Background: Quality of life can be used to measure the effect of intervention on health related conditions. Health insurance contributes positive effect on availability of medical supplies and empowerment of women and children on financial healthcare. Therefore, the study was aimed to measure the impact of Community-Based Health Insurance on HRQoL and associated socio-demographic factors. Methods: A comparative community based cross-sectional study was employed. Data was collected by trained enumerators using World Health Organization QoL-BREF tool from a sample of 1964 (982 CBHI insured and 982 un-insured) household heads selected by probability proportional to size. A descriptive summery, simple and multiple linear regression analysis was applied to describe the functional predictors of HRQoL. The study was ethically approved by IRB of Wolkite University. Results: The HRQoL score among CBHI insured family heads was 63.02 and 58.92 for un-insured family heads. The overall variation in HRQoL was explained due to; separated marital condition which reduced the HRQoL by 4.30% β β than those living together [ = − 0.044, 95% CI (− 5.67, − 0.10)], daily laborer decreased HRQoL by 7.50% [ = − 0.078, 95% CI (− 12.91, − 4.10)], but employment increased by 5.65% than farmers [ = 0.055, 95% CI (2.58, 17.59)]. QoL increased by 6.4 and 6.93% among primary and secondary level educated household heads than those household β β heads who could not read and write [ = 0.062, 95% CI (0.75, 4.31)] and [ = 0.067, 95% CI (1.84, 7.99)], respectively. As family size increased by one households’ head, HRQoL decreased by 18.21% [ = − 0.201, 95% CI (− 2.55, − 1.63)], as wealth index increased by one unit, HRQoL decreased by 32.90% [ = − 0.306, 95% CI (− 5.15, − 3.86)] and QoL among CBHI insured household heads increased by 12.41% than those un-insured family heads [ = 0.117, 95% CI (2.98, 6.16)]. Conclusions: The study revealed that significant difference in quality of life was found among the two groups; health insurance had positive effect on quality of life. Triggered, the government shall expand the scheme into other similar areas’ and further efforts should be made on the scheme service satisfaction to ensure its continuity. Keywords: Household head, Health insurance, Quality of life * Correspondence: email@example.com; firstname.lastname@example.org Department of Public Health, College of Medicine and Health Science, Wolkite University, Addis Ababa, Ethiopia © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Gebru and Lentiro Health and Quality of Life Outcomes (2018) 16:110 Page 2 of 6 Background of 15,927,649, including 7,916,042 men and 8,011,607 In terms health related indicators; Ethiopia ranks low women. The region was administratively divided into 13 even as compared to other low income countries . zones, 133 Woredas (administrative levels higher than The country bears high burden of preventable commu- kebeles) and 3512 Kebeles (the smallest administrative nicable diseases. According to the country ministry of level in the country). Healthcare service of the region was health (MoH) 2015 report, the top leading causes of renders through 45 Hospitals, 248 Health Centers and mortality were malaria, pneumonia and respiratory tract 3729 Health Posts. The study was conducted in Dale diseases [2, 3]. In spite to this high burden, utilization of Woreda (Yirgalem) as a best pilot CBHI implementer modern health care services is limited . One of the since 2011 and non-CBHI covered Gorche Woreda which reasons for low utilization of healthcare services is the has similar socio-demographic character with the piloted direct user-fee charges . Woreda (Yirgalem) . The study was employed in In Ethiopia, 38.5% of the total health expenditure was February, 2017. covered through out-of-pocket charges, which is higher than that of other African countries, which was 30.6% in Study design and population 2008 [3, 5]. Nevertheless, Ethiopia’s per capita public A community based comparative cross-sectional study spending for health (14 US$ in 2008) remains far below was employed. Source populations of the study were all even that of other African and low income countries (83 household heads whereas households heads found in US$ and 32 US$, respectively in 2008) . randomly sampled household heads’ were study popula- Health care expenses are devastating and have long tion. Household heads reside at least for six months in term effect on economic situations to majority of house- the study area were included in the study however hold heads in Ethiopia. Consequently, it was suggested household heads’ who were included in to the scheme that alternative mechanisms such as health taxes should within six months of the study were excluded to be established to cover health care expenses . Moving minimize immature effect of the scheme. away from out of pocket charges for healthcare at the time of use is an important step towards in averting the Sample size determination and procedure financial hardship related with paying for health service Sample size was calculated using STATCALC program . But, in 2008 the government planned to cover the of EPI INFO version 7 statistical packages for windows prepaid only 1.5% of the total private expenditure on by assuming; mean score 2.4 (± 0.8) difference of health in Ethiopia . HRQoL (as outcome variable), 95% confidence interval To increase the prepaid plan coverage and access to (Zα ), 80% power, insured and un-insured household /2 modern health care services, the Ethiopian government head ratio of 1:1, and 10% expected non-response rate has introduced Community Based Health Insurance . Accordingly, the required sample size was 1967 (CBHI). The scheme was piloted since 2011 in 13 household heads. The best CBHI pilot implementation districts with the objective to draw a lesson for scale-up performer of the country Dale Woreda was selected as at countrywide level. Currently the scheme covers 202 CBHI covered Woreda and Gorche Woreda as districts, including 52 from South Nation Nationality non-CBHI covered Woreda which has similar socio People’s Region (SNNPR). Increased and improved cash demographic character was selected, in these two Wore- flow has had a positive effect on the availability of medi- das there were 36 and 22 Kebeles, and 48,971 and cations and other supplies, which turn to improve the 23,705 households, respectively. Sampling frame was quality of health services. However, there is no quanti- prepared with cumulative frequency for each district . fied evidence whether the CBHI may contribute to Then independently for each district probability propor- Health-Related Quality of Life (HRQoL) or yet not. tional to size sampling method was used to select the Therefore, the purpose of this study was to assess the sampling unit household. Accordingly, we plan to collect impact of CBHI on health-related quality of life. It was data and distributed a questionnaire of 1967 that con- believed that this study will helps to provide evidence sists socio-demographic and HRQoL related issues. based decisions by policy makers to benefit the commu- nity of the country and beyond. Data collection and quality assurance An adapted World Health Organization Quality of Life Methods Biomedical Research and Educational Facility (WHO- Study area and period QoL-BREF) data collection tool was used in this study. The study was conducted in SNNPR which is one of the First, the tool was adopted in English then translated largest regions in Ethiopia, covers for more than 10% of into Amharic and finally back translated in to English by the country’s land area with an estimated 112,343.19 km . another expert to keep its consistency. The tool consists Based on 2016 point estimate, the region has a population two items on overall general health and 24 items divided Gebru and Lentiro Health and Quality of Life Outcomes (2018) 16:110 Page 3 of 6 into four domains; 7 items physical health, 6 items (67.4%) were husband (male) respondents. The mean psychological health, 3 items social relationships and 8 age of study participants were 40 with standard items environmental health rated on five-point likert deviation of 11 years. Of the respondents 1234 (72.1%) scale . Twenty health extension worker data collectors were farmers by their jobs. More than half of the study and four BSc holder public health supervisors were in- participant 1121 (57.3%) could not read & write. On the volved in the data collection process. The overall data other hand, among the interviewed household heads, collection process was coordinated by researchers. 1031 (52.7%) have greater than five family size. More- Moreover, the questioner was pre-tested on 5% of the over, by their socio-demographic characteristics, the two actual sample size in the area with population having populations were statistically homogenous only by edu- similar socio-demographic status with study population. cational status and marital condition whereas they were Training was given to data collectors prior to the start statistically different by age, gender, family size and of data collection process for three-day. Lecture, mock wealth index (Table 1). interview and field practice were included in the training process. Even through, supervisors were trained together Health related quality of life score with the data collectors; orientation was given separately Among the two group of the population, the highest on how to supervise the data collectors. Moreover, daily mean and percentage satisfaction of HRQoL for insured based check-up on 10% of the filled questionnaire each family were revealed psychological domain (mean = 31.12 day were made and incomplete questionnaire was (± 5.65); percentage = 86.13) and un-insured were physical referred back for completion and data was validated for health domain (mean = 28.63 (± 6.76); percentage = 66.54) similarity through double data entry by Epi Data 3.1 and the lowest satisfaction of HRQoL for both group statistical software for windows. of the population were social relationship domain (mean = 8.86 (± 2.94); percentage = 48.79) and (mean = Data processing and analysis 8.67 (± 3.62); percentage = 60.75), respectively. From the The collected data was checked for completeness, edited, Levene’s test for homogeneity, the two populations were coded, entered to Epi-data 3.1 software and cleaned. statistically different by physical health and Psychological Then for analysis it was export to Statistical Package for domains (F = 128.95.77, p =<0.00) and (F = (1, 1953) (1, 1953) Social Science (SPSS) version 20.0 for windows. Descrip- 309.61, p = < 0.00), respectively. The transformed total tive summary was calculated for socio-demographic HRQoL score among CBHI insured family heads was characteristics such as mean and proportions. Based on 63.02 and 58.92 for un-insured family and the two the WHOQoL-BREF guideline after inversely coding populations were statistically different (F =21.77, (1, 1953) negatively coded items raw domain score was computed p = < 0.00) (Table 2). and transformed the total HRQoL score. Moreover, Levene’s test was used to assess homogeneity of the two populations (insured and un-insured) and principal Factors associated with health related quality of life component analysis was employed for wealth index. The overall variation in HRQoL was contributed due Cronbach’s alpha coefficient with 0.70 and above was to; separated marital condition was reduced the accepted. HRQoL by 4.30% than those living together [ = − Simple linear regression was applied to see the associ- 0.044, 95% CI (− 5.67, − 0.10)], daily laborer house- ation between factors and HRQoL as a first phase hold heads decreased HRQoL by 7.50% [ = − 0.078, screening. To avoid unstable estimate variables with 95% CI (− 12.91, − 4.10)], but employment increased p-value = < 0.25 were candidate for the final regression by 5.65% than those who were farmers [ = 0.055, model . Then, multiple leaner regression was applied 95% CI (2.58, 17.59)]. HRQoL among primary and in order to control the effect of confounding factors and secondary educated household heads increased by 6.4 to describe the functional association between the and 6.93% than those household heads who could not socio-demographic factors and the total score HRQoL. read and write [ = 0.062, 95% CI (0.75, 4.31)] and Beta was determined to estimate the strength of associ- [ = 0.067, 95% CI (1.84, 7.99)], respectively. As fam- ation with 95% confidence interval (CI). For all statistical ily size increased by one households’ head, HRQoL significance tests, the cut- off value set is p < 0.05. decreased by 18.21% [ = − 0.201, 95% CI: (− 2.55, − 1.63)]. Moreover, as wealth index increased by one Results unit, HRQoL decreased by 32.90% [ = − 0.306, 95% Socio-demographic characteristics CI (− 5.15, − 3.86)] and HRQoL among CBHI insured We planned to participate 1967 household heads, how- household heads were increased by 12.41% than those ever, 1955 were included in the study; this makes 99.44% un-insured family heads [ = 0.117, 95% CI (2.98, 6.16)] response rate. Among the study participants 1318 (Table 3). Gebru and Lentiro Health and Quality of Life Outcomes (2018) 16:110 Page 4 of 6 Table 1 Socio-demographic characteristics of study participants in SNNPR, n = 1955, February, 2017 Variable Household condition on CBHI Homogeneity test Insured Un-insured Count (n) Percent (%) Count (n) Percent (%) Chi-Square P-value Respondent type 17.74 < 0.00 Husband 613 62.9 705 71.9 Wife 361 37.1 276 28.1 Age in year (mean, SD) 41(± 10) 40(± 11) 531.34 < 0.00 Current job 40.59 < 0.00 House wife 355 36.4 264 26.9 Farmer 556 57.1 678 69.1 Laborer 35 3.6 30 3.1 Employed 28 2.9 9 0.9 Marital condition 0.32 0.57 Live together 872 89.5 863 88.0 Separate/divorce/widow 102 10.4 118 12.0 Educational level 6.34 0.09 Could not read & write 564 57.9 557 56.8 Primary 332 34.1 344 35.1 Secondary and above 78 8.0 74 8.1 Family size 99.95 < 0.00 Less than or equal to 5 350 35.9 574 58.5 Greater than 5 624 64.1 407 41.5 Wealth index (quintile) 48.57 < 0.00 Poorest 149 15.30 129 13.1 Poor 244 25.1 204 20.8 Medium 244 26.1 176 17.9 Rich 166 17.0 270 27.5 Richest 161 16.5 202 20.6 Discussion life on the domain psychological and environmental In this study it was revealed that community based health than un-insured family heads whereas low on health insurance impacted the health-related quality of physical health and almost equal on social relationship life among insured and un-insured family heads. Quality domain. of life among insured household heads were higher Quality of life scores in our study participants living compared with un-insured household heads; more separately by their marital condition were low compared specifically insured family heads had higher quality of with living together. This finding was consistent with Table 2 Quality of life score among insured and un-insured household head study participants in SNNPR, n = 1955, February, 2017 Variables Household condition on CBHI Test of Homogeneity Insured Un-insured Mean (±SD) Transformed score Mean (±SD) Transformed score F P-value Physical health 22.17(± 6.72) 54.17 28.63(± 6.76 66.54 128.95 < 0.00 Psychological 31.12(± 5.65) 86.13 24.01(± 11.28) 47.24 309.61 < 0.00 Social relationship 8.86(± 2.94) 48.79 8.67(± 3.62) 60.75 1.56 0.21 Environment 29.15(± 6.39) 62.97 25.57(± 8.54) 61.14 2.94 0.09 Quality of life 63.02 58.92 21.77 < 0.00 Gebru and Lentiro Health and Quality of Life Outcomes (2018) 16:110 Page 5 of 6 Table 3 Predictors to quality of life among household head study participants in SNNPR, n = 1955, February, 2017 Variable Quality of life Beta with 95% CI Unstandardized Standardized P-value Age in year −0.041(− 0.16, 0.01) − 0.038 (− 0.15, 0.01) 0.069 Marital condition Live together –– – Separate/divorce/widow − 0121(−10.96, −5.12) − 0.044(−5.67, − 0.10) 0.043 Educational level Could not read & write –– – Primary 0.130(3.53, 7.14) 0.062(0.75, 4.31) 0.005 Secondary and above 0.110(4.78, 11.21) 0.067(1.84, 7.99) 0.002 Current job Farmer –– – Daily laborer −0.076(−13,06, −3.42) − 0.078(− 12.91, −4.10) < 0.001 Employer 0.023(− 4.71, 15.14) 0.055(2.58, 17.59) < 0.001 Family size − 0.332(− 0.79, 0.13) −0.201(−2.55, − 1.63) < 0.001 Wealth Index − 0.382(−8.26, −6.66) −0.399 (− 8.67, − 6.90) < 0.001 CBHI condition Un-insured –– – Insured 0.105(2.38, 5.82) 0.117 (2.98, 6.16) < 0.001 reports conducted elsewhere . The score repre- opportunity to overview the effect of health insurance on sents the effect of living alone lead to deterioration of health related quality of life. life which could be explained due to burden on tak- ing family responsibility alone than joint care . Conclusions On the other hand, quality of life was markedly From this work, we revealed that the two populations increased as an educational level increased. This re- were different in their quality of life. Moreover, being sult was consistent with the study finding conducted member of community based health insurance had in nine European countries [13–15]. positive effect on health related quality of life. Triggered In addition, Study participants who engaged on daily this, the government shall expand the community based labor during the study period negatively affected the health insurance into additional districts of Ethiopia and quality of life. In contrary employed participants were further actions should be established on the scheme positively associated for better quality of life as satisfaction to ensure its continuity. compared to farmers by their occupational status. This Abbreviations finding was similar with research report from Chinese CBHI: Community Based Health Insurance; HRQoL: Health Related Quality of and others [16, 17]. This effect may be explained due to Life; IRB: Institutional Review Board; QOL: Quality of Life; SNNPR: South Nation Nationality People’s Region; SPSS: Statistical Package for Social job security and level of satisfaction on their job. Science; US$: United State Dollar; WHOQoL-BREF: World Health Organization Furthermore, the study revealed that the effect of family Quality of Life Biomedical Research and Educational Facility size and wealth index increment worth’s family head’s quality of life and it could be explained due to increased Acknowledgments First and foremost, we would like to express our deepest gratitude to family responsibility. data collectors, supervisors and the study participants for their cooperation. We extend our appreciation to Sidama zone health Limitation of the study department for its cooperation. Finally, we would like to acknowledge Wolkite University for the financial support. The major limitation of this study was related to causal re- lationship as it was not allowed to established cause and Funding effect relationship. Except for gross socio-demographic This study was financially supported only for data collection by Wolkite University, Ethiopia. character such as age, marital condition, educational status and job, this study did not allow to link with other Availability of data and materials potential factors that may affect quality of life. Despite The datasets used and analysed during the study available from the these limitations, our study provided a comprehensive corresponding author on reasonable request. Gebru and Lentiro Health and Quality of Life Outcomes (2018) 16:110 Page 6 of 6 Authors’ contributions 16. Lathan CS, Tucker-Seeley R, Zafar SY, Ayanian JZ, Schrag D. Association of TG framed the study and analyzed the data and KL contributed to the Financial Strain with symptom burden and quality of life for patients with design and data collection of the study. The manuscript was prepared by lung or colorectal cancer. J Am Soc Clin Oncol. 2016:1732–40. both authors and approved its final version submitted for publication. 17. Zhuyan ST, Ping Z, Qiang W, Dan L, Shihua W. Association of financial status and the quality of life in Chinese women with recurrent ovarian cancer. Health Qual Life Outcomes. 2017;15:144. Ethics approval and consent to participate The study was ethically approved by the institutional review board of Wolkite University. Official letter was obtained from Wolkite university health Science College. Letter of co-operation was obtained from Sidama Zone health department after informed about the purpose and importance of the study. From the prepared information sheet; study participants were introduced about the study, their right, autonomy and their participation willingness and then written consent was obtained prior to each interview. Names and other personal information which can violate the confidentiality of the respondents was not taken or recorded. Any information was kept confidential and only used for the research purpose and not exposed to third party for any other reason. During interview privacy of respondents was kept, and free to withdrawal from the interviewed at any time. Competing interests The authors declare that they have no competing interests. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Received: 29 August 2017 Accepted: 23 May 2018 References 1. Agency CS. (CSA) [Ethiopia] and ICF. Ethiopia demographic and health survey. Key Indicators Report. 2016;2016:19–59. 2. Mebratie D. Enrolment in Ethiopia’s community based health insurance scheme; Erasmus International Institute of Social Studies. In: The Hague; 3. Ministry of Finance and Economic Development. Macroeconomic Developments in Ethiopia, Annual Report. Addis Ababa; 2016. 4. EThiopian FMoH. A directive to provide legal backing for piloting and promotion of CBHI. Addis Ababa; 2011. 5. United Nations Development Programme (UNDP). Human Development Report 2011. In: Sustainability and equity: a better future for all. New York; 2011. 6. EThiopian FMoH. Health and health related indicators of 2011. Addis Ababa; 7. EThiopian FMOH. Health Care Financing Team, Policy, Planning and Finance General Directorat, USAID Health Sector Financing Reform Project: Ethiopia’s Fourth National Health Accounts. Addis Abeba. 2010. 8. Bureau of finance and economic development. Development data collection dissemination core process, annual statistics. In: SNNPR Hawassa; 9. WHO. Program on mental health;WHOQOL user manual. In: Geneva; 2012. 10. Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models (statistics for biology and health). San Francisco. 2005; 11. Ormel JKG, Deeg DJ, Brilman EI, VanSonderen E, Relyveld J. Functioning, wellbeing, and health perception in late middle-aged and older people: comparing the effects of depressive symptoms and chronic medical conditions. J Am Geriatr Soc. 1998;46:39–48. 12. Marie LL, Nic JV, Dirk JV. Marital status, quality of life and clinical outcome in patients with heart failure. Heart Lung. 2006;35:3–8. 13. Dominik O, Justine R, Stephen MC, Michel W, Tobias F. Health-related qualityoflifeand risk factor control: theimportanceofeducational level in prevention of cardiovascular diseases. Eur J Pub Health. 2013;24(4):679–84. 14. Fenn KM, McCorkle R, DiGiovanna MP. Impact of financial burden of cancer on survivors' quality of life. J Oncol Pract. 2014;10(5):332–8. 15. Kale HP. Self-reported financial burden of cancer care and its effect on physical and mental health-related quality of life among US cancer survivors. Cancer. 2016;122(8):283–9.
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Published: May 31, 2018
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