Is proxy means testing ideal for Kenya’s healthcare landscape?Is proxy means testing ideal for Kenya’s healthcare landscape?

The Kenyan Government has set an ambitious goal to achieve Universal Health Coverage (UHC) within its first two years, aiming to realise the constitutional right to health under Article 43. To support this, the government passed the Social Health Insurance Fund (SHIF) Act of 2023, replacing the National Hospital Insurance Fund (NHIF) with the Social Health Authority (SHA). SHA manages three funds: the Primary Healthcare (PHC) Fund, the Social Health Insurance Fund (SHIF), and the Emergency Critical and Chronic Illnesses (ECCI) Fund.

The SHIF is designed as a mandatory contributory fund, whose main barrier for success is how to effectively target contributions from Kenya's informal sector. To ensure that the fund is affordable for all, particularly the poor, the government plans to deploy Proxy Means Testing (PMT). PMT is a method used to estimate household income or consumption based on observable characteristics such as household composition, housing quality, asset ownership, education, employment, health indicators, and location.

However, PMT faces several challenges that could hinder its effectiveness. One of the primary concerns is the potential for exclusion errors, where genuinely poor households might be wrongly classified as non-poor and thus denied benefits. This risk is heightened by several inherent weaknesses in PMT, such as imperfect information, measurement errors, inappropriate indicators, the dynamic nature of poverty, and the inflexibility of the testing tool.

PMT relies on observable household characteristics to estimate economic status, but these indicators may not always capture the true financial situation due to temporary income shocks or unreported informal earnings. Additionally, measurement errors in data collection, such as misreporting or survey inconsistencies, can lead to inaccurate estimates of household income or consumption. Inappropriate or insufficient indicators might also fail to reflect poverty accurately, especially in diverse contexts across Kenya.

Cabinet Secretary for Health in Kenya Susan Nakhumicha launches the National Health Facility Assessment program in July 2023.

Moreover, poverty is not static; it fluctuates due to economic cycles, health crises, or employment changes. PMT, however, is based on static data and may not account for these shifts, leading to outdated or inaccurate targeting. The model's rigidity further complicates its ability to adapt to changes in economic conditions or poverty demographics.

The implementation of PMT in Kenya also faces practical challenges, including the high costs and complexity of data collection, processing, and management. The Kenya National Bureau of Statistics (KNBS), which is responsible for developing the PMT tool, is underfunded and short-staffed. Conducting regular household surveys to maintain the accuracy of PMT would place additional strain on KNBS's resources. The need for continuous updates to the database to reflect household changes also adds to the costs.

Furthermore, developing and calibrating the PMT model requires expertise in econometrics, data analysis, and database management. The Ministry of Health (MOH) has received support from donor agencies like Palladium International under the USAID-funded PROPEL Health project to aid in this process. However, the timeline for developing a statistically significant PMT model by July 1, 2024, is tight, raising concerns about the feasibility of onboarding informal sector contributors in time.

Social acceptance of PMT is another significant challenge. The complexity of the PMT rules may make it difficult for the public to understand how their premiums are determined, leading to suspicion and mistrust. The initial PMT model is based on data from the Kenya Continuous Household Survey 2021 (KCHS 2021), which estimates household premiums based on whether they are urban, rural, or in a specific county. However, premiums derived from mathematical estimates rather than actual surveys of household circumstances are likely to face opposition, necessitating a robust verification and appeals process.

In conclusion, while PMT is a method designed to identify poor households and effectively target social assistance, it is complex, expensive, and prone to errors. For a lower-middle-income country like Kenya, the costs of implementing PMT might outweigh the benefits, particularly when simpler and more transparent methods could achieve the same objectives. The government should explore alternative approaches to ensure that those in need receive the necessary support without placing an undue burden on Kenya's already strained administrative and financial resources.

Dr Makamu Lishenga is the Chairman, Rural & Urban Private Hospitals Association of Kenya (RUPHA) and was part of panel discussions held at the Medic East Africa 2024, the region's leading congress featuring state-of-the-art innovation and expert-led sessions.

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