Estimating additional health and social costs in eating disorder care for young people during the COVID-19 pandemic: implications for surveillance and system transformation
Bibliographic record
Abstract
BACKGROUND: The impact of the COVID-19 pandemic on young people with eating disorders (EDs) and their families was profound, with surging rates of hospitalizations and referrals reported internationally. This paper provides an account of the additional health and social costs of ED care for young people living in Canada incurred during the COVID-19 pandemic, drawing attention to the available data to inform these estimates while noting gaps in data capacities to account for a full view of the ED system of care. METHODS: Three methodologies were used to capture costs: (1) provincial administrative data holdings available at the Canadian Institute of Health Information (CIHI) were used by Deloitte Access Economics to conduct analyses on costs related to hospitalizations, emergency room visits, outpatient visits with physicians and loss of well-being from being on a waitlist. These were examined across three fiscal years (April 1 to March 31, 2019-2022) to compare costs from one year before to two years after the onset of the pandemic, (2) data collected on support-based community ED organizations and, (3) costs identified by young people, caregivers and health care professionals. RESULTS: Estimates of additional health care costs and social costs arising from ED care waitlists were estimated to have increased by 21% across the two years after the onset of the pandemic and is likely to represent an underestimate of costs. Costs related to some standard ED care services (e.g. day treatment programs) and support-based community ED organizations that saw a 118% increase in services during this time, are some examples of costs not captured in the current cost estimate. CONCLUSIONS: This paper provides a first account of the additional health and social ED care costs associated with the pandemic, which indicate at minimum, a 21% increase. The results invite discussion for more investments in ED services for young people in Canada, as it is unclear if needs are expected to remain elevated. We suggest a call for a national surveillance strategy to improve data holdings to aid in managing services and informing policy. A robust strategy could open the door for much-needed, data-informed, system transformation efforts that can improve ED care for youth, families and clinicians.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".