Pandemic effects on health condition specific healthcare encounters in British Columbia, Canada.
Bibliographic record
Abstract
ObjectiveWhile overall health service use declined following the start of the pandemic, the aim of this analysis is to generate insights to inform public health priorities by identifying higher-than-expected patterns of health care service use for some health condition and population groups. ApproachHealth care encounters for hospital, emergency department, and primary care encounters between 2011 and 2021 were categorized into condition groups according to the CIHI Population Grouping Methodology (British Columbia version). Actual health condition encounters were compared with ARIMA-based encounter forecasts to identify conditions with different-from-expected encounter rates in 2020 and 2021. For each of 225 CIHI-defined health conditions, we identified health conditions for which service use was higher-than-expected. Area-based socioeconomic status and virtual care visit data are examined to further explore conditions that continue to differ from their pre-pandemic encounter patterns. ResultsThis analysis demonstrates that some health condition groups have seen dramatic increases in service use. The three most impacted groups with higher-than-expected encounters are hypercholesterolaemia/high cholesterol [47.8% increase in average monthly encounters since 2019], emotional and behavioural disorder (w/onset generally in childhood) [+37.3%] and neurotic/anxiety/obsessive compulsive disorder [+28.0%]. Since the start of the pandemic in British Columbia, the health condition groups with both the highest volumes of services and higher than expected service use included: hypercholesterolemia & hypothyroidism, mental health conditions (eating disorder, depression, and others), hypertension and heart failure, and diabetes. Additional descriptive analysis explores potential inequities in encounters by socio-economic status and how virtual care has changed service patterns. ConclusionIncreased service use may reflect greater need, better access to virtual care or potential changes in diagnoses. Identifying patterns of higher-than-expected use can support program planning to address growing need in certain regions or populations. Additional exploration will be undertaken to examine lower-than-expected service use as potential unmet need.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".