Inequality as a Powerful Predictor of Infant and Maternal Mortality around the World.
BACKGROUND
Maternal and infant mortality are highly devastating, yet, in many cases, preventable events for a community. The human development of a country is a strong predictor of maternal and infant mortality, reflecting the importance of socioeconomic factors in determinants of health. Previous research has shown that the Human Development Index (HDI) predicts infant mortality rate (IMR) and the maternal mortality ratio (MMR). Inequality has also been shown to be associated with worse health in certain populations. The main purpose of the present study was to determine the correlation and predictive power of the Inequality Adjusted Human Development Index (IHDI) as a measure of inequality with the Infant Mortality Rate (IMR), Maternal Mortality Rate (MMR), Early Neonatal Mortality Rate (ENMR), Late Neonatal Mortality Rate (LNMR), and the Post Neonatal Mortality Rate (PNMR).


RESULTS
Data for the present study were downloaded from two sources: infant and maternal mortality data were downloaded from the Global Burden of Disease 2013 Cause of Death Database and the Human Development Index (HDI) and Inequality-Adjusted Human Development Index (IHDI) data were downloaded from the United Nations Development Program (UNDP). Pearson correlation coefficients were estimated, following logarithmic transformations to the data, to examine the relationship between HDI and IHDI with MMR, IMR, ENMR, LNMR, and PNMR. Steiger's Z test for the equality of two dependent correlations was utilized in order to determine whether the HDI or IHDI was more strongly associated with the outcome variables. Lastly, we constructed OLS regression models in order to determine the predictive power of the HDI and IHDI in terms of the MMR, IMR, ENMR, LNMR, and PNMR. Maternal and infant mortality were both strongly and negatively correlated with both HDI and IHDI; however, Steiger's Z test for the equality of two dependent correlations revealed that IHDI was more strongly correlated than HDI with MMR (Z = 4.897, p < 0.001), IMR (Z = 2.524, p = 0.012), ENMR (Z = 2.936, p = 0.003), LNMR (Z = 2.272, p = 0.023), and PNMR (Z = 2.277, p = 0.023). Furthermore, side-by-side OLS regression models revealed that, when IHDI was used as the predictor variable instead of HDI, the R2 value was 0.053 higher for MMR, 0.025 higher for IMR, 0.038 higher for ENMR, 0.029 higher for LNMR, and 0.026 higher for PNMR.


CONCLUSIONS
Even when both the HDI and the IHDI correlate with the infant and maternal mortality rates, the IHDI is a better predictor for these two health indicators. Therefore, these results add more evidence that inequality is playing an important role in determining the health status of various populations in the world and more efforts should be put into programs to fight inequality.
Department of Health Education and Recreation, College of Education and Human Services, Southern Illinois University Carbondale, Carbondale, Illinois, United States of America.
Juan Ignacio
JI
Ruiz
Department of Health Education and Recreation, College of Education and Human Services, Southern Illinois University Carbondale, Carbondale, Illinois, United States of America.
Kaamel
K
Nuhu
Department of Health Education and Recreation, College of Education and Human Services, Southern Illinois University Carbondale, Carbondale, Illinois, United States of America.
Justin Tyler
JT
McDaniel
Servicio de Clínica Médica, Hospital Aleman, Ciudad Autónoma de Buenos Aires, Argentina.
Federico
F
Popoff
Servicio de Clínica Médica, Hospital Aleman, Ciudad Autónoma de Buenos Aires, Argentina.
Ariel
A
Izcovich
Servicio de Clínica Médica, Hospital Aleman, Ciudad Autónoma de Buenos Aires, Argentina.
Juan Martin
JM
Criniti
D003906:Developing Countries; D054625:Healthcare Disparities / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D008428:Maternal Mortality; D012959:Socioeconomic Factors; D035862:Vulnerable Populations