Trends in hospital coding for people experiencing homelessness in Canada, 2015–2020: a descriptive study
Bibliographic record
Abstract
BACKGROUND: (ICD-10-CA code Z59.0). We sought to answer whether the coding mandate affected the volume of patients identified as experiencing homelessness in acute inpatient hospitalizations and if there was any geographic variation. METHODS: We conducted a serial cross-sectional study describing 6 fiscal years (2015/16 to 2020/21) of hospital administrative data from the Hospital Morbidity Database. We reported frequencies and percentages of hospitalizations with a Z59.0 diagnostic code and disaggregated by several types of Canadian geographies. Controlling for fiscal quarter (coded Q1 to Q4) and province or territory, adjusted logistic regression models quantified the odds of Z59.0 being coded during hospital stays. RESULTS: The frequency and percentage of people experiencing homelessness in hospitalization records across Canada increased from 6934 (0.12%) in 2015/16 to 21 529 (0.41%) in 2020/21. Trends varied by province and territory. Recording of the Z59.0 code increased following the mandate (adjusted odds ratio 2.29, 95% confidence interval 2.25-2.32), relative to the pre-mandate period. INTERPRETATION: The 2018 coding mandate coincided with an increase in the use of the Z59.0 code to document homelessness in health care administrative data; however, trends varied by jurisdiction. The ICD-10-CA code Z59.0 presents a promising opportunity for standardized and routinely collected data to identify people experiencing homelessness in hospital administrative data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".