Top

BMC Public Health

Published in:

Open Access 01-12-2019 | Research article

Impact of rurality on diabetes screening in the US

Authors: Phoebe Tran, Lam Tran, Liem Tran

Published in: BMC Public Health | Issue 1/2019

Abstract

Background

Due to the high prevalence of diabetes risk factors in rural areas, it is important to identify whether differences in diabetes screening rates between rural and urban areas exist. Thus, the purpose of this study is to examine if living in a rural area, rurality, has any influence on diabetes screening across the US.

Methods

Participants from the 2011, 2013, 2015, and 2017 nationally representative Behavioral Risk Factor Surveillance System (BRFSS) surveys who responded to a question on diabetes screening were included in the study (n = 1,889,712). Two types of marginal probabilities, average adjusted predictions (AAPs) and average marginal effects (AMEs), were estimated at the national level using this data. AAPs and AMEs allow for the assessment of the independent role of rurality on diabetes screening while controlling for important covariates.

Results

People who lived in urban, suburban, and rural areas all had comparable odds (Urban compared to Rural Odds Ratio (OR): 1.01, Suburbans compared to Rural OR: 0.95, 0.94) and probabilities of diabetes screening (Urban AAP: 70.47%, Suburban AAPs: 69.31 and 69.05%, Rural AAP: 70.27%). Statistically significant differences in probability of diabetes screening were observed between residents in suburban areas and rural residents (AMEs: − 0.96% and − 1.22%) but not between urban and rural residents (AME: 0.20%).

Conclusions

While similar levels of diabetes screening were found in urban, suburban, and rural areas, there is arguably a need for increased diabetes screening in rural areas where the prevalence of diabetes risk factors is higher than in urban areas.

Centers for Disease Control and Prevention. New CDC report: More than 100 million Americans have diabetes or prediabetes Diabetes growth rate steady, adding to health care burden. 2017. https://www.cdc.gov/media/releases/2017/p0718-diabetes-report.html.

American Diabetes Association. The Cost of Diabetes. 2018. http://www.diabetes.org/advocacy/news-events/cost-of-diabetes.html.

Centers for Disease Control and Prevention. Putting the Brakes on Diabetes Complications. 2017. https://www.cdc.gov/features/preventing-diabetes-complications/index.html.

Rural Health Information Hub. The burden of diabetes in rural America. 2018. https://www.ruralhealthinfo.org/toolkits/diabetes/1/rural-concerns.

National Institute of Diabetes and Digestive and Kidney Diseases. Prediabetes Screening: How and Why. 2018. https://www.niddk.nih.gov/health-information/communication-programs/ndep/health-professionals/game-plan-preventing-type-2-diabetes/prediabetes-screening-how-why.

Rural Health Information Hub. Chronic disease in rural America. 2017. https://www.ruralhealthinfo.org/topics/chronic-disease.

Befort CA, Nazir N, Perri MG. Prevalence of obesity among adults from rural and urban areas of the United States: findings from NHANES (2005-2008). The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association. 2012;28(4):392–7. https://doi.org/10.1111/j.1748-0361.2012.00411.x.CrossRef

Weaver KE, Palmer N, Lu L, Case LD, Geiger AM. Rural-urban differences in health behaviors and implications for health status among US cancer survivors. Cancer causes & control : CCC. 2013;24(8):1481–90. https://doi.org/10.1007/s10552-013-0225-x.CrossRefPubMed

Kilmer G, Hughes E, Zhang X, Elam-Evans L. Diabetes and prediabetes: screening and prevalence among adults with coronary heart disease. Am J Prev Med. 2011;40(2):159–65. https://doi.org/10.1016/j.amepre.2010.09.021.CrossRefPubMed

10.

Kidney RS, Peacock JM, Smith SA. Blood glucose screening rates among Minnesota adults with hypertension, behavioral risk factor surveillance system, 2011. Prev Chronic Dis. 2014;11:E207. https://doi.org/10.5888/pcd11.140204.CrossRefPubMedPubMedCentral

11.

Man B, Turyk ME, Kominiarek MA, Xia Y, Gerber BS. Diabetes screening in US women with a history of gestational diabetes, National Health and nutrition examination survey, 2007-2012. Prev Chronic Dis. 2016;13:E124. https://doi.org/10.5888/pcd13.160106.CrossRefPubMedPubMedCentral

12.

Lobo JM, Anderson R, Stukenborg GJ, McCall A, Kang H, Camacho F, et al. Disparities in the use of diabetes screening in Appalachia. J Rural Health. 2017;34(2):173–81. https://doi.org/10.1111/jrh.12247.CrossRefPubMed

13.

Misra R, Fitch C, Roberts D, Wright D. Community-Based Diabetes Screening and Risk Assessment in Rural West Virginia. J Diabetes Res. 2016;2016:9. https://doi.org/10.1155/2016/2456518.CrossRef

14.

Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Comparability of Data BRFSS. 2017:2018. https://www.cdc.gov/brfss/annual_data/2017/pdf/compare-2017-508.pdf.

15.

Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System 2013. Codebook Report Land-Line and Cell-Phone data. 2014. https://www.cdc.gov/brfss/annual_data/2013/pdf/codebook13_llcp.pdf.

16.

Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System 2015. Codebook Report Land-Line and Cell-Phone data. 2016. https://www.cdc.gov/brfss/annual_data/2015/pdf/codebook15_llcp.pdf.

17.

Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System 2011. Codebook Report Land-Line and Cell-Phone data. 2013; https://www.cdc.gov/brfss/annual_data/2011/pdf/codebook11_llcp.pdf.

18.

Centers for Disease Control and Prevention. LLCP 2017 Codebook Report Overall version data weighted with _LLCPWT Behavioral Risk Factor Surveillance System. 2018. https://www.cdc.gov/brfss/annual_data/2017/pdf/codebook17_llcp-v2-508.pdf.

19.

Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the behavioral risk factor surveillance system (BRFSS), 2004–2011. BMC Med Res Methodol. 2013;13(1):49. https://doi.org/10.1186/1471-2288-13-49.CrossRefPubMedPubMedCentral

20.

Centers for Disease Control and Prevention. BRFSS Data Quality, Validity, and Reliability. 2017. https://www.cdc.gov/brfss/publications/data_qvr.htm.

21.

Centers for Disease Control and Prevention. BRFSS Questionnaires. 2019. https://www.cdc.gov/brfss/questionnaires/index.htm.

22.

Centers for Disease Control and Prevention. The BRFSS Data User Guide. 2013. https://www.cdc.gov/brfss/data_documentation/pdf/UserguideJune2013.pdf.

23.

Centers for Disease Control and Prevention. Annual Survey Data. 2018. https://www.cdc.gov/brfss/annual_data/annual_data.htm.

24.

Montana Department of Public Health & Human Services. Interpreting and Reporting BRFSS Data. 2015. https://dphhs.mt.gov/Portals/85/publichealth/documents/BRFSS/Factors/2015Factors2.pdf.

25.

North Carolina Department of Health and Human Services. Technical Notes 2012. 2012. https://schs.dph.ncdhhs.gov/data/brfss/2012/technical.htm.

26.

Alaska Department of Health and Social Services. Complete Health Indicator Report of Mental Health - Frequent Mental Distress - Adults (18+). 2018. http://ibis.dhss.alaska.gov/indicator/complete_profile/FMD.html.

27.

Oberoi S, Chaudhary N, Patnaik S, Singh A. Understanding health seeking behavior. Journal of Family Medicine and Primary Care. 2016;5(2):463–4. https://doi.org/10.4103/2249-4863.192376.CrossRefPubMedPubMedCentral

28.

National Institute of Diabetes and Digestive and Kidney Diseases. Risk Factors for Type 2 Diabetes. 2016. https://www.niddk.nih.gov/health-information/diabetes/overview/risk-factors-type-2-diabetes.

29.

Fletcher B, Gulanick M, Lamendola C. Risk factors for type 2 diabetes mellitus. J Cardiovasc Nurs. 2002;16(2):17–23.CrossRefPubMed

30.

American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2019. Diabetes Care. 2019;42(Supplement 1):S13-S28.

31.

Tung EL, Baig AA, Huang ES, Laiteerapong N, Chua KP. Racial and ethnic disparities in diabetes screening between Asian Americans and other adults: BRFSS 2012-2014. J Gen Intern Med. 2017;32(4):423–9. https://doi.org/10.1007/s11606-016-3913-x.CrossRefPubMed

32.

Morales L, Rogowski J, A Freedman V, L Wickstrom S, Adams J, Escarce J. Sociodemographic Differences in Use of Preventive Services by Women Enrolled in Medicare+Choice Plans. 2004;39(4):738-45.

33.

US Office of Management and Budget. 2010 standards for delineating metropolitan and micropolitan statistical areas; Notice. Fed Regist. 2010;75(123):37246–52.

34.

Isserman AM. In the national interest: defining rural and urban correctly in research and public policy. Int Reg Sci Rev. 2005;28(4):465–99.CrossRef

35.

Hart LG, Larson EH, Lishner DM. Rural definitions for health policy and research. Am J Public Health. 2005;95(7):1149–55.CrossRefPubMedPubMedCentral

36.

Woods M. Rural geography: blurring boundaries and making connections. Prog Hum Geogr. 2009;33(6):849–58.CrossRef

37.

Hall SA, Kaufman JS, Ricketts TC. Defining urban and rural areas in US epidemiologic studies. J Urban Health. 2006;83(2):162–75.CrossRefPubMedPubMedCentral

38.

SAS Institute Inc. SAS 9.4. Cary, North Carolina 2017.

39.

Jann B. Predictive margins and marginal effects in Stata; 2013.

40.

Williams R. Using Stata’s margins command to estimate and interpret adjusted predictions and marginal effects. Stata J. 2012;12(2):308–31.CrossRef

41.

StataCorp. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC; 2017.

42.

Rural Health Information Hub. Healthcare access in rural communities. 2017. https://www.ruralhealthinfo.org/topics/healthcare-access.

43.

Lobo P. New diabetes screening recommendations in the USA. The Lancet Diabetes & Endocrinology. 2014;2(12):934. https://doi.org/10.1016/S2213-8587(14)70182-2.CrossRef

44.

Basu J, Cooper J. Out-of-area travel from rural and urban counties: a study of ambulatory care sensitive hospitalizations for New York state residents. J Rural Health. 2000;16(2):129–38.CrossRefPubMed

45.

Nhim K, Khan T, Gruss SM, Wozniak G, Kirley K, Schumacher P, et al. Primary care providers’ prediabetes screening, testing, and referral behaviors. Am J Prev Med. 2018;55(2):e39–47. https://doi.org/10.1016/j.amepre.2018.04.017.CrossRefPubMedPubMedCentral

46.

Bowlin SJ, Morrill BD, Nafziger AN, Jenkins PL, Lewis C, Pearson TA. Validity of cardiovascular disease risk factors assessed by telephone survey: the behavioral risk factor survey. J Clin Epidemiol. 1993;46(6):561–71.CrossRefPubMed

47.

Scribani M, Shelton J, Chapel D, Krupa N, Wyckoff L, Jenkins P. Comparison of bias resulting from two methods of self-reporting height and weight: a validation study. RSM Open. 2014;5(6):1–7.

48.

Klompas M, Cocoros NM, Menchaca JT, Erani D, Hafer E, Herrick B, et al. State and local chronic disease surveillance using electronic health record systems. Am J Public Health. 2017;107(9):1406–12. https://doi.org/10.2105/ajph.2017.303874.CrossRefPubMedPubMedCentral

49.

Fewell Z, Davey Smith G, Sterne JAC. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol. 2007;166(6):646–55. https://doi.org/10.1093/aje/kwm165.CrossRefPubMed

Title: Impact of rurality on diabetes screening in the US
Authors: Phoebe Tran
Lam Tran
Liem Tran
Publication date: 01-12-2019
Publisher: BioMed Central
Published in: BMC Public Health / Issue 1/2019
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-019-7491-9

At a glance: The STEP trials

Springer Medicine

Impact of rurality on diabetes screening in the US

Abstract

Background

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2019

A study protocol to determine the association between lifetime lead exposure and violent criminal behaviour in young males in conflict with the law

Gender-based violence and engagement in biomedical HIV prevention, care and treatment: a scoping review

The measurement scale of resilience among family caregivers of children with cancer: a psychometric evaluation

La Vida Buena (The Good Life) evaluation: a quasi experimental intervention of a community health worker-led family-based childhood obesity program for Latino children 5–8 years of age on the US-Mexico border

Co-morbidities of mental disorders and chronic physical diseases in developing and emerging countries: a meta-analysis

Effect of diet with or without exercise on abdominal fat in postmenopausal women – a randomised trial