Assessing catastrophic health expenditures using median out-of-pocket costs for inpatient and outpatient care in Kenya's journey to universal health coverage

Abstract

Background

Kenya's transition to Universal Health Coverage (UHC) faces persistent challenges from high out-of-pocket expenditures (OOP), exposing households to catastrophic health expenditures (CHE). With the recent shift from the National Hospital Insurance Fund (NHIF) to the Social Health Insurance Fund (SHIF), understanding the drivers and inequalities of CHE is critical. This study evaluates CHE using a novel median-based approach in the absence of income/consumption data, providing insights for Kenya’s UHC reforms.

Methods

We analyzed data from the 2022 Kenya Demographic and Health Survey (KDHS), employing a median-based method to calculate CHE at thresholds of 10 %, 20 %, 30 %, and 40 % of household OOP spending. Survey-weighted generalized linear models (gamma distribution, log-link) assessed determinants of inpatient/outpatient costs, while logistic regression and marginal effects identified CHE predictors. Erreygers Normalized Concentration Indices (ENCI) quantified socioeconomic inequalities.

Results

CHE prevalence was high, affecting 88.9 % of households at the 10 % threshold and 70.9 % at 40 %. Key drivers included: Socioeconomic status: Wealthier households incurred higher inpatient costs (richest quintile: KES 40,201 vs. poorest: KES 11,697; p < 0.01), reflecting utilization disparities. Education: Households with secondary-educated heads had 33 % lower outpatient costs (p = 0.04). Vulnerable groups: Widowed/separated households faced 56 % higher outpatient costs (p < 0.01), while high-dependency households had 44 % higher costs (p = 0.02). Insurance: Reduced CHE odds by 76 % at the 10 % threshold (OR=0.24, p < 0.01), though gaps persisted. ENCI revealed pro-poor CHE distribution (−0.13 at 10 % threshold, p < 0.01), with poorer households disproportionately affected.

Conclusions

The median-based approach offers a pragmatic solution for CHE estimation where income data are lacking, though results require cautious interpretation. Findings underscore the need for SHIF reforms to: (1) expand benefits to cover outpatient services and diagnostics, (2) target subsidies for low-income and informal workers, and (3) address non-medical costs (transportation vouchers). Strengthening equity-focused financial protection is essential to mitigate disparities and achieve UHC.