Abstract
BACKGROUND: Accurate forecasting of cardiovascular disease mortality is crucial to guide policy and programming efforts. Prior forecasts often have not incorporated past trends in rates of reduction in cardiovascular disease mortality. This creates uncertainties about future trends in cardiovascular disease mortality and disparities. METHODS AND RESULTS: To forecast US cardiovascular disease mortality and disparities to 2030, we developed a hierarchical bayesian model to determine and incorporate prior age, period, and cohort effects from 1979 to 2012, stratified by age, sex, and race, which we combined with expected demographic shifts to 2030. Data sources included the National Vital Statistics System, Surveillance, Epidemiology, and End Results (SEER) single-year population estimates, and US Bureau of Statistics 2012 national population projections. We projected coronary disease and stroke deaths to 2030, first on the basis of constant age, period, and cohort effects at 2012 values, as is most commonly done (conventional), and then with the use of more rigorous projections incorporating expected trends in age, period, and cohort effects (trend based). We primarily evaluated absolute mortality. The conventional model projected total coronary and stroke deaths by 2030 to increase by ≈18% (67 000 additional coronary deaths per year) and 50% (64 000 additional stroke deaths per year). Conversely, the trend-based model projected that coronary mortality would decrease by 2030 by ≈27% (79 000 fewer deaths per year) and stroke mortality would remain unchanged (200 fewer deaths per year). Health disparities will be improved in stroke deaths but not coronary deaths. CONCLUSIONS: After prior mortality trends and expected demographic shifts are accounted for, total US coronary deaths are expected to decline, whereas stroke mortality will remain relatively constant. Health disparities in stroke but not coronary deaths will be improved but not eliminated. These age, period, and cohort approaches offer more plausible predictions than conventional estimates.