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
Abstract Views: 1588
PDF: 616
HTML: 102
HTML: 102
Publisher's note
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.
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.
Authors
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.
Abhijit B, Rohini P, Michael W, 2012. Delhi’s slum-dwellers: deprivation, preferences and political engagement among the urban poor. Available from: https://www.theigc.org/wpcontent/uploads/2014/10/Banerjee-Et-Al-2012-Working-Paper.pdf Accessed: June 15, 2021.
Australian Bureau of Statistics, 2014. Sample and frame maintenance procedures for census and household surveys. Available from: www.abs.gov.au. Accessed: June 15, 2021.
Carl G, Kühn I, 2017. Spind: a package for computing spatially corrected accuracy measures. – Ecography 40:675-82.
DOI: https://doi.org/10.1111/ecog.02593
Christakos G, 2005. Random field models in earth sciences. Dover Publications, New York.
Cochran WG, 1977. Sampling techniques. 3rd Edition, John Wiley and Sons, New York.
Cressie N, 1991. Statistics for Spatial Data. New York: Wiley. 900 pp.
Dessard H, Bar-Hen A, 2005. Experimental design for spatial sampling applied to the study of tropical forest regeneration. Can J For Res 35:1149-55.
DOI: https://doi.org/10.1139/x04-187
EPA, 2002. Available fromhttps://www.epa.gov/sites/production/files/2015-06/documents/g5s-final.pdf Accessed: June 15, 2021.
Eric D, 2012 . Spatial Sampling. Available from https://pages.uncc.edu/eric-delmelle/wp-content/uploads/sites/150/2012/12/spatial-sampling-delmelle.pdf Accessed: June 15, 2021.
Escamilla V, Emch M, Dandalo L, Miller WC, Hoffman I, 2014. Sampling at community level by using satellite imagery and geographical analysis. Bull World Health Organ 92:690-4. Available from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208578/Accessed: June 15, 2021.
DOI: https://doi.org/10.2471/BLT.14.140756
Grais RF, Rose AM, Guthmann JP, 2007. Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys. Emerg Themes Epidemiol 1;4:8.
DOI: https://doi.org/10.1186/1742-7622-4-8
Grzegorz S, Zoltán B, Zygmunt, Daniel P, Ludwik Ż, 2017. A performance comparison of sampling methods in the assessment of species composition patterns and environment–vegetation relationships in species-rich grasslands. Acta Soc Bot Pol 86:3561
Haining RP, 2001. Spatial sampling. In: International Encyclopedia of the Social & Behavioral Sciences. Available from https://www.sciencedirect.com/topics/computer-science/spatial-sampling Accessed: June 15, 2021.
DOI: https://doi.org/10.1016/B0-08-043076-7/02510-9
Haining RP, 2003. Spatial data analysis: Theory and practice. Cambridge University Press. 452 pp.
DOI: https://doi.org/10.1017/CBO9780511754944
Hernandez JA, Mulla DJ, 2002. Comparing statistical analysis for landscape scale experimental designs. 6th International Congress on Precision Agriculture and Other Precision Resource Management, July 15, Minneapolis, MN.
Improving Health in Slums Collaborative, 2019. 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 19:109.
DOI: https://doi.org/10.1186/s12874-019-0732-x
Joshua V, Sunitha K, Muthu G, Sinduja V, Venkatesh P, Nandini P, Shantaraman K, ManickamP, Murhekar MV, Yuvaraj J, 2020. Self-reported morbidity profile among geriatric population in ICMR-model rural health research unit, Kallur, Tirunelveli. J Indian Acad Geriatr 16:95-100.
DOI: https://doi.org/10.4103/jiag.jiag_2_20
Kalton G, Kali J, Sigman R, 2014. Handling frame problems when address-based sampling is used for in-person household surveys. J Surv Stat Methodol 2:283-304.
DOI: https://doi.org/10.1093/jssam/smu013
Kolbe AR, Hutson RA, 2006. Human rights abuse and other criminal violations in Port-au-Prince, Haiti: a random survey of households. Lancet 368:864-73.
DOI: https://doi.org/10.1016/S0140-6736(06)69211-8
Kumar N, 2007. Spatial Sampling Design for a Demographic and Health Survey. Popul Res Policy Rev 26:581-99
DOI: https://doi.org/10.1007/s11113-007-9044-7
Lee C, Moudon AV, Courbois JYP, 2006. Built environment and behavior: Spatial sampling using parcel data. Ann Epidemiol 16:387-94.
DOI: https://doi.org/10.1016/j.annepidem.2005.03.003
Lepowski JM, 1988. Telephone sampling methods in the United States. Telephone Survey Methodology (Eds. Groves RM, Biemer PP, Lyberg LE, Massey JT, Nicholls II WL, Waksberg J). New York: Wiley 73-98 pp.
Maduekwe E, Vries WTD, 2019. Random spatial and systematic random sampling approach to development survey data: evidence from field application in Malawi. Sustainability 11:6899.
DOI: https://doi.org/10.3390/su11246899
Malhotra NK, Birks, DF, 2006. Marketing research: an applied approach, 3rd Edition, Prentice Hall, Upper Saddle River.
Miller AC, Rohloff P, Blake A, Dhaenens E, Shaw L, Tuiz E, Grandesso F, Mendoza MC, Thomson DR, 2020. Feasibility of satellite image and GIS sampling for population representative surveys: a case study from rural Guatemala. Int J Health Geogr19:56.
DOI: https://doi.org/10.1186/s12942-020-00250-0
Muller W, 1998. Collecting spatial data: optimal design of experiments for random Fields, revised edition. Contribution to statistics. Heidelberg: Physica-Verlag.
Ripley, B, 1981. Spatial Statistics. Wiley, New York, 252.
DOI: https://doi.org/10.1002/0471725218
Rogerson PA, Delmelle E, Batta R, Akella M, Blatt A, Wilson G, 2004. Optimal sampling design for variables with varying spatial importance. Geogr Anal 36:177–94.
DOI: https://doi.org/10.1111/j.1538-4632.2004.tb01131.x
Speizer IS, Nanda P, Achyut P, Pillai G, Guilkey DK, 2012. Family planning use among urban poor women from six cities of Uttar Pradesh, India. J Urban Health 89:639-58.
DOI: https://doi.org/10.1007/s11524-011-9667-1
Stehman SV, Overton SW, 1996. Spatial sampling. In: Arlinghaus SL (ed.) Practical Handbook of Spatial Statistics; Boca Raton, FL: CRC Press 31–64 pp.
DOI: https://doi.org/10.1201/9781003067689-3
Swacha G, Botta-Dukát Z, Kącki Z, Pruchniewicz D, Żołnierz L, 2017. A performance comparison of sampling methods in the assessment of species composition patterns and environment–vegetation relationships in species-rich grasslands. Acta Soc Bot Pol 86:3561.
DOI: https://doi.org/10.5586/asbp.3561
Thomas B, Alexander K, Magdalena S, Giovanna B, Danuté K, 2019. Available from https://ec.europa.eu/eurostat/cros/system/files/qgfss-v1.51.pdf Accessed June 15, 2021.
Thomson DR, Shitole S, Shitole T, Sawant K, Subbaraman R, David EB, Anita PM, 2014. A system for household enumeration and re-identification in densely populated slums to facilitate community research, education, and advocacy. PLoS One 9:e93925.
DOI: https://doi.org/10.1371/journal.pone.0093925
Valliant R, Hubbard F, Lee S, Chang C, 2014. Efficient use of commercial lists in US Household Sampling. J Surv Stat Methodol2:182- 209.
DOI: https://doi.org/10.1093/jssam/smu006
Wang JF, Haining RP, Cao ZD, 2010. Sample surveying to estimate the mean of a heterogeneous surface: reducing the error variance through zoning. Int J Geogr Inf Sci 24:523-43.
DOI: https://doi.org/10.1080/13658810902873512
WHO STEPS Surveillance Manual,2017. The WHO STEP wise Approach non-communicable disease risk factor surveillance. Geneva: World Health Organization. Available at https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps/manuals Accessed: June 15, 2021.
Yihan L, David PK, 2016. Using satellite imagery and GPS technology to create random sampling frames in high-risk environments Int J Surg 32:123-8.
DOI: https://doi.org/10.1016/j.ijsu.2016.06.044
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
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
