DETERMINANTS OF CHOICE OF HEALTH INSURANCE IN KENYA

CHAPTER ONE

INTRODUCTION

Background to the Problem

Health care financing in developing countries remain a policy issue with few countries able to spend the $34 per capita recommended by the World Health Organisation as minimum requirement for basic health care. Lack of financial resources to adequately meet the increasing demand for health care needs of the African population remain a persistent problem, and is becoming more critical in the context of increasing incidences of non- communicable diseases.

Consequently, there have been attempts by African governments to explore different methods of health care financing. The 2005 World Health Assembly encouraged its member states to move towards achieving universal coverage. Universal coverage does not only relate to generation of health care funds but implies equity in access and guaranteed financial risk protection. As it is the desire of all countries to move towards a system of universal coverage,6 it is argued that ‘irrespective of the source of financing for the health system selected, prepayment and pooling of resources and risks arbasic principles in financial-risk protection’. Further recognition of the importance of universal coverage for countries led to the WHO proposing the 2010 World Health Report to address financing for universal health coverage (UHC).

Since independence, one of the overall objectives of the government of Kenya has been to promote and improve the health status of Kenyans. This objective is motivated by the evidence that investing in health produces positive outcomes in human capital that have long term impacts in the overall socio-economic development of a country (World Bank 1993; Mwabu 1998). In a number of government policy documents and in successive National Development Plans, the government has set forth that the provision of health services should be available, accessible and affordable to those in most need of healthcare (sessional paper No. 10 of 1965; KHPFP, various Development Plans).

Different health financing policy initiatives have been undertaken in Kenya, all aimed largely at addressing affordability and access to health care services. Universalist ‘free health for all’ policy saw a rapid expansion of the healthcare infrastructure, particularly in the 1970s and 1980s, and advances in health and social indicators. During this period, health financing system was supported primarily via general tax revenue. With the growing population and worsening socio-economic and political factors, a severe crisis of health and social development unraveled in the 1990s (UNDP 2002). As a result of the crisis, the government’s objectives and commitments to free healthcare provision for all eroded dramatically forcing it to implement a cost-sharing scheme in 1989. User fees were abolished for outpatient care in 1990, inspired by concerns about social justice, but re-introduced in 1992 because of budgetary constraints. Today, these fees have remained, with their impact on access to health care the subject of several empirical studies. The user fee system was significantly altered in June 2004, when the Ministry of Health stipulated that health care at dispensary and health centre level be free for all citizens, except for a minimal registration fee in government health facilities.

Health financing in Kenya is characterized by a high out of pocket expenditure. The Annual Health Sector Statistics Report (2008), indicate that the out of pocket expenditure as a proportion of total expenditure stands at 36% while public expenditure as a proportion of total health expenditure is 29% per cent. 31 per cent of the total health expenditure comes from the development partners while the private companies contribute 3%. This kind of scenario makes access to health a big problem for the majority of the people below the poverty line that constitute about 45.9 per cent of the population. According to the 2007 Kenya Household Expenditure Survey, 37.7% of Kenyans who were ill and did not seek care were hindered by cost. Health insurance is emerging as the most preferred form of health financing mechanism in situations where private out-of-pocket expenditures on health are significantly high and cost recovery strategies affect the access to healthcare. The need for health insurance in Kenya has been recognized by policymakers for quite some time now, as exemplified by the establishment of NHIF in 1966 through an Act of Parliament. The most significant event in the recent past has been the government’s interest in social health insurance as a health financing method and its possible implementation in Kenya. The aim is to ensure equity and access to healthcare services by all Kenyans.

Despite the recognition of the importance of health insurance by the government, the number of people in Kenya enrolled in health insurance schemes is low (KNBS, 2009). In view of this, there is need to carry out a study on factors determining choice of health insurance.

Overview of Health Insurance in Kenya

Kimani et al (2004) put forward that health insurance in Kenya has been provided by both private and public systems. The main objective of the health systems has been to insure Kenyans against health risks that they may encounter in future.

The broad categories of health insurance in Kenya are as discussed below:

Private Healthcare Insurance

Health insurance is considered private when the third party (insurer) is a profit organisation (Republic of Kenya, 2003a). In private insurance, people pay premiums related to the expected cost of providing services to them, that is, people who are in high health risk groups pay more, and those at low risk pay less. Cross-subsidy between people with different risks of ill health is limited. Membership of a private insurance scheme is usually voluntary.

Private health insurance has been offered by general insurance firms, which offer healthcare insurance as one of their portfolio of products. Therefore, their intention may be driven by the profit motive as business enterprises rather that the pursuit to promote the general health of Kenyans.

Wang’ombe et al (1994) identify two categories of private health insurance in Kenya: direct private health insurance and, employment based insurance. Nderitu (2002) notes that direct private health insurance is very expensive and only the middle and high-income groups afford it In the employment-based plans, the employer provides care directly through employer-owned on site health facility, or through employer contracts with health facilities or healthcare organisations. These are both voluntary health schemes and are not legislated by the government.

According to Techlink International Report (1999), few firms provide healthcare insurance in the strict sense of insurance in private healthcare insurance in Kenya. The general insurance firms offering healthcare insurance as one of their portfolio of products include American Life Insurance Company (ALICO), Apollo Insurance, GMD Kenya, Kenya Alliance Insurance Company Ltd, and UAP Provincial Insurance. Other firms run medical schemes and they are in two categories: the first category provides healthcare through own clinics and hospitals (these include AAR Health Services, Avenue Healthcare Ltd, Comprehensive Medical Services, Health Plan Services), while the other category provides healthcare through third party facilities (examples are Bupa International, Health Management Services and Health First International). These medical schemes are also known as Health Management Organisations (HMOs). HMOs are registered as companies under the Companies Act. The concept originated in the US, where HMOs also help the government to disseminate preventive messages to the public. They were introduced in Kenya a decade ago in response to a 1994 Government call on the private sector to assist in medical care. HMOs are filling a vacuum left by the public health insurance scheme. In HMOs, the patient pays a fixed annual fee, called a capitation fee, to cover the medical costs. Members of a HMO must go to the doctors of that HMO. In addition, to see a specialist, their HMO family doctor must refer them. HMOs have grown rapidly especially in the last few years, especially among those who are covered by employer-provided health plans, mainly because they have helped contain cost increases.

National Hospital Insurance Fund (NHIF)

The NHIF was established by an Act of Parliament in 1966 as a department in the Ministry of Health, which oversaw its operations, but responsible to the government Treasury for fiscal matters. The Fund was set up “to provide for a national contributory hospital insurance scheme for all residents in Kenya.” The Act establishing the NHIF provided for the enrolment in the NHIF of all Kenyans between the ages of 18 and 65 and mandates employers to deduct premium from wages and salaries. Contributions and membership are compulsory for all salaried employees earning a net salary of Kshs. 1000 per month and above. The level of contribution is graduated according to income, ranging from Ksh 30 to Ksh 320 per month.

The Fund covers up to 180 inpatient hospital days per member and his/her beneficiaries per year. Besides being self-financing and self-administering, the Fund monitors its own collections and distributes benefits to providers. The NHIF Act also provides for the Fund to make loans from its reserves to hospitals for service improvement.

Over the years, the original Act of Parliament has been reviewed to accommodate the changing healthcare needs of the Kenyan population, employment and restructuring in the health sector. The government restructured the NHIF Act in 1998 to make the Fund an autonomous parastatal. The apex of NHIF is no longer the Ministry but a Board of Directors. The Fund was given the task of enabling as many Kenyans as possible to have access to quality and affordable healthcare against a background of rising medical costs and a dwindling share of resources.

According to the amended NHIF Act, beneficiaries are both in-patients and outpatients (section 22 of NHIF Act, 1998), but outpatient services are not yet operational. NHIF Management Board pays benefits to declared hospitals for expenses incurred at those hospitals by any contributor, his/her named spouse, child or other named dependant. According to the NHIF Act, the benefits payable from the Fund are limited to expenses incurred in respect of drugs, laboratory tests and diagnostic services, surgical, dental, or medical procedures or equipment, physiotherapy care and doctors’ fees, food and boarding costs (Republic of Kenya, 1999).

Though the NHIF is meant to be a health insurance scheme after the amendment of the NHIF Act in 1998, it is still a hospital insurance scheme since it only pays for inpatient services only. Currently, NHIF pays more than half of a typical inpatient bill in private-for-profit sector in urban areas. Although benefit rates have been increased since the onset of the cost-sharing programme, the Fund’s reimbursement levels remain a small proportion of the total costs of care in many for-profit facilities

The relevance of NHIF has been questioned in the light of access and affordability of healthcare for the poor, together with its coverage. It is for this reason that the Kenyan Government has proposed a scheme that is supposed to address fundamental concerns regarding equity, access, affordability and quality in the provision of health services in Kenya.

National Social Health Insurance Fund

The proposed mandatory social health insurance scheme, seeks to transform the NHIF into a National Social Health Insurance Fund (NSHIF) to provide health insurance cover to both outpatients and inpatients. The main objective of the Fund is to facilitate the provision of accessible, affordable and quality healthcare services to all its members irrespective of their age, economic or social status (Republic of Kenya, 2003b).

It will be compulsory for every Kenyan and every permanent resident to become a member through enrolment and payment of a subscription either monthly or annually, or as may be deemed convenient to different socio-economic groups. Subscriptions for the poor will be paid for with funds from the government and other sources.

The current cost sharing fees will be replaced by pre-paid contribution into the new scheme. Some of the services that the members will enjoy under the new outpatient cover include: general consultation with general practitioners; prescribed laboratory tests/investigations; drugs/medicines; prescribed X-rays and ultra sound diagnosis; treatment of Sexually Transmitted Infections (STIs); Treatment, dressing or diagnostic testing; family planning; ante-natal and post-natal care; clinical counseling services; health and wellness education (Ministry of Health, 2004a)

Statement of the Problem

Health insurance is an institutional and financial mechanism which is seen as one option of obtaining additional resources for the financing of health care without deterring the poor and the vulnerable group from seeking care when they need it. It has the potential of generating substantial funds for equitable health care. Government’s funds so saved could then be diverted to the development and expansion of primary health care services and other infrastructure. It is a way of improving quality and access to health care as well as managing resources more efficiently.

Health insurance helps households and private individuals to set aside financial resources to meet costs of medical care in event of illness. It is based on the principle of pooling funds and entrusting management of such funds to a third party (government, employer or insurance company or a provider) that pays for healthcare costs of members who contribute to the pool.

Lack of health insurance promotes deferment in seeking care, non-compliance of the treatment regime and results in an overall poor health outcome (Hadley, 2002).

Tropical diseases, especially malaria and tuberculosis have long been a public problem in Kenya. However, Beyond grappling with a persistent high burden of infectious disease, including malaria, HIV/AIDS, and tuberculosis, Kenya faces an emerging chronic diseases problem characterized by increasing rates of cardiovascular disease, cancers, and diabetes.  Since the 1990s some of Kenya’s early achievements in health have begun to reverse: Over the past two decades life expectancy has declined to 53 years, and mortality among children under the age of five has risen slightly.

In Kenya, only about 10% of the population has some form of health insurance (KNBS, 2010; Republic of Kenya, 2009; Kinuthia, 2002). Coverage has remained the same since 2003. This implies that a huge segment of Kenyans are still not covered hence the burden of paying bills lies with themselves or through fund raising. In addition, most of the insurance firms are located in urban areas where a substantial number of population can afford as compared to rural areas.

With the current debate on the introduction of National Social Health insurance, there is need to examine the factors which affect individual’s decisions of enrolling in health insurance scheme.

Purpose of the Study

The purpose of this study is to identify the factors that influence choice of health insurance among Kenyans.

Specific Objectives

To evaluate socio-economic factors influencing choice of health insurance in Kenya.

To determine the role of information on the choice factors of health insurance in Kenya.

To determine how location factor influences the choice of health insurance in Kenya.

Make policy recommendations

Chapter two

LITERATURE REVIEW

Theoretical framework

The theory of demand for health insurance is based on expected utility theory of

The standard economic theory of behavior under uncertainty is well known; risk

averse individuals will pay to avoid severe financial consequences of the “unfortunate”

state of the world. In some markets, that willingness to pay to avoid risk leads to the

existence of contingent contracts, or insurance markets. In the health insurance context,

the “unfortunate” state of the world can be described as the event of illness or fear of

illness serious enough to require an individual or family to pay the full cost of necessary

and efficacious medical care solely out of current income or wealth. Risk averse

individuals facing actuarially fair prices will fully insure, but with unavoidable loading

costs in the real world, individuals prefer incomplete insurance. The optimal degree of

coverage in the face of loading costs is increasing in the degree of risk aversion.

One’s degree or intensity of risk aversion to not having health insurance can be

reasonably posited to depend upon wealth (W), because the potential financial loss from

catastrophic illness is increasing in wealth, although after a very high threshold level of

wealth is reached, risk aversion may decline again; education (ED), because more

educated people know the consequences of not having insurance, they know the

likelihood of appropriate health care being efficacious, and they also may have more

confidence that they can obtain efficacious care within any insurance and delivery

system; income (Y), because financial protection — both of wealth and of current income

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or consumption streams — is a normal good; family status (FS), since parents and

married partners may be more likely to seek coverage for family members whom they

care about and/or for whom they feel responsible; other access to insurance

(OTHER_ESI, ELIG), since the value placed on any particular insurance option may be

different if one is married to a worker whose employer offers coverage, or if some family

member(s) is(are) eligible for public insurance; health status (HS) of everyone in the

family; perceived risk (RISK) to health status, increasing in age and other sometimes

observable clinical factors which we summarize with _, so that RISK = RISK(age,_);

gender (SEX), since men and women have different health use profiles; and then,

contingent on a health shock that requires an intervention, one’s aversion to the risk of

illness also depends upon expected expenditures (EX) and the variance of possible

expenditures (_EX). These expenditure functions depend upon the quantity (C) and

quality (q) of medical care that may be necessary (and efficacious) as well as the

expected price of each unit of that medical care (PC). Note, when it comes to risk

aversion and demand for health insurance, the expected value of necessary medical care

is not more important than the variance of that potential demand or need for medical care,

i.e., the upper bound of potentially required medical care affects demand. In other words,

the first two moments of the health services utilization and expenditure distribution

matter, a priori, to insurance demand.

We find it useful to think about an individual’s demand for health insurance

having two classes of arguments: those that reflect influences on the subjective value of

insurance coverage per se, and those that determine the net price to the consumer. From

the above, one may summarize the value of a particular package of health benefits, V(Bi),

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as:

V(Bi) = V(W, ED, Y, FS, OTHER_ESI, ELIG, HS, RISK, SEX, EX(C,q,PC), _EX).

Let the price of health insurance (to the individual) be P*. Health insurance demand for a

particular package of benefits is then:

HId = 0 if V(Bi) < P*,

HId > 0 if V(Bi) _ P*.

Thus we have the truism, people will be uninsured if the value to them of the insurance

benefit package they can buy is less than the price they have to pay. We also note the

obvious that those which value health insurance the most are likely to buy the most of it,

conditional on a given price. This concept of V(B) is similar to Pauly and Herring’s

notion of reservation price for health insurance (Pauly and Herring, 2002, forthcoming),

and V(B) – P* is similar to consumer surplus.

An interesting feature of health insurance markets is that some of those with the

highest V(B) are also those most likely to make choices — such as seeking jobs from

employers that offer health insurance — that lead them to find the lowest prices of health

insurance (P*). Thus purchasers of insurance are likely to obtain substantial consumer

surplus. Other people with high demand – say those who expect to be very sick – are

unable to work. They often either qualify for public programs or end up facing very high

prices in the private non-group insurance market, and sometimes can find no one willing

to sell insurance to them at any actuarially fair price.3 Therefore, it is difficult to sustain

the interpretation that observed prices paid in health insurance markets reflect

equilibrium marginal subjective values of having health insurance.{my argument is that

3Pollitz K, R Sorian, and K Thomas, “How Accessible is Individual Health Insurance for Consumers in

Less-Than-Perfect Health?” Report to the Henry J. Kaiser Family Foundation, June 2001.

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buyers have CS, so nobody’s marginal utility is revealed in these markets. I inserted a

new CS sentence above}.

The arguments in our expressions of health insurance demand are useful for

general expressions of demand, but we also need to make clear that some eligible people

do not enroll in insurance even though the monetary cost is zero . This would not seem

possible from our characterization of health insurance demand. The important point is

that P* in our framework represents more than just monetary cost. P* includes time cost

and any disutility from an enrollment process that is perceived as burdensome or

embarrassing (e.g. some say a kind of stigma is associated with Medicaid since it was for

so long associated with people on cash assistance). We explain more in section 4 what is

known about the ways P* exceeds zero for various public insurance programs with zero

nominal fees.

2.2 Socially

Empirical Literature

Kirigia et al (2005), using data from the 1994 South African Health Inequalities Survey (SANHIS) examined the relationship between health insurance ownership and the demographic, economic and educational characteristics of South African women. Applying binary logistic regression technique, they found out that environmental rating, residence, smoking and marital status variables determined health insurance coverage.

The 2002 Jamaican Survey of Living Conditions was used to model the determinants of private health insurance coverage. Bourne and Kerr-Campbell (2010), using logistic regression to estimate the determinants of health insurance coverage, found out that social standing, durable goods, income, marital status, area of residence, education, social support, crowding, psychological conditions, retirement benefits, living arrangements, the number of males in the household and good health determined health insurance coverage.

Nketiah-Amponsah (2009) investigated the determinants of public health insurance among women aged 15-49 in Ghana using primary data collected in three districts in Ghana in 2008. Using the logit model the paper concludes that marital status, income, age, religion and access to television and newspapers are the most significant determinants of women’s insurance coverage. In addition, health inputs like medical personnel and health infrastructure increase demand for health insurance and health care. Another study using primary data was conducted in Ghana by Sarpong et al (2010) to explore the association between socio-economic status and subscription to the Ghanaian National Health Insurance Scheme (NHIS). Applying logistic regression, they concluded that economic well being and distance to the closest health facility were important determinants of National health insurance coverage.

Gius (2010), using data from the 2008 National Health Interview Survey (NHIS) estimated the logistic model for determinants of health insurance coverage for young adults. They posit that socioeconomic factors among them, age, sex, race, employment, area of residence, cost of insurance and beliefs held about health insurance are important in determining the health insurance coverage.

In Malawi, Makoka et al (2007), based on a logistic regression found income and education as significant determinants of private health care where public health services are free. This study used primary data collected from Blantyre and Zomba cities in 2003.

A working paper study by Bhat and Jain (2006) examined factors affecting the demand for health insurance in a micro health insurance scheme setting. Estimating

Takeuchi et al (1998) estimating the logistic model for factors associated with health insurance coverage among Chinese Americans in Los Angeles county found out that marital status, length of stay in the United States, education, employment and household income were important factors determining health insurance coverage.

Hopkins and Kidd (1992), utilizing data from the 1989-90 National Health Survey examined the socio-economic variables which influence the demand for health insurance under medicare in Australia using the binary logit model. They conclude that age, income, health status, material wellbeing and geographical location are important determinants of decision to purchase insurance.

Owando (2006) carried out a study on factors influencing the demand for health insurance in Kenya. Using the probit model, they found out that age, self evaluated health status, marital status, income, level of educational attainment, household size, risk behavior and employment status were important determinants of health insurance ownership in Kenya.

CHAPTER 3

METHODOLOGY

Theoretical Framework

This study borrows heavily from the demand theory. Health Insurance is treated just like any other good. Hence, demand for health insurance should be affected by variables such as price of the commodity, price of related commodities, income, tastes and preferences among others.

The demand equation for health insurance is modeled as follows:

Model Specification

The decision to buy health insurance will be formulated in two interrelated choices. First, the choice is related to the decision to buy or not the health insurance. Since the dependent variable takes two forms, will use binary logit model to study this choice. Theory and previous empirical work (Kirigia et al ,2005; Bourne and Kerr-Campbell, 2010) suggest that the probability that an individual owns a health insurance is conditional on several socio economic variables including age, education, area of residence, household size, occupation, marital status, health status among others.

In this study, the relationship between the binary status variable and its determinants is specified as follows:

Where are the following independent variables: age, sex, marital status, area of residence, level of education, proxy measures for economic welfare (land ownership availability of electricity, characteristics of dwelling place), knowledge (access to radio, television and newspaper), household size, occupation, health status (HIV and Tuberclosis), cigarette smoking.

The second step, if the decision to buy insurance is positive is to focus attention to the types of health insurance, that is, community based health insurance, health insurance trough employer, social security and private health insurance. This can be handled by applying a polychotomous model, more in particular a multinomial logit model. This approach is justifiable because the categories refer to choices being made that are mutually exclusive.

The regression model is expressed as follows:

Data Sources and Variables

The study will utilize survey methodology in which secondary data relating to the issue under investigation will be obtained from the 2008-09 Kenya Demographic Health Survey (KDHS). This is a nationally representative sample survey of 8,444 women aged between 18-44 years and 3465 men aged between 15 and 54 years of age selected from 400 sample points (clusters) throughout Kenya. Data collection was done from the month of November, 2008 and February, 2009.

Dependent and Independent variable

The dependent variable will be health insurance ownership. For purposes of coding the health insurance ownership outcome