Rob van Hest Capture-recapture Methods in Surveillance - RePub ...
Rob van Hest Capture-recapture Methods in Surveillance - RePub ...
Rob van Hest Capture-recapture Methods in Surveillance - RePub ...
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General discussion<br />
and hence positive predictive value, will often already considerably improve the<br />
knowledge of the number of patients and <strong>in</strong>fectious disease <strong>in</strong>cidence rates, as well as the<br />
completeness of <strong>in</strong>formation on specific demographic, diagnostic or epidemiological<br />
variables. An example of an <strong>in</strong>fectious disease surveillance system that rout<strong>in</strong>ely l<strong>in</strong>ks<br />
notification data with laboratory data is the ETS <strong>in</strong> England and Wales. This type of<br />
record-l<strong>in</strong>kage should ideally be web-based for a timely reflection of trends. An example<br />
of a web-based notification system is OSIRIS <strong>in</strong> the Netherlands but this system is not<br />
l<strong>in</strong>ked to laboratory reports. 13 For improv<strong>in</strong>g quality, completeness and timel<strong>in</strong>ess of<br />
<strong>in</strong>fectious disease surveillance, a web-based <strong>in</strong>fectious disease surveillance system that<br />
rout<strong>in</strong>ely l<strong>in</strong>ks notification data with laboratory data could essentially fulfil the qualities<br />
once attributed to capture-<strong>recapture</strong> analysis.<br />
11.3 Conclusions and recommendations<br />
Conclusions<br />
• Infectious disease <strong>in</strong>cidence capture-<strong>recapture</strong> analysis requires adequate knowledge<br />
of disease, patients and registrations.<br />
• In capture-<strong>recapture</strong> analysis small variations <strong>in</strong> the quality of data and record-l<strong>in</strong>kage<br />
can lead to highly variable outcomes. Therefore previous successful <strong>in</strong>fectious disease<br />
capture-<strong>recapture</strong> studies cannot be repeated uncritically.<br />
• Hospital episode statistics often conta<strong>in</strong> many false-positive records, which, when not<br />
identified, lead to biased capture-<strong>recapture</strong> estimates.<br />
• When categorical covariates associated with the probability of capture <strong>in</strong> an <strong>in</strong>fectious<br />
disease register are present, covariate capture-<strong>recapture</strong> analysis can reduce bias as a<br />
result of heterogeneity.<br />
• In the absence of a gold standard, truncated models can be used as a heuristic tool to<br />
identify possible failure <strong>in</strong> log-l<strong>in</strong>ear models, especially when saturated models are<br />
selected.<br />
Recommendations<br />
• Infectious diseases capture-<strong>recapture</strong> studies should be performed with a multidiscipl<strong>in</strong>ary<br />
team <strong>in</strong>clud<strong>in</strong>g public health physicians, cl<strong>in</strong>icians, statisticians and data<br />
managers.<br />
• For more reliable record-l<strong>in</strong>kage of notifiable <strong>in</strong>fectious disease registers the Dutch<br />
Infectious Disease Act 1999 should be amended and provide record<strong>in</strong>g the date of<br />
birth of the patients <strong>in</strong>stead of the year of birth.<br />
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