Predictive modeling of anxiety and depression DALYs in Indonesia before and after the COVID-19 pandemic: Insights from the Global Burden of Disease 2021 study.
Source: PubMed, NCBI / U.S. National Library of Medicine
This study aims to develop a predictive model to estimate the burden of anxiety and depression, measured by Disability-Adjusted Life Years (DALYs) lost, in Indonesia before and after the COVID-19 pandemic. A quantitative predictive design was applied to examine factors associated with anxiety and depression DALYs lost in Indonesia before (2019) and after (2021) the COVID-19 pandemic. Data were compiled from the Global Burden of Disease (GBD) 2021, Statistics Indonesia (BPS), Human Development Index (HDI), Indonesia Health Profile, and other national statistical indicators. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed to construct a predictive model. Predictor variables included infrastructure resources, unemployment rate, income level, Gross Regional Domestic Product (GRDP) growth, human development index, sociodemographic index, social environment, health access, physical environment, food insecurity, and chronic disease prevalence. The predictive models explained 65.3% (2019) and 68.1% (2021) of the variance in DALYs lost due to anxiety and depression. In 2019, food insecurity, chronic disease, and physical environment had negative direct effects, while sociodemographic index and GRDP growth indirectly influenced DALYs lost through food insecurity and health access. In 2021, food insecurity and chronic disease remained significant negative predictors, with the Human Development Index (HDI) emerging as an indirect determinant through its effect o
Abstract
This study aims to develop a predictive model to estimate the burden of anxiety and depression, measured by Disability-Adjusted Life Years (DALYs) lost, in Indonesia before and after the COVID-19 pandemic. A quantitative predictive design was applied to examine factors associated with anxiety and depression DALYs lost in Indonesia before (2019) and after (2021) the COVID-19 pandemic. Data were compiled from the Global Burden of Disease (GBD) 2021, Statistics Indonesia (BPS), Human Development Index (HDI), Indonesia Health Profile, and other national statistical indicators. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed to construct a predictive model. Predictor variables included infrastructure resources, unemployment rate, income level, Gross Regional Domestic Product (GRDP) growth, human development index, sociodemographic index, social environment, health access, physical environment, food insecurity, and chronic disease prevalence. The predictive models explained 65.3% (2019) and 68.1% (2021) of the variance in DALYs lost due to anxiety and depression. In 2019, food insecurity, chronic disease, and physical environment had negative direct effects, while sociodemographic index and GRDP growth indirectly influenced DALYs lost through food insecurity and health access. In 2021, food insecurity and chronic disease remained significant negative predictors, with the Human Development Index (HDI) emerging as an indirect determinant through its effect on food insecurity, forming a predictive pathway HDI Food Insecurity DALYs lost. These findings indicate a post-pandemic shift toward stronger socioeconomic influences on mental health outcomes in Indonesia. Before the pandemic, depression-related DALYs lost were mainly influenced by food insecurity and chronic disease, whereas after the pandemic, the Human Development Index indirectly affected DALYs lost through food insecurity.