{"paper_id":"649ef4a6-921d-47a7-a798-32fa40776ff0","body_text":"National Health Insurance Coverage and COVID-19 vaccine acceptance in Uganda. Implications \non Uganda's achievement of Universal Healthcare Coverage and Sustainable Development Goals. \nJudith Aloyo1,2, Freddy Wathum Drinkwater Oyat1, Lawence Obalim3, Eric Nzirakaindi Ikoona4, \nDavid Lagoro Kitara1,5* \n \n \n1Uganda Medical Association (UMA), Acholi branch, Gulu City, Uganda. \n2Rhites-N, Acholi, Gulu City, Uganda. \n3Gulu University, Faculty of Science, Department of Computer Science, Gulu City, Uganda.  \n4ICAP at Columbia University, Sierra Leone. \n5Gulu University, Faculty of Medicine, Department of Surgery, Gulu City, Uganda. \n \n \n \n \n \n \n \n \n \n \n \n \n \n*Corresponding Author: David Lagoro Kitara is a Takemi fellow of Harvard University and Faculty at Gulu University, \nFaculty of Medicine, Department of Surgery, P.0. Box 166, Gulu City, Uganda, email: klagoro2@gmail.com; phone: \n(+256)772524474 ORCID: 0000-0001-7282-5026. \n \n \n \n \n \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nAbstract \nBackground: With the advent of the novel coronavirus disease (COVID -19) and the severe second wave that \ncaused high-profile deaths, hospitalization, and high treatment costs in Uganda, the population has raised concerns \nabout the enactment of the national health insurance coverage bill. \nAs of March 31, 2021, when Uganda was beginning to experience the second wave of COVID-19, the Parliament \nof Uganda passed a national health insurance bill that outlined the general structure for the first nationa l health \ninsurance scheme. The bill had pre-set benefit packages including a wide range of essential health services such as \nfamily planning, vaccination, and counseling. The plan was proposed to be financed by a combination of employers \nand government con tributions and aimed to cover all Ugandans when fully implemented. The policy and \nimplementation details would evolve when the President enacts it into law. However, the President has not \nassented to the bill. \nThis study aimed to determine the prevalence of health insurance coverage and factors associated with COVID-\n19 vaccine acceptance among participants in northern Uganda and use findings to show its implications on \nUganda's achievement of Universal Health Coverage and Sustainable Development Goals. \nMethods: We conducted a cross-sectional study among seven hundred and twenty-three adult participants from \nnorthern Uganda. Participants were selected randomly and consecutively. We used a questionnaire with an \ninternal validity of Cronbach's a=0.772 to colle ct quantitative data from participants. A local IRB approved the \nstudy, and we used SPSS version 25.0 for data analysis. A p-value less or equal to 0.05 was considered significant. \nResults: The prevalence of health insurance coverage among the study popula tion was low, 57/723(7.9%), with \nmost insured 42/57(73.7%), accepting the COVID -19 vaccine with a mean age of 33.81 years SD+8.863 at 95% \nCI:31.46-36.16 and a median age of 35 years. Participants without insurance cover age but accepted the COVID-\n19 vaccine were 538/723(74.4%) with a mean age of 31.15 years SD+10.149 at 95% CI:30.38-31.92 and a median \nof 29 years. The insured and uninsured ages range from 18 -52 years and 18 -75 years, respectively. COVID -19 \nvaccine acceptance was higher among the insured 42/ 57(73.7%), and the likelihood ratio for insured participants \nto accept than reject the COVID-19 vaccine was 9.813; df=4; p=0.044. Widows, divorcees, and married separate, \nparticipants from remote districts (Nwoya and Lamwo), and those without formal educat ion had no health \ninsurance cover. However, in a multivariable logistic regression analysis, health insurance coverage was not an \nindependent predictor of COVID-19 vaccine acceptance AoR=1.501,95%CI:0.807-2.791; p=0.199. \nConclusion: As the world grapples w ith the control of COVID -19, vaccine acceptance and health insurance \ncoverage have become critical issues to be handled by each country. The health insurance coverage among \nparticipants from northern Uganda was low at 57/723(7.9%). Most participants with h ealth insurance cover age \naccepted the COVID-19 vaccines compared to those who did not. The lack of health insurance coverage among \nmost study participants is problematic as the world looks toward attaining UHC and SDGs. We proposed that \nUganda's national s ocial health insurance scheme, which is not legal, is urgently reviewed and signed to allow \nUganda's population access to the needed health services. \nKeywords: COVID-19 vaccination, Universal Health Coverage, Uganda's health insurance scheme, SDGs. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nIntroduction: According to the World Bank (WB) and the World Health Organization (WHO), at least half the \nworld's population can't access essential health services [1]. Nearly one hundred million people worldwide are \nbeing pushed into extreme poverty by healthcar e costs, meaning that after paying for vital health services, \nprocedures, and medications, they have less than $1.90 a day to live on [1,2]. A lack of access to quality, affordable \nhealth care isn't a problem in developing countries or countries with a hig h poverty rate, and it is an issue that \npeople worldwide, from the United States of America to Chad to China, are dealing with [1]. But a world where \nthere are adequate and affordable health services is not impossible to achieve [1]. Evidence from countrie s \nworking to achieve universal health coverage (UHC), the dream of health care for all is already within sight [1,2]. \nEvery United Nations (UN) member state has committed to achieving UHC by 2030 as part of the Sustainable \nDevelopment Goals (SDGs), but not  all have started taking the steps necessary to make healthcare services \naccessible and affordable within the next 12 years [1,2]. The greatest obstacle to establishing UHC is a lack of \npolitical will but as said, where there's a will, there's a way [1,2]. \nSince 2000, many countries, including Canada, Saudi Arabia, Rwanda, Cuba, Indonesia, and Kenya, have begun \nimplementing reforms to establish UHC [1]. The reform initiatives of Rwanda and Indonesia prove that countries \ndon't have to be wealthy to provide affordable healthcare coverage [1]. In addition to overcoming a lack of political \nwill, several other significant barriers to countries' hope to achieve UHC by 2030 must be tackled [1,2]. These \ninclude a lack of trained healthcare workers, vaccines, medication, equipment, and infrastructures. \nIn some countries, a lack of infrastructure means that the population will travel long distances to receive the \nneeded health services, which can be costly and, at times, dangerous to reach healthcare services and facil ities \n[1,2]. The lack of infrastructure does not only mean individuals may be discouraged from getting preventative \ntreatment like vaccines but also means they will wait until their lives hang in the balance to seek medical attention \n[1,2]. A lack of vaccines and medication implies that even when they are willing and able to seek care, treatment \nmay not be available or affordable, or there may not be enough skilled workers to provide care [1,2]. In the past \n16 years, despite its low -income economy, Rwanda h as provided healthcare coverage to about 90% of its \npopulation [1]. It managed to do so by, passing policies that direct the use of tax revenue and foreign aid to cover \nhealthcare costs and asking its citizens to pay voluntary premiums scaled by income (the New York Times \nreported) [1]. \nIn Uganda, the situation is somewhat different. It is the only country in East Africa that has not enacted a national \nhealth insurance scheme but has one of the region's highest out -of-pocket health costs [3]. An estimated 38% \npercent of Uganda's health expenditures are paid by individuals through out -of-pocket expenses, followed by \ndevelopment partners (41%), the government (16%), and others (5%) [4]. Uganda's current health insurance \noptions are an employer or community-based schemes and are estimated to cover less than 2% of the population \n[5]. Health insurers only contribute around 1% to health spending in Uganda [6].  \nIn addition, Uganda's national insurance scheme, which is not yet legal, will allow the insured clients to receive \ninformation and services in both public and private sectors, increasing accountability fo r providers to offer \ncompetitive and high-quality services. \nCOVID-19 has dramatically interrupted the lives and livelihoods of many communities in Uganda, especially during \nthe second wave, which swept across the country with thousands of high-profile deaths, hospitalization, and high \ntreatment costs unaffordable to many communities. This problem was made worse by the high COVID-19 vaccine \nhesitancy and curiosity among the Ugandan communities. \nThis study aimed to determine the prevalence of health insurance coverage and factors associated with COVID-\n19 vaccine acceptance among participants in northern Uganda and to use it to explain its implications on Uganda's \nachievement of Universal Health Coverage (UHC) and Sustainable Development Goals (SDGs). \nMethods \nWe conducted a cross-sectional survey in northern Uganda between March and April 2022 in twenty-four health \nfacilities of the Acholi sub-region. We selected the health centers based on their participation in offering COVID-\n19 vaccination to the region's pop ulation. We recruited participants (adults/>18 years) admitted or clients to \noutpatient clinics of health facilities in northern Uganda's nine districts of the Acholi sub-region and had consented \nto the study. We excluded those who were critically ill and were unable to answer research questions. We \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\ncalculated the sample size using the Raosoft sample size calculation in which the computation is on a 50% response \ndistribution, 5% margin of error, and 95% Confidence Interval. We used this online software foundation because \nit is a widely utilized descriptive study formula for sample size estimation [7,8]. The research team chose this \nsoftware calculator because Raosoft, Inc. form and survey software comprise a database management system of \ngreat strength and r eliability that communicates with other proprietary formats. Raosoft database is a highly \nrobust, proven system with high data integrity and security [7,8]. \nThe sample size was calculated using the formula = (z-score)2 x StdDev x (1-StdDev) \n                                                                                 (Confidence Interval)2 \nBased on a population size of 45,000 clients and OPD visitors in one month in all health facilities in the Acholi \nsubregion, a minimum sample size grounded on the above assumptions and factoring in a 10% non-response rate \nis 396 participants. We used a simple random sampling technique to recruit participants. We chose this sampling \ntechnique because it is one of the most popular and simple data collection  methods in research fields (in terms \nof probability, statistics, and mathematics). It allows for a balanced data collection, aiding studies to arrive at \nunbiased conclusions. The dependent variable was COVID -19 vaccine acceptance (\"Have you received a jab  of \nCOVID-19 vaccine? And the answer was either \"Yes\" or \"No\"). The independent variables were the socio -\ndemographic characteristics: age, sex, occupation, religion, level of education, tribe, marital status, district, \npresence of comorbidities, nationality, race, health insurance coverage, and participants' self -confidence that the \nvaccines available in health facilities in northern Uganda were safe. \nOur research team used face -to-face questionnaire interviews, strictly following Uganda's standard COVID -19 \ninfection, prevention, and control (IPC) guidelines. The questionnaire was constructed in English, consisting of \nquestions on socio-demographic characteristics and views on the safety of vaccines in health facilities in the sub -\nregion (Additional file 1). The questionnaire was developed and grounded on literature reviews and discussions \nwith the research team [9,10], pretested in a regional hospital and had an internal validity of Cronbach's α= 0.772. \nWe assured participants’ confidentiality and privacy of their responses to reduce the potential bias introduced by \nself-reported data. In addition, the questionnaire was short and precise and thus minimized lethargy in participants' \nresponses. We conducted data analysis using SPSS statistical software version 25.0, where continuous variables \nwere in means, standard deviations, medians, and interquartile ranges depending on the distribution of the data. \nCategorical data were in frequencies and percentages. The Chi -square and crosstabs tests were performed on \ncategorical data when comparing two or more groups. In addition, w e conducted a multivariable logistic \nregression analysis to identify independent factors associated with the COVID-19 vaccine acceptance among the \ninsured participants and relationships between  independent and dependent variables. A p -value less or equal to \n0.05 was considered statistically significant.  \nSt. Mary's Hospital, Lacor Institutional, Ethics, and Review Committee (Lacor IREC) approved the study. We \nobtained administrative clearance fr om the health facilities and informed consent from each participant. The \nresearch team ensured confidentiality of personal information during and after the investigation, and we only \nretained unique identifiers of participants on public records. In addition, only the principal investigator had access \nto the database during and after the project, which was archived at the Gulu University, Faculty of Medicine, \nDepartment of Surgery. \nResults \nThe prevalence of health insurance coverage among the study population was low, 57/723(7.9%), with most \ninsured, 42 out of 57(73.7%) accepted the COVID -19 vaccine with a mean age of 33.81 years SD+8.863 at 95% \nCI:31.46-36.16. The uninsured but accepted the  COVID-19 vaccine were 538/723(74.4%) and younger, with a \nmean age of 31.15 years SD+10.149 at 95% CI:30.38 -31.92 and a median age of 29 years. The insured and \nuninsured ages range between 18 -52 years and 18 -75 years, respectively. The Likelihood ratio for  COVID-19 \nvaccine acceptance with the insured participants was among age groups 9.813; df=4; p=0.044.  \nWidows, divorcees, and marriage separate participants, those from the remote districts of northern Uganda \n(Nwoya and Lamwo) and those with no formal educ ation had no health insurance coverage. A multivariable \nlogistic regression analysis showed that health insurance coverage among participants was not an independent \npredictor of COVID-19 vaccine acceptance AoR=1.304, 95%CI:0.657-2.732; p=0.421. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nTable 1 shows participants with health insurance coverage and accepted the COVID-19 vaccine, with a prevalence \nrate of 42/57(73.7%), the mean age of 33.81 years SD+8.863 at 95% CI:31.46-36.16 and a median age of 35 years. \nThose without insurance but accepted the COVID-19 vaccine were 538/723(74.4%), and the mean age was 31.15 \nyears SD+10.149 at 95% CI:30.38-31.92 and a median of 29 years. The insured and uninsured ages ranged between \n18-52 years and 18-75 years, respectively. \nFigure 1 is a box plot showing ages, COVID-19 vaccine acceptance, and health insurance cover among participants. \nInsured participants had an older average age (33.84 years) compared to those without 31.15 years. \nTable 2 shows the association factors between health insurance coverage and other soci o-demographic \ncharacteristics. Near significant association was observed with age groups χ2=8.122; df=4; p=0.087; with a \nLikelihood ratio of 9.813; df=4; p=0.044. The most vulnerable participants, such as widows, divorcees, and \nmarriage separate participants, had no one with health insurance coverage. In addition, participants from remote \ndistricts of northern Uganda (Nwoya and Lamwo, and those without formal education had no health insurance \ncoverage. \nTable 3 shows the COVID-19 vaccine acceptance among the insured participants. COVID-19 vaccine acceptance \namong the insured participants was associated with no worries that they would take a COVID -19 vaccine by \nforce χ2=8.036; df=1; p=0.005; did not get a fever after vaccination χ2=5.631; df=1; p=0.018; and mainstream media \nwas not their most trusted sources of information on the COVID-19 χ2=4.619; df=1;p=0.032. \nHealth insurance coverage was not an independent predictor of COVID-19 vaccine acceptance at a multivariable \nlogistic regression analysis AoR=1.501, 95%CI:0.807-2.791; p=0.199. \nTable 1: Health Insurance Coverage and acceptance of the COVID-19 vaccine among \nparticipants in northern Uganda. \ns/no Variables Yes (%) No (%) \n1 Health Insurance Coverage (HIC) 42/723(5.8%) 538/723(74.4%) \n2 Mean age (years) 33.81 31.15 \n3 The standard error (SE) 1.174 0.393 \n4 95% Confidence Interval (CI) 31.46-36.16 30.38-31.92 \n5 Median 35.00 29.00 \n6 Variance 78.551 103.012 \n7 Standard Deviation (SD) 8.863 10.149 \n8 Minimum 18.00 18.00 \n9 Maximum 52.00 75.00 \n10 Range 34.00 57.00 \n11 Interquartile range 13.00 14.00 \n12 Skewness 0.167(SE=0.316) 1.118(SE=0.095) \n13 Kurtosis -0.797 (SE=0.623) 1.325(SE=0.189) \nTable 1 shows the participants with health insurance coverage that accepted the COVID-19 vaccine, with a prevalence of 42/723(5.8%), \nthe mean age of 33.81 years SD+8.863 at 95% CI:31.46-36.16 and a median age of 35 years. Those without insurance cover but accepted \nthe COVID-19 vaccine were 538/723(74.4%), and the mean age was 31.15 years SD+10.149 at 95% CI:30.38-31.92 and a median age of \n29 years. The age ranges for the insured and uninsured were 18-52 years and 18-75 years, respectively. \n \n \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nFigure 1: The ages, COVID-19 vaccine acceptance, and health insurance coverage among \nparticipants \n \nFigure 1 is a box plot showing ages, COVID-19 vaccine acceptance, and health insurance cover among participants. \nInsured participants had an older average age (33.84 years) compared to those without 31.15 years. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nTable 1: Health Insurance Coverage and COVID-19 vaccine acceptance among participants. \ns/no Variables Yes (N=42) (%) χ2 df p-value \n1 Age (years)      \n <20  3 7.14    \n 20-29 17 40.48    \n 30-39 12 28.57 8.122 4 0.087 \n 40-49 7 16.67    \n > 50 3 7.14    \n2 Marital status      \n Married 24 57.14 0.037 1 0.847 \n Single 18 42.86    \n Widowed 0 0.00    \n Separated 0 0.00    \n Divorced 0 0.00    \n3 Religion      \n Catholics 24 57.14 4.373 4 0.358 \n Protestants 7 16.67    \n Born Again 6 14.29    \n Muslims 3 7.14    \n Others 2 4.76    \n4 Tribes      \n Acholi 25 59.52 7.289 4 0.121 \n Langi 3 7.14    \n Baganda 7 16.67    \n Itesot 0 0.00    \n Others 7 16.67    \n5 Districts      \n Gulu  20 47.62 4.93 5 0.424 \n Pader 11 26.19    \n Agago 5 11.90    \n Kitgum 4 9.52    \n Lamwo 0 0.00    \n Nwoya 0 0.00    \n Amuru 1 2.38    \n Omoro 1 2.38    \n6 Level of education attained     \n No formal education 0 0.00    \n Primary 2 4.76    \n Secondary 13 30.95    \n Diploma 6 14.29    \n Degree 14 33.33    \n Postgraduate 7 16.67 3.853 4 0.426 \n7 Occupation  0.00    \n Health workers 14 33.33    \n Non-Health workers 28 66.67 0.844 1 0.358 \n8 Nationality  0.00    \n Ugandan 40 95.24    \n American 0 0.00    \n Kenyan 1 2.38    \n Italian 1 2.38 0.74 2 0.691 \n9 Race  0.00    \n Black African 41 97.62    \n European 1 2.38 0.364 1 0.547 \n10 Smoking status      \n Ex-smoker 1 2.38 0.434 2 0.805 \n Never smoked 39 92.86    \n Smoker 2 4.76    \n11 Alcohol drinking status      \n Drinks 18 42.86 2.674 2 0.263 \n Never drank 15 35.71    \n Quit drinking 9 21.43    \n12 Comorbidities      \n Yes 31 73.81 0.001 1 0.971 \n No 11 26.19    \n13 Sex      \n Males 23 54.76    \n  Females 19 45.24 0.123 1 0.726 \nTable 2 shows the associations among health insurance coverage, vaccine acceptance and other socio-demographic characteristics. Near \nsignificant association was observed with age groups χ2=8.122; df=4; p=0.087; with a Likelihood ratio of 9.813; df=4; p=0.044. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nTable 3: Perceptions of COVID-19 among the insured participants in Northern Uganda \ns/no Variables Yes (%) No (%) chi df p-values \n1 The mainstream media is my most trusted source of information  15(35.7) 27(64.3) 4.619 1 0.032 \n2 I got dizzy after receiving the COVID-19 vaccine 2(4.8) 40(95.2) 0.740 1 0.390 \n3 I got a blood clot after the COVID-19 vaccination 7(16.7) 35(83.3) 0.916 1 0.339 \n4 I have job-related worries with the COVID-19 pandemic 9(21.4) 33(78.6) 3.487 1 0.062 \n5 I am worried about the unavailability of the COVID-19 vaccine 8(19.0) 34(81.0) 1.281 1 0.258 \n6 I have food insecurity worries during this pandemic 8(19.0) 34(81.0) 0.249 1 0.617 \n7 The coronavirus is a plot or a conspiracy theory 4(9.5) 38(90.5) 0.113 1 0.737 \n8 I may be forced to take medicine for the virus 0(0.0) 42(100.0) 2.850 1 0.091 \n9 I may be forced to take the COVID-19 vaccine 5(11.9) 37(88.1) 8.036 1 0.005 \n10 I am not worried about any COVID-19 issues 1(2.4) 41(91.6) 2.659 1 0.103 \n11 I got a fever after the COVID-19 vaccination 2(4.8) 40(95.2) 5.631 1 0.018 \nTable 3 shows the COVID-19 vaccine acceptance among insured participants was associated with no worries that they would be forced \nto take a COVID -19 vaccine χ2=8.036; df=1; p=0.005; did not get  a fever after vaccination χ2=5.631; df=1; p=0.018; and mainstr eam \nmedia was not the most trusted source of information on the COVID-19 χ2=4.619; df=1;p=0.032. \nDiscussions \nThe most significant finding from this study was the low prevalence of health insurance coverage among the study \npopulation, 57/723(7.9%), with 42 of 57(73.7%) insured having accepted the COVID-19 vaccine with a mean age \nof 33.81 years SD +8.863 at 95% CI:31.46-36.16 (Table 1, Table 2). The insured but rejected the COVID -19 \nvaccine were 15 of 57 (26.3%) . On the other hand, the uninsured but accepted the COVID -19 vaccine were \n538/723(74.4%) and were younger, with a mean age of 31.15 years SD+10.149 at 95% CI:30.38-31.92 and a median \nage of 29 years (Figure 1 and Table 1). The insured participants and those without had ages ranging between 18-\n52 years and 18-75 years, respectively. The Likelihood ratio for COVID-19 vaccine acceptance among the insured \nwas in the age group 9.813; df=4; p=0.044 (Table 1). The most vulnerable participants were widows, divorcees, \nand marriage separate participants who had no one with insurance coverage. In addition, participants from remote \ndistricts of northern Uganda and those without formal education had no insurance cover age too (Table 2). This \nfinding has implications that during the COVID-19 pandemic, these vulnerable groups were adversely affected by \nthe lack of access to health information and services because they had no health insurance coverage.  \nAccording to the WHO, Universal health coverage means that everyone has access to the health services they \nneed, when and where they need them, without financial hardships [2]. It includes a full range of essential health \nservices, from health promotion to prevention, treatment, rehabilitation, and palliative care [1,2]. In addition, \nUniversal health coverage (UHC) ensures that people have access to the healthcare they need without suffering \nfinancial hardships [1,2,4]. UHC is also critical for achieving the World Bank Group's (WBG) twin goals of ending \nextreme poverty and increasing equity and shared prosperity [1]. Ending extreme poverty and increasing equity \nare the driving forces behind the WBG's health and nutrition investments [1].  \nCurrently, at least half of the world's population does not receive the health services they need [1,4,11,12]. About \n100 million people are pushed into extreme poverty each year because of out-of-pocket spending on health [1,4].  \nTo make health a reality for all, individuals, and communities with access to high-quality health services for taking \ncare of their health and the health of their families are necessary. Skilled health workers provide quality, people -\ncentered care, and policymakers are committed to investing in universal health coverage [1,2,12]. Universal health \ncoverage must be stron g, people -centered primary health care with sound health systems rooted in the \ncommunities they serve [1,11,12]. They focus not only on preventing and treating diseases and illnesses but on \nhelping to improve the general well-being and quality of life [1,2]. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nOur study found that the most vulnerable participants, such as widows, divorcees, and married separate, had no \nhealth insurance coverage (Table 2). In addition, participants from remote districts of northern Uganda and those \nwithout formal education were not insured (Table 2). The lack of health insurance coverage among most of the \nstudy participants has implications. During the COVID-19 pandemic, the population was adversely affected by a \nlack of access to health information and services, usually available to those with health insurance coverage.  \nInterestingly, discussions on the national health insurance scheme in Uganda started in the late 1980s and evolved \nslowly over the years [3]. In 2017, discussions on the plan accelerated when Advance Family Planning's local \npartner, PPD ARO, stakehold ers, Population Action International (PAI), and The William and Flora Hewlett \nFoundation worked to ensure that the draft scheme had family planning programs [3]. The coalition steered \nadvocacy efforts to provide family planning commodities and services tha t the stakeholders formed throughout \nthe scheme's development processes [3]. Between 2016 and 2020, Parliamentary champions asked PPD ARO to \norganize a series of meetings with members of Parliament, the Ministry of Health, the Ministry of Finance, and the \nUganda Reproductive, Maternal, Newborn, Child, and Adolescent Health (RMNCAH+N) civil society platform to \ncontinue this momentum [3]. For three consecutive years, Uganda's representatives in the Network of African \nParliamentary Committees of health, a regional entity, committed to passing the national health insurance scheme \nbut struggled to get the Ugandan Ministry of Health to draft and introduce the bill to Parliament [3]. However, in \nJune 2019, Parliamentary champions sensed a stalling of the draft bill  in the Ministry of Health [3]. They asked \nPPD ARO for technical support to inform a private member's account for Hon. Dr. Michael Bukenya, the \nchairperson of the parliamentary health committee [3]. The introduction of the private member's bill put pressure \non the government to revive, update, and present their bill to Parliament in August 2019. Eventually, the Ugandan \nMinistry of Health's revised bill incorporated elements from the private member's bill in its final form [3]. \nTherefore, in February 2020, P PD ARO published an issue brief on the draft scheme and organized a series of \nmedia engagement activities to share details and benefits of the plan with the public, including its coverage of \nfamily planning [3]. The advocacy kept the issue central to parli amentarians all over the country as the COVID -\n19 pandemic unfolded and challenged national health financing [3]. The media were instrumental in keeping the \npressure on Parliament to pass the bill, which finally happened on March 31, 2021 [3]. However, this bill has not \nbeen assented to and has effectively prevented the population from accessing services as required by the UHC \ndeclarations and SDGs [3]. \nInterestingly, every country worldwide has committed to achieving universal health coverage (UHC) by 2 030 as \npart of the United Nations Sustainable Development Goals (SDGs) [4,11,12]. But, some countries are progressing \nfaster than others in delivering equitable access to health services, affordable medicines, and vaccines [11].  \nAmong those leading the pack is Vi etnam. Today, 87.7% of Vietnam's population, or 83.6 million people, are \ncovered by health insurance [11]. According to the latest Global Monitoring Report on UHC, published jointly \nby the World Health Organization and the World Bank, 97% of Vietnamese chi ldren now receive standard \nimmunizations, compared to 95% of children in the United States [11]. Since 1990, the country's maternal \nmortality rate has fallen by 75% [11]. Vietnam has reached such impressive milestones ahead of schedule, despite \nhaving an a verage per capita income of just $2,342 as of 2017 [11]. The key to its success is not the scale of \ninvestment in healthcare, which amounts to a modest $142 per person annually (including both public funding and \nout-of-pocket expenses), but rather how the government uses its resources, including the country's intellectual \ncapital [11]. \nVietnam's strategic approach was in its Ministry of Health's directing healthcare activities scheme, which required \nhealth facilities at the central and provincial levels of government administration to help build up the capacity of \ndistrict and community facilities [11]. A key objective of this scheme was to shift more of the burden of delivering \nmedical services from higher-level hospitals onto lower-level primary healthcare facilities [11]. Given a long history \nof deep disparities in health outcomes between urban and rural areas, Vietnamese still often tried to bypass their \nlocal healthcare centers in favor of major hospitals in urban centers [11]. This created inefficiencies in the health \nsystem and increased out-of-pocket costs for patients and their families without guaranteeing the best care [11]. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nThus, going beyond ensuring that community health facilities can offer affordable, quality care, there is a need to \nchange public perceptions [11]. Families need to trust that they can get a dependable diagnosis of malaria, chronic \nobstructive pulmonary disease, or diabetes locally, as well as the necessary medications and other treatments. \nTherefore, health facilities must stren gthen their relationships with local communities by routinely providing a \nlevel of service that satisfies patients [11]. Such connections will help to advance another health-improving, cost-\nsaving imperatives where local health workers should be able to edu cate their communities to maintain health \nand avoid illnesses. Success will require good working conditions and access to the ongoing training and \nmanagement support that are critical to job satisfaction [11]. \nVietnam's government recognized that to implement its healthcare strategy effectively, it needed help from other \npartners [11]. It then established a Working Group for primary healthcare transformation led by the Vietnamese \nMinistry of Health and included diverse actors from the public, nonprofit, and  private sectors [11]. The group's \nfounding partners were the World Economic Forum, Harvard Medical School, and Novartis [11]. The working \ngroup aimed to strengthen existing primary care demonstration projects in 30 Vietnamese provinces and applied \nthe lessons learned to develop holistic solutions that could be replicated and scaled up in Vietnam [11]. It also \nprioritized rigorous measurements and evaluation of outputs, from the quality of community-level health services \nto the cost -effectiveness of primary  health care [11]. The government invited each partner to contribute \ncapabilities, resources, and knowledge to this endeavor. For example, Harvard Medical School brought world -\nclass expertise in the organizational management of primary healthcare teams [11 ]. National partners brought, \namong others, a deep understanding of the local context, which was essential for developing and implementing \nsustainable solutions [11]. For its part, Novartis offered insight on how to deploy digital technology at a large \nscale, engage rural communities in health education, and expand education programs for healthcare practitioners \nin rural communities [11]. Notably, Novartis made similar contributions through another successful public-private \npartnership in Vietnam, Cùng Sông Khòe [11]. \nIn partnership with Vietnam's government, Cùng Sông Khòe (CSK) has been delivering services to underserved \nrural communities in Vietnam since 2012 [11]. That initiative expanded healthcare for common medical conditions \nlike diabetes, hypertension, and respiratory diseases, patient health education, and continuing medical education \nfor health professionals [11]. Since 2012, CSK has reached out to more than 570,000 people, mainly adults, across \nthe sixteen provinces of Vietnam [11]. \nIt would there fore be naïve to think that all going on in Vietnam are beds of roses because Vietnam is facing \nsignificant challenges ahead [11]. It is grappling with behavioral and environmental factors underlying poor health \nand diseases, especially high rates of smoking among males, high rates of alcohol consumption, air pollution, and \nthe rapidly growing number of aging populations [11]. Governments must conduct critical healthcare reforms to \nimprove healthcare outcomes; for example, the government should incentivize doctors to be more selective in \nreferring patients to higher-level hospitals and sending more patients to local primary-health-care centers [11]. \nNonetheless, Vietnam's progress toward UHC has been remarkable, partly due to the government's embrace of \nstrategic public-private partnerships (PPP) [11]. For countries that have struggled to move forward, this model \nand approaches from other high performers in the race for UHC, such as Indonesia, Rwanda, and Thailand, may \nbe worth embracing [11]. \nThe global movement toward Universal Healthcare Coverage (UHC): Health is an essential part of the \nSustainable Development Goals (SDGs). For example, the SDG 3.8 target aims to achieve universal health \ncoverage, including financial risk protection, access to quality esse ntial healthcare services, and safe, adequate, \nquality, and affordable essential medicines and vaccines for all [1,2,4,11,12]. In addition, SDG 1, which calls to \"end \npoverty in all its forms everywhere, could be in peril without UHC, as almost 90 million people worldwide are \nimpoverished by health expenses every year [4,11,12]. Access to affordable, quality primary healthcare is the \ncornerstone of UHC, but many people worldwide still struggle to fulfill their immediate healthcare needs \n[11,12,1,13,14]. Often overlooked, mental health is also an essential element of UHC, as it is critical to people's \nability to lead productive lives [11]. \nIn recent years, the UHC movement has gained global momentum, with the first -ever UN high-level meeting on \nUHC held in Se ptember 2019  in New Y ork, U SA [4,11]. Member states unanimously adopted a Political \nDeclaration, affirming their high-level political commitment to UHC and outlining several necessary actions [11]. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nTwelve co-signatories, including the WBG, also launched the Global Action Plan (GAP) for healthy lives and well-\nbeing to support countries in jointly delivering on the SDG3 targets [11]. In January 2020, the second UHC forum \nwas held in Bangkok to enhance political momentum on UHC in international outlets [11]. \nProviding affordable and quality primary healthcare : Providing affordable, quality health services to the \ncommunity, women, children, adolescents, and people affected by mental health issues represents a long -term \ninvestment in human capital [11,14,15]. Primary health services are a fundamental element of UHC, yet research \nwarns that, if current trends continue, up to 5 billion people will still be unable to access health care in 2030 [14]. \nMaternal and child mortality remains high in several parts of the world. More than a fourth of girls and women in \nsub-Saharan Africa cannot access family planning services, encouraging unplanned pregnancies and maternal, infant, \nand child mortality and morbidity [14,15].  \nIn 2015, the WBG and partners set up the Global Financing Facility ( GFF), a multi -stakeholder initiative that \nfocuses on helping countries improved maternal, child, and adolescent health services [15,16]. Many countries \nexperiencing rapid population growth have young populations that could drive economic growth and reduce \npoverty [15,16,17]. But to unleash the benefits of the demographic dividend, governments must invest in the \nhealth and well-being of their people to build human capital and boost inclusive growth [15,16,17,18]. Improving \nreproductive, maternal, newborn, child, and adolescent health (RMNCAH) and addressing mental health disorders \nare crucial for achieving UHC because significant challenges exist.   \nMaternal mortality: Most of the world's maternal deaths occur in developing regions; in the least developed \ncountries, the lifetime risk of maternal death for women is, on average, one in fifty-six compared to one in 7,800 \nin high-income countries like Australia or New Zealand [14,15,16]. In sub-Saharan Africa, which accounts for two \nin three maternal deaths (66%),  the risk is one in 37 [15,16]. A further 20% of maternal deaths occur in South \nAsia, and most of these fatalities are preventable if pregnant women have timely access to the necessary healthcare \n[16,17]. \nChild mortality: Reports show that mortality rates among children under five have more than halved from 12.5 \nmillion to 5.2 million between 1990 and 2018, according to a joint 2020 report published by the WBG, WHO, \nand UNICEF [17]. Yet a child's chance of survival depends on where they are born [17]. World wide, 15,000 \nchildren under five still die every day [17]. In sub -Saharan Africa, one child in 13 dies before their fifth birthday \ncompared to one in 199 in high-income countries [17,18].  \nThe WBG, WHO, and UNICEF also collaborated on another 2020 publication that highlighted stillbirths, an issue \nthat remains largely overlooked [18]. Every year, 2 million babies are stillborn worldwide, and progress in reducing \nthese numbers has not kept up with the decline in under -five mortality [18]. In 2000, the ratio of stillbirths to \nunder-five deaths was 0.30, but by 2019, it had risen to 0.38 worldwide. In sub-Saharan Africa, stillbirths increased \nfrom 0.77 million in 2000 to 0.82 million in 2019 [18]. \nHigh fertility rates:  Globally, women are giving birth to fewer children today than three decades ago [19]. \nHowever, there are still a handful of countries with persistently high fertility rates, such as Niger (7.0), Mali (6.0), \nand the Democratic Republic of Congo (6.0) [19]. In countries with lower fertility, such as Ethiopia, fertility varies \nwithin different regions [19]. It ranges from 1.7 in Addis Ababa, the capital city, to 6.4 in Somali, a regional state \n[19]. Countries with persistently high fertility often face high maternal, infant, and child mortality burdens [19]. \nAdolescent fertility: More teenage girls are giving birth in countries with high fertility rates [19,20]. In the sub-\nSaharan Africa, the adolescent fertility rate is 102 births per 1,000 girls [19,20,21,22]. Underage mothers are more \nlikely to experience complications due to pregnancy, such as obstructed labor and eclampsia, increasing their risks \nof death [20,21,22,23,24]. In addition, children born to adolescents are also more likely to have a low birth weight, \nill-health, stunting, and other poor nutritional outcomes [19,20-25]. \nMental, neurological, and substance use disorders (MNS):  These are common, highly disabling disorders \nthat are associated with significant premature mortality, and they impose a human, social and economic toll [26 -\n27]. Every 40 seconds, a person commits suicide worldwide [26 -27]. Therefore, to fully realize th e goal of \nuniversal health coverage and improve human capital outcomes worldwide, mental health programs must be \nintegrated with service delivery at the community level and covered under financial protection arrangements \n[26,27]. Estimates suggest that nea rly 1 billion people live with a mental disorder worldwide [26,27]. In low -\nincome countries, more than 75% of people with the disease do not receive treatment, and approximately half of \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nall mental health disorders emerge by age 14 and some other illnesses [26,27]. More than one in five people \n(22.1%) suffer from mental ill-health in countries affected by fragility, conflict, and violence [26,27]. Women and \nchildren who have experienced violence, soldiers returning from war, migrants and refugees displaced by conflict, \nthe poor, and other vulnerable groups in society are disproportionately affected [26,27]. The COVID-19 pandemic \nhas caused a global increase in mental health disorders due to various factors, including anxiety, lockdowns, and \njob losses while disrupting or halting critical mental health services in many African countries [28,29]. Since MSN \nhave an early onset, often in childhood or early adolescence, and are highly prevalent in the working -age \npopulation, they contribute to economic output losse s estimated between $2.5 -8.5 trillion globally. The World \nBank and World Health Organization project that mental health issues will double by 2030 [26,27]. \nMobilizing resources for UHC:  In June 2019, the President of Japan hosted the first -ever G20 Finance  and \nHealth Ministers joint session on resource mobilization for UHC [30]. The discussion aimed to galvanize G20 \ncountries towards the common goal of financing UHC in developing countries [30]. A World Bank report showed \nthat people in developing countries  spend half a trillion dollars annually, over $80 per person, out of their own \npockets to access health services [30]. Such expenses hit the poor the hardest and threatened decades -long \nprogress in health [30]. \nWorld Bank/World Health Organization (WHO) re search from 2019 shows that countries must increase \nspending on primary health care by at least 1% of their gross domestic product (GDP) if the world is to close \nglaring coverage gaps and meet the health targets agreed under the SDGs [30]. A lack of univer sal access to \nquality, affordable health services endanger countries' long -term economic prospects and make them more \nvulnerable to pandemic risks [30]. Developing countries, faced with a growing number of aging populations and \nburdens of non-communicable diseases, need urgent action. They find themselves increasingly challenged to close \nthe gap between the demand for health spending and available public resources, which prolongs patients' families' \nreliance on out-of-pocket expenditures [30]. \nUniversal Health Coverage for inclusive and sustainable development:  In 2011, Japan celebrated the \n50th anniversary of its achievement of universal health coverage (UHC) [12-15]. On this occasion, the government \nof Japan and the World Bank Group decided to undertake a multi-country study to share varied experiences from \ncountries at different stages of adopting and implementing strategies for UHC, including Japan itself [12-16].  \nThe initiative resulted in an in -depth report on Japan's experience entitled \"Universal He alth Coverage for \nInclusive and Sustainable Development: Lessons from Japan\" [12,13]. The goals of UHC are to ensure that all \npeople can access quality health services. To safeguard all people from public health risks, and protect people \nfrom impoverishment due to illness, whether from out-of-pocket payments for healthcare or loss of income when \na household member falls sick [12,31-36]. \nCountries as diverse as Brazil, France, Japan, Thailand, and Turkey have shown how UHC can serve as a vital \nmechanism for improving the health and welfare of their citizens, as well as lay the foundation for economic \ngrowth grounded in the principles of equity and sustainability [12,31-37]. Ensuring universal access to affordable, \nquality health services will end extreme poverty by 2030 and boost shared prosperity in low- and middle-income \ncountries, where most of the world’s poor reside [12,38].  \nWhile governments can, and should, play a leading role in the global UHC movement, to make the dream of UHC \nfor all a reality, governments, nonprofit organizations, and businesses must work together to create and invest in \nrobust health systems [12,13,31 -40]. Global Citizen and Johnson & Johnson support the UN Sustainable \nDevelopment Goal of ensuring people's healthy lives and well -being are taken care of no matter who they are, \nwhere they live, or their income [11,12]. \nIn Uganda, the Parliament has passed the national health insurance scheme, but more advocacy is needed to \nensure that the President signs it promptly [3]. However, priv ate employers have publicly opposed the plan, \nfearing that paying employees' contributions would raise business costs [3]. The scheme advocates need to \ncontinue to engage the President to highlight the benefits of the national health insurance scheme [3]. They should \nplan to maintain pressure to sign the bill by continuing media coverage and strategic messaging [3]. Furthermore, \ncivil society champions should remain engaged when the bill becomes law and support the scheme's regulations \nprocess and implementation [3]. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\nIn summary, the study found that Ugandans' health insurance coverage is low, with the most vulnerable population \nsuch as divorcees, widows, marri age separate participants, those without formal education, and from remote \ndistricts of northern Uganda were the most affected. The problem Uganda is experiencing is not the unaffordability \nof the COVID-19 vaccine and its related costs because vaccines are offered free and at the nearest health facility \nby the Government of Uganda. It is instead the access to information and cost of treatment when one is affected \nby COVID-19 that are not readily available and affordable to most Ugandans because of the lack of national health \nInsurance coverage. Also, when vaccinated, the fear of vaccine side effects, complications, and  where to find \nremedies have created vaccine hesitancy/ inquisitiveness among the population. Whether true or false, these \nperceptions are the reality in Ugandan communities that the Government of Uganda must handle. Therefore, if \nUganda's national health insurance coverage bill became law, access to health information and treatment for \nCOVID-19 would have been available and open to the general population, especially those without health \ninsurance coverage. \nSo, the COVID -19 pandemic exposed Uganda's health service delivery vulnerability, which should provide a \nblueprint for future epidemic preparedness. The need for a law on Uganda's national health insurance coverage \nhas become necessary for this country if we are to achieve Universal Health Coverage  (UHC) and Sustainable \ndevelopment goals (SDGs) soon. \nStrengths and Limitations: The number of participants with health insurance coverage was small, which limited \nus from performing some of the analysis as some cells did not have enough numbers. A more extensive sa mple \nsurvey including many regions of Uganda would provide the power and accuracy of findings. However, this data \nis vital as it is one of the well -documented and completed data for over 723 participants from the Acholi sub -\nregion regarding COVID -19 vaccin e acceptance in the recent period. Findings from this study show a high \nacceptance rate of the COVID -19 vaccine, especially among the insured, despite results from other parts of \nUganda. \nGeneralizability of results:  Findings from this study should be inter preted cautiously in regions with low -\nresource settings in Uganda. \nConclusions: As the world grapples with the control of COVID -19, vaccine acceptance and health insurance \ncoverage have become critical issues to be handled by each country. The health insur ance coverage among \nparticipants from northern Uganda was low at 57/723(7.9%). Most participants with health insurance cover age \naccepted the COVID-19 vaccines compared to those who did not. The lack of health insurance coverage among \nmost study participants i s problematic as the world looks toward attaining UHC and SDGs. We propose that \nUganda's national social health insurance scheme, which is not law, is urgently reviewed and signed to allow \nUganda's population access to the needed health services. \nDeclarations \nEthics approval and consent to participate: The St. Mary's Lacor Hospital Institutional and Ethics Committee \n(LHIREC) approved this study. In addition, the study followed the relevant institutional guidelines and regulations. \nAvailability of data and m aterial: All datasets supporting this article's conclusion are within the paper and \nare accessible by a reasonable request to the corresponding author. \nCompeting interests: All authors declare no conflict of interest. \nFunding: Most funding for this study w as contributions by individual research members of the Uganda Medical \nAssociation (UMA) Acholi branch. \nAuthors' contributions: DLK, ENI, JA and FWDO participated in designing the study, JNO, LO, FWDO, and \nDLK were responsible for supervising data collectio n, LO and DLK were responsible for data analysis and \ninterpretation, CO, DA, JNO, ENI, FWDO, LO, JA, DLK for writing and revising the manuscript. All Authors \napproved the manuscript. \nAuthors' Information: Dr. Eric Nzirakaindi Ikoona (ENI) is a Technical Di rector at ICAP at the University of \nColumbia, Sierra Leone; Dr. Freddy Wathum Drinkwater Oyat (FWDO) is a senior physician, a public health \nspecialist, and a member of Uganda Medical Association, UMA -Acholi branch, Gulu City, Uganda; Mr. Lawrence \nOballim is a member of Gulu University, Faculty of Science, Department of Computer Science, Gulu City, Uganda; \nDr. Judith Aloyo (JA) is a Technical Director at the Rhites-N, Acholi, Gulu City, Uganda; Prof. David Lagoro Kitara \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint \n\n(DLK) is a Takemi fellow of Harvard Un iversity and a Professor at Gulu University, Faculty of Medicine, \nDepartment of Surgery, Gulu City, Uganda \nAcknowledgment: We acknowledge with many thanks for assistance from the administration of health facilities \nin the region for the information obtained. Financial support from UMA Acholi branch members, which enabled \nthe team to conduct this study successfully, is most appreciated.  \nReferences \n1. Daniele Selby and Erica Sánchez. What Is Universal Health Coverage, and How Can We Achieve It? \nGlobal citizen. 2018. https://www.globalcitizen.org/en/content/universal-health-coverage-uhc-healthcare-\nworkers-2/ \n2. World Health Organization (WHO). 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HNP Discussion Paper. Washington, DC: World Bank, 2015.  \n39. A GUIDANCE NOTE IS Araujo E, Maeda A. How to recruit and retain health workers in rural and \nremote areas in developing countries. HNP Discussion Paper 78506. Washington, DC: World Bank, \n2013.   \n40. Wang H, Ramana GNV. The country summary report for Ethiopia is universal health coverage for \ninclusive and sustainable development. Washington, DC: World Bank, 2014. \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 10, 2022. ; https://doi.org/10.1101/2022.08.09.22278595doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}