Download Data Mining for Racial, Gender, and Social Economic Status

Sylvia Le
April 29, 2010
Disparities in Health Care
 Disparities in health care have serious impact on the
quality of health care.
 Identifying health disparities may seem difficult at
times, but recognition is essential.
 Data mining has been used to effectively recognize
several disparities in different areas of health care.
The Thyroid
 The thyroid is an endocrine gland
(hormone secreting and
producing organ)
located anterior to the trachea.
 This organ secretes hormones thyroxine (T4) and
triiodothyronnine (T3) which help regulate metabolism and
growth.
 Thyroid cancer can occur at any age, and there are over
20,000 new cases of thyroid cancer every year.
Purpose
Are there race, gender, and socioeconomic
disparities affecting treatment and survival for
thyroid cancer patients?
SEER for Data Mining
 Surveillance, Epidemiology and End Results (SEER)
program of the National Cancer Institute is the data and
software source for this data mining process.

Software:
SEER*Stat 6.5.2

Dataset:
Incidence - SEER 17 Regs Limited-Use + Hurricane
Katrina Impacted Louisiana Cases, Nov 2009 Sub
(2000-2007) <Katrina/Rita Population Adjustment>
Data Selection
 Only Thyroid cancer patients
 from Variable Set {Site and Morphology. Site rec with Kaposi and mesothelioma}
 where there exists known data for
 Race - White, Black, American Indian/Alaska Native, Asian/Pacific Islander,
Non-white Hispanic
 Sex – Male, Female
 Age - 00 - 85+ years
 To limit data size and ensure the more recent information is used, only
patients diagnosed from 2000 - 2006 will be used.
 Information about socioeconomic status (SES) is not directly available
from the patients but rather this data is calculated from US Census data
by County. This variable was formed into Quintiles based on Median
Household Income.
 Total number of patients = 47,278.
Data Mining for Survival Disparities
 Compare variables to Survival time in 6-month
intervals (000-083 months)
 Used SEER*Stat Frequency Session to calculate Count
and Frequency (Column%)
 Exported tables to .txt and visualized using Excel
graphs
Survival Time vs Sex
Count
 Observe Sex Ratio in these two kinds of stacked bar
graphs.
 Females make up ~75% of the thyroid cancer patients.
78
Survival Time (months)
Survival Time (months)
78
66
54
42
30
18
66
54
Male
42
Female
30
18
6
6
0
2,000
4,000
Number of Patients
6,000
8,000
0%
20%
40%
60%
80%
100%
Survival Time vs Sex
Frequency
 Observe that survival time has rapid decrease in the
first year after diagnosis.
 The two survival curves do not seem to indicate a
disparity between male and female survival.
18.00%
16.00%
Frequency (%)
14.00%
12.00%
10.00%
8.00%
Male
6.00%
Female
4.00%
2.00%
0.00%
6
12
18
24
30
36
42
48
54
Survival Time (months)
60
66
72
78
83
Survival Time vs Race
Count
 Observe Rate Ratio in these two kinds of stacked bar
78
66
White
54
Black
42
AmerInd
30
Asia/Pac
18
Hispan
6
Survival Time (months)
Survival Time (months)
graphs.
 Notice whites make up ~70% of the patients.
78
66
54
42
30
18
6
0
2,000
4,000
6,000
Number of Patients
8,000
0%
20%
40%
60%
80%
100%
Survival Time vs Race
Frequency
 The five survival curves do not seem to indicate a disparity between
races in survival.
 American Indian/Alaska Native have a few unusual blips probably
because their patient population is very low, making the curve
susceptible to such increases/decreases.
18.00%
16.00%
Frequency (%)
14.00%
12.00%
White
10.00%
Black
8.00%
AmerInd
6.00%
Asia/Pac
4.00%
Hispan
2.00%
0.00%
6
12
18
24
30
36
42
48
54
Survival Time (months)
60
66
72
78
83
Survival Time vs SES
Count
 Observe Quintile Ratio in these two kinds of stacked
bar graphs.
 Notice Third and Fourth Quintile make up ~40% and
~55% of total number of patients.
78
66
First Quintile - Lowest
54
Second Quintile
42
Third Quintile
30
Fourth Quintile
18
Fifth Quintile - Highest
Survival Time (months)
Survival Time (months)
78
66
54
42
30
18
6
6
0
2000
4000
6000
Number of Patients
8000
0%
20%
40%
60%
80%
100%
Survival vs SES
Frequency
 The five survival curves do not seem to indicate a disparity between
socioeconomic status and survival.
 The Second Quintile has a few unusual blips for similar reasons as Native
American/Alaska Native did for previous frequency graph; the patient count for
this groups is very low.
18%
16%
Frequency (%)
14%
12%
First Quintile - Lowest
10%
8%
Second Quintile
6%
Third Quintile
4%
Fourth Quintile
2%
Fifth Quintile - Highest
0%
6
12
18
24 30 36 42 48 54 60 66 72 78 83
Survival Time (months)
Treatment vs Sex
Count
 Observe Sex Ratio in these two kinds of stacked bar
graphs.
 Notice that the fraction of females is still greater, but
less clear for each treatment.
Radiation after Surgery
No Surgery, Not recommended
Treatment
Treatment
No Surgery, Recommended but no…
No Surgery, Constraindicated due to…
Radiation before Surgery
No Surgery, Recommended but patient…
Male
Female
Radiation before and after Surgery
Intraoperative Radiation
No Surgery, patient died before…
0
5000
10000
15000
20000
Number of Patients
25000
0%
50%
100%
Treatment vs Sex
Frequency
 Because proportion of Radiation before and after
Treatment is so large, it was removed to form the graph on
the right so view other treatment graphs.
 Females appear to have less Intraoperative Radiation and
Males tend to not have recommended surgeries.
No Surgery,
patient died…
100.00%
Radiation after
80.00%
Surgery
60.00%
40.00%
No Surgery,
20.00%
Not…
0.00%
No Surgery,
Recommende…
No Surgery,
Constraindica…
Intraoperative
Radiation
8.00%
Intraoperative
Radiation
Radiation after
Surgery
6.00%
Radiation before
and after Surgery
4.00%
Radiation
before and…
Male
Female
No Surgery,
Recommende…
Radiation
before Surgery
2.00%
No Surgery, Not
recommended
0.00%
No Surgery,
Recommended
but no perform…
No Surgery,
Recommended
but patient…
Radiation before
Surgery
No Surgery,
Treatment vs Race
Count
 Observe Race Ratios in these two kinds of stacked bar
graphs.
 Notice that the fraction of whites is still greater, but
less clear for each treatment.
Radiation after Surgery
Treatment
No Surgery, Recommended but no perform for…
White
No Surgery, Constraindicated due to other conditions
Black
Radiation before Surgery
No Surgery, Recommended but patient refused
AmerInd
Radiation before and after Surgery
Asia/Pac
Intraoperative Radiation
Treatment
No Surgery, Not recommended
Hispan
No Surgery, patient died before recommended surgery
0%
0
5000
10000
15000
20000
Number of Patients
25000
50%
100%
Treatment vs Race
Frequency
 Because proportion of Radiation before and after Treatment is so large, it was
removed to form the graph on the right so view other treatment graphs.
 Several disparities seem to appear:
 Blacks seem to have the most non-recommended surgeries.
 American Indian/Alaska Natives most frequently seem to have no surgery
due to patient refusal or other constraints..
Rad after Surg
100.00%
No Surg, Pt died
80.00%
before
60.00%
No Surg, Not
recommend
White
40.00%
Intraoperative Rad
20.00%
0.00%
Rad before and
after Surg
No Surg, Rec but
Pt refuse
Intraoperative
Radiation
8.00%
No Surg, Rec but
no perform
No Surg,
Constrain other
Rad before Surg
Radiation after
Surgery
Black
AmerIn
d
Asia/Pac
No Surgery, Not
recommended
6.00%
4.00%
2.00%
0.00%
Radiation before
and after…
No Surgery,
Recommended…
Hispan
No Surgery,
Recommended…
Radiation before
Surgery
No Surgery,
Constraindicat…
Treatment vs SES
Count
 Observe Quintile Ratio in these two kinds of stacked bar graphs.
 Notice that Third and Fourth Quintile still make up the majority
of patients, but fraction is less clear that in Survival set.
 Notice also that Radiation before and after Surgery treatment
makes up ~90% of all treatment counts.
Radiation after Surgery
No Surgery, Recommended but no perform for…
First Quintile Lowest
Second Quintile
Treatment
Treatment
No Surgery, Not recommended
No Surgery, Constraindicated due to other conditions
Radiation before Surgery
No Surgery, Recommended but patient refused
Radiation before and after Surgery
Third Quintile
Fourth Quintile
Intraoperative Radiation
No Surgery, patient died before recommended surgery
0
10000
20000
Number of Patients
30000
0%
50%
100%
Treatment vs SES
Frequency
 Because proportion of Radiation before and after
Treatment is so large, it was removed to form the graph on
the right so view other treatment graphs.
 Radar graphs do not seem to indicate disparity in
treatment based on socioeconomic status.
Radiation after
Surgery
No Surgery,
Not
recommended
No Surgery,
patient died
before…
100%
80%
60%
40%
20%
0%
Radiation
after Surgery
Intraoperative
Radiation
Radiation
before and
after Surgery
First Quintile - Lowest
Second Quintile
Third Quintile
Fourth Quintile
No Surgery,
Recommended
but no…
No Surgery,
Constraindicat
ed due to…
No Surgery,
Recommended
but patient…
Radiation
before Surgery
Fifth Quintile - Highest
No Surgery,
Not
recommende
d
No Surgery,
Recommende
d but no
perform for…
Intraoperative
Radiation
6%
5%
4%
3%
2%
1%
0%
No Surgery,
Constraindica
ted due to
other…
Radiation
before and
after Surgery
No Surgery,
Recommende
d but patient
refused
Radiation
before
Surgery
Conclusions
 None of Race, Gender or Socioeconomic Status appear
to have Survival disparities.
 However, several disparities can be observed with
patient Treatment for Race and Gender.
 Treatment does not seem to have as significant of
disparities for Socioeconomic Status.
Questions?