Objective: This study seeks to forecast the global burden of diabetes-related mortality by type, age group, WHO region, and income classification through 2030, and to assess progress toward Sustainable Development Goal (SDG) 3.4, which aims to reduce premature mortality (among people age 30–70 years) from noncommunicable diseases (including diabetes) by one-third. Methods: We analyzed diabetes mortality data from the Institute for Health Metrics and Evaluation, Global Burden of Disease 2019, covering 30 years (1990–2019). Using this historical dataset, we generated 11-year prospective forecasts (2020–2030) globally and stratified by diabetes type (type 1, type 2), age groups, WHO regions, and World Bank income classifications. We employed multiple time series and epidemic modeling approaches to enhance predictive accuracy, including ARIMA, GAM, GLM, Facebook’s Prophet, n-sub-epidemic, and spatial wave models. We compared model outputs to identify consistent patterns and trends. Results: Our forecasts indicate a substantial increase in global diabetes-related mortality, with type 2 diabetes driving the majority of deaths. By 2030, annual diabetes mortality is projected to reach 1.63 million deaths (95% PI: 1.48–1.91 million), reflecting a 10% increase compared to 2019. Particularly concerning is the projected rise in mortality among adults aged 15–49 and 50–69 years, especially in Southeast Asia and low- and middle-income countries. Mortality in upper-middle-income countries is also expected to increase significantly, exceeding a 50% rise compared to 2019. Conclusions: Diabetes-related deaths are rising globally, particularly in younger and middle-aged adults in resource-limited settings. These trends jeopardize the achievement of SDG 3.4. Urgent action is needed to strengthen prevention, early detection, and management strategies, especially in Southeast Asia and low-income regions. Our findings provide data-driven insights to inform global policy and target public health interventions.

Aims: This study characterizes the diabetes burden across USA–Mexico border states (1990–2021) by examining current patterns, temporal trends, binational comparisons, and demographic variations. Methods: Using Global Burden of Disease 2021 estimates, we calculated age-standardized rates for mortality, prevalence, incidence, and DALYs. We applied joinpoint regression, age-period-cohort models, and decomposition analyses to characterize trends and identify drivers of burden change, comparing border versus non-border states within and between countries. Results: Border states showed 9-14% higher burden than non-border states. Despite similar prevalence (9,010 vs 8,298 per 100,000) and incidence (435 vs 426 per 100,000), Mexican border states had nearly five times higher mortality (67.2 vs 12.0 per 100,000) and three times higher DALYs (2,355 vs 896 per 100,000) than USA counterparts. YLLs comprised 64.3% of Mexican burden versus 31.4% in USA. From 1990-2021, USA border states showed rising incidence (+3.04% annually) with declining mortality (- 1.28%), while Mexican states demonstrated stable-to-declining incidence (-0.15%) with plateauing mortality post-2009. Males experienced higher mortality in both countries. Conclusions: Diabetes occurrence has converged across the border, yet outcomes remain profoundly unequal. This demands coordinated binational surveillance and policy action. Focus on prevention in the US and diabetes care management in Mexico to reduce health disparities.

The COVID-19 pandemic disrupted diabetes care globally and created a complex bidirectional health crisis. Recent forecasting efforts using pre-pandemic data projected substantial increases in diabetes mortality through 2030, raising concerns about achieving Sustainable Development Goal (SDG) 3.4. However, these projections did not account for pandemic-related disruptions to health systems and chronic disease management. The newly released Global Burden of Disease (GBD) 2023 data, covering the pandemic period through 2023, now provide a comprehensive empirical reference for assessing COVID-19’s observed impact on diabetes trends. This perspective adopts a forecast reconciliation and interpretation approach by examining counterfactual pre-pandemic diabetes mortality projections alongside GBD 2023 data, thereby shedding light on how pandemic-era mortality diverged from pre-pandemic trajectories. Critically, we note that insulin-dependent diabetes mellitus (IDDM, Type 1) and non-insulin-dependent diabetes mellitus (NIDDM, Type 2) have distinct etiologies and pandemic vulnerabilities, a distinction this article addresses. The evidence is striking: by 2023, global diabetes deaths had already exceeded 2.0 million per year, surpassing the 2030 upper forecast bound of 1.91 million, seven years ahead of the forecast horizon. NIDDM was the primary driver, with deaths crossing 1.9 million per year in 2023. These findings underscore the urgent need to strengthen diabetes prevention and management strategies as the world recovers from the pandemic-era disruptions in health systems and chronic disease care.

Objectives: The USA-Mexico border area faces an elevated diabetes burden, with prevalence 2-3 times higher than national averages in both countries. Yet, no forward-looking projections exist beyond 2021, limiting healthcare system planning. We provide the first multi-indicator, multi-model forecasts of diabetes burden through 2030 across border states. Methods: Diabetes mortality, prevalence, and disability-adjusted life years (DALYs) data were extracted from the Global Burden of Disease Study 2021 for all ten border states in both countries (1990-2021). Eight different time-series models including ARIMA, generalized additive models, generalized linear models, Prophet, and n -sub-epidemic framework were used to project burden from 2022-2030. Models were evaluated and compared using weighted interval scores, with forecasts stratified by age, sex, and geography. Results: Multi-model ensembles project 15-17 million people with diabetes, 23,000-26,000 annual deaths, and 1.6-1.8 million Disability-Adjusted Life Years (DALYs) due to diabetes in USA-Mexico border states by 2030. Mexican border states face accelerating mortality among adults aged 20-59 years (15-58% increases), while USA states show prevalence increases of 35-52% in youth and 47-49% in older adults. Males experienced consistently higher projected burden than females. ARIMA demonstrated superior performance for mortality and prevalence forecasting, while ensemble methods performed better for DALYs projections. Conclusions: Diabetes burden is projected to increase 30-50% across border states by 2030, driven by distinct regional patterns requiring divergent public health responses. Mexican states require community health worker programs, subsidized medications, and mobile clinics for mortality prevention, while US states must expand school-based screening and nutrition program capacity to address rising youth prevalence. These projections provide actionable intelligence to support coordinated cross-border efforts in diabetes prevention and management. Strengths and Limitations of This Study: Strengths: – Multi-model ensemble approach (eight models) with rigorous comparative evaluation using standardized metrics, providing transparency on performance variability across outcomes and demographics. – Comprehensive stratification by age group (five categories), sex, and geography enables demographically targeted healthcare planning, addressing a gap in prior border health forecasting studies. – Explicit quantification of forecast uncertainty through 95% prediction intervals and formal calibration assessment, identifying outcome-specific and geographic instances of substantial uncertainty. Limitation: – Forecasts use statistically modeled disease burden estimates (GBD 2021) rather than primary surveillance data, potentially introducing measurement error and limiting precision in capturing true state-level variation. – Time-series models assume historical trends continue unchanged, unable to account for policy interventions, technological innovations, or epidemiological shocks. Forecasts are most reliable in short term.