Modeling and calibration for exposure to time-varying, modifiable risk factors: the example of smoking behavior in India

Background: Risk factors increase the incidence and severity of chronic disease. To examine future trends and develop policies addressing chronic diseases, it is important to capture the relationship between exposure and disease development, which is challenging given limited data.

Objective: To develop parsimonious risk factor models embeddable in chronic disease models, which are useful when longitudinal data are unavailable.

Design: The model structures encode relevant features of risk factors (e.g., time-varying, modifiable) and can be embedded in chronic disease models. Calibration captures time-varying exposures for the risk factor models using available cross-sectional data. We illustrate feasibility with the policy-relevant example of smoking in India.

Methods: The model is calibrated to the prevalence of male smoking in 12 Indian regions estimated from the 2009-2010 Indian Global Adult Tobacco Survey. Nelder-Mead searches (250,000 starting locations) identify distributions of starting, quitting, and restarting rates that minimize the difference between modeled and observed age-specific prevalence. We compare modeled life expectancies to estimates in the absence of time-varying risk exposures and consider gains from hypothetical smoking cessation programs delivered for 1 to 30 years.

Results: Calibration achieves concordance between modeled and observed outcomes. Probabilities of starting to smoke rise and fall with age, while quitting and restarting probabilities fall with age. Accounting for time-varying smoking exposures is important, as not doing so produces smaller estimates of life expectancy losses. Estimated impacts of smoking cessation programs delivered for different periods depend on the fact that people who have been induced to abstain from smoking longer are less likely to restart.

Conclusions: The approach described is feasible for important risk factors for numerous chronic diseases. Incorporating exposure-change rates can improve modeled estimates of chronic disease outcomes and of the long-term effects of interventions targeting risk factors.