Assessment of Willingness to pay for Community-Based Health Insurance among Artisans in a selected Community of Ekiti State, Southwest Nigeria

Elegbede OE1,2, Durowade KA1,2, Sanni TA2, Ipinnimo TM2, Alabi AK2

Abstract

Background: Community-based health insurance (CBHI) has emerged as a more efficient and equitable approach to healthcare financing. It was designed to ensure that sufficient resources are made available for members to access effective healthcare. This study assessed the willingness to pay (WTP) for CBHI among artisans in a town in Ekiti State, South West Nigeria.
Methods: This was a cross-sectional survey conducted among 416 artisans in a town in Ekiti State. A semi-structured interviewer-administered questionnaire was designed and used for data collection on sociodemographic data and WTP for CBHIS. Data entry and analysis was done using IBM SPSS software version 25.0.
Results: The mean age and standard deviation of the respondents was 29.7±10.9 years with male to female ratio of 1:1.4. Most of the respondents were willing to pay (86.3%) and willing to enroll other household members (73.6%) in the CBHI. A large percentage (44.3%) of those willing to pay were ready to pay between ₦1,000-₦5,000 (US$2.63–US$13.16) per year while 39.6% preferred frequency of payment to be annually. Positive predictors of WTP for CBHI were age groups ≥50 years and 40-49 years than <20 years (AOR:13.270, 95%CI: 1.597-110.267; AOR:142.996, 95%CI: 10.689-1913.009). Females than males (AOR:9.155, 95%CI: 3.680-22.775), tertiary level of education than no formal of education (AOR:23.420, 95%CI: 1.648-850.921), no children than ≥5 children (AOR:20.099, 95%CI: 2.705-149.364), earn ≥₦30,000 (US$78.95) than <₦30,000 (AOR:2.248, 95%CI: 1.278-6.499). often and somethings fall ill than seldom fall ill (AOR:6.505, 95%CI: 1.623-26.065; AOR:4.889, 95%CI: 1.674-14.279)
Conclusion: WTP for CBHI was high among the artisans, however, there is a variation across the amount and frequency of payment. Therefore, policy that is flexible enough to allow artisans enroll and pay a premium that is affordable, at an acceptable frequency, should be formulated by the Government.

Keywords: Artisans, Assessment, Community-Based, Insurance, Willingness

Introduction
The public health system in Nigeria is characterized by low funding and inequitable access to health care services.1 Access to health care services by the poor households has been greatly affected due to their low purchasing power evidenced by their earning and expenditure patterns.2 This is because the predominant health care financing mechanism in Nigeria is out-of-pocket (OOP) payment.3 To get around this problem and safeguard the poor from the catastrophic nature of this health financing method, prepayment schemes such as community-based health insurance (CBHI) scheme has been advocated.4
A recent review of health-system financing for universal health coverage in Nigeria shows high OOP expenses for health care, a very low budget for health at all levels of government, and poor health insurance uptake.5 With the increasing need to reduce the burden for funding health care services amongst the populace, especially for preventive and emergency services, the Nigerian federal government introduced the National Health Insurance Scheme (NHIS) under the Act 35, l999.6 This scheme has been running for over a decade, but has failed to fully incorporate the informal sector which contributes the largest percentage of the country’s population and has thus, still made OOP the predominant form of healthcare financing in the country.7 NHIS currently has limited coverage, covering only about 10% of the general population including the federal civil servants, the armed forces and paramilitary forces.8 However, the states have showed little or no interest in enrolling artisans and other categories of workers.8
OOP payment resulting from medical fees brings about a serious barrier to heath seeking behavior in Nigeria.9 It has resulted in members of the informal sector neglecting most of their ailments, resulting to use of self-medication and frequent visit and consultation of herbalists and traditionalists. The World Health Organization (WHO) views medical fees as a significant obstacle to healthcare coverage and utilization, and has stated that the only way to reduce reliance on direct payments is for governments to encourage the risk-pooling prepayment approach.10 In our environment, CBHI has emerged as an alternative to user fees. CBHI schemes are designed to ensure that sufficient resources are made available for members to access effective health care. Contributions are accumulated and managed to spread the risk of payment for health care among all scheme members.
A study done in Kaduna State, Nigeria shows that 82% were willing to pay an average of ₦513 ± ₦47 ($1.68) per month for health insurance premium.11 Another study in Ghana found that 98% of household heads would be willing to pay a premium to obtain health insurance cover for all their household members.12 In Ethiopia, 73.6% were willing to enroll in CBHI and the reasons given for the willingness were: Free access to medical care (73.6%), security and peace of mind during illness (18.9%) and to help others (7.5%).13 However, the reasons why they would not enroll in CBHI were: Not having enough money (11.9%), preferring OOP (3.6%) and lack of trust in CBHI practitioners (2.2%).13 Other reasons for non-willingness were absence of worries about health care prices and good family health.12
Globally, the mean willingness to pay (WTP) for health insurance among the lower and middle-income countries is estimated at 1.18% of gross domestic product per capita and 1.39% of adjusted net national income per capital.14 Several factors have been found to affect WTP for CBHI, for example, males and unmarried individuals were willing to pay higher in a previous study.11 Income, size of the household, level of education and formal employment matter in people’s choice and WTP premiums, as well as the amount they are willing to pay for contributory health insurance schemes in Nigeria.15
It was found that the poorest indicated the lowest WTP of ₦193 compared with the least poor who suggested a WTP of ₦329.16 On the effect of age and frequency of falling sick on WTP, Oyekale found a strong negative correlation.9 It was also revealed that WTP for CBHI increased significantly with awareness.9 A similar finding was reported by Biosca and Brown in their study.17 However, this was not the case in another study done by Bawa and Ruchita in India where 71% of the respondents reported being aware but did not subscribe to health insurance.18
Individuals must be willing to pay and subscribe to CBHI as it cannot be forced even though it has good benefits.19 Artisans contribute to a large number of the population of Ido-Ekiti20 and to a level they have a source of income which with good guidance can cater for their health needs. Moreover, some of them are exposed to occupational hazards too. To benefit from health insurance, they have to be willing to pay a particular amount of money for them to have a health insurance cover. Having this will go a long way in catering for their needs and put a stop to being stranded in hospital wards and at the mercy of well-wishers and charity givers. Carrying out a study to know if they are willing to pay for a health insurance scheme will improve the health status of this particular group of people, the community and the nation at large. This study therefore aims to assess the WTP for CBHI among artisans in Ido-Ekiti, Ekiti State.

Materials and methods
A cross-sectional survey of WTP for CBHI among artisans was conducted in Ido-Ekiti, a town in Ekiti State, South West region of Nigeria. Ido-Ekiti is situated in the Northern part of the State where routes from Oyo, Osun and Kwara States converge. The town is the headquarters of Ido-Osi local government area and is bounded in the East by Ipere and Iludun Ekiti, in the South by Igbole and Ifisin Ekiti and in the North and North West by Usi and Ilogbo Ekiti. Ido-Osi local government has an estimated population of 2l8,100 (Projection from 2006 population census).
The study included all artisans of any gender in Ido-Ekiti. An artisan is a worker in a skilled trade, especially one that involves making things by hand. Apprentices were excluded from the study.
A minimum sample size of 422 was calculated using the Leslie Fischer’s formula for population greater than 10,000 after assuming a 10% non-response, a proportion of 50% for WTP among artisans, standard normal deviate of 1.96 and degree of accuracy of 0.05. Using stratified sampling technique, the artisans were initially grouped into their various occupations. The respondents were then selected by simple random sampling method using proportionate allocation based on occupational population.
The study instrument used for data collection is a semi-structured interviewer-administered questionnaire. The questionnaire was designed and administered by the researchers to the respondents, and was constructed in English language. The questionnaire contains questions on sociodemographic data as well as questions on WTP for CBHI. The questionnaire was translated into the local language (Yoruba) for use on the field and back translated into English to ensure consistency of content. Questionnaire was scrutinized for content validity by experts in health economics and consultant community medicine physicians. Research assistant were trained for data collection and research supervisor ensured the procedures were followed precisely, to ensure that the data were valid, reliable and useful. The artisans were informed about the purpose of the study before their verbal consent was obtained. The questionnaires were then administered by the research assistants and it took about 15 minutes to fill in. The participants were followed in their respective work places during the day. Data were collected within 1 week in March, 2020.
Questionnaires were checked for errors by the research supervisor at the end of each day. All data collected were entered and analyzed using IBM SPSS for Windows, version 25.0 (IBM Corp., Armonk, N.Y., USA). The data were presented in frequency tables and percentages. Mean and standard deviation (SD) were computed for age while median and range were computed for income. Chi square test was used to determine the association between socio-demographic characteristics and WTP for CBHI. Binary logistic regression was performed to measure adjusted odd ratios for the significant factors associated with WTP for CBHI. Results were interpreted and a conclusion was drawn.

Ethical Consideration
Verbal informed consent was obtained from each and every respondent prior to participation in the study, and those who did not consent to participate were excluded from the study. Confidentiality and anonymity of the respondents were fully guaranteed. The purpose and benefits of the research were clearly explained to the respondents before questionnaires were administered. Ethical approval to conduct this study was obtained from Human Ethics and Research Review Committee of Federal Teaching Hospital, Ido-Ekiti, Ekiti State.

Results
A total of 422 questionnaires were administered for the research and 416 were received giving a non-response rate of 1.42%. As shown in table 1, most (44.0%) of the respondents were within the age group 20-29 years with a mean age ± SD of 29.7 ± 10.9 years. The male to female ratio was 1:1.4. About half were married (51.0%) and have their highest level of education at secondary level (47.8%). As regards occupation, 38.5% were tailors representing the occupation with the most population. Majority (44.2%) had no child and were earning less than ₦30,000 monthly (73.1%). More than half (57.2%) seldom fall ill, two-third (68.5%) did not visit the hospital in one year, 84.6% were not admitted in the hospital in one year and half (50.0%) did not spend any money on health care yearly.
Table 2 describes response to enrollment in CBHI scheme, majority (84.1%) have not been enrolled in any health insurance scheme while 63.0% find CBHI scheme acceptable for paying for their health care. Most (84.6%) believe that access to CBHI will improve access to healthcare services and make healthcare more affordable.
Table 3 describes the WTP for CBHI. Most of the respondents were willing to pay for CBHI (86.3%) and willing to enroll other household members in the CBHI scheme (73.6%). A large percentage of respondents were willing to pay between ₦1,000 to ₦5,000 (US$2.63 to US$13.16) per year for CBHI (44.3%). The preferred frequency of payment was annually among 39.6% of the respondents. Feeding was responsible for the major (63.7%) monthly expense of the respondents.
As shown in table 4, there were statistically significant associations between the WTP for CBHI and age (p=0.003), gender (p<0.001), level of education (p<0.001), income (p=0.018), number of children (p=0.026), frequency of illness (p=0.037) and estimated amount spent on health care last year (p<0.001) of respondents. There was no statistically significant association between WTP for CBHI and other factors.
Table 5 shows the binary logistic regression for the factors associated with WTP for CBHI in this study. Age group, gender, level of education, number of children, income and frequency of illness were significant predictors of WTP for CBHI. Respondents who were 50 years or older and those who were between 40-49 years were about 13 and 143 times respectively more willing to pay for CBHI than respondents <20 years of age (AOR:13.270, 95%CI: 1.597-110.267; AOR:142.996, 95%CI: 10.689-1913.009). Female respondents were about 9 times more willing to pay for CBHI than male respondents (AOR:9.155, 95%CI: 3.680-22.775). Respondents with tertiary level of education are about 23 times more willing to pay for CBHI than respondents with no formal education (AOR:23.420, 95%CI: 1.648-850.921). Respondents with no children are 20 times more willing to pay for CBHI than respondent with 5 children and above (AOR:20.099, 95%CI: 2.705-149.364). Respondents who earn ≥₦30,000 are about 2 times more willing to pay for CBHI than respondents who earn <₦30,000 (AOR:2.248, 95%CI: 1.278-6.499). Likewise, respondents who often and somethings fall ill are 6.5 and 4.9 times respectively more willing to pay for CBHI than respondents who seldom fall ill (AOR:6.505, 95%CI: 1.623-26.065; AOR:4.889, 95%CI: 1.674-14.279).

Discussion
This study assessed the WTP for CBHI among artisans in a community in Ekiti State. Majority of the respondents replied positively when asked if willing to pay for CBHI and most of them were willing to enroll other household members into the program as well. This level of WTP is similar to that of a study carried out in Nigeria.11 However, it is lower than that of another study done in Ghana.12 It was observed that the level of their willingness varied with the amount as well as with the frequency of premium payments. Most of them were willing to pay lower premium rates compared with a small proportion who were willing to pay higher rates. Therefore, increasing amounts yielded a substantial decrease in WTP. Also, a large proportion of the respondents preferred an annual premium payment of the insurance scheme to the more frequent payments method.
Although enrollment among the participants was generally poor, with only 15% enrollment in any health insurance program, there seemed to be a widespread acceptance and acknowledgement of the CBHI scheme after it had been explained. About two-third of the respondents found it acceptable as a strategy for paying for health care and majority believed that CBHI would improve access to health care services and make it more affordable. These findings are similar to that of a study done among surgical patients in a rural area in Niger-Delta, Nigeria which showed that patients paid for care mostly with personal savings and most of them did not enroll for a health insurance program. However, after giving them information, majority of them were willing to enroll in the program.21 This suggests that information must be disseminated to promote acceptance of CBHI.
In Nigeria, it has been documented that there is a clear desire on the part of the less well-off households to join health insurance schemes and most of them stated that CBHI was an acceptable means of paying for health with the poorest households expressing the greatest willingness to enroll.10 Also, another study done in Enugu and Anambra State, Southeast Nigeria among different population groups showed that the poor has higher tendency of using health insurance.22 Although our study was restricted to those of artisans in a community, a similar pattern was observed. Respondents with a monthly income of ₦30,000 were two times more willing to pay for CBHI than those with higher income.
This study found the following characteristics to be associated with WTP for CBHI; age, gender, level of education, number of children, income, frequency of illness and estimated amount spent on healthcare. It was shown that those within the age group of 40-49 were willing the most to pay for CBHI. Lower age groups were less willing to pay for CBHI. This result is similar to that of a study done among artisans in Ebonyi State, Southeast Nigeria.23 This could be attributed to the health status of the younger age group, knowing well that most of the chronic diseases that may require huge continuous health expenditure is commoner in the older age group. It could also be attributed to the reduced levels of education of the younger age groups. For the younger respondents, data analysis revealed that they were less educated. As a result, there is a reluctance to participate and pay for what they may have little or no knowledge about and what they perceive as being of no concern to them.
This study also found that females were more willing to pay for CBHI. A similar finding in Tanzania24 revealed that females had a higher mean WTP than males and this was statistically significant. Also, majority of households who were not willing to pay for CBHI had male household heads and only 20% had female household heads. However, this result is at variance with other findings where it was noted that males were more willing to pay than females in two different communities in Nigeria and Ghana.16,25 Closely related to this finding is the finding in Namibia where more individuals living in male-headed households were insured than in female-headed households.26
With regards to level of education, the proportion of respondents willing to pay for CBHI with tertiary education was the highest. Level of education was directly proportional to WTP for CBHI in this study. The results of a study done in the rural areas of Kwara State, Nigeria showed that the more the educational level attained, the more amounts and WTP.27 This is also consistent with findings from previous studies where people with higher education were more willing to pay.16,25
There was an increasing WTP with less children, participants with no child were 20 times more willing to pay than those with 5 or more children. A study done in Osun state, Nigeria had similar findings.27 Income was another predictor of WTP for CBHI in this study, with those with higher income having increased WTP. Income-regressive flat-rate payments are a problem in Nigeria and inability to pay premiums is a big obstacle which is further complicated by the absence of mechanisms in place to help those who cannot afford to join.10 A study done in Kaduna State, Northwest Nigeria shows that the higher the income of the household or individual, the more likely they are to participate health insurance schemes.28 Lower income earner not willing to pay for CBHI may result from an inability to afford it. The economic intuition behind this suggests that income is a very important variable in determining the demand for products including health insurance.29 This finding with respect to income has been the debate and argument about the WTP approach in health care evaluation as the amount households or respondents are willing to pay is an increasing function of their ability to pay.
The frequency of illness of respondents also affects WTP for CBHI. Those who often and sometimes fell ill were about seven and five times more likely to pay for CBHI than those who seldom fell ill. This may indicate that frequent illnesses inquire more costs and so increase the need for payment subsidies. Additionally, a pattern revealing a decreasing WTP with less amount spent on healthcare was observed. The other socio-demographic factors were not significant, consequently, they were less likely to influence the respondents’ WTP for CBHIS.
The cross-sectional nature of the study design may not allow cause and effect relationship between characteristics of interest.

Conclusion
The study revealed that majority of respondents were willing to pay for CBHI and willing to enroll other household members in the CBHI. The level of WTP however varies with the amount to be paid and the frequency of payment. Age, gender, level of education, number of children, income and frequency of illness were characteristics that predict WTP for CBHIS in this study. Therefore, given this level of WTP, there is a need for government policy that will allow artisans to easily enroll for CBHI in Ido-Ekiti. This policy should be flexible enough to allow the artisans pay a premium that would be affordable and at a frequency that would be acceptable to them.

References:

  1. Hodges T; Nigeria. National Planning Commission; UNICEF. Nigeria Country Office. Children’s and women’s rights in Nigeria: a wake-up call: situation assessment and analysis. Nigeria. National Planning Commission; UNICEF Nigeria; 2001
  2. Braveman P, Gottlieb L. The social determinants of health: it's time to consider the causes of the causes. Public Health Rep. 2014;129(2):19-31. doi:10.1177/00333549141291S206
  3. Garba MB, Ejembi CL. The role of National Health Insurance Scheme on structural development of health facilities in Zaria, Kaduna State, North Western Nigeria. Annals of Nigerian Medicine 2015;9(1):9–14. doi: 10.4103/0331-3131.163327
  4. Dong H, Kouyate B, Cairns J, Mugisha F, Sauerbom R. Willingness-to-pay for community- based insurance in Burkina Faso. Health Econ. 2003;12(10): 849-62.
  5. Uzochukwu B, Ughasoro MD, Etiaba E, Okwuosa C, Envuladu E, Onwujekwe OE. Health care financing in Nigeria: implications for achieving universal health. Nigerian Journal of Clinical Practice. 2015;18:437-444.
  6. Welcome MO. The Nigerian health care system: Need for integrating adequate medical intelligence and surveillance systems. J Pharm Bioallied Sci. 2011;3(4):470-478. doi:10.4103/0975-7406.90100
  7. Aregbeshola BS, Khan SM. Out-of-Pocket Payments, Catastrophic Health Expenditure and Poverty Among Households in Nigeria 2010. Int J Health Policy Manag. 2018;7(9):798-806. doi:10.15171/ijhpm.2018.19
  8. Adewole DA, Adebayo AM, Udeh EI, Shaahu VN, Dairo MD. Payment for Health Care and Perception of the National Health Insurance Scheme in a Rural Area in Southwest Nigeria. Am J Trop Med Hyg. 2015;93(3):648-654. doi:10.4269/ajtmh.14-0245
  9. Oyekale AS. Factors influencing households’ willingness to pay for National Health Insurance scheme (NHIS) in Osun state. Nigeria. Ethno Med. 2012; 6(3):167-172.
  10. Odeyemi IA. Community-based health insurance programmes and the national health insurance scheme of Nigeria: challenges to uptake and integration. International Journal for Equity in Health. 2014; 13(20):799—811.
  11. Bamidele JO, Adebimpe WO. Awareness, attitude and willingness of artisans in Osun state South-western Nigeria to participate in community-based health insurance. Journal of Community Medicine and Primary Health Care. 2012. 24:1-10
  12. Arhin DC. Willingness to pay rural health insurance: Evidence from three African countries. 2014. Available from: http://etheses.lse.ac.uk/2863/ (Last accessed on 3/3/2020)
  13. Ebrahim K, Yonas F, Kaso M. Willingness of community to enrol in community based health insurance and associated factors at household level in Siraro District, West Arsi Zone, Ethiopia. Journal of Public Health and Epidemiology. 2019;8 (1): 137-144.
  14. Nosratnejad S, Rashidian A, Dror D. Systematic review of willingness to pay for health Insurance in low and Middle-Income Countries. PLOS ONE. 2016; 11(6):eO 157470.
  15. Ogundeji YK, Babatunde A, Ohiri K, Butuwa NN. Factors influencing willingness and ability to pay for social health insurance in Nigeria. PLOS ONE. 2019;14(8):e0220558.
  16. Onwujekwe O, Okereke E, Onoka C, Uzochukwu B, Kiriga J, Petu A. Willingness to pay for community-based health insurance in Nigeria: do economic status and place of residence matter? Health Policy Planning. 2009;25(2):155-161.
  17. Biosca O, Brown H. Boosting Health Insurance in Developing Countries: Do Conditional Cash Transfer Programmes Matter in Mexico? Health Policy and Planning. 2Ol5;30(2):155-162. https://doi.org/10.1093/heapol/czt109.
  18. Bawa SK, Ruchita M. Awareness and Willingness to Pay for Health Insurance: An Empirical Study with Reference to Punjab India. International Journal of Humanities and Social Science. 2011;1(7):l00-108.
  19. United Health Care. Understanding health insurance. Available from: https://www.uhc.com/individual-and-family/understanding-health-insurance. (Last accessed on 3/3/2020)
  20. Wikipedia. Ido-Ekiti. Available from: https://en.wikipedia.org/wiki/ldo Ekiti. (Last accessed on 3/3/2020.)
  21. Dienye PO, Brisibe SF, Eke R. Sources of healthcare financing among surgical patients in a rural Niger Delta practice in Nigeria. Rural Remote Health. 2011;11(2):1577.
  22. Onwujekwe O, Onoka C, Uguru N, Tasie N, Uzochukwu B, Kirigia J et al. Socioeconomic and geographic differences in acceptability of community-based health insurance. Public Health. 2011;125(11):806-808. https//doi.org/10.101 6/j.puhe.2011.09.006.
  23. Azuogu NB, Eze CN. Awareness and Willingness to Participate in Community Based Health Insurance among Artisans in Abakaliki, Southeast Nigeria. Asian Journal of Research in Medical and Pharmaceutical Sciences. 2018; 4(3):1-8. https://doi.org/10.9734/AJRIMPS/2018/42839
  24. Dror DM, Radermacher R, Koren R. Willingness to pay for health insurance among rural and poor persons: Field evidence from seven micro health insurance units in India. Health Policy(Amsterdam,Netherlands).2007;82(1):12-27. https//doi.org/10.1016/j.healthpol.2006.07.011.
  25. Atagbua J, Ichoku EH, Fonta W. Estimating the willingness to pay for community health care insurance in rural Nigeria. Working papers PM/VIA, PEP-PMI[A [Internet]. Available from: http://www.pep-net.org/sites/pep-net.org/files/typo3doc/pdf/files_events/ataguba-pa.pdf
  26. Gustafsson-Wright E, Asfaw A, van der Gaag J. Willingness to pay for health insurance: an analysis of the potential market for new low-cost health insurance products in Namibia. Soc Sci Med. 2009;69(9):1351-9. doi: 10.1016/j.socscimed.2009.08.011.
  27. Babatunde OA, Akande TM, Salaudeen AG, Aderibigbe SA, Elegbede OE, Ayodele LM. Willingness to Pay for Community Health Insurance and its Determinants among Household Heads in Rural Communities in North-Central Nigeria. International Review of Social Sciences and Humanities. 2012;(2):133-142.
  28. Ogundeji YK, Akomolafe B, Ohiri K, Butawa NN. Factors influencing willingness and ability to pay for social health insurance in Nigeria. PLoS One. 2019;14(8):e0220558. doi: 10.1371/journal.pone.0220558.
  29. Usman D, Bukola A. Willingness to pay for Community Based Health Care Financing Scheme: A Comparative Study among Rural and Urban Households in Osun State, Nigeria. IOSR Journal of Dental and Medicinal Sciences. 2013;5 (6):27-40.