Deriving causes of child mortality by re–analyzing national verbal autopsy data applying a standardized computer algorithm in Uganda, Rwanda and Ghana
Identifieur interne : 000013 ( Pmc/Curation ); précédent : 000012; suivant : 000014Deriving causes of child mortality by re–analyzing national verbal autopsy data applying a standardized computer algorithm in Uganda, Rwanda and Ghana
Auteurs : Li Liu [États-Unis] ; Mengying Li [États-Unis] ; Stirling Cummings [États-Unis] ; Robert E. Black [États-Unis]Source :
- Journal of Global Health [ 2047-2978 ] ; ????.
Abstract
To accelerate progress toward the Millennium Development Goal 4, reliable information on causes of child mortality is critical. With more national verbal autopsy (VA) studies becoming available, how to improve consistency of national VA derived child causes of death should be considered for the purpose of global comparison. We aimed to adapt a standardized computer algorithm to re–analyze national child VA studies conducted in Uganda, Rwanda and Ghana recently, and compare our results with those derived from physician review to explore issues surrounding the application of the standardized algorithm in place of physician review.
We adapted the standardized computer algorithm considering the disease profile in Uganda, Rwanda and Ghana. We then derived cause–specific mortality fractions applying the adapted algorithm and compared the results with those ascertained by physician review by examining the individual– and population–level agreement. Our results showed that the leading causes of child mortality in Uganda, Rwanda and Ghana were pneumonia (16.5–21.1%) and malaria (16.8–25.6%) among children below five years and intrapartum–related complications (6.4–10.7%) and preterm birth complications (4.5–6.3%) among neonates. The individual level agreement was poor to substantial across causes (kappa statistics: –0.03 to 0.83), with moderate to substantial agreement observed for injury, congenital malformation, preterm birth complications, malaria and measles. At the population level, despite fairly different cause–specific mortality fractions, the ranking of the leading causes was largely similar.
The standardized computer algorithm produced internally consistent distribution of causes of child mortality. The results were also qualitatively comparable to those based on physician review from the perspective of public health policy. The standardized computer algorithm has the advantage of requiring minimal resources from the health care system and represents a promising way to re–analyze national or sub-national VA studies in place of physician review for the purpose of global comparison.
Url:
DOI: 10.7189/jogh.05.010414
PubMed: 26110053
PubMed Central: 4467513
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<author><name sortKey="Liu, Li" sort="Liu, Li" uniqKey="Liu L" first="Li" last="Liu">Li Liu</name>
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<series><title level="j">Journal of Global Health</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>To accelerate progress toward the Millennium Development Goal 4, reliable information on causes of child mortality is critical. With more national verbal autopsy (VA) studies becoming available, how to improve consistency of national VA derived child causes of death should be considered for the purpose of global comparison. We aimed to adapt a standardized computer algorithm to re–analyze national child VA studies conducted in Uganda, Rwanda and Ghana recently, and compare our results with those derived from physician review to explore issues surrounding the application of the standardized algorithm in place of physician review.</p>
</sec>
<sec><title>Methods and Findings</title>
<p>We adapted the standardized computer algorithm considering the disease profile in Uganda, Rwanda and Ghana. We then derived cause–specific mortality fractions applying the adapted algorithm and compared the results with those ascertained by physician review by examining the individual– and population–level agreement. Our results showed that the leading causes of child mortality in Uganda, Rwanda and Ghana were pneumonia (16.5–21.1%) and malaria (16.8–25.6%) among children below five years and intrapartum–related complications (6.4–10.7%) and preterm birth complications (4.5–6.3%) among neonates. The individual level agreement was poor to substantial across causes (kappa statistics: –0.03 to 0.83), with moderate to substantial agreement observed for injury, congenital malformation, preterm birth complications, malaria and measles. At the population level, despite fairly different cause–specific mortality fractions, the ranking of the leading causes was largely similar.</p>
</sec>
<sec><title>Conclusions</title>
<p>The standardized computer algorithm produced internally consistent distribution of causes of child mortality. The results were also qualitatively comparable to those based on physician review from the perspective of public health policy. The standardized computer algorithm has the advantage of requiring minimal resources from the health care system and represents a promising way to re–analyze national or sub-national VA studies in place of physician review for the purpose of global comparison.</p>
</sec>
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<author><name sortKey="Chandramohan, D" uniqKey="Chandramohan D">D Chandramohan</name>
</author>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Glob Health</journal-id>
<journal-id journal-id-type="iso-abbrev">J Glob Health</journal-id>
<journal-id journal-id-type="publisher-id">JGH</journal-id>
<journal-title-group><journal-title>Journal of Global Health</journal-title>
</journal-title-group>
<issn pub-type="ppub">2047-2978</issn>
<issn pub-type="epub">2047-2986</issn>
<publisher><publisher-name>Edinburgh University Global Health Society</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26110053</article-id>
<article-id pub-id-type="pmc">4467513</article-id>
<article-id pub-id-type="publisher-id">jogh-05-010414</article-id>
<article-id pub-id-type="doi">10.7189/jogh.05.010414</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Articles</subject>
</subj-group>
</article-categories>
<title-group><article-title>Deriving causes of child mortality by re–analyzing national verbal autopsy data applying a standardized computer algorithm in Uganda, Rwanda and Ghana</article-title>
<alt-title alt-title-type="running-head">Liu and Li et al. Deriving causes of child death using national verbal autopsy</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Liu</surname>
<given-names>Li</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff4">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Mengying</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff4">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Cummings</surname>
<given-names>Stirling</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Black</surname>
<given-names>Robert E.</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<aff id="aff1"><label>1</label>
Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA</aff>
<aff id="aff2"><label>2</label>
Institute of International Programs, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA</aff>
<aff id="aff3"><label>3</label>
MEASURE Evaluation, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA</aff>
<aff id="aff4"><label>*</label>
Joint first authors.</aff>
</contrib-group>
<author-notes><corresp id="cor1"><bold>Correspondence to:</bold>
Li Liu
Department of Population, Family, and Reproductive Health
Johns Hopkins Bloomberg School of Public Health
615 N Wolfe Street
Baltimore, MD 21205, USA
<email xlink:href="lliu26@jhu.edu">lliu26@jhu.edu</email>
</corresp>
</author-notes>
<pub-date date-type="pub" publication-format="print"><month>6</month>
<year>2015</year>
</pub-date>
<pub-date date-type="pub" publication-format="electronic"><day>19</day>
<month>5</month>
<year>2015</year>
</pub-date>
<volume>5</volume>
<issue>1</issue>
<elocation-id>010414</elocation-id>
<permissions><copyright-statement>Copyright © 2015 by the Journal of Global Health. All rights reserved.</copyright-statement>
<copyright-year>2015</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><license-p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<abstract><sec><title>Background</title>
<p>To accelerate progress toward the Millennium Development Goal 4, reliable information on causes of child mortality is critical. With more national verbal autopsy (VA) studies becoming available, how to improve consistency of national VA derived child causes of death should be considered for the purpose of global comparison. We aimed to adapt a standardized computer algorithm to re–analyze national child VA studies conducted in Uganda, Rwanda and Ghana recently, and compare our results with those derived from physician review to explore issues surrounding the application of the standardized algorithm in place of physician review.</p>
</sec>
<sec><title>Methods and Findings</title>
<p>We adapted the standardized computer algorithm considering the disease profile in Uganda, Rwanda and Ghana. We then derived cause–specific mortality fractions applying the adapted algorithm and compared the results with those ascertained by physician review by examining the individual– and population–level agreement. Our results showed that the leading causes of child mortality in Uganda, Rwanda and Ghana were pneumonia (16.5–21.1%) and malaria (16.8–25.6%) among children below five years and intrapartum–related complications (6.4–10.7%) and preterm birth complications (4.5–6.3%) among neonates. The individual level agreement was poor to substantial across causes (kappa statistics: –0.03 to 0.83), with moderate to substantial agreement observed for injury, congenital malformation, preterm birth complications, malaria and measles. At the population level, despite fairly different cause–specific mortality fractions, the ranking of the leading causes was largely similar.</p>
</sec>
<sec><title>Conclusions</title>
<p>The standardized computer algorithm produced internally consistent distribution of causes of child mortality. The results were also qualitatively comparable to those based on physician review from the perspective of public health policy. The standardized computer algorithm has the advantage of requiring minimal resources from the health care system and represents a promising way to re–analyze national or sub-national VA studies in place of physician review for the purpose of global comparison.</p>
</sec>
</abstract>
<counts><page-count count="10"></page-count>
</counts>
</article-meta>
</front>
</pmc>
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