1

MORTALITY AND HEALTH TRANSITION INAFRICA:

EXPLAINING THE REVERSAL TREND INCHILDHOOD MORTALITY

Barthélémy Kuate-Defo, Ph.D., M.P.M.

Professor of Demography & Preventive Medicine –Epidemiology

University of MontrealC.P. 6128 Succursale Centre-VilleMontreal, QC H3C 3J7 Canada

Paper for Presentation at the IUSSP Seminar in Rostock,Germany, June 19-21, 2002.

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THE PROBLEM

After a long period of continuous decline in all countries,childhood mortality levels in several African countries haveleveled off, or are even on the rise. Both this stagnation,and the fact that child mortality remains high in developingcountries, can be explained by its concentration withinparticular families, communities or geographic localities.

This study develops a new conceptualization of childhoodmortality research to explain the strong heterogeneity ofmortality risks between families and communities acrossAfrican countries.

It focuses on neonatal mortality and uses multilevelmodeling and nationally representative data from 15African countries with hierarchically structured informationon children, families, households and communities, toattempt to disentangle the determinants of the geography ofchild mortality concentration.

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THE EPIDEMIOLOGICAL TRANSITION IN AFRICA

One of the most gratifying phenomenon of the twentieth centuryhas been the unprecedented decline of mortality in general andinfant and child mortality in particular, especially in the developingworld following the World War II (WWII) and resulting both fromimprovements in standards of living and advances in medicaltechnology and national and international public health efforts.

These declines were welcomed with great optimism in the 1960sand 1970s, but in the 1980s, however, considerable pessimismabout the performance in improving child survival and aboutprospects for sustainable ‘child survival revolution in the ThirdWorld’, started to surface. Just around that time, the humanimmunodeficiency virus (HIV) and the AcquiredImmunodeficiency Syndrome (AIDS) emerged as health threatsand took epidemic proportions in most countries since the late1990s. Since then, there has a heated debate as to whether thischange in attitude reflect a shift in underlying mortality trends.

Proponents of the optimist view contend that the pace of mortalitydecline in the developing world has changed little over time, whilethe pessimists identify at least three major areas of concern.

First, some observers have worried that the pace of mortalitydecline achieved in many developing countries following WWIIhas not been, and will not be, sustained because of the slow pace ofeconomic development. This argument hinges on the belief thatexogenously developed, technologically sophisticated public healthinterventions could not bring about sustained mortality decline inthe face of only limited improvements in Third World living

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standards. More recently, significant deterioration of livingstandards in many countries in Africa and Latin America has led toworries about the possibility of increases in child mortality rates.Furthermore, some observers have argued that the economicstabilization or structural-adjustment programs designed to copewith deteriorating economic conditions may themselves contributeto increases in child mortality, because the package of measuresincorporated in the program often includes reductions ingovernment spending for social services, increases in food prices,mounting unemployment and poverty, leading to sharp reductionsin family income.

The second concern relates to the effectiveness of the particularhealth programs targeted at diseases amenable to interventions.This focus on vertical (narrow) as opposed to horizontal (broadlybased community) interventions has led to concerns about possiblesubstitution effects in morbidity and mortality; that is, manychildren will die anyway, because they may be prevented fromdying of immunizable diseases such as measles just to die of othercompeting causes such as non-immunizable diseases.

Third, there is a concern about the potential effect on childmortality of recent developments in disease patterns. Two suchdevelopments are likely to have a particularly important impact ontrends in infant and child mortality rates in the 1990s in Africa,namely the AIDS epidemic and the spread of chloroquine-resistantmalaria.

The purpose of this paper is to examine the empirical support forthese concerns, by examining the levels, trends, age patterns anddeterminants of mortality in over a dozen African countries.

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Like other regions of the developing world, many Africancountries are in the midst of a demographic and epidemiologicaltransition that is profoundly transforming their health profile(Mosley and Cowley, 1991). The major phases of these transitionsare as follows:

• Prior to World War II. Most developing countriesexperienced high mortality rates, with most deaths caused byinfectious and parasitic diseases.

• After World War II and until the late 70s. Largely as a resultof advancements in medical technology directed againstinfectious and parasitic diseases, there have been rapiddeclines in infant and child mortality that coupled with thehigh levels of fertility, have resulted in population increasesand shifts in population age structure towards younger ages.

• During the 80s and 90s, mortality reductions have continuedin many countries along with fertility declines, while in otherthere have been reversal in mortality decline.

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Why focus on infant and child mortality? Three reasons:

(i) The mortality rates remain high. For example, justneonatal mortality in Cameroon varied from 33.1 perthousand to 37.2 per thousand between 1991 and 1998 (arelative increase of 4 percent in less than 10 years) (Kuate-Defo, 2000), a level at least five times the infant mortalityrate in most developed nations.

(ii) There are a wide range of health conditions affectinginfants and children in developing countries that havelong-term consequences for subsequent ages in life. Theseinclude: conditions acquired in the perinatal period,infectious diseases of childhood, nutritional deficiencies ofinfant and childhood, environmental hazards.

(iii) As the experience of secular decline in mortality intoday’s developed countries indicate, improvements inliving standards play an important role in mortalityreductions. Infant and child mortality in many developingcountries are largely the result of infectious and parasiticdiseases that are less amenable to control throughdeployment of high-tech medical technologies, but areinstead very sensitive to improvements in livingconditions (National Research Council, 1993; Gribble etPreston, 1993).

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DATA AND METHODS

A number of factors prevent a precise understanding of theepidemiological/mortality transition and impede policyformulation in developing countries. Paramount among these hasbeen until recently the absence of good data on mortality by causeof death or data representative of the entire population.

With the availability of large-scale cross-national and repeatDemographic and Health Surveys with comparable methodologies,it is now possible to assess the determinants of mortality changesover time in many developing countries.

Studying infant and child mortality is not only important in its ownright, but also because of the influence of early childhood healthconditions on health conditions in subsequent years. For instanceMosley and Gray (1993) suggest that as many as one-third ofpreventable deaths in developing countries are the consequences ofinfection and other conditions acquired during infancy andchildhood.

This life-cycle connection between childhood health conditionsand health prospects later in life indicate that programs targeted atchildren have payoffs across lifespan. The main accuratemeasurement of ill-health in developing countries is mortalitybecause perceptions of illness vary across cultures and limitedaccess to health services by the whole population impedesgathering data on morbidity and precludes the use of data forselected groups of the population to make inference about theentire population. The focus on infant and child deaths hinges onthe fact that they still form the vast majority of mortality in thepoorer societies of developing nations.

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We use individual, family, community and regional levels data andmulti-level analyses, to examine the conjectures above and toattempt to disentangle the role played by socio-economicdeterioration, sanitation and medical and health care, pre- and post-natal care, family planning, and contextual variables on changingmortality levels and trends.

We try to examine in particular the roles of changes in thesevariables and changes in the structures of relationships betweenthese variables and infant and child mortality variations inCameroon over time using decomposition techniques.

Detailed information is available only for births that occurredwithin five years preceding the survey date; thus, left-censoring isalso important. However, additional analyses based on variablesfor all births (available from upon request) indicate that thecharacteristics of women who gave birth during the five-yearperiod are very similar to the characteristics of all sampled women;therefore, left-censoring did not present problems.

This departs from previous studies of determinants of infant andchild mortality which have considered the unit of analysis to be theperson-months lived by the child, and the event examined beingthe death of the child or infant during the first year (or first twoyears) of life. In this approach, it is assumed that observations arenot correlated (e.g., children born of the same mother share anumber of mother-level characteristics and are therefore not trulyindependent observations in the statistical sense).

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1. DATA AND SELECTED VARIABLES

1.1. Data sources

• DHS Data from 15 African countries, between 1990 – 1998

• 1 316 communities

• 13 392 households

• 38 672 women

• 54 2 58 children

1.2. Selected variables

1.2.1. Dependent variable: Neonatal Mortality

1.2.2. Independent variables

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2. Child-Level, Woman/Family-Level And Community-Level InfluencesOn Infant And Childhood Mortality

• Multi-level framework of mortality determination because changingmortality levels and patterns are the product not only of changes inage structure but also changes in the distribution of child-level,mother-level, household-level, community-level and country-levelrisk factors and age-specific incidence and case-fatality rates ofvarious diseases.

• In most developing countries, the roles of women, families andcommunities in preventing illness and providing health servicesduring the demographic and epidemiological transitions have beenconsistently documented.

• For example, Caldwell and Caldwell (1993) suggest that as societiesmodernize, health usually improves because of greater availability ofhealth amenities and changes in attitudes and norms pertaining towomen’s behavior and the value of life.

• The demographic and epidemiological changes that are taking place indeveloping countries are often aggregated into national-levelestimates, but there are important variations within sub-groups withina population typically following different epidemiological anddemographic trajectories.

• The wide range of health conditions and influences affecting infantsand children in developing countries include conditions specific to thechild (e.g., acquired in the perinatal period), maternal attributes,household characteristics, environmental hazards and communityhealth-related resources, and other influential variables at the nationallevel.

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3. Multilevel Modelling Of Influences On Neonatal Mortality

3.1. Formulation of the models

• Multilevel techniques allow to analyze data with complex patterns ofvariability, with a focus on nested sources of variability. In theanalysis of mortality, the evidence tends to support the view that it isilluminating to take account of the variability associated with eachlevel of nesting.

• The basic data structure for a two-level logistic regression (e.g., childas level 1 unit and mother as level 2 unit) model can be written in thegeneral form as follows:

)1()(1

log jijijij

ijZf

P

Pβπ ==

−

where ijπ is the expected values of the responses (the two outcome are

coded 0 for alive and 1 for death within the observation period) for child i ofmother j and f is a non-linear, cumulative logistic distribution function.

A 2-level variance components logistic regression model with a vector ofexplanatory ijZ is specified as follows :

[ ]( ) )2(exp(1

1

1log

0110 jijij

ij

ij

uZP

P

++−+==

− ββ

π

• From (2), we can develop the basic structure of a five-level (1=child,2= mother, 3= household, 4= community) logistic regression is acollection of K children (i=1, …, K) born to M mothers (m = j, …, M)residing in H households (h=1, …, H) belonging to C communities(k= 1, …, C), in R countries (r=1, …, R).

• The dependent variable is the conditional probability that a child i ofmother j from household h in community k within country r, dies attime t, given that this child has survived until t is denoted )(tPijhkr .

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• We specify a multilevel logistic regression model that relates )(tPijhkr

to child-level, mother-level, household-level and community-levelrisk factors of mortality at time t by the following general equation:

( )( )( )( )( ) )3(.)()()()()(

)()()()(

)()()(

)()()()(

)()()()()()(1

)(log

55

)4(4

)3(3

)2(2

)1(1

21

4321

543210

tetutvtwt

RRtRtRtRt

QMtQYtMYt

QXtMXtYXtXXt

SQtMtYtXtttP

tP

ijhkrjhkrhkrkrr

rkhkrkrjhkrhkrjhkr

krijhkrhkrijhkrjhkrijhkrijhkrijhkr

rkrhkrjhkrijhkrijhkrijhkr

ijhkr

ijhkrijhkrijhkrijhkrijhkr

++++

+++++

+++

++++

++++++==

−

φ

ηηηηη

γδδαααα

ββββββπ

where ijhkrX , jhkrY , hkrM and krQ , rS refer to child-level, mother-level,

household-level, community-level, and country-level explanatory variablesspecified in Table 1.

0β is the constant term in the fixed part of the model; 1β , 2β , 3β , 4β , 5β andare vectors of coefficients for fixed effects of independent variables at thechild-level, mother-level, household-level, community-level, and country-level respectively; 1α , 2α , 3α , 4α , 1δ , 2δ γ are the interaction effects ofthe fixed part of the model; 1η , 2η , 3η , 4η , and 5η are random partexplanatory variables at levels 1, 2, 3, 4, and 5 respectively

54321 ,,,, ijhkrijhkrijhkrijhkrijhkr RRRRR (with their associated parameters in the random

part of the model). Finally, rφ krw , hkrv , jhkru , and ijhkre are the total variance at

the following levels: country-level, community-level, household-level,woman-level and child-level, respectively. We can write:

)4(,,,,1234

0000

5

0lijhkr

l

llijhkrijhkrljhkr

l

lljhkrjhkrlhkr

l

llhkrhkr

l

llkrlkrklr

l

llrr XeeYuuMvvQwwS ∑∑∑∑∑

=====

===== φφ

where l indexes the explanatory variables and their coefficients within eachlevel.

Normally ijhkrX 0 , jhkrY0 , hkrM 0 , krQ0 and rS0 refer to the constant defining a basic

or intercept variance term at each level.

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3.2. Estimation of the multilevel logistic model for neonatalmortality

• Rodriguez and Goldman (1995) showed that the first-order marginalquasi-likelihood (MQL) and predictive quasi-likelihood (PQL)estimates of the variance parameters of the random part of the binarymultilevel model have an appreciable downward bias.

• Goldstein (1999) and Goldstein and Rasbash (1996) showed that inmany applications the marginal quasi-likelihood (MQL) procedurewill tend to underestimate the values of both the fixed and randomparameters, especially where the lowest level unit is small, comparedto the predictive quasi-likelihood (PQL).

• In addition, greater accuracy is to be expected if the second orderapproximation is used rather than the first order based upon the firstterm in the Taylor expansion used in order to work with a linearizedmodel (Goldstein , 1999; McCullagh and Nelder, 1989).

• In most applications, the second order PQL using IGLS (using IGLSif the sample is large enough – e.g., as a rule of thumb: 50 level 2units with 20 level 1 units in each level 2 unit) model producesestimates close to the true values (Goldstein, 1999).

• With (0,1) binary data the likelihood ratio test statistic is unreliableand so we carry out an approximate test on the random parameters forthe null hypothesis that the additional variation is zero.

• Hence, to test for heterogeneity of probabilities of neonatal death, thatis, to test whether there are systematic differences between groups, weused the well-known chi squared test. For a large number of groupsthe null distribution of the test statistic of the chi squared can beapproximated by a normal distribution with the correct mean andvariance (McCullagh and Nelder, 1989).

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4. RESULTS

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5. CONCLUSIONS AND IMPLICATIONS

• IMPORTANCE OF REPRODUCTION-RELATED COVARIATES :ROLE OF PRECEDING BIRTH INTERVAL MORE IMPORTANTTHAN PARITY IN PREDICTING NEONATAL DEATH.

• IMPORTANCE OF HEALTH-SEEKING BEHAVIOR: ROLE OFANTI-TETANUS INJECTION AND PRENATAL CARE VISITS

• IMPORTANCE OF HETEROGENEITY IN MORTALITY RISKS:COUNTRY-LEVEL AND COMMUNITY-LEVEL VARIATIONS.

• CONCENTRATION OF MORTALITY RISKS: SIGNIFICANCE OFTHE SURVIVAL STATUS OF THE PRECEDING SIBLING ATTHE TIME OF THE CONCEPTION OF THE INDEX CHILD.

• OUR NEONATAL MORTALITY ESTIMATES ARE LESSLIKELY TO BE AFFECTED IN ANY SIGNIFICANT WAY BYTHE AIDS PANDEMIC.

e.g.: Heligman et al. (1993) show that in the 15 Africancountries examined, about one-fourth of projected AIDS deathswill take place among the children under age 5, the death rateunder age 5 being 13 percent higher than originally expected.

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Table 1. Levels and percent changes in the probabilities of infant and childmortality according to the three main mortality regimes in Africa since the1980s

Probability of InfantDeaths

Probability of deaths underage 5

Country Survey year

Level(‰)

Percentchange

Level (‰) Percentchange

Mortality Regime No. 1: Infant Mortality DeclinesDHS, 1988 73 102EgyptDHS, 1996 63 -14 81 -21DHS, 1988 77 155GhanaDHS, 1998 56 -27 107 -31DHS, 1987 73 102MoroccoDHS, 1995 62 -15 80 -22DHS, 1986 86 191SenegalDHS, 1992/93 68 -21 131 -31DHS, 1988 101 180UgandaDHS, 1995 81 -20 147 -18Mortality Regime No. 2: Stagnation of Infant Mortality Levels

DHS, 1992 93 163MadagascarDHS, 1997 96 3 159 2DHS, 1992 123 318NigerDHS, 1998 123 0 274 -14DHS, 1992/93 68 131SenegalDHS, 1997 68 0 139 6DHS, 1988 81 158TogoDHS, 1998 80 1 158 0DHS, 1988 53 75ZimbabweDHS, 1994 53 0 77 2

Mortality Regime No. 3: Infant Mortality IncreasesDHS, 1991 65 126CameroonDHS, 1998 77 19 151 20DHS, 1994 88 149Côte d’IvoireDHS, 1998/99 112 27 181 21DHS, 1989 60 89KenyaDHS, 1998 74 23 112 26DHS, 1987 108 249MaliDHS, 1996 123 14 238 -4

Sources : DHS country reports. Mortality regimes and changes are our ownderivations.

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Table 2. Distribution of Selected Variables: Pooled Weighted Data (N =54,258)Variables Non-residual group Residual group

Cases % Cases %Burkina Faso (CDHS, 1993) 3 315 6.11Central African Republic (CDHS, 1994/95) 2 758 5.08Cameroon (CDHS, 1991) 1 953 3.60Côte-d’Ivoire (CDHS, 1994) 3 857 7.10Kenya (CDHS, 1993) 3 553 6.55Madagascar (CDHS, 1992) 3 177 5.85Mali (CDHS, 1995/96) 5 852 10.78Morocco (CDHS, 1992) 2 982 5.50Malawi (CDHS, 1992) 2 705 5.00Nigeria (CDHS, 1990) 4 599 8.48Niger (CDHS, 1998) 4 630 8.53Senegal (CDHS, 1992/93) 3 278 6.04Tanzania (CDHS, 1991/92) 4 932 9.10Uganda (CDHS, 1995) 4 295 7.92Zimbabwe (CDHS, 1994) 2 372 4.37Preceding birth interval/birth orderFirst birth 11 009 20.29<15 months/2-4 876 1.6115-30/2-4 9 922 18.2931+/2-4 12 245 22.57<15/5+ 713 1.3115-30/5+ 8 023 14.7931+/5+ 11 470 21.13Low birthweight 11 902 21.94 42 356 78.06Preceding sibling dead 6 342 50.00 47 916 88.31Male sex 27 253 50.30 27 005 49.70Some prenatal care visits 40 626 74.88 13 632 25.12Anti-tetanus vaccine 37 596 69.29 16 662 30.71Child residence in main cities 3 112 5.73Child residence in other urban areas 10 834 19.98

40 312 74.29

Never married 2 599 4.79 51 659 95.21Mother has primary education 18 471 34.04Mother has secondary+ education 6 405 11.80

29381 54.15

Partner has primary education 16 478 30.37Partner has secondary+ education 9 772 18.01

27 708 51.67

Never used contraception 33 959 62.59 20 299 37.41Has flushed toilet 34 743 64.03 19 515 35.97Current residence in main cities 5 807 10.70Current residence in other urban areas 11 188 20.62

37 263 68.68

Notes: (1) Data from the Demographic and Health Surveys; (2) percentages may not addup to 100% due to rounding.

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Table3. Neonatal Mortality Levels in (%0) by Selected Covariates: weighteddataCountry Non-residual group Residual groupOverall neonatal mortality for the 15 countries 32Burkina Faso 35Central African Republic 32Cameroon 32Côte-d’Ivoire 28Kenya 25Madagascar 32Mali 49Morocco 30Malawi 40Nigeria 40Niger 34Senegal 31Tanzania 29Uganda 20Zimbabwe 18

First birth 54<15 months/2-4 7115-30/2-4 29<15/5+ 9415-30/5+ 3231+/5+

(Residualgroup:

31+/2-4)23

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Low birthweight (Ref: otherwise) 50 28Preceding sibling dead (Ref: otherwise) 50 30Male sex (Ref: otherwise) 36 29Some prenatal care visits (Ref: otherwise) 28 46Anti-tetanus vaccine (Ref: otherwise) 25 49

Child residence in main cities 28Child residence in other urban areas

(Res.: ruralresidence) 32

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Never married 35 32Mother has primary education 30Mother has secondary+ education

(Res.: noeducation) 25

36

Partner has primary education 31Partner has secondary+ education

(Res.: noeducation) 24

36

Never used contraception (Ref: otherwise) 36 26Has flushed toilet (Ref: otherwise) 31 35Current residence in main cities 28Current residence in other urban areas

(Res.: ruralresidence) 31

33

Note: (1) Data from the Demographic and Health Surveys.

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Table 4. Multilevel Estimates of the Covariates of Neonatal Mortality: Pooled Weighted Data (N = 54,258)Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Fixed effectsIntercept -3.91(0.07)* -3.46(0.03)* -3.00(0.05)* -3.56(0.04)* -2.40(0.37)* -3.47(0.07)* -3.36(0.04)* -2.61(0.38)* -2.62(0.38)*

Child-level covariatesFirst birth 1.06(0.08)* 1.01(0.09)* 1.05(0.09)* 1.04(0.09)*<15 months/2-4 1.31(0.15)* 1.02(0.15)* 1.03(0.15)* 1.03(0.15)*15-30/2-4 0.40(0.09)* 0.34(0.09)* 0.34(0.08)* 0.34(0.09)*<15/5+ 1.59(0.15)* 1.25(0.15)* 1.24(0.15)* 1.23(0.15)*15-30/5+ 0.48(0.09)* 0.32(0.09)* 0.29(0.09)* 0.30(0.10)*31+/5+ 0.14(0.09) -0.04(0.10) -0.06(0.10) -0.05(0.10)Prec. sibling dead 0.46(0.06)* 0.51(0.07)* 0.50(0.07)* 0.51(0.07)*Low birthweight 0.65(0.05)* 0.56(0.05)* 0.56(0.05)* 0.56(0.05)*Some prenatal care -0.53(0.05)* -0.17(0.06)¶ -0.17(0.07)¶ -0.18(0.07)¶Anti-tetanus vaccine -0.57(0.06)* -0.56(0.06)* -0.56(0.06)*Male sex 0.28(0.04)* 0.29(0.05)* 0.29(0.05)*Mom age at birth -0.11(0.02)* -0.10(0.02)* -0.10(0.03)*(Mom age at birth)2 0.00(0.00) 0.00(0.00) 0.00(0.00)

Mother/household-level covariatesChild residence inmain cities -0.05(0.12) -0.01(0.12) -0.02(0.12)Child residence inother urban areas 0.12(0.07) 0.16(0.07)¶ 0.14(0.07)¶Never married 0.11(0.11) -0.26(0.12)¶ -0.17(0.11)Mom prim. educ. -0.05(0.06) -0.00(0.06) -0.05(0.06)Mother sec.+ educ. -0.07(0.07) -0.08(0.11) -0.18(0.09)¶Partner prim. educ. -0.05(0.06) 0.04(0.06) 0.10(0.06)Partner sec.+ educ. -0.27(0.09)* -0.22(0.09)¶ -0.23(0.09)¶Never used contrac. 0.26(0.05)* 0.12(0.06)¶ 0.13(0.05)¶Has flushed toilet -0.07(0.06) 0.05(0.06) 0.04(0.06)

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Table 4 (cont.)Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Community-level covariatesCurrent residencemain cities -0.16(0.12) 0.07(0.12) 0.06(0.12)Current residenceother urban -0.08(0.07) 0.02(0.08) 0.02(0.08)

Random EffectsCountry-level

2

0vσ (between-

country variance) 0.08(0.03) 0.06(0.03) 0.07(0.03) 0.09(0.03) 0.09(0.03) 0.06(0.03) 0.06(0.03) 0.08(0.03) 0.08(0.03)

Community-level2

0uσ (between-

communityvariance)

0.11(0.03) 0.11(0.03) 0.06(0.03) 0.11(0.03) 0.04(0.03) 0.10(0.03) 0.12(0.04) 0.04(0.03) 0.05(0.03)

2

1uσ (between-community variance in the effects of preceding child death) 0.34(0.14)

01uσ (covariance of the between-community variance in the effects of the preceding child death) -0.04(0.05)

Child-level2

0eσ (between

children variance) 0.99(0.01) 0.99(0.01) 0.98(0.01) 0.96(0.01) 0.99(0.01) 0.99(0.01) 0.97(0.01) 0.96(0.01)

Residual Intra-Class CorrelationCoefficientsIntra-countrycorrelation 0.024 0.018 0.021 0.027 0.027 0.018 0.018 0.024 0.024Intra-communitycorrelation 0.032 0.032 0.018 0.032 0.012 0.029 0.035 0.012 0.015* p<.01; ¶ p<0.05.

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Table 5. Multilevel Estimates of the Probability of Dying during theNeonatal Period : Singleton Children Born 12-59 Months Before the SurveyDate

Kenya, 1989 Kenya, 1998 Change over timeLevel-specific Covariates Coeff. SE Coeff. SE Est. Signif.

PART I : FIXED EFFECTS Intercept -1.066** 0.541 -0.415 0.763 -0.651 Signif.

Level 4 : Community-specific characteristics Rural residence (D) -0.477** 0.197 -0.174 0.297 -0.303 Signif.

Level 3 : Household-specific characteristicsPiped water (D) -0.291* 0.167 -0.086 0.203 0.205 Signif.

Level 2 : Woman-specific characteristicsPrimary educ. (D) -0.398** 0.148 0.039 0.300 0.437 Signif.Secondary educ. (D) -0.587*** 0.200 -0.077 0.364 -0.510 Signif.Christian religions (D) -0.221 0.220 0.370 0.359 0.591 Signif.Never in union (D) 0.323 0.267 0.205 0.345 -0.118 Not signif.Age first intercourse (D) -0.058 0.029 -0.114*** 0.036 0.056 Not signif.

Level 1 : Infant-specific characteristicsBoy (D) 0.115 0.120 0.039 0.184 -0.076 Not signif.PBI < 24 months 0.469*** 0.129 0.053 0.238 -0.416 Signif.SBI < 24 months 1.375*** 0.128 1.823*** 0.206 0.448 Not signif.Child prenatal care (D) -0.587*** 0.140 -1.230*** 0.302 0.643 Signif.

PART I : RANDOM EFFECTSBetween-ChildrenVariation 0.897*** 0.023 0.758*** 0.041Between-HouseholdVariation 1.566*** 0.347 2.767*** 0.808Between-CommunityVariation 0.493 0.384 2.119*** 0.518Between-CommunityVariation in effects ofpreceding birth intervals 0.404 0.384 2.041 1.129 Between-CommunityVariation in effects ofsucceeding birth intervals -0.493 0.306 -2.529*** 0.677 Between-CommunityVariation in effects ofprenatal care -0.179 0.354 0.0

* p<.10 (two-tailed test).** p<.05 (two-tailed test).*** p<.01 (two-tailed test).