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

Research output: Contribution to journalArticlepeer-review


Colleges, School and Institutes


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 In poor urban environments, such as slums, censuses can often be out-of-date or fail to record transient residents, maps may be incomplete, and access to sites can be limit, all of which prohibits obtaining an accurate sampling frame.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.


Original languageEnglish
Pages (from-to)109
JournalBMC Medical Research Methodology
Issue number1
Publication statusPublished - 30 May 2019


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