Health Insurance Is Associated with Decreased Odds for Undiagnosed Prediabetes and Type 2 Diabetes in American Adults

Social and Structural Determinants of Cardiovascular Complications of Diabetes

PURPOSE OF REVIEW

Cardiovascular disease (CVD) is the leading cause of mortality in people who have diabetes. Racial and ethnic minorities with diabetes have suboptimal management of cardiovascular risk factors, leading to higher mortality. Social and structural determinants of health are external factors that influence an individual's ability to choose positive health behaviors. In this review, we will discuss cardiovascular complications in people who have diabetes and their relationship to social determinants of health (SDOH).

RECENT FINDINGS

Recent innovations in diabetes treatment, including new devices and medications, have improved care and survival. However, disparities in the availability of these treatments to racial and ethnic minorities may contribute to continued inequities in CVD outcomes. Racial/ethnic disparities in CVD relate to inequities in economic opportunity, education and health literacy, neighborhoods and social cohesion, and health care access and quality driven by structural racism.

Health insurance status, lifestyle choices and the presence of non-communicable diseases: a systematic review

BACKGROUND

Although health insurance (HI) has effectively mitigated healthcare financial burdens, its contribution to healthy lifestyle choices and the presence of non-communicable diseases (NCDs) is not well established. We aimed to systematically review the existing evidence on the effect of HI on healthy lifestyle choices and NCDs.

METHODS

A systematic review was conducted across PubMed, Medline, Embase, Cochrane Library and CINAHLComplet@EBSCOhost from inception until 30 September 2022, capturing studies that reported the effect of HI on healthy lifestyle and NCDs. A narrative synthesis of the studies was done. The review concluded both longitudinal and cross-sectional studies. A critical appraisal checklist for survey-based studies and the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies were used for the quality assessment.

RESULT

Twenty-four studies met the inclusion criteria. HI was associated with the propensity to engage in physical activities (6/11 studies), consume healthy diets (4/7 studies), not to smoke (5/11 studies) or take alcohol (5/10 studies). Six (of nine) studies showed that HI coverage was associated with a lowered prevalence of NCDs.

CONCLUSION

This evidence suggests that HI is beneficial. More reports showed that it propitiated a healthy lifestyle and was associated with a reduced prevalence of NCDs.

National Prevalence of Diabetes and Barriers to Care Among U.S. Farmworkers and Association With Migrant Worker Status

OBJECTIVE

To quantify the prevalence of diabetes and barriers to care among U.S. migrant farmworkers (i.e., those who travel from their permanent residence for seasonal farmwork).

RESEARCH DESIGN AND METHODS

Age-adjusted prevalence of self-reported diabetes and barriers to care were calculated among adult U.S. farmworkers from 2008 to 2017 National Agricultural Workers Surveys.

RESULTS

Among 16,913 farmworkers, 30.7% reported one or more barriers to care, most often due to cost. Age-adjusted self-reported prevalence of diabetes was 13.51% (95% CI 10.0-17.1) among migrant farmworkers and 10.8% (95% CI 9.0-12.6) among nonmigrant farmworkers with access to health care. Migrant farmworkers without recent health care had 83% lower odds of reporting known diabetes (adjusted odds ratio 0.17; 95% CI 0.06-0.54) compared with nonmigrant farmworkers, likely because of poor health care access and/or a healthy worker effect.

CONCLUSIONS

Many migrant farmworkers face barriers to care, which may lead to significant underdiagnosis of diabetes in this vulnerable population.

A new metabolic syndrome prediction model for self-evaluation as a primary screening tool in an apparently MetS-free population

BACKGROUND

Metabolic syndrome (MetS) is a growing global public health concern associated with increased morbidity and mortality. The study aimed to establish a simple self-evaluated prediction model to identify MetS.

METHODS

A cross-sectional study based on the American National Health and Nutrition Examination Survey database was performed. Participants aged ≥20 in the 2009-2018 surveys, with no current pregnancy or major morbidities, were included. The model was built with data from 2009 to 2016 and validated using 2017-2018 data. MetS was defined according to AHA/NHLBI guidelines. Multivariable logistic regression was applied to build a prediction model. The area under the receiver operating characteristic curve (AUC) was used to assess the discrimination ability and the maximal Youden's index was used to identify the optimal cut-off value.

RESULTS

The study included 4245 individuals (median age 37, IQR 28-49, 51.8% females) in the training group and 911 individuals (median age 37, IQR 28-52, 52.5% females) in the validation group. Older age, male gender, non-Black race, no postsecondary education, and higher BMI were significantly associated with increased risk of MetS. The final model included age, gender, race, education, and BMI, and showed good discrimination ability (AUC = 0.810, 95% CI 0.793-0.827, sensitivity 80.4%, specificity 66.2%, positive likelihood ratio 2.379, negative likelihood ratio 0.296, PPV 59.6% and NPV 84.5%).

CONCLUSION

A new model for self-evaluation may serve as a primary, easy-to-use screening tool to identify MetS in an apparently MetS-free population. A simple application may serve for primary and secondary prevention, thus enabling risk reduction in the development of cardiovascular morbidity and health expenditure.

Predictors of Undiagnosed Diabetes among Middle-Aged and Seniors in China: Application of Andersen's Behavioral Model

Undiagnosed diabetes is a threat to public health. This study aims to identify potential variables related to undiagnosed diabetes using Andersen’s behavioral model. Baseline data including blood test data from the China Health and Retirement Longitudinal Study (CHARLS) were adopted. First, we constructed health service related variables based on Andersen model. Second, univariate analysis and multiple logistic regression were used to analyze the relations of variables to undiagnosed diabetes. The strength of relationships was presented by odds ratios (ORs) and 95% confidence intervals (CIs). Finally, the prediction of multiple logistic regression model was assessed using the Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC). According to diagnosis standards, 1234 respondents had diabetes, among which 560 were undiagnosed and 674 were previously diagnosed. Further analysis showed that the following variables were significantly associated with undiagnosed diabetes: age as the predisposing factor; medical insurance, residential places and geographical regions as enabling factors; having other chronic diseases and self-perceived health status as need factors. Moreover, the prediction of regression model was assessed well in the form of ROC and AUC. Andersen model provided a theoretical framework for detecting variables of health service utilization, which may not only explain the undiagnosed reasons but also provide clues for policy-makers to balance health services among diverse social groups in China.