Temporal Association of US Tobacco Taxation on Smoking Attributable Bladder Cancer Mortality and Disability-Adjusted Life Years.

PURPOSE: The causal link between smoking and bladder cancer (BC) development is well established but the long-term impact of tobacco taxation and health policy on BC mortality and disability-adjusted life years (DALYs) has not been fully elucidated. Given the protracted latency of carcinogenesis, this study examines whether historical changes in tobacco taxation and smoke-free laws are associated with reductions in BC disease burden and mortality in the United States.

METHODS

Smoking-attributable BC mortality and DALY data, and federal and state tobacco taxation data were differenced as time series to achieve stationarity. Cross-correlation analysis identified optimal lag times. A semilogarithmic multivariable linear regression was used to estimate the percent change in BC outcomes per 1% increase in tobacco tax. Analyses were adjusted for national health expenditures and stratified by state.

RESULTS

The median lag time between tobacco tax changes and smoking-attributable BC mortality was 17 years, whereas the lag to DALYs was 24 years. National-level regression showed no significant association between taxation and BC mortality (-0.09%, P = .64) or DALYs (1.77%, P = .051). However, 22 states exhibited significant reductions in mortality, with the greatest observed in Arkansas (-3.64%, P < .001), California, and Indiana. Sixteen states showed significant DALY reductions, led by California (-4.68%). The implementation of smoke-free laws alone was not associated with decreases in smoking-attributable BC mortality and DALY.

CONCLUSION

Tobacco taxation is significantly associated with long-term reductions in smoking-attributable BC mortality and DALYs at the state level, but not nationally. These findings demonstrate the importance of adjunctive localized public health policy and the delayed impact of tobacco control measures on cancer outcomes. Further investigation is warranted to understand the mechanisms driving state-level variability and to inform targeted prevention strategies.