42 datasets found
  1. Main causes of deaths in Kenya 2021, by type

    • statista.com
    Updated Jun 3, 2025
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    Statista (2025). Main causes of deaths in Kenya 2021, by type [Dataset]. https://www.statista.com/statistics/1221721/main-causes-of-deaths-in-kenya/
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    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    Kenya
    Description

    Respiratory infections and tuberculosis were the most frequent cause of casualties in Kenya as of 2021, with a rate of almost 208 deaths per 100,000. In addition, cardiovascular diseases, and HIV/AIDS and sexually transmitted infections caused high number of deaths compared to other disorders, at about 76 deaths per 100,000 and 66 deaths per 100,000 respectively.

  2. Main causes of deaths among men in Kenya 2019

    • statista.com
    Updated Jun 3, 2025
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    Statista (2025). Main causes of deaths among men in Kenya 2019 [Dataset]. https://www.statista.com/statistics/1282417/main-causes-of-deaths-among-men-in-kenya/
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    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Kenya
    Description

    HIV/AIDS and sexually transmitted infections were the main cause of deaths among men in Kenya as of 2019, with a rate of roughly 99 casualties per 100,000 population. In 2020, Kenya was the ninth country worldwide with the highest number of AIDS-related deaths. In addition, cardiovascular diseases, and respiratory infections and tuberculosis caused a high number of deaths, by rates of 92.7 and 82.8 casualties per 100,000 population, respectively.

  3. Main causes of deaths in Kenya 2019

    • statista.com
    Updated Jun 3, 2025
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    Statista (2025). Main causes of deaths in Kenya 2019 [Dataset]. https://www.statista.com/statistics/1282386/main-causes-of-deaths-in-kenya/
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    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Kenya
    Description

    HIV/AIDS and sexually transmitted infections were the main death causes among women in Kenya as of 2019, with a rate of 108.5 deaths per 100,000 population. In 2020, Kenya was the ninth country worldwide with the highest number of AIDS-related deaths. In addition, cardiovascular diseases, and respiratory infections and tuberculosis caused a high number of deaths among women in the country, by rates of 78.9 and 60.2 deaths per 100,000 population, respectively.

  4. f

    Leading Causes of Death Kisumu Kenya

    • figshare.com
    xlsx
    Updated Jun 18, 2021
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    Anthony Waruru (2021). Leading Causes of Death Kisumu Kenya [Dataset]. http://doi.org/10.6084/m9.figshare.14806176.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 18, 2021
    Dataset provided by
    figshare
    Authors
    Anthony Waruru
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kisumu, Kenya
    Description

    The Excel Workbook contains raw minimal deidentified dataset that were used for the manuscript "Leading causes of death and high mortality rates in an HIV endemic setting (Kisumu county, Kenya, 2019)"

  5. Kenya - Demographics, Health and Infant Mortality Rates

    • data.unicef.org
    Updated Sep 29, 2016
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    UNICEF (2016). Kenya - Demographics, Health and Infant Mortality Rates [Dataset]. https://data.unicef.org/country/ken/
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    Dataset updated
    Sep 29, 2016
    Dataset authored and provided by
    UNICEFhttp://www.unicef.org/
    Description

    UNICEF's country profile for Kenya, including under-five mortality rates, child health, education and sanitation data.

  6. Estimated all-cause and cause-specific mortality rates by GBD and HIV...

    • plos.figshare.com
    xls
    Updated Jun 9, 2023
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    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young (2023). Estimated all-cause and cause-specific mortality rates by GBD and HIV disease classifications in Kisumu County, Kenya (2019). [Dataset]. http://doi.org/10.1371/journal.pone.0261162.t005
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    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kisumu County, Kenya
    Description

    Estimated all-cause and cause-specific mortality rates by GBD and HIV disease classifications in Kisumu County, Kenya (2019).

  7. Ascertained leading causes of death at two large hospitals and among all...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jun 15, 2023
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    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young (2023). Ascertained leading causes of death at two large hospitals and among all persons and sex, Kisumu County, Kenya (2019). [Dataset]. http://doi.org/10.1371/journal.pone.0261162.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kisumu County, Kenya
    Description

    Ascertained leading causes of death at two large hospitals and among all persons and sex, Kisumu County, Kenya (2019).

  8. Reduction of HIV-associated excess mortality by antiretroviral treatment...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated May 31, 2023
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    Dickens O. Onyango; Courtney M. Yuen; Kevin P. Cain; Faith Ngari; Enos O. Masini; Martien W. Borgdorff (2023). Reduction of HIV-associated excess mortality by antiretroviral treatment among tuberculosis patients in Kenya [Dataset]. http://doi.org/10.1371/journal.pone.0188235
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Dickens O. Onyango; Courtney M. Yuen; Kevin P. Cain; Faith Ngari; Enos O. Masini; Martien W. Borgdorff
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kenya
    Description

    BackgroundMortality from TB continues to be a global public health challenge. TB ranks alongside Human Immunodeficiency Virus (HIV) as the leading infectious causes of death globally. HIV is a major driver of TB related morbidity and mortality while TB is the leading cause of mortality among people living with HIV/AIDS. We sought to determine excess mortality associated with HIV and the effect of antiretroviral therapy on reducing mortality among tuberculosis patients in Kenya.MethodsWe conducted a retrospective analysis of Kenya national tuberculosis program data of patients enrolled from 2013 through 2014. We used direct standardization to obtain standardized mortality ratios for tuberculosis patients compared with the general population. We calculated the population attributable fraction of tuberculosis deaths due to HIV based on the standardized mortality ratio for deaths among TB patients with HIV compared to TB patients without HIV. We used Cox proportional hazards regression for assessing risk factors for mortality.ResultsOf 162,014 patients included in the analysis, 6% died. Mortality was 10.6 (95% CI: 10.4–10.8) times higher among TB patients than the general population; 42% of deaths were attributable to HIV infection. Patients with HIV who were not receiving ART had an over four-fold risk of death compared to patients without HIV (aHR = 4.2, 95% CI 3.9–4.6). In contrast, patients with HIV who were receiving ART had only 2.6 times the risk of death (aHR = 2.6, 95% CI 2.5–2.7).ConclusionHIV was a significant contributor to TB-associated deaths in Kenya. Mortality among HIV-infected individuals was higher among those not on ART than those on ART. Early initiation of ART among HIV infected people (a “test and treat” approach) should further reduce TB-associated deaths.

  9. f

    Comparison of notified versus the ascertained cause of death (COD) in Kisumu...

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jun 16, 2023
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    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young (2023). Comparison of notified versus the ascertained cause of death (COD) in Kisumu County, Kenya (2019). [Dataset]. http://doi.org/10.1371/journal.pone.0261162.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kisumu County, Kenya
    Description

    Comparison of notified versus the ascertained cause of death (COD) in Kisumu County, Kenya (2019).

  10. Deaths from non-communicable diseases in Kenya 2010-2021

    • statista.com
    Updated Jul 18, 2025
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    Statista (2025). Deaths from non-communicable diseases in Kenya 2010-2021 [Dataset]. https://www.statista.com/statistics/1309021/deaths-from-non-communicable-diseases-in-kenya/
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    Dataset updated
    Jul 18, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Kenya
    Description

    In 2021, the number of deaths from non-communicable diseases (NCDs) in Kenya decreased to *******. This represents an almost ** percent decrease from the highest level of ******* deaths recorded in 2019. NCDs are chronic conditions which include cardiovascular diseases, cancers, chronic respiratory diseases among others

  11. f

    Ascertained leading causes of death at two large hospitals and among...

    • plos.figshare.com
    xls
    Updated Jun 16, 2023
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    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young (2023). Ascertained leading causes of death at two large hospitals and among children aged 0–4 years in Kisumu County, Kenya (2019). [Dataset]. http://doi.org/10.1371/journal.pone.0261162.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Anthony Waruru; Dickens Onyango; Lilly Nyagah; Alex Sila; Wanjiru Waruiru; Solomon Sava; Elizabeth Oele; Emmanuel Nyakeriga; Sheru W. Muuo; Jacqueline Kiboye; Paul K. Musingila; Marianne A. B. van der Sande; Thaddeus Massawa; Emily A. Rogena; Kevin M. DeCock; Peter W. Young
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kisumu County, Kenya
    Description

    Ascertained leading causes of death at two large hospitals and among children aged 0–4 years in Kisumu County, Kenya (2019).

  12. Countries with the highest number of AIDS-related deaths 2024

    • statista.com
    Updated Jul 14, 2025
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    Statista (2025). Countries with the highest number of AIDS-related deaths 2024 [Dataset]. https://www.statista.com/statistics/281396/countries-with-highest-number-of-aids-deaths/
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    Dataset updated
    Jul 14, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Worldwide
    Description

    In 2024, South Africa and Mozambique had the highest number of deaths due to AIDS worldwide, with around ** thousand and ** thousand such deaths, respectively. African countries account for eight of the top 10 countries with the highest number of AIDS-related deaths worldwide. AIDS-related deaths worldwide have been gradually declining over the past decade, decreasing from *** million deaths in 2010 to *** thousand deaths in 2024. HIV/AIDS HIV (human immunodeficiency virus) is an infectious sexually transmitted disease that is transmitted via exposure to infected semen, blood, vaginal and anal fluids and breast milk. HIV weakens the human immune system, resulting in the affected person being unable to fight off opportunistic infections. The top 15 countries worldwide with the highest prevalence of new HIV infections as of 2024 were all African. HIV treatment Although there is currently no effective cure for HIV, death can be prevented by taking HIV antiretroviral therapy (ART). Access to antiretroviral therapy worldwide has significantly increased in the past decade. As of 2024, around **** million people with HIV worldwide were receiving ART. The countries with the highest percentage of HIV-infected children who were receiving ART were Eswatini, Kenya, and Lesotho.

  13. Data from: High temperatures and human pressures interact to influence...

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated Feb 13, 2025
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    Daniella Rabaiotti; Rosemary Groom; J. W. McNutt; Jessica Watermeyer; Helen O'Neill; Rosie Woodroffe (2025). High temperatures and human pressures interact to influence mortality in an African carnivore [Dataset]. http://doi.org/10.5061/dryad.4j0zpc8b9
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    zipAvailable download formats
    Dataset updated
    Feb 13, 2025
    Dataset provided by
    Botswana Predator Conservation Trusthttps://www.bpctrust.org/
    University of Kent
    Institute of Zoology
    African Wildlife Conservation Fund
    Authors
    Daniella Rabaiotti; Rosemary Groom; J. W. McNutt; Jessica Watermeyer; Helen O'Neill; Rosie Woodroffe
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    The impacts of high ambient temperatures on mortality in humans and domestic animals are well understood. However, much less is known about how hot weather affects mortality in wild animals. High ambient temperatures have been associated with African wild dog Lycaon pictus pup mortality, suggesting that high temperatures might also be linked to high adult mortality. We analysed mortality patterns in African wild dogs radio-collared in Kenya (0°N), Botswana (20°S), and Zimbabwe (20°S), to examine whether ambient temperature was associated with adult mortality. We found that high ambient temperatures were associated with increased adult wild dog mortality at the Kenya site, and there was some evidence for temperature associations with mortality at the Botswana and Zimbabwe sites. At the Kenya study site, which had the highest human impact, high ambient temperatures were associated with increased risks of wild dogs being killed by people, and by domestic dog diseases. In contrast, temperature was not associated with the risk of snare-related mortality at the Zimbabwe site, which had the second-highest human impact. Causes of death varied markedly between sites. Pack size was positively associated with survival at all three sites. These findings suggest that while climate change may not lead to new causes of mortality, rising temperatures may exacerbate existing anthropogenic threats to this endangered species, with implications for conservation. This evidence suggests that temperature-related mortality, including interactions between temperature and other anthropogenic threats, should be investigated in a greater number of species to understand and mitigate the likely impacts of climate change. Methods Study sites We analysed adult African wild dog mortality at three sites: the Ewaso ecosystem, Kenya; the Okavango Delta, Botswana; and Savé Valley Conservancy, Zimbabwe. All three study sites fall within semi-arid savanna ecosystems.

    Field Data Collection At the Kenya study site 130 African wild dogs (56 female, 74 male) from 41 packs were monitored using either Vectronics GPS collars (GPS Plus, Vectronic Aerospace GmbH), Televilt GPS collars (GPS-Posrec, Televilt, Lindesberg, Sweden), Berlin, Germany), or VHF radio-collars (Telonics, Mesa AZ, USA). All three collar types included a mortality sensor programmed to emit a characteristic radio signal if stationary for ≥4h. At the Zimbabwe study site, 59 African wild dogs (22 female, 37 male) from 34 packs were monitored using either radio collars or GPS collars (African Wildlife Tracking, Rietondale, Pretoria, South Africa). Using radio-collars (Sirtrack, Havelock West, New Zealand) 31 African wild dogs (10 female, 21 male) from 16 packs were monitored at the Botswana site. Collars were fitted using the procedures outlined in McNutt (1996), Woodroffe (2011) and Jackson et al. (2017). At all three sites, packs were located every 1-2 weeks where possible. Any collared animal found dead was carefully examined with the aim of establishing a cause of death. At the Kenya site necropsies were carried out on all dead individuals located. At the Botswana site cause of death was only recorded in cases where the death was directly observed, or during disease outbreaks, and therefore the majority of causes of death were unconfirmed. Most deaths at the Botswana site are likely to be due to natural causes given the low human activity in this area. For all three sites, the date of first detection of a mortality signal from the collar was used to estimate the date of death when not observed directly, and where this was not possible an estimated date of mortality was made based on the date midway between the last sighting, or the last detection of the radio-collar without a mortality signal, and the discovery of the carcass or collar. If any study animal was not observed in its resident pack for over 30 days, no mortality signal was detected, and no carcass was found, it was considered lost from the study and censored from the day of the last observation (Kenya: n=51, Zimbabwe: n=34, Botswana: n=8). If a carcass or collar was discovered more than 30 days after the last sighting (n=2), the animal was considered lost from the study due to the inaccuracy of the date of death and was censored from the date of the last sighting. Group and individual characteristics were recorded at each site. At all three sites dispersal status of the individual was recorded. Individuals were defined as dispersing if they left their pack for multiple days and did not return, otherwise they were defined as resident (Woodroffe et al. 2019b). Group size – either the pack size for resident individuals or the dispersal group size for dispersing individuals – was recorded for each individual, and was defined as the number of adults (>12 months in age) in the group. African wild dog pup-rearing involves the pups being left at a den site for the first three months of life while the majority of the rest of the pack hunt daily, bringing food back to provision the pups. This pup rearing period is referred to as denning. For each pack, denning periods were identified using either direct observations or GPS-collar data. At the Kenya site a number of other individual and pack characteristics were also monitored. Individuals’ alpha status was inferred based on consistent close association with a specific individual of the opposite sex, coordinated scent marking, and reproductive activity; all animals not identified as alpha were considered subdominant. African wild dog age was known for many individuals, otherwise it was estimated from tooth wear when the individual was collared (Woodroffe et al. 2019b). Age range at collaring ranged from 1 to 7 years old (mean: 2.43 ±1.27). The age of the majority of individuals at the Zimbabwe and Botswana sites was not known. Weather data is from weather stations within the field site at Mpala research station at the Kenya site (detailed in Caylor K., Gitonga, J. and Martins 2016), 30km outside the study site at Maun airport for the Botswana site and the Middle Sabi Research Station, 12km from the study area boundary at the Zimbabwe research site.

    Data Processing The average mean temperature was taken on a 90 day rolling average at the Kenya and Zimbabwe sites, and a 30 day rolling average at the Botswana site. Rainfall was summed over a 30 day rolling time period at the Kenya and Botswana sites and a 90 day rolling period at the Zimbabwe site.

  14. d

    PTBi EA simulation and team training – knowledge and skills assessment

    • search.dataone.org
    Updated Apr 1, 2025
    + more versions
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    Lara Miller (2025). PTBi EA simulation and team training – knowledge and skills assessment [Dataset]. http://doi.org/10.7272/Q61Z42PB
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Lara Miller
    Time period covered
    Jan 1, 2023
    Description

    Introduction: Simulation training in basic and emergency obstetric and neonatal care has previously shown success in reducing maternal and neonatal mortality in low-resource settings. Though preterm birth is the leading cause of neonatal deaths, application of this training methodology geared specifically towards reducing preterm birth mortality and morbidity has not yet been implemented and evaluated. Methods: The East Africa Preterm Birth Initiative (PTBi-EA) was a multi-country cluster randomized controlled (CRCT) trial that successfully improved outcomes of preterm neonates in Migori County, Kenya, and the Busoga region of Uganda through an intrapartum package of interventions. PRONTO simulation and team training (STT) was one component of this package and was introduced to maternity unit providers in 13 facilities. This analysis was nested within the larger CRCT and specifically looked at the impact of the STT portion of the intervention package. The PRONTO STT curriculum was modi...

  15. f

    DataSheet_1_Baseline assessment of cervical cancer screening and treatment...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    Updated Jul 2, 2024
    + more versions
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    Njiri, Patricia; Tummers, Philippe; Bor, Joan-Paula; Mwenda, Valerian; Nyangasi, Mary; Murage, David; Osiro, Lance; Temmerman, Marleen; Arbyn, Marc; Kilonzo, Catherine (2024). DataSheet_1_Baseline assessment of cervical cancer screening and treatment capacity in 25 counties in Kenya, 2022.csv [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001493668
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    Dataset updated
    Jul 2, 2024
    Authors
    Njiri, Patricia; Tummers, Philippe; Bor, Joan-Paula; Mwenda, Valerian; Nyangasi, Mary; Murage, David; Osiro, Lance; Temmerman, Marleen; Arbyn, Marc; Kilonzo, Catherine
    Area covered
    Kenya
    Description

    BackgroundCervical cancer is the leading cause of cancer deaths among women in Kenya. In the context of the Global strategy to accelerate the elimination of cervical cancer as a public health problem, Kenya is currently implementing screening and treatment scale-up. For effectively tracking the scale-up, a baseline assessment of cervical cancer screening and treatment service availability and readiness was conducted in 25 priority counties. We describe the findings of this assessment in the context of elimination efforts in Kenya.MethodsThe survey was conducted from February 2021 to January 2022. All public hospitals in the target counties were included. We utilized healthcare workers trained in preparation for the scale-up as data collectors in each sub-county. Two electronic survey questionnaires (screening and treatment; and laboratory components) were used for data collection. All the health system building blocks were assessed. We used descriptive statistics to summarize the main service readiness indicators.ResultsOf 3,150 hospitals surveyed, 47.6% (1,499) offered cervical cancer screening only, while 5.3% (166) offered both screening and treatment for precancer lesions. Visual inspection with acetic acid (VIA) was used in 96.0% (1,599/1,665) of the hospitals as primary screening modality and HPV testing was available in 31 (1.0%) hospitals. Among the 166 hospitals offering treatment for precancerous lesions, 79.5% (132/166) used cryotherapy, 18.7% (31/166) performed thermal ablation and 25.3% (42/166) performed large loop excision of the transformation zone (LLETZ). Pathology services were offered in only 7.1% (17/238) of the hospitals expected to have the service (level 4 and above). Only 10.8% (2,955/27,363) of healthcare workers were trained in cervical cancer screening and treatment; of these, 71.0% (2,097/2,955) were offering the services. Less than half of the hospitals had cervical cancer screening and treatment commodities at time of survey. The main health system strength was presence of multiple screening points at hospitals, but frequent commodity stock-outs was a key weakness.ConclusionTraining, commodities, and diagnostic services are major gaps in the cervical cancer program in Kenya. To meet the 2030 elimination targets, the national and county governments should ensure adequate financing, training, and service integration, especially at primary care level.

  16. f

    Table_2_Stroke Epidemiology, Care, and Outcomes in Kenya: A Scoping...

    • frontiersin.figshare.com
    docx
    Updated Jun 1, 2023
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    Peter Waweru; Samwel Maina Gatimu (2023). Table_2_Stroke Epidemiology, Care, and Outcomes in Kenya: A Scoping Review.docx [Dataset]. http://doi.org/10.3389/fneur.2021.785607.s002
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    docxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers
    Authors
    Peter Waweru; Samwel Maina Gatimu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kenya
    Description

    Background: Stroke is a leading cause of death and disability in sub-Saharan Africa with increasing incidence. In Kenya, it is a neglected condition with a paucity of evidence despite its need for urgent care and hefty economic burden. Therefore, we reviewed studies on stroke epidemiology, care, and outcomes in Kenya to highlight existing evidence and gaps on stroke in Kenya.Methods: We reviewed all published studies on epidemiology, care, and outcomes of stroke in Kenya between 1 January 1990 to 31 December 2020 from PubMed, Web of Science, EBSCOhost, Scopus, and African journal online. We excluded case reports, reviews, and commentaries. We used the Newcastle-Ottawa scale adapted for cross-sectional studies to assess the quality of included studies.Results: Twelve articles were reviewed after excluding 111 duplicates and 94 articles that did not meet the inclusion criteria. Five studies were of low quality, two of medium quality, and five of high quality. All studies were hospital-based and conducted between 2003 and 2017. Of the included studies, six were prospective and five were single-center. Stroke patients in the studies were predominantly female, in their seventh decade with systemic hypertension. The mortality rate ranged from 5 to 27% in-hospital and 23.4 to 26.7% in 1 month.Conclusions: Our study highlights that stroke is a significant problem in Kenya, but current evidence is of low quality and limited in guiding policy development and improving stroke care. There is thus a need for increased investment in hospital- and community-based stroke care and research.

  17. f

    Mortality rates for top 3 specific causes of death pre COVID-19 Period by...

    • plos.figshare.com
    xls
    Updated Aug 23, 2023
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    Clifford Oduor; Allan Audi; Samwel Kiplangat; Joshua Auko; Alice Ouma; George Aol; Carolyne Nasimiyu; George O. Agogo; Terrence Lo; Peninah Munyua; Amy Herman-Roloff; Godfrey Bigogo; Patrick K. Munywoki (2023). Mortality rates for top 3 specific causes of death pre COVID-19 Period by age group in Kibera and Asembo. [Dataset]. http://doi.org/10.1371/journal.pgph.0002141.t004
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    xlsAvailable download formats
    Dataset updated
    Aug 23, 2023
    Dataset provided by
    PLOS Global Public Health
    Authors
    Clifford Oduor; Allan Audi; Samwel Kiplangat; Joshua Auko; Alice Ouma; George Aol; Carolyne Nasimiyu; George O. Agogo; Terrence Lo; Peninah Munyua; Amy Herman-Roloff; Godfrey Bigogo; Patrick K. Munywoki
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kibera, Asembo
    Description

    Mortality rates for top 3 specific causes of death pre COVID-19 Period by age group in Kibera and Asembo.

  18. d

    Lake Victoria region block sub-county level risk data

    • datadryad.org
    • search.dataone.org
    zip
    Updated Sep 10, 2025
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    Megan Kowalcyk (2025). Lake Victoria region block sub-county level risk data [Dataset]. http://doi.org/10.5061/dryad.crjdfn3dj
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    zipAvailable download formats
    Dataset updated
    Sep 10, 2025
    Dataset provided by
    Dryad
    Authors
    Megan Kowalcyk
    Time period covered
    Sep 5, 2024
    Description

    The health risks of climate change need to be identified to inform the prioritization of adaptation efforts. This is particularly true within low- and middle-income countries (LMICs) with limited resources, heterogenous climates, and varying degrees of social vulnerability. In Kenya, diarrheal disease is one of the leading causes of death and identifying risk factors of diarrheal disease is critical. This research aims to characterize factors associated with a high risk of diarrheal disease in western Kenya by developing a risk index based on the Intergovernmental Panel on Climate Change (IPCC) risk framework. We developed a conceptual model of risk factors based on prior research with risk factors grouped into the four components of the IPCC risk framework: hazard, exposure, and vulnerability (which is comprised of sensitivity and adaptive capacity). We obtained 30 data elements corresponding to the four components for 99 sub-counties in 14 western Kenya counties. We conducted pr...

  19. o

    Perceptions on Covid-19 and Malaria Vaccine Acceptance: Evidence from Kenya

    • osf.io
    url
    Updated Jan 5, 2024
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    Hamid Oskorouchi; Jennifer Svaldi; Alfonso Sousa-Poza; Philipp Schroeder (2024). Perceptions on Covid-19 and Malaria Vaccine Acceptance: Evidence from Kenya [Dataset]. http://doi.org/10.17605/OSF.IO/3JC24
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    urlAvailable download formats
    Dataset updated
    Jan 5, 2024
    Dataset provided by
    Center For Open Science
    Authors
    Hamid Oskorouchi; Jennifer Svaldi; Alfonso Sousa-Poza; Philipp Schroeder
    License

    http://opensource.org/licenses/AFL-3.0http://opensource.org/licenses/AFL-3.0

    Description

    In October 2021, WHO recommended widespread use among children of the first ever malaria vaccine (RTS,S/AS01). Because in Sub-Saharan Africa malaria remains a primary cause of death, successful immunization campaigns against this disease could save tens of thousands young lives every year. However, since the COVID-19 pandemic, a growing body of research on vaccine attitudes suggests that immunization acceptance has declined not only for vaccines against COVID-19 but also against other diseases (e.g., influenza). In fact, most determinants of vaccination hesitancy are not specific to the COVID-19 vaccine (e.g., mistrust in pharmaceutical companies, (too) rapid development of vaccines, and concerns about severe side effects). Although a number of articles on how COVID-19 vaccine (mis)perceptions affect hesitancy for other immunization campaigns (He et al. 2022; Dubé and MacDonald 2020; Wang et al. 2022), no experimental study investigates this nexus for the case of the newly licensed malaria vaccine (Shah et al. 2022). In this study, we investigate whether recalling personal opinions about COVID-19 immunization affects short-term malaria vaccination hesitancy. We test this hypothesis by means of a randomized controlled trial (RCT) where the individuals in the treatment group are asked to reveal their beliefs about COVID-19 vaccine efficacy and safety before being asked about their attitudes towards the newly WHO licensed malaria vaccine. Conversely, the control group will answer the same questions about malaria immunization without any direct or indirect recall to COVID-19. The study’s primary data will consist of telephone surveys from a randomly drawn representative sample of adult individuals from selected Kenyan counties facing medium to high risk of P. falciparum malaria transmission .

  20. f

    Antecedent/immediate causes of death for infant (1 to

    • plos.figshare.com
    xls
    Updated Jun 25, 2025
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    Portia Chipo Mutevedzi; Zachary J. Madewell; Karen L. Kotloff; Quique Bassat; Percina Joao Chirinda; Anelsio C. A. Cossa; Elisio G. Xerinda; Victor Akelo; Paul K. Mitei; Elizabeth Oele; Richard Omore; Dickens Onyango; Joseph Bangura; Ronita Luke; Andrew Moseray; Ikechukwu Udo Ogbuanu; Tom Sesay; Nega Assefa; Temesgen Teferi Libe; Lola Madrid; Melisachew M. Yeshi; J. Anthony G. Scott; Nelesh P. Govender; Sanjay G. Lala; Shabir A. Madhi; Sana Mahtab; Adama Mamby Keita; Doh Sanogo; Samba O. Sow; Milagritos D. Tapia; Shams El Arifeen; Emily S. Gurley; Beth A. Tippett Barr; Cynthia G. Whitney; Dianna M. Blau; Inacio Mandomando (2025). Antecedent/immediate causes of death for infant (1 to [Dataset]. http://doi.org/10.1371/journal.pgph.0004772.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 25, 2025
    Dataset provided by
    PLOS Global Public Health
    Authors
    Portia Chipo Mutevedzi; Zachary J. Madewell; Karen L. Kotloff; Quique Bassat; Percina Joao Chirinda; Anelsio C. A. Cossa; Elisio G. Xerinda; Victor Akelo; Paul K. Mitei; Elizabeth Oele; Richard Omore; Dickens Onyango; Joseph Bangura; Ronita Luke; Andrew Moseray; Ikechukwu Udo Ogbuanu; Tom Sesay; Nega Assefa; Temesgen Teferi Libe; Lola Madrid; Melisachew M. Yeshi; J. Anthony G. Scott; Nelesh P. Govender; Sanjay G. Lala; Shabir A. Madhi; Sana Mahtab; Adama Mamby Keita; Doh Sanogo; Samba O. Sow; Milagritos D. Tapia; Shams El Arifeen; Emily S. Gurley; Beth A. Tippett Barr; Cynthia G. Whitney; Dianna M. Blau; Inacio Mandomando
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Antecedent/immediate causes of death for infant (1 to

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Statista (2025). Main causes of deaths in Kenya 2021, by type [Dataset]. https://www.statista.com/statistics/1221721/main-causes-of-deaths-in-kenya/
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Main causes of deaths in Kenya 2021, by type

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2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 3, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2021
Area covered
Kenya
Description

Respiratory infections and tuberculosis were the most frequent cause of casualties in Kenya as of 2021, with a rate of almost 208 deaths per 100,000. In addition, cardiovascular diseases, and HIV/AIDS and sexually transmitted infections caused high number of deaths compared to other disorders, at about 76 deaths per 100,000 and 66 deaths per 100,000 respectively.

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