Predictors of Healthcare Expenditure: Aging, Disability or Development?

Objective: Studies show that almost every country across the world will experience a remarkable increase in their healthcare costs and ageing population by 2030. Also, people with disabilities are more likely to impose considerable healthcare costs on families and governments than their counterparts. On the other hand, socioeconomic status of countries can be an important factor to predict healthcare costs. In this study, we aimed to evaluate the relationship between disability rate, ageing rate and development rate of countries with their current health expenditure. Materials & Methods: This is a descriptive correlational study conducted based on secondary analysis of existing data of 202 countries under six different regions of African Region (AFRO), Eastern Mediterranean Region (EMRO), South-East Asia Region (SEARO), Western Pacific Region (WPRO), European Region (EURO), and Region of the Americas (PAHO) in 2016 . The linear regression analysis was applied to investigate the association between the explanatory variables of age, Years Lost due to Disability (YLD) per 100000 general population, Human Development Index (HDI), Gross Domestic Product (GDP) growth, and unemployment rate with Current Health Expenditure (CHE) per capita as the outcome variable. The costs were expressed based on Power Purchasing Parties (PPP) in USD. One-way ANOVA was applied to compare the means of YLD and CHE per capita between three levels of HDI.  Results: The highest mean YLD (13272.76±1577.22 per 100000 general population) and mean CHE (2698.39± 1915.01 USD) was belonged to EURO region, while AFRO region showed the lowest mean YLD (10005.65± 847.03 per 100000 general population) and mean CHE (281.11± 335.84 USD). In Iran, the mean YLD and CHE was lower than that of EURO region but higher than that of other five regions. EURO, PAHO, and WPRO had the highest rate of ageing compared to other regions. For Iran, the rate of ageing population was higher than AFRO and EMRO. In the regression model, population aged 15 to 49 (YLD=0.167), aged >65 years (YLD=0.651), aged ≥70 years (YLD=0.359) and HDI (0.391) had a positive association with the CHE per capita. In contrast, the population aged ≤5 years (YLD=-0.585), aged 15-64 years (YLD=-0.274), and aged 50-69 years (YLD=-0.938) and the unemployment rate (-0.138) showed a negative association with the CHE per capita. Moreover, ANOVA results revealed that the rate of ageing population (P<0.001), YLD (P<0.001) and CHE per capita (P<0.001) were significantly higher in countries with higher HDI than in countries with lower HDI.  Conclusion: In the study period, YLD can predict healthcare expenditure of countries better compared to HDI and ageing population. Therefore, it is suggested that cost control interventions in ageing period should be implemented through programs aimed at preventing chronic diseases.