A protocol for a multi-site, spatially-referenced household survey in slum settings: methods for access, sampling frame construction, sampling, and field data collection

BMC Med Res Methodol. 2019 May 30;19(1):109. doi: 10.1186/s12874-019-0732-x.

Abstract

Background: Household surveys are a key epidemiological, medical, and social research method. In poor urban environments, such as slums, censuses can often be out-of-date or fail to record transient residents, maps may be incomplete, and access to sites can be limit, all of which prohibits obtaining an accurate sampling frame. This article describes a method to conduct a survey in slum settings in the context of the NIHR Global Health Research Unit on Improving Health in Slums project.

Methods: We identify four key steps: obtaining site access, generation of a sampling frame, sampling, and field data collection. Stakeholder identification and engagement is required to negotiate access. A spatially-referenced sampling frame can be generated by: remote participatory mapping from satellite imagery; local participatory mapping and ground-truthing; and identification of all residents of each structure. We propose to use a spatially-regulated sampling method to ensure spatial coverage across the site. Finally, data collection using tablet devices and open-source software can be conducted using the generated sample and maps.

Discussion: Slums are home to a growing population who face some of the highest burdens of disease yet who remain relatively understudied. Difficulties conducting surveys in these locations may explain this disparity. We propose a generalisable, scientifically valid method that is sustainable and ensures community engagement.

Keywords: GIS; Sampling; Slum; Survey.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Epidemiological Monitoring*
  • Family Characteristics
  • Geographic Information Systems
  • Health Surveys / methods*
  • Humans
  • Poverty Areas*
  • Urban Population
  • Vulnerable Populations / statistics & numerical data*