Estimating Local Intervention Impact with Individual Level National Data by Using GBD Studies to Guide Models

Abstract

Background: Non-communicable diseases account for the majority of the global burden of disease. Interventions, such as exercise and healthy diet, have the potential to decrease the prevalence and burden of diseases. While global academic studies represent the domain knowledge and provide the marginal effect of risk factors, individual level observational data reflects the joint distribution of local behaviors and socioeconomics, and their local impact on disease prevalence. Methods: We developed a method to evaluate the potential impact of a public health intervention by guiding individual level disease prevalence models of local observational data with the published meta-analysis results of Global Burden of Disease (GBD) studies. We use a Hierarchical Bayes framework to ensure that the model parameters are in agreement with the qualitative patterns in the meta-analysis results. The method provides impact estimates with uncertainty intervals at the population or any targetable subpopulation level. The intervention impact accounts for the burden due to multiple diseases without double counting and controls for demographics, socioeconomics and behaviors. We evaluate the potential impact of behavioral interventions involving smoking, physical exercise and diet on prevalence and disability adjusted life years (DALY) due to Ischemic Heart Disease (IHD) and Diabetes in the Turkish adult population. Findings: As expected, eliminating smoking and secondhand smoking and encouraging exercise have the potential to reduce DALYs associated with IHD and Diabetes by 10% (8% to 12%) and 8% (7% to 10%), respectively in the overall population. Somewhat surprisingly, increasing fruit and vegetable consumption would increase the expected DALYs for about one in five individuals due to its adverse effect on Diabetes, while bringing about a 2% (1% to 3%) reduction at the population level DALYs. Interpretation: The findings are in line with the qualitative patterns of GBD meta-analysis results, but differ quantitatively for some risk factors and demographic groups. Beyond evaluating the potential impact at the national level, the method allows the decision maker to see the heterogeneity of the intervention impact due to joint distribution of behaviors and other socioeconomic/ lifestyle factors in addition to different relative risk estimates by age and gender. Funding Statement: The authors are employees of Koc University and there was no additional funding from other sources for this research. Declaration of Interests: The authors do not have any competing interest. Ethics Approval Statement: We received a waiver from the Koc University Ethics Committee. We also got approval from the Turkish Statistical Institute for the study to be able to use the micro-level data.