MétaCan
Menu
Back to cohort
Record W4389487955 · doi:10.1186/s12962-023-00502-3

Cost analysis of the management of end-stage renal disease patients in Abuja, Nigeria

2023· article· en· W4389487955 on OpenAlexaff
Yakubu Adole Agada-Amade, Daniel Chukwuemeka Ogbuabor, Ejemai Eboreime, Obinna Onwujekwe

Bibliographic record

VenueCost Effectiveness and Resource Allocation · 2023
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineEnd stage renal diseasePublic healthHealth administrationEmergency medicineHealth careHealth services researchCross-sectional studyDescriptive statisticsHealth economicsHemodialysisIndirect costsEnvironmental healthInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Although the treatment for end-stage renal disease (ESRD) under Nigeria's National Health Insurance Authority is haemodialysis (HD), the cost of managing ESRD is understudied in Nigeria. Therefore, this study estimated the provider and patient direct costs of haemodialysis and managing ESRD in Abuja, Nigeria. METHOD: The study was a cross-sectional survey from both healthcare provider and consumer perspectives. We collected data from public and private tertiary hospitals (n = 6) and ESRD patients (n = 230) receiving haemodialysis in the selected hospitals. We estimated the direct providers' costs using fixed and variable costs. Patients' direct costs included drugs, laboratory services, transportation, feeding, and comorbidities. Additionally, data on the sociodemographic and clinical characteristics of patients were collected. The costs were summarized in descriptive statistics using means and percentages. A generalized linear model (gamma with log link) was used to predict the patient characteristics associated with patients' cost of haemodialysis. RESULTS: The mean direct cost of haemodialysis was $152.20 per session (providers: $123.69; and patients: $28.51) and $23,742.96 annually (providers: $19,295.64; and patients: $4,447.32). Additionally, patients spent an average of $2,968.23 managing comorbidities. The drivers of providers' haemodialysis costs were personnel and supplies. Residing in other towns (HD:β = 0.55, ρ = 0.001; ESRD:β = 0.59, ρ = 0.004), lacking health insurance (HD:β = 0.24, ρ = 0.038), attending private health facility (HD:β = 0.46, ρ < 0.001; ESRD: β = 0.75, ρ < 0.001), and greater than six haemodialysis sessions per month (HD:β = 0.79, ρ < 0.001; ESRD: β = 0.99, ρ < 0.001) significantly increased the patient's out-of-pocket spending on haemodialysis and ESRD. CONCLUSION: The costs of haemodialysis and managing ESRD patients are high. Providing public subsidies for dialysis and expanding social health insurance coverage for ESRD patients might reduce the costs.

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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCost Effectiveness and Resource AllocationSame topicDialysis and Renal Disease ManagementFrench-language works237,207