Health disparities across the counties of Kenya and implications for policy makers, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Achoki, Tom; Miller-Petrie, Molly K.; Glenn, Scott; Kalra, Nikhila; Lesego, Abaleng; Gathecha, Gladwell; Alam, Uzma; Kiarie, Helen; Maina, Isabella; Adetifa, Ifedayo; Barsosio, Hellen C.; Degfie, Tizta Tilahun; Keiyoro, Peter Njenga; Kiirithio, Daniel N; Kinfu, Yohannes; Kinyoki, Damaris K.; Kisia, James M; Krish, Varsha; Lagat, Abraham K.; Mooney, Meghan; Moturi, W. N.; Newton, Charles R.; Ngunjiri, Josephine W; Nixon, Molly R; Soti, David; Vijver, Steven van de; Yonga, Gerald; Hay, Simon I.; Murray, Christopher J L; Naghavi, Mohsen

doi:10.1016/s2214-109x(18)30472-8

Cited by 60 publications

(71 citation statements)

References 55 publications

(91 reference statements)

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“…However, 43% of the 44 counties (n = 19) do report cost-related unmet need higher than the average in the whole country. This is consistent with other studies in Kenya that have highlighted that health outcomes remain heterogeneous at the county level, although with convergence across counties overtime [49]. This means that cost-related unmet need might impose inequities in access to healthcare between counties.…”

Section: Discussionsupporting

confidence: 90%

Cost–related unmet need for healthcare services in Kenya

Njagi

Arsenijevic

Groot

2020

BMC Health Serv Res

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Background: The assessment of unmet need is one way to gauge inequities in access to healthcare services. While there are multiple reasons for unmet need, financial barriers are a major reason particularly in low-and middleincome countries where healthcare systems do not offer financial protection. Moreover, accessibility and affordability are paramount in achieving universal health coverage. This study examines the extent of unmet need in Kenya due to financial barriers, the associated determinants, and the influence of regional variations. Methods: We use data from the 2013 Kenya household health expenditure and utilization (KHHEUS) cross sectional survey. Self-reported unmet need due to lack of money and high costs of care is used to compute the outcome of interest. A multilevel regression model is employed to assess the determinants of cost-related unmet need, confounding for the effect of variations at the regional level. Results: Cost-related barriers are the main cause of unmet need for outpatient and inpatient services, with wide variations across the counties. A positive association between county poverty rates and cost-related unmet is noted. Results reveal a higher intraclass correlation coefficient (ICC) of 0.359(35.9%) for inpatient services relative to 0.091(9.1%) for outpatient services. Overall, differences between counties accounted for 9.4% (ICC~0.094) of the total variance in cost-related unmet need. Factors that positively influence cost-related unmet need include older household heads, inpatient services, and urban residence. Education of household head, good self-rated health, larger household size, insured households, and higher wealth quintiles are negatively associated with cost-related unmet need. Conclusion: The findings underscore the important role of cost in enabling access to healthcare services. The county level is seen to have a significant influence on cost-related unmet need. The variations noted in cost-related unmet need across the counties signify the existence of wide disparities within and between counties. Scaling up of health financing mechanisms would fundamentally require a multi-layered approach with a focus on the relatively poor counties to address the variations in access. Further segmentation of the population for better targeting of health financing policies is paramount, to address equity in access for the most vulnerable and marginalized populations.

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Section: Discussionsupporting

confidence: 90%

Cost–related unmet need for healthcare services in Kenya

Njagi

Arsenijevic

Groot

2020

BMC Health Serv Res

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“…Our analysis also suggests that transitional areas around endemic areas need to be more vigilant in terms of malaria prevention since they are potentially at risk. Malaria in urban areas has seen effective vector control, but the increasing urban sprawl and expansion into semi-urban areas now pose new challenges in vector control and dissemination of public warnings [74,75]. Regional pattern and clustering of indicators used in the study suggest a need to continue and bolster county level and locally focused prevention and eradication programs.…”

Section: Discussionmentioning

confidence: 94%

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Gopal

Chen

et al. 2019

IJERPH

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The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

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“…Conflicting policy positions aside, progressive universalism has emerged as the preferred approach towards UHC in Kenya, with most interviewees prioritising an equity-based approach towards healthcare service access and financial risk protection. This strategy is particularly pertinent given the regressive nature of household healthcare contributions in Kenya, as well as the impact of developmental, financial and epidemiological inequity on the health system's responsiveness and resilience [35][36][37]. It further reflects the global consensus towards a pro-poor UHC approach that prioritises equity and equality, and underlines a commitment towards a more holistic healthcare approach under the UHC banner [1,[38][39][40][41].…”

Section: Discussionmentioning

confidence: 99%

Policy levers and priority-setting in universal health coverage: a qualitative analysis of healthcare financing agenda setting in Kenya

Oraro-Lawrence

Wyss

2020

BMC Health Serv Res

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Background: Competing priorities in health systems necessitate difficult choices on which health actions and investments to fund: decisions that are complex, value-based, and highly political. In light of the centrality of universal health coverage (UHC) in driving current health policy, we sought to examine the value interests that influence agenda setting in the country's health financing space. Given the plurality of Kenya's health policy levers, we aimed to examine how the perspectives of stakeholders involved in policy decision-making and implementation shape discussions on health financing within the UHC framework. Methods: A series of in-depth key informant interviews were conducted at national and county level (n = 13) between April and May 2018. Final thematic analysis using the Framework Method was conducted to identify similarities and differences amongst stakeholders on the challenges hindering Kenya's achievement of UHC in terms of its the optimisation of health service coverage; expansion of the population that benefits from essential healthcare services; and the minimisation of out-of-pocket costs associated with health-seeking behaviour. Results: Our findings indicate that the perceived lack of strategic leadership from Kenya's national government has led to a lack of agreement on stakeholders' interpretation of what is to be understood by UHC, its contextual values and priorities. We observe material differences between and within policy networks on the country's priorities for population coverage, healthcare service provision, and cost-sharing under the UHC dispensation. In spite of this, we note that progressive universalism is considered as the preferred approach towards UHC in Kenya, with most interviewees prioritising an equity-based approach that prioritises better access to healthcare services and financial risk protection. However, the conflicting priorities of key stakeholders risk derailing progress towards the expansion of access to health services and financial risk protection. Conclusions: This study adds to existing knowledge of UHC in Kenya by contextualising the competing and evolving priorities that should be taken into consideration as the country strategises over its UHC process. We suggest that clear policy action is required from national government and county governments in order to develop a logical and consistent approach towards UHC in Kenya.

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Health disparities across the counties of Kenya and implications for policy makers, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Cited by 60 publications

References 55 publications

Cost–related unmet need for healthcare services in Kenya

Cost–related unmet need for healthcare services in Kenya

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Policy levers and priority-setting in universal health coverage: a qualitative analysis of healthcare financing agenda setting in Kenya

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