tetano
Editor, Senior Moderator
BMC Med Res Methodol. 2016 Nov 15;16(1):156.
[h=1]Improving incidence estimation in practice-based sentinel surveillance networks using spatial variation in general practitioner density.[/h] Souty C[SUP]1[/SUP], Bo?lle PY[SUP]2,[/SUP][SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] In 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.
[h=4]METHODS:[/h] There 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.
[h=4]RESULTS:[/h] In 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%.
[h=4]CONCLUSIONS:[/h] Local 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.
[h=4]KEYWORDS:[/h] Epidemiological surveillance; GP density; General practitioners; Incidence estimation; Influenza-like illness; Surveillance; Surveillance networks
PMID: 27846798 DOI: 10.1186/s12874-016-0260-x
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[h=1]Improving incidence estimation in practice-based sentinel surveillance networks using spatial variation in general practitioner density.[/h] Souty C[SUP]1[/SUP], Bo?lle PY[SUP]2,[/SUP][SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] In 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.
[h=4]METHODS:[/h] There 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.
[h=4]RESULTS:[/h] In 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%.
[h=4]CONCLUSIONS:[/h] Local 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.
[h=4]KEYWORDS:[/h] Epidemiological surveillance; GP density; General practitioners; Incidence estimation; Influenza-like illness; Surveillance; Surveillance networks
PMID: 27846798 DOI: 10.1186/s12874-016-0260-x
[PubMed - in process] Free full text