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Improving disease incidence estimates in primary care surveillance systems

Abstract : Background:In primary care surveillance systems based on voluntary participation, biased results may arise fromthe lack of representativeness of the monitored population and uncertainty regarding the population denominator,especially in health systems where patient registration is not required.Methods:Based on the observation of a positive association between number of cases reported and number ofconsultations by the participating general practitioners (GPs), we define several weighted incidence estimatorsusing external information on consultation volume in GPs. These estimators are applied to data reported in aFrench primary care surveillance system based on voluntary GPs (theSentinellesnetwork) for comparison.Results:Depending on hypotheses for weight computations, relative changes in weekly national-level incidenceestimates up to 3% for influenza, 6% for diarrhea, and 11% for varicella were observed. The use of consultation-weightedestimates led to bias reduction in the estimates. At the regional level (NUTS2 level -Nomenclature of Statistical TerritorialUnits Level 2), relative changes were even larger betweenincidence estimates, with changes between -40% and +55%.Using bias-reduced weights decreased variation in incidence between regions and increased spatial autocorrelation.Conclusions:Post-stratification using external administrative data may improve incidence estimates in surveillancesystems based on voluntary participation
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https://www.hal.inserm.fr/inserm-01088631
Contributor : Nadia Taibi <>
Submitted on : Friday, November 28, 2014 - 12:04:01 PM
Last modification on : Wednesday, August 19, 2020 - 11:58:39 AM
Long-term archiving on: : Friday, April 14, 2017 - 10:48:06 PM

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Cécile Souty, Clément Turbelin, Thierry Blanchon, Thomas Hanslik, Yann Le Strat, et al.. Improving disease incidence estimates in primary care surveillance systems. Population Health Metrics, BioMed Central, 2014, 12 (1), pp.19. ⟨10.1186/s12963-014-0019-8⟩. ⟨inserm-01088631⟩

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