title: Improving incidence estimation in practice-based sentinel surveillance networks using spatial variation in general practitioner density.
abstract: 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.
  
  
  
  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.
  
  
  
  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%.
  
  
  
  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.
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), F-75012, Paris, France. cecile.souty@upmc.fr.
authors.forename: Cécile
authors.initials: C
authors.lastname: Souty
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), F-75012, Paris, France.|Département de santé publique, AP-HP, Hôpital Saint-Antoine, F-75012, Paris, France.
authors.forename: Pierre-Yves
authors.initials: PY
authors.lastname: Boëlle
mesh_terms: D000465:Algorithms; D019058:Community Networks / Q000706:statistics & numerical data*; D003198:Computer Simulation; D058872:Epidemics; D005602:France / Q000453:epidemiology; D058006:General Practice / Q000706:statistics & numerical data*; D058005:General Practitioners / Q000706:statistics & numerical data*; D005843:Geography; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology*; D008962:Models, Theoretical; D012621:Seasons; D018571:Sentinel Surveillance; D062206:Spatial Analysis
keywords: Epidemiological surveillance; GP density; General practitioners; Incidence estimation; Influenza-like illness; Surveillance; Surveillance networks