The Kenyan health sector relies heavily on OOP expenditure (Health accounts 2005/2006). The sector is largely underfunded and health care contributions are regressive, that is, the poor contribute a larger portion of their income to healthcare than the rich (WHO 2010). Health expenditure risk is of considerable importance as it has a significant impact on a household’s or an individual’s consumption, saving and asset allocation. Researchers such as Kiplagat I. (2006) have attempted to establish the causal relationship between health insurance membership and health status. Other extensive studies have aimed at determining the impact of health insurance on health care utilization, cost of illness on households, social health insurance schemes; opportunities and sustainability potential, and market assessment of private prepaid schemes among other studies. (WHO 2010, Mclntypue et al 2006, Nguli M et al, and Deloitte Consulting limited 2001).
Sandra Hopkins (2010) using Cross-sectional health expenditure data was concerned with comparison of health expenditure aggregates and the contribution of the public and private sectors in a selection of 31 low, middle and high income countries. Hopkins noted that Low and middle income countries heavily relied on private funding especially household out-of-pocket payments while Public funding is more rife for funding of curative care than for funding of pharmaceuticals in all three categories of countries.
Kiplagat, et al (2013) in their paper determinants of choice of health insurance schemes in Kenya showed that wealth index, employment status, education level and household size are important determinants of health insurance ownership and choice, and that lack of awareness prevents many from enrolling in any form of health insurance scheme. They explored those determinants using a Multinomial logit model on the 2008-2009 Kenya, Demographic Health Survey (KDHS). According to D. Kimani, D Muthaka (2009) contributions of private health insurance are likely to be progressive as a financing mechanism for healthcare utilization, but often cream skim and fail to cover people with chronic conditions or the premiums are unaffordable thus people opt for OOP payments.
Ilesanmi Olowolabi (2014) used Raw data from 2004/05 Household income and expenditure surveys conducted by Kenya Bureau of Statistics to analyze determinants of household health expenditure which included age, education, gender, settlement, household size and total income. Further results were achieved by analyzing average household healthcare expenditure by determinants. Regression analysis was done using 95% confidence interval level. This study showed that Education and Settlement are the main significant determinants of Household healthcare expenditure using multiple regression analysis with P-value being significant at 5% error level. No other explanatory variable is significant at this level, not even at 0.1000.The elderly age group was found to have the highest expenditure at 141KShs compared with other age groups while the youngest age group has the second highest expenditure at 129.5 Kshs. However, age as a determinant is not found to be significant determinant using the regression analysis.
Mhere (2013) examined the determinants of health insurance participation in Gweru
Urban in Zimbabweans. Using a probit model he showed that the household head’s level of education, household income, age, family size, and chronic illnesses, are all Significant predictors of participation in health insurance schemes.
KeXu, et al (2006), used a log-linear model to explore the determinants of OOP spending given utilization of health services. The unit of analysis was the individual. Apart from area of residence whereby urban- rural difference was found to be an insignificant predictor of OOP spending, age, chronic health conditions and insurance membership were found to be key determinants of OOP spending. Empirical findings were that young children pay less OOP and those aged older than 65 pay more OOP, also people with chronic health conditions paid higher OOP. Although income was not controlled for in the regression models used, researchers expected those with higher incomes to pay higher OOP. Insurance membership was seen to increase OOP considerably, with the insured individuals paying more for health care services than the uninsured. A study done by WHO (2000) showed that health insurance is emerging as the most preferred form of health mechanism in developing countries like Kenya where OOP expenditures on health are significantly high and cost recovery strategies affect access and utilization of healthcare.
According to karimo and apere using Heckman selection two-step model, they Examined the determinant of out-of-pocket healthcare expenditure in the south-south geopolitical zone of Nigeria using the 2010 national harmonized living standard survey data, they concluded that state of residence, age of household head, family size, per capita consumption expenditure and adult equivalent weight together determines whether a person who falls sick will spend out-of-pocket for healthcare. Also, if a person falls sick and seeks healthcare service(s) age, age squared, household size, household size squared and per capita consumption expenditure proxy for per capita income. These are the factors that determine how much he/she spends out-of-pocket for healthcare, while aged people (those beyond 50 years of age) spend more out-of-pocket for healthcare households with more than 7 members have little to spare and so spend less out-of-pocket on healthcare. This reveals that healthcare is a normal good. The study therefore, recommends a comprehensive health insurance scheme irrespective of the state of residence for households in the zone.
Brinda, et al (2014), investigated the determinants of out-of-pocket health expenditure among adults population in the United Republic of Tanzania. They also investigated the prevalence and associated determinants of household catastrophic healthcare expenditure. Employing multiple generalized linear and logistic regression models they showed the major determinants of out-of-pocket healthcare expenditure to be age, gender, obesity, functional disability and visits to traditional healers. Further, large household size, household head’s occupation as a manual laborer, household member with chronic illness, domestic violence against women and traditional healer’s visits were associated with high catastrophic health expenditure in the United Republic of Tanzania.
Abolhallaje et al (2012) examined the determinants of unpredictable healthcare expenditure in
Iran. They analyzed the shares of households ‘expenditures on main groups of goods and services in urban and rural areas and in groups of deciles using data from households’ expenditure surveys. They growth of spending in nominal values within the year 2002-2008 was considerably high and the rate for out of pocket payments is nearly the same or greater than the rate for total health expenditure. Uzochukwu, and Uju, (2012) Using intensity and
incidence methods showed that 24% of Nigerian households incur catastrophic health expenditure and this was more prevalent among the richest income quintiles in Nigeria and as such has succeeded in changing the poverty situation of most households who were originally on or above the poverty line. The study recommended the need for social health insurance expansion in order to accommodate informal sector to achieve universal access to health services and financial protection of the poor and vulnerable.