Extending coverage to informal sector populations in Kenya: design preferences and implications for financing policy

Since the 58^th World Health Assembly, many low- and-middle-income countries (LMICs) are reforming their health systems for universal health coverage (UHC). UHC has two key goals: financial risk protection and access to needed care. Implicit are objectives related to equity, quality of services and broader social protection [1]. The World Health Organization (WHO) recommends greater use of mandatory prepayment mechanisms including general government revenue (tax funding) and social health insurance (SHI). Both tax funding and SHI can mobilise substantial resources, create income and risk cross-subsidies and benefit from economies of scale. Large risk pools are more financially secure and promote equity in financing and access to health care across socio-economic groups [2, 3].

The need for UHC is well documented. However, the measures to increase mandatory prepaid funds to progress toward UHC are faced with a number of challenges including inefficiencies in revenue collection and use of health resources, inequitable access and utilisation of health services and large populations working in the informal sector [4‐6].

The ILO & WTO [7] indicates that up to 65% of the total population in low- and middle-income countries (LMICs) work in the informal sector. In Kenya, informal sector workers constitute about 80% of the total workforce [8, 9]. The sector is characterized by low and irregular incomes which make prepaying for health care difficult. As a result, existing prepayment systems including funding from government revenue and premium contributions in Kenya and many LMIC tend to exclude informal sector workers because either the funds are inadequate (in the case of funding from general government revenue) or they are too poor to pay for insurance premiums [3]. Moreover SHI schemes historically have focused on the formal sector workers because it is relatively easy by law to enforce mandatory contributions through salary deductions [3, 5].

A number of developing countries are reforming their health systems for UHC. In Kenya, financing reforms for UHC are underway and the process of finalizing a health financing strategy (HFS) has been going on for more than two years. The draft HFS proposes a contributory health insurance model as the main health financing strategy for Kenya where both formal and informal sector workers contribute premiums to a scheme, and the government subsidizes premiums for the poor and other vulnerable groups.

The contributory policy approach to financing UHC in Kenya is a technocrat-led top-down strategy with limited public participation and partly informed by the assumption that there are sufficient financial resources in the informal sector to support the UHC agenda. There is limited evidence to suggest that the views of informal sector workers regarding their preferred prepayment design were put into consideration. Thornton et al. [10] reiterate the scarcity of evidence on the best possible design of a prepayment system that targets the informal sector in terms of ability to achieve high coverage or to increase utilisation of quality health services across different contexts. Extending coverage to the informal sector through an inclusive process increases their participation in decision-making processes and break away from exclusion [11, 12], for example from prepayment systems. This study fills this gap in evidence by exploring informal sector workers’ preference of health financing mechanisms for UHC. It presents the views of informal sector workers on the design of future prepayment mechanisms and how such a design could drive up an all-inclusive population coverage in Kenya. The aim of this paper is to illustrate the preferences of the informal sector in designing a prepayment mechanism with the aim of expanding population and service coverage to achieve UHC.

Data presented in this paper were part of a larger study whose aim was to contribute to national and international policy debates on UHC in contexts with large informal sector populations. The study was conducted in two counties: Nyeri, a rural county that relies predominantly on agriculture; and Mombasa, the second largest city in Kenya. The combination of an urban and a rural county provided an opportunity to compare and contrast the views of agricultural and non-agricultural informal workers. In addition, existing studies on the informal sector in Kenya including [13‐15], have all been conducted in Nairobi, the largest urban setting in Kenya. Nyeri County was purposively selected based on the fact that it has a long history of community based health insurance (CBHI) schemes. There was clear indication that the population was potentially aware of health insurance issues and hence their insights and experiences were important for this study, particularly in relation to exploring alternative prepayment mechanisms.

A stratified five-stage and four-stage sample designs were applied in the rural and urban sites respectively, to arrive at primary sampling units (PSU) (Figs. 1 & 2). Expert opinion was used to select areas where informal sector entities were most active.

The rural site was stratified along existing administrative zones comprising of divisions, locations and villages. The village, as the smallest sampling unit, was selected as the Enumeration Area (EA). Since the divisions were on the whole, homogenously engaged in subsistence agriculture, one division with a large market was purposively selected to obtain a mix of the agricultural and non-agricultural informal sector activities in rural areas. From this division, a list of locations and villages were selected from which economic entities were mapped and randomly selected. Households represented economic units (farms) in the agricultural informal sector. The owners or managers working within the agricultural informal sector were interviewed at home except for the few who were found at the farms and preferred to be interviewed on site.

In the urban site, a list of estates was obtained from the town authorities. The estates including the central business district (CBD) constituted four purposively selected enumeration areas (EAs) for the urban area. The four EAs were selected because they had the largest concentration of informal sector entities and so provided the best opportunity to capture a large diversity in the type and size of informal sector entities. From the four urban EAs, a list of locations was obtained from which economic entities were mapped and randomly selected.

A representative sample size for quantitative component of the study was calculated using the following formula (+30% to account for non-response):

-where N represented the sample size of the study group, the Z _crit value is = 1.96, p (=0.5) is based on previous studies [16] and represents the pre-study estimate of the proportion to be measured (that is, the proportion of informal sector entities that have financial potential to prepay for health care) and D (=0.05) is the total width of the expected confidence interval (CI). The urban sample consisted of 350 economic entities and 150 for the rural area. The allocation was based on a mapping of informal sector entities which showed that for every informal sector entity in the rural site, there were about 2.3 entities in the urban site.

For the qualitative component, a conservative estimate was given as 12 focus group discussions (FGDs) stratified by gender, 17 key informant interviews at community level and five policy level in-depth interviews. Purposive and snow-balling sampling strategies were used to collect the qualitative data from key informants. Data were collected to saturation.

Phase 1 of data collection involved mapping informal sector entities in terms of geographical location and types of entities (N = 2721). Phase 2 included FGDs and in-depth interviews (IDIs). A total of 16 FGDs (Rural = 7, Urban = 9) and 16 community level IDIs (Rural = 14, Urban = 12 and Policy Level = 9) were conducted. Phase 3 was an interviewer administered questionnaire survey which achieved 91% response rate [N = 455 out of 500; Rural = 64 females, 65 males; Urban = 92 females, 234 males)]. The variety of data collection tools mentioned above were used to collect data to meet various objectives of the larger study and what is presented in this article constitutes a small section of the data generated by these tools. So not all the data generated from the large qualitative study were relevant to this study. The broad topics explored that were relevant to this paper included design preferences of future prepayment system including choice of a prepayment system, who should prepay for health care and management of prepaid funds for health. In particular, the study participants were asked what type of prepayment system between contributory and non-contributory mechanisms, they would prefer to be the main financing approach if all Kenyans were to receive quality health care under a universal coverage system. The preference for a dominant financing mechanism was with the knowledge that both contributory and non-contributory mechanisms operate concurrently in many health systems. The study participants were also asked to discuss who should and who should not pay for health care. The design of the benefit package was also discussed; however, the description of the benefit package was broadly presented as it is one that requires more technical considerations beyond the views of informal sector workers. Lastly, the study participants were asked to discuss how the management of a future prepaid system could be made more transparent to avoid corruption. Key issues raised from FGDs including choice of a prepayment system and payment for health care, were further explored with key informants.

Quantitative data were double-entered into predesigned data entry spread-sheets in FoxPro and transferred to Stata Version 11 for analysis. They were categorised and grouped to give a summary of results using descriptive statistics.

All qualitative data were audio recorded and translated into English where necessary. Data were organised and coded using QSR NVivo 7.0. Analysis was performed by developing a matrix of emerging categories and themes. Data from each theme or category was identified and analysed using constant comparison [17]. Concepts and themes identified from various qualitative data sources were compared. The analytical categories were then explained and interpreted in line with the research objectives and the themes developed from the data [18].

To expand population coverage to its members, key design elements were preferred by informal sector workers (See Additional files 1, 2 & 3 for tools used in data collection).

At least on average, the majority of informal sector workers prefer a non-contributory financing design as the dominant financing strategy for UHC. However, an overwhelming majority of rural informal sector workers is in support of the contributory system as the dominant approach. The Government of Kenya has stated its desire to expand coverage through social insurance contributions which seems to be the financing strategy of choice for the rural area but not for the urban area. Despite the differences in the choice of the dominant financing strategy for UHC, both rural and urban study participants expressed high levels of mistrust towards the government. Fears of mismanagement of funds as well as political interference and divisions, were highlighted as potential bottlenecks in designing and developing effective prepayment mechanisms for UHC.

Despite the stated bottlenecks, there were strong justifications for a non-contributory financing mechanism to be the main financing approach for UHC. The reasons put forward included the fact that such a system, compared to social insurance contributions, would be more equitable, affordable, easy to implement and sustainable. Such a system was also viewed as inclusive hence elimination of such issues as defaulting payment and dropping out of coverage, meaning that fund flows are likely to be predictable to support UHC. A number of authors [2, 3, 19, 20] have expressed similar views and are supportive of financing UHC through a non-contributory mechanism in contexts with large informal sector populations.

The inclusivity of a non-contributory financing strategy embodies institutional universalism which makes the redistribution process more effective than under a contributory system where population coverage is often gradual beginning with the formal sector. McIntyre [21] and Sachs [22] state that institutional universalism brings about entitlement to services on the basis of citizenship as opposed to targeting. However, this entitlement requires a high level of collective responsibility to facilitate allocation and redistribution of resources to ensure equitable and efficient delivery of services of the same range and quality [23, 24].

An important observation in support for a non-contributory approach to UHC focused on the political economy of expanding coverage in a setting where social cohesion is not strong and therefore the high degree of collective responsibility required for effective redistribution is compromised. In such a setting, the findings indicated, a non-contributory approach to UHC is less likely to accentuate ethnic/regional division current in Kenya where wealthier regions would find it difficult to participate in a redistributive platform. There is evidence that UHC is a less likely scenario in societies with low social solidarity including divisions along ethnic and religious lines, and high income inequalities [25]. Social solidarity is critical in sustaining redistributive policies such as UHC [25, 26]. Without social solidarity, it is difficult to implement policies that require popular support, particularly those that require certain resources from some groups to support others. The political acceptability and sustainability of a financing system for UHC needs to be seriously considered in the design of future prepayment system. However, there are some level of social and political development in Kenya that could help in establishing UHC as a realistic political goal. The implementation of social welfare programmes such as free primary care and free maternity health are important milestones in the country’s quest for UHC. Stuckler et al. [25] explain that expansion of health care coverage normally occurs as part of a broader process of increasing social welfare programmes such as indicated.

The issue of affordability was a key factor in the design preferences of a future prepaid health system. Affordability is not simply having or not having money to purchase health care but also the type of financing mechanism as well as the timing of contributions if in a contributory system. The non-contributory mechanism was viewed as more affordable especially to the poor and other low-income groups because they are not obligated to pay if they do not fall within the taxable income bracket or do not consume ‘vatable’ products.

For the contributory mechanism, there is substantial evidence suggesting that many developing countries with large informal sector populations that have attempted to expand coverage through contributory mechanisms have experienced difficulties. Tangcharoensathien et al. [27] observe that implementing contributory schemes among large informal sector populations is not feasible because of the difficulties in collecting premiums and high administrative costs. Although Rwanda has often been given as a success story pursuing a contributory system, the system is heavily donor dependent and questions have been raised about its sustainability. For example, enrolment to the mutuelles dropped from about 90% in 2012 to 79% in 2017 [28].

In terms of affordability, the contributory mechanism may not be affordable to many low-income earners because contributions are either flat-rated [29] or the structure/frequency of payments are unfavourable to low-income groups. Kenya’s public insurer, the National Hospital Insurance Fund (NHIF) has instituted reforms allowing informal sector workers to pay their monthly premiums in up to three instalments which could resolve some of the unfavourable terms of contributions. The difficulty of prepaying yearly lump-sum premiums has been recorded in Rwanda where timing of contributions coincided with other household obligations such as school fees which together made yearly contribution expensive, leading to defaults in payments [30]. This means that even among those who indicate that they can afford yearly premiums there are those who will default because of other urgent household obligations. To avoid such a scenario, a non-contributory approach to financing UHC would be the most appropriate for a setting such as Kenya.

In terms of management, the proposal for a semi-autonomous entity and the national government by rural and urban informal sector workers respectively, to manage a prepaid system, was somewhat surprising because public sentiments largely perceive both the government and the NHIF as corrupt and untrustworthy with public funds. In the view of the researcher drawn from contextual experience, the choice of government and an entity such as the NHIF were to some extent driven by compromise. For instance, the rural area had already indicated their preference for a contributory system so an institution moulded along the lines of the NHIF would clearly be their choice because a private entity would likely drive up costs. Likewise, the urban area, as demonstrated earlier, strongly preferred a non-contributory system so their choice of the government as the manager of prepaid funds was not unexpected. There were however, strong suggestions for accountability to ensure that the management of funds for UHC remain transparent including public declaration of financial statements, service costs and service entitlement as well as measures taken to prevent fraud.

Finally, drawing from the negative governance culture as perceived by most informal sector workers, it would be difficult to expand coverage whether under contributory or non-contributory approach. A number of reports [31‐33] suggesting financial impropriety in the country are likely to make people reluctant to buy into government programmes. In Ghana for example, reports of widespread mismanagement of funds meant for health care eroded trust and social solidarity to make people reluctant to prepay for health care [34, 35]. Therefore, the suggestion by informal sector workers that governance systems need to be more efficient in controlling wastage and fraud need to be seriously considered as an important step toward raising revenues for a non-contributory approach to financing UHC in many LMIC. The European Union and the African Union [36] joint report on social protection suggests that controlling corruption among other measures would improve fiscal space to advance coverage for informal sector populations in Sub-Saharan Africa. The WHO [1] has also urged countries to effectively and efficiently use resources at their disposal to facilitate progress towards universal coverage.

There was a clear understanding among study participants of the concept of prepaying for health care and why this is important. Why such clarity of information about prepaying for health care had not translated into actual prepayment participation by informal sector populations was linked to several factors including the choice of a prepayment mechanism which emphasises premium payments and is difficult to enforce in law, as well as a number of social, economic and political factors that require interventions sometimes from outside the health system. The need for an acceptable prepayment mechanism for the informal sector including non-contributory financing, a benefit package that is affordable and aligned to the health needs of the population, and accountability measures that punish corruption would go a long way in expanding population coverage and attainment of UHC in Kenya.

The evidence from this study has been presented to the Government of Kenya through the Ministry of Health (MoH) in various fora. The findings are also summarized into policy briefs to be shared with all health sector stakeholders in Kenya.