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

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Table 10.3 Comparison of the various log-linear model, truncated Poisson mixture model, truncated Poisson heterogeneity model and truncated binomial model estimates of three-source capture-recapture studies of infectious diseases since 1997, with the ratio between the number of patients registered once and twice and coefficient of variation of the data source probabilities. Study Log-linear model Truncated binomial model number (95% CI) est(N) = obs(N) + (f1) 2/3f2 Truncated Poisson mixture model (PI) 22 Poisson heterogeneity model est(N)= obs(N)/[1–exp(-2f2 /f1)] (PI) 23,24 est(N) = obs(N) + (f1) 2/2f2 f1/f2 Coefficient (PI) of variation 1 291 (276-308) 302 (281-322) 304 (271-346) 323 (299-362) 1.09 0.52 2 190 (181-203) 192 (172-212) 192 (170-222) 201 (188-226) 0.87 0.07 3a 895 (735-1055) 1272 (1022-1522) 1685 (1363-2206) 1691 (1335-2187) 6.70 0.76 3b 1153 (785-1521) 1542 (1159-1925) 2098 (1640-2910) 2103 (1590-2841) 8.95 0.90 4 1065 (913-1217) 1933 (1576-2291) 2642 (2133-3469) 2596 (2088-3278) 7.65 0.86 5 46 (31-71) 43 (36-50) 46 (43-76) 48 (38-78) 1.58 0.33 6 54 55 (42-68) 55 (47-68) 56 (54-67) 0.64 0.07 7 81 81 (63-99) 81 (71-94) 81 (95% PI inestimable) 0.07 0.09 8 704 (688-728) 713 (672-754) 710 (666-759) 741 (715-776) 0.77 0.27 9 236 (206-306) 222 (200-244) 236 (210-270) 234 (217-262) 1.08 0.26 10 774 (740-821) 769 (733-805) 794 (739-857) 823 (781-877) 1.10 0.26 11 1124 (973-1275) 927 (893-962) 1018 (936-1115) 1033 (959-1125) 1.65 0.06 12 1314 (685-2899) 479 (392-586) 601 (494-769) 583 (480-737) 3.30 0.04 13a 2053 (1871-2443) 1615 (1552-1679) 1606 (1541-1677) 1673 (1635-1721) 0.74 0.29 13b 1547 (1513-1600) 1510 (1433-1592) 1481 (1424-1544) 1545 (1519-1580) 0.56 0.33 14 42 969 (41 360-44 783) 32 973 (32 732-33 215) 34 149 (33 773-34 532) 35 121 (34 822-35 434) 1.09 0.30 15 1253 (1019-1715) 1061 (1017-1106) 1241 (1136-1367) 1202 (1108-1323) 2.02 0.39 16a 5962 (5581-6343) 4881 (4738-5029) 5016 (4908-5129) 5023 (4959-5098) 0.80 0.12 16b 5577 4741 (4548-4940) 4821 (4719-4927) 4858 (4802-4924) 0.73 0.12 CI: Confidence Interval; est(N): number of estimated patients; obs(N): number of observed patients; f1: number of patients registered once; f2: number of patients registered twice; PI: Probability Interval; exp: exponential
Chapter 10 independent (without interactions) or parsimonious log-linear model while Chao’s model estimates were slightly higher. When a saturated log-linear model (with all twoway interactions) was selected the truncated estimates were considerably lower than the log-linear model estimates. c. 1.5 < f1/f2 < 3.5 (datasets 5, 11, 12, 15). In the first study the results of all truncated models were similar to the parsimonious log-linear model estimate but the number of observed patients was small. In the second study the estimates of Zelterman’s and Chao’s truncated models were lower but within the 95% confidence interval (CI) of the parsimonious log-linear model estimate while the truncated binomial model estimate was considerably lower. In the third study all truncated model estimates were considerably lower than the saturated log-linear model estimate, the truncated binomial estimate again being lowest. In the fourth study all truncated model estimates were lower than the saturated log-linear model estimate but fell within the broad 95% CI, the truncated binomial model estimates again lowest. d. f1/f2 > 3.5: datasets (3a, 3b, 4). In all studies the truncated model estimates were considerably higher than the parsimonious log-linear model estimates, especially the Zelterman and Chao models. Selected log-linear model On the basis of the selected log-linear model the studies can be divided in three categories: a. Independent log-linear model (datasets 1, 2). In these studies the truncated models produce similar estimates as the log-linear model. b. Parsimonious log-linear model (datasets 3-11). In the 11 studies with a parsimonious log-linear model selected three observations can be made: - In the three studies with f1 >> f2 (datasets 3, 4) the truncated binomial model estimates a higher number of patients than the log-linear model while the truncated Poisson and Poisson mixture models estimate a considerably higher number of patients. - In the three studies (datasets 5-7) with a small number of observed patients the estimates of the log-linear model and truncated Poisson, Poisson mixture and binomial models are comparable. - In the studies with the f1/f2 ratio between 0.5 and 1.5 (datasets 8-10, 13b) the truncated model estimates are similar to the log-linear model but the Chao models can be relatively higher and in one study the truncated Poisson mixture estimate was relatively low. c. Saturated log-linear model (datasets 12-16, apart from 13b). In all but one of the studies with a saturated model selected (datasets 12, 13a, 14, 16) the truncated models gave considerable lower and mutually comparable estimates. 148
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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
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Chapter 6 Abstract The aim of this
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Chapter 6 3. Hospitalised patients
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Chapter 6 Record-linkage of all 537
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Chapter 6 Figure 6.1 Schematic view
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Chapter 6 Table 6.2 Total and strat
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Chapter 6 administrative discrepanc
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Chapter 6 the patients, and complet
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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 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 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
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
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