Poverty, Income, and Unemployment as Determinants of Life Expectancy: Empirical Evidence from Panel Data of Thirteen Malaysian States.

BACKGROUND: The primary indicator of public health, which all nations aim to prolong, is life expectancy at birth. Uncovering its socioeconomic determinants is key to extending life expectancy. This study examined the determinants of life expectancy in Malaysia. METHODS: This observational study employs secondary data from various official sources of 12 states and one federal territory in Malaysia (2002-2014). Panel data of 78 observations (13 cross-sections at six points in time) were used in multivariate, fixed-effect, regressions to estimate the effects of socioeconomic variables on life expectancy at birth for male, female and both-gender. RESULTS: Poverty and income significantly determine female, male, and total life expectancies. Unemployment significantly determines female and total life expectancies, but not male. Income inequality and public spending on health (as a percentage of total health spending) do not significantly determine life expectancy. The coefficients of the multivariate regressions suggest that a 1% reduction in poverty, 1% reduction in unemployment, and around USD 23.20 increase in household monthly income prolong total life expectancy at birth by 17.9, 72.0, and 16.3 d, respectively. The magnitudes of the effects of the socioeconomic variables on life expectancy vary somewhat by gender. CONCLUSION: Life expectancy in Malaysia is higher than the world average and higher than that in some developing countries in the region. However, it is far lower than the advanced world. Reducing poverty and unemployment and increasing income are three effective channels to enhance longevity.

Demographic and Socioeconomic Variables Associated With Health Care-Seeking Behavior Among Foreign Workers in Malaysia.

Foreign workers in Malaysia face various barriers in accessing health care, which results in many of them being unable to obtain appropriate medical treatment in case of sickness. This study investigates the foreign workers' health care-seeking behavior and the demographic and socioeconomic variables that influence it. Data were collected from 502 foreign workers using a self-administered questionnaire. Multiple logistic regression was used to estimate the influence of demographic and socioeconomic variables on health care-seeking behavior among foreign workers. In cases of severe sickness, 20.5% of foreign workers stated that they will not go or are unlikely to go to a clinic or hospital. The multiple logistic regression revealed that foreign workers' tendency to avoid medical treatment is associated with gender, marital status, monthly income, preferred language of communication, and work classification. Nonetheless, in cases of mild sickness, demographic and socioeconomic variables do not influence foreign workers' health care-seeking behavior.

The Power of Multidimensional Poverty in Explaining Life Expectancy: Empirical Evidence from Cross-Sectional Data of 62 Developing Countries.

BACKGROUND: We examined whether multidimensional poverty index (MPI) explained variations in life expectancy (LE) better than income poverty; and assessed the relative importance of MPI indicators in influencing LE. METHODS: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE. RESULTS: Adjusting for controls, both MPI (ß =-0.245, P<0.001) and IPG at 3.1 USD (ß=-0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (ß=-0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (ß=-0.210, P<0.001) than male (ß=-0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity. CONCLUSION: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.