Assessment of equity in healthcare financing in Fiji and Timor-Leste: A study protocol

Introduction: Equitable health financing remains a key health policy objective worldwide. In low and middle-income countries (LMICs), there is evidence that many people are unable to access the health services they need due to financial and other barriers. There are growing calls for fairer health financing systems that will protect people from catastrophic and impoverishing health payments in times of illness. This study aims to assess equity in healthcare financing in Fiji and Timor-Leste in order to support government efforts to improve access to healthcare and move towards universal health coverage in the two countries. Methods and analysis: The study employs two standard measures of equity in health financing increasingly being applied in LMICs – benefit incidence analysis (BIA) and financing incidence analysis (FIA). In Fiji, we will use a combination of secondary and primary data including a Household Income and Expenditure Survey, National Health Accounts, and data from a cross-sectional household survey on healthcare utilisation. In Timor-Leste, the World Bank recently completed a health equity and financial protection analysis that incorporates BIA and FIA, and found that the distribution of benefits from healthcare financing is pro-rich. Building on this work, we will explore the factors that influence the pro-rich distribution. Ethics and dissemination: The study is approved by the Human Research Ethics Committee of University of New South Wales, Australia (Approval number: HC13269); the Fiji National Health Research Committee (Approval # 201371); and the Timor-Leste Ministry of Health (Ref MS/UNSW/VI/218). Results: Study outcomes will be disseminated through stakeholder meetings, targeted multidisciplinary seminars, peer-reviewed journal publications, policy briefs and the use of other web-based technologies including social media. A user-friendly toolkit on how to analyse healthcare financing equity will be developed for use by policymakers and development partners in the region. Fiji is a Pacific island nation with a population of about 875 000 in 2012.31 Approximately 57% of the population are ethnic Fijians and about 37% are Indo-Fijian.24 The health system of Fiji is the most complex and developed among the Pacific island countries. The government provides the largest share of healthcare services—about 71% of total health services in 2011.32 The private sector is small but has experienced significant growth in recent decades and there are a number of non-government organisations providing specific health services to the public.33 Access in terms of availability of basic healthcare is relatively good with primary healthcare services available to about 80% of the population.34 National health indicators, including life expectancy at birth (69 years) and infant mortality rate (18/1000 live-births) are also good compared to developing countries elsewhere.24 About 30% of healthcare expenditure, including 20% OOP payment, is financed from private sources and 9% is financed by development partners.35 Government health expenditure is almost exclusively financed through taxation. Only1% of revenue is raised internally by health facilities through user fees.33 Timor-Leste, a new island nation with 1.1 million people, has seen some significant health improvements in its relatively short history.28 The 2010 infant mortality rate of 44/1000 live-births and under-five mortality rate of 64/1000 were better than the country’s Millennium Development Goals (MDG) targets of 53 and 96/1000 live-births, respectively.36 In contrast, the maternal mortality ratio of 557/100 000 live-births36 is among the highest in the Asia Pacific region and more than double the country’s MDG target of 252/100 000. A quarter of households travel for more than 2 hours to reach the closest health facility and 1 in 10 households do not consult a health provider when sick.37 Total government health expenditure has more than doubled from US$18.3 million in 2006–2007 to US$38.2 million in 2011, with much of the increase attributable to the high capital expenditure in rebuilding health infrastructure destroyed during the independence struggle.25 Despite this, government health expenditure as a proportion of total government expenditure declined from 7% in 2007 to 2.9% in 2011.38 The Fiji component of the study will use benefit and financing incidence analyses to assess equity in health financing and service use. The Fiji National Health Accounts (NHA) 2011–2012 and Household Income and Expenditure Surveys (HIES) 2008–2009 will be used to estimate the healthcare financing mix and household contributions to health financing through direct and indirect taxation and OOP payments required for the FIA. Tax thresholds and actual revenue generated through different forms of taxation will be obtained from the Ministry of Finance and will be used to triangulate with estimated tax revenue from the NHA and HIES. The BIA also requires data on health service utilisation and the cost of accessing healthcare. As Fiji has no nationally representative household data for utilisation of healthcare, a cross-sectional household survey will be conducted to obtain estimates of health service use and the cost incurred for using health services. Socioeconomic information will also be collected to enable the ranking of households by their living standards and for the assessment of ATP for healthcare. A two-stage sampling strategy will be used to select 2000 households, with 1000 each from urban and rural areas. This will enable the determination of prevalence for characteristics with a 95% CI and a precision of ±3%. It will also allow at least 80% power and a significance level of 5% to be able to detect differences of 7% for comparisons between urban and rural areas. The sample will be selected from 50 enumeration areas (EAs) based on the Fiji Bureau of Statistics (FBoS) census divisions. The EAs will be selected from three of the four main administrative divisions in Fiji. The fourth division will be excluded due to accessibility challenges, the small and dispersed population and study resource constraints. In the first stage, the total sample frame will be divided into six strata and representative samples of urban and rural EAs will be selected from these strata to obtain the primary sampling unit (PSU). The sample of rural and urban EAs within each PSU (stratum EA) will be based on probability proportional to size, measured in terms of the total number of households in the frame. In the second stage, we will select 40 households from each of the 50 EAs using systematic random sampling. The sampling interval will be estimated based on the total number of households divided by the sample size. The first house to be visited will be randomly determined. Electronic data collection involving the use of laptops by enumerators will be employed. The e-questionnaire will be designed using the NOVA Research Company’s Questionnaire Development System (QDS) 3.0 and administered with the computer-assisted personal interview (CAPI) program. The questionnaire will be piloted in selected EAs to test logistics and gather information to improve the quality and efficiency of the main survey. Enumerators and supervisors will be trained in e-data collection and administrative procedures including the content of the questionnaire, how to save completed interviews and how to transfer data to the Central Data Processing Centre for the study. A project manual has already been developed and published on the project website: https://research.unsw.edu.au/projects/sustainable-health-financing-fiji-and-timor-leste-shift-study. The primary caregiver or head of the household will be interviewed in each household. The entire study will be implemented over a period of 3 years from July 2013 to June 2016. Data collection is ongoing. The Timor-Leste component of the study investigates one of the key drivers of the pro-rich distribution of healthcare benefits identified in the recent World Bank health equity and financial protection study—the limited use of hospital services by the poor.30 The main question asked will be: why do the poor use less hospital services than the rich in Timor-Leste? To address this question we will use a mixed methods approach23 that combines qualitative and quantitative methods to explore three key dimensions of access: availability (physical access), affordability (financial access) and acceptability (cultural access). The qualitative approach will involve focus group discussions (FGDs) with household members to explore views and experiences about access to hospital care, including the costs of accessing hospital services, the quality of services, and access to and use of hospital referrals. In-depth interviews (IDIs) with healthcare providers will explore the functioning of the referral system and the use of hospital referral by households. Key informant interviews (KIIs) with policymakers will probe into general access to hospital care in Timor-Leste and the functioning of the referral system. The quantitative aspect will involve a cross-sectional survey of households to identify the factors influencing access and utilisation of hospital services across different socioeconomic groups. Secondary data on distribution of health facilities from the MoH and hospital referral records of selected Community Health Centres will also be analysed to complement and corroborate data from the household survey. The qualitative and quantitative data will be collected simultaneously and integrated at the data analysis stage in a concurrent triangulation strategy to collaborate and confirm results.23 39 The specific research questions, methods to address each including data sources and data collection tools are presented in table 3. Research questions and methods FGD, focus group discussions; IDS, in-depth interviews. We will follow a similar sampling method as the one proposed for Fiji. A two-stage sampling procedure will be used to select 1500 participants; 750 each from urban and rural areas. The households will be selected from 150 EAs. Administratively, Timor-Leste is divided into 13 districts and 1828 EAs based on the 2010 national census.40 The sample frame of 13 districts will be grouped into five strata in the first stage. Representative samples of urban and rural EAs will be selected from these strata to obtain the PSU. The sample of rural and urban EAs within each stratum will be based on probability proportional to size, measured in terms of the total households in the frame. In the second stage, we will select 10 households from each of the 150 EAs using systematic random sampling. The qualitative component will use a purposive sampling technique to select participants. A total of 20 FGDs, IDIs and KIIs will be conducted. At the household level eight FGDs (two in each stratum), each consisting of approximately 6–8 adult women and men randomly selected, who have not already responded to a household survey, will be carried out. For healthcare providers, we will conduct eight IDIs, two in each stratum, while for policymakers four KIIs will be conducted. We will begin by conducting four FGDs—two in an urban area and the others in a rural area—to inform the design of the household survey. The household survey will be undertaken using electronic data collection. The e-questionnaire will be translated into one of the national languages—Tetum—which is spoken in all districts, and will be piloted in selected EAs around Dili (the capital) to ensure that all the questions and administrative arrangements work as expected. The questionnaire will be reviewed for cultural appropriateness by a local member of the study team before being rolled out. In addition to socioeconomic information, the e-questionnaire will cover the three key dimensions of access: physical accessibility—including distance from health facilities, means of transport, and availability of drugs and medical supplies; financial accessibility—particularly information on costs of accessing health services including transport costs and OOP payments; and cultural accessibility—including information on the quality of health services, referral procedures, attitudes of health workers and the use of traditional medicine. Enumerators and supervisors will be recruited and trained in e-data collection and administrative procedures including training on the content of the questionnaire, how to save completed interviews and how to securely transfer data to the Central Data Processing Centre for the study. In each selected household, the primary caregiver or head of the household will be interviewed. The qualitative data (apart from the initial 4 FGDs to inform the design of the household survey) will be collected at the same time as the household survey and will be guided by an interview schedule. It will explore several of the key issues covered in the household survey in more depth. This will include topics in the domain of financial, physical and cultural access to health services, particularly access to secondary and tertiary services; healthcare-related payments; and access to domestic and overseas referrals. Interviews will be conducted by two experienced local researchers in Tetum and will be audiotaped for transcription and analysis. The survey will be piloted to test logistics and gather information to improve the main survey. The study will be integrated at the data analysis stage, with data from Fiji and Timor-Leste being analysed simultaneously (figure 2). Integration of the Fiji and Timor-Leste components of the study. BIA, benefit incidence analysis; FIA, financing incidence analysis; NHA, National Health Accounts; HIES, Household Income and Expenditure Surveys. Analysis of the BIA and FIA data from Fiji and the data from the household survey in Timor-Leste will be undertaken using STATA version 13. The BIA data analysis will seek to ascertain whether the distribution of benefits from healthcare spending for a given provider is pro-rich or pro-poor and in line with need for services. We will construct bar charts indicating the relative share of total benefits received by each quintile of a socioeconomic group. We will then compare the distribution of benefits, depicted by the concentration curve, against the 45° line of perfect equality. Dominance tests will be carried out to ascertain whether the differences are significant.41 The gender dimension of benefit from health spending will be given specific attention given the role of women as primary caregivers in times of illness or disability.42 The FIA data analysis will assess healthcare financing equity by examining the level of contribution to healthcare (through direct payments and taxation) reported by socioeconomic quintile. We will assess the progressivity of the health financing system by evaluating the payments made towards healthcare across different socioeconomic groups in relation to their ATP. The socioeconomic measure will be based on a household’s reported expenditure on food consumption, housing and other non-food items.43 We will adjust the total consumption variable to obtain per adult equivalent household consumption using the formula: where A is the number of adults in the household, K is the number of children (0–14), α is the ‘cost of children’ (given a value of 0.5 in this study) and θ determines the degree of economies of scale (given a value of 0.75 in this study).44 Analysis of the data from the Timor-Leste household survey and other quantitative data from documents will involve running a series of regressions to determine associations between household variables and the use of hospital services. Socioeconomic status of households will be measured to establish relative wealth using per capita consumption expenditure. Households will be ranked and allocated into wealth quintiles of equal size, from the poorest 20% (quintile 1) to the richest 20% (quintile 5). The qualitative data will be analysed using QSR NVivo 8. A thematic content analysis approach with a framework of core access dimensions: availability, affordability and acceptability, will be applied. Short summaries of the FGDs, IDIs and KIIs will be compiled and access themes will be used to guide data coding.45 Independent coding will be carried out by two members of the research team and codes will be repeatedly reviewed for validation and reliability, and compared with the initial data summaries. The qualitative data will be triangulated with quantitative data wherever possible to establish validity. For example, data on availability of medicines in health facilities from the household survey will be triangulated with information on medicines in health facilities from the IDIs with providers and FGDs with household members. We will conduct sensitivity analysis to assess how the results of the study, particularly the BIA and FIA, will differ under different assumptions and test whether any difference is statistically significant. For BIA, Wagstaff17 recently argued that the two key assumptions often made—the constant unit subsidy assumption and the constant unit cost assumption—may produce different pictures of equity in the distribution of government health spending, depending on the nature of utilisation and fees paid to public providers. We will assess the sensitivity of the results under three different assumptions: the constant unit cost assumption, which treats the sum of individual fees and government subsidies as constant; the constant unit subsidy assumption, which allocates the same subsidy to each unit of service used irrespective of the fees paid; and the proportional unit cost assumption, which makes the cost of care proportional to the fees paid.46 Under FIA, household per capita consumption is often used as a proxy measure for socioeconomic status, especially in LMICs. We will use data on household income from the Fiji Household Income and Expenditure Survey as an alternative measure of socioeconomic status in the sensitivity analysis. Further, there is no consensus on equivalence scales used in FIA to disaggregate household consumption to the individual level. Different scales may result in different progressivity measures. We will test whether any observed differences resulting from the use of different scales are statistically significant using the bootstrap method.47 We will adapt the SQUIRE (Standards for QUality Improvement Reporting Excellence) guidelines for reporting the findings for this study.48 SQUIRE is generally viewed as appropriate for reporting mixed-methods studies such as this one. All research materials and data from this study will be held and preserved in accordance with the UNSW Research Data management guidelines: http://www.gs.unsw.edu.au/policy/documents/researchdataproc.pdf. Quality assurance procedures will be built into the data management system and implemented alongside other data management activities to ensure timely detection and resolution of errors in the data. A central project database that is password protected will be established using the UNSW research data portal. This will be the ultimate home of the data and will be established in advance of data collection. Access to the database will be given only to members of the study team and country institutions collaborating on the project such as the MoH. The use of e-data collection method means that data can be transferred directly from the field to the project central database immediately after collection. There will be a dedicated staff member to receive all data and prepare it for analysis. The data will be archived using the UNSW long-term data archiving system.