A spatial epidemiologic study of giardiasis cases reported in southern Ontario, 1990-1998
Bibliographic record
Abstract
The objectives of this study were to describe spatial and temporal characteristics of human giardiasis reported in southern Ontario, and to assess the determinants of disease distribution. Giardiasis data were extracted from the Reportable Disease Information Systems database while the Canadian Institute for Health Information database supplied data on hospitalized cases of gastrointestinal illnesses. Drinking water data were obtained from public water works (PWWs). Geographical Information Systems (GIS) and spatial models were used to assess spatial disease patterns and determinants of disease distribution, respectively. Seasonal patterns were observed, with giardiasis rates peaking in the summer and those of non-specific gastrointestinal illness in the winter. Significant decreasing temporal trends in both health outcomes were also observed. There was no evidence that giardiasis cases made up a significant proportion of patients hospitalized for gastrointestinal problems. Standardized and Spatial Empirical Bayesian smoothed rates were appropriate for disease mapping at the county and census sub-division spatial scales, respectively. Significant giardiasis 'hot-spots' were identified in several areas across the province. Most PWWs (64%) used surface water while 34% and 2% used ground water and mixed water sources, respectively. Water treatment regime was a function of water source and not location or operator of the PWW. Twelve percent of the PWWs did not meet the minimum treatment requirements of the Ontario Water Standards guidelines. High giardiasis rates were significantly associated with surface water (rate ratio, RR = 2.36; 95% CI 1.38,4.05) and rural areas (RR = 1.79; 95% CI 1.32,2.37), whereas low rates were associated with water filtration (RR = 0.55; 95% CI 0.42,0.94) and high median income (RR = 0.623, 95% CI 0.42,0.92). Livestock density and manure application seemed to be important only in some areas. Through integration of GIS with health data, this study has shown that there are 'hot-spots' of giardiasis in Southern Ontario, and that their distribution may be influenced by drinking water characteristics and socio-economic factors. This information could aid public health resource allocation and guide individual level studies. Future case-control studies to investigate the risk factors at individual level and assess the burden of illness of giardiasis could be performed in the identified 'hot-spots'.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".