Pattern of Antibiotic Usage in Rural and Sub-Urban Settings in Bangladesh: Experience From a Primary Health Care Facility
Bibliographic record
Abstract
Background: Bangladesh is not exempted from the grave dangers of antibiotic resistance and the other problems arising from its misuse. The prevalent aberrations in the practice of using antibiotics in Bangladesh include self-medication, prescription by quacks and non-physicians, non-registered “village doctors”, irrational prescription, etc. The resultant rises in resistant strains of several bacteria are also seen, and probably much is yet unseen and unnoticed. Methods: This observational descriptive study attempted to describe the actual scenario. A total of 155 patients (60% male and 40% female) were included in this study with consecutive purposive sampling who took antibiotics prior to coming to the primary health care facility. Results: Most common problems were common cold symptoms, only fever with or without body ache but without any other specific features and gastrointestinal upset (constipation/diarrhea/abdominal pain) with/without fever. Most (67.9%) of the patients also had taken some antibiotics in the past year for similar reasons, most common of which is “mixed reasons”, followed by common cold. Most of the time, the antibiotic was prescribed by drug seller at medicine store (89, 57.4%), followed by quack/village doctor (29, 18.7%). In mere 14.2% of cases, the antibiotic was prescribed by registered Bachelor of Medicine and Bachelor of Surgery (MBBS) doctors (22, 14.2%). Most have no clear idea about the definition of a doctor, and most (137, 89%) consider non-medical as doctors and think them to be qualified enough to prescribe antibiotic. About 80% of the respondents had no idea or flawed idea about importance of regularity in taking antibiotics, or completion of the course or the concept and threat of antibiotic resistance. Most commonly taken antibiotic was ciprofloxacin (38, 24.7%), followed by cefixime (30, 19.5%), and azithromycin (26, 16.9%). Patients had already spent from a minimum of 12 Bangladeshi Taka (BDT) to a maximum of 675 BDT, average being 166.61 BDT and a median of 117 BDT, before they came for consultation. So the economic impact is also not negligible. Conclusion: This study shows that gross misuse of antibiotics is being done in Bangladesh. Most of the responsibilities of gross misuse belong to two parties, non-physicians, illegal medical practitioners, quacks, and lack of awareness among the patients. Clin Infect Immun. 2017;2(2-3):19-26 doi: https://doi.org/10.14740/cii33w
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".