Transcript CHANGING CHILDHOOD MORTALITY CONDITIONS IN KENYA: …

Family-level clustering of childhood
mortality risk in Kenya
D. Walter Rasugu Omariba
Department of Sociology
Population Studies Centre
University of Western Ontario
London, Ontario
Background
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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.
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Figure 1: Child mortality trends 19741998, Kenya
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Mortality rate
100
80
Infant mortality
60
Under-five mortality
40
20
0
1974-78 1979-83 1984-88 1989-93 1994-98
Reference period
Source: National Council for Population and Development and Macro
International, 1989, 1994; 1999.
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Existing research
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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 modelscorrelation of risks at different levels.
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Rationale
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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.
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Sources of unobserved heterogeneity
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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.
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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.
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
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Implications of data structure
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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.
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Implications of data structure
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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.
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Data and methods
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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.
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Conceptual framework
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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.
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Figure 2: Conceptual framework for
studying the determinants of infant
and childhood mortality
Distant Factors
-Socio-economic factors:
e.g. maternal & paternal
education, place of
residence, region,
migration, occupation,
household socioeconomic
status, marital status, year
of birth, period of child
birth.
-Socio-cultural factors:
e.g. religion, ethnicity.
Proximate Determinants
-Reproductive healthcare
behaviour
e.g. prenatal care, place of
delivery, delivery care, tetanus
injection, breastfeeding
-Biodemographic factors
e.g. maternal age at birth,
birth interval, birth order, age
at marriage, child loss
experience
-Household environmental
conditions
e.g. source of water, toilet
facility.
Outcome
Variable
Risk of child
death
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Data description:
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Of the 7881, 5716 had at least one child,
while 2165 had never had a child.
23348 children born to 5716 women
(family)
2325 children had died before their fifth
birthday:
 Infancy1620(0-12 months)
 Childhood- 705 (Age 13-59 months)
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Table 3: Distribution of children and child deaths per family in Kenya, DHS 1998
Children
per/fam
Percent of
Deaths in family
0
1
2
3
4
5
6
7
8
Total
Children
Deaths
1
1012
87
0
0
0
0
0
0
0
1099
4.7
3.7
2
884
99
8
0
0
0
0
0
0
991
8.5
4.9
3
632
130
16
0
0
0
0
0
0
778
10.0
7.0
4
523
131
30
3
2
0
0
0
0
689
11.8
9.0
5
366
128
36
11
1
0
0
0
0
542
11.6
10.2
6
327
115
47
15
3
2
0
0
0
509
13.1
11.9
7
193
100
42
14
9
1
0
0
0
359
10.8
11.5
8
129
81
35
19
7
4
0
0
0
275
9.4
11.0
9
105
62
29
18
9
3
0
0
1
227
8.7
10.0
10
41
40
23
18
8
6
2
1
0
139
5.9
9.5
11
14
11
12
6
5
2
3
0
0
53
2.5
4.3
12
6
6
6
3
12
2
1
2
0
38
2.0
4.5
13
1
2
1
4
2
0
2
0
0
12
0.7
1.6
14
0
1
0
0
0
1
1
0
0
3
0.2
0.5
15
0
0
1
0
0
0
0
1
0
2
0.1
0.4
Total
4233
993
286
111
58
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9
4
1
5716
100
100
% of
children
62
22
8
4
2
.8
.5
.2
.03
100
----
-----
% of
deaths
0
43
25
14
10
5
2
1
0.3
100
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Does clustering exists?
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Over 80 percent of the children belong to families
contributing two or more children to the sample.
Families with six or more children comprise about
28 percent of the families yet contribute over half
of the children.
57 percent of the deaths occurred to 8.6 percent of
the families with two or more deaths.
About 2 percent of the families contribute four or
more deaths; together accounting for about 18
percent of the deaths.
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Results
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There is significant unobserved heterogeneity both in
infancy and childhood (Tables 3 &4):
 The estimated random parameters, θ, in the models with
unobserved heterogeneity are 0.40 and 0.78 for infant
and child mortality respectively.
 There is significant familial variation in the risk of
infant and child death.
Maternal education, period of birth, ethnicity, type of toilet
facility, birth interval and maternal age at birth of child
important for both infant and child survival (Tables 1&2).
Migration status, religion, survival status of previous child
and birth order significant only for infant mortality, while
household SES significant only for child mortality.
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Results
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There are large ethnic differences in risk of death with
children Luo mothers being most disadvantaged.
Secondary or higher education associated with a 22 % and
42% reduction in risk of infant mortality and child
mortality respectively.
Risk of infant death higher for children born after 1990,
while that of child death is higher for all children born after
1985.
The risk of infant death is higher for children whose
sibling died, were born less than 19 months after preceding
sibling, and when the mother was less than 20.
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Conclusions
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The determinants of death have different effects on infant
and childhood mortality. Biodemographic factors have
greater effect in infancy, while education and ethnicity
have greater effect in childhood.
Suggests varied policy actions:
 Infancy: longer birth intervals through family planning
and breastfeeding, later age at birth etc.
 Childhood: improvement in education, socioeconomic
status and poverty eradication programs.
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Conclusions
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Death clustering is non-ignorable – Needs further
research:
 Healthcare factors- Information available only for
children born three years before the survey.
 Qualitative research at community level.
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Death clustering, another measurement: Consider
unobserved heterogeneity in the context of each woman’s
sequence of births. The heterogeneity term used in this
paper does not reflect this fact.
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