Evidence map›Paper›PMID 25435814›Full record

ArticlePopulation health metrics2014

Improving disease incidence estimates in primary care surveillance systems.

Cécile Souty, Clément Turbelin, Thierry Blanchon, Thomas Hanslik, Yann Le Strat, Pierre-Yves Boëlle

Open access · goldAbstract read
In one paragraph

Article in Population health metrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 60 citations in OpenAlex.

  1. Article
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  9. Spatial and seasonal determinants of Lyme borreliosis incidence in France, 2016 to 2021.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023
    Article
  10. Article
  11. Creation and Implementation of a New Sentinel Surveillance Model in Pharmacy Offices in Southern Europe.International journal of environmental research and public health · 2022
    Article
  12. Pertussis surveillance results from a French general practitioner network, France, 2017 to 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2022
    Article
  13. Article
  14. Observational
  15. Article
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  20. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Cécile SoutyINSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France ; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France.
Clément TurbelinINSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France ; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France.
Thierry BlanchonINSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France ; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France.
Thomas HanslikINSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France ; AP-HP, Hôpital Ambroise Paré, service de médecine interne, Boulogne-Billancourt F-92100, France ; Université Versailles Saint-Quentin-en-Yvelines, Versailles F-78000, France.
Yann Le StratDépartement des maladies infectieuses, Institut de Veille Sanitaire (InVS), St Maurice F-94415, France.
Pierre-Yves BoëlleINSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France ; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris F-75012, France ; AP-HP, Hôpital Saint-Antoine, unité de santé publique, Paris F-75012, France.
Inserm · FRInstitut de Veille Sanitaire · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn primary care surveillance systems based on voluntary participation, biased results may arise from the lack of representativeness of the monitored population and uncertainty regarding the population denominator, especially in health systems where patient registration is not required.

methodsBased on the observation of a positive association between number of cases reported and number of consultations by the participating general practitioners (GPs), we define several weighted incidence estimators using external information on consultation volume in GPs. These estimators are applied to data reported in a French primary care surveillance system based on voluntary GPs (the Sentinelles network) for comparison.

resultsDepending on hypotheses for weight computations, relative changes in weekly national-level incidence estimates up to 3% for influenza, 6% for diarrhea, and 11% for varicella were observed. The use of consultation-weighted estimates led to bias reduction in the estimates. At the regional level (NUTS2 level - Nomenclature of Statistical Territorial Units Level 2), relative changes were even larger between incidence estimates, with changes between -40% and +55%. Using bias-reduced weights decreased variation in incidence between regions and increased spatial autocorrelation.

conclusionsPost-stratification using external administrative data may improve incidence estimates in surveillance systems based on voluntary participation.

Indexed as

AdjustmentGeneral practitionersIncidence estimationSentinel networkSurveillanceVolume of consultations

Identifiers

PMID25435814
PMCPMC4244096
OpenAlexW2098619718

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.