Barriers to Increasing Use of Peritoneal Dialysis in Bangladesh: A Survey of Patients and Providers
Bibliographic record
Abstract
Despite a lower requirement for technology and equipment than hemodialysis (HD), peritoneal dialysis (PD) is an underutilized modality in low- and middle-income countries (LMICs). Bangladesh has the lowest use of PD in the world (fewer than 2% of prevalent patients). We evaluated nephrologists’ attitudes toward PD and examined differences between patients on HD and PD in Dhaka. We asked nephrologists to fill out an English-language questionnaire. Using convenience sampling but targeting both public and private hospitals in Dhaka, we asked trained nurses to administer a Bangla-language questionnaire to patients on HD ( n = 116) and PD ( n = 41). We validated the questionnaires on a sub-sample (n = 10 for each group). Of the 43 nephrologists surveyed, 27 (63%) had patients on PD. When compared with nephrologists without patients on PD, those with patients on PD were less likely to believe that survival and quality of life on PD was worse than on HD (odds ratio [OR] = 0.21, 95% confidence interval [CI] 0.05 - 0.83 and OR = 0.11, 95% CI 0.02 - 0.67 respectively) but were not more likely to have received training for PD. Nephrologists named cost of PD as the predominant barrier to increasing use of PD, followed by concerns about patient hygiene and lack of trained nurses. Fifty-two HD patients (45%) did not know about a home-based modality. When compared with patients on HD, patients on PD were more likely to have been educated by non-nephrologists about dialysis, to be “forewarned” about the need for dialysis, to be paying fully, and to be living in a permanent home with a non-communal water source. Some barriers to increasing access to PD—i.e., patient living conditions and cost—are unique to LMICs. Our study also highlights that issues encountered in high-income countries—i.e., nephrologists’ subjective preference and lack of patient knowledge about an alternate modality to HD—may play a role as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".