Download The Capture of Morbidity Information in General Practice

The Capture of Morbidity
Information in General Practice
Douglas Fleming
Director, RCGP, Birmingham Research Unit
Nottingham: July 20, 2005
Content of Presentation
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History of morbidity surveys
Purpose of morbidity surveys
THE POTENTIAL OF ROUTINE
ELECTRONIC MEDICAL RECORDS
FOR EPIDEMIOLOGY IN PRIMARY
CARE
The Weekly Returns Service
SELECTED RESULTS FROM
MORBIDITY SURVEYS
History of morbidity surveys
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Deaths by cause 1851 William Farr:
the establishment of the ICD-now
version 10
1946 the London Sickness Survey
1956 The first national practice
based morbidity survey
1971/72 and on to 1976; 1981/82;
1991/92- the 2nd 3rd and 4th surveys
Regular General Household surveys
since 1972
Purpose of morbidity surveys
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To describe disease prevalence
To examine social and regional
inequalities
To monitor changes in prevalence
and to seek for evidence of trend
To study co-morbidity
To examine GP workload
To provide information for health
service planning
Electronic Medical Records
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Have arrived
Let us ensure we use them to
maximum advantage
Remember we will arrive at a time
when the record is uniform across
primary and secondary care.
Common classification systems and
standardises recording protocols are
needed
What is the practice EMR used for?
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Patient Registration
Patient Consultation Record
Complete Prescribing Record
Limited Prescribing Record (e.g.
repeat prescriptions)
Research Facilitator – completion of
templates
Accessing System for patient lists
Quality Assurance
Epidemiology
EMR for epidemiology
If you focus on EMR for epidemiology you
can achieve all the other functions.
Conversely if your primary focus is on any
other purpose you may exclude the
possibility of use for epidemiological
research.
The EMR is a filing cabinet containing
medical information
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Everything put
in it can be
retrieved
But if we want
to retrieve
information
readily we must
put in in an
orderly fashion.
Example patient record
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Abdominal Pain for 3 days, radiation
to RIF, Vomiting 24 hours, pain
increasing. No diarrhoea and no
urinary symptoms.
O/E tender RIF, no guarding,
t.38.0o C
Urine no protein, no sugar, no blood.
Rectal examination not done. Patient
told he may have appendicitis and
hospital admission (QE) arranged.
Structure record for filing and decide
what you wish to analyse
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Subjective
Abdo pain, vomiting
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Objective
Tender RIF t 38.0oC
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Assessment Appendicitis
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Plan
Admit hospital
In structuring for filing, for analytical purposes, you will
l lose the free text describing the negative information
and qualifying details
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S No urinary symptoms, no diarrhoea.
O Rectal examination not done, Urine no
sugar.
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A May have appendicitis.
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P QE hospital
All boxes need to be filled in a
structured patient electronic record
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Essential for an accurate patient record.
Free text is important. Negative and
qualifying details need to be stored but
not in a way that confuses analysis
Sometimes you can bring data together
from different consultations in order to
fill every box
Many episodes of illness involve only one
consultation, therefore complete the
assessment box at each consultation.
The meaning of Asthma: an orderly record:
information in the right place
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Recent hospital admission for asthma
Had asthma as a child.
Never had asthma.
Reversibility test for asthma
Worried about son with serious asthma.
Father died of asthma.
Occupational asthma.
Asthma attack.
Asthma review.
Who does the filing?
The Classification System,
but you must use it properly
The Read Thesaurus
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Is a medical terminology containing many
more codes than ICD.
By using Read codes, you can process the
information and analyse by ICD (or ICPC)
but you are able to retain a higher level of
detail in your patient centred record.
There are separate codes for patient
complaints (presenting symptoms) and
symptom diagnoses.
Consultation/episode type
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Used to distinguish incidence from
ongoing illness, but not needed for
prevalence. The fact of consultation for
the specified condition determines
prevalence.
A repeat prescription (without
consultation) is sometimes an indicator of
prevalence (eg. Hay fever, glaucoma)
Intelligent interrogation of database
needed
Episode typing in use:
the example of otitis media
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Feb 1
Feb 4
Oct 10
Oct 12
Oct19
Record= Otitis Media F
Record= Otitis Media O
Record= Otitis Media N
Record= Otitis Media O
Record= Otitis Media O
1 person = annual prevalence
2 episodes and when they occurred =
incidence
5 consultations = workload
The importance of episode type
Much epidemiological research is
concerned with the timing of events.
For this type of research it is important
to identify when new episodes of
illness occur. For example we may be
interested in the factors which
precipitate asthma attacks and we need
to know when patients consult with new
episodes as opposed to consulting
simply to renew medication or as part
of routine management. The simplest
episode typing must distinguish new
episodes from ongoing consultations.
RCGP Weekly Returns Service
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Weekly Returns Service (WRS)
established in 1964
Fully computerised data entry and
automated data extraction since 1994
Record all new episodes (and
consultations) of illness (per 100,000
population)
Report on a twice weekly basis (daily
possible)
Monitor at national, regional and practice
level
Age and gender specific data
Now also provide annual prevalence data
ASTHMA
WRS and hospital admissions 1990-97
2.5
0-4 years
5-14 years
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
0
1
6 11 16 21 26 31 36 41 46 51
1
6
week
WRS
Admissions
11 16 21 26 31 36 41 46 51
Acute Otitis Media & Common Cold (per
100,000 All Ages) 10yr av. incidence in yrs
1991-2000
400
140
350
120
300
100
250
80
200
60
150
40
100
20
50
0
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Cold (All Ages)
OM (all Ages)
Incidence of influenza-like illness: Virus
isolations in sentinel networks 1996/97
England/Wales
Incidence rate
The Netherlands
FluA
FluB
Incidence
Baseline
Confidence interval
300
250
200
Virus isolates
30
25
20
150
15
100
10
50
5
0
0
37 41 45 49 1
5
9 13 17 19 37 41 45 49 1
Week
From Fleming DM. Zambon M, Bartelds AIM, de Jong JC. The duration and magnitude of influenza epidemics:
European Journal of Epidemiology 15: 467-473 1999
5
9 13 17 19
Episodes, Admissions and Deaths For Respiratory disease (Age 75+)
45000
4000
40000
3500
35000
3000
30000
2500
25000
2000
20000
1500
15000
1000
10000
500
5000
0
age group (years)
excess admissions
excess bed days
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
0
number of excess bed days
4500
15-19
number of excess admissions
Figure 5: Average excess admissions and excess bed days by age group, 1989/90
to 2000/01
Acute bronchitis: weekly incidence in 0-4 and 65+
age groups by winter weeks in alternate
years 1995-2002
1200
1000
65+ years
800
600
400
200
2
4
6
8
10
12
14
16
18
40
42
44
46
48
50
52
2
4
6
8
10
12
14
16
18
20
0
40
42
44
46
48
50
52
rate per 100,000
0-4 years
week
1995/96
1997/98
1999/00
2001/02
1400
1400
1200
1200
1200
1200
1000
1000
1000
1000
800
800
800
800
600
600
600
600
400
400
400
400
200
200
200
200
0
40 42 44 46 48 50 52 2
4
6
0
8 10 12 14 16 18 20
0
40 42 44 46 48 50 52 2
6
8 10 12 14 16 18 20
RSV
w eek
Bronchitis
1400
1400
1400
1200
1200
1200
1200
1000
1000
1000
1000
800
800
800
800
600
600
600
600
400
400
400
400
200
200
200
200
0
0
40 42 44 46 48 50 52 2
4
w eek
6
8 10 12 14 16 18 20
RSV reports
1400
bronchitis incidence rate per 100,000
RSV reports
w eek
4
0
0
40 42 44 46 48 50 52 2
4
w eek
6
8 10 12 14 16 18 20
bronchitis incidence rate per 100,000
0
bronchitis incidence rate per 100,000
1400
RSV reports
1400
bronchitis incidenc rate per 100,000
RSV reports
Weekly incidence of acute bronchitis contrasted with
RSV reports from the Health Protection Agency:
winter weeks from selected years
1996/97 - 1999/00
Acute Respiratory
Infections
1000
16
14
12
10
8
6
4
2
0
800
600
400
200
0
94
95
96
97
98
99
Year (1994-2000)
Ac. Resp Inf
Presc
00
N of Precps (millions)
Mean weekly incidence of acute respiratory
infections vs antibiotic prescriptions
800
50
700
45
Antibacterial prescriptions (x
1,000,000)
Consultation rate per 1,000
Respiratory illness and
antibiotic prescribing
40
600
35
500
30
400
25
300
20
15
200
10
100
5
0
0
1994
1995
1996
1997
1998
1999
2000
2001
2002
Year
Antibacterial prescriptions
Upper respiratory tract infection
Lower respiratory tract infection
Combined
2003
INFECTIONS OF SKIN & SUBCUTANEOUS TISSUE
Mean weekly incidence rate by Gender
90
80
70
60
50
40
30
20
10
0
04
03
02
FEMALE
01
00
99
98
97
96
95
94
MALE
Prostate and Breast Cancer
Prevalence per 10,000
by age and gender
400
350
300
250
200
150
100
50
0
0-1 1-4 5- 15- 25- 45- 65- 75+ 0-1 1-4 5- 15- 25- 45- 65- 75+
14 24 44 64 74
14 24 44 64 74
NL - Male
Eng - Male
NL - Female
Eng - Female
Benign Prostatic Hypertrophy
Prevalence per 10,000
by age and gender
400
350
300
250
200
150
100
50
0
0-1 1-4 5- 15- 25- 45- 65- 75+ 0-1 1-4 5- 15- 25- 45- 65- 75+
14 24 44 64 74
14 24 44 64 74
NL - Male
Eng - Male
NL - Female
Eng - Female
WRS; Influenza vaccination uptake 2003
compared with 2002
70
60
50
40
0-44 (2002)
0-44 (2003)
45-64 (2002)
45-64 (2003)
65-74 (2002)
65-74 (2003)
75+ (2002)
75+ (2003)
30
20
10
0
39
40
41
42
43
44
45
46
47
48
49
50
51
52
Summary
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Computer storage of medical records is replacing paper
records.
The computer is a filing cabinet, but you need a good
filing system.
Disciplined data capture is at the heart of a good record
whether for routine patient management of for
epidemiological research.
The classification system does the filing. Select it
carefully according to your purpose and collaborators. Be
wary of mapping programmes across classifications.
SOAP is a good structure on which to base your recording
but if you want to concentrate your analysis on one or
two of these boxes you must make appropriate entries in
every box at every consultation including home visits.
Episode typing is needed to study seasonality for
contemporary surveillance.