Estimating population size in emergencies.

One of the first challenges in a natural disaster or humanitarian emergency is to obtain accurate estimates of affected populations (1). In the aftermath of rapid-onset disasters such as the recent tsunami disaster in south Asia, there is frequently an absence of adequate baseline data against which to measure the impact of the disaster. Available population data vary widely in quality, and the movement of persons can result in inaccurate estimates. The population may be widely dispersed, highly mobile, or in refugee camps. Uncertainty over population figures and demographic information constitutes one of the main barriers to accurate needs assessment. A basic problem faced by all humanitarian relief agencies in an emergency is that of counting the numbers of displaced persons and assessing their general well-being. The various methods of estimating population size in disasters have their advantages and disadvantages, and demographic techniques are continually being updated and improved. Social scientists, epidemiologists and statisticians alike are familiar with the principles of sampling: selecting a subset of the population of interest in order to gain information about the entire population. A global positioning system (GPS — a system of satellites that provide precise location information that can be accessed using handheld electronic units) is increasingly used to identify a sampling frame in the conflict setting. Alternatively, satellite imagery can be used to estimate population density and select a geographical area for sampling. Different techniques and types of samples need to be considered in crisis situations, so it is important to understand the limitations and potential weaknesses of the various methodologies. Cluster sampling consists of assigning each member of the population to a group (cluster); clusters are randomly selected and all members of selected clusters are included in the sample (2). Spatial sampling — which is a variant of cluster sampling and is also known