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BACKGROUNDExcess all-cause mortality rates in Mexico in 2020 during the COVID-19 pandemic were among the highest globally. Recent reports suggest that diabetes-related deaths were also higher, but the contribution of diabetes as a cause of excess mortality in Mexico during 2020 compared to prior years has not yet been characterized. METHODSWe conducted a retrospective, state-level study using national death registries from Mexican adults [≥]20 years for the 2017-2020 period. Diabetes-related deaths were classified using ICD-10 codes that listed diabetes as the primary cause of death, excluding certificates which listed COVID-19 as a cause of death. Excess mortality was estimated as the increase in diabetes-related mortality in 2020 compared to average rates in 2017-2019. Analyses were stratified by diabetes type, diabetes-related complication, and in-hospital vs. out-of-hospital death. We evaluated the geographic distribution of diabetes-related excess mortality and its socio-demographic and epidemiologic correlates using spatial analyses and negative binomial regression models. RESULTSWe identified 148,437 diabetes-related deaths in 2020 (177/100,000 inhabitants), 41.6% higher than the average for 2017-2019, with the excess occurring after the onset of the COVID-19 pandemic. In-hospital diabetes-related deaths decreased by 17.8% in 2020 compared to 2017-2019, whereas out-of-hospital deaths increased by 89.4%. Most deaths were attributable to type 2 diabetes and type 1 diabetes (129.7 and 4.0/100,000 population). Diabetes-related emergencies as contributing causes of death also increased in 2020 compared to 2017-2019 for hyperglycemic hyperosmolar state (128%), and ketoacidosis (116%). Diabetes-related excess mortality clustered in southern Mexico and was highest in states with higher social lag, higher rates of COVID-19 hospitalization, and higher prevalence of HbA1c [≥]7.5%. INTERPRETATIONDiabetes-related mortality increased among Mexican adults by 41.6% in 2020 after the onset of the pandemic compared to 2017-2019, largely attributable to type 2 diabetes. Excess diabetes-related deaths occurred disproportionately out-of-hospital, clustered in southern Mexico, and were associated with higher state-level marginalization, rates of COVID-19 hospitalizations, and higher prevalence of suboptimal glycemic control. Urgent policies to mitigate mortality due to diabetes in Mexico are needed, particularly given the ongoing challenges in caring for people with diabetes posed by the COVID-19 pandemic. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar for research articles published up to February 15, 2022, using the terms [("diabetes-related mortality" OR ("excess mortality" AND "diabetes"))]. No language restriction was applied. This search revealed few international studies evaluating nationwide diabetes-related mortality in general. In Mexico, only one unpublished study evaluated diabetes-related mortality up to 2019. We identified no studies which evaluated diabetes-related excess mortality in Mexico or elsewhere during 2020 or which explored correlates of diabetes-related excess mortality in 2020. Added value of this studyThis is the first report and characterization of an excess in diabetes-related mortality in Mexico during 2020 compared to recent years. Diabetes as a primary cause of death in Mexico was higher in 2020 compared to 2017-2019, particularly for people living with type 2 diabetes, starting in March 2020 with the onset of the COVID-19 pandemic. Compared to the 2017-2019 period, most of these excess deaths occurred out of hospital, with a concurrent decrease in in-hospital diabetes-related mortality. Hyperosmolar hyperglycemic state and ketoacidosis as primary causes of diabetes-related deaths also increased in 2020 compared to prior years. Our study also identified substantial geographic variation in diabetes-related excess mortality in Mexico, with southern, poorer States bearing a disproportionate burden. Finally, we report that diabetes-related excess mortality was associated with higher marginalization, suboptimal glycemic control, and higher rates of COVID-19 hospitalization, which were clustered in southern Mexico. Implications of the available evidenceReadily treatable, high morbidity diabetes-related conditions were likely untreated due to the constraints of the health care system during the COVID-19 pandemic, leading to diabetes-related excess mortality. This is a problem for Mexico, but it is likely to be generalizable to other countries and other conditions, as seen even in high-income countries. Given the ongoing challenges posed by the COVID-19 pandemic on healthcare systems, policies that can strengthen care for diabetes and other chronic conditions are urgently needed to mitigate the dramatic rise in diabetes-related mortality occurring in the out-of-hospital setting and its disproportionate burden on populations with high levels of marginalization.
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BackgroundDuring the COVID-19 pandemic, convolutional neural networks (CNNs) have been used in clinical medicine (e.g., to classify chest X-rays for COVID-19 diagnosis). Whether CNNs could also inform the epidemiology of COVID-19 analysing street images has been understudied, though it could identify high-risk places and relevant features of the built environment. We trained CNNs to classify bus stops (Lima, Peru) into moderate or extreme COVID-19 risk. MethodsWe used five images per bus stop. The outcome label (moderate or extreme) for each bus stop was extracted from the local transport authority. We used transfer learning and updated the output layer of five CNNs: NASNetLarge, InceptionResNetV2, Xception, ResNet152V2, and ResNet101V2. We chose the best performing network which was further tuned to increase performance. ResultsThere were 1,788 bus stops (1,173 moderate and 615 extreme), totalling 8,940 images. NASNetLarge outperformed the other CNNs except in the recall metric for the extreme label: 57% versus 59% in NASNetLarge and ResNet152V2, respectively. NASNetLarge was further tuned and reached: training loss of 0.50; training accuracy of 75%; precision, recall and F1 score for the moderate label of 80%, 83% and 82%, respectively; these metrics for the extreme label were 65%, 51% and 63%. ConclusionsCNNs has the potential to accurately classify street images into levels of COVID-19 risk. In addition to applications in clinical medicine, CNNs and street images could also advance the epidemiology of COVID-19 at the population level.
