Diabetes and obesity are highly prevalent among hospitalized patients with coronavirus disease 2019 (COVID-19), but little is known about their contributions to early COVID-19 outcomes. We tested the hypothesis that diabetes is a risk factor for poor early outcomes, after adjustment for obesity, among a cohort of patients hospitalized with COVID-19. RESEARCH DESIGN AND METHODS We used data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry of patients hospitalized with COVID-19 between 11 March 2020 and 30 April 2020. Primary outcomes were admission to the intensive care unit (ICU), need for mechanical ventilation, and death within 14 days of presentation to care. Logistic regression models were adjusted for demographic characteristics, obesity, and relevant comorbidities. RESULTS Among 450 patients, 178 (39.6%) had diabetesdmostly type 2 diabetes. Among patients with diabetes versus patients without diabetes, a higher proportion was admitted to the ICU (42.1% vs. 29.8%, respectively, P 5 0.007), required mechanical ventilation (37.1% vs. 23.2%, P 5 0.001), and died (15.9% vs. 7.9%, P 5 0.009). In multivariable logistic regression models, diabetes was associated with greater odds of ICU admission (odds ratio 1.59 [95% CI 1.01-2.52]), mechanical ventilation (1.97 [1.21-3.20]), and death (2.02 [1.01-4.03]) at 14 days. Obesity was associated with greater odds of ICU admission (2.16 [1.20-3.88]) and mechanical ventilation (2.13 [1.14-4.00]) but not with death. CONCLUSIONS Among hospitalized patients with COVID-19, diabetes was associated with poor early outcomes, after adjustment for obesity. These findings can help inform patient-centered care decision making for people with diabetes at risk for COVID-19. Diabetes is one of the most common chronic conditions in the U.S., currently estimated to affect 34.2 million people (10.5% of the U.S. population) (1). In addition to the well-documented adverse health outcomes associated with this disease, diabetes has also emerged as a commonly reported risk factor among people hospitalized with coronavirus disease 2019 (COVID-19), caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (2-7). Following initial reports of COVID-19 in Washington state (January 2020) (8), COVID-19 has spread rapidly and resulted in .3.0 million cases and nearly 135,000 deaths in the U.S.

Background Approximately 80% of the 463 million adults worldwide with diabetes live in low-income and middle-income countries (LMICs). A major obstacle to designing evidence-based policies to improve diabetes outcomes in LMICs is the scarce availability of nationally representative data on the current patterns of treatment coverage. The objectives of this study were to estimate the proportion of adults with diabetes in LMICs who receive coverage of recommended pharmacological and non-pharmacological diabetes treatment; and to describe country-level and individual-level characteristics that are associated with treatment. Methods We did a cross-sectional analysis of pooled, individual data from 55 nationally representative surveys in LMICs. Our primary outcome of self-reported diabetes treatment coverage was based on population-level monitoring indicators recommended in the 2020 WHO Package of Essential Noncommunicable Disease Interventions. Surveys were included if they were done in 2008 or after in an LMIC, as classified by the World Bank in the year the survey was done; were nationally representative; had individual-level data; contained a diabetes biomarker (fasting glucose, random glucose, or glycated haemoglobin); and had data on one or more diabetes treatments. Our sample included non-pregnant individuals with an available diabetes biomarker who were at least 25 years of age. We assessed coverage of three pharmacological and three non-pharmacological treatments among people with diabetes. At the country level, we estimated the proportion of individuals reporting coverage by per-capita gross national income and geographical region. At the individual level, we used logistic regression models to assess coverage along several key individual characteristics including sex, age, body-mass index, wealth quintile, and educational attainment. In the primary analysis, we scaled sample weights such that countries were weighted equally. Findings The final pooled sample from the 55 LMICs included 680 102 total individuals and 37 094 individuals with diabetes. Using equal weights for each country, diabetes prevalence was 9•0% (95% CI 8•7-9•4), with 43•9% (41•9-45•9) reporting a previous diabetes diagnosis. Overall, 4•6% (3•9-5•4) of individuals with diabetes selfreported meeting need for all treatments recommended for them. Coverage of glucose-lowering medication was 50•5% (48•6-52•5); antihypertensive medication was 41•3% (39•3-43•3); cholesterol-lowering medication was 6•3% (5•5-7•2); diet counselling was 32•2% (30•7-33•7); exercise counselling was 28•2% (26•6-29•8); and weight-loss counselling was 31•5% (29•3-33•7). Countries at higher-income levels tended to have greater coverage. Female sex and higher age, body-mass index, educational attainment, and household wealth were also associated with greater coverage. Interpretation Fewer than one in ten people with diabetes in LMICs receive coverage of guideline-based comprehensive diabetes treatment. Scaling up the capacity of health systems to deliver treatment not only to...

Diabetes is a rapidly growing health problem in low-and middle-income countries (LMICs), but empirical data on its prevalence and relationship to socioeconomic status are scarce. We estimated diabetes prevalence and the subset with undiagnosed diabetes in 29 LMICs and evaluated the relationship of education, household wealth, and BMI with diabetes risk. RESEARCH DESIGN AND METHODSWe pooled individual-level data from 29 nationally representative surveys conducted between 2008 and 2016, totaling 588,574 participants aged ‡25 years. Diabetes prevalence and the subset with undiagnosed diabetes was calculated overall and by country, World Bank income group (WBIG), and geographic region. Multivariable Poisson regression models were used to estimate relative risk (RR). RESULTSOverall, prevalence of diabetes in 29 LMICs was 7.5% (95% CI 7.1-8.0) and of undiagnosed diabetes 4.9% (4.6-5.3). Diabetes prevalence increased with increasing WBIG: countries with low-income economies (LICs) 6.7% (5.5-8.1), lowermiddle-income economies (LMIs) 7.1% (6.6-7.6), and upper-middle-income economies (UMIs) 8.2% (7.5-9.0). Compared with no formal education, greater educational attainment was associated with an increased risk of diabetes across WBIGs, after adjusting for BMI (

Countries were categorized according to the NCD Risk Factor Collaboration regions. 1 1. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants.

Diabetes is an important risk factor for severe coronavirus disease 2019 (COVID-19), but little is known about the marginal effect of additional risk factors for severe COVID-19 among individuals with diabetes. We tested the hypothesis that sociodemographic, access to health care, and presentation to care characteristics among individuals with diabetes in Mexico confer an additional risk of hospitalization with COVID-19. RESEARCH DESIGN AND METHODS We conducted a cross-sectional study using public data from the General Directorate of Epidemiology of the Mexican Ministry of Health. We included individuals with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 between 1 March and 31 July 2020. The primary outcome was the predicted probability of hospitalization, inclusive of 8.5% of patients who required intensive care unit admission. RESULTS Among 373,963 adults with COVID-19, 16.1% (95% CI 16.0-16.3) self-reported diabetes. The predicted probability of hospitalization was 38.4% (37.6-39.2) for patients with diabetes only and 42.9% (42.2-43.7) for patients with diabetes and one or more comorbidities (obesity, hypertension, cardiovascular disease, and chronic kidney disease). High municipality-level of social deprivation and low statelevel health care resources were associated with a 9.5% (6.3-12.7) and 17.5% (14.5-20.4) increased probability of hospitalization among patients with diabetes, respectively. In age-, sex-, and comorbidity-adjusted models, living in a context of high social vulnerability and low health care resources was associated with the highest predicted probability of hospitalization. CONCLUSIONS Social vulnerability contributes considerably to the probability of hospitalization among individuals with COVID-19 and diabetes with associated comorbidities. These findings can inform mitigation strategies for populations at the highest risk of severe COVID-19.

Background Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care. Methods and findings We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases (‘NCD readiness indicators’ from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08–3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09–4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02–1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06–1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11–1.32], p < 0.001) per 1,000 people were positively associated with being diagnosed and, similarly, with being treated; and the number of physicians was additionally associated with being controlled (OR, 1.12 [95% CI 1.01–1.23], p = 0.03). For diabetes, no positive associations were seen between NCD readiness indicators and outcomes. There was no association between country development, health service finance, or health service performance and readiness indicators and any outcome, apart from GDP (OR, 1.70 [95% CI 1.12–2.59], p = 0.01), HDI (OR, 1.21 [95% CI 1.01–1.44], p = 0.04), and number of physicians per 1,000 people (OR, 1.28 [95% CI 1.09–1.51], p = 0.003), which were associated with being diagnosed. Six countries had data on cascades of care and nationwide-level data on facility preparedness. Of the 27 associations tested between facility preparedness indicators and outcomes, the only association that was significant was having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01–1.81], p = 0.04). The main limitation was use of blood pressure measurement on a single occasion to diagnose hypertension and a single blood glucose measurement to diagnose diabetes. Conclusion In this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries’ preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care.

Background Obesity has been linked to severe clinical outcomes among people who are hospitalized with COVID-19. We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). Methods We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with PCR-confirmed SARS-CoV-2 infection from March 11 - May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from CT scans during or prior to the hospital admission. VAT quantification took an average 2±0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm2 and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI and diabetes status. Results 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared to those with low VAT (p&lt;0.005), especially in those with BMI &lt;30 kg/m 2 (p&lt;0.005). In multivariable models, the aHR for high vs. low VAT was unchanged [aHR 1.97 (1.24 – 3.09)], whereas BMI was no longer significant [aHR for obese vs. normal BMI 1.14 (0.71 – 1.82)]. Conclusions High VAT is associated with a greater risk of severe disease or death in COVID-19, and can offer more precise information to risk stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.

Objective To describe the temporal and geographical patterns of the continuum of maternal health care in Mexico, as well as the sociodemographic characteristics that affect the likelihood of receiving this care. Methods We conducted a pooled cross-sectional analysis using the 1997, 2009, 2014 and 2018 waves of the National Survey of Demographic Dynamics, collating sociodemographic and obstetric characteristics of 93 745 women aged 12–54 years at last delivery. We defined eight variables along the antenatal–postnatal continuum, both independently and conditionally. We used a pooled fixed-effects multivariable logistic model to determine the likelihood of receiving the continuum of care for various properties. We also mapped the quintiles of adjusted state-level absolute change in continuum of care coverage during 1994–2018. Findings We observed large absolute increases in the proportion of women receiving timely antenatal and postnatal care (from 48.9% to 88.2% and from 39.1% to 68.7%, respectively). In our conditional analysis, we found that the proportion of women receiving adequate antenatal care doubled over this period. We showed that having social security and a higher level of education is positively associated with receiving the continuum of care. We observed the largest relative increases in continuum of care coverage in Chiapas (181.5%) and Durango (160.6%), assigned human development index categories of low and medium, respectively. Conclusion Despite significant progress in coverage of the continuum of maternal health care, disparities remain. While ensuring progress towards achievement of the health-related sustainable development goal, government intervention must also target underserved populations.