Epidemiological Trends of Waterborne Infectious Diseases and the Role of Community Health Nurses, Health Inspectors, and Epidemiology Workers in Prevention
Bibliographic record
Abstract
Waterborne infectious diseases (WBD), contracted via contamination in water supplies, are one of the most significant healthcare problems worldwide. More than 7 million people become ill each year in the United States from WBD, and WBD contributes to 1.5 million deaths per year around the world, which is primarily in low- and middle-income countries. This study explores the changing epidemiology of WBD with consideration of the decline in enteric pathogenic WBD (i.e., Cryptosporidium, Giardia, and Norovirus, etc.) compared to the increase in biofilm WBD, such as Legionella pneumophila and Nontuberculous Mycobacteria (NTM) in developed countries. Furthermore, in low-resource areas, a lack of water, sanitation, and hygiene (WASH) contributes to continuing WBD, such as cholera and typhoid, in the communities utilizing these resources. Data is obtained from the World Health Organization, Centers for Disease Control and Prevention, and the wastewater-based epidemiology (WBE) programs initiated in Vancouver (Canada). The study identified the variation in pathogens, outbreak trends, and risk factors (e.g., climate change, aging infrastructure, etc). Finally, the roles of community health nurses, health inspectors, and epidemiology workers for surveillance, education, and enforcement or regulations, and reporting were examined as major elements of WBD prevention. Evidence-based strategies for prevention of WBD based on the overall enhancement of communities, for example, the improvement of the water, sanitation, and hygiene infrastructure, and community engagement and participation, were considered. The outcome of this study may serve as a guide to inform strategies for public health to lessen the burden caused by WBD illnesses throughout the world.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".