Evidence map›Paper›PMID 41053776›Full record

ArticleBMC health services research2025

Analysing the determinants of healthcare insurance uptake in Nigeria.

Wasiu Adekunle, Oluwaseyi Vincent

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wasiu AdekunleResearch and Development Department, The Nigerian Economic Summit Group (NESG), Lagos, Nigeria. wasiu.adekunle@nesgroup.org.ORCID http://orcid.org/0000-0003-4968-3642
Oluwaseyi VincentResearch and Development Department, The Nigerian Economic Summit Group (NESG), Lagos, Nigeria.

Funding

Consortium pour la recherche économique en Afrique RC21624
6 · The paper itself

Abstract

Low health insurance coverage has remained a persistent challenge in Nigeria, restricting access to healthcare despite the coexistence of various insurance schemes. While previous research has primarily focused on the National Health Insurance Scheme (NHIS) or community-based health insurance (CBHI), few studies have collectively examined the factors influencing enrolment across different types of insurance-namely, community-based, employer-sponsored, and private schemes. This study addresses this research gap using household data from the 2018 Nigeria Demographic and Health Survey (DHS). Employing logit and probit regression models, the analysis explores the likelihood of health insurance enrolment. The results reveal that six socioeconomic factors-education attainment, age of the household head, bank account ownership, wealth status, marital status, and employment type-significantly influence enrolment in both overall insurance (all types of insurance) and employer-sponsored insurance. However, these factors vary in importance for community-based and private insurance. For instance, individuals with low education have a higher likelihood of enrolment in CBHI than those with high education. The study also finds that financial constraints may reduce the possibility of enrolment among low-income groups compared to their middle- and high-income counterparts. The findings underscore the need for targeted interventions to enhance enrolment, particularly among individuals with lower educational levels, limited income, and those employed in the informal sector.

Indexed as

Community-Based Health InsuranceInsurance CoverageInsurance, HealthAdolescentAdultEducational StatusFemaleHumansMaleMiddle AgedNigeriaSocioeconomic FactorsYoung AdultHealthcareInsurance uptakeNHIANHISSocioeconomic factors

Identifiers

PMID41053776
PMCPMC12502249

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.