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. 2022 Sep 27;22(1):1828.
doi: 10.1186/s12889-022-14141-5.

All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries

Wanjiru Waruiru  1 , Violet Oramisi  2 , Alex Sila  3 , Dickens Onyango  4 , Anthony Waruru  5 , Mary N Mwangome  6 , Peter W Young  5 , Sheru Muuo  6 , Lilly M Nyagah  7 , John Ollongo  8 , Catherine Ngugi  2 , George W Rutherford  9
Affiliations

All-cause and cause-specific mortality rates for Kisumu County: a comparison with Kenya, low-and middle-income countries

Wanjiru Waruiru et al. BMC Public Health. .

Abstract

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.

Keywords: Cause of death; Kenya; Kisumu; LMICs; Mortality.

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Conflict of interest statement

We, the authors, have no conflicts of interests to declare.

Figures

Fig. 1
Fig. 1
All-cause mortality by age in Kisumu, Kenya & LMICs, 2019
Fig. 2
Fig. 2
Cause-specific Mortality by GBD and Age, Kisumu, Kenya & LMICs
See this image and copyright information in PMC

References

    1. Fottrell E. Dying to count: mortality surveillance in resource-poor settings. Glob Health Action. 2009. 10.3402/gha.v2i0.1926. - PMC - PubMed
    1. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018. 10.1016/S0140-6736(18)32203-7. - PMC - PubMed
    1. AbouZahr C, Cleland J, Coullare F, et al. The way forward. Lancet. 2007. 10.1016/S0140-6736(07)61310-5.
    1. Baingana FK, Bos ER, et al. Changing patterns of disease and mortality in sub-Saharan Africa: an overview. In: Jamison DT, Feachem RG, Makgoba MW, et al., editors. Disease and mortality in sub-Saharan Africa. 2. Washington (DC): The International Bank for Reconstruction and Development/The World Bank; 2006. - PubMed
    1. Nojilana B, Groenewald P, Bradshaw D, Reagon G. Quality of cause of death certification at an academic hospital in Cape Town. South Africa S Afr Med J. 2009;99(9):648–652. - PubMed

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