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Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach

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  • Ifechukwude Obiamaka Okwechime
  • Shamarial Roberson
  • Agricola Odoi

Abstract

Background: Individuals with pre-diabetes and diabetes have increased risks of developing macro-vascular complications including heart disease and stroke; which are the leading causes of death globally. The objective of this study was to estimate the prevalence of pre-diabetes and diabetes, and to investigate their predictors among adults ≥18 years in Florida. Methods: Data covering the time period January-December 2013, were obtained from Florida’s Behavioral Risk Factor Surveillance System (BRFSS). Survey design of the study was declared using SVYSET statement of STATA 13.1. Descriptive analyses were performed to estimate the prevalence of pre-diabetes and diabetes. Predictors of pre-diabetes and diabetes were investigated using multinomial logistic regression model. Model goodness-of-fit was evaluated using both the multinomial goodness-of-fit test proposed by Fagerland, Hosmer, and Bofin, as well as, the Hosmer-Lemeshow’s goodness of fit test. Results: There were approximately 2,983 (7.3%) and 5,189 (12.1%) adults in Florida diagnosed with pre-diabetes and diabetes, respectively. Over half of the study respondents were white, married and over the age of 45 years while 36.4% reported being physically inactive, overweight (36.4%) or obese (26.4%), hypertensive (34.6%), hypercholesteremic (40.3%), and 26% were arthritic. Based on the final multivariable multinomial model, only being overweight (Relative Risk Ratio [RRR] = 1.85, 95% Confidence Interval [95% CI] = 1.41, 2.42), obese (RRR = 3.41, 95% CI = 2.61, 4.45), hypertensive (RRR = 1.69, 95% CI = 1.33, 2.15), hypercholesterolemic (RRR = 1.94, 95% CI = 1.55, 2.43), and arthritic (RRR = 1.24, 95% CI = 1.00, 1.55) had significant associations with pre-diabetes. However, more predictors had significant associations with diabetes and the strengths of associations tended to be higher than for the association with pre-diabetes. For instance, the relative risk ratios for the association between diabetes and being overweight (RRR = 2.00, 95% CI = 1.55, 2.57), or obese (RRR = 4.04, 95% CI = 3.22, 5.07), hypertensive (RRR = 2.66, 95% CI = 2.08, 3.41), hypercholesterolemic (RRR = 1.98, 95% CI = 1.61, 2.45) and arthritic (RRR = 1.28, 95% CI = 1.04, 1.58) were all further away from the null than their associations with pre-diabetes. Moreover, a number of variables such as age, income level, sex, and level of physical activity had significant association with diabetes but not pre-diabetes. The risk of diabetes increased with increasing age, lower income, in males, and with physical inactivity. Insufficient physical activity had no significant association with the risk of diabetes or pre-diabetes. Conclusions: There is evidence of differences in the strength of association of the predictors across levels of diabetes status (pre-diabetes and diabetes) among adults ≥18 years in Florida. It is important to monitor populations at high risk for pre-diabetes and diabetes, so as to help guide health programming decisions and resource allocations to control the condition.

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  • Ifechukwude Obiamaka Okwechime & Shamarial Roberson & Agricola Odoi, 2015. "Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0145781
    DOI: 10.1371/journal.pone.0145781
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    References listed on IDEAS

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    1. Morten W. Fagerland & David W. Hosmer, 2012. "A generalized Hosmer–Lemeshow goodness-of-fit test for multinomial logistic regression models," Stata Journal, StataCorp LP, vol. 12(3), pages 447-453, September.
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    3. Dinca-Panaitescu, Serban & Dinca-Panaitescu, Mihaela & Bryant, Toba & Daiski, Isolde & Pilkington, Beryl & Raphael, Dennis, 2011. "Diabetes prevalence and income: Results of the Canadian Community Health Survey," Health Policy, Elsevier, vol. 99(2), pages 116-123, February.
    4. Braveman, P.A. & Cubbin, C. & Egerter, S. & Williams, D.R. & Pamuk, E., 2010. "Socioeconomic disparities in health in the united States: What the patterns tell us," American Journal of Public Health, American Public Health Association, vol. 100(S1), pages 186-196.
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    1. Sapkota, Jeet Bahadur & Paudel, Damaru Ballabha & Neupane, Pramila & Thapa, Rajesh Bahadur, 2018. "Preference for Sex of Children Among Women in Nepal," MPRA Paper 106095, University Library of Munich, Germany.
    2. Ming Zhao & Hongbo Lin & Yanyan Yuan & Fuyan Wang & Yang Xi & Li Ming Wen & Peng Shen & Shizhong Bu, 2016. "Prevalence of Pre-Diabetes and Its Associated Risk Factors in Rural Areas of Ningbo, China," IJERPH, MDPI, vol. 13(8), pages 1-13, August.

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