This article is part of a series on Methods for Health Surveys in Difficult Settings, edited by Oleg Bilukha (Centers for Disease Control), Kristof Bostoen (London School of Hygiene and Tropical Medicine), Francesco Checchi (London School of Hygiene and Tropical Medicine), Bridget Fenn (London School of Hygiene and Tropical Medicine), Oliver Morgan (London School of Hygiene and Tropical Medicine) and Anne-Marie ter Veen (London School of Hygiene and Tropical Medicine).

Methodology

Sampling in health geography: reconciling geographical objectives and probabilistic methods. An example of a health survey in Vientiane (Lao PDR)

Julie Vallée^1,2 , Marc Souris^3,5,6 , Florence Fournet⁴ , Audrey Bochaton^1,2 , Virginie Mobillion^1,2 , Karine Peyronnie¹ and Gérard Salem²

¹  Conditions et Territoires d'Emergence des Maladies (UR178), Institut de Recherche pour le Développement (IRD), UR 178, PO. 5992, Vientiane, Laos

²  Laboratoire Espace, Santé et Territoire, Université Paris X- Nanterre, 200 avenue de la République, 92000 Nanterre, France

³  Conditions et Territoires d'Emergence des Maladies (UR178), Institut de Recherche pour le Développement (IRD), Mahidol University, Bangkok, Thailand

⁴  Conditions et Territoires d'Emergence des Maladies (UR 178), Institut de Recherche pour le Développement, BP 182, Ouagadougou, Burkina Faso

⁵  Center for Vectors and Vector-Borne Diseases, Faculty of Science, Mahidol University, Bangkok, Thailand

⁶  Asian Institute of Technology, RS & GIS FoS, School of Engineering and Technology, Thailand

author email corresponding author email

Emerging Themes in Epidemiology 2007, 4:6doi:10.1186/1742-7622-4-6

Published:	1 June 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.