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African Population Studies
Union for African Population Studies
ISSN: 0850-5780
Vol. 11, Num. 1, 1996, pp. 29-62

African Population Studies/Etude de la Population Africaine, Vol. 11, October/octobre 1996

PREVENTIVE HEALTH STRATEGIES AND INFANT SURVIVAL IN ZIMBABWE

Assata ZERAI*

Code Number: ep96002

ABSTRACT

Socioeconomic and demographic variables are examined in a multilevel framework to determine conditions influencing infant survival in Zimbabwe (1983-88) using Demographic and Health Survey data. Community level child health inputs consistently influence infant survival. The most unique finding is that childbearing-aged women's average educational levels in their community exert a greater effect on infant survival than the individual mother's educational level. This result supports assertions that child survival is strongly impacted by mass education (Caldwell, 1989). This research also contributes evidence to theories postulating that widespread social development is necessary to sustain recent declines in developing country mortality rates (Palloni, 1989).

RÉSUMÉ

Les variables socio-économiques et démographiques sont étudiées dans un cadre à niveaux multiples pour déterminer les conditions qui influencent la survie des enfants à bas âge au Zimbabwé (1983-88) en utilisant les données d'une enquête démographique et de santé. Les apports communautaires à la santé de l'enfant exercent une influence régulière sur la survie des enfants âgés de moins de cinq ans. Le résultat le plus remarquable est que le niveau moyen d'instruction des femmes en âge de procréation au sein de leur communauté a un plus grand effet sur la survie des enfants de moins de cinq ans que le niveau d'instruction de la mère prise isolément. Ce résultat conforte les thèses selon lesquelles l'éducation populaire a un impact décisif sur la survie de l'enfant (Calwell, 1989). Cette recherche fournit également des preuves supplémentaires aux théories fondées sur le postulat qu'un développement social généralisé est nécessaire pour que les baisses relevées recemment au niveau des taux de mortalité dans les pays en développement soient durables (Palloni, 1989).

Infant mortality levels are still high in less developed countries today. The major causes of infant mortality are preventable and are tied to decreasing levels of household contamination, increasing nutritional intake and improving preventive and curative health practice (Preston, 1980). Inputs affecting infant health can take place on a community level, and additionally community-level infant health inputs can sometimes exert an even greater influence on infant survival outcomes than individual-level characteristics.

I examine demographic and socioeconomic variables measured at the individual level as well as community conditions influencing infant survival in Zimbabwe. The theoretical model used suggests that the socioeconomic status of the household and community conditions are mediated by proximate determinants, including maternal fertility, personal disease control, nutrient availability, and household contamination (Mosley and Chen, 1984).

There is not an extensive literature on community level effects on child survival from which to chart theoretical direction for this research (notable exceptions include: Bailey 1988; Mosley and Chen 1984). But there is a developing literature on multilevel analysis (Entwisle 1989; Billy and Moore 1992; Tsui 1985), and an extensive literature supportive the positive effect of the individual mother's education on child survival (Caldwell 1986; Caldwell 1989; Das Gupta 1990; Cleland and van Ginneken 1988; Cleland 1990). In a survey of 99 countries Caldwell (1986:179) has found that average levels of female education is the main predictor of child survival at the societal level. "There is... as close a correlation between child survival and general levels of education in a community as there is between child survival and maternal education" (Caldwell 1989:103). A multilevel analysis which incorporates both the individual mother's level of education with the average levels of education of women in the community is necessary to get a better understanding of the relative importance of education at the individual and community level for promoting child health.

Theoretically, this research is motivated by the basic premise that economic development and unidimensional health interventions such as immunization programs are not enough to ensure constant mortality decline in low-income countries; widespread social development is necessary to sustain a consistent mortality decline (Palloni 1985). There have been not only slowdowns in the apce of mortality decline in African and Latin American countries, but also reversals in infant and child mortality trends in such countries as Zambia (Gaisie, Cross and Nsemukila 1993:81) and Nigeria (Federal Office of Statistics 1992:78) which are witnessing recent increases in child mortality. While medical technology and economic development are important, programs that ensure the distribution of social development resources throughout the country to improve the living standards of families are necessary to sustain declines in infant mortality. Infant health is chosen as the dependent variable because infants are the most vulnerable population to the changes in economic and social development (Hill and Palloni 1992).

Further theoretical development is needed on many fronts in order to advance our understanding of determinants of child health. The main analytical difficulty has been in specifying the mechanisms of multilevel models that delineate the pathways by which community characteristics influence individual behavior (Tienda 1991) and specifying the mechanisms through which maternal education affects child survival (Cleland and van Ginneken 1988). Even less attention has been paid to delineating the mechanisms through which the education of women in the community impacts child health.

Unfortunately, this project does not solve any of those dilemmas. But it contributes to the state of knowledge on the use of multilevel analysis and on the effect of maternal education on child survival by incorporating both level of education of the infant's mother and the level of education in the infant's community into the same model to see which exerts a greater effect on infant survival. While the mechanisms tying community and individual behavior to child survival are still largely unknown, the average levels of education of child bearing aged women in the community turns out to be a type of proxy for development at the community level. Further research which incorporates participant observation or ethnographic methodology to delineate social networks and spheres of influence within and between networks in a community is necessary to determine the actual mechanisms.

ZIMBABWE SOCIAL SETTING

Zimbabwe is an appropriate social setting in which to look at effects of maternal education on child health because it is a newly independent country in which citizens are exercising a greater sense of political and personal efficacy. The transition in control of education and health institutions from the hands of colonial settlers to a nascent government elected by the previously dispossessed majority has allowed policymakers to institute progressive health strategies motivated to benefit the majority populace.

The Zimbabwe government believes that development is not just economic in nature. It is also social, political, and cultural. When assessing their country's development, the people of Zimbabwe chose leadership committed to addressing the relevant questions regarding poverty, unemployment, and inequality. Their concern for social development grew out of their struggle for liberation from colonialism as an leadership is that market oriented development does not give preference to the needs of the masses. So the Zimbabwe government chose not to follow a strategy that measured progress solely by export orientated economic growth. Instead, they chose to couple economic schemes with social development strategies, and to give priority to the social betterment of the majority of its citizens. This strategy reflects the underlying belief that equitable distribution of resources is an important part of development.

Zimbabwe is an interesting context in which to study child health because of the substantial improvements in indicators of living conditions since political independence (attained April 18, 1980). The country's 1960 infant mortality rate was 113 deaths per 1000 live births; according to the ZDHS, it dropped to 72/1000 in 1988 and the Population Reference Bureau has projected its 1994 infant mortality rate at 59/1000 (United Nations 1989; ZDHS 1989; Population Reference Bureau 1994). Zimbabwe's Equity in Health program reflects the government's commitment to social development in many ways. Zimbabwe has encompassed some of the most progressive strategies for improving child health. Some of these include integrating traditional health practitioners into the health care infrastructure, balancing curative and preventive health measures, and a focus on redistributing health services so that the poorest citizens in greatest need have improved access to health services.

The result of the abandonment of the racially discriminatory and inequitable colonial practice in controlling health care access has been overwhelming. There is majority access for Zimbabwe's population to health centers and hospitals in even the remotest areas. During the first 10 years of independence, rural areas were targeted for the more than 500 health centers that were built or upgraded, and more than a dozen district hospitals were completed or are under constructions (World Bank 1992:1). Additionally, between 1980 and 1985, more than 4000 Village Health Workers who disseminate health information and provide preventive and simple curative services in the rural areas were trained. More than 200 State Certified Nurses were trained to staff clinics (World Bank 1992:1). The result has been that "millions of people have for the first time gained access to basic care" (World Bank 1992:ix). "In 1980 (at the time that Zimbabwe attained its independence), 44% of the publicly funding services went to the urban-based sophisticated central hospitals serving about 15% of the population" (Sanders and Davies 1988:724). But as of 1988, "in Zimbabwe, nearly health of all rural women (were) within 5 kilometers of some type of health provider, and only 12% are more than 15 km distant from one" (Wilkinson, Njogu, and Abderrahim 1993:24). The median distance to a health center for married women ages 15 to 49 interviewed in the nationally representative Demographic and Heath Survey sample is 3.1 kilometers: 5,1 km in rural areas and 1 km in urban areas (Wilkinson, Njogu, and Abderrahim 1993:26). Immunization data show no inequality in distribution between rural and urban areas of Zimbabwe (Zimbabwe Central Statistical Office 1989)1, although there is some inequality by province (Zerai 1992). Additionally, health education has promoted low-risk maternal fertility practices such as delayed child bearing, parities of four or fewer children, and adequate spacing of births (Zimbabwe Federal Office of Statistics 1989).

MULTILEVEL APPROACH

The multilevel approach guards against the myopia of micro level analyses which "implicitly assume that variations in (behavior associated with the dependent variable) are due only to individual characteristics and that the social contexts in which people live have invariant effects..." on the outcome variable (Billy and Moore 1992:978). The advantage of the multilevel approach is that the researcher simultaneously estimates the individual-level effects on the outcome variable that macro level analyses omit. "A multilevel approach bridges the gap between the strictly macro and strictly micro orientations" (Billy and Moore 1992:978).

While the multilevel frameworks have been widely praised for incorporating both micro and macro level models (Billy and Moore 1992; Tsui 1985; Brewster, Billy, and Grady 1990; Entwisle, Casterline, and Sayed 1989; etc), the approach has been criticized for mechanically connecting the individual and contextual models without specifying the mechanisms through which macro forces exert an effect on micro level behavior and outcomes (Tienda, 1991).

When putting together multilevel models and infant health, the average level of female education is an excellent independent variable to focus on because considerable effort has been expanded in an attempt to identify mechanisms linking maternal education to child survival (Caldwell 1986; Caldwell 1989; Cleland and van Ginneken 1988; Mosley and Chen 1984). The problem of specifying the mechanisms organically linking macro and micro infuences is exemplified by the problem of specifying the mechanisms through which education affects child health. Education is clearly relevant to the society in general even though it is measured at the individual level. Formal education prepares the young to fulfill the expectations of their society. Its function is largely socialization. Unfortunately, although several mechanisms, through which maternal education affects child health, have been proposed in the literature, we still lack a definitive knowledge of these mechanisms (Caldwell 1989:106).

Importance of Maternal Education

A review of the literature shows that while the higher socioeconomic status of better educated women explains about half of the magnitude of the relationship between maternal education and child survival (Cleland and van Ginneken 1988), the domestic health practice of individual women is probably the new most salient mechanism in the maternal education-child mortality relationship². The fact that mother's education is a more important determinant of child survival than father's education shows that there is greater maternal involvement in child-health related care (Caldwell 1989). The mother's education influences her choices and skills in health care practices (Bailey 1988; Das Gupta 1990; Caldwell 1989). For instance, both educated and illiterate mothers recognize when their child is sick but the educated mother more frequently will take action "without waiting for (her) husband or mother-in-law to notice the child's condition too" (Caldwell 1989:106). "This is partly because illiterate women do feel a lack of capability when dealing with the modern world" (Caldwell 1989:106). Caldwell found that the educated mother is "more likely to report back to the health center if the treatment does not seem to be affecting a cure. Educated women see the (health process) as experimental... (And do not feel it is an attack on the health care parctitioner to give this important feedback)" (Caldwell 1989:106). Joshi has postulated that it is through the acquisition of skills and identity that education impacts the health behavior of women, but says, "while these findings are interesting, they are still incomplete. More studies, especially longitudinal ones, are needed before these findings can be woven into a meaningful theory" (Joshi 1994:24).

Education as a Community Phenomenon

We know even less about the reason average levels of female education in the community exert a positive effect on infant survival. But this is an important relationship to consider. Caldwell points that "an uneducated woman may feel more deprived in a country where most other women are educated than one in which they are not: nevertheless, her children stand a much greater chance of survival" (Caldwell 1989:103). The mechanisms operating in the mass education-child mortality relationship are likely to be muldimensional. One possibility is health service advocacy. The equitable distribution of community health services influences child health in Kerala State India (Preston 1978). Advocacy to ensure better distribution is imperative. Bailey (1988) found that the shortage of health facilities and personnel affect child health in Sierra Leone. "As argued powerfully by Caldwell (1986) the key to low mortality at the societal level may be a synergy between mass education and egalitarian politics which leads to demands for a health service that caters for the needs of all" (Cleland and van Ginneken 1988).

Another important possibility is the improvements in status that are accrued to all women as a result of mass education. This impacts how women are viewed in society and also how women view themselves. "The quality of schooling seems relatively unimportant...; it is not so much what you learn or understand, but how you see yourself and how others see you" (Caldwell 1989:106). "Women who had been to school thought of the school as part of the whole modern system which included independence, five-year plans, government programs, health centers, modern medicine and themselves" (Caldwell 1989:106). So mass education has an important influence on both the individual and the society.

Another result of mass education that may have important implications for child survival is its compounding multigenerational effects (Caldwell 1989). Wealth flows reverse as there are larger investments by families in the development of their individual children (Caldwell 1982).

FRAMEWORK

The general theoretical question addressed examines the relationship between development and population. The specific question asks to what extent social development determines infant mortality. There is a debate on the causes of mortality decline and the position argued here is that mortality decline is sustained in less developed countries as a result of economic development, humanistic social development, and equitable distribution of development resources (Palloni 1985; Caldwell 1986).

Distribution of development resources to all in society ensures that the standard of living is adequate, women and men have the freedom to choose their contribution to society, and that children's health received a high priority. Having clean water supplies, proper sanitation, educational facilities, and hospitals, promotes the well-being of citizens which finally has positive effects on child health. When universal education is encouraged and high percentages of women complete schooling, women have a more egalitarian role in society (Caldwell 1986) that allows them to delay childbearing and encourages the lower parities conducive to improved child health (Chen 1983). When men and women exercise their right to choose their education and employment, living standards rise as a result of increased household economic resources. This produces living environments with reduced contamination that promote child health. Development resources such as piped water and adequate sanitation promote health because they also reduce contamination. Finally, when child health is a high priority in the public sector, as reflected in the building of hospitals and health centers that include prenatal care, immunization, and growth-monitoring programs, children's survival chances improve.

The theoretical framework of child health in Zimbabwe here begins with some of the individual-level variables in the classical proximate determinants framework (Mosley and Chen 1984). Other crucial variables are added that operate through the proximate determinants, such as maternal education, parents' occupation, and community-level variable (see figure 1). The community conditions are mediated by proximate determinants of child health. Proximate determinants operate through specific mechanisms to determine child health outcomes.

The causal ordering is that macroeconomic, political, and social factors set the stage for community conditions and socioeconomic realities in which families find themselves. Community conditions and socioeconomic variables operate through the proximate determinants of child health. Community conditions also exert an impact on socioeconomic conditions to influence proximate determinants of child health indirectly.

Presently, maternal fertility variables are typified by later marriage and childbearing, lower parities, and longer birth intervals than in preceding periods in Zimbabwe. Operating through the biological mechanism of improved production of breastmilk and through the social mechanism of less competition with siblings for mother's attention and household resources, children get better nutrition and better care, thus improving their survival chances (Chen 1983).

In Zimbabwe, household contamination is still a big problem. Piped water is provided to a minority of households. Sanitation measures are still not adequate. But improvements in these areas since independence work through the mechanism of less exposure of children to contamination to make them less susceptible to disease and eventually lower mortality.

Nutrient availability has improved due to drought feeding programs (Agere 1986), land reform and increased economic opportunities (Sanders and Davies 1988). Operating through the mechanism of decreased susceptibility to illness, improved nutrient availability leads to improvements in child health (Preston 1980).

Personal disease control is typified by better immunization coverage and increased access to treatment. The goals set by the Ministry of Health of universal immunization of children and targeting rural areas for building primary health care centers (Manga 1988) have improved the timeliness of disease prevention and treatment. Timely personal disease control is critical for improving child survival chances.

Mother's higher levels of education and increased professional and blue collar employment of the household head lead to low child-health-risk fertility, timely immunization of children, adequate nutritional intake by children, and household environments with lessened contaminants. These socioeconomic variables operate through the proximate determinants to influence child health.

Community conditions have become more conducive to child health in Zimbabwe today than in the past. Some characteristics include free and compulsory education (Zvobgo 1986), widespread family planning education, land reform measures, free health services to citizens who do not make minimum wage (Sanders and Davies 1988), prenatal care programs (Agere 1986), and postnatal care including growth monitoring. These conditions make it easier for individual households and mothers to maximize the health of their children.

Community conditions operate through socioeconomic variables and proximate determinants to influence child health. For instance, higher percentages of women receiving primary level and more education lead to improved socioeconomic outlooks of families, which in turn influence fertility variables, personal disease control, nutrient availability, and household contamination.

MODELS

The Zimbabwe Demographic and Health Survey (ZDHS) and ZDHS Service Availability data are used to analyze the effect of individual and community-level determinant on child mortality between 1983 and 1988 in Zimbabwe. To incorporate individual level and contextual determinants in this study of infant health in Zimbabwe, a three-tiered analysis is conducted by establishing a baseline model that includes individual-level determinants and background characteristics and then analyzing both a community model that includes distal determinants and finally an expanded model that incorporates all the variables in the first two models (see figure 2).

First, the individual level risk factors to neonatal and postneonatal survival are evaluated for children born during 1983-88 in Zimbabwe. Background variables are added as controls. The maternal histories collected in the Zimbabwe Demographic and Health Surveys provide adequate data to examine effects of environmental contamination and maternal variables on individual-level child survival3. However, to understand child health status better, it is important to understand the context in which child health outcomes unfold. This analysis reveals that individual level risk factors are expectedly not the whole picture, but mediate contextual determinants.

In the second part of the analysis, the effect of resource distributive programs (as reflected in contextual variables) are examined. These programs were established after Zimbabwe attained independence. The expanded model examines the joint effects of the individual-level determinants and community conditions on neonatal and postneonatal mortality.

DATA

The Demographic and Health Surveys (DHS) are a rich source of data on developing countries in general, and Africa in particular. The national probability surveys interview all or ever-married women ages 15-49 and provide a wealth of information on child health, the proximate determinants of fertility, fertility preferences, and other social and economic characteristics unmatched by comparative data from developing countries to date.4 Retrospective data is collected to provide complete birth histories, as well as more detailed information on the five years preceding the survey (1983-88).

The sample size in the Zimbabwe Demographic and Health Survey is 4201 women. It was fielded between June and September 1988. To prepare the data for analysis, files with mothers, children, and finally child-months as three different units of observation from the ZDHS data set were constructed. While the ZDHS data give very specific information on 1983-88 they only give limited birth history and child mortality information for previous years. Therefore, the empirical analyses here are restricted to the 3393 children born during this short period.

Variables analyzed for all models are listed in Table 1. Descriptive statistics for all variables are listed in Table 2. The analysis of child survival is limited to infants under 1 year where a majority of child mortality usually occurs. That infant mortality rates are sensitive to social development inputs permits an assessment of the impact of proximate and distal determinants on child health in Zimbabwe. Different sets of variables influence neonatal and postneonatal mortality, requiring separate investigations for the latter. Analysis of neonatal and postneonatal survival allows examinations of two important aspects of early child health in Zimbabwe; neonatal mortality reflects "the preexisting health conditions of the mother and the health care she and her infant received during pregnancy and shortly after delivery" and postneonatal mortality reflects "post-birth socioeconomic, environmental, and medical care" indicators (Campbell 1989).

Proximate Determinants

The study used individual-level variables whose relationship to infant survival has been well established (Chen 1983; Mosley and Chen 1984; Pebley and Millman 1986; Curtis, Diamons, and McDOnald 1993; Phuapradit et al 1990). Mother's age at childbearing, parity, and the preceding birth interval are examined in the form of a high risk fertility index. If any of these high risk conditions existed at the time of the birth, a pregnancy was deemed to be at high risk of complications, negative birth outcomes such as neonatal mortality, or problems with postneonatal survival. If none of the conditions existed, the index child was designated a low risk birth category5. Half (51%) of children in the subsample were born in conditions of high risk to pregnancy, birth, or postbirth complications. The high-risk maternal and child health index was constructed to address the problem of collinearity among maternal fertility variables and also in preparation for further bivariate analysis which will treat risk as endogenous to the relationship between background and community variables affecting infant survival.

Other variables from the classical proximate determinants model such as nutrient availability, personal disease control, and incidence of injury were not examined because of insufficient information on the variable itself (in the case of incidence of injury for all infants and immunization or other indicators of personal disease control for infants that died), and inadequate information that would allow causal inference (such as nutrient availability and succeeding birth intervals).

Background Characteristics

Socioeconomic variables determine the availability of nutritional resources, which is especially important because once infants reach the age of 6 months, they can no longer depend on nourishment from breastmilk alone. Mother's education is important because it facilitates her integration into a society impacted by traditional customs, colonialism, and neo-colonialism. Education heightens her ability to make use of government and private health care resources and it may also increase the autonomy necessary to advocate for her child in the household and the outer world. If a mother has not completed primary level education, her child is more likely to die.

The other socioeconomic variable measured is parents' occupation. If parents do not have regular employment, they will be less able to supply nutritional needs of children and consequently their children will be more susceptible to disease and other causes of death.

Household contamination is an important indicator of child susceptibility to contagion leading to disease. The higher the value of the household through lack of easy access to water, modern toilet facilities, and refrigeration and having nonporous floor material. A factor score was constructed for these variables to address statistical problems presented by the high collinearity among them. The number of people in the household is added as a measure of crowing in the household. Finally rural/urban residence of the infant is included in the model as a control variable.

Distal Determinants

The ZDHS-Service Availability (ZDHS-SA) data set was collected on 166 at 167 sample clusters6 in Zimbabwe. ZDHS-SA variables were matched to the individual-level and child-month working files by cluster. Two of the variables for examining the effect of community-level variables on infant survival between 1983 and 1988 are extracted from the ZDHS-SA. General categories of variables measuring community-level inputs into child health include existence of family planning education and contraceptive distribution programs, female education, and health care availability7.

Clusters are the primary sampling unit used in the ZDHS. Families living in the same cluster live in close proximity to each other -- in the same community. In the sample of 3393 children, an average of 20 survey households is represented by each cluster (with a standard deviation of 11). This ranges from 1 to 84 of the households in which women were interviewed. The total number of households in a cluster is less than 500 for a majority (72%) of the communities and no more than 2000 for all communities (Wilkinson, Njogu, and Abderrahim 1993).

The first community-level variable of interest is the average level of women's education. A variable indicating the mean level of education for childbearing aged women was created by calculating educational levels of all women by cluster from the original ZDHS file. Caldwell (1986:191) points out that the percentage of women in the community with primary schooling is an important determinant of child health because it indicates female autonomy and the extent of egalitarianism in the society. The more empowered childbearing-aged women are as a group, the more likely individual women in that community will be able to promote actively the health of their children. Children living in communities where women on the average complete primary and secondary level education are expected to have higher probalities of survival than those living in communities in which women on the average do not complete primary-level education.

The second community-level variable is whether or not the locale has family planning education and contraceptive distribution programs, which is important because it is hypothesized to increase the likelihood that parents contracept -- at least to space their births, to the benefit of mother and child's health. Children who live in a community in which family planning education and contraceptive distribution programs are available, are more likely to be healthy.

The availability of community health care facilities indicates the extent of health services, including prenatal and postnatal care, immunization, and growth monitoring. Hospital and clinic availability is mainly important for personal disease control. If resources for disease prevention, such as prenatal care, immunization, and growth monitoring are available in the community, the child is more likely to be healthy. If resources for disease diagnosis and treatment, such as hospitals, health clinics, and health workers are available, the child is more likely to survive.

In Zimbabwe, most hospitals and health clinics have prenatal care, immunization, and growth monitoring. Availability of hospitals and health centers in the community is also an indicator of social development. The greater the development infrastructure, the greater the access to hospitals and other health resources.

METHODS

The logistic regression and Cox proportional hazards models are used to estimate the effect of proximate and distal covariates on child mortality8. Neonatal mortality models are estimated with logistic regression. Postneonatal mortality models are estimated with the proportional hazards model. The Cox proportional hazards model allows researchers to study events, or the change from one state to the other at a specific point in time (Allison 1984). For the first part of this analysis the transition from the state of being alive to the state of death is examined.

RESULTS

The model selection results appear in Table 3. The overall fit for all neonatal and postneonatal mortality models is good. The chi-square statistic is significant at the .001 level for proximate, community and saturated models. Chi-square tests result in significant improvements of the expanded models over the proximate and community models. This shows that individual and community-level variables contribute significantly to our understanding of infant mortality in Zimbabwe.

Tables 4 and 5 list the results of the logistic regression and Cox regression analyses of neonatal and postneonatal mortality, respectively. Odds ratios are presented for all logit models, and hazard ratios are presented for Cox regression models. Estimated hazard and odds ratios are relative risks --for a one-unit change in the independent variable the instantaneous risk of death increases by a multiplicative factor of the reported value of the hazard ratio (Computing Resource Center 1992:212). The first column in Tables 4 and 5 list the independent variables for all three models. The second and third columns list the results of the individual-level model, the fourth and fifth columns show the results of the community model, and the sixth and seventh columns report the results of the saturated model.

Determinants of Neonatal Mortality

All children at risk of dying before the first completed month of life are included. Of the 3393 children in the sample, 108 infants did not survive to age 1 month (a neonatal mortality rate of 32.1 deaths per 1000 live births). Proximate and background variables that determine whether a child survives to age 1 month are listed in table 4.

The high-risk maternal fertility index is the first significant determinant of neonatal mortality (& = .05). If the child's birth order is greater than 4, if the child's preceding birth interval is 24 months or greater, or if the mother was not the optimal age at birth, as indicated by a value of 1 on the high-risk index, the child is 1.42 times as likely to die as infants who were born under low risk conditions. A low risk birth is one in which the child has higher chances of survival because there has been a sufficiently lengthy time interval after the previous birth (or because the index birth is a first order birth). The mother is at her biologically and socially optimal age for childbearing, and because of lessened competition for maternal resources due to lower parities.

The only significant household contamination variable is the number of persons residing in the household. For each additional member in the household, children are only 89% as likely to die in the first month of life as children in smaller households (& = .001). Whereas, originally theory predicted that the number of people in the household could be used as a proxy for crowding and increased susceptibility to disease through contagion (Aaby 1988), it appears the number of household members may be a better proxy for the number of caretakers available to children under 5 years of age. As shown in Table 6, the main compositional difference between large and small households is that larger households contain greater numbers of adults. This finding supports the idea that these additional members contribute to the pool of caretakers or free the primary caretaker to spend more time with the newborn.

Personal disease control is measured by whether or not the pregnant mother was cared for by a nurse or doctor prior to the birth of her child. Newborns whose mothers received prenatal care from a health professional are 35% less likely to die as those whose parents had not obtained prenatal care from a nurse or doctor (& = .001).

Neonates whose parents are unemployed are 2.79 times as likely to die during the first month of life as children whose parents are working in professional occupations. This variable indicates socioeconomic resources of the household. Households that can afford to provide proper nutrition, personal disease control, and highly sanitary living environments are more likely to have healthy children residing in them.

The last variable in the individual-level model is rural/urban residence. Newborns in urban areas are approximately half as likely to die as newborns living in rural areas during the first month of life (& +.05). Conditions are still more favorable to child health in urban areas of Zimbabwe.

Logistic regression analysis of the effects of community-level variables on neonatal mortality are reported in columns 4 and 5 of Table 49. The average educational level of women in the community significantly influences survival of newborns. Infants living in communities where women have a secondary level education are .33 times as likely to die and those living in communities where women have on the average completed a primary level education are .49 times as likely to die the first month of life, as infants living in communities where the average woman is uneducated. Both results are highly statistically significant (& = .01).

The only shared variable in the proximate model and community model is rural/urban residence. Similar to the residence variable in the individual model, urban neonates are .62 times as likely to die as rural neonates.

Model 3 encompasses the individual level variables in model 1 and community-level variables of model 2 (see columns 6 and 7 of table 4). The magnitude and direction of effects are the same as in the results for the constrained individual and community models. The significance is virtually the same as well.

Determinants of Postneonatal Survival

Determinants of infant survival to age 1 year are examined (see Table 5) using Cox regression10. Of the original 3393 children observed, 108 were deleted because they died in the first month of life, and 62 observations were deleted because they were censored11. Of the 3223 children at risk, 68 did not survive to 12 months of age (a postneonatal mortality rate of 23.2 deaths per 1000 live births). The independent variables are the same as in the analysis of neonatal mortality.

The significant household influence on infant mortality was the number of members in the household. For each additional member in the household, children were only 86% as likely to die before reaching age one as children living in households with fewer members (& = .001). The more household members there were, the more likely the infant to survive. The magnitude of the effect is similar to that in the neonatal mortality model.

Infants whose mothers obtained professional prenatal care were .30 times as likely to die in Zimbabwe as infants whose mothers had not obtained such care. This statistic is significant at the & = 0.001 level.

Family-level socioeconomic variables are important for infant survival. Mother's education (at least primary level) and parents' employment status significantly influence survival of infants. Infants whose mothers had a secondary level education were .49 times as likely to die (& = .001) and infants whose mothers completed primary level education were only .84 times as likely to die, as infants whose mothers did not complete a primary level education (& = .05). While mother's individual-level education was not significant in neonatal mortality models, it was significant in infant mortality models. In individual-level postneonatal model, if neither parent has a regular occupation, the infants were 1.74 times as likely to die as children whose parents work in non-manual (including professional) occupations (& = .01).

According to individual level model results, infants living in urban areas are .41 times as likely to die as infants living in rural areas (& = .001). And according to results from the community model, infants living in urban areas are .62 times as likely to die as those living in rural areas (& = .001).

Results from the community model reveal that in Zimbabwe infants living in communities where adult female educational attainment is on the average primary or secondary level are .38 and .08 times as likely to die as infants living in communities where women are largely uneducated (& = .001). Infants living in communities with Zimbabwe National Family Planning Council (ZNFPC) contraceptive distribution programs were .76 times as likely to die as those without this resource in their communities (& = .001); and those living in communities with access to hospitals or health centers are .66 times as likely to die within the first year of life as those that do not have access to modern health care (& = .001).

There are some differences between the results of neonatal and postneonatal mortality community models. ZNFPC and hospitals or health care centers in the community do not significantly influence neonatal mortality. This social determinant is less important for neonatal than biological determinants influencing fetal health. But the average level of adult female education in the community is important in both neonatal and postneonatal mortality models.

Results from the expanded model reveal that the number of people in the household, professionally administered prenatal care, mother's secondary level education, parents' regular employment, urban versus rural residence, the average level of women's education in the community, ZNFPC contraceptive distribution programs and hospitals and health centers in the community all significantly influence infant surivival (Table 5). The magnitudes of effects are similar to the proximate model of individual-level family and household variables. The main difference is that education of individual mothers is a weaker determinant of postneonatal mortality in the saturated model. Whereas maternal secondary level education was a strong determinant of child survival in the model that excluded community variables, once community variables are added in a saturated model, the average level of female education in the community overshadows the effect of maternal education of infant survival. Among neonates, the effect of individual mother's education in the saturated model is not significant in either the individual-level or saturated model, although the level of education of women in the community is a significant determinant of neonatal survival (refer to table 4).

DISCUSSION

The individual-level model effectively sets the stage for results to follow in the community and expanded models. Its performance is supported by chi-square statistics in Table 3 which report that the community model is improved by the addition of proximate and background variables. The proximate determinants framework yields some useful predictors, but not all variables significantly infuenced neonatal and postneonatal mortality in Zimbabwe.

Individual-level variables that are consistently significant predictors of child survival include the number of members in the household, whether the mother obtained prenatal care from a doctor or nurse and parents' employment. The household contamination variables did not yield significant results as was expected. Household contamination, as measured by a factor of porous floor material, refrigerators, distance to household water, and modern toilet facilities, is not an important indicator of newborns and infants' health status once other socioeconomic variables are taken into account. And although the number of household members was an important indicator in all four models in which its effect was estimated, it operated less as an crowding and increased contagion variable and more as a possible childcare variable.

As was expected, community-level variables gave added explanatory power to the individual-level models. High proportions of women receiving a secondary level of education in the community was the most important community-level variable influencing infant mortality, with access to hospitals and the existence of ZNFPC programs being important as significant determinants of postneonatal survival.

Educational levels of women in the infant's community proved to be more significant than the education of an infant's own mother in neonatal and postneonatal models that include both variables. This indicates that equitable distribution of this resource is beneficial to infant and newborn survival (Caldwell 1986). The proportion of young girls enrolled in primary school has declined in most African countries since the 1980s (according to the World Bank and UNICEF), and will probably decline further in coming years. The positive relationship between mass schooling and infant survival will translate into lower infant mortality in Zimbabwe only insofar as the country's population and economic growth levels allow for increased enrollment.

CONCLUSION

While a clear delineation of the mechanisms linking education and infant survival still remains an unresolved issue, results of this analysis show that secondary-level education of individual mothers and of women in the community promotes child survival. Results reveal that when maternal education and average levels of female education in the community are both observed, the state of female educational advancement (or lack of education) in the community overshadows the effect of the individual mother's educational attainment on infant survival. It is not clear what the average level of female education in the community is actually measuring. At least, it measures the existence of a phenomenon at the contextual level which exerts an effect on infant mortality. We need to do further research to help us more specifically define this context in social terms (and not just as an area demarcated by a specific geopolitical boundary) and then examine specifically what characteristics of this context correlate to proximate causes of health behavior and outcomes. Is it that uneducated women are mimicking the health promoting behavior of educated women who live close to them? Or are women who live in close proximity sharing child care information? Another possibility is that there is a greater demand for social amenities that benefit child health and that all benefit regardless of educational level. Ethnographic child care practice studies are needed to get a better grasp of these mechanisms (Feyisetan 1988).

Figure 1:Causal ordering of factors affecting child health in Zimbabwe

Figure 2:Proximate and distal determinants of survival for infants born between 1983 and 1988 in Zimbabwe

Table 1:Variable names and values for all models

Variable type and name

Description

Value

DEPENDENT VARIABLES
Neonatal mortality Mortality before completing first month of life 1= Died, 0= Survived
Postneonatal mortality Time varying: Mortality after surviving the first month and before completing the 1st year of life 1= Child died in observation month,

0= Child survived the observation month

INDEPENDENT VARIABLES
1. High-risk maternal fertilty index Index accounting for the presence of at least one high risk variable: Parity greater than 4, Insufficient preceding birth interval: interval of <24 months for second or high parity child; or Maternal age under 18 or over 36 1= At least one high risk factor

0= None

SOCIOECONOMIC STATUS    
Educational level of mother Recoded to 3 dummy variables  
2. Incomplete primary (omitted category) 1= yes, 0= no
3. Complete primary

4. Secondary

-

-

1= yes, 0= no

1= yes, 0= no

Parent's usual occupation Father's occupation measured; mother's occupation substituted if father unemployed or absent  
5. Missing Father's and mother's occupational data missing 1= yes, 0= no
6. None Parent has no usual occupation 1= yes, 0= no
7. Agricultural Includes farmworkers and farmers 1= yes, 0= no
8. Manual labor Non-agricultural 1= yes, 0= no
9. Nonmanual labor (omitted category)  
HOUSEHOLD CONTAMINATION
10. Household contamination factor Factor score coefficient comprised of presence of refrigerator in household; toilet on premises; porous floor material; and distance to household water supply (ordinal codes: 1 = on premises, 2= less than 5 meters from premises, 3= 6 to 100 meters away, 4= 101 to 500 meters away,
6= 501 meters to 1 km away,
7= 1-3 kilometers away,
8= 3-5 kilometers away, to
9= greater than 5 kilometers away).
mean= 0; s.d.=1
11. Number of household members Number at time of survey Continuous
12. Prenatal care

13. Residence

Care from a medical professional

Place of residence

1= yes, 0= no

1= urban, 0= rural

Table 1:Variable names and values for all models (continued)

 

Variable type and name

Description

Value

COMMUNITY CONDITIONS

Average level of females'

education in the community

14. Incomplete primary

15. Complete primary

16. Secondary education

17. ZNFPC community based

distribution program

18. Hospital or health center

in the community

Ordinal variable recoded to three

dichotomous variables

(omitted category)

-

-

-

-

 

 

1= incomplete primary

1= complete primary

1= secondary

1= yes, 0= no

1= yes, 0= no

Source : ZDHS data (1989).

 

Table 2:Descriptive statistics on all variables in models

Variables

N

Mean

Standard

division

Neonatal mortality

Postneonatal mortality

3393

3223

0.03

0.02

0.14

0.14

PROXIMATE VARIABLES
1. High risk fertility

2. Mother under age 18

3. (Mother 18-36)

4. Mother over age 36

5. Preceding birth interval <24 months

6. Birth order >4

7. Number of household members

8. Professional prenatal care

3390

3393

3393

3393

3388

3393

3393

3393

0.51

0.04

0.82

0.14

0.19

0.36

7.41

0.84

0.50

0.19

0.38

0.35

0.39

0.48

3.37

0.37

BACKGROUND CHARACTERISTICS
9. (Mother did not complete primary level education)

10.Mother completed a primary level of education

11.Mother completed secondary level or more education

12.Parents unemployed

13.Agricultural occupation

14.Manual occupation

15.(Non-manual occupation)

16.Missing information on occupation

3393

3393

3393

3393

3393

3393

3393

3393

0.19

0.63

0.18

0.02

0.26

0.50

0.17

0.05

0.39

0.48

0.38

0.14

0.44

0.50

0.38

0.22

TYPE OF PLACE OF RESIDENCE
17.Rural/urban residence 3393 0.27 0.44
COMMUNITY CONDITIONS (Average level of female's education in the community)
18.(Incomplete primary)

19.Complete primary

20.Secondary education

21.ZNFPC distribution program in locale

22.Hospital or health center in the community

3393

3393

3393

3387

3387

0.04

0.82

0.14

0.66

0.81

0.19

0.38

0.35

0.47

0.39

Source: ZDHS data (1989)

Note: Residual categories in parentheses.

Table 3:Mortality model selection

Model Overall model significance -2*Log Likelihood Degrees of freedom Chi-square (comparing constrained and saturated models)
Neonatal mortality:
Base (proximate model)

Comunity model

Expanded (saturated model)

82.86 ***

23.86 ***

95.41 ***

1450.38

1517.92

1440.48

4

10

-

9.90 *

77.44 ***

---

Postneonatal mortality:
Base (proximate model)

Community model

Expanded (saturated model)

544.41 ***

278.19 ***

669.70 ***

12618.82

12883.52

12490.48

4

10

-

128.34 ***

393.04 ***

---

Source: ZDHS data (1989)

Notes: *p<.05, **p<.01, and ***p<.001.

Negative log likelihoods are reported in tables 4 and 5.

Table 4:Determinants of neonatal mortality

Variables

Individual model

Community model

Saturated model

Odds Ratio

z Value

Odd
Ratio

z
Value

Odds
Ratio

z
Value

PROXIMATE VARIABLES
1. High risk maternal fertility

2. Number of household members

3. Household contamination factor

4. Professional prenatal care

1.42*

0.89***

0.89

0.35***

2.246

-4.430

-0.953

-6.591

 

 

 

 

 

 

 

 

1.42*

0.86***

0.88

0.35***

2.170

-4.227

-0.978

-6.596

BACKGROUND VARIABLES
5. Mother completed secondary or more education

6. Mother completed primary level education

7. (Mother did not complete primary level education)

8. Parents unemployed

9. Agricultural

10.Manual

11.Missing

12.(Non manual)

13.Rural/urban residence

0.73

0.89

 

 

2.79*

1.11

1.00

1.73

 

0.53*

-1.082

-0.659

 

 

2.388

0.391

0.035

1.499

 

-2.497

 

 

 

 

 

 

 

 

 


0.62*

 


 

 

 

 

 

 

 

-2.063

0.86

1.02

 

 

2.85

1.09

1.00

1.96

 

0.54*

-0.496

0.086

 

 

2.426

0.317

0.018

1.330

 

-2.165

COMMUNITY VARIABLES
14.Secondary level education

15.Completed primary level education

16.(Incomplete primary level education)

17.ZNFPC distribution program in locale

18.Community hospital or health center

 

 

 

 

 

 

 

 

 

 

 

0.33**

0.49**


0.76

 

0.97

-2.715

-2.568


-1.697

 

-0.169

0.41*

0.58


0.73

 

1.04

-2.022

-1.737


-1.846

 

0.243

Source: ZDHS data (1989)

Note: Logistic regression analysis of neonatal mortality; *p<.05, **p<.01, and *** p<.001. Omitted category in parentheses.

 

Table 5:Determinants of postneonatal mortality

Variables

Individual model

Community model

Saturated model

Hazard Ratio

z Value

Hazard Ratio

z  Value

Hazard Ratio

z Value

PROXIMATE VARIABLES
1. High risk maternal fertility

2. Number of household members

3. Household contamination factor

4. Professional prenatal care

1.10

0.86***

1.02

0.30***

1.268

-10.973

0.330

-16.483

 

 

 

 

 

 

 

 

1.08

0.87***

0.99

0.31***

0.991

-10.380

-0.208

-16.222

BACKGROUND VARIABLES
5. Mother completed secondary or more education

6. Mother completed primary level education

7. (Mother did not complete primary level education)

8. Parents unemployed

9. Agricultural

10.Manual

11.Missing

12.(Non manual)

13.Rural/urban residence

0.49***


0.84*


1.74**

0.92

0.90

1.50*

 

0.41***

-4.517


-2.031

 


2.639

-0.720

-0.959

2.206

 

-6.553

 

 

 

 

 

 

 

 



0.62***

 

 

 

 

 

 

 

 



-4.044

0.71*


1.10

 


1.80**

0.85

0.85

1.33

 

0.54***

-2.096


0.989

 


2.426

0.317

0.018

1.330

 

-2.165

COMMUNITY VARIABLES
14.Secondary level education

15.Completed primary level education

16.(Incomplete primary level education)

17.ZNFPC distribution program in locale

18.Community hospital or health center

 

 

 

 

 

 

0.08***

0.38***

 

 

0.76***


0.66***

-9.284

-8.274

 

 

-3.432


-5.178

0.11***

0.45***

 

 

0.72***


0.74***

-2.022

-1.737

 

 

-1.846


0.243

Source: ZDHS data (1989)

Note: Cox regression analysis of postneonatal mortality; *p<.05, **p<.01, and *** p<.001. Omitted category in parentheses.

Table 6:Household composition by household size

  All households [n=2228] Number of people in household
    Six or fewer [n=1035] More than six [n=1193]
Average number of children under age 5 in household

Average number of children born to index mother 1983-88

1.81


1.52

1.36


1.47

2.20


1.57

Percentage of households with children born between 1983 and 1988 not residing in household

Percentage of households with children under age 5 in addition to those born between 1983 and 1988

17.00

 

32.40

24.20

 

16.60

10.70

 

46.20

Average number of members over age 5

Average number of eligible women in household

5.49

2.00

3.23

1.00

7.45

2.00

Average household size 7.30 4.59 9.66

Source: ZDHS data (1989)

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NOTES

  1. According to the ZDHS, among all children 12-23 months, 79,8% living in rural areas had health cards with immunization data recorded on them and 71,3% of children living in urban areas had health cards. Among children with health cards, 98% received the BCG vaccine in rural areas and 96% received the vaccine in urban areas; 96% of children living in rural areas received their measles vaccine and in rural areas this number was 92% of children aged 12-23 months (Zimbabwe Central Statistical Office 1989:87).
  2. There is supporting evidence for this in a recent article by Arun R. Joshi "Maternal Schooling and Child Health: Preliminary Analysis of the Intervening Mechanisms in Rural Nepal" (1994).
  3. Values of socioeconomic variables collected at the time of the survey are used as a proxy for socioeconomic measures at the time the child was exposed to risk.
  4. Some of the limitations of the Demographic and Health Survey data set pointed out by Brockerhoff and De Rose (1994) that need to be aknowledged include the fact that there is no information on childbearing history. This analysis assumes that all children live with their birth mothers. Another limitation is that there is insufficient observation of morbidity and health interventions --educated mothers are more likely to report health interventions. The result in this analysis is that the effect of prenatal care is possibly underestimated. Finally, there are at least two instances in which the effect of educational levels of infant survival might be underestimated. Because the birth history data is collected retrospectively and mothers with greater levels of education are probably less likely to omit births that end in death than are mothers with less education, the effect of education on infant survival may be underestimated. And because maternal education is related to maternal mortality, and birth history information is not collected on mothers who died in the locale, the effect of education on infant survival might again be underestimated.
  5. The combined index more closely aligns this analysis with policy prescriptives that identify high risk pregnancies than with theoretical concerns with the distinctive impact of fertility limitation, birth spacing, and maternal age on infant survival.
  6. Often these clusters were located in independent communities.
  7. Since information collected on community health facilities within the Zimbabwe Demographic and Health Survey consisted of data from only the nearest health facility, these data are not nationally representative.
  8. These were estimated with the State corporation statistical analysis software.
  9. My assumptions for analysis of multilevel models include the following: that contextual variables are not picking up effects of correlated, excluded individual-level variables; that mothers have not selected their contexts by perceived levels of education, existence of contraceptive distribution programs, or access to health facilities; and that mothers' community of residence at the time of the survey are where the children were exposed to the risk of mortality in earlier years.
  10. In this section postneonatal mortality, the probability of death from ages 1 month through the twelfth month of life, is estimated.
  11. These children were less than 1 month old because they were born during the month the survey was being taken.

Copyright 1996 - Union for African Population Studies.

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