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      Diabetes Knowledge, Behaviors, and Perceptions of Risk in Rural West Virginia Counties

      research-article
      , PhD, CHES, FASHA, , DO, , PT, DPT, OCS , PharmD, BCPS, BCGP, CDE , MPH, , EdD
      Journal of Appalachian Health
      The University of Kentucky
      Appalachia, diabetes risk factors, community screening

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          Abstract

          Introduction

          A little less than half of American adults have diabetes or pre-diabetes. In 2016, West Virginia (WV) had the highest percentage (15.2%) of adults with diagnosed diabetes in the U.S.

          Purpose

          In partnership with the Health Sciences and Technology Academy (HSTA), a cross-sectional study was preformed to assess knowledge, behaviors, and perceptions of diabetes risk.

          Methods

          Data was collected by trained HSTA students and teachers who lived in rural counties in WV. Information was assessed using validated surveys, and HbA1c was obtained by utilizing professional point-of-care (Bayer) kits.

          Results

          Mean age and Body Mass Index (BMI) was 36.11±17.86 years and 27.80±6.09 kg/m 2, respectively. More than half of the participants had a family history of diabetes (58.8%) and hypertension (60.2%), and a majority had elevated BMI (65.9%). However, only 29.2% rated their future risk for diabetes as moderate to high. Eighty percent (80%) had an inadequate amount of weekly exercise, and 36% had lower quality of diet. Overall, dietary quality and diabetes knowledge was associated with a low to moderate diabetes risk score; risk score positively correlated with higher HbA1c (r=0.439, P<.001). Participants’ HbA1c, perceived future risk of diabetes and family history of diabetes emerged as significant predictors of diabetes risk in the regression model, controlling for health behavior and diabetes knowledge.

          Implications

          HbA1c, perceived future risk of diabetes and family history of diabetes may be the best predictors of developing diabetes in the future and, therefore, are important to assess during community screening. Perception of diabetes risk is lower than actual diabetes risk in WV.

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          Most cited references22

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          Economic Costs of Diabetes in the U.S. in 2017

          (2018)
          OBJECTIVE This study updates previous estimates of the economic burden of diagnosed diabetes and quantifies the increased health resource use and lost productivity associated with diabetes in 2017. RESEARCH DESIGN AND METHODS We use a prevalence-based approach that combines the demographics of the U.S. population in 2017 with diabetes prevalence, epidemiological data, health care cost, and economic data into a Cost of Diabetes Model. Health resource use and associated medical costs are analyzed by age, sex, race/ethnicity, insurance coverage, medical condition, and health service category. Data sources include national surveys, Medicare standard analytical files, and one of the largest claims databases for the commercially insured population in the U.S. RESULTS The total estimated cost of diagnosed diabetes in 2017 is $327 billion, including $237 billion in direct medical costs and $90 billion in reduced productivity. For the cost categories analyzed, care for people with diagnosed diabetes accounts for 1 in 4 health care dollars in the U.S., and more than half of that expenditure is directly attributable to diabetes. People with diagnosed diabetes incur average medical expenditures of ∼$16,750 per year, of which ∼$9,600 is attributed to diabetes. People with diagnosed diabetes, on average, have medical expenditures ∼2.3 times higher than what expenditures would be in the absence of diabetes. Indirect costs include increased absenteeism ($3.3 billion) and reduced productivity while at work ($26.9 billion) for the employed population, reduced productivity for those not in the labor force ($2.3 billion), inability to work because of disease-related disability ($37.5 billion), and lost productivity due to 277,000 premature deaths attributed to diabetes ($19.9 billion). CONCLUSIONS After adjusting for inflation, economic costs of diabetes increased by 26% from 2012 to 2017 due to the increased prevalence of diabetes and the increased cost per person with diabetes. The growth in diabetes prevalence and medical costs is primarily among the population aged 65 years and older, contributing to a growing economic cost to the Medicare program. The estimates in this article highlight the substantial financial burden that diabetes imposes on society, in addition to intangible costs from pain and suffering, resources from care provided by nonpaid caregivers, and costs associated with undiagnosed diabetes.
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            Prediabetes: a high-risk state for diabetes development

            Prediabetes (intermediate hyperglycaemia) is a high-risk state for diabetes that is defined by glycaemic variables that are higher than normal, but lower than diabetes thresholds. 5-10% of people per year with prediabetes will progress to diabetes, with the same proportion converting back to normoglycaemia. Prevalence of prediabetes is increasing worldwide and experts have projected that more than 470 million people will have prediabetes by 2030. Prediabetes is associated with the simultaneous presence of insulin resistance and β-cell dysfunction-abnormalities that start before glucose changes are detectable. Observational evidence shows associations between prediabetes and early forms of nephropathy, chronic kidney disease, small fibre neuropathy, diabetic retinopathy, and increased risk of macrovascular disease. Multifactorial risk scores using non-invasive measures and blood-based metabolic traits, in addition to glycaemic values, could optimise estimation of diabetes risk. For prediabetic individuals, lifestyle modification is the cornerstone of diabetes prevention, with evidence of a 40-70% relative-risk reduction. Accumulating data also show potential benefits from pharmacotherapy. Copyright © 2012 Elsevier Ltd. All rights reserved.
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              The diabetes risk score: a practical tool to predict type 2 diabetes risk.

              Interventions to prevent type 2 diabetes should be directed toward individuals at increased risk for the disease. To identify such individuals without laboratory tests, we developed the Diabetes Risk Score. A random population sample of 35- to 64-year-old men and women with no antidiabetic drug treatment at baseline were followed for 10 years. New cases of drug-treated type 2 diabetes were ascertained from the National Drug Registry. Multivariate logistic regression model coefficients were used to assign each variable category a score. The Diabetes Risk Score was composed as the sum of these individual scores. The validity of the score was tested in an independent population survey performed in 1992 with prospective follow-up for 5 years. Age, BMI, waist circumference, history of antihypertensive drug treatment and high blood glucose, physical activity, and daily consumption of fruits, berries, or vegetables were selected as categorical variables. Complete baseline risk data were found in 4435 subjects with 182 incident cases of diabetes. The Diabetes Risk Score value varied from 0 to 20. To predict drug-treated diabetes, the score value >or=9 had sensitivity of 0.78 and 0.81, specificity of 0.77 and 0.76, and positive predictive value of 0.13 and 0.05 in the 1987 and 1992 cohorts, respectively. The Diabetes Risk Score is a simple, fast, inexpensive, noninvasive, and reliable tool to identify individuals at high risk for type 2 diabetes.
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                Author and article information

                Journal
                J Appalach Health
                J Appalach Health
                Journal of Appalachian Health
                The University of Kentucky
                2641-7804
                2021
                25 July 2021
                : 3
                : 3
                : 51-67
                Affiliations
                West Virginia University
                Cleveland Clinic Akron General Medical Center
                West Virginia University
                Article
                jah-3-3-51
                10.13023/jah.0303.05
                9192115
                35770034
                e3378c44-35e9-42eb-95ed-3711a394a7d3
                Copyright © 2021 Ranjita Misra, Sara Farjo, Renee McGinnis, Megan Adelman Elavsky, Summer Kuhn, and Catherine Morton-McSwain

                This work is licensed under a Creative Commons Attribution 4.0 License.

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                appalachia,diabetes risk factors,community screening

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