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

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.
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal-univ-paris10.archives-ouvertes.fr/hal-01713523
Contributor : Julie Vallée <>
Submitted on : Monday, March 5, 2018 - 8:57:51 AM
Last modification on : Tuesday, November 19, 2019 - 9:40:03 AM
Long-term archiving on : Wednesday, June 6, 2018 - 2:08:28 PM

File

2007_Vallée et al_Sampling in...
Publisher files allowed on an open archive

Identifiers

Collections

Citation

Julie Vallée, Marc Souris, Florence Fournet, Audrey Bochaton, Virginie Mobillion, et al.. Sampling in health geography: reconciling geographical objectives and probabilistic methods. An example of a health survey in Vientiane (Lao PDR). Emerging Themes in Epidemiology, BioMed Central, 2007, 4, pp.6. ⟨10.1186/1742-7622-4-6⟩. ⟨hal-01713523v2⟩

Share

Metrics

Record views

131

Files downloads

173