Family-level clustering of childhood mortality risk in Kenya

1.     Family-level clustering of childhoodmortality risk in Kenya  D. Walter Rasugu Omariba Department of Sociology Population Studies Centre University of Western Ontario London, Ontario

2. 2 Background Mortality decline in Kenya began in late 1940s. E.g. under-five mortality: 220 in 1958-62 period, declined to 89 in 1984-1989 period Reversals in the downward trend started in 1986 (see figure 1). Infant mortality increased by 24 % and Under-five mortality by 25 % in 1988-98 period. -Source- (NCPD and Macro International, 1989; Brass and Jolly, 1993). -Similar declines were also recorded in infant mortality rates. It declined from 103 deaths per one thousand live births in 1975, to 83 in 1985 and reached 67 in 1995 (UN, 2001). -Source- (NCPD and Macro International, 1989; Brass and Jolly, 1993). -Similar declines were also recorded in infant mortality rates. It declined from 103 deaths per one thousand live births in 1975, to 83 in 1985 and reached 67 in 1995 (UN, 2001).

3. 3 Figure 1: Child mortality trends 1974-1998, Kenya Source: National Council for Population and Development and Macro International, 1989, 1994; 1999. -It is evident that mortality increased from 1986. -It is evident that mortality increased from 1986.

4. 4 Existing research Focuses on determinants and differentials of mortality (See, for instance, Kibet, 1981; Ewbank et al., 1986; Kichamu, 1986; Omariba, 1993; Obungu et al., 1994; Ikamari, 2000). This study’s focus: Familial child death clustering: In the literature, defined in two ways: 1) Expected vs. observed- Higher observed deaths indicate death clustering 2) Control for unobserved heterogeneity through inclusion of random effects in models- correlation of risks at different levels. -Relied on census and survey data-Relied on census and survey data

5. 5 Rationale Random-effects models used yet to be applied on Kenyan data. Child mortality remains an important public health issue. Reducing mortality important for sustaining country’s incipient fertility transition. -Recent increases in mortality are likely to affect the government’s two-pronged approach to population growth management, i.e., through birth spacing and reduction of mortality. Fertility declined from 5.4 in 1993 to 4.7 in 1998 and expected to reach about 2.5 in 2010. -Need to reexamine mortality determinants especially in changing conditions -Recent increases in mortality are likely to affect the government’s two-pronged approach to population growth management, i.e., through birth spacing and reduction of mortality. Fertility declined from 5.4 in 1993 to 4.7 in 1998 and expected to reach about 2.5 in 2010. -Need to reexamine mortality determinants especially in changing conditions

6. 6 Sources of unobserved heterogeneity Differential competence in childcare (Das Gupta, 1997). Biological factors e.g. genetically determined frailty, ‘improvident maternity’ syndrome (Guo, 1993; Das Gupta, 1997). Socioeconomic, cultural factors and environmental factors. All unmeasured and unmeasurable factors. -In relation to child care, such women are likely to be poor at making effective home diagnoses of their children’s symptoms and taking active steps to help them (Das Gupta, 1990). Guo (1993): -Gupta (1990): ‘Improvident maternity’ syndrome whereby certain families suffer brief birth intervals and/or large families in which higher-parity children receive less parental care and other resources. -Socioeconomic and socio-cultural factors: Poverty in certain households/ communities, lack of infrastructure in community, cultural practices that favour boys in childcare and feeding practices, infanticide in certain cultures -Environmental factors: ecological conditions associated with the aetiology of diseases -In relation to child care, such women are likely to be poor at making effective home diagnoses of their children’s symptoms and taking active steps to help them (Das Gupta, 1990). Guo (1993): -Gupta (1990): ‘Improvident maternity’ syndrome whereby certain families suffer brief birth intervals and/or large families in which higher-parity children receive less parental care and other resources. -Socioeconomic and socio-cultural factors: Poverty in certain households/ communities, lack of infrastructure in community, cultural practices that favour boys in childcare and feeding practices, infanticide in certain cultures -Environmental factors: ecological conditions associated with the aetiology of diseases

7. 7 Death clustering? In this study: Measured by unobserved heterogeneity term indicating correlation of risks in family. Most studies only select one child, truncate data by certain date or ignore first child- Biased results especially when variables such as preceding birth interval and survival status are considered. -Clustering of mortality risks among siblings (or among children residing in the same community) is due in part to children sharing the same observed family and community characteristics. But the correlation may persist even after controlling for observed covariates- The remaining correlation is a consequence of genetic, behavioural and environmental factors that are related mortality risks and are common to groups of children but that are unmeasured or unmeasurable. -Correlated observations can be used to estimate the extent of clustering in mortality risks and can help us to determine the importance of unmeasured factors for child survival.-Clustering of mortality risks among siblings (or among children residing in the same community) is due in part to children sharing the same observed family and community characteristics. But the correlation may persist even after controlling for observed covariates- The remaining correlation is a consequence of genetic, behavioural and environmental factors that are related mortality risks and are common to groups of children but that are unmeasured or unmeasurable. -Correlated observations can be used to estimate the extent of clustering in mortality risks and can help us to determine the importance of unmeasured factors for child survival.

8. 8 Implications of data structure Children in same family are more alike than children from different families. covariates’ estimates biased. Consequences of violation of independence: standard errors of parameters underestimated– spurious precision. biases baseline hazard duration pattern downward in survival analysis. -The sameness of children from same community/family violates the assumption of independence of observations required for statistical analyses. -If standard errors are underestimated we are likely to make Type I error- rejecting true null hypothesis. -biases baseline hazard duration pattern downward- This is similar to having a constant hazard (The best way of understanding this is by imagining a process of constant hazard). -The sameness of children from same community/family violates the assumption of independence of observations required for statistical analyses. -If standard errors are underestimated we are likely to make Type I error- rejecting true null hypothesis. -biases baseline hazard duration pattern downward- This is similar to having a constant hazard (The best way of understanding this is by imagining a process of constant hazard).

9. 9 Implications of data structure Random-effects models: Correct for the biases in parameter estimates, provides correct standard errors and correct confidence intervals and significance tests Separates impact of individual and social context If contextual effects significant, using a random effect (or multilevel model is reasonable). If not, then we need only adjust the error term for dependence of units. -Covariates’ estimates biased? Means magnitude may be erroneously large and direction of effect could be wrong. -Random-effects models? The random-effects have to correspond with the levels that the data is conceptualized to have.-Covariates’ estimates biased? Means magnitude may be erroneously large and direction of effect could be wrong. -Random-effects models? The random-effects have to correspond with the levels that the data is conceptualized to have.

10. 10 Data and methods Data source: Demographic and Health Survey for Kenya, 1998. 7,881 women 15-49, all marital statuses from 8,380 households and 8,233 eligible women. 3,407 husbands/partners of the women Largely rural sample, 81.4% of the women’s sample Methods: Weibull hazard models and random-effect hazard models. The latter tests for family-level variance. -DHS are a replacement of the WFS for developing countries. The 1998 DHS for Kenya was the third such survey in the country. Others in 1989 and 1993. The 2003 DHS is yet to be released to the public. - Although we can include a random term for unobserved heterogeneity at the individual-level, a major advantage of the multilevel analysis is that by splitting of the variance in the multilevel model enables us to determine how much of the variation between individual survival chances is due to family or community effects. -DHS are a replacement of the WFS for developing countries. The 1998 DHS for Kenya was the third such survey in the country. Others in 1989 and 1993. The 2003 DHS is yet to be released to the public. - Although we can include a random term for unobserved heterogeneity at the individual-level, a major advantage of the multilevel analysis is that by splitting of the variance in the multilevel model enables us to determine how much of the variation between individual survival chances is due to family or community effects.

11. 11 Conceptual framework Study is guided by the Mosley and Chen (1984) ‘proximate determinants’ model (see Figure 2). Individual characteristics: Migration status, education, year of birth, ethnicity, religion, survival status of preceding child, birth interval, birth order and maternal age at birth. Household characteristics: socioeconomic status, sanitation and source of water. Strategy of analysis: first estimate effect of distant factors and then include proximate factors. Strategy of analysis: first estimate effect of distant factors and then include proximate factors.

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