Population survey sampling methods in a rural African setting: measuring mortality

Abstract

Background: Population-based sample surveys and sentinel surveillance methods are commonly used as substitutes for more widespread health and demographic monitoring and intervention studies in resource-poor settings. Such methods have been criticised as only being worthwhile if the results can be extrapolated to the surrounding 100-fold population. With an emphasis on measuring mortality, this study explores the extent to which choice of sampling method affects the representativeness of 1% sample data in relation to various demographic and health parameters in a rural, developing-country setting.

Methods: Data from a large community based census and health survey conducted in rural Burkina Faso were used as a basis for modelling. Twenty 1% samples incorporating a range of health and demographic parameters were drawn at random from the overall dataset for each of seven different sampling procedures at two different levels of local administrative units. Each sample was compared with the overall 'gold standard' survey results, thus enabling comparisons between the different sampling procedures.

Results: All sampling methods and parameters tested performed reasonably well in representing the overall population. Nevertheless, a degree of variation could be observed both between sampling approaches and between different parameters, relating to their overall distribution in the total population.

Conclusion: Sample surveys are able to provide useful demographic and health profiles of local populations. However, various parameters being measured and their distribution within the sampling unit of interest may not all be best represented by a particular sampling method. It is likely therefore that compromises may have to be made in choosing a sampling strategy, with costs, logistics the intended use of the data being important considerations.

Figure 1

Overall proportions and maximum and minimum values of selected parameters disaggregated by three administrative levels.

Figure 2

Proportion of male residents (%) by sample (blue circle), mean of 20 samples (red square), and unsampled population value (green line) for each of 7 sampling methods at two administrative levels, ZD and concession.

Figure 3

Proportion of children under 5 years of age (%) by sample (blue circle), mean of 20 samples (red square), and unsampled population value (green line) for each of 7 sampling methods at two administrative levels, ZD and concession.

Figure 4

Proportion of individuals educated to secondary level or higher (%) by sample (blue circle), mean of 20 samples (red square), and unsampled population value (green line) for each of 7 sampling methods at two administrative levels, ZD and concession.

Figure 5

Proportion of households in the poorest wealth quintile (%) by sample (blue circle), mean of 20 samples (red square), and unsampled population value (green line) for each of 7 sampling methods at two administrative levels, ZD and concession.

Figure 6

Proportion of adult female residents (%) by sample (blue circle), mean of 20 samples (red square), and unsampled population value (green line) for each of 7 sampling methods at two administrative levels, ZD and concession.

Figure 7

Maternal mortality rate by sample (blue circle), mean of 20 samples (red square), and unsampled population value (green line) for each of 7 sampling methods at two administrative levels, ZD and concession.