CHILD MORTALITY IN DEVELOPING COUNTRIES

CHILD MORTALITY IN DEVELOPING COUNTRIES: CHALLENGES AND POLICY OPTIONS James A. Oloo* Abstract: The objective of this study is to understand determinants of mortality rates of children under the age of five years in developing countries. The study uses secondary data to investigate the relationship between underfive mortality rates and such socioeconomic variables as fertility, literacy, immunization, access to clean drinking water, HIV/AIDS prevalence, and human and material resources using linear regression analysis. Results show that while most of these variables have a significant relationship with under-five mortality rate, the proportion of doctors for every 100,000 population, and health expenditure per capita have an insignificant predictive value. Conclusion: Reducing child mortality rates requires multiple intervention strategies, such as access to safe drinking water, improvement in education opportunities, family planning, and tackling HIV/AIDS.

Key words: Under-five mortality, developing countries, United Nations, World Bank, fertility, linear regression

1. INTRODUCTION Child mortality in developing countries has been a focus of researches in the recent decades, especially by donor agencies and such international organizations as the World Health Organization (WHO), UNICEF, and Oxfam (Gwatkin 1999). This indicator is influenced both directly and indirectly by a number of factors. Because the immune system of young children is not as developed as that of a healthy adult, children are more likely to be affected by the availability of health facilities, nutritious food, and sanitation, or lack thereof. In developing countries, child mortality accounts for a relatively higher proportion of all deaths, whereas in the developed countries, it represents an increasingly small segment of total mortality (Poerwanto et al. 2003).1 Child mortality is calculated by comparing the number of children who die before their fifth birthday to 1,000 live births. Sweden is the lowestranking country on the child mortality scale, reporting just three infant deaths for every 1,000 live births. Sierra Leone, which holds the last rung on the United Nations' Human Development Index, remains the most *

Simon Fraser University, 10-6395 Hawthorne Lane, Vancouver, BC, V6T 1Z4,Canada, E-mail : [email protected]

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EASSRR, vol. XXI, no. 2 (June 2005)

dangerous place for infants, reporting 284 deaths for every 1,000 live births (UNICEF 2005). On average, Latin American and Caribbean countries have experienced a substantial improvement in reducing child mortality in the last four decades; but disturbing gaps are still opening within nations there. In Haiti, for example, more than one in ten children die before age five. In 2000, the under-five mortality rate for the Middle East and North Africa was 71/1000 while it was 178/1000 for that of Sub-Saharan Africa (UNICEF 2005).

2. HYPOTHESIS This study examines the correlation between child mortality and explanatory variables hypothesized in the demographic literature: fertility, literacy rates of females, HIV/AIDS prevalence, GDP per capita of a country, immunization, and accessibility to clean water and sanitation. Each of these variables is defined and its hypothetical relationship to child mortality is explained. Dependent variable Child mortality rate This refers to the total number of deaths of both males and females aged 0-5 years old for every 1000 live births (It includes infants counted by the infant mortality rate). The under-five mortality rate was used as the indicator of child mortality as it provides the best means of capturing mortality risks during the most vulnerable years of childhood (Ahmad et al. 2000). Mortality can be classified into two broad categories: endogenous and exogenous (Cigno 1998). Endogenous mortality is presumed to arise from genetic causes, such as degenerative diseases (e.g. cancer, heart disease, and diabetes) and from causes related to early infancy such as birth injuries, congenital disorders, premature births, and postnatal asphyxia. Exogenous mortality may be attributed to environmental or external causes, such as infections and accidents. For the purposes of this study, no distinction is made between the two types of mortality. Independent variables Fertility rates Fertility, as used in this study, refers to the average number of children born per woman. While scholars have different opinions on whether or not there is a causal relationship between infant mortality and fertility, and the possible direction of the causality flows between the two variables (Galloway et al. 1998; Jain 1998), most agree that there is a relationship

James A. Oloo. Child Mortality in Developing Countries: Challenges ...

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between fertility and child mortality (Bairagi et al. 1999; Pandey 1997; The Alan Guttmacher Institute 2004). It is hypothesized here that increase in fertility will have a positive correlation with child mortality. HIV/AIDS prevalence rates This refers to the percentage of the proportion of a country’s total population who has tested positive for HIV/AIDS. HIV/AIDS causes death among children both directly through infection and indirectly by contributing to poverty and hardships for children of AIDS devastated families. HIV/AIDS has caused adult mortality rates to increase in many countries and there is some indication that child mortality rate is also rising due to vertical transmission (Ngom and Clark 2003). However, some scholars assert that since HIV positive women suffer increased mortality and decreased fertility, the widely used retrospective techniques for measuring child mortality (directly, through birth history analysis, or indirectly, using the Brass techniques) no longer yield reliable estimates, as the independence between the mortality experience of mothers and children cannot be assumed (Mahy 2003; Ngom and Clark 2003; Ntozi and Nakanaab 1997). It is hereby hypothesized that there is a positive correlation between HIV/AIDS prevalence and child mortality. Female literacy rates Female literacy refers to females, aged 15 years and over, who can read and write in any language. Education of mothers is perhaps the best long-term investment to determine the well-being of children. A female who has not gone to school is more likely to be poor, marry early, die in childbirth, lose a child to sickness or disease, have many closely- spaced births and have children who are chronically ill (Teicher 2005). A negative correlation between female literacy and child mortality is hypothesized. Gross domestic product (GDP) per capita2 A lot has been written about the effects of well being on child mortality. According to a World Bank report, the share of the population in developing countries living on less than US$ 1 a day fell from about 28 percent in 1987 to 23 percent in 1999 (The World Bank Group 2004). The number of ‘poor’ people remained roughly constant as the population increased. During the same period, the number of people living on less than “US$ 2 per day- a more relevant threshold for middle income economies” such as those of East Asia and Latin America – showed roughly similar trends (p. 71). The World Bank Group also noted that while child mortality has declined by about 50 percent in low-income countries, over the last forty years it has been increasing in sub-Saharan Africa; and there are also

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large differences between the health status of poor and non-poor children within countries. It is hypothesized that there is a negative correlation between GDP per capita and child mortality. Health expenditure per capita A review of literature on public expenditure on health in developing countries revealed that, on average, health spending has risen slowly and is now typically four to six percent of GDP with about 40 percent of expenditure being public (UNICEF 2004). Available data suggest that, except in South Asia and sub-Saharan Africa, health expenditure per capita may be enough to cover the cost of a minimum package of interventions; and that some public health programmes, such as immunisation, have been very successful but they are often minor elements in health budgets (Luther and Thapa 1999; Mahy 2003; The World Bank Group 2000; WHO 2003b). It is hypothesized that increase in health expenditure per capita is likely to lead to a decline in child mortality. Proportion of completely immunized children between ages 0-5 years According to the World Health Organization (WHO 2003a), vaccines save about three million lives annually, and more than 1.5 million deaths of children under five years of age could be prevented if all children under five had access to vaccines. It is, thus, hypothesized that the proportion of completely immunized children between ages 0-5 has a negative correlation with child mortality. Human resource indicators Both UNICEF (2004) and WHO (2003) regard the proportion of physicians and that of nurses to the population as an important health indicator. Two hypotheses are, therefore, made: First, there is a negative correlation between the number of physicians for every 100,000 population and child mortality. Second, there is a negative correlation between the number of nurses for every 100,000 population of a country and child mortality. Proportion of population with access to safe drinking water The UN Secretary-General Kofi Annan asserts, “No single measure would do more to reduce disease and save lives in the developing world than bringing safe water … to all” (as cited in Water Matters 2003). Measured by the number of people who have a reasonable means of getting an adequate amount of clean water (expressed as a percentage of the total population), access to safe drinking water reflects the health of a country's people and the country's ability to collect, clean, and distribute water (The


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World Bank Group 2004; Water Matters 2003). Research shows that contaminated drinking water and an inadequate supply of water cause diseases that account for ten percent of the total burden of disease in developing countries (The World Bank Group 2004). It is hypothesized that there is a negative correlation between the proportion of population with access to safe drinking water and child mortality.

3. METHODOLOGY This study analyzes secondary data using linear regression to investigate the correlation between under-five mortality and the nine independent variables described above. The method used involves a brief discussion of the data, theoretical framework, and the model of analysis employed. The data To understand the determinants of mortality, it is important to measure health indicators using reliable data sources (Ahmad et al. 2000). In his examination of potential problems associated with cross-country data, Srinivasan (1994 as cited in Mosley 1985) argues that child mortality is particularly sensitive to estimation methods, data sources and comparability. The data analyzed in this study was collected by credible institutions – the CIA (2004), UNICEF (2004), WHO (2003a;b) and the World Bank Group reports (2004). Though not without inherent limitations (for example due to estimation methods for countries that do not keep ‘accurate’ data), these data sources are among the best that are available, and therefore, are expected to generate reliable results. The data are from hundred countries, selected from the so-called ‘developing countries’ as described by the World Bank (2004). The countries chosen have data on all the variables tested in this study. Theoretical framework This study uses the ecological approach to relate under-five mortality rates at the population level to various characteristics of developing countries. Specifically, it employs the conceptual model developed by Mosley (1985) for the study of the determinants of infant mortality in the Middle East. The analytical framework he proposed identifies the related environmental, susceptibility of, as well as the intermediate variables through which social and economic determinants may operate to influence the levels of mortality. The model The analytical model utilizes linear regression to evaluate the relationship between under-five mortality and various socio-economic indicators. Nine independent variables are tested and their ability to predict child mortality is assessed.

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4. RESULTS The strength of the model to explain the variance in child mortality was measured using R-square. Two-step linear regression model yielded Rsquare value of 0.727 (Table 1) with ANOVA of 0.000, meaning the result was not a fluke. The R-square value can be interpreted to mean that the model predicts that 72.7 percent of the outcomes for under-five mortality can be explained by the nine independent variables. The model shows a fairly strong association between the independent and dependent variables. Durbin-Watson statistic was requested as well. This statistic informs us whether the assumption of independent errors is tenable. Its value of 1.603 confirms that the assumption has almost certainly been met. Table 1. Regression model summary b

Model Summary Model

1

R

R Square

Adjusted R Square

Std. Error of the Estimate

DurbinWatson

.852a

.727

.699

21.55465

1.603

a.

Predictors: (Constant), DOCTORS, HEALTHPE, HIV, GDPPERC, IMMUNIZE, FEMALE, WATER, NURSES, FERTILITY

b.

Dependent Variable: MORTALITY

Coefficients A test for collinearity in the data was done, and results are displayed in Table 2. This was to determine if explanatory variables are confounded due to their intercorrelations. The Tolerance values for this model were all above 0.1, while VIFs were below 10. This indicates that there was no collinearity within the data used. Unstandardized coefficient value of each independent variable is described below.


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Table 2. Results of regression analysis Coefficients a Unstandardized Coefficients

Standardized Coefficients

Model

1 (Constant) NURSES FERTILITY HIV FEMALE GDPPERC HEALTHPE IMMUNIZE WATER DOCTORS

Collinearity Statistics t

B

Std. Error

96.196 4.578E.02 7.481 1.000 -.367 1.749E.03 -.279 -.222 -.389 -1.054E.02

25.177 .015 2.800 .320 .153 .001 1.215 .154 .150 .032

Sig.

Beta

.263 .303 .198 -.242 .189 -.014 -.115 -.225 -.030

3.821 3.041 2.672 3.121 -.405 2.342 -.230 -1.437 -2.596 -.334

.000 .003 .009 .002 .018 .021 .819 .154 .011 .739

Tolerance

VIF

.408 .237 .753 .299 .468 .786 .476 .404 .373

2.453 4.220 1.329 3.342 2.135 1.272 2.101 2.475 2.680

a. Dependent Variable: Mortality

Number of nurses for every 100,000 population had a coefficient of 0.045 with a significance of 0.003. That is, an increase in the number of nurses by one for every 100,000 population of a country is likely to result in an increase in child mortality by 0.045 for every 1000 live births (or 45 for every one million live births). The hypothesis that increase in the number of nurses is likely to lead to a fall in child mortality rates was thus not confirmed. On the contrary, the study predicted a positive correlation between nurses and child mortality. This is very surprising. However, while it contradicts the findings by Camacho et al. (2003), it concurs with those by Suwal (2001) and Younger (2001).3 A possible explanation for the positive correlation between the number of nurses and child mortality- though not supported by data- is that because doctors are more expensive to train, hence are relatively few in proportion to the total population in most developing countries, nurses usually assume doctors’ roles. The author’s experience in developing countries shows that in many cases nursing qualifications are not ‘regulated’ by the governments in those countries. Again, physicians tend to concentrate in urban areas where a minority of the population dwells. These, coupled with increasing demand for nurses, have led to less qualified nurses entering the profession.4

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Fertility rates had a coefficient of 7.481 with a significance of 0.009. That is, as fertility increases by one additional child per woman, child mortality is likely to increase by about seven for every 1000 live births. This confirms our hypothesis that there is a positive correlation between fertility and child mortality. HIV/AIDS prevalence was significant at a coefficient of 1.00. Hence, as HIV/AIDS prevalence rates in a country increase by one percentage point of the population, child mortality is likely to increase by one for every 1000 live births. This supports the hypothesis that there is a positive correlation between HIV/AIDS prevalence and child mortality. Since HIV/AIDS prevalence level is ‘high’, and still increasing in many developing countries, its effect on child mortality is likely to persist for several years. Female literacy rates had a coefficient of –0.367 with a significance of 0.018. That is, as the number of females aged 15 years and over who can read and write increases by one percent, child mortality rates are likely to fall by about three for every 10,000 live births. This validates our hypothesis. GDP per capita had a coefficient of 0.0017 and a significance of 0.021. This means that as a country’s GDP per capita increases by one unit, child mortality is likely to increase by about 0.002 for every 1000 live births. Although this is a ‘small’ rate of change, the positive correlation between the two variables is surprising. Our hypothesis has not been confirmed, which could be due to the problems inherent in the use of GDP per capita as an indicator of human development (Barro 2000). The GDP does not record the informal economy, which is estimated to account for between 30 and 60 percent of the GDP of developing countries (European Commission 1998).5 Also, because GDP per capita is unlikely to change rapidly over time, it may be a proxy for some other country characteristics unaccounted for in the regression.6 The proportion of population with access to safe drinking water had a coefficient of –0.389 with a significance of 0.011. That is, as the proportion on the population with access to safe drinking water increases by one percent, under-five mortality rates are likely to decrease by about four for every 10,000 live births. Not only does this finding support our hypothesis, but it also helps explain the Poerwanto et al. (2003) assertion that the probability of diarrhea in children under five years old, one of the principal causes of infant mortality and malnutrition in developing countries, is inversely proportional to the availability of safe drinking water. Three independent variables- the proportion of completely immunized infants, health expenditure per capita, and the number of doctors per 100,000 population had no significance. Insignificance of immunization in


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reducing child mortality is very surprising given that, measles, a vaccinepreventable disease, accounts for over half a million deaths of children aged 0-5 in developing countries annually (UNICEF 2004; WHO 2003b). In addition, given the number of projects in developing countries which are funded by such international organizations as the Canadian International Development Agency (CIDA), the Norwegian Development Agency (NORAD) and others that are offering immunization programmes, it was assumed that immunization is an essential component in reducing child mortality rates (Mahy 2003; WHO 2003a). CONCLUSION This model has explained a respectable amount of variation in child mortality amongst developing countries, all things considered. Most of the hypotheses were supported by the results of the analysis. However, the negative correlation between number of nurses and child mortality was surprising. Similarly, immunization, health expenditure per capita and GDP were statistically insignificant. GDP per capita did not have a strong coefficient, but was nevertheless significant. This seems to suggest that factors other than GDP per capita explain higher mortality rates. As this model suggests, fertility rates tend to have a larger impact on child mortality. Hence, developing countries should address the issue of fertility by promoting family planning campaigns. Policy options Reducing child mortality will require multiple, complementary interventions. These include access to safe water, better sanitation facilities, and improvements in education, especially for girls and mothers. Also needed are programmes to sensitize people on how to prevent HIV/AIDS. Based on this analysis, the following policy options are recommended. 1. Connecting all households to a reliable source of water that is reasonably protected from contamination would be an important step toward improving health and reducing child mortality. Where possible, underground water should be tapped, treated, and used as a source of safe water, especially for the majority of the population who live in rural areas in developing countries. Also needed are greater efficiency in the use of water and fair allocation to balance the limited supply with the growing demand. 2. The study found a strong relationship between child mortality and mothers’ literacy. The knowledge gained through education will enable mothers, among others, to have a greater awareness of sanitation, adapt a more hygienic way of living and healthy lifestyles, as well as to use health-care facilities and family planning more.

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Developing countries and their partners should invest in education. They should provide free and compulsory primary education to all citizens. Special attention should be paid to the countries where females are seen to be ‘less equal’ to males. Some countries are making better progress than others. For example, in Kenya, which has a per capita GDP of [US]$1,020 (in 2004), there are 99 girls in school for every 100 boys. Moreover, 71 percent of girls at primary school age are enrolled in class. Contrastingly, in Saudi Arabia, where GDP was twelve times higher, only 57 percent of girls were enrolled (Teicher 2005). 3.

Some countries, such as Burma and Guinea, have less than twenty physicians for every 100,000 population, and relatively higher numbers of nurses. It is thus possible that some nurses are playing the role of physicians. The other more plausible explanation is that highly trained health staff (not just nurses) are more likely to be found disproportionately in urban areas where fewer people live. In addition to the urban-rural divide, there tends to be over-concentration of quality health staff in the private sector, as opposed to the public sector, catering for fewer people. Although this factor has been known by analysts for quite some time, little action has been taken to remedy the situation. Governments should give incentives to attract and retain health care professionals in the rural areas. Further, nursing schools need to be regulated, their exams standardized, and professional groups (similar to the College of Nurses of Alberta) be formed to ensure professionalism in the field. Equally important, the countries should develop policies that emphasize, not just the number of nurses, but also medication, hospital beds and measures on prevention of diseases. The latter could be done in ways such as improving drainage systems so that stagnant waters, which are breeding grounds for mosquitoes, are drained. This is a positive step, albeit a minor one, in addressing the problem of malaria.

NOTES 1. An exception is the mortality rates of Aboriginal children in such developed countries as Canada, Australia, and New Zealand. For example, between 1998 and 2000, mortality rate for Indigenous infants was four times the rate of the total Australian population. As well, in 1999 infant mortality rate for Aboriginal populations was 1.5 times higher than the national Canadian average (Australian Bureau of Statistics 2002; Government of Canada 2002). 2. The World Bank defines GDP in two broad ways: (1) it is the sum total valueadded of all production units, including all taxes and subsidies on products


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which are not included in the valuation of output. (2) It is the sum of final uses of goods and services (except intermediate consumption) measured in purchasers' prices, less the value of imports of goods and services. Levels of GDP per capita are obtained by dividing annual or period GDP at current market prices by population. A variation of the indicator could be the growth of real GDP per capita which is derived by computing the annual or period growth rate of GDP in constant basic producers' or purchasers' prices divided by corresponding population (The World Bank Group 2004). 3. Younger’s finding is even more surprising. He concludes: “… measures of the availability of … doctors, nurses, and hospital beds per 1000 inhabitants- have no impact at all on the rate of decline of infant mortality” (p. 10). Younger attributes his findings to two main factors. First, that these variables “are not too relevant to national infant mortality rates because they measure mostly urban, formal sector health care availability” that is not available to most poor people whose children are most at risk (p. 10); second, that his variables may have been “measured with substantial error, which could account for their low t-statistics (from attenuation bias)” (pp. 10-11). However, that would not explain why their coefficients are positive both in Younger’s and this study. 4. In Kenya, only two percent of nurses have university education while in Uganda a great majority of nurses have only a two-year post-secondary training (Kenyaweb 2004; Government of Uganda 2003). In addition, other factors may be at play. These include allocation of large proportion of Ministry of Health budgets to hiring more nurses while factors like malaria and nutrition which may put the lives of children at risk are not addressed. 5. As a comparison, the shadow economy in Italy and Greece is about 20-25 percent of the GDP (European Commission 1998). 6. The use of the GDP, not the GDP itself, has been criticized as scientifically incorrect, Simon Kuznets (the economist that in the early 1940s devised GDP), himself and many other experts of national budgets and accounting tried for years to prevent the use of GDP as an indicator of prosperity and human development. Because GDP does not measure important aspect of development such as health, crime, poverty, environmental health/decay and destruction of the natural environment, loss of leisure time, lack of concern for future generations, and income gap (women/men; poor/wealthy), it may not be a ‘good’ indicator of human development (Organization for Economic Cooperation and Development 2003).

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