Abstract
BackgroundIn surveillance networks based on voluntary participation of health-care professionals, there is little choice regarding the selection of participants’ characteristics. External information about participants, for example local physician density, can help reduce bias in incidence estimates reported by the surveillance network.MethodsThere is an inverse association between the number of reported influenza-like illness (ILI) cases and local general practitioners (GP) density. We formulated and compared estimates of ILI incidence using this relationship. To compare estimates, we simulated epidemics using a spatially explicit disease model and their observation by surveillance networks with different characteristics: random, maximum coverage, largest cities, etc.ResultsIn the French practice-based surveillance network – the “Sentinelles” network – GPs reported 3.6% (95% CI [3;4]) less ILI cases as local GP density increased by 1 GP per 10,000 inhabitants. Incidence estimates varied markedly depending on scenarios for participant selection in surveillance. Yet accounting for change in GP density for participants allowed reducing bias. Applied on data from the Sentinelles network, changes in overall incidence ranged between 1.6 and 9.9%.ConclusionsLocal GP density is a simple measure that provides a way to reduce bias in estimating disease incidence in general practice. It can contribute to improving disease monitoring when it is not possible to choose the characteristics of participants.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0260-x) contains supplementary material, which is available to authorized users.
Highlights
In surveillance networks based on voluntary participation of health-care professionals, there is little choice regarding the selection of participants’ characteristics
Number of cases per Sentinel general practitioner (SGP) and general practitioners (GP) density using data from the French Sentinelles network SGPs practicing in places with high GP density, reported fewer influenza-like illness (ILI) cases over the duration of an epidemic (Fig. 2)
The number of cases was reduced by 3.6% as GP density increased by 10 GP per 100,000 inhabitants (p < 2.10−16)
Summary
In surveillance networks based on voluntary participation of health-care professionals, there is little choice regarding the selection of participants’ characteristics. For example local physician density, can help reduce bias in incidence estimates reported by the surveillance network. These a priori methods for selection implicitly assume that all health professionals or organizations would be willing to participate in surveillance upon proposal and not subject to turnover once recruited. These assumptions may be reasonable in hospital or laboratory based surveillance networks, but not in surveillance networks based on general practitioners (GPs) in primary. In our experience, only a few percent of practicing GPs agree to participate in surveillance networks [8] and their participation is most of the times for a couple of years only [9]
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