Diabetes Preventive Care Research Articles

Diabetes Belt has lower efficiency in providing diabetes preventive care than surrounding counties

Diabetes Belt has lower efficiency in providing diabetes preventive care than surrounding counties

Health literacy, self-efficacy, self-care behaviors, and glycemic control among older adults with type 2 diabetes mellitus: a cross-sectional study in Thai communities

BackgroundProperly understanding the health information of people with type 2 diabetes mellitus (T2DM) is the basis for better risk factor management, which also positively affects their quality of life. The aim of this study was to investigate diabetes health literacy (HL), self-efficacy, and self-care behaviors associated with glycemic control among older adults with T2DM in northern Thai communities.MethodsA cross-sectional study was conducted among 414 older adults over the age of 60 diagnosed with T2DM. The study was conducted in Phayao Province from January to May 2022. Simple random sampling of the patient list for the Java Health Center Information System program was used. Questionnaires were used to collect data on diabetes HL, self-efficacy, and self-care behaviors. Blood samples were tested for estimated glomerular filtration rate (eGFR) and glycemic controls, such as fasting blood sugar (FBS) and glycated hemoglobin (HbA1c).ResultsThe mean age of participants was 67.1 years. FBS (mean ± SD = 108.5 ± 29.5 mg/dL) and HbA1c (mean ± SD = 6.6 ± 1.2%) levels were found to be abnormal in 50.5% (≥ 126 mg/dL) and 17.4% (≥ 6.5%) of the subjects, respectively. There was a strong correlation between HL and self-efficacy (r = 0.78), HL and self-care behaviors (r = 0.76), and self-efficacy and self-care behaviors (r = 0.84). The eGFR was significantly correlated with diabetes HL (r = 0.23), self-efficacy (r = 0.14), self-care behaviors (r = 0.16), and HbA1c (r = -0.16) scores. Linear regression after adjusting for sex, age, education, DM duration, smoking, and drinking alcohol showed that FBS level was inversely associated with diabetes HL (Beta = -0.21, R2 = 11.0%), self-efficacy (Beta = -0.43, R2 = 22.2%), and self-care behavior (Beta = -0.35, R2 = 17.8%), whereas HbA1C level was negatively associated with diabetes HL (Beta = -0.52, R2 = 23.8%), self-efficacy (Beta = -0.39, R2 = 19.1%), and self-care behavior (Beta = -0.42, R2 = 20.7%).ConclusionDiabetes HL was related to self-efficacy and self-care behaviors in elderly T2DM patients and was shown to influence their health, including glycemic control. These findings suggest that implementing HL programs to build competence in self-efficacy expectations is important for contributing to improvements in diabetes preventive care behaviors and HbA1c control.

Doubled-Up Households, Self-Management Behaviors, Diabetes Preventive Care Services, and Hospital Use in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) 2015-2020.

We investigated associations of living in a doubled-up household (i.e., adults living with adult children, other related adults, or other unrelated adults) with diabetes self-management behaviors, occurrence of diabetes preventive care services, and hospital use by Hispanic/Latino adults with diabetes. We analyzed data from the second clinical visit (2014-2017) through subsequent annual follow-up interviews completed through January 2020 of all participants with diabetes in the Hispanic Community Health Study/Study of Latinos. Multivariable regression was used to test associations between doubled-up status with diabetes self-management behaviors (i.e., checking blood glucose level, checking feet for sores), diabetes preventive care services done by a doctor (i.e., dilated-eye examination, feet checked, hemoglobin A1c measured, urine analysis for kidney function), and hospital use (i.e., emergency department [ED] visits and hospitalizations). Hispanic/Latino adults living doubled up were less likely to have their urine checked by a doctor for kidney disease compared with adults not in doubled-up households. Doubled-up status was not associated with diabetes self-management behaviors. Adults living doubled up in a household with other related adults had a 33% increased risk of ED visits compared with adults living doubled up in a household with adult children. Health care settings where Hispanic/Latino adults with diabetes receive trusted care should add housing characteristics such as doubled-up status to social-needs screening to identify residents in need of connecting with housing or social services and more targeted diabetes management services.

921-P: Diabetes Preventive Care for Medicare Beneficiaries: Comparing Efficiency between the Diabetes Belt and Surrounding Areas

Objective: Evidence-based preventive care services are crucial for people with diabetes to improve their health outcomes, but the utilization of the services among older adults is suboptimal. Our objective is to compare relative performance of the Diabetes Belt and surrounding counties in the uptake of preventive services among Medicare beneficiaries with diabetes given available health care resources in the counties. The Diabetes Belt includes 644 counties in the Appalachian and deep south areas of the US that have high diabetes prevalence. Methods: We used data envelopment analysis (DEA) to compute technical efficiency of counties for 2009-2015. Technical efficiency measures how many outputs a decision-making unit produces given a set of inputs. Inputs included the number of physicians and nurses per 1000 adults in the counties, and outputs included the number of foot exams, a1c tests, and microalbumin tests per 1000 Medicare beneficiaries with diabetes. The Diabetes Belt includes counties in the Appalachian and deep south areas of the US that have high diabetes prevalence. We performed t-tests to compare efficiency between the Diabetes Belt and surrounding counties. We excluded counties with 0 doctors or 0 nurses for >1 year in 2009-2015. Results: Of 1057 counties, 861 (496 in the Belt and 365 in surrounding areas) were included. During the study period, the average technical efficiency of the counties ranged from 79% to 83%, with only 2.- 3.4% of counties achieving 100% technical efficiency. Diabetes Belt counties had consistently lower efficiency than surrounding counties during the study period (p <0.05) . The mean efficiency was 78-81% in the Diabetes Belt and 81-85% in the surrounding counties. Discussion: Our results suggest that the degree of inefficiencies was higher in the Diabetes Belt compared to surrounding counties. Given relatively low available resources in these areas, more attention to improve diabetes preventive care is needed. Disclosure H.Kang: None. S.Kim: None. J.M.Lobo: n/a. M.Sohn: None. Funding National Institutes of Health (1R01DK113295-01A1)

Adverse childhood experiences, diabetes and associated conditions, preventive care practices and health care access: A population-based study

Our objective was to examine how Adverse Childhood Experiences (ACEs) are associated with diabetes mellitus, diabetes-related conditions, and preventive care practices. We used data from the Behavioral Risk Factor Surveillance System (BRFSS) 2009–2012, a cross-sectional, population-based survey, to assess ACEs, diabetes, and health care access in 179,375 adults. In those with diabetes (n = 21,007), we assessed the association of ACEs with myocardial infarction, stroke, and five Healthy People 2020 (HP2020) diabetes-related preventive-care objectives (n = 13,152). Healthcare access indicators included lack of a regular health care provider, insurance, and difficulty affording health care. Regression analyses adjusted for age, sex, and race. The adjusted odds ratio (AOR) of diabetes increased in a stepwise fashion by ACE exposure, ranging from 1.2 (95% CI 1.1–1.3) for 1 ACE to 1.7 (95% CI 1.6–1.9) for ≥4 ACEs, versus having no ACEs. In persons with diabetes, those with ≥4 ACEs had an elevated adjusted odds of myocardial infarction (AOR = 1.6, 95% CI 1.2–2.0) and stroke (AOR = 1.8, 95% CI 1.3–2.4), versus having no ACEs. ACEs were also associated with a reduction in the adjusted percent of HP2020 diabetes objectives met: 72.9% (95% CI 71.3–74.5) for those with no ACEs versus only 66.5% (95% CI 63.8–69.3%) for those with ≥4 ACEs (p = 0.0002). Finally, ACEs predicted worse health care access in a stepwise fashion for all indicators. In conclusion, ACEs are associated with greater prevalence of diabetes and associated disease conditions, and with meeting fewer HP2020 prevention goals. Implementing ACE screening and trauma-informed health care practices are thus recommended.

Are racial/ethnic minorities recently diagnosed with diabetes less likely than white individuals to receive guideline-directed diabetes preventive care?

BackgroundDiabetes mellitus has reached epidemic proportions in the United States. As the prevalence of diabetes continues to rise, the burden of disease is divided unevenly among different populations. Racial/ethnic disparities in diabetes care are pervasive, including the provision of care for prevention of complications. Prevention efforts should be focused on the time that immediately follows a diagnosis of diabetes. The aim of this study was to assess racial/ethnic differences in the receipt of guideline-directed diabetes care for complication prevention by individuals recently diagnosed with diabetes.MethodsWe used repeated cross-sections of individuals recently diagnosed with diabetes (within the past 5 years) from the National Health Interview Survey from 2011 to 2017. Multivariate regression was used to estimate the associations between race/ethnicity (non-Hispanic White, non-Hispanic Black and Hispanic) and guideline-directed process measures for prevention of diabetes complications (visits to an eye and foot specialist, and blood pressure and cholesterol checks by a health professional - each in the prior year). We assessed effect modification of these associations by socioeconomic status (SES).ResultsIn a sample of 7,341 participants, Hispanics had lower rates of having any insurance coverage (75.9 %) than Non-Hispanic Whites (93.2 %) and Blacks (88.1 %; p<0.001). After adjustment for demographics, total comorbidities, SES, and health insurance status, Hispanics were less likely to have an eye exam in the prior year (OR 0.80; (95 % CI 0.65-0.99); p=0.04) and a blood pressure check (OR 0.42; (95 % CI 0.28-0.65); p<0.001) compared to Non-Hispanic Whites. There was no significant effect modification of race/ethnicity by SES.ConclusionsHispanics recently diagnosed with diabetes were less likely to receive some indicators of guideline-directed care for the prevention of complications. Lack of insurance and SES may partially explain those differences. Future work should consider policy change and providers’ behaviors linked to racial/ethnic disparities in diabetes care.

313-OR: Disparities in Preventive Care Utilization among Patients with Diabetes in the Diabetes Belt and Surrounding Areas

Objective: Preventive care has been shown to reduce both the incidence and progression of diabetic complications. American Diabetes Association treatment guidelines recommend annual preventive care such as annual doctor visits and A1c tests. Our objective is to compare utilization rates of four annually recommended preventive services for persons with diabetes in the Diabetes Belt and those in the surrounding area. Methods: We used 2015 Medicare claims data to identify preventive service utilization using ICD9/10 and CPT/HCPCS codes. Diabetes Belt consists of 644 counties in the Appalachian region and southeastern part of US that had diabetes prevalence over 11% in 2008 compared to ~8.5% in the rest of the country. Surrounding counties include 118 counties that are adjacent but not contiguous to the Diabetes Belt counties. Results: Patients in the Belt had significantly lower utilization than those in surrounding counties for all four preventive services: foot exam, 81% vs. 88%; eye exam, 53% vs. 58%; microalbuminuria test, 31% vs. 34%; one or more A1c tests, 73% vs. 74%. After adjusting for age, sex, and race/ethnicity, patients in the Diabetes Belt had 38%, 18%, 16%, and 11% lower odds of using foot exams, microalbuminuria tests, eye exams, and A1c tests, respectively, compared to those in the surrounding counties (p for all comparisons &amp;lt; 0.001). Even after adjusting for socioeconomic, access-to-care factors, and service availability, patients in the Diabetes Belt had consistently lower rates of utilization for these four services than surrounding counties. Conclusion: Patients with diabetes in the Diabetes Belt are doubly jeopardized for diabetic complications because of higher prevalence diabetes and lower preventive care utilization than surrounding counties. Health policy makers may want to pay more attention to the Diabetes Belt in reducing the regional differences in the utilization of these services. Disclosure M. Sohn: None. H. Kang: None. T. L. Mcmurry: None. S. Kim: None. J. M. Lobo: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK113295)

Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis.

For patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from the 2010 Behavioral Risk Factor Surveillance System. 1112 US counties were analyzed. Cluster analysis was used to place counties into three similar groups in terms of economic wellbeing and population characteristics. Group 1 consisted of metropolitan counties with prosperous or comfortable economic levels. Group 2 mostly consisted of non-metropolitan areas between distress and mid-tier levels, while Group 3 were mostly prosperous or comfortable counties in metropolitan areas. We used data enveopement analysis to assess efficiencies within each group. The majority of counties had modest efficiency in providing diabetes preventive care; 36 counties (57.1%), 345 counties (61.1%), and 263 counties (54.3%) were inefficient (efficiency scores < 1) in Group 1, Group 2, and Group 3, respectively. For inefficient counties, foot and eye exams were often identified as sources of inefficiency. Available health professionals in some counties were not fully utilized to provide diabetes preventive care. Identifying benchmarking targets from counties with similar resources can help counties and policy makers develop actionable strategies to improve performance.

Identifying Bright Spot Counties for Appropriate Diabetes Preventive Care: A Geospatial, Positive Deviance Approach.

Positive deviance approaches, which have been used to identify and study high performers (bright spots) and translate their successes to poorer performers, offer great potential for chronic disease management. However, there are few examples of applying positive deviance approaches across different geographic contexts. Building on prior research that developed a new measure for appropriate diabetes preventive care (DMPrevCare) and identified priority counties for this strategy, we introduce a geospatial approach for identifying bright spot counties and case matching them to priority counties that need improvement. We used the Local Moran's I tool to identify DMPrevCare spatial outliers, which are counties with larger percentages of Medicare beneficiaries receiving appropriate diabetes preventive care (DMPrevCare) surrounded by counties with smaller percentages of Medicare beneficiaries receiving DMPrevCare. We define these spatial outliers as bright spots. The Robert Wood Johnson Foundation County Health Rankings Peer Counties tool was used to link bright spot counties to previously identified priority counties. We identified 25 bright spot counties throughout the southern and mountain western United States. Bright spot counties were linked to 45 priority counties, resulting in 23 peer (bright/priority) county groups. A geospatial approach was shown to be effective in identifying peer counties across the United States that had either poor or strong metrics related to DMPrevCare, but were otherwise similar in terms of demographics and socioeconomic characteristics. We describe a framework for the next steps in the positive deviance process, which identifies potential factors in bright spot counties that positively impact diabetes care and how they may be applied to their peer priority counties.

Diabetes Diagnosis and Control: Missed Opportunities to Improve Health : The 2018 Kelly West Award Lecture.

Diabetes is a prevalent condition in the U.S. and worldwide, with expanding impact over time as it affects progressively younger ages as well as older ages as people live longer. Costs of diabetes to those affected and to society as a whole continue to increase. Costs are realized through daily treatment regimens throughout life to control glycemia and other risk factors for complications as diabetes progresses, diabetes complications and disability and their treatments, health care visits and hospitalization, and as indirect costs via lower quality of life and lost productivity. Diagnosing diabetes is key to affording the opportunity to treat diabetes, and diabetes control is key to reducing the risk of complications. Yet the magnitude of undiagnosed diabetes and poor control of diabetes is large. And just as certain subgroups of the population are affected disproportionately by diabetes and diabetes complications, so are they affected disproportionately by undiagnosed diabetes and poor control. This review addresses the epidemiology of undiagnosed diabetes and diabetes control, largely covering their magnitude, demographic variation, trends over time, and predictors. For diabetes control, it focuses on control of A1C, blood pressure, and lipid levels, although there are many other facets of diabetes control and preventive care that also could be examined. The review is based predominantly on data from the National Health and Nutrition Examination Survey (NHANES), a U.S. health survey that includes both an interview and examination component that has been conducted continuously since 1999 and episodically for decades earlier. The interview elicits self-reported health responses pertaining to diabetes and other medical conditions and an examination that measures glycemic indicators, blood pressure, and lipids, which provide much of the material presented herein. Data from other studies are also presented and described.

Identifying Priority and "Bright-Spot" Counties for Diabetes Preventive Care in Appalachia: An Exploratory Analysis.

IntroductionType 2 diabetes mellitus (T2DM) prevalence and mortality in Appalachian counties is substantially higher when compared to non-Appalachian counties, although there is significant variation within Appalachia.PurposeThe objectives of this research were to identify low-performing (priority) and high-performing (bright spot) counties with respect to improving T2DM preventive care.MethodsUsing data from the Centers for Medicare and Medicaid (CMS), the Dartmouth Atlas of Health Care, and the Appalachia Regional Commission, conditional maps were created using county-level estimates for T2DM prevalence, mortality, and annual hemoglobin A1c (HbA1c) testing rates. Priority counties were identified using the following criteria: top 33rd percentile for T2DM mortality; top 33rd percentile for T2DM prevalence; bottom 50th percentile for A1c testing rates. Bright spot counties were identified as counties in the bottom 33rd percentile for T2DM mortality, the top 33rd percentile for T2DM prevalence; and the top 50th percentile for HbA1c testing rates.ResultsForty-one priority counties were identified (those with high T2DM mortality, high T2DM prevalence, and low HbA1c testing rates), which were located primarily in Central and North Central Appalachia; and 17 bright spot counties were identified (high T2DM prevalence, low T2DM mortality, and high HbA1c testing rates), which were scattered throughout Appalachia. Eight of the 17 bright spot counties were adjacent to priority counties.ImplicationsBy employing conditional mapping to T2DM, multiple variables can be summarized into a single, easily interpretable map. This could be valuable for T2DM-prevention programs seeking to prioritize diagnostic and intervention resources for the management of T2DM in Appalachia.

Diabetes Self-Management Education (DSME) and Diabetes Preventive Care Practices among People With and Without Vision Impairment

Objective: To examine whether participating in Diabetes Self-Management Education (DSME) was associated with increased diabetes preventive care practices among those with and without self-reported vision impairment (VI). Background: Over 30 million people in the United States have diagnosed diabetes. DSME is a vital component of care, facilitating the knowledge and skills needed to improve self-care for diabetes. Those with VI are more likely to have diabetes than those without, highlighting a need to examine DSME participation and preventive care practices among those with and without VI. Methods: We used data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS), a large ongoing landline and cellular telephone based survey. The question “Have you ever taken a course or class in how to manage your diabetes yourself?” was asked in 37 states plus the District of Columbia and Guam. In a logistic regression, we examined if the association between DSME and 9 preventive care practices was significantly modified by VI status. Results: Regardless of VI status, respondents with diabetes who participated in DSME had a greater predicted probability of engaging in each preventive care practice than those who did not. Compared to respondents with VI who did not participate in DSME, those with VI who participated in DSME had a significantly greater predicted probability of having administered a daily glucose check (83.7% vs. 59.3%) and having had an annual visit to a health care professional for their diabetes (95.3% vs. 82.1%). Conclusions: Participants with VI who received DSME have increased predicted probability of engaging in certain self-care practices compared to those with VI who did not receive DSME. This highlights the importance of people with diabetes and VI acquiring the appropriate tools to positively affect their own health. Disclosure S. Sekar: None. R.B. Gerzoff: None. J.B. Saaddine: None. M.M. Saunders: None.

Health Care Access and Receipt of Clinical Diabetes Preventive Care for Working-Age Adults With Diabetes in States With and Without Medicaid Expansion: Results from the 2013 and 2015 BRFSS.

This study aimed to assess whether the recent Medicaid expansion, as a natural experiment, was associated with better access to care and, as a consequence, better receipt of clinical diabetes care services. Data were from the Behavioral Risk Factor Surveillance System (BRFSS). The analytical sample included 20 708 working-age adults with diabetes aged 18 to 64 years from 22 states. The outcome variables included 4 measures of access to care and 4 measures of receipt of clinical diabetes care services. A difference-in-difference logistic regression model was used to compare changes in outcomes between respondents in Medicaid expansion and nonexpansion states. Data from the 2013 survey provided pre-Medicaid expansion information, and data from the 2015 survey provided postexpansion information. Analyses were conducted using Stata 13 using survey commands to account for the complex survey design of BRFSS. A significant increase was observed in health insurance coverage for people with diabetes from 2013 to 2015 (P < .05) in both Medicaid expansion and nonexpansion states, with a larger increase in the Medicaid expansion states. The Time by Medicaid expansion interaction term was significant for 2 measures of access to care: health insurance coverage (adjusted odds ratio [AOR] = 1.43, 95% confidence interval: 1.04-1.96) and having an annual checkup (AOR = 1.30, 95% confidence interval: 1.00-1.71). Respondents in expansion states were more likely to have a personal doctor and more likely to be able to afford a physician visit than those in nonexpansion states. The Time by Medicaid expansion was close to significance for one of the measures of clinical diabetes care: getting flu shots (AOR = 1.20, P = .08). Medicaid expansion did improve health care access but no significant improvement was found for receipt of clinical diabetes care for people with diabetes. Resources provided through Medicaid are vital for diabetes control and management.

Diabetes Preventive Care Practices in North Carolina, 2000-2015.

This analysis assessed trends in measures of diabetes preventive care overall and by race/ethnicity and socioeconomic status in the North Carolina Behavioral Risk Factor Surveillance System (2000–2015). We found increasing trends in 5 measures: diabetes self-management education (DSME), daily blood glucose self-monitoring, hemoglobin A1c tests, foot examinations, and flu shots. Non-Hispanic black and non-Hispanic white respondents showed increases in blood glucose self-monitoring, and a significant time-by-race interaction was observed for annual flu shots. Predisposing, enabling, and need factors were significantly associated with most measures. DSME was positively associated with 7 measures. Expanding access to health insurance and health care providers is key to improving diabetes management, with DSME being the gateway to optimal care.

Comparison of Diabetic Foot Care with Other Diabetic Preventive Care Services.

Diabetes is a major chronic disease with high morbidity and mortality. Diabetic preventive care services are essential in the management and outcome of the disease. More than other preventive diabetic care services, preventive care of diabetic retinopathy has been emphasized and recommended by practitioners and insurance companies. We investigated the status of preventive care in the diabetic population. Information was collected from 420 outpatients aged 30 to 80 years. The patients were divided into two groups: those with well-controlled blood sugar levels (hemoglobin A1c [HbA1c] level ≤7%) and those with uncontrolled blood sugar levels (HbA1c level >7%). Data analysis indicated that for both groups, 93% of the participants were seen for diabetic eye care at least once and 78% were getting an annual eye examination regularly. In the controlled and uncontrolled blood sugar groups, 26% and 32% of patients, respectively, had ever seen a nephrologist and 38% and 49%, respectively, had ever seen a cardiologist. In the controlled and uncontrolled blood sugar groups, 32% and 38% of patients, respectively, had visited a podiatric physician. For statistical analysis and comparison of results between the two groups, we applied the χ2 test and calculated 95% confidence intervals. There were some significant differences regarding the complications of diabetes mellitus and preventive care. There is a need for greater engagement by podiatric physicians and health-care providers to promote regular visits for the diabetic population to podiatric medical clinics.

Frequency and predictors of missed visits to primary care and eye care providers for annually recommended diabetes preventive care services over a two-year period among U.S. adults with diabetes

This paper examines the patterns of visits to primary care and eye care providers for annually recommended diabetes preventive care services over a two-year period in a sample of U.S. adults aged 18years and older with diabetes drawn from the 2008–2013 Medical Expenditure Panel Survey-Household Component (n=3982). The four-category outcome variable was defined based on the four possible combinations of whether an individual missed an annual visit to a primary care provider for preventive care services in at least one year over the two-year period and whether the individual missed an annual visit to an eye care provider in at least one year over this period. Descriptive statistics and a multinomial logistic regression model were estimated. 51.9% of the sample missed an annual visit to a primary care or eye care provider for preventive care services over a two-year period. Although 48.8% of the sample missed at least one annual visit to an eye care provider, 85.6% of these individuals did visit a primary care provider for preventive care services during the year they did not visit an eye care provider. This suggests that primary care-based screening for diabetic retinopathy could reach most individuals who do not receive regular eye care. Regression results particularly relevant for potential interventions were that missing annual visits was significantly more likely for individuals without health insurance or a usual source of healthcare and for those who were not receiving diabetes treatments and did not have or were not aware of having diabetes-related complications.

Role of Insurance Coverage on Diabetes Preventive Care

Minorities with diabetes are at an increased risk for low quality care and access to resource utilization, specifically health insurance coverage which often determines access to health care. The purpose of the study was to examine the relationship between insurance coverage and diabetes preventive care after adjusting for patient, physician care, and contextual characteristics. Study used data from the 2010 Medical Expenditure Panel Survey (MEPS). Patients represented a weighted total of 20,741,963 (n=2,182). The dependent variables included five measures of the recommended components of diabetes preventive care: receipt of A1C test, retinal eye exam, foot care, blood cholesterol check, and flu vaccination. The independent variables consisted of sociodemographic, physician care, and contextual characteristics. Receipt of each of the diabetes quality and preventive care measures was strongly associated with insurance coverage. Insured individuals were more likely to receive treatment than uninsured. However, the impact of insurance coverage was attenuated after adjusting for individual, physician care, and contextual characteristics. Physician care was the strongest predictor of diabetes preventive care. Our study identified the importance of health insurance coverage on diabetes preventive care. Physician care and contextual factors made a significant impact in receipt of diabetes preventive care.

Role of Insurance Coverage On Diabetes Preventive Care

Role of Insurance Coverage On Diabetes Preventive Care

U.S. Associated Pacific Islands Health Care Teams Chart a Course for Improved Health Systems: Implementation and Evaluation of a Non-communicable Disease Collaborative Model.

The burden of non-communicable disease (NCD) is increasing in the U.S. Associated Pacific Islands (USAPI). We describe the implementation and evaluation of a NCD Collaborative pilot, using local trainers, as an evidence-based strategy to systematically strengthen NCD health care quality and outcomes, focusing on diabetes preventive care across five health systems in the region.

Receipt of diabetes preventive services differs by insurance status at visit.

Lack of insurance is associated with suboptimal receipt of diabetes preventive care. One known reason for this is an access barrier to obtaining healthcare visits; however, little is known about whether insurance status is associated with differential rates of receipt of diabetes care during visits. To examine the association between health insurance and receipt of diabetes preventive care during an office visit. This retrospective cohort study used electronic health record and Medicaid data from 38 Oregon community health centers. Logistic regression was used to test the association between insurance and receipt of four diabetes services during an office visit among patients who were continuously uninsured (n=1,117); continuously insured (n=1,466); and discontinuously insured (n=336) in 2006-2007. Generalized estimating equations were used to account for within-patient correlation. Data were analyzed in 2013. Overall, continuously uninsured patients had lower odds of receiving services at visits when due, compared to those who were continuously insured (AOR=0.73, 95% CI=0.66, 0.80). Among the discontinuously insured, being uninsured at a visit was associated with lower odds of receipt of services due at that visit (AOR=0.77, 95% CI=0.64, 0.92) than being insured at a visit. Lack of insurance is associated with a lower probability of receiving recommended services that are due during a clinic visit. Thus, the association between being uninsured and receiving fewer preventive services may not be completely mediated by access to clinic visits.