In the United States, rural areas have a higher burden of type 2 diabetes (T2DM) compared to urban areas. However, there is limited information on risk factors and interventions that improve the primary prevention and management of T2DM in rural areas. To synthesize current knowledge on T2DM in rural areas and to guide healthcare providers and policy makers, we reviewed five scientific databases and the grey literature over the last decade (2010–2020). We described classification systems for rurality and the T2DM burden based on rurality and region (West, South, Midwest, and Northeast). We highlighted risk factors for T2DM in rural compared to urban areas, and summarized interventions to screen and manage T2DM based on opportunistic screening, T2DM self‐management, community‐based initiatives, as well as interventions targeting comorbidities and T2DM. Several studies identified the co‐existence of T2DM and depression/psychological symptoms, which could reduce adherence to non‐pharmacologic and pharmacologic management of T2DM. We highlighted the role of technology in education and counselling of patients with geographic and financial barriers to accessing care, which is exacerbated by the SARS‐CoV‐2 coronavirus disease‐19 pandemic. We identified knowledge gaps and next steps in improving T2DM care in rural areas. There is an urgent need for interventions tailored to rural areas given that rural Americans currently experience a disproportionate burden of T2DM and are encumbered by its associated morbidity, mortality, and loss in economic productivity.

Importance: US rural vs nonrural populations have striking disparities in diabetes care. Whether rurality contributes to disparities in diabetes mortality is unknown.Objective: To examine rates and trends in diabetes mortality based on county urbanization.Design, Setting, and Participants: In this observational, cross-sectional study, the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was searched from January 1, 1999, to December 31, 2018, for diabetes as a multiple cause and the underlying cause of death among residents aged 25 years or older in US counties. County urbanization was categorized as metro, medium-small, and rural. Weighted multiple linear regression models and jackknife resampling, with a 3-segment time component, were used. The models included exposures with up to 3-way interactions and were age standardized to the 2009-2010 population. The analyses were conducted from July 1, 2020, to February 1, 2022.Exposures: County urbanization (metro, medium-small, or rural), gender (men or women), age group (25-54, 55-74, or ≥75 years), and region (Midwest, Northeast, South, or West).Main Outcomes and Measures: Annual diabetes mortality rate per 100 000 people.Results: From 1999-2018, based on 4 022 238 309 person-years, diabetes was a multiple cause of death for 4 735 849 adults aged 25 years or older. As a multiple cause, diabetes mortality rates in 2017-2018 vs 1999-2000 were highest and unchanged in rural counties (157.2 [95% CI, 150.7-163.7] vs 154.1 [95% CI, 148.2-160.1];P = .49) but lower in medium-small counties (123.6 [95% CI, 119.6-127.6] vs 133.6 [95% CI, 128.4-138.8];P = .003) and urban counties (92.9 [95% CI, 90.5-95.3] vs 109.7 [95% CI, 105.2-114.1];P < .001). In 2017-2018 vs 1999-2000, mortality rates were higher in rural men (+18.2; 95% CI, 14.3-22.1) but lower in rural women (−14.0; 95% CI, −17.7 to −10.3) (P < .001 for both). In the 25- to 54-year age group, mortality rates in 2017-2018 vs 1999-2000 showed a greater increase in rural counties (+9.4; 95% CI, 8.6-10.2) compared with medium-small counties (+4.5; 95% CI, 4.0-5.0) and metro counties (+0.9; 95% CI, 0.4-1.4) (P < .001 for all). Of all regions and urbanization levels, the mortality rate in 2017-2018 vs 1999-2000 was higher only in the rural South (+13.8; 95% CI, 7.6-20.0;P < .001).Conclusions and Relevance: In this cross-sectional study, US rural counties had the highest overall diabetes mortality rate. The determinants of persistent rural disparities, in particular for rural men and for adults in the rural South, require investigation.

To evaluate the magnitude of the association between risk factors and premature myocardial infarction (MI) (men aged 18-55 years; women aged 18-65 years). We searched MEDLINE and other databases from inception through April 30, 2020, as well as bibliography of articles selected for data extraction. We selected observational studies reporting the magnitude of the association of at least 1 risk factor (demographic characteristics, lifestyle factors, clinical risk factors, or biomarkers) with premature MI and a control group. Pooled risk estimates (random effects) from all studies unadjusted and adjusted for risk factors were reported as summary odds ratios (ORs) with 95% CIs. From 35,320 articles of 12.7 million participants, we extracted data on 19 risk factors from 77 studies across 58 countries. Men had a higher risk of premature MI (OR, 2.39; 95% CI, 1.71 to 3.35) than did women. Family history of cardiac disease was associated with a higher risk of premature MI (OR, 2.67; 95% CI, 2.29 to 3.27). Major modifiable risk factors associated with higher risk were current smoking (OR, 4.34; 95% CI, 3.68 to 5.12 vs no/former), diabetes mellitus (OR, 3.54; 95% CI, 2.69 to 4.65), dyslipidemia (OR, 2.94; 95% CI, 1.76 to 4.91), and hypertension (OR, 2.85; 95% CI, 2.48 to 3.27). Higher body mass index carried higher risk (OR, 1.46; 95% CI, 1.24 to 1.71 for ≥25 kg/m2 vs <25 kg/m2). Biomarkers associated with 2- to 3-fold higher risk were total cholesterol levels greater than 200 mg/dL, triglyceride levels higher than 150 mg/dL, and high-density lipoprotein cholesterol levels less than 60 mg/dL (to convert to mmol/L, multiply by 0.0259). Major risk factors for premature MI are mostly amenable to patient, population, and policy level interventions. Mild elevations in body mass index and triglyceride levels were associated with higher risk, which has implications for the growing worldwide epidemic of cardiometabolic diseases.

Patients with COVID-19 infection requiring in-hospital care are frequently managed by Internal Medicine hospitalists, comprised of physicians, nurse practitioners and physician assistants. There is...

The Middle East and North Africa (MENA) region has a high burden of morbidity and mortality due to premature (≤55 years in men; ≤65 years in women) myocardial infarction (MI) and acute coronary syndrome (ACS). Despite this, the prevalence of risk factors in patients presenting with premature MI or ACS is incompletely described. We compared lifestyle, clinical risk factors, and biomarkers associated with premature MI/ACS in the MENA region with selected non-MENA high-income countries. We identified English-language, peer-reviewed publications through PubMed (up to March 2018). We used the World Bank classification system to categorize countries. Patients with premature MI/ACS in the MENA region had a higher prevalence of smoking than older patients with MI/ACS but a lower prevalence of diabetes, hypertension, and dyslipidemia. Men with premature MI/ACS had a higher prevalence of smoking than women but a lower prevalence of diabetes and hypertension. The MENA region had sparse data on lifestyle, diet, psychological stress, and physical activity. To address these knowledge gaps, we initiated the ongoing Gulf Population Risks and Epidemiology of Vascular Events and Treatment (Gulf PREVENT) case–control study to improve primary and secondary prevention of premature MI in the United Arab Emirates, a high-income country in the MENA region.

Introduction Premature myocardial infarction (MI) generally refers to MI in men ≤55 years or women ≤65 years. Premature MI is a major contributor to cardiovascular disease (CVD), which claimed 17.6 million lives globally in 2016. Reducing premature MI and CVD is a key priority for all nations; however, there is sparse synthesis of information on risk factors associated with premature MI. To address this knowledge gap, we are conducting a systematic review to describe the association between risk factors (demographics, lifestyle factors and biomarkers) and premature MI. Methods and analysis The following databases were searched from inception to June 2018: CENTRAL, CINAHL, Clinical Trials, EMBASE and MEDLINE. We will include original research articles (case–control, cohort and cross-sectional studies) that report a quantitative relationship between at least one risk factor and premature MI. Two investigators will use predetermined selection criteria and independently screen articles based on title and abstract (primary screening). Articles that meet selection criteria will undergo full-text screening based on criteria used for primary screening (secondary screening). Data will be extracted using predetermined data extraction forms. The Newcastle-Ottawa Scale for case–control and cohort studies will be used to evaluate the risk of bias and will be adapted for cross-sectional studies. Whenever feasible, data will be summarised into a random-effects meta-analysis. Ethics and dissemination To our knowledge, this will be the first study to synthesise results on the relationship between risk factors and premature MI. These findings will inform healthcare providers on factors associated with risk of premature MI and potentially improve primary prevention efforts by guiding development of interventions. These findings will be summarised and presented at conferences and through publication in a peer-reviewed journal. PROSPERO registration number CRD42018076862.