Community water service and incidence of respiratory, skin, and gastrointestinal infections in rural Alaska, 2013–2015
Bibliographic record
Abstract
BACKGROUND: Communities in rural Alaska have access to multiple types of water service (piped, vehicle-hauled, and self-hauled) and experience varying levels of water service coverage. We assessed the incidence rate of inpatient and outpatient infectious disease visits among communities with different water service types and coverage levels. METHODS: We classified ICD-9 codes for inpatient and outpatient visits to the Yukon-Kuskokwim Health Corporation facilities between 2013 and 2015 into six infectious disease categories. Using Poisson models, we compared the incidence of visits in each category across communities with differing water service coverage levels as defined by water service billing data for the same years. Using census data, we adjusted for community median household income, median age, crowding, and health aide staffing. RESULTS: We included 48 communities in this analysis. After adjusting for possible confounders, each 10% increase in piped water coverage was associated with a 4% lower incidence of pneumonia/influenza visits (adjusted incidence rate ratio [IRR] 0.96, 95% CI 0.93-0.98), a 2% lower incidence of other respiratory infection visits (adjusted IRR 0.98, 95% CI 0.97-0.99), an 8% lower incidence of methicillin-resistant Staphylococcus visits (adjusted IRR 0.92, 95% CI 0.87-0.97), and a 4% lower incidence of other skin infections visits (adjusted IRR 0.96, 95% CI 0.95-0.98). Each 10% increase in vehicle-hauled water coverage was associated with a 2% lower incidence of respiratory infection visits (adjusted IRR 0.98, 95% CI 0.97-0.996) and a 3% lower incidence of skin infection visits (adjusted IRR 0.97, 95% CI 0.95-0.99), also after adjustment. CONCLUSIONS: Higher levels of water service coverage were associated with lower incidence rates of visits for several infectious disease categories. These associations were more pronounced for communities with piped water service compared to vehicle-hauled water service.
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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 | 0.000 |
| 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".