Achieving MDG 4 in Sub-Saharan Africa: What Has Contributed to the Accelerated Child Mortality Decline in Ghana?
Identifieur interne : 002B59 ( Pmc/Corpus ); précédent : 002B58; suivant : 002B60Achieving MDG 4 in Sub-Saharan Africa: What Has Contributed to the Accelerated Child Mortality Decline in Ghana?
Auteurs : Haruyo Nakamura ; Nayu Ikeda ; Andrew Stickley ; Rintaro Mori ; Kenji ShibuyaSource :
- PLoS ONE [ 1932-6203 ] ; 2011.
Abstract
Recent analyses have suggested an accelerated decline in child mortality in Ghana since 2000. This study examines the long-term child mortality trends in the country, relates them to changes in the key drivers of mortality decline, and assesses the feasibility of the country's MDG 4 attainment.
Data from five Demographic and Health Surveys (DHS) between 1988 and 2008 and the Maternal Health Survey 2007 were used to generate two-year estimates of under-five mortality rates back to 1967. Lowess regression fitted past and future trends towards 2015. A modified Poisson approach was applied on the person-period data created from the DHS 2003 and 2008 to examine determinants of under-five mortality and their contributions to the change in mortality. A policy-modelling system assessed the feasibility of the country's MDG 4 attainment.
The under-five mortality rate has steadily declined over the past 40 years with acceleration since 2000, and is projected to reach between 45 and 69 per 1000 live births in 2015. Preceding birth interval (reference: 36+ months, relative risk [RR] increased as the interval shortened), bed net use (RR 0.71, 95% confidence interval [CI]: 0.52–0.95), maternal education (reference: secondary/higher, RR 1.71, 95% CI: 1.18–2.47 for primary), and maternal age at birth (reference: 17+ years, RR 2.13, 95% CI: 1.12–4.05) were primarily associated with under-five mortality. Increased bed-net use made a substantial contribution to the mortality decline. The scale-up of key interventions will allow the possibility of Ghana's MDG 4 attainment.
National and global efforts for scaling up key child survival interventions in Ghana are paying off ― these concerted efforts need to be sustained in order to achieve MDG 4.
Url:
DOI: 10.1371/journal.pone.0017774
PubMed: 21445299
PubMed Central: 3061869
Links to Exploration step
PMC:3061869Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Achieving MDG 4 in Sub-Saharan Africa: What Has Contributed to the
Accelerated Child Mortality Decline in Ghana?</title>
<author><name sortKey="Nakamura, Haruyo" sort="Nakamura, Haruyo" uniqKey="Nakamura H" first="Haruyo" last="Nakamura">Haruyo Nakamura</name>
<affiliation><nlm:aff id="aff1"><addr-line>Japan International Cooperation Agency (JICA), Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Ikeda, Nayu" sort="Ikeda, Nayu" uniqKey="Ikeda N" first="Nayu" last="Ikeda">Nayu Ikeda</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Stickley, Andrew" sort="Stickley, Andrew" uniqKey="Stickley A" first="Andrew" last="Stickley">Andrew Stickley</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="aff3"><addr-line>London School of Hygiene and Tropical Medicine, London, United Kingdom</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Mori, Rintaro" sort="Mori, Rintaro" uniqKey="Mori R" first="Rintaro" last="Mori">Rintaro Mori</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Shibuya, Kenji" sort="Shibuya, Kenji" uniqKey="Shibuya K" first="Kenji" last="Shibuya">Kenji Shibuya</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Achieving MDG 4 in Sub-Saharan Africa: What Has Contributed to the
Accelerated Child Mortality Decline in Ghana?</title>
<author><name sortKey="Nakamura, Haruyo" sort="Nakamura, Haruyo" uniqKey="Nakamura H" first="Haruyo" last="Nakamura">Haruyo Nakamura</name>
<affiliation><nlm:aff id="aff1"><addr-line>Japan International Cooperation Agency (JICA), Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Ikeda, Nayu" sort="Ikeda, Nayu" uniqKey="Ikeda N" first="Nayu" last="Ikeda">Nayu Ikeda</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Stickley, Andrew" sort="Stickley, Andrew" uniqKey="Stickley A" first="Andrew" last="Stickley">Andrew Stickley</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="aff3"><addr-line>London School of Hygiene and Tropical Medicine, London, United Kingdom</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Mori, Rintaro" sort="Mori, Rintaro" uniqKey="Mori R" first="Rintaro" last="Mori">Rintaro Mori</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Shibuya, Kenji" sort="Shibuya, Kenji" uniqKey="Shibuya K" first="Kenji" last="Shibuya">Kenji Shibuya</name>
<affiliation><nlm:aff id="aff2"><addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
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<series><title level="j">PLoS ONE</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2011">2011</date>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Recent analyses have suggested an accelerated decline in child mortality in
Ghana since 2000. This study examines the long-term child mortality trends
in the country, relates them to changes in the key drivers of mortality
decline, and assesses the feasibility of the country's MDG 4
attainment.</p>
</sec>
<sec><title>Methodology</title>
<p>Data from five Demographic and Health Surveys (DHS) between 1988 and 2008 and
the Maternal Health Survey 2007 were used to generate two-year estimates of
under-five mortality rates back to 1967. Lowess regression fitted past and
future trends towards 2015. A modified Poisson approach was applied on the
person-period data created from the DHS 2003 and 2008 to examine
determinants of under-five mortality and their contributions to the change
in mortality. A policy-modelling system assessed the feasibility of the
country's MDG 4 attainment.</p>
</sec>
<sec><title>Findings</title>
<p>The under-five mortality rate has steadily declined over the past 40 years
with acceleration since 2000, and is projected to reach between 45 and 69
per 1000 live births in 2015. Preceding birth interval (reference: 36+
months, relative risk [RR] increased as the interval shortened),
bed net use (RR 0.71, 95% confidence interval [CI]:
0.52–0.95), maternal education (reference: secondary/higher, RR 1.71,
95% CI: 1.18–2.47 for primary), and maternal age at birth
(reference: 17+ years, RR 2.13, 95% CI: 1.12–4.05) were
primarily associated with under-five mortality. Increased bed-net use made a
substantial contribution to the mortality decline. The scale-up of key
interventions will allow the possibility of Ghana's MDG 4
attainment.</p>
</sec>
<sec><title>Conclusions</title>
<p>National and global efforts for scaling up key child survival interventions
in Ghana are paying off ― these concerted efforts need to be
sustained in order to achieve MDG 4.</p>
</sec>
</div>
</front>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">21445299</article-id>
<article-id pub-id-type="pmc">3061869</article-id>
<article-id pub-id-type="publisher-id">PONE-D-10-02772</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0017774</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Medicine</subject>
<subj-group><subject>Global Health</subject>
</subj-group>
<subj-group><subject>Non-Clinical Medicine</subject>
<subj-group><subject>Health Care Policy</subject>
<subject>Health Services Research</subject>
<subject>Socioeconomic Aspects of Health</subject>
</subj-group>
</subj-group>
<subj-group><subject>Public Health</subject>
<subj-group><subject>Child Health</subject>
</subj-group>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Achieving MDG 4 in Sub-Saharan Africa: What Has Contributed to the
Accelerated Child Mortality Decline in Ghana?</article-title>
<alt-title alt-title-type="running-head">Accelerated Child Mortality Decline in
Ghana</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Nakamura</surname>
<given-names>Haruyo</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ikeda</surname>
<given-names>Nayu</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Stickley</surname>
<given-names>Andrew</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mori</surname>
<given-names>Rintaro</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Shibuya</surname>
<given-names>Kenji</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>Japan International Cooperation Agency (JICA), Tokyo, Japan</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>London School of Hygiene and Tropical Medicine, London, United Kingdom</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Vitzthum</surname>
<given-names>Virginia</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1">Indiana University, United States of America</aff>
<author-notes><corresp id="cor1">* E-mail: <email>fv5h-smz@asahi-net.or.jp</email>
</corresp>
<fn fn-type="con"><p>Conceived and designed the experiments: HN NI KS. Performed the experiments:
HN NI. Analyzed the data: HN NI. Contributed reagents/materials/analysis
tools: HN NI KS. Wrote the paper: HN. Interpreted data: NH NI AS RM KS.
Revised the manuscript for intellectual content: NI AS RM KS. Revised the
manuscript for language content: AS. Supervised the study: KS.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2011</year>
</pub-date>
<pub-date pub-type="epub"><day>21</day>
<month>3</month>
<year>2011</year>
</pub-date>
<volume>6</volume>
<issue>3</issue>
<elocation-id>e17774</elocation-id>
<history><date date-type="received"><day>25</day>
<month>9</month>
<year>2010</year>
</date>
<date date-type="accepted"><day>14</day>
<month>2</month>
<year>2011</year>
</date>
</history>
<permissions><copyright-statement>Nakamura et al. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited.</copyright-statement>
</permissions>
<abstract><sec><title>Background</title>
<p>Recent analyses have suggested an accelerated decline in child mortality in
Ghana since 2000. This study examines the long-term child mortality trends
in the country, relates them to changes in the key drivers of mortality
decline, and assesses the feasibility of the country's MDG 4
attainment.</p>
</sec>
<sec><title>Methodology</title>
<p>Data from five Demographic and Health Surveys (DHS) between 1988 and 2008 and
the Maternal Health Survey 2007 were used to generate two-year estimates of
under-five mortality rates back to 1967. Lowess regression fitted past and
future trends towards 2015. A modified Poisson approach was applied on the
person-period data created from the DHS 2003 and 2008 to examine
determinants of under-five mortality and their contributions to the change
in mortality. A policy-modelling system assessed the feasibility of the
country's MDG 4 attainment.</p>
</sec>
<sec><title>Findings</title>
<p>The under-five mortality rate has steadily declined over the past 40 years
with acceleration since 2000, and is projected to reach between 45 and 69
per 1000 live births in 2015. Preceding birth interval (reference: 36+
months, relative risk [RR] increased as the interval shortened),
bed net use (RR 0.71, 95% confidence interval [CI]:
0.52–0.95), maternal education (reference: secondary/higher, RR 1.71,
95% CI: 1.18–2.47 for primary), and maternal age at birth
(reference: 17+ years, RR 2.13, 95% CI: 1.12–4.05) were
primarily associated with under-five mortality. Increased bed-net use made a
substantial contribution to the mortality decline. The scale-up of key
interventions will allow the possibility of Ghana's MDG 4
attainment.</p>
</sec>
<sec><title>Conclusions</title>
<p>National and global efforts for scaling up key child survival interventions
in Ghana are paying off ― these concerted efforts need to be
sustained in order to achieve MDG 4.</p>
</sec>
</abstract>
<counts><page-count count="6"></page-count>
</counts>
</article-meta>
</front>
<body><sec id="s1"><title>Introduction</title>
<p>Millennium Development Goal 4 (MDG 4) is targeted at reducing global under-five
mortality rates by two-thirds between 1990 and 2015.<xref ref-type="bibr" rid="pone.0017774-UNICEF1">[1]</xref>
The number of children dying
before reaching the age of five decreased from over 12.6 million in 1990 to around 9
million in 2007 worldwide, with the global under-five mortality rate declining from
93 to 67 per 1,000 live births in this period.<xref ref-type="bibr" rid="pone.0017774-World1">[2]</xref>
, <xref ref-type="bibr" rid="pone.0017774-UNICEF2">[3]</xref>
, <xref ref-type="bibr" rid="pone.0017774-United1">[4]</xref>
However, it was reported that
little or no progress has been made in 68 countdown priority countries, which are
responsible for 97% of maternal and child deaths worldwide.<xref ref-type="bibr" rid="pone.0017774-UNICEF1">[1]</xref>
These priority
countries are concentrated in sub-Saharan Africa, where the absolute number of
under-five deaths increased from 4.2 to 4.6 million during the above-mentioned
period, primarily due to the persistently high fertility levels as well as the
continuing high rates of mortality.<xref ref-type="bibr" rid="pone.0017774-United1">[4]</xref>
</p>
<p>In Ghana, the MDG 4 target of a two-thirds reduction in under-five mortality equates
to 40 deaths per 1,000 live births, relative to the figure in 1990 which was 120 per
1,000 live births.<xref ref-type="bibr" rid="pone.0017774-World1">[2]</xref>
, <xref ref-type="bibr" rid="pone.0017774-UNICEF2">[3]</xref>
However, by 2007 the figure had only declined to 115.<xref ref-type="bibr" rid="pone.0017774-UNICEF2">[3]</xref>
The MDG
Countdown Report in 2008 rated Ghana as a “No Progress” country and
warned that the country had to reduce its under-five mortality rate by 12%
annually on average between 2007 and 2015 in order to reach the MDG 4 target.<xref ref-type="bibr" rid="pone.0017774-UNICEF1">[1]</xref>
</p>
<p>In contrast, two recent surveys―the Maternal Health Survey (MHS) 2007 and the
Demographic and Health Survey (DHS) 2008―have both suggested a high rate of
decline since 2003 (26% and 28%, respectively).<xref ref-type="bibr" rid="pone.0017774-UNICEF1">[1]</xref>
A more recent systematic review
of child mortality worldwide and the updated MDG Countdown Report 2010 have also
indicated an accelerated decline since 2000.<xref ref-type="bibr" rid="pone.0017774-Bhutta1">[5]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Rajaratnam1">[6]</xref>
However, no study has
explained this occurrence in detail.<xref ref-type="bibr" rid="pone.0017774-Rajaratnam1">[6]</xref>
</p>
<p>This study, therefore, attempts to estimate the most up-to-date under-five mortality
trends in Ghana, relates them to changes in the key drivers of child mortality
decline, and assesses the feasibility of the country's MDG 4 attainment given
the current child mortality reduction efforts in place.</p>
</sec>
<sec sec-type="materials|methods" id="s2"><title>Materials and Methods</title>
<sec id="s2a"><title>Trends in under-five mortality between 1967 and 2008</title>
<p>We used data from the five DHS conducted in Ghana between 1988 and 2008, and the
MHS 2007. These were nationally representative cluster sample surveys that
covered 4,406, 5,822, 6,003, 6,251, 10,858, and 11,778 households in 1988, 1993,
1998, 2003, 2007, and 2008, respectively.<xref ref-type="bibr" rid="pone.0017774-MEASURE1">[7]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Ghana1">[8]</xref>
This cross-sectional survey
data provided direct estimates of child mortality, as well as detailed
information on household demographics, asset ownership, the health and
nutritional status of women and children, the coverage of health care services,
and current knowledge and practices related to health. Survey data were obtained
from interviews undertaken by trained personnel.<xref ref-type="bibr" rid="pone.0017774-ORC1">[9]</xref>
</p>
<p>Mortality among children aged younger than five years was estimated for every
two-year period before each respective survey going back to 1967, using the
direct method based on the complete birth histories of women aged between 15 and
49 years. Periods of exposure and deaths were grouped into two calendar years,
and a separate life table was constructed for each period in the birth histories
to show the probability that children would die before their next birthday. We
then calculated the individual death probability by counting the number of
children born in a certain period of time and the number of children dying in
the same period. Then we used the synthetic cohort life table approach, in which
the probabilities of death for small age segments based on real cohort mortality
experience were combined into the more common age segments.<xref ref-type="bibr" rid="pone.0017774-Rutstein1">[10]</xref>
We used biannual
mortality estimates up to 22 years back from the respective survey year to
minimize maternal recall bias. In total, 66 mortality estimates were generated
for the period between 1967 and 2008.</p>
<p>Mortality trends from 1967 to 2008 were estimated by fitting Lowess regression of
the natural log of mortality in children younger than five years to time with
bandwidths ranging from 0.3 (representing high sensitivity to recent data) to
2.0 (lower sensitivity), while the trend towards 2015 was forecast with the same
bandwidths. Mean annual reduction rates were calculated by using the results
from the Lowess regression analysis with the 0.3 bandwidth, which can precisely
detect changes that occur over time.<xref ref-type="bibr" rid="pone.0017774-Cleveland1">[11]</xref>
</p>
</sec>
<sec id="s2b"><title>Determinants of under-five mortality</title>
<p>In order to examine potential determinants of under-five mortality in Ghana, we
pooled data of children's records from DHS 2003 and 2008. We used only
these two surveys as they allowed us to maximize the number of intervention
variables that could be examined. The pooled data had 6,609 under-five children
born to 4,259 ever-married women aged 15–49 years at the time of the
survey. We then transformed individual records of children to survival-time
data, in order to consider the time to event and censoring and reflect the
changing rates of mortality during childhood. In this risk dataset, children
first became at risk and came under observation at date of birth. A row
represented each child for each age band in which he/she survived and was
therefore at risk. The analysis-time variable was time since birth measured in
months, and we split childhood into five age bands: the first month of life (age
0 months), months 1–5, months 6–11, months 12–23 and months
24–59. We obtained 27,011 episodes for 6,609 children. The total exposure
time was 184,307 child-months and 499 children died. The median time of exiting
the risk set due to experiencing the event or censoring was 26 months.</p>
<p>We employed Poisson regression with robust error variance to model a binary
outcome of death.<xref ref-type="bibr" rid="pone.0017774-Zou1">[12]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Ozaltin1">[13]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Subramanian1">[14]</xref>
The Poisson regression
model is appropriate for analyzing the risk of rare events such as under-five
mortality which is not very common. However, the error for estimated relative
risks may be overestimated when Poisson regression is applied to binomial data.
We therefore used a sandwich estimator to obtain robust variance estimates. We
also accounted for the clustering of children by mothers and included fixed
effects of survey years in the model, specifying the exposure time for each
child within each age band as defined above. Our log-linear hazard function of
an underlying risk of death was<disp-formula><graphic xlink:href="pone.0017774.e001"></graphic>
</disp-formula>
where
<italic>μ<sub>si</sub>
</italic>
and
<italic>x<underline><sub>s</sub>
</underline>
<sub>i</sub>
</italic>
were
the risk of dying and a set of covariates, respectively, during age band
<italic>s</italic>
for child <italic>i</italic>
; the exponential of the
constant term, <italic>α<sub>s</sub>
</italic>
, was the vector of underlying
age-specific risks of death given survival to the beginning of each age
interval; and <italic>β</italic>
was a set of estimated coefficients.</p>
<p>When selecting covariates for the model, we considered their relevance to child
mortality in Ghana and potential confounding factors. Covariates consequently
covered socioeconomic and demographic characteristics (urban/rural residence,
region, religion, economic status, maternal age at birth, maternal highest
education, maternal marital status, child's sex and multiplicity in birth)
and maternal and child health practices (birth order, preceding birth interval,
breastfeeding duration, use of a bed net, use of oral rehydration salt
[ORS] and the number of antenatal care [ANC] visits). As a
measure of economic status, we constructed wealth quintiles to make them
comparable between the two surveys. We appended household survey datasets to
estimate wealth index scores through a principal component analysis on the
ownership of household assets that were available from both surveys.<xref ref-type="bibr" rid="pone.0017774-Rutstein2">[15]</xref>
Then, we
computed cutoffs of wealth quintiles among household members in the pooled
population. The wealth quintiles variable therefore refers to the economic level
of a household to which the woman aged 15 to 49 years belongs. Furthermore, two
dummy variables were created for the duration of breastfeeding to indicate for
each age band whether children were breastfed for a sufficient length of time
and whether breastfeeding terminated in preceding age bands or was still ongoing
at the start of each age band: (1) children in age bands of 1–5 months or
later who were never breastfed or breastfed for less than one month, and (2)
children in age bands of 6–11 months or later who were never breastfed or
had stopped being breastfed before entering the age band of 6–11 months.
We determined cutoffs for the continuous variables of maternal age at birth and
preceding birth interval to indicate early childbearing and short intervals,
respectively, so that we could maintain a sufficient sample size for analysis
and maximize the statistical significance of the model, while retaining
consistency with the original purpose of the variable. After excluding children
with missing values on any of these covariates, 24,228 episodes of 5,886
under-five children (3,359 in 2003 and 2,527 in 2008) were included in this
regression.</p>
<p>For the relative contribution of each covariate to the changes in child mortality
from 2003 to 2008, first we used the estimated beta coefficients and means of
the explanatory variables in 2003 to calculate the risk of mortality in that
year. Then, for each explanatory variable, we changed its values to its mean in
2008 to compute the change in the risk of mortality, setting other variables to
their means in 2003. We divided the difference by the risk of mortality in 2003
to obtain the relative contribution of each explanatory variable.</p>
</sec>
<sec id="s2c"><title>Potential impact of scaling up child-survival interventions</title>
<p>To estimate reductions in under-five mortality rates as a result of scaling up
child-survival interventions in Ghana, we used the Lives Saved Tool (LiST) in
the Spectrum Policy Modelling System.<xref ref-type="bibr" rid="pone.0017774-Plosky1">[16]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Plosky2">[17]</xref>
LiST calculates the impact
of scaling up interventions on child death under the assumption that the number
or proportion of deaths averted increases linearly as coverage increases from 0
to 100%, with some ability to incorporate a non-linear impact of
component interventions, such as herd-immunity and increased quality. This
programme assigns weights to the impact of each intervention to obtain the
percentage of deaths averted across interventions.</p>
<p>LiST requires background information for projections of the future based on UN
population division estimates and under-five mortality trends from the UN child
mortality coordination group, child morbidity and nutritional status, deaths by
causes, and coverage of child health interventions as well as assumptions
concerning the efficacy of those interventions. The programme provides default
values for the required information based on a review of scientific studies<xref ref-type="bibr" rid="pone.0017774-Plosky2">[17]</xref>
and we used
the built-in data, which included the DHS datasets, for the analysis of
intervention impact on child mortality. The intervention coverage values come
from the latest data sources, including DHS, Multiple Indicator Cluster Surveys,
and other domestic household surveys conducted in the country. The particular
coverage values for vaccinations and vitamin A came from UNICEF. The coverage
values for 2015 were based on an expert review of trials on the effectiveness
and consequent theoretical scale-up for each intervention. We set 2003 as the
baseline year for our projection because the default data for the new LiST
projections were collected to respond closely to the year 2003.</p>
<p>The tool projected the under-five mortality rates in 2009 and 2015 based on the
background information explained above, and made an alternative child survival
projection for 2015, in which the selected eight interventions were further
up-scaled in pursuit of the MDG 4 target. The interventions were selected based
on the causes of death among children aged under-five in Ghana (malaria,
32%; neonatal death, 29%; pneumonia, 15%; diarrhoea,
12%; HIV/AIDS, 6%; measles, 3%; and injuries,
3%)<xref ref-type="bibr" rid="pone.0017774-UNICEF1">[1]</xref>
, the effectiveness of the interventions for these
causes of death, and the past trends in intervention coverage in the country.
Insecticide-treated materials are a 55% effective intervention to prevent
malaria. Vitamin A as a preventive measure is 32% effective for diarrhoea
and 19% effective for measles. Measles vaccination is 85%
effective. Hib vaccination is 18% and DPT is 10% effective for
pneumonia prevention. ORS is 93% and zinc is 23% effective for
diarrhoea treatment.<xref ref-type="bibr" rid="pone.0017774-Plosky1">[16]</xref>
Case management of neonatal severe infection was
selected based on the evidence that coverage of postnatal care is closely
related to institutional delivery and that specific case management can be
provided by trained community workers, which suggests cost-effectiveness and
swiftness of the intervention.<xref ref-type="bibr" rid="pone.0017774-Fort1">[18]</xref>
</p>
<p>The trend and determinant analyses were conducted using Stata/SE version 10.0
(StataCorp LP, College Station, TX, United States of America). LiST was used in
the Spectrum version 3.45, downloaded from the website (Futures Institute,
Glastonbury, CT, United States of America). The complex nature of the survey
design, including such elements as stratification and clustering, was considered
in the trend analysis and descriptive statistics of the study population.</p>
</sec>
</sec>
<sec id="s3"><title>Results</title>
<sec id="s3a"><title>Trends in under-five mortality rates</title>
<p><xref ref-type="fig" rid="pone-0017774-g001">Figure 1</xref>
presents biannual
trends in under-five mortality rates. Estimates from the Lowess regression
indicated that under-five mortality had declined from 88 per 1,000 live births
(95% CI: 86−89) in 2003 to 73 (95%CI: 66−80) in 2008.
The under-five mortality rate steadily declined between 1967 and 2008 in Ghana
and is projected to reach somewhere between 45 and 69 deaths per 1,000 live
births in 2015. Mean annual rates of reduction in under-five mortality remained
around 1.5–1.7% from the 1960s to the 1990s, but they have
accelerated to 4.6% since 2000.<xref ref-type="bibr" rid="pone.0017774-Bhutta1">[5]</xref>
</p>
<fig id="pone-0017774-g001" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0017774.g001</object-id>
<label>Figure 1</label>
<caption><title>Two-year estimates of under-five mortality between 1967 and 2008 and
projection towards 2015 in Ghana.</title>
<p>Data are from Ghana Demographic and Health Surveys 1988–2008 and
Ghana Maternal Health Survey 2007. Note: The MDG 4 target is 40 per 1000
live births, indicated as the red round-shaped point at the end of the
dotted MDG track. MDG, Millennium Development Goal; DHS, Demographic and
Health Survey; MHS, Maternal and Health Survey.</p>
</caption>
<graphic xlink:href="pone.0017774.g001"></graphic>
</fig>
</sec>
<sec id="s3b"><title>Factors associated with under-five mortality</title>
<p><xref ref-type="supplementary-material" rid="pone.0017774.s001">Table S1</xref>
presents information on the socioeconomic, geographical and demographic
characteristics and health service utilization of the respondents and their
children aged younger than five years in the DHS 2003 and 2008. Between the two
survey years, the poorest quintile and the poorer quintile decreased by
27% and 22%, while the richer quintile and the richest quintile
increased by 50% and 54%, respectively. The proportion of women
who had no education was reduced by 18% during the period. Coverage of
bed net use for under-five children grew substantially by 196%, while the
use of ORS and the coverage of ANC occurring more than 4 times also increased by
51% and 13%, respectively.</p>
<p><xref ref-type="supplementary-material" rid="pone.0017774.s002">Table S2</xref>
shows the relative risk for under-five death in Ghana. Early child-bearing
(<17 years) doubled the risk of under-five death
(p = 0.021). Compared with children whose mothers had a
secondary or higher education, children whose mothers received a primary
education had a 1.7 times higher risk of dying before their fifth birthday
(p = 0.004), while the likelihood also increased but was
not statistically significant for those born to mothers who had no education
(p = 0.103). A child who was born less than 1.5 years after
a previous birth was 3.6 times more likely to die before their fifth birthday,
compared to a baby born after a preceding birth interval of more than three
years (p<0.001). Moreover, the risk of dying gradually reduced as the birth
interval widened (RR 2.3, p<0·001 for 19–23 months; RR 1.6,
p = 0.003 for 24–35 months). The risk of an
under-five death increased three-fold when a child older than a month was never
breastfed or breastfed for less than one month (p = 0.001),
and the risk increases five-fold when a child older than six-months was never
breastfed or breastfed for less than 6 months (p<0.001). The risk of child
death was reduced by nearly 30% when all or some of the children aged
younger than five slept under bed nets on the night before the interview
(p = 0.023).</p>
<p><xref ref-type="fig" rid="pone-0017774-g002">Figure 2</xref>
presents information
on how much each determinant has contributed to the mortality decline between
2003 and 2008. Out of 23.1 percentage points reduction in the probability of
under-five mortality between the two consecutive survey years, −10.6
percentage points (95% CI: −19.2 to −1.0), came from an
increased use of bed nets. The other significant factor was a widened birth
interval, which contributed −1.6 percentage points (95% CI:
−2.6 to −0.4) to the reduction in the probability of a child's
death. Finally, −13.7 percentage points remained unexplained (95%
CI: −33.6 to 12.2), which would include unobserved variables in the
household surveys including immunization, and the use of zinc and
antibiotics.<xref ref-type="bibr" rid="pone.0017774-Jones1">[19]</xref>
</p>
<fig id="pone-0017774-g002" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0017774.g002</object-id>
<label>Figure 2</label>
<caption><title>Estimated contributions of explanatory variables to the change in
under-five mortality in Ghana between 2003 and 2008.</title>
<p>Data are from Ghana Demographic and Health Surveys 2003 and 2008. The
change in mean predicted probabilities of death under five years of age
between 2003 and 2008 was decomposed into contributory factors.</p>
</caption>
<graphic xlink:href="pone.0017774.g002"></graphic>
</fig>
</sec>
<sec id="s3c"><title>Mortality impact of changes in the intervention coverage</title>
<p><xref ref-type="supplementary-material" rid="pone.0017774.s003">Table S3</xref>
presents the adjusted and projected coverage of the child survival interventions
in 2003, 2009 and 2015 in Ghana. Using LiST the under-five mortality rate is
estimated to be 68 per 1000 live births in 2009, which is within the range of
the Lowess regression estimates of between 62 and 78 per 1000 live births. The
intervention coverage was then projected to increase towards 2015, yielding an
under-five mortality rate of 49 per 1000 live births, which was also within the
Lowess estimates of 45 to 69 per 1000 live births.</p>
<p>We suggested an alternative scenario with the further up-scaling of eight
interventions by 2015, which is indicated as “2015a” in the fourth
column of <xref ref-type="supplementary-material" rid="pone.0017774.s003">Table
S3</xref>
. If these interventions were up-scaled to this alternative level
and other interventions remained at the 2015 level, Ghana would attain its MDG 4
target: 40 deaths per 1000 live births. In this scenario, Ghana can upscale the
eight interventions through the following four strategies. First, extra efforts
are made to further extend the target coverage of rapidly up-scaled
insecticide-treated materials from 80% to 90%. Second, the
successful three child-survival immunizations: i.e. measles, diphtheria,
pertussis, tetanus (DPT), and Haemophilus influenzae type B (Hib), are provided
altogether with vitamin A supplements, which could bring a reduction of up to
25% in under-five mortality in sub-Saharan Africa.<xref ref-type="bibr" rid="pone.0017774-Aguayo1">[20]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Aguayo2">[21]</xref>
Third, based on the
assumption that 79.4% of women would have institutional delivery by 2015
in Ghana,<xref ref-type="bibr" rid="pone.0017774-Plosky2">[17]</xref>
deaths caused by severe neonatal infections could be widely averted by the
proper training of health personnel to continually monitor mothers before,
during and after delivery and swiftly diagnose and treat neonatal illnesses, as
coverage of postnatal care is closely related to institutional delivery.
Finally, the use of ORS for children with diarrhoea is further promoted based on
the fact that more than 90% of mothers have knowledge of ORS,<xref ref-type="bibr" rid="pone.0017774-Ghana2">[22]</xref>
and use of
zinc can be introduced as an adjunct to ORS.</p>
</sec>
</sec>
<sec id="s4"><title>Discussion</title>
<p>We confirm a steady decline in under-five mortality between 1967 and 2008 with an
accelerated decline since 2000. The mean annual rate of reduction: 4.6% since
2000 exceeded the rate required for achieving MDG4 (i.e. 4. 4%).<xref ref-type="bibr" rid="pone.0017774-Bhutta1">[5]</xref>
Our estimate is
higher than the MDG 4 target, but is substantially lower than previous
estimates<xref ref-type="bibr" rid="pone.0017774-UNICEF1">[1]</xref>
and consistent with new estimates,<xref ref-type="bibr" rid="pone.0017774-Bhutta1">[5]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Rajaratnam1">[6]</xref>
<xref ref-type="bibr" rid="pone.0017774-Murray1">[23]</xref>
which
included the recent data from MHS and DHS.<xref ref-type="bibr" rid="pone.0017774-Ghana1">[8]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Ghana2">[22]</xref>
</p>
<p>This study analysed the trends and determininants of under-five mortality and the
relative contribution of different factors to the decline in child mortality. This
series of population-level analyses, together with the practical estimates of child
survival intervention coverage as a supplementary tool, has suggested a way of
directing policy-initiatives towards MDG 4 attainment, especially for other priority
countries.</p>
<p>Specifically, we have identified bed net use for children aged under five and
preceding birth interval as being important determinants of child mortality as well
as major contributors to the decline in mortality. We also found that mothers'
educational attainment, mother's age at birth and breatfeeding duration were
important determinants of child mortality. The results of this study suggest that
under-five mortality was successfully reduced by a combination of the recent scale
up of child survival interventions and an overall improvement in the socioeconomic
conditions in Ghana.</p>
<p>In addition to the factors identified in this study, there are several other possible
reasons for the decline in child mortality in Ghana. First, Ghana's economic
improvement must have been behind the success. In fact, Ghana has enjoyed constant
increases in gross domestic product (GDP) over the past 15 years <xref ref-type="bibr" rid="pone.0017774-International1">[24]</xref>
and real
GDP growth rates of 5 to 7 percent since 2003.<xref ref-type="bibr" rid="pone.0017774-Central1">[25]</xref>
Second, the country's
pursuit of MDG 4 attainment itself may have played a role and the scaling up of
other key child survival interventions has certainly reduced under-five mortality.
<xref ref-type="bibr" rid="pone.0017774-Ghana2">[22]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Bryce1">[26]</xref>
In particular,
the coverage of the key child immunizations was successfully increased from less
than 20% in 1980 to over 90% in 2007.<xref ref-type="bibr" rid="pone.0017774-World2">[27]</xref>
With a few exceptions,
child-survival interventions were up-scaled by 10 to 30% in Ghana between
2003 and 2008.<xref ref-type="bibr" rid="pone.0017774-Ghana2">[22]</xref>
,
<xref ref-type="bibr" rid="pone.0017774-Ghana3">[28]</xref>
</p>
<p>Ghana has also achieved equity in intervention coverage. A recent analysis revealed
that Ghana had the highest coverage in the richest quintile and the second poorest
quintile among the 16 Sub-Saharan African Countries with 2009 estimated GDP per
capita of less than International $1,000. <xref ref-type="bibr" rid="pone.0017774-International2">[29]</xref>
Various trials have
shown remarkable reductions in child mortality with the case management of ill
children by community health workers <xref ref-type="bibr" rid="pone.0017774-Haines1">[30]</xref>
and it can be conjectured that
the piloting and scale-up of the nation-wide “High Impact Rapid Delivery
(HIRD)” strategy,<xref ref-type="bibr" rid="pone.0017774-UNICEF3">[31]</xref>
together with a community health service delivery
framework, Community-based Health Planning and Services (CHPS), may have played a
pivotal role in accelerating child mortality reduction in Ghana, though a recent
retrospective evaluation of HIRD highlighted the mixed results of the Program in
West Africa.<xref ref-type="bibr" rid="pone.0017774-Bryce2">[32]</xref>
Finally, the decentralization of the health system, initiated in 1996 by introducing
sector-wide capitation grants and giving districts sufficient financial resources,
has also been relatively successful in Ghana, owing to the early establishment of
the District Health Management Team as a primary health care strategy in the
1980s.<xref ref-type="bibr" rid="pone.0017774-Shimizu1">[33]</xref>
</p>
<p>Several limitations of this study should be mentioned. First, the limited number of
variables in the DHS datasets did not allow us to explore a wider range of
intervention variables, particularly the direct impact of child survival
interventions on under-five mortality, including immunizations and the case
management of malaria, pneumonia and diarrhoea, as well as neonatal mortality, which
accounts for 29% of under-five mortality in Ghana.<xref ref-type="bibr" rid="pone.0017774-UNICEF1">[1]</xref>
The limited nature of the
variables examined in this study highlights the large unexplained element in the
mortality reduction. In order to maximize the range of intervention variables, we
used only DHS 2003 and 2008, which in turn reduced the number of observations in the
analysis. Second, the nature of the cross-sectional data also did not allow us to
investigate any causal relations between variables or effectiveness of interventions
on child mortality. Third, DHS-style direct birth histories may be subject to recall
bias and/or the underreporting of child mortality by mothers, which could result in
more than a 7% underestimation of deaths.<xref ref-type="bibr" rid="pone.0017774-Murray1">[23]</xref>
, <xref ref-type="bibr" rid="pone.0017774-Mahy1">[34]</xref>
The non-inclusion of other data
sets meant that the current study may also have been subject to this potential
problem. Finally, the LiST assumption of a linear decrease in the number of deaths
with increasing intervention coverage remains somewhat uncertain as there are
non-linear effects on child mortality which are not controlled by the tool.</p>
<p>In conclusion, Ghana has experienced a slow, but nevertheless, steady decline in its
under-five mortality rate over the past forty years with an accelerated reduction
since 2000. National and global efforts for scaling up key child survival
interventions in Ghana are paying off and need to be sustained. Strategic scale-up
of key interventions will lead the country even further towards the attainment of
its MDG 4 target. Identification and enhancement of key child-survival
interventions, following a thorough examination of each country's specific
context, are recommended for other low-income, “No progress” countries,
particularly in sub-Saharan Africa.</p>
</sec>
<sec sec-type="supplementary-material" id="s5"><title>Supporting Information</title>
<supplementary-material content-type="local-data" id="pone.0017774.s001"><label>Table S1</label>
<caption><p><bold>Socioeconomic, geographic and demographic characteristics and health
service utilization of women aged 15 to 49 years and their children aged
under five, the Demographic and Health Surveys 2003 and 2008,
Ghana.</bold>
Values are percentages with 95% confidence
intervals in parentheses.</p>
<p>(DOCX)</p>
</caption>
<media xlink:href="pone.0017774.s001.docx" mimetype="application" mime-subtype="msword"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="pone.0017774.s002"><label>Table S2</label>
<caption><p><bold>Relative risk of death among children aged under five years of age in
Ghana, the Demographic and Health Surveys 2003 and 2008, Ghana.</bold>
CI, confidence interval; RR, relative risk.</p>
<p>(DOCX)</p>
</caption>
<media xlink:href="pone.0017774.s002.docx" mimetype="application" mime-subtype="msword"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="pone.0017774.s003"><label>Table S3</label>
<caption><p><bold>Projected/alternative coverage (%) of key child-survival
interventions in Ghana.</bold>
BCG, Bacillus Calmette-Guérin;
DPT, diphtheria, pertussis, tetanus; Hib, Haemophilus influenzae type B.</p>
<p>(DOCX)</p>
</caption>
<media xlink:href="pone.0017774.s003.docx" mimetype="application" mime-subtype="msword"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
</sec>
</body>
<back><ack><p>We would like to express our sincere gratitude to Dr. Julie Rajaratnam for her
technical support, and Dr. Ai Koyanagi and Dr. Moazzam Ali for their helpful
comments.</p>
</ack>
<fn-group><fn fn-type="conflict"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="financial-disclosure"><p><bold>Funding: </bold>
This study was in part funded by a research grant from the Japan Ministry of
Health, Labour and Welfare. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the
manuscript.</p>
</fn>
</fn-group>
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