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The impact of health insurance schemes on the population's health in the Buea health district

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Statistical analysis
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This study is set to determine the  the impact of health insurance schemes on the population’s health in the Buea health district. The study adopted a survey research design with the researcher going to the field.

The target population of the study consists of the population of the Buea health district from which three hospitals; Mount Mary hospital, Buea Regional Hospital, and Muea Integrated Health Center were purposively selected with a sample size of 250 respondents.

Simple random sampling techniques were used to collect primary data by use of a structured questionnaire. F-ratio and P-value were used to validate the significant level of the results.

From the analysis, the insurance coverage increased access to public facility services, with the insured having better health status and better protection from large financial burden due to health expenditures than the uninsured.

As far as gender is a concern, the findings show that sex negatively and insignificantly affects the health status of individuals.

Furthermore, the results show that individuals who are single are healthier than married persons while those widowed are somehow less healthy than their actively married peers and the value was insignificant.

Findings also indicate that health status increases with educational attainment. Also, income is positive at all levels but only comparatively higher and significant at higher levels of income.

The study recommends that the state should subsidize the cost associated with registering for health insurance schemes and create more public health centres, especially in interior villages so that many Cameroonians can belong to at least a health insurance scheme and that more attention needs to be paid to expanding insurance coverage and setting an appropriate benefit.

Keywords: Awareness, Enrolment, Utilization of Health care services, health status



1.1 Background to the Study

Households across the developing world are vulnerable to poverty due to various forms of uninsured risks, (Gertler and Gruber 2002, Derconet al. 2005, Wagstaff 2007, Hoddinot 2006, Sparrow et al. 2014). One of these sources of uninsured risk is ill-health, which involves financial risk due to the direct and indirect costs of medical care and forgone income (Islam and Maitra 2012).

The welfare implication of these adverse events is often examined by assessing the extent to which a particular risk affects the ability of households to maintain consumption levels, often referred to as a test of consumption insurance (e.g. Gertler and Gruber 2002, Asfaw and Von Braun 2004, De Weerdt and Dercon 2006, Wagstaff 2007, Gertleret al. 2009, Davies 2010).

Justifications for introducing social insurance schemes and setting policy priorities often rely on tests of consumption insurance (e.g. Morduch 1995, Gertler and Gruber 2002, Asfaw and Von Braun 2004). As a result, policy emphasis has been on designing schemes to deal with covariate risks as compared to health risks.

Also, the economic, social and political circumstances of most developing countries have made them not able to fulfil the health care needs of their poor population. These circumstances are characterised by; shrinking budgetary support for health care services, inefficiency in public health provision, an unacceptable low quality of public health and the resultant imposition of user charges reflecting their inability to meet health care needs of the poor – which is a majority of their population (World Bank, 1993).

Neither the state nor the natural forces of demand and supply (normal market conditions) have been effective in providing health insurance to low-income class earners in rural and informal sectors. The few formal health institutions that seek to provide such services are often at an informational disadvantage and face high transaction costs, thereby reducing the extent to which their objectives are attained to reach out to the masses.

Given the prevailing challenges, health insurance schemes rooted in local organisation potentially score better than alternate health insurance arrangements. In most rural and informal sectors where the supply of health services is expected to be weak, both the financial aspect and the service provision aspects need to be tackled simultaneously if the health care goals are to be attained.

Most of the Health Micro Insurance (HMI) schemes have either been initiated by the health providers (missionary hospitals) or tend to be set up by the providers themselves (Atim, 1998: Musau 1999). Thus the potential benefit of the schemes is seen not just in terms of mobilisation of resources but also in the improvement and organisation of health care services (Jutting, 2003).

Proponents argue that health insurance schemes are a potential instrument of protection from the impoverishing effects of health expenditures for low-income populations. It is argued that HMI schemes are effective in reaching a large number of poor people who would otherwise have no financial protection against the cost of illness (Dror&Jacquier, 1999).

In some situations, community structures have been used as alternatives but they may not necessarily reflect the views of the wider population, critical decisions may not take into account the interest of the poorest, and they may be excluded from decision making (Gilson et al, 2000).

It is further argued that the risk pool is often too small, that adverse selection problems arise and the schemes are heavily dependent on subsidies that financial and managerial difficulties arise and that the overall sustainability, seems not to be assured (Atim, 1998, Bennett, Creese and Monash, 1998: Criel, 1998). More than half of health expenditure in poor countries is covered by Out-Of-Pocket (OOP) payments incurred by households (Aregawi,2012).

Increased expenditure caused by the need to cope with injury and illness has been identified as one of the main factors responsible for driving vulnerable households further into a more critical poverty state (Aregawi, 2012, WHO, 2000).

  Due to the limited ability of public health systems in developing countries to provide adequate access to health care and the shortcoming of informal coping strategies to provide financial protection against health shocks, a large number of health financing schemes have been established in several low and middle-income countries (Aregawi,2012).

Generally, HMI schemes are non-profit initiatives built upon the principles of “social solidarity” and designed to provide financial protection against the impoverishing effects of health expenditure for households in the informal sector particularly the low-income communities. Matching the roll-out of these schemes, theoretical and especially empirical studies that examine their impact on outcomes such as the utilisation of health care, financial protection, resource mobilisation and social exclusion have flourished. (Aregawi, 2012)

Community-Based Health Insurance Schemes (CBHIS) is one of the numerous alternatives designed in the least developed countries since the 1990s to improve health care service utilisation through sharing the financial burden of the cost of illness.

Health insurance becomes new findings and concepts, which address health care challenges faced particularly by the poor (WHO, 2000). This health security is deliberately being recognised as an integral and mechanical tool to any poverty reduction strategy and it has been argued that these schemes are effective in reaching a large number of poor people who would otherwise have no financial protection against the cost of health care services (WHO, 2000).

Given the fact that people may be willing to spend more money on security access to health care than they can actually pay as user fees at the time of illness and that the healthy carry the financial burden of illness together with the sick via the insurance scheme. Additional resources may be mobilised for health care provision, and consequently, utilisation of health facilities will probably increase desirable effect given the prevailing under-utilisation in developing countries (Jutting, 2003: Muller, Cham, Jaffar, and Greenwood, 1990).

These Insurance schemes can be an important tool for protecting low-income populations from falling into poverty as a result of their health expenditure and by so doing, effectively reaching poorer households who would otherwise have no way to cope with this risk. Though these schemes have come to address some health issues, the HMI schemes do have some disadvantages compared with traditional insurance mechanisms.

The peculiar issues are associated to their small size, limited technical and managerial skills and the quality and accessibility of service providers. Their small risk pools and dependence on subsidies also cause some concern for their sustainability (WHO, 2000).

Certainly, the occurrence of illness is unpredictable. But individuals are not only uncertain about the timing of their future health care consumption, they are also uncertain about the form and consequently the cost of that consumption. Such uncertainties lead to welfare losses and therefore individuals seek Insurance to mitigate or avoid such uncertainties.

Welfare is then improved given that the risk associated to health is spread. It has also been argued that insurance may increase welfare by releasing the consumer from concerns over health care prices and income constraints at the time of consumption. Fact that the costs is directly associated with decision making, even without such considerations, the cost will still be high in many cases (Fuchs, 1979).

In considering the welfare losses associated with risk-bearing Arrow (1963) shows that risk-averse individuals will demand full coverage if insurance is available at actuarially fair prices.

In fact, he goes further by arguing that even if the insurer is risk-averse and loads the premium to cover his risk, (i.e. the premium is set at a higher rate than the actuarially fair value) the insurance will still be purchased, provided that the loading is not perceived by the individual to be too unfair.

Arrow (1963) further discusses the conditions under which an individual will prefer a deductible or coinsurance scheme. The former is better suited to cover high loading and the latter to coverage of any uncertainty associated with the risk insured against (Henderson, 1987).

In most circumstances then, the demand for health care should lead to a demand for health insurance. If the utility is positively linked to income and the cost of health care is seen as a deductible from that income, the risk-averse individual is likely to purchase more insurance as the risks increase.

Indeed it is also argued that events which have a low probability of occurrence but a high associated loss, such as hospital care, are more likely to be insured against than events that have a high risk of occurrence but low loss, such as check-ups, everything being equal (Hershey et al, 1984; Phelps, 1983).

In addition, despite being better positioned to reach poor rural households than most market-based insurance mechanisms, they are still often unable to meet the poorest groups because of the costs of premiums (Jutting, 2009). It is based on this, that the Cameroon government recently authorized the creation and running of health insurance schemes through some health providers (missionary centres) such as the Catholic and Baptist health boards (Jutting,2003)

The success of health insurance depends first and foremost on the effective and sustained demand for the insurance scheme. In the absence of real-world experience, economists gauge the willingness to pay for health insurance by means of the so-called contingent valuation approach.

This approach elicits directly what an individual would be willing to pay for a potential non-market or public good. No single individual of any community is compelled to be a member of the HMI program; it’s simply based on interest. Respective local administrations play a great role in enrolling people into the program.

Any household can enrol into the program in its renewable time. Based on experience, households also have the right to get out of the program and cancel their membership, and new entrances are allowed at any time.  Even in an event of new birth in the household is also allowed to be a member based on the previous premium payments of the household since the requirement for registration is just the child’s name and photograph to be attached to the program membership card (Tesfay, 2014).

Every member is aware of the fact that his/her premium payment is used to recover health services costs until the fixed time to renew, which is usually a year, without which the household will be out of the membership and no service will be delivered from the scheme.

Premium payments will not be paid back even if no member of the household uses health service since it is a precautionary motive for the uncertain future in relation to health status. But it is not like a bank saving, in that, neither the principal nor interest is paid back (Tesfay, 2014)

In order to successfully carry out their activities, there is usually a financial agreement between the insurance scheme and the health care service provider such that members are simply expected to present to the health care providers their membership identity card and obtain health services without any payment or delay (Jutting 2003).

An additional potential impact of health insurance is increased utilisation among nonparticipants members due to spillover effects (in some case when insurance is made available, participating facilities are upgraded). We might also expect individuals to have better health if the quality of the health care they receive is improved. Health insurance is also expected to provide financial protection because it reduces the financial risk associated with falling ill. Financial risk in the absence of health insurance is equal to the out-of-pocket expenditures because of illness. Additional financial risk includes lost income due to the inability to work (Wagstaff& Moreno-Serra, 2007).

However, recently, the World Health Organisation and the World Bank have argued in favour of the introduction of various forms of prepayment methods in order to prevent the impoverishing effects of dealing with out-of-pocket payments for health care and the deleterious effects of prolonged untreated illness (WHO & World Bank 2013).

In part, the increasing proliferation of health insurance schemes in Sub Saharan Africa (SSA) emanates from such concerns. The recent introduction of a pilot voluntary Community-Based Health Insurance (CBHI) scheme in rural Cameroon and Ghana’s National Health Insurance (NHI) scheme are two examples in this regard. Both aim at providing financial protection against illness-related expenses and increasing access to modern healthcare (Mensah et al. 2010, USAID 2011).

Looking at Health Outcomes, it should be recalled that the International Consortium for Health Outcomes Measurement (ICHOM) was founded in 2012 by recognised experts from the Institute for Strategy and Competitiveness, the Boston Consulting Group and the Karolinska Institute, ICHOM organises global teams of physician leaders, outcomes researchers and patient advocates to define Standard Sets of outcomes per medical condition, and then drives adoption to enable health care providers globally to compare, learns, and improve’.

ICHOM aims to define global Standard Sets of outcome measures and then drive adoption and reporting of these measures worldwide. The prioritisation of developing each new Standard Set depends on the disease burden, the level of engagement among clinicians who can help develop and promote the Standard Set and available funding.

The ICHOM focus on what matters to patients in determining outcomes comes from the work of one of its founders, (Michael Porter, 2010) ‘in any field, quality should be measured from the customer’s perspective, not the supplier’s. In health care, outcomes should be centred on the patient, not the individual units or specialities involved in care’.

Porter points out that this means outcomes measures ought to consider the success of all the acute care, related complications, rehabilitation and reoccurrences a patient experience for a particular condition or as part of preventive care, rather the outcome of a single intervention that is part of on-going care.

This is because a single intervention, such as a surgical procedure, may be successful in its aims but if the patient’s subsequent rehabilitation fails, for example, the outcome is poor. Porter sums this up as “patient satisfaction with care is a process measure, not an outcome. Patient satisfaction with health is an outcome measure”.

Health outcomes, according to Porter and the work of ICHOM, can be defined according to health status, the process of recovery and sustainability of health. These should be defined specifically for each medical condition.

Determining health outcomes can be a major hurdle in progressing to collecting and using outcomes data. There is, as yet, no standard definition of health outcomes in the UK or internationally. It is important to distinguish outcomes from outputs.

Health outputs have been the traditional way to quantify healthcare delivery and are an important source of data but do not provide the information required to measure value and improve healthcare. Outcomes include patient-reported measures about patients’ care and specific data about the efficacy of the treatment patients receive in addressing their condition.

Health outcomes, although not defined precisely by clinicians, are understood in a similar way. According to Australia’s New South Wales Health Department, a health outcome is a change in the health of an individual, group of people or population which is attributable to an intervention or series of interventions.

This definition is helpful because it makes clear that determining health outcomes, first and foremost, involves measuring a change. Secondly, they can relate to individual patients or entire populations and finally, the outcomes are related to specific interventions.

According to the African development bank, African health systems face huge financing deficits. Compared to a global average of 5.4 per cent of GDP, current government spending averages 2.5 per cent of GDP and falls far short of that needed even to provide basic care.

While spending on health care in high-income countries exceeded US$ 2,000 per person per year, in Africa it averaged between US$ 13 and US$ 21 in 2001 (Commission for Africa, 2004). The Commission for Macroeconomics and Health (2001) recommended that spending for health care in sub-Saharan Africa should rise to US$ 34 per person per year by 2007, and to US$ 38 by 2015, which represents roughly 12 per cent of GNP.

This is the minimum amount needed to deliver basic treatment and care for the major communicable diseases (HIV/AIDS, TB and malaria), and early childhood and maternal illnesses. Similarly, some argue for a massive scaling up of public health and other social sector expenditure (Sachs, 2004).

Key issues for policy and health sector strategy are how far public expenditure has been instrumental in bringing about the progress in health status experienced in developing countries over the five decades, and what programs have been particularly effective (Roberts, 2003).

1.3.1 Main Question

To what extent does Health Insurance Schemes affect the health status of the population in the Buea health district?

1.3.2 Specific Questions

  • To what extent is the population of the Buea Health District aware of the existence of Various Health Insurance Schemes?
  • How enrolment in Health Insurance Schemes does influences Health status?
  • Is there any difference in health status between the insured and non-insured persons in the Buea Health District?
  • Do socio-economic characteristics of individuals in the Buea Health District such as age, marital status, religion, level of education and income) influence their health status?
  • What are the fundamental challenges associated with the use of these insurance schemes by the insured in the Buea Health District?

The impact of health insurance schemes on the population’s health in the Buea health district. The impact of health insurance schemes on the population’s health in the Buea health district

The impact of health insurance schemes on the population’s health in the Buea health district. The impact of health insurance schemes on the population’s health in the Buea health district

The impact of health insurance schemes on the population’s health in the Buea health district. The impact of health insurance schemes on the population’s health in the Buea health district

The impact of health insurance schemes on the population’s health in the Buea health district. The impact of health insurance schemes on the population’s health in the Buea health district

The impact of health insurance schemes on the population’s health in the Buea health district. The impact of health insurance schemes on the population’s health in the Buea health district

The impact of health insurance schemes on the population’s health in the Buea health district. The impact of health insurance schemes on the population’s health in the Buea health district


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