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Record W4388728458 · doi:10.1371/journal.pone.0289507

Willingness to pay for antiretroviral therapy, viral load, and premium services; A contingent valuation survey of people living with HIV in southern Nigeria

2023· article· en· W4388728458 on OpenAlexaff
Olusola Sanwo, Ihoghosa Iyamu, Augustine Idemudia, Titilope Badru, Sylvia Ekponimo, Dorothy Oqua, Olusesan Ayodeji Makinde, Gambo Aliyu, Abimbola Kola-Jebutu, Jemeh Egwuagu-Pius, Chika Obiora‐Okafo, Moses Bateganya, Iorwakwagh Apera, Satish Raj Pandey, Hadiza Khamofu

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British Columbia
FundersFHI 360United States Agency for International Development
KeywordsWillingness to payContingent valuationMedicinePaymentWillingness to acceptViral loadValuation (finance)DemographyFamily medicineBusinessHuman immunodeficiency virus (HIV)EconomicsFinance

Abstract

fetched live from OpenAlex

BACKGROUND: With stagnating funding for HIV and AIDS control programs in Nigeria, alternative funding models for antiretroviral therapy (ART) including out of pocket payment are being considered to sustain momentum epidemic control targets. We assessed willingness to pay for ART related services, and factors associated with willingness to pay. METHODS: Between July and August 2019, we conducted a survey among people living with HIV (PLHIV) on ART in 3 states in southern Nigeria. Randomly sampled respondents on ART for at least 6 months, aged ≥ 18 years, able to communicate in English or pidgin English, and consenting to the survey were enrolled. Respondents were asked if they were willing to pay for clinical consultation, antiretroviral drugs (ARVs), viral load testing services and premium ART services (including fast track services). Respondents indicating willingness to pay for any of these services were asked the maximum amount they were willing to pay using contingent valuation methodology. We assessed the weighted proportions of PLHIV on ART willing to pay for ART and used survey-featured logistic regression measures to assess sociodemographic and ART related factors associated with willingness to pay for ART services. RESULTS: Overall, 1,598 PLHIV with a mean age of 39.03 years (standard deviation [SD]: 11.23 years), were included in this analysis. Of these, 65.8% (1,079), 73.9% (1,192), 61.0% (995) and 33.6% (472) were willing to pay for ART consultation, ARVs, viral load testing services and premium ART services respectively. The median maximum amount PLHIV were willing to pay for clinical consultation and for ARVs was NGN1,000 (USD equivalent of $2.78; interquartile range [IQR]: 500-2,000) respectively, and NGN2,500 (USD equivalent of $6.94; IQR: NGN1,000-5,000) and NGN2,000 (USD equivalent of $5.56; IQR: NGN1,000-3,000) for viral load testing and premium ART services respectively. Receiving ART in Lagos state, being employed and having a monthly income of NGN100,000 or more was associated with willingness to pay for the various ART services. CONCLUSION: We found generally high-level of willingness to pay for ART consultation, ARVs and viral load testing services but low willingness to pay for premium ART services among PLHIV on ART. The maximum amount PLHIV were willing to pay for various ART services fell short of benchmarks for alternative funding but can potentially supplement ART by funding differentiated service delivery models that require nominal amounts to facilitate person-centered differentiated service delivery models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.305
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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