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Abstract
ObjectiveThis paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used to estimate the impact of behavioral scenarios on the burden of disease due to ischemic heart disease (IHD) and diabetes in the Turkish adult population.MethodsDisease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-per-prevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations.FindingsThe proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%).ConclusionWhile the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors.
Highlights
Non-communicable diseases account for the majority of the global burden of disease. 72% of all deaths were estimated to be due to non-communicative diseases in 2016 [1]
While the national relative risk (RR) estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the Global Burden of Disease (GBD) analysis and allow evaluation of practical scenarios with multiple behaviors
We model the individual disease probability with a Hierarchical Bayes (HB) logistic regression, where the log odds of having the disease is a function of demographic, behavioral and other control variables
Summary
Non-communicable diseases account for the majority of the global burden of disease. 72% of all deaths were estimated to be due to non-communicative diseases in 2016 [1]. 72% of all deaths were estimated to be due to non-communicative diseases in 2016 [1]. Non-communicable diseases account for the majority of the global burden of disease. Changing behaviors, such as exercise and healthy diet has the potential to decrease, or slow the increase of public burden [2,3]. According to the World Health Organization (WHO), as of 2017, 161 out of the 194 countries had operational policy/ strategy/ action plan to decrease tobacco use, and 100 had implemented physical activity public awareness programs [4]. Quantifying the potential impact of a behavior change in the local population enables policy makers to use resources more efficiently. The policy maker’s decision as to which intervention (if any) to pursue depends on the cost and likelihood of the potential interventions to achieve the intended behavior change
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