Diabetes-Related Excess Mortality in Mexico: A Comparative Analysis of National Death Registries Between 2017–2019 and 2020

OBJECTIVE To estimate diabetes-related mortality in Mexico in 2020 compared with 2017–2019 after the onset of the coronavirus disease 2019 (COVID-19) pandemic. RESEARCH DESIGN AND METHODS This retrospective, state-level study used national death registries of Mexican adults aged ≥20 years for the 2017–2020 period. Diabetes-related death was defined using ICD-10 codes listing diabetes as the primary cause of death, excluding certificates with COVID-19 as the primary cause of death. Spatial and negative binomial regression models were used to characterize the geographic distribution and sociodemographic and epidemiologic correlates of diabetes-related excess mortality, estimated as increases in diabetes-related mortality in 2020 compared with average 2017–2019 rates. RESULTS We identified 148,437 diabetes-related deaths in 2020 (177 per 100,000 inhabitants) vs. an average of 101,496 deaths in 2017–2019 (125 per 100,000 inhabitants). In-hospital diabetes-related deaths decreased by 17.8% in 2020 versus 2017–2019, whereas out-of-hospital deaths increased by 89.4%. Most deaths were attributable to type 2 diabetes (130 per 100,000 inhabitants). Compared with 2018–2019 data, hyperglycemic hyperosmolar state and diabetic ketoacidosis were the two contributing causes with the highest increase in mortality (128% and 116% increase, respectively). Diabetes-related excess mortality clustered in southern Mexico and was highest in states with higher social lag, rates of COVID-19 hospitalization, and prevalence of HbA1c ≥7.5%. CONCLUSIONS Diabetes-related deaths increased among Mexican adults by 41.6% in 2020 after the onset of the COVID-19 pandemic, occurred disproportionately outside the hospital, and were largely attributable to type 2 diabetes and hyperglycemic emergencies. Disruptions in diabetes care and strained hospital capacity may have contributed to diabetes-related excess mortality in Mexico during 2020.

Diabetes-Related Excess Mortality in Mexico: A Comparative Analysis of National Death Registries Between 2017-2019 and 2020.

OBJECTIVE: To estimate diabetes-related mortality in Mexico in 2020 compared with 2017-2019 after the onset of the coronavirus disease 2019 (COVID-19) pandemic. RESEARCH DESIGN AND METHODS: This retrospective, state-level study used national death registries of Mexican adults aged ≥20 years for the 2017-2020 period. Diabetes-related death was defined using ICD-10 codes listing diabetes as the primary cause of death, excluding certificates with COVID-19 as the primary cause of death. Spatial and negative binomial regression models were used to characterize the geographic distribution and sociodemographic and epidemiologic correlates of diabetes-related excess mortality, estimated as increases in diabetes-related mortality in 2020 compared with average 2017-2019 rates. RESULTS: We identified 148,437 diabetes-related deaths in 2020 (177 per 100,000 inhabitants) vs. an average of 101,496 deaths in 2017-2019 (125 per 100,000 inhabitants). In-hospital diabetes-related deaths decreased by 17.8% in 2020 versus 2017-2019, whereas out-of-hospital deaths increased by 89.4%. Most deaths were attributable to type 2 diabetes (130 per 100,000 inhabitants). Compared with 2018-2019 data, hyperglycemic hyperosmolar state and diabetic ketoacidosis were the two contributing causes with the highest increase in mortality (128% and 116% increase, respectively). Diabetes-related excess mortality clustered in southern Mexico and was highest in states with higher social lag, rates of COVID-19 hospitalization, and prevalence of HbA1c ≥7.5%. CONCLUSIONS: Diabetes-related deaths increased among Mexican adults by 41.6% in 2020 after the onset of the COVID-19 pandemic, occurred disproportionately outside the hospital, and were largely attributable to type 2 diabetes and hyperglycemic emergencies. Disruptions in diabetes care and strained hospital capacity may have contributed to diabetes-related excess mortality in Mexico during 2020.

Diabetes mellitus as a risk factor for SARS-CoV-2 test positivity in Mexico: A propensity score matched study.

AIMS: We sought to investigate whether individuals with diabetes have a higher likelihood of testing positive for SARS-CoV-2, as a proxy for infection risk, than individuals without diabetes. METHODS: We conducted a cross-sectional study of publicly available data among a Mexican population, totaling 2,314,022 adults ≥ 18 years who underwent SARS-CoV-2 testing between March 1 and December 20, 2020. We used 1:1 nearest neighborhood propensity score matching by diabetes status to account for confounding among those with and without diabetes. RESULTS: In the overall study population, 1,057,779 (45.7%) individuals tested positive for SARS-CoV-2 and 270,486 (11.7%) self-reported diabetes. After propensity score matching, patient characteristics were well-balanced, with 150,487 patients in the diabetes group (mean [SD] age 55.9 [12.7] years; 51.3% women) and 150,487 patients in the no diabetes group (55.5 [13.3] years; 50.3% women). The strictest matching algorithm (1:1 nearest neighbor) showed that compared to individuals without diabetes, having diabetes was associated with 9.0% higher odds of having a positive SARS-CoV-2 test (OR 1.09 [95% CI: 1.08-1.10]). CONCLUSIONS: Presence of diabetes was associated with higher odds of testing positive for SARS-CoV-2, which could have important implications for risk mitigation efforts for people with diabetes at risk of SARS-CoV-2 infection.

Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction.

BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (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 polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 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: A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m2 (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24-3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 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.

Gender disparities in coronavirus disease 2019 clinical trial leadership.

OBJECTIVES: To compare the gender distribution of clinical trial leadership in coronavirus disease 2019 (COVID-19) clinical trials. METHODS: We searched https://clinicaltrials.gov/ and retrieved all clinical trials on COVID-19 from 1 January 2020 to 26 June 2020. As a comparator group, we have chosen two fields that are not related to emerging infections and infectious diseases: and considered not directly affected by the pandemic: breast cancer and type 2 diabetes mellitus (T2DM) and included studies within the aforementioned study period as well as those registered in the preceding year (pre-study period: 1 January 2019 to 31 December 2019). Gender of the investigator was predicted using the genderize.io application programming interface. The repository of the data sets used to collect and analyse the data are available at https://osf.io/k2r57/. RESULTS: Only 27.8% (430/1548) of principal investigators among COVID-19-related studies were women, which is significantly different compared with 54.9% (156/284) and 42.1% (56/133) for breast cancer (p < 0.005) and T2DM (p < 0.005) trials over the same period, respectively. During the pre-study period, the proportion of principal investigators who were predicted to be women were 49.7% (245/493) and 44.4% (148/333) for breast cancer and T2DM trials, respectively, and the difference was not statistically significant when compared with results from the study period (p > 0.05). CONCLUSION: We demonstrate that less than one-third of COVID-19-related clinical trials are led by women, half the proportion observed in non-COVID-19 trials over the same period, which remained similar to the pre-study period. These gender disparities during the pandemic may not only indicate a lack of female leadership in international clinical trials and involvement in new projects but also reveal imbalances in women's access to research activities and funding during health emergencies.

Incremental Risk of Developing Severe COVID-19 Among Mexican Patients With Diabetes Attributed to Social and Health Care Access Disadvantages.

OBJECTIVE: 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 state-level 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.

Diabetes as a Risk Factor for Poor Early Outcomes in Patients Hospitalized With COVID-19.

OBJECTIVE: 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 diabetes-mostly 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 = 0.007), required mechanical ventilation (37.1% vs. 23.2%, P = 0.001), and died (15.9% vs. 7.9%, P = 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.