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African Census Analysis Project (ACAP) UNIVERSITY OF PENNSYLVANIA
Population Studies Center 3718 Locust Walk Philadelphia, Pennsylvania 19104-6298 (USA)
Tele: 215-573-5219 or 215-573-5169 or 215-573-5165 Fax: 215-898-2124 http://www.acap.upenn.edu Email: [email protected]
Analysis of Regional Differentials in Under-five Mortality in Kenya Using a Count-Data Regression Model
Gideon Rutaremwa
ACAP Working Paper No 14, March 2000 This research was done as part of the African Census Analysis Project (ACAP), and was supported by grants from the Rockefeller Foundation (RF 97013 #21; RF 98014 #22), from Andrew W. Mellon Foundation, and from the Fogarty International Center and the National Institute of Child Health and Human Development (TW00655-04). We would like to thank Timothy Cheney for computer programming assistance.
Recommended citation:
Gideon Rutaremwa. 2000. Analysis of Regional Differentials in Under-five Mortality in Kenya Using a Count-Data Regression Model. ACAP Working Paper 14, March 2000, The African Census Analysis Project (ACAP), Population Studies Center, University of Pennsylvania, Philadelphia,, Pennsylvania.
Abstract
ines regional
he number of
tive binomial
sults showed
higher incidence rate ratios of under-five deaths for Nyanza and Western Provinces,
while lower rates were estimated for Central and Nairobi Provinces. These regional
ls were added
ortality exist
tality and the
anatory variables namely: age of mother, her education, marital status, type of toilet
facility, source of water and place of residence, is basically similar in all the provinces of
Kenya.
Using data from the Kenya 1989 population census the study exam
differences in under-five mortality using a count-data regression model. T
children dead for each woman is used as the dependent variable in a nega
regression equation to explore regional under-five mortality differences. Re
differences in under-five mortality were reduced in magnitude when contro
in the regression models. Nevertheless, regional differences in childhood m
in Kenya and are substantial, and the relationship between under-five mor
expl
ii
Introduction
Understanding the geographic distribution of mortality is crucial to policy
Africa tends
this paper I
a, taking into
consideration various environmental and socioeconomic factors. Among the reasons for
regional differences in child mortality include geographic conditions such as climate and
altitude, which may affect land productivity, especially in areas where the level of
also increase
and Knowels,
evant variable
for mortality differential among children under the age of three. In addition differential
access to political power has been found to explain demographic behavior patterns in
Kenya (Weinreb, 2000).
rtality in East
s the method
ich a mortality indicator is generated.
Th ble in a linear
regression model. However, these procedures rely on normal assumptions, which may
not be accurate in under-five mortality covariate analysis.
In the current study, data on reports of women concerning all their live-born
children who have subsequently died are used to estimate a negative binomial model.
This model derives from a Poisson distribution, which has also been described as the
intervention (United Nations, 1991). Mortality in most parts of sub-Saharan
to cluster by area, often identified as a high or low-mortality region. In
examine the spatial distribution of under-five mortality using census dat
technology in agricultural production is still low. Climatic conditions can
the incidence of infectious and parasitic diseases. Studies on Kenya (Anker
1977; Ewbank et al., 1986), have identified endemic malaria as the most rel
There is a large empirical literature on covariates of under-five mo
Africa. The approach used in several studies (United Nations, 1991) i
proposed by Trussell and Preston (1982), in wh
e mortality indicator, once computed, is then used as a dependent varia
1
benchmark model for count data analysis (Cameron and Trevedi, 1998, Allison, 1998,
Long 1997). Although Poisson models have been widely used in applied econometric
rial science,
any of the
rogeneity and
all have maintained the relevance of these models in the context of count regressions.
The standard generalized form of the Poisson model is the negative binomial model,
tudy because
e conditional
dent variables are used as
predictors including maternal education, marital status, region of residence, rural/urban
residence, occupational status, source of water, and type of toilet facility.
ge number of
which makes
me regional
lity. Brass and Jolly (1993) suggest the need
for greater geographic disaggregation in examining levels and trends in childhood
mortality. The relevance of such regional-level analyses of patterns, trends, and
differentials of mortality cannot be overemphasized.
The current study focuses on some of the issues that are relevant to understanding
regional differences in under-five mortality in Kenya. In so doing, the study attempts to
works, early applications of the Poisson model took place in actua
biostatistics, and demography (Cameron and Trevedi, 1998). M
developments in analysis of count data have addressed such issues as hete
overdispersion as well as true versus apparent contagion in Poisson models, but have
derived by Greenwood and Yule (1920).
The negative binomial regression model is useful in the current s
of the role of covariates that are thought to affect the parameters of th
distribution of deaths among children. A number of indepen
The analysis uses census data as opposed to surveys because a lar
cases are required for adequate analysis of mortality at the regional level,
surveys unsuitable. Census data also offer an opportunity to examine so
covariates and patterns of under-five morta
2
answer the following questions: first, are there substantial regional variations in under-five
mortality? Second, what factors are most related to under-five mortality in the different
finally, do these differences disappear once individual and
hou
e 1989 Kenya
cluded in the
censuses. For example, census data do not contain information on causes of death,
immunization, access to and use of health facilities, and nutritional status in the various
regions of the country, yet these factors are known influence levels of under-five mortality
ries.
The study considers 135,459 Kenyan women of reproductive ages (15-49) and
who had borne at least one child by the time of the census. These data contain certain
ity in child mortality
incl r province of
, and marital
r.
It is important to note that the data used for this study have certain errors; notably,
about 15 percent of the women in the Kenyan sample did not state their parity. The
majority (about 60 percent) of these women were aged 15-19 and about 71 percent of
them were single. Given the age and marital status distribution of women in the Kenyan
sample, it seems reasonable to assume that there is little impact of these missing data on
regions of Kenya? And
sehold factors are controlled?
In an attempt to provide answers to these questions, this study uses th
census data. It is important to note that some of the relevant questions are not in
in developing count
Data and Methodology
variables that are important in analyzing geographical divers
uding information on children ever born and those surviving, region o
residence, district of residence, rural or urban residence, educational status
status. Household-level data include type of toilet facility and source of wate
3
the overall reporting of children ever born and surviving and also on child mortality
estimates derived from these data.
Cha
e presented in
Table 1. Overall, less than 10 percent of the respondents were in the age-group 15-19, this
proportion increases to slightly above 20 percent in the age groups 20-24 and 25-29, and
45-49. Age is
erspective, in
untries births to older women are usually of high order and are prone to higher
mor oung mothers
Results in Table 1 also suggest that about 20 percent of the Kenyan population are
rning level of
ment, Nairobi as well as Central Province had the highest proportion of
wom y about 1
North Eastern
The marital status variable shows that overall over 80 percent of the study population
was currently married. The expectation is that women who are currently married have more
support from their partner and are in a better position to look after their children well,
resulting in higher child survival compared to those not in a marital union. Results in Table 1
show that Provinces that were mainly rural had higher proportions of married women.
racteristics of the Respondents
Data on some of the characteristics of the respondents in this study ar
subsequently declines gradually to less than 10 percent in the age groups
important because it is closely related to child survival. From a theoretical p
developing co
tality risks. This is also true of first-order births that occur to very y
(Rutstein, 1992).
urban. There are regional variations in proportion of urban population. Conce
educational attain
en with a secondary and higher education while the North Eastern had onl
percent within this category. The worst educational indicators are observed in
and Coastal provinces.
4
Results in Table 1 also show the sanitation indicators, source of water and type of
toilet facility. Better sanitation was most lacking in North Eastern, South Rift and Western
Province had
y
or standard of
environmental
sanitation in these households. In addition, they show variation in the level of regional
development, since we expect more developed regions to have higher levels of infrastructure
t facility are
ldren in these
services that could benefit child health are
imp ilities for safe
in this paper.
with human,
that either aid
. For example,
Ewbank and others (1986), explaining regional differences in infant mortality in Kenya, point
to the fact that the high level of mortality observed for the Coastal and Nyanza Provinces
could have been partly due to malaria infestation in these regions. However, other factors,
such as political influence on access to health and other development infrastructure, may be
related to different levels of mortality (Weinreb, 2000).
Provinces of Kenya: approximately 80 percent of women from North Eastern
no toilet facility. Source of water and type of toilet facility, each with three dumm
categories, are included in the analysis because not only are they proxies f
living of the households of the respondents but they are also indicators of
use. Respondents from households where pipe-borne water and flush toile
available enjoy a higher standard of living, and child survival among chi
households is higher. Among the basic
rovements in the quality of the drinking water and the provision of fac
disposal of human excreta (Esrey and Habicht, 1986).
Finally, region of residence is examined as an explanatory variable
Each of the nine provinces of Kenya is expected to be diversely endowed
environmental and other resources that can promote child survival. The factors
or otherwise reduce child survival may be different in the respective provinces
5
Table 1: Selected characteristics of the sample women by region of residence and other selec eted background variable, K nya 1989
We otal
3.4
5.786.5
0.19.9
7.80.6
3.511.1
19.7
2.6
76.0 9.8
13.
6.84.6
86.13.2
22.
12.9.8
32.3
26.0
10.7
684.6
.4
8514.7
19.9
1411.4
.3
44.6
14.5
15.5
5.188.2
7.0
20.7
13.311.1
15.0
15.4
7 5 8
7 2 0
7 3
9 6 5 4 7 2 7
3 2 5
7 6
41
1
8
81
22111
7.3.1.8.
2
5
Region of residence Selected variable/ category Nairobi Central Coastal East North East Nyanza N. Rift S. Rift st T
Education None Primary Secondary +
Marital status Never married Previously married Currently married
Place of residence Rural Urban
Age group 15-19 20-24 25-29 30-34 35-39 40-44 45-49
Toilet facility None Other Flush
Source of water Other safe Other-unsafe Tap/ pipe-borne
12.5 40.9 46.6
21.3 5.7
73.0
- 100.0
5.5
23.7 28.4 17.7 12.2 7.7 4.8
1.8
35.4 62.8
1.0 2.1
96.9
21.4 54.7 23.9
17.5 5.8
76.7
91.5 8.5
4.1
21.8 23.2 16.0 13.8 11.8 9.8
0.6
93.2 6.2
13.0 53.2 33.8
62.5 27.2 10.3
8.8 8.5
82.8
68.4 31.6
5.9
18.4 22.5 19.2 14.5 11.2
8.2
39.5 51.1
9.4
14.7 32.5 52.8
40.9 45.4 13.6
12.0 6.3
81.7
94.2 5.8
4.1
19.4 22.2 17.7 14.5 12.6 9.5
24.3 73.6 2.1
22.3 58.0 19.7
95.9 2.9 1.2
1.7 8.2
90.1
76.8 23.2
3.5
18.2 22.9 20.8 13.0 14.6 7.0
79.0 19.7 1.3
42.5 45.2 12.3
40.6 45.9 1
7.8
9
220.5 17.2 1
9.4
77.8
14.3
41.7 44.3
9
13.4 0
8
7.7
1 22.4 16.7
9
8.5
61.7 6.0
11.8 62.2
56.5 32.8
9.0
.3
5.4
22.5 17.8
8.7
51.5 3.9
33.7 51.8
43.4 41.1
6.7
93.0
6.9
21.0 18.2
8.9
83.2 1.8
49.3 35.3
40.2.6.
1.6.2.
1.8.
5.0.2.
3.68.8.
18.2.
28.7 TOTAL (N) 9476 20454 12879 24838 2456 24096 20499 12626 8135 135459
6
Model Specification and Estimation
equently died
th the various
covariates. Let us assume that a discrete random variable Y (number of children dead)
is Poisson-distributed with intensity or rate parameter µ, µ>0, and t is the exposure,
defined as the length of time during which the events occur. Y is defined by the
following density distribution function:
Data on reports by women of all their live births who have subs
are used to estimate under-five mortality incident rate ratios associated wi
... 1,2,)(e yµt
===− tµ
0,y ,y!
y]Pr[Y
Equality of the mean with the variance is kn
Where E[Y], the expected value of Y, equals the variance - V[Y] = µt.
own as the equidispersion property
of eal-life data.
d Rodriguez
In this study, the dependent variable Y is the count of the number of children
born alive who have subsequently died for mother i, i=1, 2, 3,…, n, where n denotes
the sample size. The count-datum y s distribution depends on a set of exogenous
t ui represent
t:
the Poisson model. This property is frequently violated in r
Overdispersion means that the variance exceeds the mean (Trussell an
1990; Long 1997; Allison 1998a; Cameron and Trevedi 1998).
i
i
variables, some of which are observed (the xi) and some unobserved. Le
unobserved variables and measurement errors on the data and le
{ } λ)u,β,λ(xu,x|YE iiiiiii ==
Where E stands for the expectation operator, β is the k-dimensional parameter vector to
be estimated and u is the unobserved variables and measurement errors in the data.
e:
Implicitly the latter equation assumes that all individuals with the same
characteristics Xi have a Poisson distribution with the same mean. Suppose the ui
(source disturbance not included in Xi) was observed; we could work with the
i
The general form of the log-linear regression model specification would b
uβXuβXlog λk
1kikikiii ∑
=
+=+=
7
unconditional distribution of β given ui, which is Poisson with the m
However the ui is not observed and
ean of λi.
may not even be observable and we are forced to
con
ability of her
of mortality,
hence children ever born and duration of exposure to the risk of dying. This then
ntered in the study.
Fur
ying.
model as an
offset variable. By including ln[Children ever born]=Ψ as an offset in the equation, it
is differentiated from other coefficients in the regression model by being carried
through as a constant and forced to have a coefficient of 1.0. The final model that is
sider the unconditional distribution of βi.
To proceed, we assume that for each individual mother, the prob
children dying depends on the number of children exposed to the risk
allows us to control both the duration of child exposure to the risk of mortality and the
number of children exposed to the risk for a given woman e
thermore, we include age group of mother in the regression models as one of the
covariates in order to account for the age-varying exposure to the risk of d
The logarithm of children ever born is introduced in the regression
estimated is therefore the following:
σε)Xβ...,Xββ(βiD jij2i21i1 ++++ X
0e iψ=
Wh i i hildren born,
β is the vector parameters affecting under-five mortality levels while Xs are the
inear models
case of error or
stoc ectation of the
dependent variable and the linear predictor is a logarithmic function and the linear
of maximum
tatistics.
The model suggests that both sets of parameters are dependent on the covariates.
Furthermore, the number of children born will be equal to the observed deaths if the
coefficients of the independent variables, denoted by β, are equal to zero. Since Ψ is a
constant, any variation in the coefficients of the independent variables will show up
affecting the dependent variable and not the number of children born. The procedure
ere D is number of children dead, ψ is the logarithm of the number of c
covariates of interest.
This final model falls within the framework of generalized l
described by Nelder and Wedderburn (1972), representing a special
hastic structure, which is Poisson-distributed. The link between the exp
predictor contains a known part or offset. This allows for the estimation
likelihood, standard errors, and likelihood ratio goodness-of-fit chi-squares s
8
therefore allows us to obtain maximum likelihood regression coefficient
easily interpreted in terms of differentials in the dependent variable. Using
binomial regression procedure, several regression equations are estimated
relationship between under-five mortality changes when control var
s that can be
the negative
to show the
iables earlier
men
pressed on a
l scale, thus
interpretation of the parameters (β) obtained from the negative binomial regression
models are in terms of incident rate ratios. The incident rate ratios are obtained by
exponentiation of the regression coefficients, that is, exp[β]. For ease of interpretation,
xpression 100*(exp[β]-1) tells us the percentage change in the incidence or risk of
und
The findings from the regression models are presented in Tables 2 and 3. The
results suggest wide and significant regional variations in child mortality in Kenya.
ts change in
mortality was
nza Province
ovince for all
the models fitted to the data and was least for Central Province. According to Table 2,
results in Model I further suggest that the risk of child mortality was 17 percent lower in
the Central region compared to Nairobi. These regional differences in the risk of child
mortality between the Central and Nairobi regions widen when more controls are
introduced in the model. In Table 2 (Model I), results show that the risk of child mortality
was approximately two times higher among children born to mothers in North Eastern
tioned are introduced. All the regressions include an offset variable mentioned above.
Results from the negative binomial models are sometimes better ex
more convenient scale. All coefficients have been put on an exponentia
the e
er-five mortality for each unit increase in the independent variable.
Results
Depending on the variables included in the model, regional coefficien
magnitude and direction. Results in Table 2 show that the risk of child
slightly over three times higher among children born to mothers in Nya
compared to Nairobi. The risk of child mortality was highest in Nyanza Pr
9
Kenya compared to Nairobi. However, addition of control variables in the models
significantly alters the relationship between the various provinces. This indicates the
rtance of the control variables in understand child mortality risks in the
Under-five mo ality inc nce rat s for lected ind endent variables, (Kenya)
Model
relative impo
regions of Kenya.
Table 2: rt ide e ratio se ep
able/cat IV
Nairobi (CenCoastEasteNorth-Eastern NyanzNSW
1520
30-3435-40-44
cation
PrimaSe
Marital stNever Previously
Residence Rural U
Unsafe(RC) Other-safe Tap/ pipe-borne
-
*0.84 2.07
*1.27 *1.81
1.15 *1.70 *2.25
-
-
-
-
***11915.9 8
*******1*****2.38
*****
-
-
-
*0.75 *1.93
.14 *1.67
*1.07 *1.57 *2.08
****1.20 ***1.68 ***1.82 ***2.23 ***2.55
-
-
-
***11915.9 14
***
***1.70 *
*
- *1.09
-
-
-
**0.59 **1.34 **0.80
0.98
**0.74 1.02
**1.50
****1.23 ***1.71 *****2.57
-
-
-
***0.9***0.92
- ***0.73 ***0.49
***13405.1
18
***0.89
***1.85
***1.17 ***1.50
***1.92
***0.67
1.03
-
***15393.2 19
-
***0.64 ***1.24 ***0.82 ***0.87 ***1.72 ***0.75
1.00 ***1.52
-
***1.13 ***1.20 ***1.55 ***1.59 ***1.84 ***2.02
- ***0.70 ***0.43
-
***1.22 1.01
-
***1.12
- ***0.97 ***0.92
- ***0.80 ***0.63
***15997.1
23
- **1.11
**1.85 **2.26
- 7
- ***0.66***1.38
*1.08
***0.83***1.15***1.64
- ***1.12
***1.53***1.76
-
***0.40
- ***1.25
- 0.99
-
10
Vari egory I II III V Region of residence
RC) tral
al rn
a orth-Rift outh Rift est
Age group -19 (RC) -24
25-29
39
45-49 Edu
None (RC) ry
condary & over atus
married (RC) married
Currently married
(RC) rban
Water source
Toilet facility
None (RC) Other Flush
Likelihood ratio X2
Degrees of freedom
************2.55 *******
*** = p<1%; **= p<5%; *= p<10%; N=135459
Table 3: Under-five mortality incident rate ratios by region of residence and some selected indep va les enyendent riab , (K a) VINCE OF RE
North East an North Rift South Rift
8 1.90
5 4
2.
- 0
0.0.38
- 9 1.
1.09
- 1.16
- 9 1.0
1.14
- 1.00
1.23
***1.34 ***1.44
***0.75 0.** 43
***1.42 ***1.19
-*1.07
-890.
***0.85
-***0.78
***1.37 .7*1
***2.46
***0.75 .4* *0 5
***1.26 1.02
1.07
*1***1.41
.1
***0.89
*2.13
*2.*2.
39
8 0
*0.*0.
65
4 2 *
- 5 1.0
0.94
- 1.19
- 2 1.0
0.98
- ***0.74
**1.34 1.6
***1.98 ***2.17
***0.74 0.5**
- 9 4
- 0
- 5
1.31.1
***1.2
0.9***0.78
***0.88
REGION/PRO SIDENCE Variable/category
Nairobi Central Coastal Eastern Ny za West Age group
15-19 (RC) 20-24 25-29 30-34 35-39 40-44 45-49
Educational level attainment None Primary Secondary & over (RC)
Marital status Never married Previously married Currently married (RC)
Residence Rural Urban (RC)
Water source Safe Unsafe Tap/ pipe-borne (RC)
Toilet facility None Other Flush (RC) Likelihood ratio X2
Degrees of freedom Number of cases
-
1.07 0.85 1.09 0.94 1.09 1.07
-
***0.72 ***0.36
-
**1.22 0.99
- -
- 1.48 1.16
-
0.98 **0.75
***389.6
14 9476
-
1.17 1.30
***1.71 ***1.88 ***2.24 ***2.43
-
***0.64 ***0.46
-
**1.17 **0.90
-
1.12
- 0.96
***0.86
- 0.80 0.86
***877.0
15 20454
-
*1.24 ***1.46 ***1.90 ***1.97 ***2.24 ***2.40
-
***0.64 ***0.43
-
***1.22 1.05
-
1.01
- **0.92 **0.92
-
***0.85 ***0.63
***1056.6
15 12879
-
**1.49 ***1.91 ***2.45 ***2.82 ***3.35 ***3.76
-
***0.66 ***0.42
-
***1.11 **0.89
-
***1.25
- 1.02
***0.77
- ***0.76 ***0.61
***1962.8
15 24838
-
1.20 1.3
*1.77
**2.2** 3
** 5*
3
1.25
***82.9 15
2454
-
1.02 0.99
*** ***1.19
-
*
-
***
***0.63
***2053.0 15
24096
-
***1.18
** 9 ***1.90 ***2.25
-
*
-
-
-
** 2
-
***0.72
***896.7 15
20499
-
1.26 **1.48
*****2.20 ****
- **
*
***0.51
***1012.8 15
12626
-
**1.34
*** 6 ***1.68
-
* 0
***
**
-
***0.67
***683.5 15
8135 *** = p<1%; **= p<5%; *= p<10%;
11
Results in Tables 2 and 3 also provide estimates of the effect of some selected
individual- and household-level characteristics of mothers on the mortality of their
en of mothers
lity compared
kewise, these
oximately 30
percent those of mothers with a primary education. A look at region-specific result (Table
3) reveals that maternal education effects are very strong in all the individual province
a significantly
never married
usly married
mothers. This result is also reflected in the regional-level models in Table 3 except for the
North Eastern and South Rift Provinces where marital status was not significant in the
five mortality
f never married mothers and those of currently married
mo provinces, as
ong children
whose mothers were previously married.
Regression results in Tables 2 and 3 appear to be inconclusive with regard to rural-
urban child mortality differentials. Rural and urban residence coefficients were barely
significant in the models except in Coastal, Nyanza, and Western Provinces. The key
question raised is why child mortality should be lower in some rural areas of the respective
children. Results in Models IV and V (Table 2) show that in Kenya childr
with no education had approximately 60 percent higher risk of child morta
to their counterparts whose mothers had a secondary or higher education. Li
children of mothers with no education experienced mortality risks appr
models and follow the pattern discussed above.
In Table 2 results suggest that children of never married mothers had
lower mortality compared to those who were currently married. Children of
mothers experienced lower risk of mortality compared to those of previo
models at all. It also appears that there is no significant difference in under-
risks experienced by children o
thers. This fact is reflected both in Table 2 and in six out of the nine
indicated in Table 3. Under-five mortality risks were therefore highest am
12
regions and higher in others, even when the expectation is a lower mortality in the urban
areas? In fact, Model V (Table 2) suggests that overall child mortality risks in Kenya were
s. However, in Table 3 the results
men
increased the
province the
findings seem to suggest that the risk of child survival was higher among children of
mothers who resided in households using pipe-borne water compared to those who used
uns rtifact of data
oilet facilities
ept in
Central and North Eastern, where the variable type of toilet facility was not significant in the
models. The age coefficients were not significant in the model estimated for Nairobi (Table
3), however, results for other provinces generally show the pattern already observed in
Mortality risk among children increased with the age of mother. This perhaps
refl ers.
Conclusions
The purpose of this paper was to examine the regional differences in infant and child
mortality in Kenya using a count data regression procedure. The study uses the 1989 Kenya
census to examine some of the factors that are most responsible for regional differences in
under-five mortality. Kenya has in the recent past experienced substantial and sustained
significantly higher in the urban areas than in rural area
tioned earlier reveal that this is a feature of only three provinces.
In Table 2, results also show that, as expected, unsafe water sources
risk of child mortality. Considering the results in Table 3 for North Rift
afe water sources. This latter finding was unexpected, and is perhaps an a
in this province.
As expected, children of mothers from households with flush t
experienced the lowest mortality risks. This result is consistent in all provinces exc
Table 2.
ects the longer duration of exposure to mortality for children of older moth
13
decline in mortality associated with general socioeconomic development and changes
related to education, perhaps facilitated by the long political stability (Brass and Jolly, 1993;
still remain.
ity rates have
DHS suggest
998). This is
mainly attributed to the introduction of structural adjustment programs which have
substantially reduced government subsidies to the health sector and the onset of HIV/AIDS.
est under-five
gest likewise
). According
lity reflect the
importance of socioeconomic development. In this study, even after controlling for other
variables, education of mother remained significant in the regression equations. Further,
rtality rates in Nyanza and Coastal Provinces
are ese potential
mortality rates
This paper further reinforces the findings of previous studies concerning the
relationship between maternal education and child survival. The coefficients for education
were highly significant in all the models estimated, even in the regional models. The results
indicate an inverse relationship between maternal education and child mortality.
Ewbank et al., 1986). However, regional differences in under-five mortality
Evidence from the data suggests that since around 1985, under-five mortal
been increasing in Kenya. Moreover, preliminary results from the 1998 Kenya
that childhood mortality rates in Kenya increased in the recent past (NCPD, 1
In Kenya, Nyanza, Coastal, and Western Provinces registered the high
mortality risks. This finding is in line with previous studies, which sug
(Ewbank et al. 1986; United Nations, 1991; NCPD, 1998; Rutaremwa, 1999a
to Ewbank et al. (1986), the explanations for geographic differentials in morta
according to their study the high under-five mo
attributable to the prevalence of malaria and diarrhea in. Because of th
cultural, socioeconomic, environmental, and historical differences, under-five
in the different regions of Kenya have also been variable.
14
With regard to level of urbanization, the results are unclear. Theoretically, all things
being equal, living in urban areas should be associated with a higher standard of living,
esults indicate
icantly higher
fact that the
his finding is
consistent with Ewbank et al. (1986) in their Kenyan study, although their analysis of rural-
urban differentials was not definitive. Preston and Haines (1991) found that America at the
han the rural areas. They
exp nd congestion,
.
ion equations.
The variations in the relative size of the coefficient for this variable for the different regions
of Kenya indicate the regional differences in relative importance of this variable. The
sign expected since
hild mortality.
ory currently
married had the least risk of child mortality. This relationship was more pronounced for
all the provinces with exception of the North Eastern Province. The risks of child
mortality were lowest among children of mothers who were never married. What is
characteristic about this category of women (never married) is that they are young and
compared to the rest of the women they have fewer children. It is possible that the
better sanitation, and better health facilities, among other things. However, r
that for some regions of Kenya, the under-five mortality indicator was signif
for the urban areas compared to the rural areas. This is perhaps due to the
models examined variables that are remotely correlated to mortality. T
turn of the century also witnessed higher mortality in urban areas t
lain that urban areas were associated with a lot of unsanitary conditions a
which could be the case in the slum environment in some parts of urban Kenya
Type of toilet facility emerges as an important variable in the regress
ificant negative coefficient for North Rift Province of Kenya is rather un
it suggests that unsafe sources of water contribute to lowering the risk of c
This finding could perhaps be an outcome of problems related to the data.
Results with regard to marital status generally suggest that the categ
15
children of single mothers have on average more resources in terms of diet and
medication relative to children from larger families. In addition, it is likely that children
question of
osal of the child and mother may play an important role in the
surv
ortality exist
and are strong in Kenya. In addition, the factors most associated with under-five
mortality are similar in all regions of Kenya. When control variables are added in the
agnitude, and
aps regional
l attributes of
also the role
of political influence. Clearly, these factors are not fully captured by census data and
most surveys, yet the same factors shape the demographic, biological, socioeconomic,
historical, and cultural background of specific regions which in turn have an impact on
regional under-five mortality levels and patterns.
of single mothers are fostered to the extended family, where again the
resources at the disp
ival status of the child.
Finally, this study concludes that regional differences in under-five m
models, regional differences in under-five mortality tend to be reduced in m
sometimes the direction of the relationship changes altogether. Perh
differences in under-five mortality are an outcome of the underlying spatia
these regions: resource endowments including climate, soils, vegetation and
16
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Last Working Papers published
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