Health Concerns on Microbiological Quality of Bottled Drinking Water Sold in Dar es Salaam, Tanzania
Bibliographic record
Abstract
Background:Dar es Salaam City has over 10 million habitants, who are constantly circumvented by water-borne diseases. The city experiences humid and hot weather throughout a year, which lead to high consumption of bottled drinking water because of being perceived as safer than tap or well water. Waterborne disease outbreaks still create havoc among the city habitants. This study determines heterotrophic plate count (HPC) values in bottled water and performs antimicrobial resistance tests on isolated microorganisms. Methods: A cross-sectional study was conducted. Fifty-four samples of bottled water were randomly purchased from shops, supermarkets and street vendors. HPC values were determined and microbial contaminants identified. The disk diffusion method was employed for testing antimicrobial resistance of microbial contaminants against four widely used antibiotics. Results: HPC values were variable (4.7 x102 to 7.0 x105 cfu/ml). Majority (86.7%) of analyzed samples revealed high HPC values. Four bacterial species were isolated and identified from 47 samples. Predominant bacterial contaminants were Brevundimonas vesicularis (54.5%), while Pseudomonas thomasii was only isolated from one brand A sample. Brand E samples had the highest HPC values (2.2 x 104 to 2.16 x105 cfu/ml) while F samples were free from microbial contaminants. A total of 21 (46.7%) bacterial isolates were resistant to commonly used antibiotics namely ciprofloxacin, ampicillin, cotrimoxazole and chloramphenicol. Conclusion: Of 54 analyzed bottled water samples, 47 had high HPC values. High rates of antibacterial resistance was exerted against ciprofloxacin (68.2%) and ampicillin (56%). This calls for responsible authorities to impose more stringent measures on in-process quality control among bottled water producers and indefatigably conduct post- production surveillance to avert the endemic water-borne outbreaks resulting from consumption of such products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".