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Rural and Urban Health Care Coverage in the United States

Ally Goerge, Grace Hotchkiss, Teegan Galdeen, Karen Niemchick, PhD

Grand Valley State University

We would like to thank Dr. Niemchick for guiding us throughout this project.

Acknowledgements

Purpose: to examine if there is an association between urban or rural residence and health care coverage in the United States.
Findings: rural residents were less likely to have health care coverage compared to urban residents.

Rural residents have higher mortality rates than urban residents (Gong et al., 2019).
Greater percentage of rural residents were uninsured compared to urban residents (Boal et al., 2018).

The results indicated that with an increase in level of education, the odds of having health care coverage increased by 1.290, CI [1.278 - 1.302].

A higher percentage of uninsured people had low health literacy (Sentell, 2012).

For employment, the odds of having health care coverage differed between groups by 1.283, CI [1.275 - 1.292].

More individuals that were unemployed for under a year reported having no health insurance compared to employed individuals (Silver et al., 2022).

As level of income increased, the odds of having health care coverage increased by 1.499 CI [1.483 - 1.514].

Low income is a barrier to having health insurance (Finegold et al., 2021; Gotanda et al., 2020).

Strengths: large sample size, nationally representative sample, and included multiple predictor variables.
Limitations: Unequal distribution of sample lived in urban areas, reported having health care coverage, and were White, non-Hispanic.

Discussion

The results indicated the odds of having health care coverage was less likely for rural residents compared to urban residents.

employment status, education, income

Public health issue: health care coverage disparity between urban and rural residents, need to target populations who are least likely to have coverage.
Future research: focus on states with high rural population, those without health care coverage, and expand upon health literacy and its impact on health care coverage status.

Conclusion

Introduction

Methods

Table 1

Characteristics of Study Participants

Results

Cross-sectional study
SPSS Version 26
2020 Behavioral Risk Factor Surveillance System (BRFSS) covered 53 states and territories.
Computer assisted telephone survey
Initial sample size: 401,958 adults over 18
Final sample size: 313,123 adults over 18
Chi-square and logistic regression

Rural areas generally have higher mortality rates in the United States (Gong et al., 2019).
Number of uninsured persons is associated with mortality rates (Gong et al., 2019).
15.4% of individuals living in non-metropolitan areas are in poverty (USDA, 2021).
Between 2018 and 2019 34.8% of unemployed individuals lacked coverage compared to 12.7% of employed individuals (Silver et al., 2022).
Uninsured individuals were more likely to have low health literacy compared to individuals with adequate health literacy (Sentell, 2012).

Research Question

Is there an association between living in a rural or urban area and having any kind of health care coverage in adults aged 18 or older in the United States?

Hypotheses

Null Hypothesis (Ho)= There is no association between living in a rural or urban area and having any kind of health care coverage in adults aged 18 or older in the United States.

Alternative Hypothesis (Ha)= There is an association between living in a rural or urban area and having any kind of health care coverage in adults aged 18 or older in the United States.

Sample was majority White, Non-Hispanic (77.3%), urban (85.1%), employed (44.6%), income greater than $50,000 (53.9%), and reported having health care coverage (91.8%).
Chi square test indicated that health care coverage and urban/rural status were related (χ 2 (1, 313123) = 4.971, p = 0.026).

Significant difference between observed and expected frequencies.

Logistic regression indicated that the odds of having health care coverage was 0.948 times less likely for rural residents compared to urban residents.

Characteristic	Frequency	Percent	Cumulative Percent
Residence Urban Rural	266508 46615	85.1 14.9	85.1 100
Health care Yes No	287601 25522	91.8 8.2	91.8 100
Employment Employed for wages Self-employed Out of work Homemaker, student, unable to work Retired	139727 28218 18047 38869 88262	44.6 9.0 5.8 12.4 28.2	44.6 53.6 59.4 71.8 100
Education Did not graduate high school High school graduate Some college or technical school College graduate	18314 80209 87752 126848	5.8 25.6 28.0 40.5	5.8 31.5 59.5 100
Income less than $15,000 $15,000 to less than $25,000 $25,000 to less than $35,000 $35,000 to less than $50,000 $50,000 or more	24474 46502 30401 42884 168862	7.8 14.9 9.7 13.7 53.9	7.8 22.7 32.4 46.1 100
Sex Male Female	147333 165790	47.1 52.9	47.1 100
Race White, Non-Hispanic Black, Non-Hispanic Other, Non-Hispanic Hispanic	242124 23818 23002 24179	77.3 7.6 7.3 7.7	77.3 84.9 92.3 100

References

Centers for Disease Control and Prevention. (2005). Behavioral risk factor surveillance system operational and user’s guide. https://www.cdc.gov/brfss/pdf/userguide.pdf

Centers for Disease Control and Prevention. (2021). Behavioral risk factor surveillance system overview. https://www.cdc.gov/brfss/annual_data/2020/pdf/overview-2020-508.pdf

Finegold, K., Conmy, A., Chu, R. C., Bosworth, A., & Sommers, B. D. (2021). Trends in the U.S. uninsured population, 2010-2020. Office of the Assistant Secretary for Planning and Evaluation. https://aspe.hhs.gov/reports/trends-us-uninsured-population-2010-2020

Gong, G., Phillips, S. G., Hudson, C., Curti, D., & Philips, B. U. (2019). Higher US rural mortality rates linked to socioeconomic status, physician shortages, and lack of health insurance. Health Affairs (Project Hope), 38(12), 2003–2010. https://doi.org/10.1377/hlthaff.2019.00722

Gotanda, H., Jha, A. K., Kominski, G. F., & Tsugawa, Y. (2020). Out-of-pocket spending and financial burden among low-income adults after Medicaid expansions in the United States: Quasi-experimental difference-in-difference study. British Medical Journal, 368, m40. https://doi.org/10.1136/bmj.m40

Sentell T. (2012). Implications for reform: survey of California adults suggests low health literacy predicts likelihood of being uninsured. Health affairs (Project Hope), 31(5), 1039–1048. https://doi.org/10.1377/hlthaff.2011.0954

Silver, S. R., Li, J., & Quay, B. (2022). Employment status, unemployment duration, and health-related metrics among US adults of prime working age: Behavioral Risk Factor Surveillance System, 2018-2019. American Journal of Industrial Medicine, 65(1), 59–71. https://doi.org/10.1002/ajim.23308

United States Department of Agriculture. (2021). Data show U.S. poverty rates in 2019 higher in rural areas than in urban for racial/ethnic groups. Economic research service. http://www.ers.usda.gov/data-products/chart-gallery/gallery/chart-detail/?chartId=101903

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