Estimating Local Prevalence of Obesity Via Survey Under Cost Constraints: Stratifying ZCTAs in Virginia’s Thomas Jefferson Health District

Abstract

Currently, the most reliable estimate of the prevalence of obesity in Virginia’s Thomas Jefferson Health District (TJHD) comes from an annual telephone survey conducted by the Centers for Disease Control and Prevention. This district-wide estimate has limited use to decision makers who must target health interventions at a more granular level. A survey is one way of obtaining more granular estimates. This article describes the process of stratifying targeted geographic units (here, ZIP Code Tabulation Areas, or ZCTAs) prior to conducting the survey for those situations where cost considerations make it infeasible to sample each geographic unit (here, ZCTA) in the region (here, TJHD). Feature selection, allocation factor analysis, and hierarchical clustering were used to stratify ZCTAs. We describe the survey sampling strategy that we developed, by creating strata of ZCTAs; the data analysis using the R survey package; and the results. The resulting maps of obesity prevalence show stark differences in prevalence depending on the area of the health district, highlighting the importance of assessing health outcomes at a granular level. Our approach is a detailed and reproducible set of steps that can be used by others who face similar scenarios. Supplementary files for this article are available online.

Keywords:

• Population health
• Small area
• Stratification
• ZIP code tabulation area

Acknowledgments

We are most grateful to the editor and the anonymous reviewers whose comments greatly improved our article. We also thank Barry Graubard (National Cancer Institute) for advice on the analysis, and Thomas Guterbock and Kara Fitzgibbon (Center for Survey Research, University of Virginia) for assistance with survey questions.

Competing interests: BL reports research support from Dexcom, Inc. handled by the University of Virginia, and royalties from Dexcom, Inc. handled by the University of Virginia’s Licensing and Ventures Group.

Ethical approval: Collection and subsequent analysis of the data from the survey was performed under the University of Virginia’s Social and Behavioral Sciences IRB protocol number 2018025900.

Supplementary Material

The supplementary material contains a map showing the TJHD ZCTAs, prevalence estimates for Underweight, Healthy Weight, and Overweight, and some survey statistics. Some analysis code and data can be found at https://github.com/bjl2n/tjhd_prevalence_of_obesity.

Notes

1 In 2021 the Thomas Jefferson Health District was renamed the Blue Ridge Health District (BRHD).

2 www.cdc.gov/obesity/data/childhood.html.

3 Geocorr 2014 is located at mcdc.missouri.edu/applications/geocorr2014.html. To generate the correlation list for the research in this article, under Input Options:

“Virginia” was selected as the State.

“2010 Geographies: ZIP/ZCTA” was selected as the Source Geography.

“2014 Geographies: County” was selected as the Target Geography.

“Housing Units (2010 census)” was selected as the Weighting Variable.

Housing units (www.census.gov/housing/hvs/definitions.pdf) were used as a proxy for the population of the ZCTA because the survey was mailed to randomly selected addresses (where an address corresponds to a housing unit).

4 Because the ranges vary greatly for these 11 features, they were first scaled using the scale function of the sklearn.preprocessing Python library (Pedregosa et al. 2011). The scipy.cluster.hierarchy Python module (Oliphant 2007; Millman and Aivazis 2011) implementation of hierarchical clustering with the Ward variance minimization algorithm was used to calculate Euclidean distances between the strata.

5 Unless otherwise noted the labels in parentheses refer to the American Community Survey 2016 5-year estimate data tables from which population totals were taken.

6 We raised the adverse consequences of a long survey during the planning stages. The survey length arose from other units in the public health department who desired additional information beyond simply estimates of the prevalence of obesity.

Additional information

Funding

BL acknowledges support in part from an internal grant from the University of Virginia’s Translational Health Research Institute of Virginia (THRIV). KK acknowledges support in part from the National Center For Advancing Translational Sciences of the National Institutes of Health (NIH) under Award Number UL1TR003015. Authors are solely responsible for the content which does not represent the official views of the NIH.