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Are health and demographic surveillance system estimates sufficiently generalisable

Bibliographic Data

ID19530744
AuthorsPhilippe Bocquier (0000-0002-6278-0597, University of the Witwatersrand, corresponding author), Osman Sankoh (0000-0003-4405-9808, University of the Witwatersrand), Peter Bya (0000-0001-5474-4361, University of the Witwatersrand)
Year2017
Volume10
Issue1
Pages1356621-1356621
Publication date2017-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueGlobal Health Action (JOURNAL)
Journal identifiersISSN: 1654-9716 • E-ISSN: 1654-9880
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/16549716.2017.1356621
PMID28820344
OpenAlexW2750514536
LanguageEN
Citations received8
References cited9

Sampling rules do not apply in a Health and Demographic Surveillance System (HDSS) that covers exhaustively a district-level population and is not meant to be representative of a national population. We highlight the advantages of HDSS data for causal analysis and identify in the literature the principles of conditional generalisation that best apply to HDSS. A probabilistic view on HDSS data is still justified by the need to model complex causal inference. Accounting for contextual knowledge, reducing omitted-variable bias, detailing order of events, and high statistical power brings credence to HDSS data. Generalisation of causal mechanisms identified in HDSS data is consolidated through systematic comparison and triangulation with national or international data

Causal inference · Credence · Data science · Econometrics · Economics · Environmental health · Machine learning · Population · Probabilistic logic · Computer Science · Data-Driven Disease Surveillance · Food Security and Health in Diverse Populations · Health disparities and outcomes · Medicine · Artificial Intelligence

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Unique citing works8
Citations per year1
Citation span2018 - 2025 (8)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 7

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