An Econometric Analysis Of Health Care Utilization In Kenya

Abstract

Background: Increasing access to health care has been a policy concern for many governments, Kenya included. The Kenyan government introduced and implemented a number of initiatives in a bid to address the healthcare utilization challenge. These initiatives include 10/20 policy, exemptions for user fees for some specific health services (treatment of children less than five years, maternity services in dispensaries and health centers, Tuberculosis treatment in public health facilities), and increase in the number of health facilities and health workforce. These initiatives notwithstanding, healthcare utilization in Kenya remains a challenge. The Kenya Household Health Expenditure and Utilization Survey of 2007 found that 17 percent of those who needed health care services could not access the services from both government and private health facilities largely due to financial constraints. This paper employed econometric analysis to examine what could be constraining health care utilization in Kenya despite all the efforts employed. Methods: Using the 2007 Kenya Household Health Expenditures and Utilization Survey (KHHEUS) data (n = 8414), this paper investigates the factors that affect health care utilization in Kenya by estimating a count data negative binomial model. The model was also applied to public and private health facilities to better understand the specificities of poverty in these two facility types. Common estimation problems of endogeneity, heterogeneity, multicollinearity and heteroskedasticity are addressed. Findings: The econometric analysis reveals that out-of-pocket expenditures, waiting time, distance, household size, income, chronic illness area of residence and working status of the household head are significant factors affecting health care utilization in Kenya. While income and distance are significant factors affecting public health care utilization they are not significant in explaining healthcare utilization in private facilities. In addition, working status of the household head, insurance cover and education are significant in explaining private and not public health care utilization. A striking finding is the positive relationship between distance and health care utilization implying that people will travel long distances to obtain treatment. This is perhaps associated with expectations of higher quality of care at far away higher level facilities, especially in rural areas. Conclusion: The paper confirms the existing evidence of the negative effects of Out-of-Pocket (OOP) expenditures and other determinants of health care utilization. With a better understanding of why people use or do not use health services, health care organizations can seek to improve the quality of human life. The bypassing of health facilities for higher level far away facilities implies that it is not so much about availing health facilities, but the quality of the services offered in those facilities. The government should therefore assure quality to increase utilization of the lower level facilities, especially in the rural areas.

An Empirical Analysis of the Effect of Poverty on Health Care Utilization in Kenya

“…This high likelihood of individuals from large households falling sick may lead to more health care utilization. This study finding was consistent with those of Kimani et al (2016) on Kenya. The author found that a ten per cent increase in household size led to 0.95 increase in the difference in logs of expected number of hospital visits.…”

“…Wealthier individuals are in most cases educated, have well earning jobs and are well informed on where they can get quality health care and are also able to pay for the services (Kyegombe, 2003). The finding that wealth increases use of health care was consistent with earlier studies in Kenya (Kimani, 2014;Kimani et al, 2016;Ochako et al, 2011), which found that increase in wealth increases number of visits to hospitals.…”

“…Following Kimani et al (2016) and Fabbri and Monfardini (2003), this study estimated equation 11using the Negative Binomial Regression Model (NBRM). In estimating models with count variables, the starting point is the standard Poisson regression model where the variable is assumed to have a Poisson distribution.…”

“…The chosen instrument variable needs to be valid. According to Awiti (2014) and Kimani et al (2016), validity entails relevance, strength and exogeneity of an instrument variable. Relevance means that the instrumental variable should be strongly correlated to the endogenous variable.…”

“…Regarding health care utilization, the Kenyan government has over time implemented various policies and initiatives aimed at addressing health care utilization challenges (Kimani, Mugo, & Kioko, 2016). These broad initiatives are contained in various policy papers such as Kenya Health Policy Framework (KHPF, 1994(KHPF, -2010 and KHPF (2012KHPF ( -2030.…”