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All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries
BACKGROUND: Understanding the magnitude and causes of mortality at national and sub-national levels for countries is critical in facilitating evidence-based prioritization of public health response. We provide comparable cause of death data from Kisumu County, a high HIV and malaria-endemic county i...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516838/ https://www.ncbi.nlm.nih.gov/pubmed/36167543 http://dx.doi.org/10.1186/s12889-022-14141-5 |
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author | Waruiru, Wanjiru Oramisi, Violet Sila, Alex Onyango, Dickens Waruru, Anthony Mwangome, Mary N. Young, Peter W. Muuo, Sheru Nyagah, Lilly M. Ollongo, John Ngugi, Catherine Rutherford, George W. |
author_facet | Waruiru, Wanjiru Oramisi, Violet Sila, Alex Onyango, Dickens Waruru, Anthony Mwangome, Mary N. Young, Peter W. Muuo, Sheru Nyagah, Lilly M. Ollongo, John Ngugi, Catherine Rutherford, George W. |
author_sort | Waruiru, Wanjiru |
collection | PubMed |
description | BACKGROUND: Understanding the magnitude and causes of mortality at national and sub-national levels for countries is critical in facilitating evidence-based prioritization of public health response. We provide comparable cause of death data from Kisumu County, a high HIV and malaria-endemic county in Kenya, and compared them with Kenya and low-and-middle income countries (LMICs). METHODS: We analyzed data from a mortuary-based study at two of the largest hospital mortuaries in Kisumu. Mortality data through 2019 for Kenya and all LMICs were downloaded from the Global Health Data Exchange. We provided age-standardized rates for comparisons of all-cause and cause-specific mortality rates, and distribution of deaths by demographics and Global Burden of Disease (GBD) classifications. RESULTS: The all-cause age-standardized mortality rate (SMR) was significantly higher in Kisumu compared to Kenya and LMICs (1118 vs. 659 vs. 547 per 100,000 population, respectively). Among women, the all-cause SMR in Kisumu was almost twice that of Kenya and double the LMICs rate (1150 vs. 606 vs. 518 per 100,000 population respectively). Among men, the all-cause SMR in Kisumu was approximately one and a half times higher than in Kenya and nearly double that of LMICs (1089 vs. 713 vs. 574 per 100,000 population). In Kisumu and LMICs non-communicable diseases accounted for most (48.0 and 58.1% respectively) deaths, while in Kenya infectious diseases accounted for the majority (49.9%) of deaths. From age 10, mortality rates increased with age across all geographies. The age-specific mortality rate among those under 1 in Kisumu was nearly twice that of Kenya and LMICs (6058 vs. 3157 and 3485 per 100,000 population, respectively). Mortality from injuries among men was at least one and half times that of women in all geographies. CONCLUSION: There is a notable difference in the patterns of mortality rates across the three geographical areas. The double burden of mortality from GBD Group I and Group II diseases with high infant mortality in Kisumu can guide prioritization of public health interventions in the county. This study demonstrates the importance of establishing reliable vital registry systems at sub-national levels as the mortality dynamics and trends are not homogeneous. |
format | Online Article Text |
id | pubmed-9516838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95168382022-09-29 All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries Waruiru, Wanjiru Oramisi, Violet Sila, Alex Onyango, Dickens Waruru, Anthony Mwangome, Mary N. Young, Peter W. Muuo, Sheru Nyagah, Lilly M. Ollongo, John Ngugi, Catherine Rutherford, George W. BMC Public Health Research BACKGROUND: Understanding the magnitude and causes of mortality at national and sub-national levels for countries is critical in facilitating evidence-based prioritization of public health response. We provide comparable cause of death data from Kisumu County, a high HIV and malaria-endemic county in Kenya, and compared them with Kenya and low-and-middle income countries (LMICs). METHODS: We analyzed data from a mortuary-based study at two of the largest hospital mortuaries in Kisumu. Mortality data through 2019 for Kenya and all LMICs were downloaded from the Global Health Data Exchange. We provided age-standardized rates for comparisons of all-cause and cause-specific mortality rates, and distribution of deaths by demographics and Global Burden of Disease (GBD) classifications. RESULTS: The all-cause age-standardized mortality rate (SMR) was significantly higher in Kisumu compared to Kenya and LMICs (1118 vs. 659 vs. 547 per 100,000 population, respectively). Among women, the all-cause SMR in Kisumu was almost twice that of Kenya and double the LMICs rate (1150 vs. 606 vs. 518 per 100,000 population respectively). Among men, the all-cause SMR in Kisumu was approximately one and a half times higher than in Kenya and nearly double that of LMICs (1089 vs. 713 vs. 574 per 100,000 population). In Kisumu and LMICs non-communicable diseases accounted for most (48.0 and 58.1% respectively) deaths, while in Kenya infectious diseases accounted for the majority (49.9%) of deaths. From age 10, mortality rates increased with age across all geographies. The age-specific mortality rate among those under 1 in Kisumu was nearly twice that of Kenya and LMICs (6058 vs. 3157 and 3485 per 100,000 population, respectively). Mortality from injuries among men was at least one and half times that of women in all geographies. CONCLUSION: There is a notable difference in the patterns of mortality rates across the three geographical areas. The double burden of mortality from GBD Group I and Group II diseases with high infant mortality in Kisumu can guide prioritization of public health interventions in the county. This study demonstrates the importance of establishing reliable vital registry systems at sub-national levels as the mortality dynamics and trends are not homogeneous. BioMed Central 2022-09-27 /pmc/articles/PMC9516838/ /pubmed/36167543 http://dx.doi.org/10.1186/s12889-022-14141-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Waruiru, Wanjiru Oramisi, Violet Sila, Alex Onyango, Dickens Waruru, Anthony Mwangome, Mary N. Young, Peter W. Muuo, Sheru Nyagah, Lilly M. Ollongo, John Ngugi, Catherine Rutherford, George W. All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries |
title | All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries |
title_full | All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries |
title_fullStr | All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries |
title_full_unstemmed | All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries |
title_short | All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries |
title_sort | all-cause and cause-specific mortality rates for kisumu county: a comparison with kenya, low-and middle-income countries |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516838/ https://www.ncbi.nlm.nih.gov/pubmed/36167543 http://dx.doi.org/10.1186/s12889-022-14141-5 |
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