What question did this study set out to answer?

To analyze the socio-economic and demographic factors that determine household healthcare expenditures in Trans-Nzoia County, Kenya.

March 26, 2026Open Access

Impact of Demographic and Socio-Economic Factors on Households Out-of-Pocket Healthcare Expenditures: A Case of Trans-Nzoia County, Kenya

Key Points

To analyze the socio-economic and demographic factors that determine household healthcare expenditures in Trans-Nzoia County, Kenya.
Non-experimental research design
Stratified random sampling of 412 households
Engel Curve framework for analysis
Binary logistic regression for catastrophic expenditures
Multinomial logistic regression for coping mechanisms
25% of households spend over 40% of their income on non-food expenses
Larger families and households with chronically ill members more likely to experience high OOP expenditures
Moderate-income households resort to alternative medicine as a coping mechanism
Insured households less likely to borrow or seek aid
Unemployed households often liquidate assets to finance healthcare costs

Abstract

Purpose: Out-of-pocket (OOP) health expenses remain a significant obstacle to equitable healthcare access in Kenya, limiting progress toward Universal Health Coverage. In Trans-Nzoia County, Kenya, low insurance coverage, high poverty rates, and large household sizes exacerbate the risk of catastrophic health spending, forcing families into impoverishment. This study aimed to analyze the socio-economic and demographic factors determining household healthcare expenditures in Trans-Nzoia County, Kenya, and establish the various coping mechanisms households adopt to finance healthcare. Design/methodology/approach: The study employed a non-experimental research design and utilized a stratified random sampling technique to collect data. Paper questionnaires were administered to 412 households across the five sub-counties of Trans Nzoia County. The methodology was based on the Engel Curve framework, with binary logistic regression used to identify determinants of catastrophic health expenditures, and multinomial logistic regression employed to analyze coping mechanisms. Findings: Results showed that 25% of the households spend more than 40% of their income on non-food expenditure. Households with chronically ill members, larger family sizes, and elderly household heads had a significantly higher likelihood of experiencing catastrophic OOP expenditure. Regarding coping mechanisms, households with moderate income (Kshs. 20,000-60,000) were more likely to use alternative medicine, insured households were less likely to seek aid or borrow, whereas unemployed households and those with larger household sizes opted for asset liquidation. Research limitations: The main limitation is that the study captured data at a single point in time restricting the ability to infer causality between socio-economic characteristics and healthcare expenditure patterns. Additionally, the study relied heavily on self-reported data, particularly concerning income, health expenditures, and coping strategies, which may affect the accuracy of the responses. Practical implications: The findings will help stakeholders identify strategic policy targets to maximize the return on investments in healthcare. The national and county government of Kenya will also rely on this study to identify and address regional disparities in OOP payments, insurance coverage, and coping mechanisms, guiding targeted interventions and reforms to improve healthcare equity. Originality/value: This study is original in its focus on the proportion of household income spent on healthcare and the coping mechanisms employed by households in Trans-Nzoia County. It addresses a gap in previous research, which primarily examined aggregate household spending and the prevalence of OOP payments, while also considering the demographic and socioeconomic factors influencing these expenditures.

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