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Capture-recapture analysis for

surveillance of tuberculosis

Capture recapture analysis and tuberculosis

e Dr. Rob van Hest, MD MSc PhD

Consultant Tuberculosis Control Physician/Epidemiologist

Department of Infectious Disease Control

Tuberculosis Control Section

Rotterdam Municipal Public Health Service

ROTTERDAM

The Netherlands


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Kopregel Surveillance

“The process of continuous and systematic

collection, processing, analysis and interpretation of

data regarding health which are important for

planning, implementing and evaluating public health

care, and reporting and distributing this information

to all those who should know this”




Kopregel

“For all these reasons, it is impossible to obtain accurate data

concerning the prevalence of tuberculous infection even during recent times.”

René and Jean Dubos

The White Plague; Tuberculosis, Man and Society

Boston: Little, Brown and Company, 1952:6




Kopregel Surveillance is an indispensable part of infectious

disease control and reliable information is essential

A common method of infectious disease surveillance uses

the notifications but not every patient is reported

(incomplete reporting or under-notification)

Apart from the notifications other registers could exist

containing information about infectious diseases, e.g.

hospital admission registers of laboratory registers




Kopregel

After record-linkage of various infectious disease registers

the completeness of the notification register can, under

certain assumptions, be estimated by a technique called

“capture-recapture analysis”

Record-linkage of various infectious disease registers

improves case-ascertainment




Kopregel

Capture-recapture analysis is a technique

originally developed within population biology

for estimation of the size of animal populations

Capture

1 5

6 7 8

9

2 3 4

10 11 12 13

Assumption 1: unique identification

1 5

6 7 8

9

2 3 4

10 11 12 13

Een ander belangrijk punt is de kwaliteit van de gekoppelde data. Hebben alle gevangen ‘patienten’ wel de betreffende ziekte? (Hoedt U voor misfits en look alikes)

2

5

11

Assumption 2: only individuals from the study population should be caught

1 5

6 7 8

9

2 3 4

10 11 12 13

Een ander belangrijk punt is de kwaliteit van de gekoppelde data. Hebben alle gevangen ‘patienten’ wel de betreffende ziekte? (Hoedt U voor misfits en look alikes)

2

5

11

Assumption 2: no false-positive captures

Assumption 3: homogeneous distribution

Recapture

!

Assumption 4: the recapture should be independent of the (first) capture

?

Assumption 5: closed population

1 5

6 7 8

9

2 3 4

10 11 12 13

Assumption 6: perfect linkage between capture and recapture is possible

Hervangst Recapture

Under these assumption this formula is valid:

1st Capture Recapture

Petersen’s estimator




Kopregel

Many years later capture-recapture analysis is also

applied within epidemiology, e.g. to estimate the size of a

group of persons with a certain disease




Kopregel

For application of capture-recapture analysis within

(infectious disease) epidemiology, being captured once,

twice or three times is translated into being known to one,

two, three or multiple (infectious disease) registers

0 0 0

0 0 1

0 1 0

0 1 1

1 0 0

1 0 1

1 1 0

1st capture 2nd capture 3rd capture

1 1 1

0 0 0

0 0 1

0 1 0

0 1 1

1 0 0

1 0 1

1 1 0

1st capture 2nd capture 3th capture

1 1 1

register 1 register 2 register 3




Kopregel

Notifications

Laboratory

Hospital admissions

301

78

93

510 30

99

388

For epidemiological applications of capture-recapture

analysis, record-linkage of at least three registers is

preferable

2nd recapture 3rd recapture 1st recapture

Vangst-hervangst

methode

Capture recapture analysis and tuberculosis in

resource-rich countries



Three sources: Notification – Laboratory - Hospital

After record-linkage completeness of case-ascertainment was 93.3%

A parsimonious capture-recapture model with only one interaction

term was preferred

Overall estimated under-notification was was 79.1% (plausible).



Three Dutch tuberculosis registers were linked:

1. Notifications (IGZ)

2. National reference laboratory cultures (RIVM)

3. Hospital admissions (Prismant)


A

B

D

F

C

Notifications

Hospital admissions Laboratory

E G



• Mycobacteria Other Than Tuberculosis?

(MOTT) (not excluded)

• Other disease than tuberculosis? (not excluded)

Remaining false-positives cases?


• Are these patients real tuberculosis patients?

• Increase positive predictive value of the registers!!

CROSS-VALIDATION!!!

A

E

F

C

Notifications

Hospital admissions Laboratory

G D

MOTT

MOTT

B

NTR

NTR

PALGA

PALGA



78 Notification (n = 1298)

23

388

Hospital (n = 610)

510

5

30

58

3

46

229

53 1

40

8

9

18

Laboratory (n = 1006)



Hospital (n = 610)

Notification (n = 1298)

78

510

30

388

301

99

93



The best-fitting log-linear capture-recapture model was

the saturated model, i.e. including all pair-wise

interactions and possibly three-source interaction.

Estimated completeness of notification: 63.2% (95% CI

53.1-69.4)!!

The model estimates 2053 tuberculosis patients (95% CI

1871-2443), i.e. 554 unobserved tuberculosis cases!!


The adjusted estimated completeness of notification of

36.8% is highly inconsistent with prior expectations!!


301

388


Hospital (n = 552)

40

548

99

30

35



The best-fitting log-linear capture-recapture model on the

adjusted data was a parsimonious model, containing two

pair-wise interactions [N*H] and [N*L]: G2 = 0.053; D.F. =

2; P = 0.974; Akaike Information Criterion = –3.95)

Estimated completeness of notification: 86.4% (95% CI

83.5-88.3)!!

The model estimates 1547 tuberculosis patients (95%CI

1513-1600), i.e. 106 unobserved tuberculosis cases!!


CONCLUSIONS

Even in a country with a well-organised system of

tuberculosis control, record-linkage and cross-validation

three source capture-recapture analysis can easily produce

erroneous results and slight adjustment for possible

misclassification of laboratory patients and remaining

false-positive hospital cases had a considerable impact on

the log-linear capture-recapture estimate


in resource-rich countries


in resource-rich countries

Three sources: Notification – Laboratory - Hospital

High number of “TB” patients only recorded in hospital episode register:

complex population-mixture model was required to estimate proportion

of true TB patients

For all estimates the saturated log-linear model was preferred

Overall estimated under-notification was 43.8% (not plausible).


in middle-income country





Saturated model estimated completeness of notifications at 78%.

After internal validity analysis and use of alternative truncated

population estimation models (truncated Poisson and truncated

Poisson mixture models) we estimate completeness of the Notification

register between 82%-85%


in resource-limited countries



247

5 76

40

0

1

41

NTP

(n = 364)

Public non-NTP

(n = 82)

Private non-NTP

(n = 82)



The selected model (AIC: -1,873) contained two (expected) interactions

(Public non-NTP referred to the NTP!) and estimated the CDR of the

NTP surveillance at 55% (95% CI 45% - 68%) for all TB cases

Internal validity analysis indicated similar relevant interactions

as the log-linear model.

The estimated CDR of NTP surveillance for sputum smear-positive

cases was 66%

Capture recapture analysis and

tuberculosis in resource-limited

countries

Yemen 2010

Yemen 2010

Yemen 2010









Iraq 2011

Iraq 2011

Iraq 2011

Iraq 2011

Iraq 2011

Iraq 2011

Iraq 2011

Iraq 2011

Iraq 2011













The log-linear model selected was a parsimonious model incorporating

two interaction effects: the centralised laboratory and tuberculosis

treatment register interaction and the centralised laboratory and hospital

laboratory interaction.

The log-linear model estimated completeness of registration of

bacteriologically confirmed pulmonary tuberculosis at 75%

Extra problem: initial defaulting!



Westbank 2013

Westbank 2013




Kopregel Thank you for your attention