Factors related to women's use of health insurance cover in Navakholo, Kakamega County, Kenya: sub-county level results based on community household register.

BACKGROUND: In concert with international commitments, the Government of Kenya identified Universal Health Coverage (UHC), mainly through the National Health Insurance Fund (NHIF), as one of its four priority agenda to enable its populations access health care without financial duress. Nevertheless, only about 19.5% of the Kenyan population is enrolled in any insurance health cover. Since 2016, Amref Health Africa and PharmAccess Foundation have been implementing the Innovative Partnership for Universal and Sustainable Healthcare (iPUSH) programme in Navakholo sub-county of Kakamega County. The main objective of this study is to examine use of health insurance cover among Women of Reproductive Age (WRA) in Navakholo sub-county, Kakamega County. METHODS: We analysed data captured during household registration conducted in February 2021 which embraced a question on use of health insurance cover including NHIF. The dataset consisted 148,957 household members within 32,262 households, 310 villages, and 32 community health units. The data had been collected using mobile phones by trained Community Health Volunteers (CHVs) and transmitted using the Amref electronic data management platform and reposited in a server. Data were analysed through frequency distributions and logistic regression (descriptive and causal methods) using STATA software. RESULTS: Insurance coverage, all providers included, in Navakholo sub-county stood at 11% among women aged 15-49 years. This is much lower than the national aggregate reported from sample surveys, but higher than the 7% found in the same survey for the region where Navakholo is situated. Social determinant variables - age, perceived condition of the household, and wealth ranking - are highly significant in the relationship with use of health insurance cover while measures of reproductive health and health vulnerability are not. CONCLUSION: In Navakholo sub-county of Western Kenya, all-health-insurance coverage is lower than the national aggregate estimated from sample surveys. Age, perception of household condition, and wealth ranking are very significantly related to use of a health insurance cover. Frequent household registrations should be conducted to help monitor the trends and impact of health insurance campaigns. Training - upstream and downstream - on community household registration and data processing should be conducted to arrive at better quality data.

Client satisfaction determinants in four Kenyan slums.

PURPOSE: In Kenya, gaps exist in health service provision to slum residents, especially service availability and access to quality care. There is also little information on the health status of people living in slums other than in Nairobi. The purpose of this paper is to generate evidence for use in designing interventions to improve health services in four mid-sized slums in Embu, Nyeri and Thika, Kenya. DESIGN/METHODOLOGY/APPROACH: A cross-sectional survey of clients receiving services in health facilities was conducted in the targeted slums. Data were collected through face-to-face interviews. Factor scores were generated using the Rasch model; simple and multivariate logistic regression analyses were done using the R statistical software. FINDINGS: Overall, 81 per cent of the 203 participants reported being satisfied with health services. Most clients (89 per cent) reported that health facility staff greeted them warmly; 82 per cent said their consultation was private. The facility type, waiting time and client experience with service providers determined their satisfaction (p<0.05). PRACTICAL IMPLICATIONS: Healthcare managers can improve client satisfaction levels by understanding the client flow in their facilities and addressing causes of client dissatisfaction, such as long waiting times, while at the same time promoting facilitating factors. ORIGINALITY/VALUE: The authors use latent variable modelling to compute client satisfaction scores, which were dichotomised into two categories and fitted into a logistic regression model to identify factors that influence client satisfaction. Health facility clients in the four slums are satisfied with services and have confidence the providers will serve them in a friendly and professional manner that promotes respect and quality care. The paper recommend healthcare managers in similar settings carry out client flow analysis and institute remedial measures to address long waiting times. Qualitative studies are recommended to determine the reasons behind the high satisfaction levels reported in this study.