Rob van Hest Capture-recapture Methods in Surveillance - RePub ...

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Estimating targeted mobile tuberculosis screening coverage The annual number of individuals in the first frequency class (seen once), second frequency class (seen twice) and total number of individuals screened respectively represent f 1, f 2 and obs(N) in the formula of the truncated models. Table 9.3 shows the annual observed and estimated number of illicit drug users and homeless persons in the target group for periodic tuberculosis screening for the two truncated models, as well as the estimated coverage of the mobile tuberculosis screening programme. The estimates of Chao’s model are slightly higher but in the same range as Zelterman’s model. The radiological mobile targeted tuberculosis screening programme reaches approximately 63% of the estimated target population at least once per year. The intended coverage of the screening programme (at least two chest X-rays per person per year) is approximately 22%, 25% and 21% in 2003, 2004 and 2005 respectively. Discussion Main findings This study demonstrates that truncated models can be used relatively easily on available single source routine data to estimate the size of a hidden population of illicit drug users and homeless persons. Our results show that a radiological mobile targeted tuberculosis screening programme among illicit drug users and homeless persons in Rotterdam reaches approximately two-third of the estimated target population at least once per year. Between 21% and 25% of the estimated target population meets the objective of the programme and has two or more chest X-rays taken per year. Limitations As with capture-recapture analysis, the validity of the estimates of truncated models depends on the possible violation of the underlying assumptions. These assumptions are perfect identification (i.e. no misclassification of the number of visits of one client), a closed population (i.e. no in-migration or out-migration in the time period studied), ideally but not necessarily a homogeneous population (i.e. no subgroups with markedly different probabilities to be observed and re-observed) and a constant probability of being observed (i.e. there should be no individual behavioural response and the probability of being re-observed should not be influenced by the experience of a previous visit) and, as explained earlier in the Methods, a specific truncated distribution of the observed data. 14,30 Perfect identification assumption In this programme individuals were assigned unique identification numbers in the Client Information System and personal identifiers were double-checked upon data entry to avoid misclassification. The staff of the facilities visited assisted the programme by providing a list of names and dates of birth of clients eligible for screening. Most clients had personal identification cards which were checked at screening. Social workers from the services furthermore assisted on the day of screening which also reduced the possibility to misclassify individuals. Violation of the perfect record-linkage assumption is therefore considered minimal. 131
Chapter 9 Closed population assumption To reduce bias as a result of violation of the closed population assumption we divided the study in one-year periods. The MDXU visits each location for one day twice a year. This limits the opportunity for passers-by and short-term clients to be observed. Table 9.1 and Table 9.2 however show that every year a substantial number of people not previously screened enter the programme. These can be individuals belonging to the target group but not yet captured by the screening programme, individuals not belonging to the target group or individuals that recently joined the target group. Influx of the last two categories will result in annual estimates of the target population of long-term illicit drug users and homeless persons being too high and hence the estimate of the screening programme coverage being too low. Homogeneity assumption Some problematic illicit drug users and homeless persons, such as cocaine users or persistent rough sleepers, will never be reached. Their probability to attend the tuberculosis screening programme is zero because they never utilise the facilities and services. This group is not included in the truncated model estimate. 28 We cannot exclude individuals entering the screening programme, e.g. among individuals entering the programme only once, that do not belong to the group of longterm illicit drug user and homeless persons. In a previous conventional log-linear capturerecapture estimation of the number of clients of a methadone maintenance programme it was demonstrated that differences in capture-probabilities of the population of interest, problematic drug users, and the sampled population, also including non-problematic drug users, could considerably overestimate the size of the population of interest. 19 We cannot exclude heterogeneity among individuals belonging to the target group entering the screening programme but the opportunities to participate in the screening (opting-out strategy) or not to participate (not attending the facility or service on the day of screening) are assumed to be largely similar for the majority. The truncated models are arguably more robust to violation of the homogeneity assumption because they are partly based upon the lower frequency classes, assumed to have more resemblance to the zero frequency class. The relative insensitivity to violation of the homogeneity assumption of Zelterman’s and Chao’s model is also supported mathematically and through simulation studies. 14,22,32 However, in the presence of heterogeneity they can underestimate the population. An alternative approach to estimate a heterogeneous population would be to use a population mixture model. Such a model (for the data in Table 9.1) regards the eligible population for each visit as a mixture of "local clients at the facilities", having six opportunities to be observed and "roaming clients" from other facilities, visiting more places than their own facility and can be captured at other facilities by the MDXU as well. They have possibly more than a total of six opportunities to be observed. For each visit the capture of the local clients could be modelled as Binomial (6, p1) and the capture of roaming clients as Poisson (lambda), where lambda is probably less than 6 times p1. However, in our population of homeless persons and illicit drug users a clear distinction 132
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Capture-recapture Methods in Survei
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Colofon Capture-recapture methods i
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Promotiecommissie Promotor: Prof.dr
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Contents Page 1 Introduction 9 2 Me
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Chapter 1 1.1 Assessing completenes
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Chapter 1 94% among AIDS patients w
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Table 1.1 Objectives, methods, data
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Researchers Objective Method Data-s
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Chapter 1 source capture-recapture
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Chapter 1 32. Bradley BL, Kerr KM,
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2 Methodology of capture-recapture
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Methodology of capture-recapture an
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Chapter 3 3.1 Application of captur
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Table 3.1 Published capture-recaptu
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Disease Authors Objective Method Da
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Chapter 3 3.3 References 1. Hook EB
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Chapter 3 52. Reintjes R, Termorshu
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Chapter 4 Abstract The aim of this
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Chapter 4 Methods Nearly all Dutch
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Chapter 4 Table 4.2 The number of m
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Chapter 4 diagnosis. Other patients
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Chapter 4 stratify the population i
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Chapter 4 25. Lambeth, Southwark an
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Chapter 5 Abstract To estimate inci
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Chapter 5 patients. Capture-recaptu
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Chapter 5 Figure 5.1 Four regions o
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Chapter 5 Hospital records From 385
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Chapter 5 Table 5.1 Epidemiological
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Table 5.2 Number and proportion of
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Chapter 5 cautious about the associ
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Chapter 5 Incidence of community-ac
Page 81 and 82: Chapter 6 Abstract The aim of this
Page 83 and 84: Chapter 6 3. Hospitalised patients
Page 85 and 86: Chapter 6 Record-linkage of all 537
Page 87 and 88: Chapter 6 Figure 6.1 Schematic view
Page 89 and 90: Chapter 6 Table 6.2 Total and strat
Page 91 and 92: Chapter 6 administrative discrepanc
Page 93 and 94: Chapter 6 the patients, and complet
Page 96 and 97: 7 Undetected burden of tuberculosis
Page 98 and 99: Introduction Underreporting of tube
Page 100 and 101: Underreporting of tuberculosis in t
Page 102 and 103: Table 7.1 Total and subset numbers
Page 104 and 105: Table 7.2 Capture-recapture estimat
Page 106 and 107: Underreporting of tuberculosis in t
Page 108: Underreporting of tuberculosis in t
Page 111 and 112: Chapter 8 Abstract In 1999 the Enha
Page 113 and 114: Chapter 8 bacteriological confirmat
Page 115 and 116: Chapter 8 Results Table 8.1 shows t
Page 117 and 118: Table 8.3 Annual and overall observ
Page 119 and 120: Table 8.4 Annual and overall estima
Page 121 and 122: Chapter 8 interaction however, i.e.
Page 123 and 124: Chapter 8 shows that observed under
Page 126 and 127: 9 Estimating the coverage of tuberc
Page 128 and 129: Introduction Estimating targeted mo
Page 130 and 131: Estimating targeted mobile tubercul
Page 134 and 135: Estimating targeted mobile tubercul
Page 136 and 137: References Estimating targeted mobi
Page 138 and 139: 10 Estimating infectious diseases i
Page 140 and 141: Introduction Estimating infectious
Page 142 and 143: Estimating infectious disease incid
Page 144 and 145: Study Disease and number of patient
Page 146 and 147: 4. Gallay A, Vaillant V, Bouvet P,
Page 148 and 149: Table 10.3 Comparison of the variou
Page 150 and 151: Discussion Estimating infectious di
Page 156: Estimating infectious disease incid
Page 159 and 160: Chapter 11 The aim of this thesis i
Page 161 and 162: Chapter 11 overestimation of the nu
Page 163 and 164: Chapter 11 clinicians to the Public
Page 165 and 166: Chapter 11 operation with the chest
Page 167 and 168: Chapter 11 of the proportion of fal
Page 169 and 170: Chapter 11 Question 3: What is the
Page 171 and 172: Chapter 11 11.2 Some findings of th
Page 173 and 174: Chapter 11 • To improve timelines
Page 176 and 177: Summary Summary Surveillance is an
Page 178 and 179: Summary linkage of notification and
Page 180 and 181: Samenvatting Samenvatting Surveilla
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Samenvatting subgroepen binnen de b
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Acknowledgements Acknowledgements W
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Curriculum vitae Rob van Hest was b
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Publications This thesis Van Hest N
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