The challenge of estimating child mortality levels and trends is due to limited data and a lack of consensus on the most effective calculation methods. This article is divided into two sections, with the first section focused on exploring the determinants of child mortality. These determinants include factors like household environmental conditions and socio-economic characteristics. The analysis examines variables such as female literacy, total fertility rate, per capita GDP, healthcare expenditure per capita, availability of physicians per population, and the proportion of the population living below the national poverty line. To evaluate how these factors impact under-five mortality rates, a multiple linear regression model is used.
Moreover, this paper discusses efforts to reduce child mortality as part of the Millennium Development Goals (MDGs) and evaluates different countries' progress in achieving this objective.
Household environmental and socio-economic characteristics
...have a significant impact on child mortality rates, so policies should prioritize improving these factors to decrease child mortality. This study examines India's initiatives in reducing child mortality rates between 1990 and 2006. Throughout this timeframe, under-five infant mortality rates decreased from 94 per 1,000 live births to 73 per 1,000, indicating a positive trajectory. Nevertheless, further efforts are necessary as the present rate still represents 1% of live births.
India has achieved success in reducing mortality rates through its significant investment in the health and social sector, as well as advancements in healthcare facilities and frontline healthcare workers. Key acronyms and abbreviations used include AARR (Average annual rate of reduction), AIDS (Acquired immunodeficiency syndrome), ASFR (Age Specific Fertility Rates), CEE/CIS (Central and Eastern Europe and the Commonwealth of Independent States), CM Child (Mortality), FLR (Female Literacy Rate), GDP (Gross Domestic
Product), HEPC (Health Expenditure Per Capita), HDR (Human Development Report), IMR (Infant mortality rate), MDG (Millennium Development Goal) , PCGDP(Per Capita Gross Domestic Product ), TFR(Total Fertility Rate ), U5MR(Under-five mortality rate ) , UNICEF(United Nations Children’s Fund ) , UNPD(United Nations Population Division ), and WHO(World Health Organization).
Child mortality, which refers to the death of infants and children under five years old, remains a global concern. Tragically, around 26,000 young children perish each day, despite most of these deaths being preventable. Notably, there has been a 60% decline since 1960, with 9.7 million child deaths recorded in 2006. It is worth mentioning that Africa alone accounts for half of all child deaths.
Approximately 60 countries make up 94% of under five child deaths. According to UNICEF, it would cost $US 1 billion (an average of $US 1000 per child) to prevent one million child deaths annually.
The under-five mortality rate, also known as Child Mortality, is the probability (measured as a rate per 1,000 live births) of a child born in a specific year dying before reaching the age of five, based on current age-specific mortality rates.
To achieve this goal, policies aimed at reducing child mortality should prioritize improving the socio-economic status of countries and households.
The international community has long prioritized enhancing the health of young children and reducing child mortality. The reduction of child mortality is one of the eight Millennium Development Goals (MDGs) established during the Millennium Summit in 2000. Donors, development agencies, the United Nations, and national governments worldwide have committed to reducing the under-five mortality rate by two-thirds between 1990 and 2015.
The importance of infant and under-five mortality rates cannot be emphasized
enough as they play a crucial role in decision-making, policy formation, program development, and progress monitoring at national and international levels. It is crucial to accurately estimate these rates in order to make well-informed funding decisions that aim to reduce child mortality. However, accurately estimating infant and under-five mortality poses challenges due to limited data in many developing countries and a lack of agreement on the most effective method for generating estimates from available data. Previously, UNICEF, The World Bank, and the World Health Organization (WHO) each independently produced and published global estimates for under-five mortality rates.
The three agencies utilized different data sources, assigned varying weights to those sources, and used diverse methodologies to extrapolate trends. Child mortality is linked to demographic factors, economic determinants, and the household's environmental and socio-economic characteristics. These factors include the Total Fertility Rate (TFR), the Per Capita Gross Domestic Product (PCGDP) of the nation, and the Female Literacy Rate (FLR). The TFR indicates the average number of children a woman would have in her lifetime based on two conditions: if she were to experience current age-specific fertility rates throughout her entire life and if she were to survive from birth until the end of her reproductive years. The TFR is calculated by summing up single-year age-specific rates at a specific point in time. The PCGDP measures the value of all final goods and services produced in a nation within a year divided by the average population for that same year. The FLR represents the percentage of educated women aged 15 and above. This study aims to analyze how socio-economic characteristics relate to child mortality.
The study aims to evaluate the
correlation between the environment and child mortality using data from different countries. It seeks to compare Under-5 mortality rates across countries categorized by their Human Development Index. Additionally, it analyzes the impact of factors such as Total Fertility Rate, Female Literacy Rate, Per Capita GDP, Health Expenditure Per Capita, Number of Physicians per lakh of population, and poverty levels on Child Mortality. The study also investigates reasons for any disparities between expected and actual relationships among these variables and examines determinants and trends of Child Mortality specifically in India.
Moreover, this research addresses the issue that while the environment is vital for human survival, it also poses significant health risks globally. Particularly in least developed nations, around one-fifth of children fail to reach their fifth birthday due to preventable environmental threats to health.
This leads to approximately 11 million preventable childhood deaths every year, along with numerous others facing health issues and disabilities that impede their overall well-being and goals. Poverty plays a crucial role in impacting health as it determines an individual's vulnerability to environmental hazards and their access to resources for addressing these risks. In developing countries, the most severe environmental health threats often exist within close proximity. Many people in these nations constantly face exposure to biological pathogens due to their impoverished living conditions. The extent of mortality within a country is heavily influenced by poverty.
The study examines the main causes of child death, particularly in India. India consistently has high rates of child mortality compared to other countries worldwide. Despite being classified as a Medium Human Development Country, India has made significant progress in reducing its under-five mortality rate. From 1960 to 2005,
the rate dropped from 236 per 1000 live births to 85 per 1000 live births, showing an impressive decrease of 65% over a period of forty-five years.
Despite our efforts, mortality rates are still alarmingly high. The reduction of child mortality is the fourth goal of the Millennium Development Goals, aiming to decrease the under-five mortality rate by two-thirds between 1990 and 2015. To achieve this goal in India, it is crucial to comprehend the factors that contribute to high mortality levels. This study investigates how household's environmental and socio-economic characteristics affect child and under five mortality in India. During this research, multiple texts and research papers were consulted, including "Reducing child mortality in India in the new millennium" by Mariam Claeson and Eduard R.
Bos, Tazim Mawji, ; Indra Pathmanathan: The study notes the decrease in infant mortality rates in India is slowing down, which deviates from long-term trends. The study also examines the main causes of childhood mortality and proposes strategic options for each state in India based on current mortality rates and progress levels. To address the deceleration of childhood mortality rates in India, new approaches are needed that go beyond focusing on specific diseases, programs, or sectors.
Poverty, Undernutrition, and Child Mortality: Some Inter-Regional Puzzles and their Implications for Research and Policy by Stephan Klasen University of Gottingen and IZA Bonn: This paper investigates the link between measures of income poverty, undernourishment, childhood undernutrition, and child mortality in developing countries. Although there is a expected close correlation between these deprivation measures at the aggregate level, the measures reveal certain inter-regional paradoxes.
Women’s economic roles and child survival: the case of India by Alaka
Malwade Basu and Kaushik Basu (Institute of Economic Growth, University Enclave, Delhi-110 007, India. Delhi School of Economics, University Enclave, Delhi): This article presents evidence suggesting that women's employment has a negative consequence on child mortality rates compared to women who do not work, despite other benefits associated with women's economic participation.
The paper "Environmental Determinants of Child Mortality in Kenya" written by Clive J. Mutunga in December 2007 explores the factors that contribute to infant and child mortality in Kenya. It investigates how household environmental and socio-economic characteristics, such as a mother's education, drinking water source, sanitation facility, cooking fuel type, and access to electricity, impact the rates of infant and child mortality. The research utilizes a hazard rate framework to analyze these determinants.
To conduct this study, data on under-5 mortality from various countries is gathered from the official websites of the World Health Organization (WHO) and the Human Development Report. Regression analysis is then performed on this data using software like Microsoft Excel and Minitab to derive different findings.
The measurement employed in this research defines under-five mortality rate as the likelihood (expressed as a rate per 1,000 live births) of a child born in a particular year dying before turning five years old if exposed to current age-specific mortality rates.
The content discusses various categories of mortality among young children, including neonatal mortality (death within the first 28 days of life), post-neonatal mortality (ages 1 to 11 months), infant mortality (between birth and age 1), child mortality (ages 1 to 4 years), and under-five mortality (between birth and age 5). These categories have practical implications for programs and policies, particularly neonatal mortality, which serves
as an indicator of maternal and newborn health. This paper primarily focuses on addressing child mortality overall, specifically under-five mortality.
There are different methods for calculating the under-five mortality rate (U5MR) depending on the available data. Data can be obtained from birth and death registrations, national census, or household surveys. Estimating U5MR becomes easier when high-quality data from registration systems are accessible by observing the survival status of different cohorts over time and at specific ages since birth. Alternatively, household survey data allows for computation of U5MR using direct or indirect methods.
The direct method uses data collected on birth histories of women of childbearing age and produces the probability of dying before age five from children born alive. Process for obtaining data and estimation UNICEF compiles U5MR country estimates derived from all sources and methods of estimation obtained either from standard reports, direct estimation from micro data sets, or from UNICEF's yearly exercise. In order to sort out differences between estimates produced from different sources, with different methods, UNICEF developed, in coordination with WHO, the WB and UNPD, an estimation methodology that minimizes the errors embodied on each estimate and harmonizes trends along time. Since the estimates are not necessarily the exact values used as input for the model, they are often not recognized as the official U5MR estimates used at the country level. However, as mentioned before, these estimates minimize errors and maximize the consistency of trends along time. Comments and limitations (data quality) In the majority of developing countries, due to difficulties in data collection, U5MR estimates are obtained from household surveys and therefore have attached confidence intervals that need to be considered
when comparing values along time or across countries.
Non-sampling errors impact recent levels and trends of U5MR similarly.
I. CHILD MORTALITY: EVIDENCE FROM CROSS-COUNTRY DATA
Child mortality has significantly decreased globally, with the number of children dying before age five falling below 10 million for the first time in 2006. This represents a 25% decrease from nearly 13 million child deaths in 1990. The worldwide probability of a newborn baby dying before reaching five years old is currently approximately 7%, compared to 10% in 1990, 12% in 1980, and 25% in1950.
The Bulletin article states that there are 57 countries that have not reached the target of reducing child mortalities to 70 per 1,000 live births. Among these countries, Niger (335), Sierra Leone (312), Afghanistan (264), Malawi (219), Guinea and Liberia (205), Guinea-Bissau (202) and Somalia (201) have estimated child mortality rates above 200 per 1,000 live births. Out of these eight countries, seven are located in WHO's African region where the average child mortality rate is approximately 150. In contrast, South-East Asia, Eastern Mediterranean, Western Pacific, Americas and Europe have rates of 88, 67, 46, 34 and 18 respectively.
Analyzing cross country experiences reveals that sub-Saharan Africa has the highest under-five mortality rates. Factors such as underdevelopment, armed conflict and HIV/AIDS have greatly impeded efforts to improve child survival in this region. In fact, ten countries within sub-Saharan Africa have estimated under-five mortality rates surpassing 200 deaths per 1,000 live births.
Child mortality rates in South Asia, a developing region, continue to remain high. However, three regions - East Asia and the Pacific, Latin America and the Caribbean, and Central and Eastern Europe and the Commonwealth
of Independent States (CEE/CIS) - have successfully achieved under-five mortality rates below 30 deaths per 1,000 live births by 2006. On the other hand, the developed world has a significantly lower child mortality rate, close to 6 deaths per 1000 live births. Infant and child mortality rates have decreased in all UNICEF regions since 1990, which serves as the baseline for the Millennium Development Goal (MDG) targets. The decline has been particularly notable in East Asia and the Pacific, Latin America and the Caribbean, and CEE/CIS regions, where estimated under-five mortality in 2006 was approximately half of what it was in 1990.
The under-five mortality rate in sub-Saharan Africa has only decreased by 14% over the same time period. To achieve the MDG4, the under-five mortality rate needs to decline by an average of 4.4% annually between 1990 and 2015. East Asia and the Pacific, Latin America and the Caribbean, and CEE/CIS regions have either achieved or come close to achieving this benchmark by 2006, putting them on track to meet the MDG4. However, sub-Saharan Africa has only seen an average annual reduction of 1% in under-five mortality since 1990.
In a number of sub-Saharan countries, the mortality rate for children under five has actually increased in recent years. Factors such as the AIDS epidemic, armed conflict, and social instability have contributed to this worsening situation. Achieving the Millennium Development Goal (MDG) target for child mortality in these countries will require significant actions. The situation is somewhat better in South Asia and the Middle East and North Africa regions, but there is still room for improvement to meet the 2015 target. There is much work to be
done in order to reach MDG4, and it will necessitate an extraordinary effort from the international community, governments, NGOs, civil society, and other stakeholders.
Effective and affordable interventions are available to prevent or treat each major cause of under-five mortality. Scaling up these interventions can reduce infant and child mortality and help countries meet the MDG4. The map below shows the regional grouping of countries used in the analyses. The histogram depicts the under-five mortality rate in these regions in 1990 and 2004, as estimated by the UN. The graph also shows the target for the under-five mortality rate by 2015 as part of MDG4. Child mortality is influenced by various socio-economic factors in households.
The level of under five mortality rate in a nation is believed to be influenced by Total Fertility Rate, Per Capita Gross Domestic Product, and Female Literacy Rate. A study collected data on these factors for 79 countries from all regions of the world. Multiple Regression was conducted to examine the relationship between these variables and child mortality. The data can be found in Appendix A (Table 1). It is expected that Child Mortality is negatively associated with Female Literacy Rate and Per Capita GDP, but positively associated with Total Fertility Rate. Countries with higher levels of female education tend to have lower rates of child mortality. This can be attributed to the fact that literate mothers tend to have healthier babies as they themselves are generally healthier than illiterate mothers.
Literate mothers have a greater likelihood of creating a healthy environment and providing nutritious food for their children compared to illiterate mothers, even under similar circumstances. Additionally, literate mothers are more likely
to possess knowledge regarding healthcare facilities and have more influence within the family when deciding to seek medical treatment for their sick children. These characteristics contribute to lower mortality rates in children under the age of five. The survival rate of a woman's children decreases as the number of children she bears during her reproductive cycle increases. Mortality rates are particularly high for first-born children and for births of extremely high orders, but low for births of order 2 or 3. First-born children are more susceptible to difficult childbirth processes, which can increase the risk of neonatal mortality. Furthermore, parents with limited skills and experience are often responsible for raising first-born children, potentially heightening the chances of infant and child mortality. Finally, births of very high order may involve mothers who are physically exhausted at the time of conception and throughout pregnancy.
Children born from high-order births are at a higher risk of conditions related to high mortality, such as foetal growth retardation and low birth weight. These births occur in families with multiple young children, leading to competition for resources and parental care. Women with high fertility rates start their reproductive cycle early in life, resulting in challenging pregnancies and deliveries due to physical immaturity. Additionally, these mothers often have limited knowledge and confidence in caring for infants and young children. Furthermore, households with higher purchasing power, as measured by PCGDP, are more likely to receive good pre and postnatal care.
When analyzing the reduction of Child Mortality, we performed a regression using the variables FLR, TFR, and PCGDP. The results of the regression analysis can be found in Appendix B, where coefficents, standard errors, t-statistics,
and p-values for each variable are presented. The intercept of the regression model is 3.44825.
696580. 13420. 89363 TFR 31. 273. 2155339.
72450 FLR -0. 480. 224423-2. 1430. 03538 PCGDP -0.
The relationship between TFR (Total Fertility Rate), FLR (Female Labor Force Participation Rate), and PCGDP (Per Capita Gross Domestic Product) follows the expected pattern.
There is a positive correlation between TFR and CM (Child Mortality Rate), and a negative correlation between FLR and both PCGDP and CM.
All the coefficients are statistically significant, as indicated by the low p-values that are close to zero.
The regression analysis shows that 80.72% of the variation in CM can be explained, while 19.28% remains unexplained. The model, which includes three variables that impact child mortality, appears to be effective. All variables have the expected signs and are statistically significant, as evidenced by their low p values. Additionally, the overall R2 is relatively high for the cross-sectional data. However, if we were to estimate the model with only one or two independent variables, we would introduce a specification bias by omitting relevant variable(s). This would result in inconsistent coefficients for the incorrectly estimated model. For instance, if we were to regress CM on TFR.
The F test is utilized to assess whether the inclusion of FLR and PCGDP is justified in the model. The formula for the F test is as follows: F = (R2ur - R2r)/m / (1-R2ur)/(n-k). This statistic follows the F distribution, with m and (n-k) degrees of freedom in the numerator and denominator, respectively. Here, R2r represents the coefficient of determination derived from the restricted regression, while R2ur represents the coefficient of determination obtained from the unrestricted
regression. The values for m, n, and k are determined as follows: m refers to the number of restrictions imposed by the restricted regression (in this case, two), n indicates the number of observations in the sample, and k represents the number of parameters estimated in the unrestricted regression (including the intercept term). In our specific example, R2ur equals 0.80727, while R2r equals 0.7875.
Using the given equation, the computed F value for a numerator of 2 and a denominator of 75 is 3.846, indicating high significance. This suggests that FLR and PCGDP should both be included in the model. Additionally, several other factors, such as income poverty, malnourishment, childhood undernutrition, and child mortality, are closely interconnected with the under-five mortality rate. Countries with higher poverty rates tend to experience more severe instances of poverty. Insufficient resources hinder the ability of individuals to provide adequate nourishment for both the mother and the child, increasing the likelihood of death.
The health status of a country's population is determined by the government's expenditure on health. However, there is a significant gap between the potential impact of public spending on health and its actual performance. Research shows that preventive and primary curative interventions can greatly reduce child mortality rates, with costs as low as $10 or less than $1000 per life saved. Additionally, the number of physicians in a country also plays a key role in the effectiveness of the government's health policy. A higher number of physicians is associated with lower rates of child mortality. Appendix A provides data on various factors influencing child mortality in 110 countries, grouped into High Human Development, Medium Human Development, and Low Human
Development categories according to the Human Development Report.
The levels and causes of child mortality vary among different groups of countries. Minitab regression was used to analyze the data for these three groups of countries separately as well as collectively. In High Human Development Countries, the regression equation is: U5MR = 32.8 + 0.230 PL + 1.42 TFR + 0.00129 HEPC - 0.
0134 P - 0. 000301 PCGDP - 0. 217 FLR The regression shows that U5MR is higher with greater poverty.
Additionally, higher the number of doctors, lower is the mortality rate among children. The impact of healthcare expenditure does not appear to be significant in this particular model. This could be because in countries with high human development and already low under-five mortality rate (U5MR), increased public spending does not result in a decrease in mortality. Mortality is attributed to inherent natural causes rather than external factors that could be addressed through healthcare spending. The d statistic and VIF indicate that there is no autocorrelation or multicollinearity present in the data. The regression analysis explains 57% of the variation in U5MR.
The reason for this low value is because factors like TFR, FLR, HEPC, PCGDP are not significant causes of child mortality in developed countries. In countries with medium human development, the regression equation for U5MR is: U5MR = 32.5 + 0.224 Pl + 18.5 TFR - 0.0248 HEPC + 0.
The regression consistently shows that in Medium Human Development Countries, an increase of $1 in health expenditure per capita will result in a reduction of child mortality rate by 0.113 P, 0.00679 PCGDP, and 0.493 FLR.
0248. The data lacks autocorrelation or multicollinearity, as indicated
by the d statistic and VIF. In Low Human Development Countries, the regression equation is: U5MR = - 257 + 0. 18 PL + 62. 2 TFR + 0. 161 HEPC + 2.
64 P - 0. 0234 PCGDP - 0. 681 FLR The coefficients show expected signs, except for HEPC and Physicians. This can be attributed to the unique structure and nature of low Human Development economies. The effect of increased availability of quality health services in the public sector on health outcomes is influenced by individual demand and market supply, which differ across countries.
Public funds are being used for costly but ineffective treatment services. In less developed economies, there is widespread corruption, fund mismanagement, and inefficiency. The gap between rural and urban areas is more pronounced in these regions, with a majority of the population living in rural areas. Urban residents, who have access to well-established healthcare facilities, and the affluent, educated individuals in rural areas reap the most benefits from public healthcare spending. Additionally, there is limited awareness of healthcare importance in rural areas.
Despite the limited resources, governments in underdeveloped nations have taken steps to improve healthcare access. However, people in these countries are hesitant to seek professional medical help and instead rely more on local treatments provided by older members of the community. The statistical measurements, d statistic and VIF, indicate no autocorrelation or multicollinearity. Approximately 67% of the variation in the dependent variable, U5MR, can be attributed to changes in the explanatory variables, while the remaining variation is attributed to random factors. This is evident from the R-Sq(adj) value of 67%, which is higher than that observed in High Human
Development countries.
This indicates that factors such as FLR, PCGDP, TFR, population below the Poverty Line etc have a significant influence on U5MR in these countries. This is due to a higher occurrence of child mortality caused by various factors other than natural causes. When regression is applied to the whole set of 110 countries, the resulting equation is: U5MR = 0.7 + 0.539 PL + 26.8 TFR + 0.00011 HEPC + 0.
The relationship depicted is consistent with what was expected. The HEPC and Physicians have inconsistent signs because of the cross-national variation in health status. It is incorrect to jump from some countries' good health outcomes to the conclusion that all (or even that any) of the unexplained differences in mortality are due to health policy. While it is possible that these countries' good health outcomes are due to health sector strategy, it is equally plausible that they share non-health characteristics like high levels of female education, better nutrition, more equal income distribution that explain their better outcomes.
81% of the variation in U5MR is attributed to the regression, surpassing the result for individual countries. The d statistic and VIF indicate no autocorrelation or multicollinearity in the data. Thus, the results demonstrate that a significant portion of the disparity in health status across countries, as indicated by the under-5 mortality rate, can be explained by factors unrelated to non-health sector policy.
Approximately 80 percent of the variation in under-5 mortality is explained with income, its distribution, female education, the total fertility rate, and other "cultural" factors. Additionally, income alone is a powerful determinant, but other factors also significantly contribute to under-5 mortality. Higher public spending
on health as a share of GDP is weakly associated with improved health status, and its effectiveness is much lower than expected. The correct interpretation of the empirical results and their policy implications depend on three factors: cost effectiveness of public spending, the net impact of additional public supply, and public sector efficacy. Each factor can explain the observed results and contributes to the low efficacy of actual public spending. The implications for reform vary based on the specific situation. The goal of the fourth MDG is to reduce under-five mortality by two-thirds between 1990 and 2015.
Achieving this goal requires a yearly decline of 4.4% in the U5MR for 25 years. To meet the MDG4 target by 2015, the rate of change needs to accelerate: global child mortality must decrease by an average of 9% per year from 2007 to 2015. Unfortunately, under-five mortality has risen in several countries, resulting in a lower chance of children surviving to their fifth birthday compared to 1990. The causes of child mortality are diverse.
The majority (60%) of deaths are linked to undernutrition. Nearly 1/3 of children under the age of five in Asia and Africa exhibit stunted growth as a result of chronic malnutrition stemming from insufficient diets and frequent illnesses. Though the situation in Asia seems to be improving, Africa lacks similar progress.
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