Forecasting the Diabetes Burden Across USA-Mexico Border States Through 2030: A Multi-Indicator, Multi-Model Analysis

ObjectivesThe 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. MethodsDiabetes 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. ResultsMulti-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. ConclusionsDiabetes 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 StudyO_ST_ABSStrengthsC_ST_ABS- 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.