Is economic growth enough to propel rehabilitation expenditures? An empirical analysis of country panel data and policy implications

• Published in: BMC public health 2024-04, Vol.24 (1), p.1154-6, Article 1154
• Main Authors: Neill, Rachel, Kautsar, Hunied, Trujillo, Antonio
• Format: Article
• Language: English
• Subjects: Analysis; Data models; Dependent variables; Developed Countries; Developing Countries; Economic aspects; Economic development; Economic Development - statistics & numerical data; Economic growth; Economics; Elasticity; Elasticity (Economics); Empirical analysis; Empirical Research; Environmental aspects; Estimates; Expenditures; Forecasts and trends; GDP; Global Health; Gross Domestic Product; Health aspects; Health care expenditures; Health Expenditures - statistics & numerical data; Health financing; Health Policy; Humans; LDCs; Macroeconomics; Medical care, Cost of; Medical policy; Methods; Multiple regression models; National health insurance; Per capita; Populations; Public finance; Public health; Regression analysis; Regression models; Rehabilitation; Rehabilitation - economics; Rehabilitation - statistics & numerical data; Specifications; World health
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-024-18601-y

Rehabilitation is a set of services designed to increase functioning and improve wellbeing across the life course. Despite being a core part of Universal Health Coverage, rehabilitation services often receive limited public expenditure, especially in lower income countries. This leads to limited service availability and high out of pocket payments for populations in need of care. The purpose of this research was to assess the association between macroeconomic conditions and rehabilitation expenditures across low-, middle-, and high-income countries and to understand its implications for overall rehabilitation expenditure trajectory across countries. We utilized a panel data set from the World Health Organization's Global Health Expenditure Database comprising the total rehabilitation expenditure for 88 countries from 2016 to 2018. Basic macroeconomic and population data served as control variables. Multiple regression models were implemented to measure the relationship between macroeconomic conditions and rehabilitation expenditures. We used four different model specifications to check the robustness of our estimates: pooled data models (or naïve model) without control, pooled data models with controls (or expanded naïve model), fixed effect models with all controls, and lag models with all controls. Log-log specifications using fixed effects and lag-dependent variable models were deemed the most appropriate and controlled for time-invariant differences. Our regression models indicate that, with a 1% increase in economic growth, rehabilitation expenditure would be associated with a 0.9% and 1.3% increase in expenditure. Given low baseline levels of existing rehabilitation expenditure, we anticipate that predicted increases in rehabilitation expenditure due to economic growth may be insufficient to meet the growing demand for rehabilitation services. Existing expenditures may also be vulnerable during periods of economic recession. This is the first known estimation of the association between rehabilitation expenditure and macroeconomic conditions. Our findings demonstrate that rehabilitation is sensitive to macroeconomic fluctuations and the path dependency of past expenditures. This would suggest the importance of increased financial prioritization of rehabilitation services and improved institutional strengthening to expand access to rehabilitation services for populations.