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.

Neighbourhood and dwelling characteristics associated with the self-reported adverse health effects of heat in most deprived urban areas: A cross-sectional study in 9 cities

“…The data were weighted sequentially according to the weight of the DA, age and sex, in order to ensure calibration of the survey frequencies; we used a procedure similar to that proposed by Vallée et al (2007). The analyses, carried out using the survey procedures in SAS 9.3, take these weights into account in the same way the sampling plan is stratified according to the municipalities.…”

“…To produce representative samples for each of these cities, we used a two-step selection procedure similar to that proposed by Vallée et al (2007). The first step established the number of highly materially and socially disadvantaged DAs corresponding to quintile 5 of a widely used Canadian deprivation index (Pampalon and Raymond, 2000) and including at least one public low-rentalhousing building.…”

An adaptation index to high summer heat associated with adverse health impacts in deprived neighborhoods

“…A selection procedure similar to that proposed by Vallée et al (2007) was used to produce representative samples for each of the 9 cities, in very materially and socially disadvantaged DAs, corresponding to quintile 5 of a widely used Canadian deprivation index (Pampalon and Raymond 2000). In total, 3,485 people were interviewed from December 21, 2010, to December 20, 2011.…”

The combined effects of activity space and neighbourhood of residence on participation in preventive health-care activities: The case of cervical screening in the Paris metropolitan area (France)

“…The SIRS survey employed a stratified, multistage cluster sampling procedure. This sample design is commonly used in health geography because it provides appropriate datasets for studying the heterogeneity of health characteristics between different neighbourhoods and the homogeneity of characteristics within a given neighbourhood (Vallée et al, 2007). The primary sampling units were census blocks called "IRISs" ("IRIS" is a French acronym for blocks for incorporating statistical information).…”