https://doi.org/10.4081/gh.2022.1097
Comparison of complete and spatial sampling frames for estimation of the prevalence of hypertension and diabetes mellitus
Submitted: 11 April 2022
Accepted: 21 September 2022
Published: 30 November 2022
Accepted: 21 September 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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A complete sampling frame (CSF) is needed for the development of probability sampling structures; utilisation of a spatial sampling frame (SSF) was the objective of the present study. We used two sampling methods, simple random sampling (SRS) and stratified random sampling (STRS), to compare the prevalence estimates delivered by a CSF to that by a SSF when applied to self-reported hypertension and diabetes mellitus in a semi-urban setting and in a rural one. A CSF based on Geodatabase of all households and all individuals was available for our study that focused on adults aged 18-69 years in the two settings. A single digitized shapefile of solely household regions/structures as SSF was developed using Google Earth and employed for the study. The results from the two sampling frames were similar and not significantly different. All 95%CI calculations contained the prevalence rates of the two medical conditions except for one occasion based on STRS and CSF. The SRS based on CSF showed a minimum 95% CI width for diabetes mellitus, whereas SSF showed a minimum 95% CI width for hypertension. The coefficient of variation exceeded 10.0% on six occasions for CSF but only once for SSF, which was found to be as efficient as CSF.
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How to Cite
Joshua, V., Pattabi, K., Jeyaraman, Y., Kaur, P., Bhatnagar, T., Arunachalam, S., Ramasamy, S., Janagaraj, V., & Murhekar, M. V. (2022). Comparison of complete and spatial sampling frames for estimation of the prevalence of hypertension and diabetes mellitus. Geospatial Health, 17(2). https://doi.org/10.4081/gh.2022.1097
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