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

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Introduction Estimating targeted mobile tuberculosis screening coverage The epidemiological pattern of tuberculosis in low incidence countries is changing, with an increasing number of tuberculosis patients living in urban areas. 1-3 This is due to overrepresentation of immigrants from countries with a high incidence of tuberculosis in large cities and to urban risk groups for tuberculosis such as illicit drug users and homeless persons. 4-6 Conventional tuberculosis control methods such as contact-tracing and preventive treatment are inadequate among marginalised care-avoiders. 5,7,8 As an alternative radiological screening programmes for illicit drug users and homeless persons have been recommended in European cities. 9-12 Tuberculosis re-emerged among illicit drug users and homeless persons in Rotterdam (population approximately 600 000) in 2001, after periodic radiological screening was discontinued in 1996. In response, a periodic radiological screening programme was re-introduced in May 2002, using a mobile digital X-ray unit (MDXU) and visiting day and night shelters and hostels for homeless persons, methadone dispensing centres and safe drug consumption rooms for opiate users, as well as the street prostitution zone in Rotterdam. The programme aimed to screen clients of these facilities and services bi-annual. 5,13 For priority setting, service planning and resource allocation it is necessary to know the number of persons in a targeted group. This number can also be used to assess the coverage of an intervention. 14 Often direct (enumeration) techniques are not feasible to estimate the size of hidden populations and indirect techniques have to be used. One such indirect technique, capture-recapture analysis, 15-17 has been used to estimate the size of hidden populations, including illicit drug users 18,19 and homeless persons. 20,21 However, capture-recapture analysis preferably needs at least three linked data sources, which are not always available for hidden populations. As an alternative, truncated models are described in the literature. 17,22,23 Contrary to conventional capture-recapture analysis, truncated models use frequency data from a single source of information. These models have been applied to estimate the size of hidden populations such as criminals, 24,25 illegal residents, 26 and illicit drug users and homeless persons. 14,27-30 The objective of this study is to estimate the coverage of a mobile tuberculosis screening programme among illicit drug users and homeless persons in Rotterdam, using simple truncated models. Methods Ethics committee approval was not required for this study. Study design, participants and study years Participants in this descriptive study are individuals using the services of shelters and hostels for homeless persons, methadone dispensing centres or safe drug consumption rooms for opiate users, or working in the street prostitution zone in Rotterdam, with at least one chest X-ray taken in the MDXU of the mobile tuberculosis screening programme between 1 January 2003 and 31 December 2005. Because 2002 was an 127
Chapter 9 incomplete year of screening and not all facilities were visited twice by the MDXU these data were excluded. A proportion of individuals in the target group use multiple facilities and their chest X-ray can be taken at different locations, sometimes more than twice yearly. Chest X-rays were read by public health tuberculosis physicians on location or within a few working days at the Public Health Service. Data collection and validation Data on participants of the MDXU screening programme, such as name, date of birth, sex, date of chest X-ray and chest X-ray result, are routinely entered into the electronic Client Information System of the Tuberculosis Control Section of the Municipal Public Health Service Rotterdam-Rijnmond, using a unique personal identification number. To avoid misclassification of individuals due to clerical errors such as misspelling of names or typing errors, all names and dates of birth of the participants were double-checked in the Client Information System during data entry. Since 2005 the Universal Mobile Telecommunications System (UMTS) provides wireless connection between the MDXU and the Client Information System facilitating checking personal data of participants on location. The number of individuals participating in the tuberculosis screening programme and the frequency of their visits per year and for the total study period were extracted from the Client Information System. Truncated models The number of illicit drug users and homeless persons in the target group for the mobile tuberculosis screening programme, and hence the coverage of the programme, was estimated through simple truncated models. Although their results are expected to be similar as two examples we used Zelterman’s truncated Poisson mixture model and Chao’s truncated heterogeneity model, which can be applied to frequency counts of observations of individuals in a single register. 31-33 They aim to estimate the number of unobserved persons in the (truncated) zero-frequency class based upon information of the lower observed frequency classes, assuming a specific truncated distribution of the observed data, e.g. Poisson, binomial or a mixture. 17,31-34 Observed frequency distributions may not be strictly Poisson and to relax this assumption Zelterman and Chao based their models on a Poisson mixture distribution. This allows greater flexibility and applicability on real life data because the models explicitly cater for departures from the strict Poisson assumption. Zelterman’s Poisson mixture model of the estimated total population size, est(N), is given by and Chao’s heterogeneity model by 128 est(N) = obs(N)/[1 – exp(-2f 2/f 1)] est(N) = obs(N) + (f 1) 2 /2f 2 where f 1 denotes the number of persons falling in the first frequency class, f 2 denotes the number of persons falling in the second frequency class, obs(N) denotes the number of all observed individuals and exp is the exponential. The simple truncated models do not need statistical packages and have performed well when compared to log-linear capture-recapture analysis. 35 They
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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
Page 77 and 78: Chapter 5 cautious about the associ
Page 79 and 80: 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 130 and 131: 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
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Summary linkage of notification and
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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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