A Cluster-based, Spatial-sampling Method for Assessing Household Healthcare Utilization Patterns in Resource-limited Settings

Alexander T Yu; Rajani Shakya; Bikram Adhikari; Dipesh Tamrakar; Krista Vaidya; Stace Maples; Kashmira Date; Isaac I Bogoch; Caryn Bern; Farah Qamar; Mohammad T Yousafzai; Denise O Garrett; Ashley T Longley; Caitlin Hemlock; Stephen Luby; Kristen Aiemjoy; Jason R Andrews

doi:10.1093/cid/ciaa1310

A Cluster-based, Spatial-sampling Method for Assessing Household Healthcare Utilization Patterns in Resource-limited Settings

Clin Infect Dis. 2020 Dec 1;71(Suppl 3):S239-S247. doi: 10.1093/cid/ciaa1310.

Authors

Alexander T Yu¹, Rajani Shakya², Bikram Adhikari², Dipesh Tamrakar², Krista Vaidya², Stace Maples³, Kashmira Date⁴, Isaac I Bogoch⁵, Caryn Bern⁶, Farah Qamar⁷, Mohammad T Yousafzai⁷, Denise O Garrett⁸, Ashley T Longley^{4

9}, Caitlin Hemlock⁸, Stephen Luby¹, Kristen Aiemjoy¹, Jason R Andrews¹

Affiliations

¹ Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, California, USA.
² Dhulikhel Hospital, Kathmandu University Hospital, Dhulikhel, Nepal.
³ Stanford Geospatial Center, Stanford University, Stanford, California, USA.
⁴ Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
⁵ Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
⁶ Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
⁷ Aga Khan University, Karachi, Pakistan.
⁸ Applied Epidemiology, Sabin Vaccine Institute, Washington, DC, USA.
⁹ National Foundation for the Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Abstract

Background: Implementation of population-based surveys is resource intensive and logistically demanding, especially in areas with rapidly changing demographics and incomplete or no enumeration of the underlying population and their residences. To remove the need for pre-enumeration and to simplify field logistics for the population healthcare utilization survey used for the Surveillance for Enteric Fever in Asia Project in Nepal, we incorporated a geographic information system-based geosurvey and field mapping system into a single-stage cluster sampling approach.

Methods: A survey was administered to ascertain healthcare-seeking behavior in individuals with recent suspected enteric fever. Catchment areas were based on residential addresses of enteric fever patients using study facilities; clusters were randomly selected from digitally created grids using available satellite images and all households within clusters were offered enrollment. A tablet-compatible geosurvey and mapping system that allowed for data-syncing and use in areas without cellular data was created using the ArcGIS suite of software.

Results: Between January 2017 and November 2018, we surveyed 25 521 households in Nepal (16 769 in urban Kathmandu and 8752 in periurban Kavrepalanchok), representing 84 202 individuals. Overall, the survey participation rate was 90.9%, with geographic heterogeneity in participation rates within each catchment area. Areas with higher average household wealth had lower participation rates.

Conclusion: A geographic information system-based geosurvey and field mapping system allowed creation of a virtual household map at the same time as survey administration, enabling a single-stage cluster sampling method to assess healthcare utilization in Nepal for the Surveillance for Enteric Fever in Asia Project . This system removed the need for pre-enumeration of households in sampling areas, simplified logistics and could be replicated in future community surveys.

Keywords: SEAP; cluster sampling; geospatial sampling; healthcare utilization survey; typhoid.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Asia
Geographic Information Systems*
Humans
Nepal / epidemiology
Patient Acceptance of Health Care*
Surveys and Questionnaires