Accuracy of health administrative data to identify cases of reportable travel or migration-related infectious diseases in Ontario, Canada
Bibliographic record
Abstract
An ongoing challenge of estimating the burden of infectious diseases known to disproportionately affect migrants (e.g. malaria, enteric fever) is that many health information systems, including reportable disease surveillance systems, do not systematically collect data on migrant status and related factors. We explored whether health administrative data linked to immigration records offered a viable alternative for accurately identifying cases of hepatitis A, malaria and enteric fever in Ontario, Canada. Using linked health care databases generated by Ontario's universal health care program, we constructed a cohort of medically-attended individuals with presumed hepatitis A, malaria or enteric fever in Peel region using diagnostic codes. Immigrant status was ascertained using linked immigration data. The sensitivity and positive predictive value (PPV) of diagnostic codes was evaluated through probabilistic linkage of the cohort to Ontario's reportable disease surveillance system (iPHIS) as the reference standard. Linkage was successful in 90.0% (289/321) of iPHIS cases. While sensitivity was high for hepatitis A and enteric fever (85.8% and 83.7%) and moderate for malaria (69.0%), PPV was poor for all diseases (0.3-41.3%). The accuracy of diagnostic codes did not vary by immigrant status. A dated coding system for outpatient physician claims and exclusion of new immigrants not yet eligible for health care were key challenges to using health administrative data to identify cases. Despite this, we show that linkages of health administrative and immigration records with reportable disease surveillance data are feasible and have the potential to bridge important gaps in estimating burden using either data source independently. .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".