Exploratory Data Analysis to Understand Social Determinants Important to Global Neonatal Mortality Rate

摘要

The Sustainable Development Goals (SDGs) are a set of targets that the UN hopes all countries will reach by 2030 broadly spanning the range of health, education, racial inequalities, environmental protections, and several other fields. Among these goals includes (Goal 3.2) an aim for all countries to reduce Neonatal Mortality Rates (NMR) to 12 per 1,000 live births. Without properly allocating resources to see the most dramatic shifts in NMR, many countries may be at risk of not meeting these ambitious goals. However, there are many factors which may influence national NMR, and while much previous work has been done to identify factors that influence NMR usually on a nation by nation basis, these factors can tend to vary. The goal of this study is to find factors that consistently lead, by changing them, to a change in NMR for many countries, in order to better inform health policy and resource allocations to the medical sector. This study will serve as an exploratory data analysis step for future studies regarding the impact of several health indicators on NMR per country. Cross-sectional data from the year 2014 were used for this Exploratory Data Analysis (EDA). To identify indicators that showed significant differences between the countries with high NMR and countries with low NMR, Mann-Whitney U Tests were performed. The p-value for each mean comparison was less than the 0.01 significance level. We have built a K-means clustering model to observe the variables' contribution to NMR, as well as a K-means clustering model to observe the same data's contributions to Gross Domestic Product (GDP), to see if both NMR and GDP follow similar trends across our target countries. The clustering for NMR groups of countries showed mostly separate clusters, while the clustering for the same data for the GDP classes showed very little separation, as the most points from each class all occupied the same cluster. To determine the actual amount that each indicator contributed to the data, Principle Component Analysis (PCA) was performed to understand the strongest contributions to the total data variance. The results of this study will serve to highlight the most important areas which must be improved in order to fulfill the Sustainable Development Goals (SDG) by the end of the next decade and to contribute to future studies that utilize longitudinal or more recent data.