Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/24842
Title: Sampling in health geography: Reconciling geographical objectives and probabilistic methods. An example of a health survey in Vientiane (Lao PDR)
Authors: Julie Vallée
Marc Souris
Florence Fournet
Audrey Bochaton
Virginie Mobillion
Karine Peyronnie
Gérard Salem
Institut de Recherche pour le Développement (IRD)
Universite Paris Nanterre
Mahidol University
Institut de recherche pour le developpement Ouagadougou
Keywords: Medicine
Issue Date: 27-Jun-2007
Citation: Emerging Themes in Epidemiology. Vol.4, (2007)
Abstract: Background. Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods. Methods. We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision. Application. We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population. Conclusion. This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy. © 2007 Vallée et al; licensee BioMed Central Ltd.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=34250338694&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/24842
ISSN: 17427622
Appears in Collections:Scopus 2006-2010

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