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

More documents

Recommendations

Info

Estimating infectious disease incidence of some truncated estimators is supported mathematically and through simulation studies but they can occasionally underestimate the true population size in the presence of heterogeneity. 22,29 Frequency counts It is possible to extract frequency counts for the truncated models from multiple-source capture-recapture data, allowing us to use the reported data from the log-linear studies for the truncated models. The ratio between the number of patients registered once (f1) and registered twice (f2) plays an important role in the truncated models. When “1” represents being known to a register and “0” represents being unknown to a register, and three linked registers are used, frequency count f1 is the sum of the cells 100, 010 and 001 in the 2 x 2 x 2 contingency table and frequency count f2 corresponds to the sum of the cells 110, 101 and 011. Similarly, patients observed in all three registers, f3, are denoted as 111. For all 19 datasets the number of patients in these seven cells are shown later. We use the f1/f2 ratio to examine a possible relationship between this ratio and the performance of truncated models vis-à-vis the log-linear models. Results Table 10.1 shows the various three-source log-linear model capture-recapture studies of infectious disease incidence and completeness of notification with the number of patients observed and their frequency counts, the objective of the study, the data sources used and the selected log-linear model. The studies involved eight infectious diseases and were performed at the local, regional or national level. One study collected data over a 4- months period, the other studies over 1- to 5-year periods. The observed number of patients varied from 33 to 28 678 persons. Notification, laboratory and hospital registers were the most conventional data sources used. The distribution of the patients over three linked registers in the various three-source capture-recapture studies of infectious diseases is shown in Table 10.2. The log-linear and truncated model estimates with their respective confidence and prediction intervals are shown in Table 10.3, as well as the f1/f2 ratio among the observed patients and the coefficient of variation of the data source probabilities (see Discussion). The capture-recapture studies varied in estimated number of patients from 46 to 42 969. A second truncated Poisson estimator, Chao’s bias-corrected homogeneity model, est(N) = obs(N) + [(f 2 1 -f1)/(2(f2-1))], gave similar estimates as Chao’s heterogeneity model. 35 A second truncated binomial estimator, est(N) = obs(N)/[1–(1/(1+f2 /f1)) 3 ], gave similar estimates as the truncated binomial model used (data not shown). f1/f2 ratio On the basis of the f1/f2 ratio the studies can be divided in four categories: a. f1/f2 < 0.5 (dataset 7). In this study all estimates were similar but the number of observed patients was small. b. 0.5 < f 1/f 2 < 1.5 (datasets 1, 2, 6, 8-10, 13a, 13b, 14, 16a, 16b). In these studies the truncated binomial model and Zelterman’s model gave similar results as the 141
Table 10.1 Overview of the various three-source capture-recapture studies of infectious diseases since 1997 Study Disease and number of patients observed Objective Data-sources Selected capture-recapture number model and interactions 1 Legionnaires' disease To estimate the level of underreporting of 1. National notification system Independent model Legionnaires' disease and to evaluate the 2. Reference laboratory database Obs(N) = 256; f1 = 126; f2 = 116; f3 = 14 feasibility of a laboratory-based reporting 3. Hospital laboratory survey system in France in 1995 Independent model 1. Prison register of AIDS patients 2. Prison register of tuberculosis patients 3. Prison register of hospital admissions To estimate the completeness of the prison AIDS register in Spain in 2000 2 HIV/AIDS Obs(N) = 173; f1 = 65; f2 = 75; f3 = 33 Parsimonious model with one two-way interaction (notification * laboratory) 1. Notification database from office for national statistics 2. Hospital admission data 3. Public health laboratory reports To estimate undernotification of whooping cough in the north west of England, 1994- 1996 Pertussis 3a (1-4 yrs) Obs(N) = 435; f1 = 375; f2 = 56; f3 = 4 Obs(N) = 420; f1 = 376; f2 = 42; f3 = 2 3b (>5 yrs) 4 Salmonella infection Parsimonious model with one two-way interaction (public health notification * veterinary notification) 1. Mandatory public health notification 2. Mandatory veterinary notification 3. National Salmonella reference centre To assess the number of foodborne Salmonella outbreaks in France in 1995 Obs(N) = 608; f1 = 520; f2 = 68; f3 = 20 Parsimonious model with one two-way interaction (hospital statistics * national statistics) 1. Hospital episode statistics 2. Enhanced laboratory pertussis surveillance 3. Office for national statistics mortality data To improve estimates of deaths from pertussis in England and to identify reasons for under ascertainment, 1994-1999 5 Pertussis Obs(N) = 33; f1 = 19; f2 = 12; f3 = 2 Parsimonious model with one two-way interaction (hospital * laboratory) 1. Mandatory notifiable disease surveillance system 2. Laboratory survey 3. Hospital information database registry To evaluate the exhaustiveness of three information sources on meningococcal disease in Tenerife, Spain, 1999-2001 6 Meningococcal meningitis Obs(N) = 53; f1 = 9; f2 = 14; f3 = 30
Page 1 and 2:
Capture-recapture Methods in Survei
Page 3 and 4:
Colofon Capture-recapture methods i
Page 5 and 6:
Promotiecommissie Promotor: Prof.dr
Page 8:
Contents Page 1 Introduction 9 2 Me
Page 11 and 12:
Chapter 1 1.1 Assessing completenes
Page 13 and 14:
Chapter 1 94% among AIDS patients w
Page 15 and 16:
Table 1.1 Objectives, methods, data
Page 17 and 18:
Researchers Objective Method Data-s
Page 19 and 20:
Chapter 1 source capture-recapture
Page 21 and 22:
Chapter 1 32. Bradley BL, Kerr KM,
Page 24 and 25:
2 Methodology of capture-recapture
Page 26 and 27:
Methodology of capture-recapture an
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36:
Page 39 and 40:
Chapter 3 3.1 Application of captur
Page 41 and 42:
Table 3.1 Published capture-recaptu
Page 43 and 44:
Disease Authors Objective Method Da
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Chapter 3 3.3 References 1. Hook EB
Page 51 and 52:
Chapter 3 52. Reintjes R, Termorshu
Page 53 and 54:
Chapter 4 Abstract The aim of this
Page 55 and 56:
Chapter 4 Methods Nearly all Dutch
Page 57 and 58:
Chapter 4 Table 4.2 The number of m
Page 59 and 60:
Chapter 4 diagnosis. Other patients
Page 61 and 62:
Chapter 4 stratify the population i
Page 63 and 64:
Chapter 4 25. Lambeth, Southwark an
Page 65 and 66:
Chapter 5 Abstract To estimate inci
Page 67 and 68:
Chapter 5 patients. Capture-recaptu
Page 69 and 70:
Chapter 5 Figure 5.1 Four regions o
Page 71 and 72:
Chapter 5 Hospital records From 385
Page 73 and 74:
Chapter 5 Table 5.1 Epidemiological
Page 75 and 76:
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 128 and 129: Introduction Estimating targeted mo
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 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 152 and 153: Estimating infectious disease incid
Page 154 and 155: Estimating infectious disease incid
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
Page 182 and 183: Samenvatting subgroepen binnen de b
Page 184 and 185: Acknowledgements Acknowledgements W
Page 186 and 187: Curriculum vitae Rob van Hest was b
Page 188 and 189: Publications This thesis Van Hest N
show all

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

Create successful ePaper yourself

Delete template?

Save as template?