Under-Five Mortality, Health and Selected Macroeconomic Variables: The Children behind the Digits

Abstract

IntroductionThe study of mortality dates back to the seventeenth century [1][2][3]. Graunt's [1] work on the 'Bill of mortality' sets a premise for the establishment of mathematical modeling in the area of mortality. In the nineteenth century, Gompertz [4] developed a mathematical formula that estimated mortality at different ages. Gompertz's [4] theory established that mortality increases at geometric progression at a particular age and forwarded that this can be represented by a mortality risk function µ(x) = α. e Makeham's [6] function that it does not hold true at older ages and that it overly estimated mortality at older ages (80+ years) [7,8], the work has laid the foundation upon which many studies have been framed [9]. The life tables is one of the creations that emerged from mortality statistics [10,11], which was used to indicate life expectancy or health of a population.The literature speaks to decreasing mortality at younger ages, Bourgeois-Pichat [12,13] proposed disaggregating infant mortality in endogenous and exogenous (accidents or infections) components and fitted this by the formula:, where a is a constant denoting the endogenous process, cumulative death in the cohort by age n (in days). Modifications are well documented in the literature to Bourgeois-Pichat's [13] work, which guide contemporary studies in the area. Examining child health, using infant and child mortality rates in Peru, Paxson and Schady [14] found that infant mortality increased during economic crisis and that infant and child mortality followed a collinear pattern over the studied period (1978-to-1999). Paxson and Schady [14] like early scholar fitted infant mortality rates with linear models for each year of birth: M it = α + X it βit + ε it , where M it is child born in year t to mother I died in the first year of life, X it is maternal characteristics (level of schooling, age, area of residence) and the error term, ε it .The earlier pioneers are still guiding the directions of contemporary scholarships. In 1992, Waldmann [15] assessing infant mortality and income, used log (infant mortality) = β log (Nonrich Income) + γRich Share. He modified the early model as follows: log (infant mortality) = β 0 + β 1 log (Poor Income) + β 2 log(Middle Income) + γRich Share + δYear 1970. Using the aforementioned model, Waldmann [15] found a positive correlation between log(Poor income) and log infant mortality and a negative one for log middle income and log infant mortality. AbstractBackground: 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.

Infant Mortality and Inflation in China: Based on the Mixed Frequency VAR Analyses

“…Some factors influence infant mortality, and there has been a lot of studies investigating the relationship between macroeconomic variables and infant mortality. Specifically, macroeconomic variables such as poverty, unemployment, and GDP per capita are associated with child mortality ( 20 ). Paxson and Norbert ( 21 ), Cnattingius and Bengt ( 22 ) argue that in countries such as Peru, Denmark, Finland, Norway, and Sweden, economic crises increase infant mortality, while economic growth decreases it.…”

Modelling the Linear and Nonlinear Effects of Malaria Risk Factors on Child Mortality

“…Adetoro and Amoo [21] identified maternal education as indicator for child mortality rate. Bourne [22] observed log poverty and gross domestic product (GDP) per capita account for ninety percent (90%) of the explanation of changes in child mortality rate. Considering the foregoing, one can observe a direct interlink between malaria and child mortality risk factors.…”