Under-Five Mortality, Health and Selected Macroeconomic Variables: The Children behind the Digits
Paul A. Bourne*
Socio-Medical Research Institute, Kingston, Jamaica, West Indies
- Corresponding Author:
- Paul A. Bourne
Socio-Medical Research Institute
66 Long Wall, Kingston 9, Kingston
Jamaica, West Indies
Tel: (876) 457 6990
E-mail: [email protected]
Received Date: February 10, 2012; Accepted Date: March 01, 2012; Published Date: March 07, 2012
Citation: Bourne PA (2012) Under-Five Mortality, Health and Selected Macroeconomic Variables: The Children behind the Digits. Epidemiol 2:115. doi: 10.4172/2161-1165.1000115
Copyright: © 2012 Bourne PA. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Mortality is filled with studies that evaluated infant mortality, child mortality, and income distribution and mortality, but no single research in the English-speaking Caribbean has wholly examined child mortality, inflation, infant mortality, poverty and economic crisis as well as modeling those phenomena.
Objectives: This work bridges the gap in the literature by assessing by modeling child mortality, inflation, infant mortality, poverty, and economic crisis as well as the appropriateness of linear modeling in addition to an assessment of under-five age-specific mortality.
Methods: This work uses data collected from various Jamaican government departments’ publications. Data were entered and stored into Statistical Packages for the Social Sciences (SPSS) for Window version 17.0 (SPSS Inc; Chicago, IL, USA) as well as Microsoft Excel to analyze the data. Pearson’s product Moment Correlation was used to assess the bivariate correlation between particular macroeconomic and other variables, and Ordinary least square regression analyses were used to establish the model for 1) log Infant mortality rate and 2) log child mortality rate.
Results: Infant mortality rate (IMR) over the last 100 years is best fitted by an inverse exponential function (R2 = 0.97) as well as child mortality rate (CMR; R2 = 0.91). Infant mortality rate is influenced by health care utilization (b= -0.004, 95%CI: -0.01 – 0.01) and GDP (b = -1.960, 95% CI: -0.52 – 0.07), and the two factors account for 55% of the variance in IMR. The factors that are correlated with child mortality rate are log poverty ((b =0.22, 95% CI: 0.33 – 0.40) and GDP per capita (b = -2.66, 95% CI: -5.07 – -0.25). Those factors account for 90% of the explanation of changes in CMR. During economic recession IMR and CMR decline and opposite is true in periods of economic growth.
Conclusion: This work provides a basis public health actions and programmes.