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Are health and demographic surveillance system estimates sufficiently generalisable?
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health. School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
2017 (English)In: Global Health Action, ISSN 1654-9716, E-ISSN 1654-9880, Vol. 10, no 1, p. 1-3, article id 1356621Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis, 2017. Vol. 10, no 1, p. 1-3, article id 1356621
Keywords [en]
Generalisation, HDSS, longitudinal data, causal inference
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:umu:diva-139148DOI: 10.1080/16549716.2017.1356621ISI: 000407953400001PubMedID: 28820344OAI: oai:DiVA.org:umu-139148DiVA, id: diva2:1141612
Available from: 2017-09-15 Created: 2017-09-15 Last updated: 2018-06-09Bibliographically approved

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Byass, Peter

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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  • de-DE
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Output format
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