Utilization of health services in a resource-limited rural area in Kenya:
                        prevalence and associated household-level factors

Ngugi, Anthony; Agoi, Felix; Mahoney, Megan; Lakhani, Amyn; Mang'ong'o, David; Nderitu, Esther; Armstrong, R. B.; Macfarlane, Sarah B.

doi:10.1016/j.aogh.2016.04.405

Cited by 9 publications

(11 citation statements)

References 8 publications

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“…Our results emphasize cost as the major reason for not seeking healthcare at 38.6%. This is in accordance with other studies in Kenya, and elsewhere that highlight cost as an important and most frequent reason for unmet need for care [25,[44][45][46]. The findings slightly differ from those of the national report because we define cost-related unmet need as a combination of two reasons "lacked money" and "high costs of care", whereas the national report focused on only the high cost of care.…”

Section: Discussionsupporting

confidence: 84%

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: 84%

Cost–related unmet need for healthcare services in Kenya

Njagi

Arsenijevic

Groot

2020

BMC Health Serv Res

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“…This might be this study only in urban residents only because urban residents have access to the health facility. It is comparable to a study done in Kenya (10). Half of them were rst to visit primarily at the public health center and 25.5% of them were at Teaching and Referral Hospital.…”

Section: Discussionsupporting

confidence: 83%

Modern Health Service utilization and associated factors among adults in Southern, Ethiopia

Workie

Mekonen

W/Michael³

et al. 2020

Preprint

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Background Ethiopian government is striving to improve the health status of its population through the expansion and strengthening of primary health care services in both rural and urban settings of the country. The study aimed to measure modern health service utilization and associated factors in Wolaita Sodo town, Ethiopia.Method: A cross-sectional study design was implemented from May to June 2019 in Wolaita Sodo town Ethiopia. All 786 study participants were selected by multistage systematic random sampling. Data were collected by face to face interviews using a pretested structured questionnaire. Data were collected by an open data kit. Stata window version 15.0 was also employed for statistical analysis. Multiple logistic regression was conducted, and a 95% confidence interval was considered for interpretation.Result Health service utilization were 77.2% with (95% CI of 74.1%,80.0%). In terms of health facility in which they visit first 50.6% were at the public health center and 25.5% of them were at Teaching and Referral Hospital. Respondents with marital status married and widowed have higher odds of utilizing health service compared to single marital status [AOR:2.96; 95% CI: 1.7–5.2 and 9.0; 95% CI: 1.69-48.0] respectively. Respondents with middle and highest wealth status have higher odds of health service utilization than poor wealth status with AOR (1.75 95%CI 1.03–2.97 and 1.58 95% CI; 1.01,2.77). Similarly, Respondents who had chronic disease and Perceived health status who had poor have higher odds of health service utilization.Conclusion Modern health services utilization was found to be unsatisfactory. Being married, wealth status being middle and high, having chronic health conditions, and having poor perceived health status were found to have a statistically significant association with health service utilization.

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“…For instance, a study in Tanzania found that despite the vast majority of the population live within 5 km of a health facility, only 40-54% of caretakers with febrile children seek healthcare [75]. Studies in Kenya reported that 76.7%-87.3% of the population sought treatment from healthcare service providers [76,77]. Like in Tanzania, relatively lower prevalence rates among rural populations of Uganda (54.1%) [78], Zambia (56.8%), [79], Ghana (55.5%) [80] and Ethiopia (43.2%) [81] seek care from conventional healthcare facilities.…”

Section: Performance Of Idsr Strategymentioning

confidence: 99%

Twenty Years of Integrated Disease Surveillance and Response in Sub-Saharan Africa: Challenges and Opportunities for Effective Management of Infectious Disease Epidemics

Mremi

George

Rumisha

et al. 2020

Preprint

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Background: Public health surveillance requires valid, timely and complete health information for early detection of outbreaks. Countries in Sub-Saharan Africa (SSA) adopted Integrated Disease Surveillance and Response (IDSR) strategy in 1998 in response to an increased frequency of emerging and re-emerging diseases in the region. This systematic review aimed to analyse how IDSR implementation has embraced advancement in information technology, big data analytics techniques and wealth of data sources to strengthen detection and management of infectious disease epidemics in SSA. Methods: A search for eligible articles was done through HINARI, PubMed, and advanced Google Scholar databases. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols checklist. Using the key search descriptors, 1,809 articles were identified and screened at two stages and 45 studies met the inclusion criteria for detailed review.Results: Of the 45 studies, 35 were country-specific, seven studies covered the region and three studies covered 3-4 countries. A total of 24 studies assessed the IDSR core functions while 42 studies assessed the support functions. Twenty-three studies addressed both the core and support functions. Most of the studies involved Tanzania (9), Ghana (6) and Uganda (5). The implementation of the IDSR strategy has shown improvements mainly in the support functions. The Health Management Information System (HMIS) has remained the main source of IDSR data. However, the HMIS system is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation as well as lack of integration of data from sources other than health care facilities. Conclusion: In most SSA, HMIS is the main source of IDSR data, characterised by incompleteness, inconsistency and inaccuracy. This data is considered to be biased and reflects only the population seeking care from healthcare facilities. Community-based event-based surveillance is weak and non-existence in the majority of the countries. Data from other systems are not effectively utilized and integrated for surveillance. It is recommended that SSA countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate the existing surveillance systems with other sources of health information to provide support to effective detection and prompt response to public health threats.

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Utilization of health services in a resource-limited rural area in Kenya: prevalence and associated household-level factors

Abstract: Background and methods

Cited by 9 publications

References 8 publications

Cost–related unmet need for healthcare services in Kenya

Cost–related unmet need for healthcare services in Kenya

Modern Health Service utilization and associated factors among adults in Southern, Ethiopia

Twenty Years of Integrated Disease Surveillance and Response in Sub-Saharan Africa: Challenges and Opportunities for Effective Management of Infectious Disease Epidemics

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