.5
Larynx
0.5
0.1
–
–
0.6
0.2
0.7
0.1
Lung
0.1
0.0
1.0
0.2
1.2
0.1
0.3
0.1
Bone
0.2
0.2
–
–
2.0
1.3
1.3
0.5
Skin, unspecified
0.6
0.8
–
–
1.6
1.3
0.8
0.6
Melanoma of skin
0.1
0.3
0.1
0.1
0.2
0.2
0.3
0.3
Kaposi sarcoma
0.0
0.0
1.4
0.4
0.3
0.2
0.2
0.1
Breast
–
12.6
0.2
5.9
0.4
9.7
0.5
15.0
Vulva
–
0.4
–
–
–
0.4
–
0.5
Vagina
–
0.4
–
–
–
0.3
–
0.3
Cervix uteri
–
9.2
–
5.5
–
9.3
–
10.4
Corpus uteri
–
2.3
–
0.3
–
2.6
–
1.1
Ovary
–
1.3
–
1.1
–
2.8
–
2.0
Penis
0.2
–
0.1
–
0.0
–
0.0
–
Prostate
8.8
–
2.8
–
2.2
–
7.7
–
Testis
0.2
–
–
–
0.1
–
0.2
–
Kidney
0.5
0.6
0.3
0.2
0.8
0.6
0.2
0.6
Bladder
1.1
1.2
0.5
0.2
1.6
1.4
1.1
0.2
Eye
0.7
0.5
0.1
0.3
0.8
0.4
1.0
0.5
Brain, nervous system
0.1
0.1
0.2
0.3
0.3
0.2
0.5
0.7
Thyroid
0.2
0.7
0.1
0.4
0.2
0.7
0.4
0.6
Lymphoma, unspecified
0.4
0.3
–
–
–
–
–
–
Hodgkin lymphoma
0.2
0.2
0.5
0.3
0.5
0.1
0.5
0.1
Non-Hodgkin lymphoma
0.1
0.2
1.9
1.7
2.4
2.1
3.4
1.7
Multiple myeloma
–
–
0.2
0.1
0.2
0.1
0.1
0.3
Lymphoid leukemia
–
–
–
–
1.0
0.9
0.1
0.1
Myeloid leukemia
–
–
–
–
0.5
0.8
0.6
0.2
Leukemia, unspecified
–
–
0.7
0.7
0.0
0.2
0.2
0.1
2.4
2.1
–
–
4.1
5.4
2.6
1.3
21.3
37.0
18.7
23.3
32.5
48.8
32.6
42.6
Other and unspecified†
All sites‡
*Crude incidence data from IARC Scientific Publication No. 153, Cancer in Africa: Epidemiology and Prevention, 2003. Cote d’Ivoire: Abidjan Cancer
Registry, 1995-1997; Niger: Cancer Registry of Niger, Niamey, 1993-1999; Nigeria: Ibadan Cancer Registry, 1998-1999.
†For Niger, and Nigeria, rate may not reflect total of “other and unspecified” as seen in this table.
‡Excluding non-melanoma skin cancer for Niger and Nigeria.
Not applicable or not available indicated by a dash.
74
Journal of Registry Management 2013 Volume 40 Number 2
the efficiency of cancer registration starting from earnest
local efforts across all departments and identifying the major
local registrars and health-care professionals involved in the
registry. This is clear in our work, which, for the first time,
linked patient records across departments and thus identified many cases of cancer when they were not explicitly
diagnosed as such, as from surgical cases only, for example.
Second, our results show the need to fully understand the
health-care system and structure when developing a cancer
registry. This is evidenced by the distribution of cancers by
both age and cancer site in this study. In our study, some
patients tended to report older ages to utilize health insurance benefits available only to older patients. Also, some
cancer sites such as leukemia were under-reported because
of lack of diagnostic facilities at the study hospital. Finally,
comparison of our results to previous estimates for Ghana
and other similar countries provide strong insight into the
reasonable nature of our estimates and point to potential
gaps in our data.
Linking cancer cases across hospital departments is
essential to ensure quality and completeness in a registration system. The departmental-based registry at KATH is
common in Africa and in many under-resourced regions,
and currently only collects data on cancer cases seen within
the medical oncology/radiotherapy department and is
therefore unable to accurately describe the cancer burden
at the hospital. The process currently in use is based on
the assumption that all cancer patients who are seen at the
department of surgery and/or diagnosed by the department
of pathology will subsequently receive chemotherapy and/
or radiotherapy. Our study found 53.2% of cancer cases were
seen only in the pathology or surgery departments and thus
no further linkage to therapy occurred because the patient
declined to receive treatment or a referral was not made.
Our results emphasize the importance of capturing cases in
all relevant departments based on the dynamics of patient
referrals between departments in local health-care systems.
This process that we conducted for the first time in Ghana is
crucial at least until formal and thorough population-based
registries are in place.
The need for locally-tailored strategies for cancer
registration is also evidenced by our work in identifying
cancer cases that were seen, but not explicitly diagnosed, in
the surgery department. Due to the incomplete database of
surgery patients and lack of separate databases or logbooks
for oncologic surgeries, cancer cases were not easily identified. To abstract cancer cases, we developed a classification
strategy to rank cases on their likelihood of being cancer
and the coauthors reviewed the data systematically. Without
this system, our study would not have captured the 725
(18.1%) patients with suspected cancer who were seen only
by the surgery department. Thus, although not without
its complexities of design, having identified such a high
percentage of cases at surgery alone again points out the
diverse nature of the patients’ navigation schemes within
the KATH system.
Understanding the intricacies of the local health-care
system is essential to effective cancer registration, which
was made clear through our attempts to age-standardize
Journal of Registry Management 2013 Volume 40 Number 2
our estimated incidence rates. The age-standardized incidence rates reported in the results (total: 41.9 per 100,000;
female: 50.8 per 100,000; male: 31.7 per 100,000) are notably
low compared to GLOBOCAN’s estimates for Ghana (total:
109.5 per 100,000; female: 125.5 per 100,000; male: 93.8 per
100,000) and age-standardized estimates for other subSaharan African countries.3,17 Our lower estimate of the
age-standardized rates is due to the large proportion of cases
aged 70 and older. It is important to note that the National
Health Insurance Scheme in Ghana provides free health care
without premiums for patients aged 70 and older.25 Based
on one of the coauthors experience in managing cancer
patients at KATH (EO-B), patients often report their age as
70 and older to avoid payment of premiums for health care,
which is seen in this data. As patients often do not have
birth records, it is difficult to validate patients’ age reporting
and so the age claimed by the patient is the one reported in
the medical records.
Our findings with respect to the distribution and
crude cancer incidence rates in the Ashanti region are
similar to estimates reported elsewhere for Ghana and
other comparable countries. First, our study confirmed
previous estimates and observations that cancer incidence
rates are higher among females. The International Agency
for Research on Cancer (IARC) and other previous studies
in Ghana13-15,17 estimated that females have overall greater
morbidity and mortality from cancer than males. Our
estimated crude incidence rates for Ghana are similar to
the estimates for other West African countries and indicate
that females have higher rates of cancers in these countries
as well. This is likely a real difference, but the magnitude
of the differences between sexes could also arise from the
available treatment options for cancer in Ghana. Treatment
for breast and cervical cancer at KATH are partially covered
by the National Health Insurance Scheme whereas other
cancers are not covered.25 This could partly explain the
greater representation of women with cancer in treatment
at KATH that were captured in this study.
This study found relatively high crude incidence rates
for breast, cervix, and prostate cancers. Previous studies1315,17
are in agreement these cancers are common in Ghana.
Furthermore, comparisons with other West African countries indicate that breast, cervix, and prostate cancers are
common in similar countries as well.3
The overall crude rates for males and females as well as
the majority of site-specific rates as estimated in this study
are quite reasonable when compared with similar West
African countries. The crude rates observed in this study
fall within ranges reported for other West African countries
with similar age distributions to Ghana.3 Previous results
from other West African countries support this observation.3
In contrast, such comparisons also show potential gaps
in our study. Other estimates for Ghana and other similar
sub-Saharan African countries found lung cancer in males
and liver cancer among both sexes to be more frequent
than indicated in our data. This is likely explained by the
fact that this study did not review autopsy records or those
in other departments such as internal medicine or laboratory departments. The lower rates of Kaposi sarcoma and
75
non-Hodgkin lymphoma relative to other West African
countries could be explained by the lack of availability
of detailed diagnostic data for many skin cancers and
lymphomas. Also, Ghana’s HIV prevalence is low relative to
both Côte d’Ivoire and Nigeria which could partly explain
the lower rates of HIV-related malignancies like Kaposi
sarcoma and non-Hodgkin lymphoma, although Niger’s
reported HIV prevalence is lower than Ghana’s.26-29 Finally,
this study did not identify certain cancers such as leukemia
and multiple myeloma that were identified in comparable
countries. Again, this is due to incomplete data collection
across all relevant departments.
This study has a number of strengths. We built upon
existing registration infrastructure at KATH to enhance
coordination of case reporting between hospital departments and improve data collection. This study was the
first at KATH to provide complete data on all cancer cases
seen in 3 departments over a 3-year period. Using the most
recent available census data, this study provided the first
estimates of incidence based on in-country data.
We also addressed numerous methodological issues in
cancer registration that are likely to be highly applicable in
other resource-limited settings. For example, KATH lacks a
uniform identification system for patients, so there was not
one variable common across departments to facilitating case
linkages. Instead, we devised a variety of strategies based
on the available data to enable these linkages. Also, we identified the most available and efficient data sources available
to each department. As these different sources had varying
degrees of completeness and quality, we identified other
sources in each department for validation purposes. Finally,
we devised strategies to identify cases of cancer when
they were not explicitly diagnosed in the available data, as
evidences by our methods for the surgery department.
This study also had a few limitations. There is the
potential for underestimation in the incidence rates provided
in this study. We did not review cases in all departments and
so may have missed cases of cancer only seen in pediatrics,
laboratory medicine or found in death registration and/or
autopsy records. In addition, KATH does not see all existing
cancer cases in the Ashanti region, so those cases that may
have only been seen at private health centers, by traditional
or alternative practitioners, or that died before diagnosis
were missed by this study. We did, however, attempt to
compensate for these missed cases using hospital- and
census-based statistics to estimate that we missed 25% of
cancer cases in the Ashanti region and used this estimate in
our calculations.
There is also a potential for errors in our data or
cases that were missed in the 3 departments reviewed.
The surgery database did not contain explicit diagnoses
of cancer and so each surgery case was categorized by
its likelihood of being cancer. These cases were, however,
reviewed carefully by medical practitioners and matched to
both pathology and oncology to ensure accuracy. We also
found data entry errors in the oncology database obtained
from the departmental-based registry. We attempted to
correct this with a systematic review of patient folders, but
could not locate 5.4% of folders for validation purposes. In
76
addition, certain cancer types such as leukemia and multiple
myeloma are not reported because of lack of collection from
the department of laboratory medicine.
In summary, this study provided the first data on
cancer cases seen in multiple departments over multiple
years at KATH. We estimated crude cancer incidence rates
of the “minimally-reported cases” or “working estimates”
for the Ashanti region that are comparable to other West
African estimates and we confirmed that breast, cervix, and
prostate cancers are common in this population. The methodology and results from this study emphasize the need to
tailor strategies for cancer registration to the local context
with an in-depth understanding of the overall healthcare
system and structure. The next steps in enhancing cancer
registration at KATH include expanding data collection
to all other relevant departments and collaborating with
data sources outside of KATH, such as private treatment
centers. Future studies should utilize the insights from
this study that may be applicable to other resource-limited
settings. Future studies should also consider additional
methods to identify the proportion of missing data such as
capture-recapture analyses30 and verification of completeness of registries.31 Future studies should also aim to verify
the reliability of the census. Methods for inclusion of
non-conforming departments into the registration system
must consider steps, barriers, and proposed solutions for
ensuring participation in the registration process. Periodic
utilization of the registry data by senior and junior physicians and faculty of departments could enhance the interest
in supporting cancer registration. Utilization of registry
data for periodic reports, theses, and dissertation materials
could also help in increasing the support of registration by
non-conforming departments.
Acknowledgments
We would like to thank the staff of the surgery,
pathology, and medical oncology/radiotherapy departments at KATH for their assistance in identifying and
retrieving data sources. We would also like to thank David
Forman, Director of Cancer Information at IARC, for his
thoughtful insights.
References
1. Boyle P, Levin B, eds; International Agency for Research on Cancer.
World Cancer Report 2008. Lyon: IARC Press; 2008.
2. Parkin DM, Sitas F, Chirenje M, Stein L, Abratt R, Wabinga H. Part I:
Cancer in Indigenous Africans—burden, distribution, and trends. Lancet
Oncol. 2008;9:683.
3. Parkin DM, Ferlay J, Hamdi-Cherif M, et al, eds. Cancer in Africa:
Epidemiology and Prevention. Lyon: IARC Press; 2003. IARC Scientific
Publications No. 153.
4. Sylla BS, Wild CP. A million Africans a year dying from cancer by 2030:
What can cancer research and control offer to the continent? Int J
Cancer. 2012;130:245-250.
5. Opoku P, Awuah B, Nyarko K. Cancer registration in low-resourced
settings: Practice and recommendations. Afr J Haematol. 2010;1:1-6.
6. Parkin DM. The role of cancer registries in cancer control. Int J Clin
Oncol. 2008;13:102-111.
7. World Health Organization. National cancer control programmes:
Policies and managerial guidelines. Geneva: World Health Organization;
2002.
Journal of Registry Management 2013 Volume 40 Number 2
8. Parkin DM. The evolution of the population-based cancer registry. Nat
Rev Cancer. 2006;6:603-612.
9. Ferlay J, Shin H, Bray F, Forman D, Mathers C, Parkin DM. Estimates of
worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer.
2010;127:2893-2917.
10.Parkin DM, Sanghvi LD. Cancer registration in developing countries.
IARC Sci Publ. 1991;(95):185-198.
11.Valsecchi MG, Steliarova-Foucher E. Cancer registration in developing
countries: Luxury or necessity? Lancet Oncol. 2008;9:159-167.
12.Curado MP, Voti L, Sortino-Rachou AM. Cancer registration data and
quality indicators in low and middle income countries: their interpretation and potential use for the improvement of cancer care. Cancer
Causes Control. 2009;20:751-756.
13.Wiredu EK, Armah HB. Cancer mortality patterns in Ghana: a 10-year
review of autopsies and hospital mortality. BMC Public Health.
2006;6:159.
14.Quayson SE. Breast cancer at Ghana: A 5 year review. Presented at:
Breast and Cervical Cancer in North Africa and the Middle East CGH
International Symposium; October 2010; Cairo, Egypt.
15.Awuah B. Cervical cancer in Ghana: 6 year review at KATH Oncology.
Presented at: Breast and Cervical Cancer in North Africa and the Middle
East CGH International Symposium; October 2010; Cairo, Egypt.
16.Clegg-Lamptey J, Hodasi W. A study of breast cancer in Korle Bu
teaching hospital: assessing the impact of health education. Ghana Med
J. 2007;41:72-77.
17.Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. GLOBOCAN
2008 v1.2, Cancer Incidence and Mortality Worldwide: IARC CancerBase
no. 10 [Internet]. Lyon, France: International Agency for Research on
Cancer; 2010.
18.de-Graft Aikins A. Ghana’s neglected chronic disease epidemic: a developmental challenge. Ghana Med J. 2007;41:154-159.
19.Ghana Statistical Service. 2010 Population and Housing Census:
Provisional Results. 2011.
20.Ghana Statistical Service, Ghana Health Service, ICF Macro. Ghana
Demographic and Health Survey 2008. Accra: GSS, GHS, and ICF
Macro; 2009.
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21.Ahmad OB, Boschi-Pinto C, Lopez AD, Murrary CJL, Lozano R, Inoue
M. Age standardization of rates: A new WHO standard World Health
Organization. 2001. GPE Discussion Paper Series No. 31.
22.Côte d’Ivoire National Institute of Statistics, Côte d’Ivoire Ministry of the
Fight Against AIDS, ICF Macro. Côte d’Ivoire Demographic and Health
Survey 2005. Calverton: NIS and ICF Macro; 2006.
23.Niger National Institute of Statistics, ICF Macro. Niger Demographic and
Health Survey 2006. Calverton: NIS and ICF Macro; 2007.
24.Nigeria National Population Commission, ICF Macro. Nigeria
Demographic and Health Survey 2008. Abuja: NPC and ICF Macro;
2009.
25.National health insurance scheme [Internet]. Accra: National Health
Insurance Authority, 2010. Available at: http://www.nhis.gov.gh/.
26.World Health Organization. Country health system fact sheet for Ghana.
World Health Organization Regional Office for Africa, 2006. Available
at: http://www.afro.who.int/en/ghana/who-country-office-ghana.html.
27.World Health Organization. Country health system fact sheet for Côte
d’Ivoire. World Health Organization Regional Office for Africa, 2006.
Available at: http://www.afro.who.int/en/cote-divoire/who-countryoffice-cote-divoire.html.
28.World Health Organization (WHO). Country health system fact sheet
for Niger. World Health Organization Regional Office for Africa, 2006.
Available at: http://www.afro.who.int/en/niger/who-country-officeniger.html.
29.World Health Organization (WHO). Country health system fact sheet for
Nigeria. World Health Organization Regional Office for Africa, 2006.
Available at: http://www.afro.who.int/en/nigeria/who-country-officenigeria.html.
30.Parkin DM, Bray F. Evaluation of data quality in the cancer registry: principles and methods Part II. Completeness. Eur J Cancer. 2009;45:756-764.
31.Parkin DM, Wabinga H, Nambooze S. Completeness in an African
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77
Original Article
Prostate Cancer Treatment Modalities and Survival
Outcomes: A Comparative Analysis of Falmouth Hospital
Versus Massachusetts and Nationwide Hospitals
Sophia Elana Shimera
Abstract: Treatment of clinically localized prostate cancer has been termed a model of “preference-sensitive” health care.
Because there is no documented consensus supporting any one of the primary treatment modalities—surgery, external
beam radiation, and brachytherapy—over the others, patient and physician preferences can significantly influence treatment approach. This can lead to substantial variation in treatment practices at the level of individual hospitals. In this
context, it is informative for individual hospitals, especially small community hospitals that lack substantial quality
assurance resources, to compare their prostate cancer care to that provided by cohort groups of hospitals. This study compares prostate cancer treatment modalities at Falmouth Hospital (FH), a 95-bed community hospital located in Falmouth,
Massachusetts, to those at hospitals in 3 cohort groups: 1) US hospitals of all types, 2) Massachusetts hospitals of all types,
and 3) Massachusetts community hospitals. It also compares survival outcomes for FH prostate cancer patients to national
averages. All data for these comparisons were obtained from the National Cancer Data Base (NCDB) Hospital Comparison
Benchmark Reports and Survival Reports applications. This study’s main finding is that FH performed markedly less
surgery and more radiation, specifically brachytherapy, than the cohort groups of hospitals. This distribution of treatment
modalities was not solely attributable to FH’s older than average patient population. Studies similar to this one could be
conducted by other community hospitals to inform quality assessment programs for prostate cancer care.
Key words: prostate cancer, Falmouth Hospital, first course treatment, National Cancer Data Base
Introduction
Prostate cancer is the most common type of non-skin
cancer diagnosed in American men.1 One in 6 men will
be diagnosed with prostate cancer over the course of his
lifetime.1 Prostate cancer is also the second most common
cause of cancer death in men, behind only lung cancer.1
The National Cancer Institute estimates2 that there will be
238,590 new cases and 29,720 deaths that are due to prostate
cancer in the United States during the year 2013.
Routine screening for early detection of prostate cancer
has been widely practiced in the United States since the
early 1990s.3 Screening involves a blood test for prostatespecific antigen (PSA), a protein produced by the prostate.
Patients who are found to have high PSA levels usually
undergo a biopsy to check for cancerous cells. Diagnosis
and staging are primarily determined by the biopsy results.4
In recent years, the PSA test’s efficacy as a screening
tool has become controversial within the medical community. The controversy intensified in May 2012, when the US
Preventive Services Task Force issued a strong recommendation against routine PSA screening.5 After analyzing recently
released results from 2 clinical trials6,7 that compared
mortality rates of men who were screened and men who
were not screened, the Task Force determined that PSA
screening could prevent prostate cancer mortality in 0 to 1
man for every 1,000 men screened over a decade. The task
force concluded that saving so few lives does not justify the
known harms of screening.5 These harms include 1) falsepositive PSA results in 100-120 out of 1,000 men screened,
which can lead to unnecessary biopsies and negative
psychological effects, 2) biopsy-related complications such
as pain and infection, and 3) treatment-related complications such as urinary incontinence and erectile dysfunction
in 50 out of 1,000 men screened.5 The task force also found
that 17%-50% of tumors detected by PSA screening are so
slow-growing that they would have remained asymptomatic for the patient’s lifetime.5 This high overdiagnosis rate
is particularly concerning because nearly 90% of men who
receive a diagnosis undergo treatment and are thus subject
to the risk of treatment-related complications.8,9
There is no documented consensus about the optimal
treatment for prostate cancers that are clinically localized,10
which constitute the vast majority (93%) of cases at diagnosis.1 Management strategies include watchful waiting
(observation with palliative treatment of symptoms) and
active surveillance (periodic monitoring with PSA tests and
repeated biopsies), which are switched to active treatment
if signs of disease progression are discovered.5 The most
common active treatment modalities are surgery (radical
prostatectomy), external beam radiation, and brachytherapy
(radioactive seed implants).10 No contemporary clinical
trials have compared outcomes among men randomly
assigned to these active treatment modalities. The Surgical
Prostatectomy vs Interstitial Radiation Intervention Trial
__________
Cancer Committee, Falmouth Hospital, Falmouth, Massachusetts.
a
Address correspondence to Sophia Shimer, PO Box 201642, New Haven, CT 06520-1642. Email: [email protected].
Peter S. Hopewood, MD, FACS, and Deborah Crockett-Rice, CTR, Falmouth Hospital, guided the design of this study.
78
Journal of Registry Management 2013 Volume 40 Number 2
(SPIRIT) closed due to inadequate accrual.11 The Prostate
Testing for Cancer and Treatment (ProtecT) trial successfully randomized men in the United Kingdom to surgery,
radiation, or active surveillance, but it has just begun the
follow-up phase and mortality results will not be available
for several years.12 In the meantime, a systematic review
commissioned by the Agency for Healthcare Research and
Quality found insufficient evidence to recommend any of
the primary active treatment modalities over the others.10
The American Urological Association does not support one
modality over another in its clinical practice guidelines.13
In the absence of conclusive evidence or clinical
guidelines regarding the comparative efficacy of treatment
options, patient and physician preferences can significantly influence treatment approach.14 In fact, treatment
of localized prostate cancer has been termed a model
of “preference-sensitive” health care, in which treatment
choice involves trade-offs and thus patient and clinician
preferences, beliefs, and values drive decision making.9,14,15
Several studies have reported substantial local variation
in treatment practices.9,16,17 One older study, using Medicare
data from the mid-1990s, found that among the 10 most
commonly performed surgical procedures in the United
States, radical prostatectomy exhibited the greatest degree
of local variation.16 A more recent and comprehensive study
by Cooperberg et al analyzed data from a large national
registry of men with prostate cancer to quantify variation
in treatment at the level of individual clinical practice sites.
This study found that a substantial proportion of treatment
variation was attributable to practice site, even after controlling for differences in case mix and patient demographics,
including clinical stage, Charlson comorbidity score, age,
race, and income.9 The authors speculated that the observed
variation by practice site reflected the impact of local culture
on patient preferences, the uneven spread of new technologies, and differences in physician training, experience,
personal outcomes, and financial incentives.9
In this context of widespread treatment variation, it
would be informative for individual hospitals to ascertain
how their prostate cancer care measures up to the care
provided by cohort groups of hospitals. This type of analysis
is especially practical in small community hospitals, which
may lack the resources of larger teaching/research hospitals,
to ensure that the care they provide is current and competitive in terms of both treatment approach and outcome. The
present study is such a comparative analysis of the prostate
cancer care at Falmouth Hospital (FH), a 95-bed community
hospital located in Falmouth, Massachusetts, about an hour
and a half away from Boston, the nearest metropolitan
center. This study compares prostate cancer treatment
modalities at FH to those at hospitals in 3 cohort groups: 1)
US hospitals of all types, 2) Massachusetts hospitals of all
types, and 3) Massachusetts community hospitals. It also
compares prostate cancer survival outcomes at FH to those
at a cohort group of US hospitals of all types.
Methods
This study analyzed data from the National Cancer
Data Base (NCDB). The NCDB was created through a
Journal of Registry Management 2013 Volume 40 Number 2
collaboration of the American College of Surgeons’
Commission on Cancer (CoC) and the American Cancer
Society. It consists of records from approximately 70% of all
invasive cancer cases diagnosed in the United States annually. Certified tumor registrars at CoC-accredited cancer
programs collect and submit data to the NCDB using
nationally standardized data item and coding definitions.18
The NCDB maintains several Web-based data applications
intended for individual hospitals to evaluate the care they
provide. These applications are accessible to the more than
1,500 hospitals nationwide that have CoC-accredited cancer
programs.18
Comparison of Treatment Modalities
Comparison of treatment modalities was carried out
using the NCDB Hospital Comparison Benchmark Reports
application,19 which allows individual hospitals to compare
themselves to hospitals in a cohort. Users can customize
the cohort by geographic region (for example, to include
reporting hospitals across the United States or only in one
state) and by hospital type (for example, to include all types
of hospitals, only community hospitals, or only teaching/
research hospitals). This application contains data from all
reported cancer cases diagnosed between 2000 and 2010.
First course treatment modalities for prostate cancer at
FH were compared to those at hospitals in 3 cohort groups.
The first cohort group consisted of CoC-accredited US
hospitals of all types (1,389 hospitals). The second cohort
group consisted of CoC-accredited Massachusetts hospitals
of all types (37 hospitals). The third cohort group consisted
of CoC-accredited Massachusetts community hospitals (12
hospitals). FH was excluded from the composition of each
cohort group. Only cases diagnosed and receiving all or
part of the first course treatment at the reporting hospital
were included. This amounted to a total of 443 cases at FH;
869,047 cases in the US hospitals cohort; 26,367 cases in
the Massachusetts hospitals cohort; and 2,893 cases in the
Massachusetts community hospitals cohort. First course
treatment modalities were categorized as surgery only,
radiation only, radiation and hormones, hormones only,
other specified therapy, or no first course treatment. For FH
and each cohort group, the percent of prostate cancer cases
receiving each treatment modality was determined.
Because surgery is less often a viable option for elderly
patients than it is for younger patients,20 the distribution of
patient ages at a particular hospital may affect the distribution of treatment modalities. To investigate this possibility,
the distribution of age at diagnosis for prostate cancer
patients was determined for FH and each cohort group.
Next, the following first course treatment modalities
were individually stratified by patient age group: surgery
only, radiation only, and radiation and hormones. The
age groups were 40-49, 50-59, 60-69, 70-79, 80-89, and 90+
years. For FH and each cohort group, the percent of cases
in each age group receiving each treatment modality was
determined.
To further investigate the particular treatment modality
of radiation therapy, this modality was broken down into the
subcategories of external beam radiation and brachytherapy.
79
For FH and each cohort group, the percent of cases receiving
each subcategory of radiation was determined.
Figure 1. First Course Treatment for Prostate
Cancer Cases Diagnosed 2000-2010
Comparison of Treatment Modalities
Figure 1 displays the distribution of first course treatment modalities for prostate cancer cases diagnosed between
2000 and 2010 at FH compared to those at hospitals in 3
cohort groups: 1) US hospitals of all types, 2) Massachusetts
hospitals of all types, and 3) Massachusetts community
hospitals. The result that stands out most clearly from this
graph is that FH performed markedly less surgery and
more radiation than all 3 cohort groups. Only 23% of cases
were treated with surgery at FH, compared to 42% at US
hospitals of all types, 38% at Massachusetts hospitals of
all types, and 48% at Massachusetts community hospitals.
On the other hand, 33% of cases at FH were treated with
radiation only, compared to 18% at US hospitals of all types,
17% at Massachusetts hospitals of all types, and 9% at
Massachusetts community hospitals. FH also treated more
patients with a combination of radiation and hormones than
all 3 cohort groups. The discrepancy in surgery vs radiation rates was biggest between FH and the cohort group
consisting of other Massachusetts community hospitals.
In the analysis of patient age at diagnosis, FH was
found to have an older-than-average prostate cancer patient
population: 49% of FH patients were diagnosed at age 70
or older, compared to 40% of patients at US hospitals of
all types, 41% of patients at Massachusetts hospitals of all
types, and 39% of patients at Massachusetts community
hospitals.19
In the analysis of first course treatment modalities
stratified by age group, it became apparent that FH’s low
surgery rate and high radiation rate cannot be solely attributed to its older patient population. Figure 2 shows that,
compared to the cohort groups, FH performed surgery less
often even on younger patients in their forties, fifties, and
sixties. Figure 3 shows that FH used radiation at higher
80
Figure 2. First Course Treatment: Surgery Only for
Prostate Cancer Cases Diagnosed 2000-2010
Percent of Cases
Results
First Course Treatment
n FH n US hospitals of all types n Massachusetts hospitals of all types n Massachusetts community hospitals
age group
n FH n US hospitals of all types n Massachusetts hospitals of all types n Massachusetts community hospitals
Figure 3. First Course Treatment: Radiation Only for
Prostate Cancer Cases Diagnosed 2000-2010
Percent of Cases
The NCDB’s Survival Reports application21 was used
to generate unadjusted 5-year observed survival rates for
prostate cancer patients at FH and at a cohort group of all
CoC-accredited US hospitals. The application displayed
both overall survival rates and 95% confidence intervals,
which were calculated as +/- 1.96 standard deviations from
the overall survival rate.21 The displayed survival rates were
stratified by clinical stage at diagnosis. However, FH did not
have enough cases to yield specific survival rates for stages
I, III, or IV, as the application did not display a rate when
fewer than 30 cases were available within a stage group.
Therefore, only overall (all stages combined) and stage
II-specific survival rates were generated. Separate survival
rates were generated for patients diagnosed in 1998-2002
(the time period covered by the fifth edition of the AJCC
Cancer Staging Manual) and patients diagnosed in 20032005 (the time period covered by the sixth edition of the
AJCC Cancer Staging Manual).
Percent of Cases
Comparison of Survival Outcomes
age group
n FH n US hospitals of all types n Massachusetts hospitals of all types n Massachusetts community hospitals
rates than all 3 cohort groups on patients in their forties,
fifties, sixties, and seventies. Furthermore, on more elderly
patients in their eighties and nineties, FH used radiation
alone at lower rates than the cohort groups, but as Figure 4
reveals, in these age groups it used a combination therapy
consisting of both radiation and hormones at much higher
rates than the cohort groups. Taken together, Figures 2, 3,
and 4 suggest that factors other than patient age have influenced FH’s distribution of treatment modalities.
Journal of Registry Management 2013 Volume 40 Number 2
Percent of Cases
Figure 4. First Course Treatment: Radiation and
Hormones for Prostate Cancer Cases Diagnosed 2000-2010
Comparison of Survival Outcomes
age group
n
more than 7 times the rate of patients in the Massachusetts
community hospital cohort: 39% of patients at FH underwent brachytherapy vs 11% of patients at US hospitals of
all types and 5% of patients at Massachusetts community
hospitals.
FH
n
US hospitals of all types
n
Massachusetts hospitals of all types
n Massachusetts community hospitals
Percent of Cases
Figure 5. Type of Radiation Therapy for Prostate
Cancer Cases Diagnosed 2000-2010
The NCDB Survival Reports application generated
unadjusted 5-year observed survival rates for prostate
cancer cases diagnosed and treated at FH compared to
those diagnosed and treated at a cohort group of all
CoC-accredited hospitals in the United States. Table 1
displays these survival rates both for all stages combined
and for stage II only. Separate survival rates are displayed
for patients diagnosed in 1998-2002 and patients diagnosed in 2003-2005. As evidenced by the overlapping 95%
confidence intervals, there was no statistically significant
difference in stage II-specific survival between FH and the
cohort group for either diagnosis period. There was also no
statistically significant difference in survival for all stages
combined in the 1998-2002 diagnosis period. On the other
hand, in the 2003-2005 diagnosis period, FH’s survival rate
for all stages combined (77.5%) was lower than the national
average (87.6%) by a statistically significant amount, as
evidenced by the non-overlapping confidence intervals.
Discussion
type of radiation
n FH n US hospitals of all types n Massachusetts hospitals of all types n Massachusetts community hospitals
Table 1. Unadjusted 5-Year Observed Survival for Prostate
Cancer Patients at Falmouth Hospital Versus
CoC-Accredited Hospitals Nationwide
5-Year Observed Survival Rate, %
(95% Confidence Interval)
All Stages
Diagnosis
Year
FH
US
Hospitals
Stage II Only
FH
US
Hospitals
19982002
87.9
85.9
92.1
88.9
(83.4 - 92.4) (85.8 - 86.0) (88.0 - 96.1) (88.8 - 89.0)
20032005
77.5
87.6
84
90.5
(70.6 - 84.4) (87.4 - 87.7) (77.5 - 90.5) (90.4 - 90.7)
n Overlapping confidence intervals
n Non-overlapping confidence intervals
When the radiation therapy treatment modality was
broken down into subcategories, as displayed in Figure 5,
it became apparent that FH’s higher than average radiation rates were almost entirely due to more patients being
treated with brachytherapy, not external beam radiation.
Approximately the same percentage of cases at FH and at
hospitals nationwide were treated with external beam radiation: 21% of cases at FH and 22% of cases at US hospitals of
all types. However, FH patients underwent brachytherapy
at more than 3 times the rate of patients nationwide and
Journal of Registry Management 2013 Volume 40 Number 2
Comparison of Treatment Modalities
This study compared prostate cancer first course treatment modalities at FH to those at hospitals in 3 cohort
groups: 1) US hospitals of all types, 2) Massachusetts hospitals of all types, and 3) Massachusetts community hospitals.
The most salient result was that FH performed markedly
less surgery and more radiation, specifically brachytherapy,
than all 3 cohort groups. The discrepancy in surgery vs radiation rates was biggest between FH and the cohort group
consisting of other Massachusetts community hospitals,
which on average had even higher surgery rates and lower
radiation rates than the state and national cohort groups
that included teaching/research hospitals. This finding was
surprising given that the other Massachusetts community
hospitals are FH’s peers in terms of having similar resources
and case volumes.
Because surgery is less often a viable option for
elderly patients than it is for younger patients,20 it was
hypothesized that FH’s low surgery rates and high radiation rates might be attributable solely to its older than
average patient population. FH’s older patient population
is consistent with its location in Barnstable County on Cape
Cod, a popular retirement destination. According to US
census data, Barnstable County has the highest proportion
of elderly people of any county in the state: 25.4% of the
Barnstable population is over 65 years old, compared to
14.0% of the overall Massachusetts population and 13.3% of
the US population.22,23 However, the analysis of first course
treatment modalities stratified by age group revealed that,
compared to the cohort groups, FH performed surgery less
often and radiation more often even on younger patients.
81
This finding indicates that factors other than patient age
have affected FH’s distribution of treatment modalities. This
result is consistent with a previous study by Cooperberg
et al,9 which found substantial variation in prostate cancer
treatment practices across practice sites. This variation was
not explained by differences in patient demographics.
Further research would be needed to pinpoint the
factors that have contributed to FH’s particular distribution of prostate cancer treatment modalities. These factors
likely include local patient and physician preferences.
Speculatively, FH’s propensity for brachytherapy may
reflect the area of expertise of FH’s radiation oncologists,
one of whom was a pioneer in the brachytherapy field.24
Notably, given the lack of conclusive evidence supporting
one treatment modality as optimal, it may be justified for
preferences to play a major role in determining treatment
practices.
Comparison of Survival Outcomes
The NCDB Survival Reports application was used to
determine unadjusted 5-year observed survival rates for
prostate cancer cases at FH compared to those at a cohort
group of all CoC-accredited hospitals in the United States.
Unlike the Hospital Comparison Benchmark Reports application, the Survival Reports application does not allow users
to refine the cohort by geographic region or hospital type.
Therefore, comparison to more restricted cohorts consisting
of Massachusetts hospitals of all types or Massachusetts
community hospitals was not possible.
There was no statistically significant difference in stage
II-specific survival between FH and the national cohort
group. On the other hand, the survival rate for all stages
combined at FH was lower than the national average by
a statistically significant amount for patients diagnosed in
2003-2005. However, it is important to bear in mind that
these observed survival rates were computed with death
from any cause as the endpoint. They were not adjusted for
patient age or hospital case-mix, so they did not take into
account that some hospitals care for older or sicker patients.
This limitation of unadjusted survival rates is particularly
relevant for this study, given that FH was found to care for
an older than average patient population. For these reasons,
the unadjusted survival rates provided by the NCDB
Survival Reports application were insufficient to support
robust conclusions about the relative quality of FH’s prostate cancer care.
To test whether FH’s lower 5-year observed survival
rate is attributable to its older patient population, the
observed survival rates could be stratified by patient age
group. However, the NCDB Survival Reports application
does not allow stratification by age, despite the availability of this data. The addition of this capability would
allow more meaningful comparison of individual hospitals’
survival outcomes to national averages.
A rigorous evaluation of FH’s quality of prostate cancer
care would require analysis of both relative survival rates
and performance measures, neither of which are provided
by the NCDB. Relative survival rates are adjusted for the
expected survival of people in the general population who
82
do not have prostate cancer. The national 5-year relative
survival rate is 28% for patients diagnosed with malignant
prostate cancer, but 100% for those with localized cancer,
which constitute the vast majority of cases.2 For all stages
combined, national 10-year and 15-year relative survival
rates are 98% and 93%, respectively.1 These high relative
survival rates illustrate prostate cancer’s characteristically
long time course.
Performance measures assess adherence to best practices in treatment. The NCDB maintains an application
dedicated to disease-specific performance measures: the
Cancer Program Practice Profile Reports.25 However, this
application currently only compiles data for breast and
colorectal cancers. It allows individual hospitals to view
performance rates that give the proportion of their breast
and colorectal cancer patients that were treated according
to recognized standards of care. An example of a best
practice is that at least 12 regional lymph nodes should be
pathologically examined for accurate staging of resected
colon cancer.25 A performance measures program for prostate cancer would allow hospitals to better assess and
improve their prostate cancer care. Given the lack of conclusive evidence supporting one optimal treatment modality
for localized prostate cancer, these performance measures
would be especially helpful in ensuring that whichever
treatment modality a patient elects is executed according to
best practices.
Conclusions and Future Directions
This study’s main finding is that FH performed
markedly less surgery and more radiation, specifically
brachytherapy, than the cohort groups of hospitals.
Furthermore, this distribution of treatment modalities was
not solely attributable to FH’s older than average patient
population. These preliminary insights provide a potential
starting point for further internal evaluation of FH’s prostate cancer care.
This study was conducted using NCDB Web-based
data applications that are available to the more than
1,500 hospitals nationwide that have CoC-accredited cancer
programs.18 The ease of access to these applications allows
this type of study to be replicable at other community hospitals that lack substantial quality assurance resources and
personnel. Similar studies could easily be carried out by a
small community hospital’s tumor registrar or other cancer
program staff. The results of these studies could inform
quality assessment programs aimed at improving prostate
cancer care.
References
1. American Cancer Society. Cancer Facts & Figures 2013. Atlanta, GA:
American Cancer Society; 2013.
2. Howlader N, Noone AM, Krapcho M, et al, eds. SEER Cancer Statistics
Review 1975-2010. National Cancer Institute. Available at: http://seer.
cancer.gov/csr/1975_2010/. Updated April 29, 2013. Accessed May
20, 2013.
3. Hernández J, Thompson IM. Prostate-specific antigen: a review of
the validation of the most commonly used cancer biomarker. Cancer.
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4. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A III, eds.
AJCC Cancer Staging Manual. 7th ed. New York, NY: Springer; 2010.
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5. Moyer VA; for U.S. Preventive Services Task Force. Screening
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doi:10.7326/0003-4819-157-2-201207170-00459.
6. Andriole GL, Crawfor ED, Grubb RL III, et al; for the PLCO Project Team.
Prostate cancer screening in the randomized Prostate, Lung, Colorectal,
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7. Schröder FH, Hugosson J, Roobol MJ, et al; for the ERSPC Investigators.
Prostate-cancer mortality at 11 years of follow-up. N Engl J Med.
2012;366(11):981-990. doi:10.1056/NEJMoa1113135.
8. Welch HG, Albertsen PC. Prostate cancer diagnosis and treatment after
the introduction of prostate-specific antigen screening: 1986-2005. J
Natl Cancer Inst. 2009;101(19):1325-1329. doi:10.1093/jnci/djp278.
9. Cooperberg MR, Broering JM, Carroll PR. Time trends and local
variation in primary treatment of localized prostate cancer. J Clin Oncol.
2010;28(7):1117-1123. doi:10.1200/JCO.2009.26.0133.
10.Wilt TJ, MacDonald R, Rutks I, Shamliyan TA, Taylor BC, Kane RL.
Systematic review: comparative effectiveness and harms of treatments for
clinically localized prostate cancer. Ann Intern Med. 2008;148(6):435438. doi:10.7326/0003-4819-148-6-200803180-00209.
11.Wallace K, Fleshner N, Jewett M, Basiuk J, Crook J. Impact of a multidisciplinary patient education session on accrual to a difficult clinical
trial: the Toronto experience with the surgical prostatectomy versus
interstitial radiation intervention trial. J Clin Oncol. 2006;24(25):41584162. doi:10.1200/JCO.2006.06.3875.
12.Lane JA, Hamdy FC, Martin RM, Turner EL, Neal DE, Donovan JL. Latest
results from the UK trials evaluating prostate cancer screening and
treatment: the CAP and ProtecT studies. Eur J Cancer. 2010;46(17):30953101. doi:10.1016/j.ejca.2010.09.016.
13.Thompson I, Thrasher JB, Aus G, et al; for the Prostate Cancer Clinical
Guideline Update Panel. Guideline for the management of clinically
localized prostate cancer: 2007 update. J Urol. 2007;177(6):2106-2131.
doi:10.1016/j.juro.2007.03.003.
14.O’Connor AM, Llewellyn-Thomas HA, Flood AB. Modifying unwarranted variations in health care: shared decision making using patient
decision aids. Health Aff (Millwood). 2004;Suppl Variation:VAR63-72.
doi:10.1377/hlthaff.var.63.
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15.Wennberg JE, Fisher ES, Skinner JS. Geography and the debate over
Medicare reform. Health Aff (Milwood). 2002 Jul-Dec;Suppl Web
Exclusives:W96-114. doi:10.1377/hlthaff.w2.96.
16.The Center for the Evaluative Clinical Sciences, Dartmouth Medical
School. The Quality of Medical Care in the United States: A Report
on the Medicare Program. Chicago, IL: Health Forum, Inc.; 1999. The
Dartmouth Atlas of Health Care in the United States. Available at: http://
www.dartmouthatlas.org/downloads/atlases/99Atlas.pdf. Accessed
May 20, 2013.
17.Shahinian VB, Kuo YF, Freeman JL, Goodwin JS. Determinants of
androgen deprivation therapy use for prostate cancer: role of the urologist. J Natl Cancer Inst. 2006;98(12):839-845. doi:10.1093/jnci/djj230.
18.Commission on Cancer of the American College of Surgeons, American
Cancer Society. National Cancer Data Base Web site. http://www.facs.
org/cancer/ncdb. Updated February 12, 2013. Accessed May 20, 2013.
19.Commission on Cancer. National Cancer Data Base Hospital Comparison
Benchmark Reports. Chicago, IL: American College of Surgeons.
Available at: http://www.facs.org/cancer/ncdb/hcbr.html. Updated
2009. Accessed May 20, 2013.
20.Trinh QD, Schmitges J, Sun M, et al. Open radical prostatectomy in
the elderly: a case for concern? BJU Int. 2012;109(9):1335:1340.
doi:10.1111/j.1464-410X.2011.10554.x.
21.Commission on Cancer. National Cancer Data Base Survival Reports.
Chicago, IL: American College of Surgeons. Available at: http://www.
facs.org/cancer/ncdb/survival.html. Updated 2004. Accessed May 20,
2013.
22.State & County QuickFacts. United States Census Bureau Web site.
http://quickfacts.census.gov/qfd/states/25/25001.html. Updated
March 11, 2013. Accessed May 20, 2013.
23.USA QuickFacts. United States Census Bureau Web site. http://quickfacts.census.gov/qfd/states/00000.html. Updated March 14, 2013.
Accessed May 20, 2013.
24.Cape Cod Healthcare Web site. http://www.capecodhealth.org/
services/regional-cancer-network/treatments-&-services/prostatecancer/. Updated 2012. Accessed May 20, 2013.
25.Commission on Cancer. National Cancer Data Base Cancer Program
Practice Profile Reports. Chicago, IL: American College of Surgeons.
Available at: http://www.facs.org/cancer/ncdb/cp3r.html. Updated
2010. Accessed May 20, 2013.
83
Original Article
Treatment Patterns for Cervical Carcinoma
In Situ in Michigan, 1998-2003
Divya A. Patel, PhDa; Mona Saraiya, MDb; Glenn Copeland, MBAc; Michele L. Cote, PhDd;
S. Deblina Datta, MDe; George F. Sawaya, MDf
Abstract: Objective: To characterize population-level surgical treatment patterns for cervical carcinoma in situ (CIS)
reported to the Michigan Cancer Surveillance Program (MCSP), and to inform data collection strategies. Methods: All
cases of cervical carcinoma in situ (CIS) (including cervical intraepithelial neoplasia grade 3 and adenocarcinoma in situ
[AIS]) reported to the MCSP during 1998–2003 were identified. First course of treatment (ablative procedure, cone biopsy,
loop electrosurgical excisional procedure [LEEP], hysterectomy, unspecified surgical treatment, no surgical treatment,
unknown if surgically treated) was described by histology, race, and age at diagnosis. Results: Of 17,022 cases of cervical
CIS, 82.8% were squamous CIS, 3% AIS/adenosquamous CIS, and 14.2% unspecified/other CIS. Over half (54.7%) of cases
were diagnosed in women under age 30. Excisional treatments (LEEP, 32.3% and cone biopsy, 17.3%) were most common,
though substantial proportions had no reported treatment (17.8%) or unknown treatment (21.1%). Less common were
hysterectomy (7.2%) and ablative procedures (2.6%). LEEP was the most common treatment for squamous cases, while
hysterectomy was the most treatment for AIS/adenosquamous CIS cases. Across histologic types, a sizeable proportion of
women diagnosed ≤30 years of age underwent excision, either LEEP (20%–38.7%) or cone biopsy (13.7%-44%). Conclusion:
Despite evidence suggesting it may be safer and equally effective as excision, ablation was rarely used for treating cervical
squamous CIS. These population-based data indicate some notable differences in treatment by histology and age at diagnosis, with observed patterns appearing consistent with consensus guidelines in place at the time of study, but favoring
more aggressive procedures. Future data collection strategies may need to validate treatment information, including the
large proportion of no or unknown treatment.
Key words: adenocarcinoma, cervical carcinoma in situ, cervical intraepithelial neoplasia, hysterectomy, squamous cell carcinoma
Introduction
Despite dramatic declines in cervical cancer in the
United States concurrent with widespread screening, about
12,280 women are diagnosed with invasive cervical cancer
each year.1 Cervical cancer is preceded by dysplastic changes
of the cervical epithelium, known as cervical intraepithelial
neoplasia (CIN). These CIN lesions are graded based on
histological severity from 1 to 3, the latter including carcinoma in situ (CIS), a pre-invasive carcinomatous change of
the cervix. High-grade CIN lesions (CIN 3) and CIS, often
synonymous, are considered the most relevant cervical
cancer precursors for diagnosis and treatment due to their
heightened invasive potential.2 Most cervical cancers (70%)
are squamous cell carcinomas.3 While adenocarcinomas
account for a smaller proportion (20%–25%) of all cervical
cancers,3,4 registry-based studies indicate their incidence
may be increasing.5 Adenocarcinoma in situ (AIS) is the
most proximate precursor of cervical adenocarcinoma.
The systematic collection of data on high-grade CIN
lesions could serve an important role in monitoring the
impact of preventive measures such as newer cervical
cancer screening guidelines and prophylactic human
papillomavirus (HPV) vaccine on future disease burden.6,7
However, cervical cancer precursors are currently not
routinely reported throughout the United States.6 Routine
collection of CIS did occur by US cancer registries for several
decades in the 1970s, 1980s, and early 1990s. In 1996, US
cancer registries discontinued this practice due to concerns
over the burden to reporting facilities; the quality of data, in
light of changing diagnostic terminology for cervical cancer
precursors8; and loss of comparability in incidence data over
time and across registries.9
Despite the national decision in 1996 to stop collection of high-grade cervical cancer precursors, the Michigan
__________
a
Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine. bDivision of Cancer Prevention and Control, Centers
for Disease Control and Prevention. cMichigan Cancer Surveillance Program, Michigan Department of Community Health. dPopulation Studies and Disparities
Research, Karmanos Cancer Institute and Wayne State University School of Medicine. eDivision of STD Prevention, Centers for Disease Control and Prevention.
f
Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, California.
Address correspondence to Mona Saraiya MD, MPH, Centers for Disease Control and Prevention, Division of Cancer Prevention and Control, 4770 Buford
Hwy NE, MS K-55, Atlanta, GA 30341. Telephone: (770) 488-4293. Fax: (770) 488-4639. Email: [email protected].
Presented at the 27th International Papillomavirus Conference (Berlin, September 2011) and at the 2012 Biennial Scientific Meeting of the American Society for
Colposcopy and Cervical Pathology (San Francisco, March 2012).
This work was supported by the National Program of Cancer Registries, Centers for Disease Control and Prevention (grant number 5U58DP000812). Dr. Patel
was supported in part through a career development award from the National Cancer Institute, National Institutes of Health (grant number K07CA120040). Dr.
Sawaya was supported in part through an interprofessional agreement with the Centers for Disease Control and Prevention. The findings and conclusions in this
report are those of the authors and do not necessarily represent the official position of the funding agencies.
84
Journal of Registry Management 2013 Volume 40 Number 2
Cancer Surveillance Program (MCSP) continued collection of cervical CIS/AIS data, with minimal additional
resources, due to the increasing use of electronic case
reporting.8 Because the Michigan program is the only
population-based data source for high-grade cervical cancer
precursors that has been continuously collected since 1985,
it provides a unique resource for the long-term systematic
monitoring of cervical cancer control efforts. Analysis of
MCSP data found increasing rates of cervical carcinoma
in situ (CIS) in Michigan that nearly doubled in less than
2 decades, increasing from 31.7 per 100,000 in 1985 to 59.2
per 100,000 in 2003. Furthermore, for every invasive cervical
cancer diagnosis reported during the same period, there
were 7 in situ cases in white women and 4 in situ cases in
black women.9
In women with a histological diagnosis of CIN, appropriate management is a critical component of cervical cancer
prevention. However, very few population-based data exist
on patterns of management for cervical cancer precursors.
Surgical management options for CIN include ablative
procedures that destroy the affected tissue in vivo (eg, cryotherapy, laser ablation), excisional procedures that remove
the affected tissue (eg, loop electrosurgical excisional procedure [LEEP], laser conization, cold knife conization), and
hysterectomy.10,11
In a systematic review published in 2000, no substantive differences were found in the persistence or resolution
of CIN among women treated with cone biopsy, cryotherapy, laser ablation, or LEEP.12 A Cochrane review of the
evidence through July 2004, from 28 randomized controlled
trials of alternative surgical treatments for CIN, found no
overwhelmingly superior technique for eradicating CIN.13
The authors concluded that the choice of treatment should
therefore be based on cost, morbidity, and the value of
obtaining biopsy specimens.
Excisional procedures are more widely used to treat
CIN in the United States,14 largely due to its provision of
a tissue specimen for assessment of histopathology and
surgical margins,15 and perhaps because it is believed by
clinicians to more effective than ablation, despite evidence
to the contrary.16-18 In contrast to these potential benefits,
there is now evidence from meta-analyses of observational
studies indicating potential increased risks for adverse
pregnancy outcomes (ie, premature rupture of membranes,
preterm delivery, low birth weight infant, and even perinatal mortality) among women treated with cold knife
conization, LEEP, or laser conization.14,19
The objectives of this study were to characterize
population-level treatment patterns for cervical CIS by
histology, age, and race in the MCSP, and to inform future
data collection strategies.
Materials and Methods
Data Source and Case Selection
This study was approved as exempt by the Michigan
Department of Community Health’s Institutional Review
Board. We used data collected by the MCSP, a statewide
population-based registry which has been in operation
Journal of Registry Management 2013 Volume 40 Number 2
since 1981, with legally mandated cancer reporting and
statewide population coverage since 1985.9 Methods for
collection of cervical CIS cases through the MCSP have
been described in detail.9 Briefly, the MCSP covers a state
population of approximately 10 million, consisting of 81.2%
whites, 14.2% blacks, 2.4% Asians, 0.6% American Indians/
Alaska Natives, and approximately 4.2% Hispanics.20 All
in situ and invasive cancers (other than basal or squamous
cell carcinoma of nongenital skin) have been reportable to
the MCSP as defined by the Michigan Administrative Code
under the authority of Public Act 82 of 1984.
Cervical CIS is collected by MSCP in either of 2 diagnostic categories: carcinoma in situ, or grade 3 cervical
intraepithelial neoplasia (CIN 3) without any qualifier.
During the study period, a diagnosis of CIN 3 qualified with
the term “severe dysplasia” was not reportable, an exclusion criterion intended to increase the specificity of cervical
CIS cases. For our analysis, all cervical CIS cases reported
between 1998 and 2003 were included. Cases were limited
to ICD-O-3 topography code C53 (cervix uteri), histology
codes 8010–8560, and behavior code 2 (in situ neoplasms).21
The majority of cases (63.6%) were ICD-O-3 histology code
8077, corresponding to squamous intraepithelial neoplasia
grade III.21 The remaining cases included histology codes
8070 (squamous cell carcinoma in situ, not otherwise
specified (NOS); 19%), 8010 (carcinoma in situ, NOS; 14.2%),
8140 (adenocarcinoma in situ, NOS; 2.8%),21 and other, less
common codes.
Classification of Variables
Histology, demographic information (race and age at
diagnosis), and first course of treatment were collected and
reported according to SEER standards.22 Histologic subtypes
were classified according to ICD-O-3 morphology codes,21
and categorized as squamous CIS, AIS/adenosquamous
CIS, unspecified CIS, or other specified CIS. For analysis
purposes, unspecified CIS and other specified CIS were
grouped, so that the final categories for histologic subtype
were as follows: squamous CIS, AIS/adenosquamous CIS,
and unspecified CIS/other CIS. Race was categorized as
white, black, other, or unknown. Age at diagnosis was
categorized as <21, 21–30, 31–45, or >45 years.
Surgical procedures were classified according to SEER
variable codes for first course of cancer-directed surgery,
and categorized as ablative procedure, cone biopsy, LEEP,
hysterectomy, unspecified surgical treatment, no surgical
treatment, and unknown if surgically treated. Details of
the surgical procedure classification, including SEER codes,
procedure descriptions and frequencies, are shown in a
supplemental table.
Ablative procedures included surgical techniques that
destroy the affected tissue in vivo. The two most widely
used excisional modalities that remove the affected tissue,
LEEP and cone biopsy, were kept as separate categories.
Hysterectomy included total, radical, modified radical, and
extended hysterectomy. Trachelectomy, or surgical removal
of the cervix, is a fertility-preserving surgical alternative
to a radical hysterectomy. This procedure was reported in
only a small number of cases (n=3) and was classified as
85
Table 1. Characteristics of Cervical Carcinoma In Situ
(CIS) Cases* in Michigan, 1998-2003 (n=17,022)
Characteristic
n (%)
Age at diagnosis (years)
<21
1,749 (10.3)
21-30
7,552 (44.4)
31-45
59,92 (35.2)
>45
1,711 (10.1)
Missing
Median (range)
18 (0.1)
29 (8-103)
Race
White
12,540 (73.7)
Black
2,508 (14.7)
Other
109 (0.6)
Unknown
1,865 (11.0)
Histologic subtype
Squamous CIS
AIS or adenosquamous CIS
Unspecified or other CIS
14,096 (82.8)
514 (3.0)
2,412 (14.2)
446 (2.6)
Cone biopsy
2,942 (17.3)
LEEP
5,504 (32.3)
Hysterectomy
Unspecified surgical treatment
1,221 (7.2)
291 (1.7)
No surgical treatment
3,032 (17.8)
Unknown if surgically treated
3,586 (21.1)
*All cases reported to the Michigan Cancer Surveillance Program during
1998-2003 and limited to ICD-O-3 topography code C53 (cervix uteri),
histology codes 8010-8560, and behavior code 2 (in situ neoplasms).
The majority of cases (63.6%) were ICD-O-3 histology code 8077, corresponding to squamous intraepithelial neoplasia grade III.
AIS = adenocarcinoma in situ, LEEP = loop electrosurgical excisional
procedure.
hysterectomy because it may be viewed as having similar
severity (though, unlike hysterectomy, trachelectomy does
not preclude future childbearing). Unspecified surgical
treatment included instances where it was assumed a
surgical treatment occurred, but the procedure could not
be readily classified with other surgical treatments on the
basis of its limited description (ie, surgery, NOS [n=4]; local
tumor excision, NOS [n=287]). The category of “no surgical
treatment” was limited to a single SEER variable code for
“none; no surgery of primary site; autopsy only.” It was
unknown if surgically treated included the SEER variable
code for “unknown if surgery performed; death certificate
only” as well as procedures considered part of diagnostic
workup rather than treatment (ie, dilation and curettage/
endocervical curettage [n=1,685]; excisional biopsy, NOS
[n=275]). The category of “no surgical treatment” was
86
Statistical Analysis
The distribution of age at diagnosis was tested for
normality using the Shapiro-Wilk test, and because the
resultant p-value was <0.05, age at diagnosis was compared
across histologic subtypes using the non-parametric
Kruskal-Wallis test. First course of treatment was described
overall and by histologic subtype, and was further stratified
by race and age at diagnosis. Analyses by race were limited
to white and black women, and excluded 109 (0.6%) women
with other race due to small numbers as well as 1,865 (11%)
women with unknown race. Analyses by age at diagnosis
excluded 18 (0.1%) women for whom age at diagnosis was
missing. The exact Cochran-Armitage trend test was used to
evaluate trends in hysterectomy by age, stratified by histologic subtype. These trend tests compared the distribution
of women undergoing hysterectomy with all individuals
who did not undergo that treatment across age groups.
Two-sided p-values <0.05 were considered statistically
significant. All analyses were conducted using SAS version
9.2 (SAS Institute, Inc., Cary, NC).23
Results
First course of cancer-directed surgery
Ablative procedure
kept separate from “unspecified surgical treatment” and
“unknown if surgically treated” since some women may
have had contraindications to surgery.
Characteristics of the Study Population
For the period 1998 through 2003, there were 17,022
cases of cervical CIS reported to the MSCP (Table 1).
Median age at diagnosis was 29 years. Over half (54.7%)
of cases were diagnosed under 30 years of age and 10.3%
of cases were diagnosed under age 21. Most women were
white (73.7%). The majority of cases diagnosed during
this period were squamous CIS (82.8%). The distribution
of age at diagnosis differed significantly across histologic
subtypes (Kruskal-Wallis p-value <0.0001; data not shown);
women with AIS/adenosquamous CIS were diagnosed at
later ages (median: 33 years) than women with squamous
CIS (median: 29 years) or unspecified/other CIS (median:
30 years). Overall, LEEP (32.3%) and cone biopsy (17.3%),
both excisional modalities, were the most commonly used
treatments. Fewer women underwent hysterectomy (7.2%),
ablative procedures (2.6%) or unspecified surgical treatments (1.7%). In addition, a substantial proportion of
women had no surgical treatment (17.8%), and for approximately one fifth of women (21.1%), it was unknown if they
were surgically treated.
First Course of Surgical Treatment Stratified by
Histologic Subtype
There were some notable differences in treatment
across histologic subtypes (Table 2). LEEP was the most
common treatment for women with squamous (33.6%) and
unspecified/other CIS (29.2%). However, the most common
form of treatment among women with AIS/adenosquamous CIS was hysterectomy (29.6%), a treatment received
by only 6% of those with squamous CIS and 9% of those
with unspecified/other CIS. Fewer women with AIS/
Journal of Registry Management 2013 Volume 40 Number 2
Table 2. First Course of Treatment for Cervical Carcinoma In Situ (CIS) Cases* by Histologic Subtype, Michigan,
1998-2003 (n=17,022)
Histologic subtype
Squamous CIS
AIS or adenosquamous CIS
Unspecified or other CIS
Overall
(n=14,096; 82.8%)
(n=514; 3.0%)
(n=2,412; 14.2%)
(n=17,022)
Treatment
n (%)
n (%)
n (%)
n (%)
Ablative procedure
380 (2.7)
2 (0.4)
64 (2.7)
446 (2.6)
Cone biopsy
2,370 (16.8)
124 (24.1)
448 (18.6)
2,942 (17.3)
LEEP
4,733 (33.6)
66 (12.8)
705 (29.2)
5,504 (32.3)
851 (6.0)
152 (29.6)
218 (9.0)
1,221 (7.2)
Hysterectomy
Unspecified surgical treatment
232 (1.7)
16 (3.1)
43 (1.8)
291 (1.7)
No surgical treatment
2,518 (17.9)
49 (9.5)
465 (19.3)
3,032 (17.8)
Unknown if surgically treated
3,012 (21.4)
105 (20.4)
469 (19.4)
3,586 (21.1)
*All cases reported to the Michigan Cancer Surveillance Program during 1998–2003 and limited to ICD-O-3 topography code C53 (cervix uteri), histology codes 8010–8560, and behavior code 2 (in situ neoplasms). The majority of cases (63.6%) were ICD-O-3 histology code 8077, corresponding to
squamous intraepithelial neoplasia grade III.
AIS = adenocarcinoma in situ, LEEP = loop electrosurgical excisional procedure.
adenosquamous CIS received no surgical treatment (9.5%),
compared to 17.9% of those with squamous CIS and 19.3%
of those with unspecified/other CIS. Ablation was the least
common treatment across all histologic types (0.4%–2.7%).
First Course of Surgical Treatment Stratified by Race
Treatment patterns by race and histologic subtype,
limited to 15,048 (88.4%) black and white women, are
shown in Table 3. Among women with squamous CIS, treatments were similar by race, with the majority of white and
black women undergoing LEEP (33.6% and 37.4%, respectively) and cone biopsy (17.8% and 17.3%, respectively).
Treatments for unspecified/other CIS were also similar
by race, with the majority of white and black women also
undergoing LEEP (30.2% and 28%, respectively) and cone
biopsy (19% and 20.5%, respectively). Some racial differences in treatment types were observed among women
with AIS/adenosquamous CIS. White women had a higher
proportion of hysterectomies (31.3%) than black women
(34.8%), while black women had a higher proportion of
cone biopsies (34.8%) than white women (23.3%). Among
women with AIS/adenosquamous CIS, a larger proportion
of black women (21.7%) underwent LEEP compared with
white women (11.7%). Of note, because there were only 23
black women with AIS/adenosquamous CIS in this study
population, these percentages and comparisons should be
interpreted with caution.
First Course of Surgical Treatment Stratified by Age
at Diagnosis
Treatment patterns by age at diagnosis and histologic
subtype, among women for whom age at diagnosis was
known (n=17,044; 99.9%), are shown in Table 4. For all
histologic subtypes, the proportion of women undergoing
hysterectomy increased significantly with increasing age
at diagnosis (Cochran-Armitage trend test p-value <0.05
for each histologic subtype). Ablative procedures were
Journal of Registry Management 2013 Volume 40 Number 2
performed in small proportions of women (<3.3%) regardless of age at diagnosis, and were used the least among
those with AIS/adenosquamous CIS. Treatment patterns
by age at diagnosis were similar for those with squamous
CIS and unspecified/other CIS, with the majority of women
with these histologic subtypes undergoing LEEP. However,
treatment patterns were more variable among women with
AIS/adenosquamous CIS. Among women diagnosed with
this histologic subtype at <21 and 21–30 years of age, cone
biopsy was the most common surgical treatment (44%
and 35%, respectively). Among women diagnosed with
this histologic subtype at later ages, hysterectomy was the
predominant form of surgical treatment (42.2% for those
diagnosed at age 31–45 years and 44.6% for those diagnosed
at age >45 years), with substantially fewer women diagnosed in these age groups undergoing cone biopsy or LEEP.
Notably, across histologic subtypes, a sizeable proportion of women diagnosed ≤30 years of age underwent an
excisional procedure, either a LEEP (20%–38.7%) or cone
biopsy (13.7%–44%). In the subgroup of 1,749 (10.3%)
women diagnosed at the youngest ages (<21 years), approximately one fourth (23.2%) received no surgical treatment.
Excisional procedures were common among women diagnosed <21 years of age, for squamous CIS (36.1% LEEP,
13.7% cone biopsy), AIS/adenosquamous CIS (20% LEEP,
44% cone biopsy), and unspecified/other CIS (38.7% LEEP,
17% cone biopsy).
Discussion
In this population-based study of 17,022 women diagnosed with cervical CIS in Michigan during 1998–2003,
excisional procedures (LEEP and cone biopsy) were the
most commonly used treatments. Cervical ablation was
rarely performed, with less than 3.3% of women across
all histological subtypes treated with these procedures.
Consensus guidelines in place during the time frame of
our study recommended both excision or ablation of the
87
Table 3. First Course of Treatment for Cervical Carcinoma
In Situ (CIS) Cases* by Histologic Subtype, Overall and by
Race, Michigan, 1998-2003 (n=15,048†)
Race
Black
(n=2,508;
16.7%)
n (%)
Overall
(n=15,048)
n (%)
313 (3.0)
38 (1.9)
351 (2.8)
Cone biopsy
1862 (17.8)
342 (17.3)
2204 (17.8)
LEEP
3509 (33.6)
741 (37.4)
4250 (34.2)
Hysterectomy
714 (6.8)
109 (5.5)
823 (6.6)
Unspecified surgical
treatment
200 (1.9)
25 (1.3)
225 (1.8)
No surgical
treatment
1744 (16.7)
276 (13.9)
2020 (16.3)
Unknown if
surgically treated
2096 (20.1)
451 (22.8)
2547 (20.5)
2 (0.4)
0 (0)
2 (0.4)
107 (23.3)
8 (34.8)
115 (23.8)
54 (11.7)
5 (21.7)
59 (12.2)
144 (31.3)
5 (21.7)
149 (30.9)
Unspecified surgical
treatment
16 (3.5)
0 (0)
16 (3.3)
No surgical
treatment
41 (8.9)
0 (0)
41 (8.5)
96 (20.9)
5 (21.7)
101 (20.9)
49 (3.0)
8 (1.6)
57 (2.7)
Cone biopsy
312 (19.0)
103 (20.5)
415 (19.4)
LEEP
496 (30.2)
141 (28.0)
637 (29.7)
Hysterectomy
174 (10.6)
39 (7.8)
213 (9.9)
38 (2.3)
1 (0.2)
39 (1.8)
No surgical
treatment
261 (15.9)
111 (22.1)
372 (17.3)
Unknown if
surgically treated
312 (19.0)
100 (19.9)
412 (19.2)
Treatment
White
(n=12,540;
83.3%)
n (%)
Squamous CIS (n=12,420)
Ablative procedure
AIS or adenosquamous CIS (n=483)
Ablative procedure
Cone biopsy
LEEP
Hysterectomy
Unknown if
surgically treated
Unspecified or other CIS (n=2,145)
Ablative procedure
Unspecified surgical
treatment
*All cases reported to the Michigan Cancer Surveillance Program during
1998-2003 and limited to ICD-O-3 topography code C53 (cervix uteri),
histology codes 8010-8560, and behavior code 2 (in situ neoplasms).
The majority of cases (63.6%) were ICD-O-3 histology code 8077, corresponding to squamous intraepithelial neoplasia grade III. †Does not
include 109 (0.6%) women of other race and 1865 (11.0%) women with
unknown race.
LEEP = loop electrosurgical excisional procedure, AIS = adenocarcinoma in situ.
88
transformation zone as acceptable treatment options for
women with biopsy-confirmed CIN2,3 and a satisfactory
colposcopy.24 In women with recurrent CIN, 2,3 excisional
treatment modalities were recommended.24 We also found
that hysterectomies increased significantly with age, and
were most commonly performed for AIS/adenosquamous
CIS, in line with the existing recommendations against
hysterectomy as primary therapy for CIN2,3 but the recommended treatment for women with AIS who have completed
childbearing.24 We observed some potential differences in
treatment for AIS/adenosquamous CIS by race, which may
be due in part to confounding by differences in desires for
future childbearing and/or preferences for definitive riskeliminating surgery compared to continued surveillance.
There are few other population-based registry studies
with which to compare the treatment patterns observed in
our study. A small study conducted by the Romagna Cancer
Registry in northern Italy found that, among 264 women
with biopsy-confirmed CIN 3 reported to the registry
during 1986–1993, the first course of treatment involved
conization (59%), hysterectomy (35%), and local destructive
therapy (6%).25 The authors attributed the limited role of
conservative therapy and high prevalence of hysterectomy
to a lack of ensuring follow-up with repeat smears and/or
colposcopy.25 We observed similar proportions of women
undergoing LEEP or conization (49.6%), but fewer women
undergoing hysterectomy (7.2%) and ablative therapy
(2.6%). The lower percentage of hysterectomies and ablative
therapy could reflect misclassification or the preferred and
popular choice of treatment during the study period.
A study linking the British Columbia Cancer Agency
cytology database with cancer registry and vital statistics
data found women with CIN 3 most often underwent
cone biopsy18; however, as the purpose of their study was
to examine rates of CIN2,3 and invasive cervical cancer
following treatment, their exclusion criteria precludes direct
comparison with our findings. Perhaps the most methodologically comparable study is an analysis published in
1990 of the Surveillance, Epidemiology, and End Results
(SEER) Program’s New Mexico Tumor Registry.26 Overall, of
the 4,585 women diagnosed with cervical CIS during 1969–
1985, 31.1% underwent conservative treatment (conization,
laser treatment, cryosurgery or trachelectomy), while 65.5%
underwent hysterectomy.26 By the end of the 17-year period,
the proportion of women undergoing hysterectomy had
declined to 45.8% while the proportion of women who
underwent more conservative treatments had increased to
50.3%.26 The use of conservative treatments increased in all
age groups with the largest increase in women under age
30.26 Interestingly, these dramatic shifts in surgical practice
occurred in the absence of any consensus guidelines for the
management of women with CIN, but may have reflected
the advent of LEEP which could be easily performed in an
outpatient setting.
In our study, the median age at diagnosis was 29 years,
coinciding with peak childbearing age among American
women.27 The few available registry-based studies have
reported later ages at diagnosis, both in areas with organized cervical cancer screening such as in Romagna, Italy
Journal of Registry Management 2013 Volume 40 Number 2
Table 4. First Course of Treatment for Cervical Carcinoma In Situ (CIS) Cases* by Age at Diagnosis and Histologic
Subtype, Michigan, 1998-2003 (n=17,004†)
Age at diagnosis (years)
Treatment
<21
(n=1,749; 10.3%)
n (%)
21-30
(n=7,552; 44.4%)
n (%)
31-45
(n=5,992; 35.2%)
n (%)
>45
(n=1,711; 10.1%)
n (%)
Squamous CIS
Ablative procedure
47 (3.1)
207 (3.3)
110 (2.3)
16 (1.2)
Cone biopsy
209 (13.7)
1,026 (16.2)
878 (18.0)
255 (19.2)
LEEP
552 (36.1)
2,383 (37.6)
1,512 (31.0)
285 (21.4)
1 (0.1)
111 (1.8)
508 (10.4)
231 (17.4)
20 (1.3)
102 (1.6)
82 (1.7)
28 (2.1)
No surgical treatment
351 (22.9)
1,166 (18.4)
765 (15.7)
231 (17.4)
Unknown if surgically treated
350 (22.9)
1,344 (21.2)
1,031 (21.1)
283 (21.3)
0 (0)
1 (0.6)
1 (0.4)
0 (0)
11 (44.0)
62 (35.0)
41 (17.3)
10 (13.5)
5 (20.0)
37 (20.9)
21 (8.9)
3 (4.1)
1 (4.0)
18 (10.2)
100 (42.2)
33 (44.6)
0 (0)
6 (3.4)
8 (3.4)
2 (2.7)
1 (4.0)
16 (9.0)
21 (8.9)
10 (13.5)
7 (28.0)
37 (20.9)
45 (19.0)
16 (21.6)
5 (2.6)
31 (3.0)
22 (2.5)
6 (2.0)
Cone biopsy
33 (17.0)
191 (18.4)
171 (19.7)
53 (17.2)
LEEP
75 (38.7)
339 (32.7)
241 (27.7)
50 (16.2)
0 (0)
27 (2.6)
119 (13.7)
71 (23.1)
3 (1.6)
21 (2.0)
17 (2.0)
2 (0.7)
No surgical treatment
53 (27.3)
200 (19.3)
136 (15.7)
72 (23.4)
Unknown if surgically treated
25 (12.9)
227 (21.9)
163 (18.8)
54 (17.5)
Hysterectomy
Unspecified surgical treatment
AIS or adenosquamous CIS
Ablative procedure
Cone biopsy
LEEP
Hysterectomy
Unspecified surgical treatment
No surgical treatment
Unknown if surgically treated
Unspecified or other CIS
Ablative procedure
Hysterectomy
Unspecified surgical treatment
*All cases reported to the Michigan Cancer Surveillance Program during 1998-2003 and limited to ICD-O-3 topography code C53 (cervix uteri), histology codes 8010–8560, and behavior code 2 (in situ neoplasms). The majority of cases (63.6%) were ICD-O-3 histology code 8077, corresponding to
squamous intraepithelial neoplasia grade III. †Does not include 18 (0.1%) women with missing age at diagnosis.
LEEP = loop electrosurgical excisional procedure, AIS = adenocarcinoma in situ.
(median: 38.5 years)25 and in areas with opportunistic
cervical cancer screening such as in Israel (mean: 38.4
years),28 which may reflect differences in risk factors for
CIN, socioeconomic characteristics, access to cervical cancer
screening, and management of abnormal screening test
results. In our study, a small proportion (10.3%) of cervical
CIS cases was diagnosed at ages under 21 years. However,
newer guidelines for screening start age combined with
HPV vaccine initiatives are likely to affect diagnoses among
the youngest women. Recent cervical cancer screening
guidelines issued by both the American Cancer Society29-31
and the US Preventive Services Task Force32 recommend
that screening start at age 21, regardless of sexual history.
Typically, CIN 3 lesions grow slowly over many years
before invasion33 and less than half (30%-50%) of CIN 3
Journal of Registry Management 2013 Volume 40 Number 2
lesions will progress to cancer34; treatment of lesions which
may ultimately regress could put women at unnecessary
risk for treatment-related side effects as well as for potential
adverse outcomes in future pregnancies. Ongoing surveillance data are needed to inform our understanding of the
epidemiology of cervical CIS, and registry-based studies
may be helpful in evaluating the population-level impact of
evolving screening guidelines and of HPV vaccination on
the burden of disease, particularly among young women.
During the time frame of our study, LEEP and cone
biopsy were commonly performed in young women with
cervical squamous CIS, despite the evidence that had accumulated during that period for adverse obstetric effects
associated with excisional treatments. Across histologic
subtypes, a sizeable proportion of women diagnosed ≤30
89
Supplemental Table. Classification and Frequency of Surgical Treatments for Cervical Carcinoma In Situ Cases,* Michigan,
1998-2003 (n=17,022)
Category
Ablative procedure
Cone biopsy
LEEP
Hysterectomy
Unspecified surgical treatment
No surgical treatment
Unknown if surgically treated
SEER site-specific
surgery codes for first
course of cancerdirected surgery
Description (SEER coding manual)
Frequency (n)
10
Local tumor destruction, NOS
66
13
Cryosurgery, no specimen sent to pathology
35
14
Laser
16
Laser ablation
17
Thermal ablation
2
21
Electrocautery
9
22
Cryosurgery
108
17
51
23
Laser ablation or excision
24
Cone biopsy with gross excision of lesion
158
27
Cone biopsy
656
15
Loop electrocautery excision procedure, no
specimen sent to pathology
157
28
Loop electrocautery excision procedure
29
Trachelectomy; removal of cervical stump;
cervicectomy
30
Total hysterectomy (simple, pan-) without removal
of tubes and ovaries
517
40
Total hysterectomy (simple, pan-) with removal of
tubes and/or ovary
495
50
Modified radical or extended hysterectomy; radical
hysterectomy; extended radical hysterectomy
51
Modified radical hysterectomy
6
53
Radical hysterectomy; Wertheim procedure
9
60
Hysterectomy, NOS, with or without removal of
tubes and ovaries
61
Hysterectomy, NOS, without removal of tubes and
ovaries
12
62
Hysterectomy, NOS, with removal of tubes and
ovaries
37
20
Local tumor excision, NOS
90
Surgery, NOS
2,286
5,347
3
16
126
287
4
0
None; no surgery of primary site; autopsy only
3,032
25
Dilation and curettage; endocervical curettage
1,685
26
Excisional biopsy, NOS
99
Unknown if surgery performed; death certificate
only
275
1,626
*All cases reported to the Michigan Cancer Surveillance Program during 1998-2003 and limited to ICD-O-3 topography code C53 (cervix uteri),
histology codes 8010-8560, and behavior code 2 (in situ neoplasms). The majority of cases (63.6%) were ICD-O-3 histology code 8077, corresponding
to squamous intraepithelial neoplasia grade III.
NOS = not otherwise specified; LEEP = loop electrosurgical excisional procedure.
90
Journal of Registry Management 2013 Volume 40 Number 2
years of age underwent either LEEP (20%–39%) or cone
biopsy (14%–44%). Even in the youngest subgroup of
women (diagnosed under age 21), excisional procedures
were common. It is conceivable that these young women
may have undergone repeat excisional procedures over
their reproductive life span, potentially further increasing
their risk of adverse outcomes in future pregnancies. A
meta-analysis showing consistent evidence linking excisional treatments for CIN and adverse outcomes in future
pregnancies, with risks of preterm delivery, low birth
weight, and preterm premature rupture of membranes
increased approximately twofold, was published after the
time frame of our study (2006).19 Risks of adverse pregnancy outcomes were not shown for women undergoing
ablative treatment.19 In the United States, practitioners may
choose more aggressive management options even for the
youngest women due to concerns about access to care and
compliance with follow-up.
While the MCSP represents some of the best available
population-level data for cervical CIS, several limitations
must be considered. The current study likely underestimates
the true burden of cervical CIS because CIN 3 qualified
with “severe dysplasia” was not reportable in Michigan
during the study period. As the MCSP began collecting
“severe dysplasia” in 2009, additional studies using more
recent data could be useful for evaluating the sensitivity
and specificity of the reporting definition for cervical CIS
and the potential need for modifying or standardizing the
definition. We were not able to evaluate the observed racial
differences in surgical treatment for AIS/adenosquamous
CIS for potential confounding (eg, by age at diagnosis) due
to the small number of black women with this histological
subtype (n=23) in our study population. As management
guidelines in place at the time of study recommended cryotherapy, laser ablation, and LEEP as acceptable treatment
modalities even for biopsy-confirmed CIN 1,24 reasons for
the high proportion of women (17.8%) with CIS that had no
surgical treatment or unknown treatment (21.1%) warrant
further investigation.
There is potential misclassification of some diagnostic
procedures as treatment in the registry. For example, in
this study, dilation and curettage or endocervical curettage
was recorded in the registry as the first course of treatment
for 1,685 (9.9%) women; for the purposes of our analyses,
these women were included in the “unknown if surgically
treated” category, as these procedures are generally considered part of the diagnostic workup rather than treatment.
Data collection and coding manuals should be reviewed
and, if needed, modified to distinguish between procedures
used for diagnostic workup and those used therapeutically, taking into account the sequence of treatments. This
process will also involve efforts to train the medical chart
abstraction staff to improve data quality. Finally, choice of
treatment may have been influenced by medical history (eg,
remote history of treated CIN) that we could not capture in
our analysis.
Looking forward, future changes being proposed by
pathology organizations to standardize classification of
HPV-related neoplasia of the lower genital tract35 may impact
Journal of Registry Management 2013 Volume 40 Number 2
the interpretation of data from long-standing surveillance
systems like the MCSP. Further, with our improving understanding of treatment-associated outcomes, the collection
of relevant treatment details (eg, cone excised depth) could
be considered. While it may be prohibitively burdensome
to add this to existing cancer registries, newer populationbased cancer registries and sentinel surveillance systems
that begin collecting cervical cancer precursors to monitor
the effects of HPV vaccination may be able to enhance their
overall impact by also collecting data on relevant treatment
characteristics and clinical outcomes.
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D. 2006 consensus guidelines for the management of women with
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12.Nuovo J, Melnikow J, Willan AR, Chan BK. Treatment outcomes for squamous intraepithelial lesions. Int J Gynaecol Obstet. 2000;68(1):25-33.
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14.Arbyn M, Kyrgiou M, Simoens C, et al. Perinatal mortality and other
severe adverse pregnancy outcomes associated with treatment of
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15.Ang C, Mukhopadhyay A, Burnley C, et al. Histological recurrence
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16.Chirenje ZM, Rusakaniko S, Akino V, Mlingo M. A randomised clinical
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18.Melnikow J, McGahan C, Sawaya GF, Ehlen T, Coldman A. Cervical
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2001 consensus guidelines for the management of women with cervical
intraepithelial neoplasia. Am J Obstet Gynecol. 2003;189(1):295-304.
25.Serafini M, Cordaro C, Montanari E, Falcini F, Bucchi L. Diagnosis and
treatment of cervical intraepithelial neoplasia grade 3: a registry-based
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November 17, 2011.
Journal of Registry Management 2013 Volume 40 Number 2
Original Article
Feasibility of Matching Vaccine Adverse Event
Reporting System and Poison Center Records
Mathias B Forrester, BSa
Abstract: Background: The Vaccine Adverse Event Reporting System (VAERS) is a US surveillance program that collects
information on adverse events that occur after the use of vaccines. Poison centers also receive calls about potentially
adverse exposures to vaccines. Since the same vaccine exposure might be reported to both VAERS and a poison center, this
study examined the feasibility of matching publicly available VAERS records to poison center records. Methods: All VAERS
records reported from Texas during 2000‑2011 were downloaded from the VAERS database. All vaccine exposures reported
to Texas poison centers during 2000‑2011 were identified. Since no unique identifiers (eg, names, dates‑of‑birth, etc) were
available in the public VAERS database, matches had to be made using other, non‑unique data fields that both databases
had in common. Matches were made using the following 4 data fields: vaccine, sex or gender, age, and date. The match rate
was determined for total poison center records and for selected poison center variables. Results: There were 13,630 VAERS
and 738 poison center records in the investigation. Twenty‑nine percent (213) of the poison center records were matched
to VAERS records. The match rate by 3‑year period was 2000‑2002 (21%), 2003‑2005 (27%), 2006‑2008 (20%), and 2009‑2011
(41%). The rate for the most common vaccines was influenza (42%), pneumococcal (39%), diphtheria‑pertussis‑tetanus
(49%), hepatitis B (14%), and diphtheria‑tetanus (29%). The match rate was 35% for adverse reactions and 32% for therapeutic errors. The rate was 28% for non-serious outcomes and 33% for serious outcomes. The match rate was 29% for patients
managed on site and 28% for patients already at or referred to a health-care facility. Conclusion: Matching between public
VAERS and poison center records can be performed. However, these matches might be considered tentative because unique
identifiers are not available. The match rate was highest during the most recent 3‑year period. The match rate varied by
vaccine but not by the exposure reason, management site, or medical outcome.
Key words: vaccine, Vaccine Adverse Event Reporting System (VAERS), poison center, match, link
Introduction
The Vaccine Adverse Event Reporting System (VAERS)
is a US surveillance program sponsored by the Centers
for Disease Control and Prevention and Food and Drug
Administration that collects information on suspected
adverse events (health problems or possible side effects)
that occur after the use of vaccines licensed in the United
States.1 VAERS is a passive surveillance system where
reports are voluntarily submitted by the persons who
receive the vaccines, their family or friends, health-care
providers, public health organizations, and others. The law
requires that all personal identifiers are kept confidential.
VAERS data are useful for studying the safety of vaccines,
identifying and understanding rare adverse events that
might not have been observed in clinical trials, and evaluating vaccines used in unique populations, such as travelers
and the military.
US poison centers are free telephone consultation
services that provide information on and assist in the
management of potentially adverse exposures to a variety
of substances such as medications, illegal drugs, household
and industrial chemicals, food, plants, and animals. Poison
centers are usually available 24 hours a day, 365 days a year.
They receive calls from health-care providers, law enforcement, and the public. Each poison center uses an electronic
database to collect demographic and clinical information
on all calls handled by the poison center’s agents. The data
fields and allowable field options are standardized by the
American Association of Poison Control Centers (AAPCC).
A subset of the data fields collected by every poison center
is submitted to the AAPCC and combined into a single
National Poison Data System (NPDS).2 Reporting exposures
to poison centers is usually not mandatory. Their databases
and the NPDS could be considered passive surveillance
programs.
Among the types of calls poison centers receive are
potentially adverse exposures to vaccines. During 2011,
US poison centers received calls about 2,151 exposures to
vaccines, serums, and toxoids, at least 457 of which were
due to adverse reactions and 110 resulted in moderate or
major effects.2
This suggests that poison centers might serve as a
potentially useful source of adverse vaccine exposures for
VAERS. In fact, an unknown portion of those vaccine exposures currently reported to poison centers might already
be reported to VAERS by the poison centers or others. To
__________
Texas Department of State Health Services, Austin, Texas.
a
Address correspondence to Mathias B. Forrester, Department of State Health Services, Environmental Epidemiology and Disease Registries Section, 1100 W
49th Street, Austin, TX 78756. Telephone: (512) 776‑6224. Fax: (512) 776-7689. Email: [email protected].
The Texas Department of State Health Services considers this study exempt from ethical review. Funding was provided by a Public Health Emergency
Preparedness grant (2U90TP617001‑11) from the Centers for Disease Control and Prevention.
Journal of Registry Management 2013 Volume 40 Number 2
93
determine the extent to which this is already occurring, it
would be useful to match poison center records of vaccine
exposures to VAERS records. This study examined the feasibility of matching publicly available VAERS records to the
records of the Texas Poison Center Network (TPCN). The
TPCN is comprised of 6 poison centers that cover the entire
state, a population of more than 25 million. The 6 poison
centers use a single, common electronic database.
Methods
All VAERS records reported from Texas during
2000‑2011 were downloaded from the VAERS database
(https://vaers.hhs.gov/data/index). All of the publicly available data were obtained. The data were imported into a
Microsoft Access database.
All vaccine exposures reported to the TPCN during
2000‑2011 were identified. Exposures to vaccines not
intended for humans were subsequently excluded from the
poison center cases since they were not likely to be reported
to VAERS. The poison center data were imported into the
same Microsoft Access database as the VAERS data.
No unique identifiers (eg, names, dates of birth, etc)
were available in the public VAERS data set. Moreover, date
of birth is not typically collected by Texas poison centers.
Thus, matches had to be made using other, non‑unique data
fields that both data sets had in common. As a result, any
matches that were made should be considered probable at
best and not definite. However, other studies have reported
using similar non-unique data for successful matching of
records in different databases.3,4 Matches were made using
the following 4 data fields: vaccine, sex or gender, age,
and date. For poison center records, the date used in the
matching process was the date the call was received; this
is likely to be the date the vaccine was received or shortly
afterward. For VAERS records, 4 different dates were used
in the matching process: the vaccination date, symptom
onset date, report completed date, and report received date.
Since the dates in the 2 databases were not equivalent, a
match was considered possible if the dates in the 2 databases were within 5 days of one another. Likewise, the ages
were considered a match if they were within 3 years of one
another.
A deterministic multi-pass match algorithm was used
to perform the matching between the 2 data sets. Both
the VAERS and TPCN tables containing record identification numbers and the variables of interest were added to
a Microsoft Access query, and all the variables of interest
were designated to be included in the query output. In a
query, if an exact match was desired for a given variable,
the variable in the VAERS table was linked to the corresponding variable in the TPCN table; if no exact match was
desired for a given variable, the corresponding variables in
the 2 tables were not linked. First, a query was run where
an automated match (link) was made between all 4 fields
(vaccine, sex, age, date). Next, queries were run where automated matches were made using combinations of 3 of the
fields, with the data in the fourth field compared between
the 2 data sets to rule out any obvious non‑matches (eg,
exact matches between vaccine, sex, and age but not date;
94
Table 1. Exact Match Rate of Texas Poison Center Network
(TPCN) Records and Vaccine Adverse Event Reporting
System (VAERS) Records by Variable Used in the Matches
Variable
Number
Percent
Vaccine type
231
90
Sex
200
78
Age
117
46
Report completed date*
159
62
Vaccination date*
109
43
Report received date*
91
36
Symptom onset date*
86
34
Total
256
213 TPCN records were matched to 256 VAERS records.
*For TPCN records, the date used in the matching process was the date
the call was received, which was matched to the 4 different VAERS
dates.
exact matches between vaccine, sex, and date but not age;
etc). Then queries were run where automated matches were
made using combinations of 2 of the fields with the data in
the other 2 fields compared between the 2 data sets to rule
out any obvious non‑matches (eg, exact matches between
vaccine and sex but not age and date, etc). Records that were
matched in a given query were excluded from subsequent
queries.
The match rate (poison center records matched to
VAERS records/total poison center records) was calculated
for total cases, the variables used to make the matches,
and the poison center data variables of year, circumstances
of the exposure, management site, and medical outcome
(severity). Differences in match rates between the subgroups
were evaluated for statistical significance by calculating the
rate ratio (RR) and 95% confidence interval (CI) by the
Newcombe-Wilson method without continuity correction.
The RRs were considered statistically significant if the 95%
CI excluded 1.00. P-values were not calculated.
For the analysis by year, the 12-year period was
divided into four 3-year periods. The circumstances of, or
reason for, the exposure were grouped into adverse reaction
(the vaccine was given as directed but the person reported
adverse effects), therapeutic error (the vaccine was given
incorrectly, such as the patient receiving a double dose),
and all other/unknown reasons (other unintentional and
intentional exposures, etc).
The medical outcome or severity of an exposure is
assigned by the poison center staff and is based on the
observed or anticipated adverse clinical effects. In the
AAPCC coding system, the medical outcome is classified by the following criteria: no effect (no symptoms due
to exposure), minor effect (some minimally troublesome
symptoms), moderate effect (more pronounced, prolonged
symptoms), major effect (symptoms that are life threatening or cause significant disability or disfigurement), and
death. A portion of exposures are not followed to a final
medical outcome because of resource constraints or the
Journal of Registry Management 2013 Volume 40 Number 2
Table 2. Match Rate of Texas Poison Center Network (TPCN) Records to Vaccine Adverse Event Reporting System (VAERS)
Records by Selected TPCN Variables
Variable
Number
Total
Percent
RR*
95% CI†
3-year period
2000-2002
26
126
21
0.51
0.35-0.74
2003-2005
57
212
27
0.66
0.50-0.86
2006-2008
32
160
20
0.49
0.35-0.69
98
240
41
Reference
Influenza vaccine
82
194
42
Reference
Pneumococcal vaccine
28
72
39
0.92
0.66-1.28
Diphtheria-pertussis-tetanus vaccine
33
68
49
1.15
0.85-1.54
9
65
14
0.33
0.17-0.61
Diphtheria-tetanus vaccine
18
62
29
0.69
0.45-1.05
Measles-mumps-rubella vaccine
21
51
41
0.97
0.68-1.41
Adverse reaction
80
231
35
Reference
Therapeutic error
86
272
32
0.91
0.71-1.17
All other/unknown
47
235
20
0.58
0.42-0.79
2009-2011
Most common vaccine type
‡
Hepatitis B vaccine
Circumstances of the exposure
Management site
On site (not healthcare facility)
158
541
29
Reference
Already at/en route to healthcare facility
32
120
27
0.91
0.66-1.26
Referred to healthcare facility
18
56
32
1.1
0.74-1.65
5
21
24
0.82
0.38-1.77
182
639
28
Reference
Serious
17
52
33
1.15
0.76-1.73
Unrelated effect
14
47
30
1.05
0.66-1.65
213
738
29
Other/unknown
Medical outcome
Not serious
Total
*RR = Rate ratio - ratio of subgroup percent to reference subgroup percent.
†CI = confidence interval.
‡Type of vaccine most commonly reported in the TPCN database. A person might have received more than one type of vaccine.
inability to obtain subsequent information on the patient. In
these instances, the poison center staff record the expected
outcome of the exposure. These expected outcomes are
grouped into the following categories: not followed but
judged as nontoxic exposure (symptoms not expected); not
followed but minimal symptoms possible (no more than
minor symptoms possible); and unable to follow but judged
as a potentially toxic exposure. Another medical outcome
category is unrelated effect where the exposure was probably not responsible for the symptoms. For this analysis,
the medical outcomes were grouped into those exposures
known or expected not to be serious (no effect, minor effect,
not followed but judged as nontoxic, not followed but
minimal symptoms possible) vs those exposures known or
expected to be serious (moderate effect, major effect, death,
unable to follow but judged as potentially toxic).
Journal of Registry Management 2013 Volume 40 Number 2
Results
There were 13,630 VAERS and 738 poison center
records included in the investigation. Twenty‑nine percent
(213) of the poison center records were matched to 256
VAERS records. Of the 213 poison center records, 189 (89%)
were matched to one VAERS record and 24 (11%) were
matched to more than one (range 2-8).
Table 1 presents the exact match rates by the 4 types of
variables used to make the match. The highest exact match
rate occurred with the vaccine type followed by patient sex.
The exact match rate varied among the 4 different VAERS
dates used.
Table 2 shows the match rate for selected poison
center variables from the TPCN database. The match rate
was higher during 2009-2011 than during the previous
three 3-year periods with the difference being statistically
95
significant. The match rate varied among the types of
vaccine most commonly reported in the TPCN database,
being highest for diphtheria-pertussis-tetanus vaccine and
lowest for hepatitis B vaccine. Exposures due to adverse
reactions and therapeutic errors had similar match rates but
the rate for other or unknown exposures was significantly
lower. The match rates also were similar for the different
management sites and medical outcomes.
Discussion
This study demonstrates that matching between poison
center records and VAERS records can be performed, even
without unique identifiers such as name and date of birth.
However, considering the variables available to be used to
make the matches are not unique identifiers, the matches
that are made should be considered tentative at best.
Nevertheless, of those TPCN records that were matched,
89% were matched to only one VAERS record.
Only 29% of the TPCN vaccine exposures were
matched to VAERS records, ie, the majority (525) of TPCN
vaccine exposures likely were not reported to VAERS. This
may be because many of the TPCN vaccine exposures were
not classified as adverse reactions but due to other circumstances such as therapeutic errors and thus possibly not
considered suitable for reporting to VAERS. The number
of poison center records that were not reported might be
considered rather small when compared to the total number
of VAERS records (525 vs 13,360). Still, poison centers could
serve as an additional data source for VAERS. When the
H1N1 influenza vaccine was first introduced in 2009, there
was concern about its safety. The Texas Department of State
Health Services (DSHS) Immunization Branch approached
the TPCN and asked whether it would be possible for the
TPCN to report all H1N1 influenza vaccine exposures to
them. A system was set up where all H1N1 influenza exposures reported to the TPCN would be forwarded to DSHS.5
As a consequence, DSHS asked if a system could be created
where all vaccine exposures received by the TPCN could be
reported to the DSHS. Such a system was created and has
been in operation since January 2010. Thus, although the
TPCN does not currently report vaccine exposures directly
to VAERS, a portion may be indirectly reported through
DSHS.
The match rate was substantially higher during 20092011 than during the 9 years prior to this time period.
Moreover, more vaccine exposures were reported to the
TPCN during 2009-2011 than during the previous three
3-year periods. The H1N1 influenza pandemic and the
introduction of the H1N1 vaccine occurred in 2009. The
number of TPCN influenza vaccine reports in 2009 were
higher than in previous years.6,7 As a consequence, the
collaboration between the TPCN and the DSHS that started
in 2009 might have led to the DSHS submitting a portion of
these TPCN exposures to VAERS, which then contributed to
a higher match rate during 2009-2011.
The match rate varied by the type of vaccine, being
highest for diphtheria-pertussis-tetanus vaccine and lowest
for hepatitis B vaccine. The differences in match rates
possibly may be due to different concerns about potentially
96
adverse effects depending on the type of vaccine.
The match rate did not vary greatly with respect to the
circumstances of the exposure, management site, or medical
outcome. It might be expected that the match rate would
be higher for adverse reactions than for therapeutic errors,
since VAERS is for reporting “adverse events.” However,
poison centers and VAERS and those individuals who
submit information to them might have different definitions of what qualifies as an “adverse event” or “adverse
reaction.”
In addition, it might be presumed that those poison
center exposures managed at a health-care facility would be
more likely to be matched to VAERS records, since healthcare providers might be expected to report adverse vaccine
exposures to VAERS. Patients and family members would be
less likely to know about the existence of VAERS. However,
some of the TPCN exposures that were managed on site also
might have been reported to health-care providers without
the TPCN staff being made aware of the fact.
Likewise, it might be anticipated that more serious
exposures, which had more serious adverse effects, would
be more likely to be matched to VAERS records. However,
the severity of the exposure was based on the symptoms
reported to TPCN agents. The agents might not have been
notified of all symptoms. Moreover, the TPCN and VAERS
might differ in what they consider serious exposures.
The exact match rate varied greatly between the 4
types of variables used to make the matches. This knowledge might help to prioritize which variables to use or
have confidence in when making matches. Nevertheless,
these exact match rates might be an artifact of the matching
methodology. For example, if a TPCN and VAERS record
matched by age, sex, and date but not by vaccine, then
the 2 records would not be considered a match. But if sex
did not match, date did not match but was close, and age
and vaccine matched, the 2 records would be considered a
potential match.
Conclusions
Matching between public VAERS and poison center
records can be performed. However, these matches might
be considered tentative because unique identifiers are not
available. The match rate was highest during the most
recent 3‑year period, possibly because in 2010 the poison
center system in this study began to report all vaccine
exposures to the state health department, which in turn
might have reported a portion of these exposures to VAERS.
The match rate varied by vaccine but not by the exposure
reason, management site, or medical outcome.
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Annual Report of the American Association of Poison Control Centers’
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3. Burkhardt M, Nienaber U, Holstein JH, et al. Trauma registry record
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response to H1N1 influenza outbreak. Clin Toxicol. 2010;48:641-642.
7. Forrester MB. Changes in Texas poison center call patterns in response to
H1N1 influenza outbreak. Tex Pub Health J. 2012;64(4):14‑18.
97
Original Article
Birth Defect Registries: the Vagaries of Management—
The British Columbia and Alberta Case Histories
R. Brian Lowrya,b,c,d; Tanya Bedarda,c
Abstract: Birth defect surveillance is of increasing interest and importance, especially since the discovery that folic acid
fortification or supplementation can prevent a large proportion of neural tube defects. Funding is a constant problem, but
management or policy can also lead to changes in ascertainment and quality, and even to the threat of actual closure. This
is a case study of 2 Canadian registries—British Columbia and Alberta—from an historical point of view. The lessons here
are applicable to many registries or surveillance systems. To succeed, 4 things must be in place—stated objectives of the
program, funding (preferably government, but may start with a grant or foundation), support from public health departments, and someone to champion the cause. The importance of medical consultants cannot be overstated.
Key words: birth defects, congenital anomalies, surveillance, registry, British Columbia, Alberta
Introduction
The discovery that thalidomide produced an epidemic
of limb and other congenital malformations resulted in the
development of many birth defects surveillance systems
or registries in the 1960s or even later. While many health
departments and vital statistics sections of government
started collecting information on live and still births with
congenital malformations (eg, New York State Department
of Health—19401; Birmingham, England, 19492), the first
formal comprehensive surveillance registry for congenital
malformations was in British Columbia, Canada, which
opened in 1952. Ascertainment also included handicapped persons to age 21. Alberta, Canada followed with
a Handicapped and Birth Defects Register in 1963. Many
other countries or areas started congenital malformation
surveillance in the 1960s, eg, Czechoslovakia in 1961,
Hungary in 1962, Finland in 1963, and Sweden, England
and Wales in 1964. The Centers for Disease Control and
Prevention birth defects program started in Atlanta in 1967.
This report is a case study of British Columbia and
Alberta and is designed to examine historical events to
demonstrate how government decisions and reorganizations affect the quality of congenital anomaly surveillance.
Although funding is a constant challenge, poor management and ineffective policy can contribute to the decline of
a surveillance system.
British Columbia (BC)
The Establishment of the British Columbia Health Status
Registry
In 1948, the Canadian government made a major
announcement of a policy shift and assigned grants to the
provinces, including national health grants (which included a
Figure 1. Dr. Donald Paterson
crippled children’s grant).3
In British Columbia (BC),
a subcommittee was established to advise the British
Columbia Ministry of
Health of a plan to deal
with crippling diseases
of children. Dr. Donald
Paterson (Figure 1) was
the most active member
of this subcommittee.
Paterson was a Canadian
who graduated from
Photo courtesy of the National
Edinburgh University and
Portrait Gallery, London, England.
rapidly became the most
prominent pediatrician in the United Kingdom.4 He developed pediatrics both as a professional specialty and as an
independent academic subject, with one of his greatest
achievements being the founding of the British Pediatric
Association. He retired to Canada in 1947 and was immediately appointed as consultant in child health to the
Metropolitan Health Committee of Vancouver, and head
of the pediatric section of the Vancouver General Hospital.
He recognized that there were many handicapped children
who were not getting appropriate services. A report was
submitted to the deputy minister of health, Dr. Gregoire
Amyot, based on the results of a provincial survey begun
in 1948 and completed in 1950 that recommended the
establishment of a voluntary register of handicapped children. Thus the Health Status Registry, initially called the
Crippled Children’s Registry, formally opened in January
1952. Administratively, it was placed in the Division of Vital
Statistics within the Ministry of Health. There were many
__________
a
Alberta Congenital Anomalies Surveillance System, Alberta Health and Wellness, Calgary, Alberta, Canada. bDepartments of Medical Genetics and Pediatrics,
University of Calgary, Alberta, Canada. cAlberta Children’s Hospital, Alberta Health Services, Calgary, Alberta, Canada. dAlberta Children’s Hospital Research
Institute, Calgary, Alberta, Canada.
Address correspondence to Dr. R. Brian Lowry, ACASS/Clinical Genetics, Alberta Children’s Hospital, 2888 Shaganappi Tr NW, Calgary, Alberta, Canada T3B
6A8. Telephone: (403) 955-7370. Fax: (403) 955-2870. Email: [email protected].
98
Journal of Registry Management 2013 Volume 40 Number 2
Figure 2. Number of Publications Using the British
Columbia Health Status Registry, 1961-2011
35
30
25
20
15
10
5
0
1961-1965 1966-1970 1971-1975 1976-1980 1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010
2011-
subsequent name changes: the Registry for Handicapped
Children; the British Columbia Registry for Handicapped
Children and Adults; Health Surveillance Registry; and
finally the Health Status Registry (HSR). Early history is
outlined by Mott5 with later updates by Lowry et al6 and
Baird.7 Table 1 provides a brief summary of HSR.
The Successes of the British Columbia Health Status
Registry (HSR)
HSR used a large number of reporting sources that not
only helped to overcome ascertainment deficits but also
provided knowledge about diagnosis or other handicaps.
HSR provided a liaison between physicians, institutions
and agencies for child care and rehabilitation. The public
health units were actively involved and there was excellent
communication between HSR and the health units. HSR
instituted a follow-up of cohorts when children became 7
years and 14 years of age, known as the 7 and 14 Follow
Up. These were invaluable and provided information that
could be used for planning with respect to future resources,
and information about who would later require assistance
regarding possible employment.
Dr. Jim Miller was appointed in 1962 as genetic consultant to HSR and Dr. Brian Lowry replaced Dr. Paterson in
1965 as the medical consultant. This was the era in which
scientific or academic publications began to be produced
using the extensive HSR data banks.8 Each study was not
only of value in itself but also served to clean the data
whereby conditions or diagnoses were looked at rigorously
and either changed if necessary or discarded if inappropriate or unreliable.
There were further increases in activities between HSR
and the public health units in the 1970s. Many in-service
visits were made by Drs. Miller and Lowry to the public
health units. The visits always served to improve ascertainment as registering agencies could see the value of their
input. Letters to the registering agency, public health unit
or physicians were written by the medical consultant to
clarify or verify diagnoses or to offer advice. Dr. Patricia
Baird replaced Dr. Lowry as the medical consultant in 1977.
Journal of Registry Management 2013 Volume 40 Number 2
There was an increase in academic and scientific activity
with many theses and papers being produced. Between
1961 and 2011, there were 127 scientific reports published
in peer reviewed journals and authored by the medical
and genetic consultants, University of British Columbia
graduate students or residents in the pediatrics or medical
genetics programs, and BC Ministry of Health staff including
research officers, data personnel and administrators (a list
of publications based on British Columbia registry data is
available from the authors of this article). Figure 2 shows the
level of productivity, which peaked from 1981-1985.
Reports were issued by HSR and provided information on the methodology of the registry, prevalence rates by
time in years and by geographical units (provincial, health
authority, health service delivery areas), and a description of the different activities of the registry. There were
4 reports issued by HSR prior to 1958 and from 1958 to
1972 they were produced annually. There was no report in
1973, but annual reports from 1974 to 1981 were produced.
Thereafter the reports appear to be approximately every
2 to 4 years, with the last report in 2005 which deals
with data up to 2002. There are also 3 reports using HSR
data by BC Ministry of Health personnel and/or medical
genetic consultants published in the now defunct Canadian
Congenital Anomalies Surveillance Network (CCASN)
publications online 2003-2005 (http://www.phac-aspc.gc.ca/
ccasn-rcsac/).
HSR became a member of the International
Clearinghouse for Birth Defects Surveillance and Research
(ICBDSR) in 2001 supplying annual data to them up to 2006.
It continued to send quarterly data up to the first quarter of
2010 on selected malformations and on a multimalformed
project and also supplied data for an international study on
the prevalence of cleft lip/palate.
The Challenges of the British Columbia Health Status
Registry
Towards the end of the 1980s, the BC Ministry of
Health indicated a desire to withdraw support for HSR.
Negotiations for the HSR takeover were held with members
of the Department of Health Care and Epidemiology (HCE)
at the University of British Columbia (UBC). When it was
realized that the Ministry of Health would not be transferring the funding to run HSR, and since UBC could not afford
it, the plans were dropped. At this time, the ministry also
made a decision to discontinue the services of the medical
consultant (Dr. Baird) who had also fulfilled the duties of
genetic consultant as well after Dr. Miller’s retirement. HSR
was then moved in 1990 from Vancouver to Victoria, which
is the capital of British Columbia.
The move to Victoria, together with the loss of a
medical consultant, resulted in a decline in ascertainment of
congenital anomalies. The decision to accept the discharge
notices from BC Children’s Hospital (the second largest
ascertainment source) in ICD-9 codes instead of text diagnoses resulted in a serious loss of detail. No permanent
medical consultant was appointed, but one of the medical
geneticists from UBC traveled from Vancouver to Victoria
3 or 4 times a year for 2 to 3 years, but then ceased. As
99
Table 1. Characteristics of the British Columbia Health Status Registry and the Alberta Registry/System
Characteristic
British Columbia Health Status Regisry
Alberta Registry for Handicapped
Children and Adults
Alberta Congenital Anomalies
Surveillance System
Date
established
1952 to date
1963-1979
1980 to date
Administration
Division of Vital Statistics, Ministry of
Health
Knowledge Management Technology
Department of Social Services and
Community Health-Vital Statistics
Vital Statistics, Ministry of Health
Ministry of Registries
Objectives
Record and classify congenital anomalies,
genetic conditions and selected
handicapping conditions
Assist in health care planning and other
appropriate services
Undertake statistical analysis for medical
and genetic research
Keep public informed with timely and
accurate information while maintaining
confidentiality
Respond to research requests
Be a useful tool for health care and social
service systems
Record and classify congenital
anomalies, genetic conditions and
selected handicapping conditions
Assist in health care planning and
other appropriate services
Undertake statistical analysis for
medical and genetic research
Keep public informed with timely
and accurate information while
maintaining confidentiality
Respond to research requests
Be a useful tool for health care and
social service systems
Sources of
ascertainment
Multiple > 50 reporting sources
Physicians’ Notice of Birth
Still birth and Death Registrations
Public Health Units (e.g. well baby clinics,
immunization, special schools for the
mentally handicapped, blind, deaf).
Hospital Discharge Abstracts
Physicians’ Notice of Birth
Still birth and Death Registrations
Health Units
Specialty clinics
Hospital admissions
Physicians’ Notice of Birth
Still birth and Death Registrations
Delivery hospitals
Pediatric and tertiary care
hospitals
Specialty clinics
Ascertainment
method
Voluntary (1952-1991)
Mandatory 1992-
Voluntary
Voluntary
Registration
Criteria
Person of any age diagnosed with genetic
condition or congenital anomaly that is not
necessarily disabling
Person <19 years diagnosed with a
physical, mental, and/or emotional problem
with long-term disabling effects that
interferes with education or prevents full
and open functional employment
Person of any age diagnosed with
genetic condition or congenital
anomaly that is not necessarily
disabling
Person <19 years diagnosed with a
physical, mental, and/or emotional
problem with long-term disabling
effects that interferes with education
or prevents full and open functional
employment
Persons up to 1 year of age
diagnosed with a congenital
anomaly
Follow up
Yes-assist local authorities with obtaining
specialist services by contacting the
reporting agency or registering physician,
thus maintaining confidentiality.
Follow-up cohorts when children were 7
and 14 years to determine if condition was
resolved or further care was needed, and
used for resource allocation and planning
Yes if suspicious increases or clusters
Yes if suspicious increases or
clusters
To verify diagnoses
Legislation
Evidence Act of 1970-protects reporting
agency from liability
Amendment to Health Act of 1992
established legislative mandate for
submission of data
None
Health Information Act
2001-allows disclosure by
reporting agencies for purpose
of public health surveillance and
allows ACASS to legally request
documents and reports
Advisory
Committee
No
Yes
Yes
100
Provide baseline data
Investigate temporal or
geographic changes
Measure trends
Assess effectiveness of
prevention
Assist in health program planning
Participate in related research
Offer advice regarding
congenital anomalies
Journal of Registry Management 2013 Volume 40 Number 2
a result, there was no medical supervision of the coders
or any attempt to verify complex or uncertain diagnoses.
There was an excellent administrator, Nancy Scott, who was
succeeded by another capable administrator, Helen Colls,
but on her death, the Ministry chose not to appoint another
dedicated administrator for HSR. Subsequent managers had
many additional duties, and administration functions were
parceled out to various parts of the BC Ministry of Health
in Vital Statistics and Knowledge Management Technology.
Thus, there was no overall guiding hand. Additionally, there
was failure to appoint a research officer when the previous
one retired. Due to the lack of feedback to the reporting
agencies, they were less inclined to report cases.
An outside consultant firm (Coopers and Lybrand)
was commissioned in 1990 to evaluate HSR. Unfortunately,
this report cannot be found, but a summary was published
which identified problems and made future recommendations9 including the formation of a 14-member health
advisory committee. It is not clear how long this committee
functioned or how often it met, but by the middle of the
decade they had ceased, and there was further decline in
activities including ascertainment.
In 2002, there was a rejuvenation of interest in HSR and
Dr. Lowry was appointed chair of a newly formed medical
advisory committee. Also on the committee were 2 Victoria
medical geneticists, the provincial epidemiologist, and
members of the BC Ministry of Health representing HSR
including the research officer, data management specialist,
and the 2 coders. The health advisory committee, realizing that ascertainment was deficient, attempted to make
improvements and did manage to get BC Children’s Hospital
to agree to send the diagnosis in text rather than ICD codes.
At one time, prenatally diagnosed cases of congenital
anomalies that underwent termination of pregnancy (ToP)
were received but no data on ToP for malformations has
been received since 2006. Lowry was replaced as chair of
this committee in 2006 and shortly thereafter the committee
disappeared completely.
HSR was probably the first birth defects and handicapped registry in the world and at one time enjoyed a
very high level and quality of ascertainment, function,
utilization, and scientific importance. The decline of HSR’s
acquisition of cases for accurate statistics, research, interaction with registering agencies, and reduced funding cannot
be attributed to the staff, coders, clerks, research officers,
and data management personnel. The responsibility resides
in the hands of BC Ministry of Health officials from the
assistant deputy minister level right up to ministers of
health and indeed the premiers, all of whom were made
fully aware in this recent decade of what was necessary to
restore HSR back to its former strength.10
Alberta (AB)
The Establishment of the Alberta Congenital Anomalies
Surveillance System
In 1963, the Alberta Department of Health started the
Registry for Handicapped Children and Adults (RHCA) as
a result of the thalidomide problem. It was modeled entirely
Journal of Registry Management 2013 Volume 40 Number 2
on the British Columbia HSR and used the same criteria for
registering cases (Table 1). With multiple government reorganizations, RHCA was ultimately placed in the Department
of Social Services and Community Health (SSCH) together
with Vital Statistics (VS). The RHCA director was a physician. Annual reports were not published but statistical data
on specific topics were available on request. There was only
1 scientific publication.11
Because of the impending retirement of the medical
director, Dr. Charlotte Dafoe, the government commissioned a review of RHCA in 1976. The report, known as
the Kaegi Report, was never given general release, though
copies were sent to all health unit directors and some
selected individuals. It raised issues such as lack of interest
and commitment, and underascertainment. Although the
minister and deputy minister of SSCH said that a decision had been made to revitalize RHCA, no action ensued
and the medical director was not replaced. In December
1979, an administrator was appointed. In January 1980
he, together with the pediatric consultant from SSCH, set
up a birth defects monitoring system, which later became
the Alberta Congenital Anomalies Surveillance System
(ACASS).12 Unfortunately, the system did not use ICD codes
but developed a sequential numerical code starting at 1. The
cases had to be recoded to ICD later.
A parallel development occurred in the province
with the start of the Alberta Hereditary Diseases Program
(AHDP) in 1979.13 A subcommittee on birth defects was
formed together with the administrator and pediatric
consultant from SSCH. The first meeting occurred in May
1981. In November 1981, the RHCA administrator resigned
and was not replaced. In April 1982, the new VS Director,
William Gilroy, supported by the Birth Defects Monitoring
Subcommittee, sent in a new proposal for restoring the
RHCA which had been inactive since 1977 with loss of the
early data. This was turned down on financial grounds and
furthermore funds for birth defects monitoring would cease
as well the following year. The Alberta Ministry of Health
did agree to transfer the congenital anomalies surveillance
component to the department of medical genetics at the
Alberta Children’s Hospital in Calgary whose director (Dr.
Lowry) undertook to find funding which was successful.
Table 1 provides a brief summary of ACASS.
Annual grants were obtained from foundations such as
Medical Services Incorporated and the Alberta Children’s
Hospital Foundation. In 1986, a federal national health grant
was obtained for a pilot project to incorporate controls into
congenital anomaly surveillance and this was followed
by a 3-year grant (1988-1991) to implement a case-control
system for the 12 congenital anomalies in the ICBDSR
surveillance reports. Mothers were interviewed using the
services of genetic nurses in all 27 health units.13 When
the grant ended, interviews and inclusion of controls were
discontinued because of time and expense. From 1992-1996,
ACASS was totally supported by the Alberta Children’s
Hospital Foundation but subsequently the Alberta Ministry
of Health (Dr. Stefan Gabos) restored funding and since
that time, the Surveillance Branch of the Alberta Ministry
of Health has provided financial support and been a very
101
active participant, although the office remains at the Alberta
Children’s Hospital.
In 2001, the Health Information Act was passed by the
Alberta government, which allows disclosure by agencies or
health personnel of any health information for the purposes
of public health surveillance. ACASS is legally entitled to
request such data.
Activities of the Alberta Congenital Anomalies Surveillance
System
The first ACASS report for the 1980-1986 data was
issued in 1989 with subsequent reports every 3 years
(www.health.alberta.ca/documents/congenitalanomaliesReport9-2012.pdf). Between 1988 and 2012, 40 publications
used ACASS data plus 3 articles in the now defunct CCASN
system (http://www.phac-aspc.gc.ca/ccasn-rcsac/index.
html). A list of these publications is available from the
authors of this article.
ACASS became a member of ICBDSR in 1996 and
continues to supply annual data for ICBDSR reports and
has supplied data for the 8 very rare defects reported in
the American Journal of Medical Genetics14; for a cleft lip/
palate study and a recent study on esophageal atresia.
Conclusion
Reorganization of the health-care system is usually
done to improve efficiency and reduce costs. In both British
Columbia and Alberta, these steps have not necessarily
aided the registry or surveillance systems. For example, in
Alberta, ACASS was moved from the Ministry of Health to
a Ministry of Registries and thus was grouped with land
titles and motor vehicle registrations. Fortunately, this was
short-lived and ACASS returned to the Ministry of Health.
In both BC and AB, health unit districts and areas were
changed to regions with loss of contact between public
health units and the registry/surveillance systems as well as
hampering geographic investigation of clusters.
Some of the methods which governments use to deal
with problems are to invite outside consultants or to set up
their own investigative commission. The results are often
disappointing, as was seen with the Coopers-Lybrand report
in BC where very little sustained action occurred, with the
exception of improving computer technology. There was
little, if anything, done towards improving ascertainment,
contact with stakeholders, or verification of diagnoses. AB
had similar experiences (see previous reference to Kaegi
report). The AB government also set up a task force to look
at services for disabled persons. The report (known as the
Klufas report) was submitted to the minister of SSCH in
1983 and made a large number of recommendations, one
of which was that the provincial government establish a
committee to assess the feasibility of an Alberta Health
Surveillance Registry similar to that of British Columbia.
There was no action on this recommendation.
There are lessons here not just for British Columbia and
Alberta but for all congenital anomaly and/or birth defects
registries. Consistent funding is essential; many systems
in Canada and the United States and indeed in the British
Isles Network of Congenital Anomaly Registers as well as
102
ICBDSR constantly admit to financial difficulties. However,
policy changes should not be implemented without having
discussions with all the major stakeholders of the system in
question.
While major congenital anomalies occur in approximately 3%-5% percent of newborn infants and in 8%-10% of
stillbirths, the World Health Assembly at their 2010 meeting
made a number of statements, including the fact that they
were “deeply concerned that birth defects are still not recognized as priorities in Public Health.” Item 1 urges member
states “to raise awareness among all relevant stakeholders,
including government officials, health professionals, civil
society and the public about the importance of birth defects
as the cause of child morbidity and mortality.”15
As stated in the Beijing manifesto, “We must continue
to collaborate to establish and maintain birth defects surveillance and monitoring systems, foster research on the causes
and prevention of birth defects and genetic diseases and
establish sustainable technologically appropriate interventions for the prevention and care of these conditions
including the provision of genetic services.”16 Thus, it
is apparent that the British Columbia HSR is no longer
meeting its objectives, as outlined in Table 1. The Alberta
Congenital Anomalies Surveillance System was evaluated
by Dr. Andrew Czeizel (Hungary) in 2005, who noted that
while the existing level of functioning was excellent and
was meeting its objectives, there were 2 major shortcomings:
lack of maternal risk factors and no controls. Negotiations
are presently underway to link ACASS with the Alberta
Perinatal Program to ascertain maternal histories and risk
factors. Because of privacy and ethical concerns, it will be
very difficult to have routine controls. The age of ascertainment needs to be increased. The thrust in congenital
anomaly surveillance is now in prevention as exemplified
by the folic acid fortification studies with a 46% reduction
of neural tube defects in Canada17 and confirmed elsewhere.
However, if we are to assess preventive measures, it is
essential that we have good quality surveillance data.
The take-home message for all registries is that in order
to remain viable, there must be well-written objectives,
funding, and an engaged provincial, state or federal government as well as medical, epidemiological, data management
and scientific support. The importance of a leader or champion cannot be overstated.
Acknowledgements
This paper is dedicated to the memory of Dr. Jim Miller
(AJMG, 2001:98;289).
ACASS is supported by the Surveillance and
Assessment Branch of the Ministry of Alberta Health.
The help of Michael Sanderson and Larry Svenson is
appreciated. The Alberta Children’s Hospital Foundation
supported ACASS for many years prior to Alberta Ministry
of Health support and without the foundation’s help,
ACASS would have closed in 1983. We thank Barbara
Sibbald for critical reading of the manuscript and Judy
Anderson for transcription.
Journal of Registry Management 2013 Volume 40 Number 2
References
1. Milham S. Congenital malformation surveillance system based on vital
records. Pub Health Rep. 1963;78:448-452.
2. Charles E. Statistical utilization of maternity and child welfare records.
Brit J Soc Med. 1951;5:41-61.
3. Martin P. A National Health Program for Canada. Can J Public Health.
1948;39:219-226.
4. Court D, Illingworth RS. Donald Hugh Paterson: ‘Through him the gale
of life blew high.’ Clin Pediatr. 1966;5:59-63.
5. Mott GA. The Registry of Handicapped Children and Adults in British
Columbia. Can J Public Health. 1963;54:239-245.
6. Lowry RB, Miller JR, Scott AE, Renwick DHG. The British Columbia
Registry for Handicapped Children and Adults: Evolutionary Changes
over Twenty Years. Can J Public Health. 1975;66:322-325.
7. Baird PA. Measuring birth defects and handicapping disorders in the
population: the British Columbia health Surveillance Registry. Can Med
Assoc J. 1987;136:109-111.
8. Miller JR. The Use of Registries and Vital Statistics in the Study of
Congenital Malformations. Proc 2nd Int Conference on Congenital
Malformations. The International Medical Congress, New York.
1964;334-340.
9. Kierens WJ, McBride AK. Health Status Registry: A Health Planning Tool
Revisited and Revitalized. Quarterly Digest Vital Statistics BC Ministry
of Health. 1992;2:22-30.
Journal of Registry Management 2013 Volume 40 Number 2
10.Lowry RB. The case for reviving the birth-defects database. The
Vancouver Sun. May 15, 2012:Opinion.
11.Smith ESO, Dafoe CS, Miller JR, Banister P. An epidemiological study
of congenital reduction deformities of the limbs. Br J Prev Soc Med.
1977;31:39-41.
12.Lowry RB, Thunem NY, Anderson-Redick S. Alberta Congenital Anomalies
Surveillance System: 1980-1986. Can Med Assoc J. 2007;141:1155-1159.
13.Lowry RB, Bowen P. The Alberta Hereditary Disease Program: a
regional model for delivery of genetic services. Can Med Assoc J.
1990;142:228-232.
14.Castilla EE, Mastroiacovo P. Special Issue: Very Rare Defects: What Can
We Learn? Am J Med Genet. 2011;157:251-373.
15.World Health Organization. Birth Defects. Sixty-Third World Health
Assembly. Provisional Agenda Item 11.7.32-33.
16.International Birth Defects Information Systems. Beijing Manifesto:
2nd International Conference on Birth Defects & Disabilities in the
Developing World, Sept 2005, Bejing, China. EB126/2010/REC2 World
Health Organization.
17.De Wals P, Tairou F, Van Allen MI, et al. Reduction in Neural-Tube
Defects after Folic Acid Fortification in Canada. N Engl J Med.
2007;357:135-142.
103
Special Feature
Raising the Bar: Was Chicken Little
a Cancer Registrar?
Michele Webb, CTR
Much talk and attention is being given to pay-forperformance, Meaningful Use 2, incentive payments, cancer
quality initiatives, and the like. As I sat at my desk to write
this quarter’s article for Raising the Bar, I could not help but
wonder if there was a hint of Chicken Little announcing that
“the sky is falling” in all the flurry of activities.
As I continued to ponder the future of cancer registry
and what it will look like in a new era of accountability,
quality control, and rapid reporting, I gave in to the need to
play and offer to you the cancer registrar’s version of The
Story of Chicken Little.1 Here’s how it goes.
•
•
•
•
•
•
•
CHARACTERS
Cancer Liaison Physician (CLP), the narrator
Sharlee Chicken, granddaughter of Chicken Little
Hennie Winny, niece of Henny Penny
Lucky Ducky, identical twin brother of Ducky Lucky
Gummy Lummy, maternal aunt to Goosey Loosey
Turkey Murkey, married name of Turkey Lurkey
The villain, Fuddy Duddy, the nerdy, bow-tie wearing
nephew of Foxy Loxy
THE STORY OF SHARLEE CHICKEN
CLP: Sharlee Chicken was in the registry one day when
a light bulb fell on her head. It scared her so much she
trembled all over. In fact, she shook so hard her hair extensions started to fall out.
Sharlee Chicken: “Help! Help! The sky is falling! I
have to go tell the CEO!”
CLP: So she ran in great fear to tell the CEO. Down the hall
and past the water fountain she met Hennie Winny.
Hennie Winny: “Where are you going, Sharlee Chicken?”
Sharlee Chicken: “Oh, help! The sky is falling!”
Hennie Winny: “How do you know?”
Sharlee Chicken: “I saw it with my own eyes, heard
it with my very own ears, and part of it fell on my head!”
Hennie Winny: “This is terrible, just terrible! We’d better
hurry.”
CLP: So they both ran down the hall as fast they could. In
front of the cafeteria they met Lucky Ducky.
Lucky Ducky: “Where are you going, Sharlee Chicken
and Hennie Winny?”
Sharlee Chicken and Hennie Winny: “The sky is
falling! They sky is falling! We’re going to tell the CEO!”
Lucky Ducky: “How do you know?”
Sharlee Chicken: “I saw it with my own eyes, and
heard it with my very own ears, and part of it fell on my
head.”
104
Lucky Ducky: “Uh oh,
freakin’ scary! We’d
better run!”
CLP: So they all ran
down the hall as fast
as they could. Soon
they met Gummy
Lummy coming out
of the elevator.
Gummy Lummy:
“Whassup? Where are
you all going in such a
hurry?”
Sharlee Chicken: “We’re
running for our lives!”
Hennie Winny: “The sky is falling!”
Lucky Ducky: “And we’re running to tell the CEO!”
Gummy Lummy: “How do you know the sky is falling?”
Sharlee Chicken: “I saw it with my own eyes, and
heard it with my very own ears, and part of it fell on my
head!”
Gummy Lummy: “Wicked cool! Then I’d better run with
you.”
CLP: And they all ran in great fear across the crowded main
lobby. There they met Turkey Murkey strutting back and
forth in front of the gift shop.
Turkey Murkey: “Hello there, Sharlee Chicken, Hennie
Winny, Lucky Ducky, and Gummy Lummy. Where are you
all going in such a hurry?”
Sharlee Chicken: “Help! Help!”
Hennie Winny: “We’re running for our lives!”
Lucky Ducky: “The sky is falling!”
Gummy Lummy: “And we’re running to tell the CEO!”
Turkey Murkey: “How do you know the sky is falling?”
Sharlee Chicken: “I saw it with my own eyes, and
heard it with my very own ears, and part of it fell on my
head!”
Turkey Murkey: “Good grief! I always suspected the sky
would fall someday. I’d better run to the CEO with you.”
CLP: So, they picked up their clogs and ran as fast they
could, until they met up with Fuddy Duddy in the CEO’s
doorway.
Fuddy Duddy: “Hello there. Where are you rushing to on
such a beautiful day?”
Sharlee Chicken, Hennie Winny, Lucky Ducky,
Gummy Lummy, Turkey Murkey (together): “Help!
Help! It’s not a beautiful day at all. The sky is falling, and
we’re here to tell the CEO!”
Journal of Registry Management 2013 Volume 40 Number 2
Fuddy Duddy: “How do you know the sky is falling?”
Sharlee Chicken: “I saw it with my own eyes, and
heard it with my very own ears, and part of it fell on my
head!”
Fuddy Duddy “I see. Well then, follow me, and I’ll take
you to the CEO right away.”
CLP: So Fuddy Duddy led Sharlee Chicken, Hennie Winny,
Lucky Ducky, Gummy Lummy and Turkey Murkey through
a side door and down a long dark hallway. He led them
straight to his office in human resources, and they never
saw the CEO to tell him that the sky was falling.
And that is the story of how 5 cancer registrars quietly
disappeared from the workforce on that fateful day! Was
Sharlee Chicken a cancer registrar? What do you think?
Is this a silly story? Perhaps. But, we know that the
landscape of oncology healthcare is changing and that
quality control initiatives, Rapid Quality Reporting System
(RQRS), workforce shortages and all the new scientific and
clinical discoveries in medicine are directly impacting the
cancer registrar’s work. We know that how we complete
our tasks, what we say about our work, what others see
in our attitudes and philosophies about service, and their
perceived value of our work is directly related to our future.
We, meaning cancer registrars worldwide, have to
make an important and life-changing decision. We can
be like Sharlee Chicken and predict the sky is falling,
talk about lack of support, poor salaries, or increasing
job demands and fail to meet the workforce expectations.
Journal of Registry Management 2013 Volume 40 Number 2
Or, we can grow individual and workforce competencies,
collaborate and serve our health-care teams to provide data
and services that meet their growing needs, volunteer and
serve our associations to reinvent the perceived value of the
cancer registry profession, and, ultimately, bring value and
quality outcomes to patient care.
These are not changes that will come about quickly
or easily. They require hard work, dedication and vision.
But, we can accomplish this with a united workforce if we
choose to abandon reactive behaviors in favor of leadership
and service that focuses on new and enhanced competences
and knowledge to promote the value of the cancer registry.
Reference
1. Ashliman DL, ed. The End of the World: The Sky Is Falling. Folktales
of Aarne-Thompson-Uther type 20C (including former type 2033), in
which storytellers from around the world make light of paranoia and
mass hysteria. Available at: http://www.pitt.edu/~dash/type2033.html.
Michele is a cancer registry speaker, educator, coach, and
independent contractor living in Rancho Cucamonga, California.
She is the founder of www.CancerRegistrar.com, http://
CancerRegistryAcademy.com, and www.RegistryMindset.
com offering cancer registry leadership, mentoring and continuing
education opportunities. Your comments are welcomed by email
to [email protected].
105
Special Feature
On Using Health Informatics to Advance Your Career
Herman R. Menck, BS, MBA, CPhil, FACE; Michele Webb, CTR; Kim Watson, CTR;
Sue Koering, MEd, RHIT, CTR; Annette Hurlbut, RHIT, CTR;
Suzan Naam, MD, CTR; Judy Williams, RHIT, CTR
Introduction
Trends in Cancer Registration/EHRs
From one perspective, the move toward electronic
hospital and medical office records and related events have
incorporated cancer registration almost completely into
the world of computerized medical information, or health
informatics. Accordingly, cancer registrars have become
accepting users of personal computers (PCs). In 2 short
decades, cancer registration has moved from no computers,
to 1 computer in the supervisor’s office, to now a PC at
virtually every registrar’s workstation. Inertia, fear, or
unwillingness to operate computers has given way to the
routine widespread use of computers by registry staff. This
is not to claim universal computer competence. There still
remain many competency and technical literacy issues
within cancer registration.
Dealing with Personal Change
For many, the dramatic move to computerization has
come at significant emotional cost and been a source of
day-to-day stress. Broad changes in previous core cancer
registration tasks such as abstracting, staging, casefinding,
and follow-up have occurred concurrently with the advent
of the electronic age in medical records. Busy registrars
have had to become masters of change and new learning
in the core certified tumor registrar (CTR) functions at the
same time that information technology (IT) is significantly
increasing. This necessary acceptance has largely been
achieved and deserves celebration. In effect, expert usage
of computers has also become one of the necessary core
competencies of cancer registrars.
Opportunity
Harnessing Opportunities in Computerization
1. Increasing IT (computer) knowledge.
2. Establishing an IT personal profile and perception within your
organization.
3. Collaborating/networking with others on organizational IT
projects.
4. Being alert and open to promotional possibilities.
Because computerization has become universal within
organizations, this tidal change has inherently increased the
value of the computer-savvy registrar within registries and
elsewhere within organizations. This major change has had
2 positive effects: 1) increasing the organizational versatility
106
and value of registrars, and 2) the potential for increased
personal satisfaction in doing one’s work. It can be more
fun, and result in a sense of pride of accomplishment. In
addition, an opportunity arises for the registrar to become
not only an experienced user of computers, but an expert
on cancer registration automation in total. Harnessing this
opportunity is summarized in the inset.
Increasing IT (Computer) Knowledge
A straightforward learning path is to read introductory
and intermediate IT books, or to take classes on IT subjects.
There are numerous journal and magazine articles that will
also be helpful. Topics might include:
Computer resources: Hardware and operating systems,
local area networks, firewalls, remote network access, use of
desktops, laptops and servers, Web servers, the Web and
cancer registry.
Evolution of medical records: Electronic medical record
(EMR)/electronic health record (EHR) environment, meaningful use, interfaces to other clinical records and systems.
Cancer registry software functionality/applications:
Data collection, database management systems, structured query language (SQL), consolidating records, data
editing/quality control (QC) software, linkage with external
sources, health information exchange/transfer, record
linkage, follow-up software, information security, statistical,
geographic information systems (GIS) software, vendors/
sources of registry software. A detailed discussion of what
such subjects as QC, SQL and information exchange are
and why they frequently involve registrars, and not just
IT personnel, is beyond the scope of this article, but their
relevance to registrars is noted.
Selected IT/registration subjects: Clinical trials,
working from home (remotely), cancer registration, informatics summary.
A comprehensive bibliography of these subjects is
available from the National Cancer Registrars Association
Web site. Also see the selective bibliography below (1-3).
Because of the completeness of this bibliographic information, it may be best to take one bibliography subject at a
time.
Also, become familiar with your software vendor’s
manual and the vendor’s representative. Get to know
people in your IT department and learn in collaboration
with IT personnel how to install, back up, and restore your
cancer registry software. Further, the registrar should make
sure IT is aware of all of the registry system requirements.
Journal of Registry Management 2013 Volume 40 Number 2
Suzan Naam suggests, “Step up and volunteer to help
Establishing an IT Personal Profile and Perception
in
many
of the tasks that used to be performed by IT in the
within Your Organization
Helpful advice from several registrars is offered.
Susan Koering suggests, “Approaches include showing
data from the registry, offering a data search for physicians,
administrative staff or researchers, and showing them
report options (departments such as dermatology). Also,
Cancer Registry Week is a good time to display data at
cancer conferences, cancer committee meetings, facility/
cancer center libraries and using hallway displays. I have
also asked to be present at department meetings to show
data on our organization. These include our annual reports,
(general) statistics and those with a cancer-site focus. The
cancer program newsletter is another option to share our
resources.”
Kim Watson says, “I came to the cancer registry field
with a degree in computer software support, so my journey
has been different than what most registrars may have
experienced. People tend to notice that my computer skills
are greater than theirs (usually), and then I end up being the
person they come to for quick answers, usually about word
processing or spreadsheet applications. We’ve recently
changed software vendors and the process would have been
much more difficult had I not had the computer background
that I have. I’ve just always tried to be available to help. I
also always try to make instructions clear and make sense
to the learner, rather than just telling them what to do. You
have to make them understand why you do something the
way you do. This helps them retain the information.”
Michele Webb suggests, “Learning about your registry
software, how it works and what the registrar can do to get
optimal performance as a result is a key to maintaining your
system. Many years ago, I found it simpler and faster to
install and maintain my registry software on my own. But,
as technology increases and the workload of the registrar
increases, it has been more successful, and even a relief,
to let IT do what they do best and to focus my efforts in
partnering with them to understand what the registry is
accountable for, and to be willing to listen, learn, and grow
my skills from what IT is almost always willing to give. It’s
like free education if we are willing to collaborate.”
registry. Participate in many cooperative projects with other
databases in our organization. Take every opportunity to
present registry data and analysis in advanced and impressive format.”
Michele Webb says, “Get involved in your organization’s efforts to maintain compliance with all quality control
measures, many undertaken through the quality control
department. IT is often a part of this effort and this is a
chance to have a voice in both sides of the picture and still
promote the activities of the cancer registry independent of
both groups.”
Being Alert and Open to Promotional Possibilities
Let your supervisor and human resources know you
are interested in job growth and promotion. Ask for their
advice on career advancement pathways, and what you can
do to help your organization. Be open to new opportunities,
even if they are somewhat different.
Your approach to registry informatics may be colored
by whether you are a central registrar, hospital based, independent contractor, or whatever. It is believed that these
core suggestions can be usefully adapted, whatever the case.
Summary
For personal satisfaction and possible promotion,
increased IT (health informatics) familiarization can be a
positive step. This article outlines self-help steps toward
making that goal possible. The authors and many of their
peer leaders believe that IT familiarization is a valuable
career goal, and is possible following the steps discussed
above.
References
1. Menck HR, Gress DM, Griffin A, et al. Cancer Registry Management
Principles & Practices. 3rd ed. Dubuque, IA: Kendall/Hunt Publishing
Company; 2011.
2. Menck HR, Scharber W, Hurlbut A, Robinson L. Guidebook on
Informatics. Alexandria, VA: National Cancer Registrars Association;
2009.
3. Menck HR, Deapen D, Phillips JL, Tucker TD. Central Cancer Registries
Design, Management and Use. 2nd ed. Dubuque, IA: Kendall/Hunt
Publishing Company; 2007.
Collaborating/Networking with Others on
Organizational IT Projects
Volunteer for or ask to be included in automation or
IT planning projects within your organization. Also initiate
other IT projects, such as additional data reporting, and
submitting outcomes data for Meaningful Use Phase 2
(MU2) as part of the Centers for Medicare and Medicaid
Services/National Quality Forum (CMS/NQF) activities.
Journal of Registry Management 2013 Volume 40 Number 2
107
Journal of Registry Management Continuing Education Quiz—Summer 2013
DESCRIPTIVE EPIDEMIOLOGY OF MALIGNANT PRIMARY OSTEOSARCOMA USING
POPULATION-BASED REGISTRIES, UNITED STATES, 1999-2008
Quiz Instructions: The multiple choice or true/false quiz below is provided as an alternative method of earning CE credit hours.
Refer to the article for the ONE best answer to each question. The questions are based solely on the content of the article.
Answer the questions and send the original quiz answer sheet and fee to the NCRA Executive Office before the processing date
listed on the answer sheet. Quizzes may not be retaken nor can NCRA staff respond to questions regarding answers. Allow 4–6
weeks for processing following the submission deadline to receive return notification of your completion of the CE process. The
CE hour will be dated when it is submitted for grading; that date will determine the CE cycle year.
After reading this article and taking the quiz, the participants will be able to:
• Compare the national incidence of osteosarcoma by age and sub-site
• Identify the source data used in this study
• Describe how the incidence of malignant primary osteosarcoma differs according to age, gender, race, ethnicity, region,
grade, and stage
1. Osteosarcoma:
a)is a common bone tumor
b)incidence peaks during adolescence
c)is most frequently diagnosed among individuals in the fourth
decade of life
d)occurs only in very young children
2. Among 15 to 29 year olds, osteosarcomas are more likely to
occur in the:
a)axial skeleton
b)proximal femur
c)metaphyses of long bones
d)distal tibia
3. Osteosarcoma affects males more frequently than females
across all age categories.
a)True
b)False
4. Incidence data for this study include:
a)radiographically confirmed cases of primary osteosarcoma
b)cases diagnosed between 2000 and 2010
c)data from 44 states and the District of Columbia
d)data from the National Program of Cancer Registries (NPCR)
and the Surveillance, Epidemiology, and End Results (SEER)
Program
5. According to Table 1, Topography Codes Used to Define
Malignant Primary Osteosarcomas by Appendicular and Axial
Sites, United States, 1999–2008, axial subsites include:
a)bone, NOS
b)bone of limb, NOS
c)overlapping lesion of bones, joints, and articular cartilage of
limbs
d)scapula
6. Cancer stage was assigned according to:
a)SEER summary stage 1977
b)SEER summary stage 2000
c)derived summary stage
d)the year of diagnosis
7. Ethnicity was categorized as Hispanic or non-Hispanic based
on:
a)race/ethnicity information taken from the face-sheet
b)patient self-disclosure of ethnicity
c)North American Association of Central Cancer Registries’
(NAACCR) Hispanic/Latino Identification Algorithm (NHIA)
d)random assignment of the Hispanic ethnicity when Hispanic
status was unknown
8. This study considered race and ethnicity to be mutually
exclusive.
a)True
b)False
9. According to Table 3. Description of Primary Osteosarcoma
Incidence by Selected Characteristics, United States,
1999−2008:
a)37% of the grade IV osteosarcomas were appendicular
b)axial sarcomas occur most commonly in the 70-79 age group
c)among appendicular and axial cases, the predominant race
group was black
d)the majority of axial osteosarcomas were diagnosed at distant
stage
10.Limitations in this study include:
a)possibility of misclassification or miscoding in the registry
data
b)data were not available from some central registries because
they did not meet case ascertainment and quality criteria
c)race and/or ethnicity misclassification
d)all of the above
The JRM Quiz and answers are now available through NCRA’s Center for Cancer Registry Education (CCRE). For your convenience, the
JRM article and quiz can be accessed online at www.CancerRegistryEducation.org/jrm-quizzes. Download the article, complete the
quiz and claim CE credit all online.
108
Journal of Registry Management 2013 Volume 40 Number 2
Journal of Registry Management Continuing Education Quiz
Answer Sheet
Available online at www.cancerregistryeducation.org/jrm-quizzes
Please print clearly in black ballpoint pen.
m.i.
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Last Name
Address
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Instructions: Mark your
answers clearly by filling in the
correct answer, like this ■ not
like this . Passing score of
70% entitles one (1) CE clock
hour per quiz.
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CTR Number
This original quiz answer sheet will not be graded, no CE credit will be
awarded, and the processing fee will be forfeited unless postmarked by:
August 31, 2014
Quiz Identification Number:
Please use black ballpoint pen.
1
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C
D
2
A
B
C
D
3
A
B
4
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B
C
D
5
A
B
C
D
6
A
B
C
D
7
A
B
C
D
8
A
B
9
A
B
C
D
10
A
B
C
D
4002
JRM Quiz Article:
DESCRIPTIVE EPIDEMIOLOGY OF MALIGNANT PRIMARY OSTEOSARCOMA USING
POPULATION-BASED REGISTRIES, UNITED STATES, 1999-2008
Processing Fee: Member $25
Nonmember $35
Payment is due with submission of answer sheet. Make check or money
order payable to NCRA. US currency only. Do not send cash. No refund
under any circumstances. Please allow 4–6 weeks following the submission
deadline for processing.
Submit the original quiz
answer sheet only!
No photocopies will be accepted.
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Journal of Registry Management 2013 Volume 40 Number 2
109
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110
Journal of Registry Management 2013 Volume 40 Number 2
National Cancer Registrars Association
CALL FOR PAPERS
Vicki G. Nelson, MPH, RHIT, CTR | EDITOR-IN-CHIEF, JRM
The Journal of Registry Management, official journal of the National Cancer Registrars Association (NCRA), announces
a call for original manuscripts on registry methodology or research findings related to the 5 subjects listed below and
related topics.
Topic:
1. Birth Defects Registries
2. Cancer Registries
Cancer Collaborative Stage
Cancer and Socioeconomic Status
History
3. Trauma Registries
4. Recruitment, Training, and Retention
5. Public Relations
Contributed manuscripts are peer-reviewed prior to publication. Manuscripts of the following types may be submitted
for publication:
1. Methodology Articles addressing topics of broad interest and appeal to the readership, including methodological
aspects of registry organization and operation.
2. Research articles reporting findings of original, reviewed, data-based research.
3. Primers providing basic and comprehensive tutorials on relevant subjects.
4. “How I Do It” Articles describe tips, techniques, or procedures for an aspect of registry operations that the author does
particularly well. The “How I Do It” feature in the Journal provides registrars with an informal forum for sharing strategies with colleagues in all types of registries.
5. Opinion papers/editorials including position papers, commentaries, essays, and interviews that analyze current or
controversial issues and provide creative, reflective treatments of topics related to registry management.
6. Bibliographies which are specifically targeted and of significant interest will be considered.
7. Letters to the Editor are also invited.
Address all manuscripts to: Vicki G. Nelson, MPH, RHIT, CTR, Editor-in-Chief, Journal of Registry Management, (770) 488-6490,
[email protected].
Manuscript submission requirements are given in “Information for Authors” found on the inside back cover of each Journal
and on the NCRA Web site at http://www.ncra-usa.org/jrm.
Journal of Registry Management 2013 Volume 40 Number 2
111
Journal of Registry Management
INFORMATION FOR AUTHORS
Journal of Registry Management (JRM), the official journal of the National Cancer Registrars Association, invites submission of original manuscripts on topics related to management of
disease registries and the collection, management, and use of cancer, trauma, AIDS, and other disease registry data. Reprinting of previously published material will be considered for
publication only when it is of special and immediate interest to the readership. JRM encourages authorship by Certified Tumor Registrars (CTRs); special value is placed on manuscripts
with CTR collaboration and publication of articles or texts related to the registry profession. CTR continuing education (CE) credits are awarded; a published chapter or full textbook
article equals 5 CE hours. Other published articles or documents equal CE hours. All correspondence and manuscripts should be addressed to the Editor-in-Chief, Vicki Nelson, MPH,
RHIT, CTR at: [email protected], or at: CDC/NCCDPHP/DCPC/CSB, 4770 Buford Drive, MS K-53, Atlanta, GA 30341-3717, 770-488-6490 (office), 770-488-4759 (fax).
Manuscripts may be submitted for publication in the following categories: Articles addressing topics of broad interest and appeal to the readership, including Methodology papers
about registry organization and operation; Research papers reporting findings of original, reviewed, data-based research; Primers providing tutorials on relevant subjects; and “How I
Do It” papers are also solicited. Opinion papers/editorials including position papers, commentaries, and essays that analyze current or controversial issues and provide creative, reflective treatments of topics related to registry management; Letters to the Editor; and specifically-targeted Bibliographies of significant interest are invited.
The following guidelines are provided to assist prospective authors in preparing manuscripts for the Journal, and to facilitate technical processing of submissions. Failure to follow the
guidelines may delay consideration of your manuscript. Authors who are unfamiliar with preparation and submission of manuscripts for publication are encouraged to contact the
Editor for clarification or additional assistance.
Submission Requirements
Manuscripts. The terms manuscripts, articles, and papers are used synonymously herein. E-mail only submission of manuscripts is encouraged. If not feasible, submit the original manuscript and 4 copies to the Editor. Manuscripts should be double-spaced on white 8-1/2” x 11” paper, with margins of at least 1 inch. Use only letter-quality printers; poor quality copies
will not be considered. Number the manuscript pages consecutively with the (first) title page as page one, followed by the abstract, text, references, and visuals. The accompanying cover
letter should include the name, mailing address, e-mail address, and telephone number of the corresponding author. For electronic submission, files should be 3-1/2”, IBM-compatible
format in Corel WordPerfect™, Microsoft® Word for Windows®, or converted to ASCII code.
Manuscripts (Research Articles). Articles should follow the standard format for research reporting (Introduction, Methods, Results, Discussion, References), and the submission instructions outlined above. The introduction will normally include background information, and a rationale/justification as to why the subject matter is of interest. The discussion often
includes a conclusion subsection. Comprehensive references are encouraged., as are an appropriate combination of tables and figures (graphs).
Manuscripts (Methodology/Process Papers). Methodology papers should follow the standard format for research reporting (Introduction, Methods, Results, Discussion), or for explanatory papers not reporting results (Introduction, Methods, Discussion), as well as the submission instructions outlined above.
Manuscripts (“How I Do It” articles). The “How I Do It” feature in the Journal provides registrars with a forum for sharing strategies with colleagues in all types of registries. These
articles describe tips, techniques, or procedures for an aspect of registry operations that the author does particularly well. When shared, these innovations can help registry professionals
improve their skills, enhance registry operations, or increase efficiency.
“How I Do It” articles should be 1,500 words or less (excepting references) and can contain up to 2 tables or figures. To the extent possible, the standard headings (Introduction, Methods,
Results, Discussion) should be used. If results are not presented, that section may be omitted. Authors should describe the problem or issue, their solution, advantages (and disadvantages) to the suggested approach, and their conclusion. All submitted “How I Do It” articles will have the benefit of peer/editorial review.
Authors. Each author’s name, degrees, certifications, title, professional affiliation, and email address must be noted on the title page exactly as it is to appear in publication. The corresponding author should be noted, with mailing address included. Joint authors should be listed in the order of their contribution to the work. Generally, a maximum of 6 authors for
each article will be listed.
Title. Authors are urged to choose a title that accurately and concisely describes the content of the manuscript. Every effort will be made to use the title as submitted, however, Journal
of Registry Management reserves the right to select a title that is consistent with editorial and production requirements.
Abstract. A brief abstract must accompany each article or research paper. The abstract should summarize the main point(s) and quickly give the reader an understanding of the manuscript’s content. It should be placed on a page by itself, immediately following the title page.
Length. Authors are invited to contact the Editor regarding submission of markedly longer manuscripts.
Style. Prepare manuscripts using the American Medical Association Manual of Style. 9th ed. (1998)
Visuals. Use visuals selectively to supplement the text. Visual elements—charts, graphs, tables, diagrams, and figures—will be reproduced exactly as received. Copies must be clear
and properly identified, and preferably e-mailed. Each visual must have a brief, self-explanatory title. Submit each visual on a separately numbered page at the end of the manuscript,
following the references.
Attribution. Authors are to provide appropriate acknowledgment of products, activities, and support especially for those articles based on, or utilizing, registry data (including
acknowledgment of hospital and central registrars). Appropriate attribution is also to be provided to acknowledge federal funding sources of registries from which the data are obtained.
References. References should be carefully selected, and relevant. References must be numbered in order of their appearance in the text. At the end of the manuscript, list the references
as they are cited; do not list references alphabetically. Journal citations should include author, title, journal, year, volume, issue, and pages. Book citations should include author, title,
city, publisher, year, and pages. Authors are responsible for the accuracy of all references. Examples:
1. LeMaster PL, Connell CM. Health education interventions among Native Americans: A review and analysis. Health Education Quarterly. 1995;21(4):521–38.
2. Hanks GE, Myers CE, Scardino PT. Cancer of the Prostate. In: DeVita VT, Hellman S, Rosenberg SA. Cancer: Principles and Practice of Oncology, 4th ed. Philadelphia, PA: J.B. Lippincott
Co.; 1993:1,073–1,113.
Key words. Authors are requested to provide up to 5, alphabetized key words or phrases which will be used in compiling the Annual Subject Index.
Affirmations
Copyright. Authors submitting a manuscript do so on the understanding that if it is accepted for publication, copyright in the article, including the right to reproduce the article in all
forms and media, shall be assigned exclusively to NCRA. NCRA will not refuse any reasonable requests by the author(s) for permission to reproduce any of his or her contributions to the
Journal. Further, the manuscript’s accompanying cover letter, signed by all authors, must include the following statement: “We, the undersigned, transfer to the National Cancer Registrars
Association, the copyright for this manuscript in the event that it is published in Journal of Registry Management.” Failure to provide the statement will delay consideration of the manuscript.
It is the author’s responsibility to obtain necessary permission when using material (including graphs, charts, pictures, etc.) that has appeared in other published works.
Originality. Articles are reviewed for publication assuming that they have not been accepted or published previously and are not under simultaneous consideration for publication
elsewhere. If the article has been previously published or significantly distributed, this should be noted in the submission for consideration.
Editing
Journal of Registry Management reserves the right to edit all contributions for clarity and length. Minor changes (punctuation, spelling, grammar, syntax) will be made at the discretion of
the editorial staff. Substantive changes will be verified with the author(s) prior to publication.
Peer Review
Contributed manuscripts are peer-reviewed prior to publication, generally by 3 reviewers. The Journal Editor makes the final decision regarding acceptance of manuscripts. Receipt of
manuscripts will be acknowledged promptly, and corresponding authors will be advised of the status of their submission as soon as possible.
Reprints
Authors receive 5 complimentary copies of the Journal in which their manuscript appears. Additional copies of reprints may be purchased from the NCRA Executive Office.
112
Journal of Registry Management 2013 Volume 40 Number 2
NCRA Marks its
Anniversary in 2014
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Address SERVICE Requested
SFI-00359
Printed on SFI fiber sourcing paper with non-petroleum,
vegetable based inks and manufactured with renewable electricity