Cost estimate of chronic hemodialysis in Kinshasa, the Democratic Republic of the Congo: A prospective study in two centers
Bibliographic record
Abstract
BACKGROUND: The number of patients on dialysis has significantly increased worldwide. However, prospective studies estimating the cost of hemodialysis (HD) in sub-Saharan Africa remain scarce. The present study aimed to evaluate the direct cost of treating end stage renal disease. Determinants of additional direct cost were also assessed. METHODS: This study is an analytical, prospective study of cost performed at two HD centers in Kinshasa for a period of 3 months among HD patients enrolled consecutively. The cost analyzed includes only expenditures: consultation, HD session, drugs, comorbidities, laboratory tests, and imaging. Transportation, patient hospitalization, and indirect costs are not taken into account. The determinants of the additional direct cost of HD are identified by multivariate logistic regression analysis. P < 0.05 is the level of statistical significance. FINDINGS: The average quarterly direct cost of chronic HD in United States Dollars (US$) is $7070 (~US$28,280 annual cost) at a rate of US$287 per patient per HD session. This cost includes the HD session (US$237) and medicine (US$33) costs, which account for 82.5% and 11.3% of the direct costs, respectively. The presence of at least 4 comorbidities (OR adjusted 4.3, 95% CI [1.23-14.95], P = 0.022) and infection (adjusted OR 4.56, 95% CI [1.05-19.85], P = 0.043) emerged as independent determinants of additional direct cost. CONCLUSION: The direct cost of HD is very high in Kinshasa, where more than 80% of Congolese people live on less than US$1.25 a day.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".