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Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model

BACKGROUND: Assessing the progress in achieving the United Nation's Millennium Development Goals in terms of population health requires consistent and reliable information on cause-specific mortality, which is often rare in resource-constrained countries. Health and demographic surveillance sys...

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Autores principales: Amek, Nyaguara O., Odhiambo, Frank O., Khagayi, Sammy, Moige, Hellen, Orwa, Gordon, Hamel, Mary J., Van Eijk, Annemieke, Vulule, John, Slutsker, Laurence, Laserson, Kayla F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Co-Action Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221497/
https://www.ncbi.nlm.nih.gov/pubmed/25377340
http://dx.doi.org/10.3402/gha.v7.25581
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author Amek, Nyaguara O.
Odhiambo, Frank O.
Khagayi, Sammy
Moige, Hellen
Orwa, Gordon
Hamel, Mary J.
Van Eijk, Annemieke
Vulule, John
Slutsker, Laurence
Laserson, Kayla F.
author_facet Amek, Nyaguara O.
Odhiambo, Frank O.
Khagayi, Sammy
Moige, Hellen
Orwa, Gordon
Hamel, Mary J.
Van Eijk, Annemieke
Vulule, John
Slutsker, Laurence
Laserson, Kayla F.
author_sort Amek, Nyaguara O.
collection PubMed
description BACKGROUND: Assessing the progress in achieving the United Nation's Millennium Development Goals in terms of population health requires consistent and reliable information on cause-specific mortality, which is often rare in resource-constrained countries. Health and demographic surveillance systems (HDSS) have largely used medical personnel to review and assign likely causes of death based on the information gathered from standardized verbal autopsy (VA) forms. However, this approach is expensive and time consuming, and it may lead to biased results based on the knowledge and experience of individual clinicians. We assessed the cause-specific mortality for children under 5 years old (under-5 deaths) in Siaya County, obtained from a computer-based probabilistic model (InterVA-4). DESIGN: Successfully completed VA interviews for under-5 deaths conducted between January 2003 and December 2010 in the Kenya Medical Research Institute/US Centers for Disease Control and Prevention HDSS were extracted from the VA database and processed using the InterVA-4 (version 4.02) model for interpretation. Cause-specific mortality fractions were then generated from the causes of death produced by the model. RESULTS: A total of 84.33% (6,621) childhood deaths had completed VA data during the study period. Children aged 1–4 years constituted 48.53% of all cases, and 42.50% were from infants. A single cause of death was assigned to 89.18% (5,940) of cases, 8.35% (556) of cases were assigned two causes, and 2.10% (140) were assigned ‘indeterminate’ as cause of death by the InterVA-4 model. Overall, malaria (28.20%) was the leading cause of death, followed by acute respiratory infection including pneumonia (25.10%), in under-5 children over the study period. But in the first 5 years of the study period, acute respiratory infection including pneumonia was the main cause of death, followed by malaria. Similar trends were also reported in infants (29 days–11 months) and children aged 1–4 years. CONCLUSIONS: Under-5 cause-specific mortality obtained using the InterVA-4 model is consistent with existing knowledge on the burden of childhood diseases in rural western Kenya.
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spelling pubmed-42214972014-12-02 Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model Amek, Nyaguara O. Odhiambo, Frank O. Khagayi, Sammy Moige, Hellen Orwa, Gordon Hamel, Mary J. Van Eijk, Annemieke Vulule, John Slutsker, Laurence Laserson, Kayla F. Glob Health Action Indepth Network Cause-Specific Mortality BACKGROUND: Assessing the progress in achieving the United Nation's Millennium Development Goals in terms of population health requires consistent and reliable information on cause-specific mortality, which is often rare in resource-constrained countries. Health and demographic surveillance systems (HDSS) have largely used medical personnel to review and assign likely causes of death based on the information gathered from standardized verbal autopsy (VA) forms. However, this approach is expensive and time consuming, and it may lead to biased results based on the knowledge and experience of individual clinicians. We assessed the cause-specific mortality for children under 5 years old (under-5 deaths) in Siaya County, obtained from a computer-based probabilistic model (InterVA-4). DESIGN: Successfully completed VA interviews for under-5 deaths conducted between January 2003 and December 2010 in the Kenya Medical Research Institute/US Centers for Disease Control and Prevention HDSS were extracted from the VA database and processed using the InterVA-4 (version 4.02) model for interpretation. Cause-specific mortality fractions were then generated from the causes of death produced by the model. RESULTS: A total of 84.33% (6,621) childhood deaths had completed VA data during the study period. Children aged 1–4 years constituted 48.53% of all cases, and 42.50% were from infants. A single cause of death was assigned to 89.18% (5,940) of cases, 8.35% (556) of cases were assigned two causes, and 2.10% (140) were assigned ‘indeterminate’ as cause of death by the InterVA-4 model. Overall, malaria (28.20%) was the leading cause of death, followed by acute respiratory infection including pneumonia (25.10%), in under-5 children over the study period. But in the first 5 years of the study period, acute respiratory infection including pneumonia was the main cause of death, followed by malaria. Similar trends were also reported in infants (29 days–11 months) and children aged 1–4 years. CONCLUSIONS: Under-5 cause-specific mortality obtained using the InterVA-4 model is consistent with existing knowledge on the burden of childhood diseases in rural western Kenya. Co-Action Publishing 2014-10-29 /pmc/articles/PMC4221497/ /pubmed/25377340 http://dx.doi.org/10.3402/gha.v7.25581 Text en © 2014 Nyaguara O. Amek et al. http://creativecommons.org/licenses/by/4.0/ 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 work is properly cited.
spellingShingle Indepth Network Cause-Specific Mortality
Amek, Nyaguara O.
Odhiambo, Frank O.
Khagayi, Sammy
Moige, Hellen
Orwa, Gordon
Hamel, Mary J.
Van Eijk, Annemieke
Vulule, John
Slutsker, Laurence
Laserson, Kayla F.
Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model
title Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model
title_full Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model
title_fullStr Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model
title_full_unstemmed Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model
title_short Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model
title_sort childhood cause-specific mortality in rural western kenya: application of the interva-4 model
topic Indepth Network Cause-Specific Mortality
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221497/
https://www.ncbi.nlm.nih.gov/pubmed/25377340
http://dx.doi.org/10.3402/gha.v7.25581
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