What influences slum residents’ choices of healthcare providers for common illnesses? Findings of a Discrete Choice Experiment in Ibadan, Nigeria

Bibliographic Data

ID19592924
AuthorsOa Fayehun (0000-0002-3769-2130, University of Ibadan, corresponding author), Jason Madan (0000-0003-4316-1480, University of Warwick), Abiola Oladejo (0000-0003-0104-7487, University of Ibadan), Omobowale Oni, Omobowale A Oni (University of Ibadan), Eme Owoaje (0000-0002-0491-6732, University of Ibadan), Motunrayo Ajisola (0000-0002-1704-0944, University of Ibadan), Richard Lilford (0000-0002-0634-984X, University of Birmingham), Akinyinka Omigbodun (0000-0002-6377-9299, University of Ibadan), Improving Health in Slums Collaborative
EditorsHannah Hogan Leslie
Year2023
Volume3
Issue3
Pagese0001664
Publication date2023-03-13
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0001664
PMID36963060
OpenAlexW4324032633
LanguageEN
References cited31

Urban slum residents have access to a broad range of facilities of varying quality. The choices they make can significantly influence their health outcomes. Discrete Choice Experiments (DCEs) are a widely-used health economic methodology for understanding how individuals make trade-offs between attributes of goods or services when choosing between them. We carried out a DCE to understand these trade-offs for residents of an urban slum in Ibadan, Nigeria. We conducted 48 in-depth interviews with slum residents to identify key attributes influencing their decision to access health care. We also developed three symptom scenarios worded to be consistent with, but not pathegonian of, malaria, cholera, and depression. This led to the design of a DCE involving eight attributes with 2–4 levels for each. A D-efficient design was created, and data was collected from 557 residents between May 2021 and July 2021. Conditional-logit models were fitted to these data initially. Mixed logit and latent class models were also fitted to explore preference heterogeneity. Conditional logit results suggested a substantial Willingness-to-pay (WTP) for attributes associated with quality. WTP estimates across scenarios 1/2/3 were N5282 / N6080 / N3715 for the government over private ownership, N2599 / N5827 / N2020 for seeing a doctor rather than an informal provider and N2196 / N5421 /N4987 for full drug availability over none. Mixed logit and latent class models indicated considerable preference heterogeneity, with the latter suggesting a substantial minority valuing private over government facilities. Higher income and educational attainment were predictive of membership of this minority. Our study suggests that slum residents value and are willing to pay for high-quality care regarding staff qualifications and drug availability. It further suggests substantial variation in the perception of private providers. Therefore, improved access to government facilities and initiatives to improve the quality of private providers are complementary strategies for improving overall care received

Actuarial science · Business · Discrete choice · Econometrics · Economic growth · Economics · Environmental health · Health care · Latent class model · Logistic regression · Mixed logit · Preference · Public economics · Slum · Statistics · Willingness to pay · Child Nutrition and Water Access · Economic and Environmental Valuation · Global Maternal and Child Health · Medicine