Healthcare costs associated with receipt of effective mental healthcare coverage in individuals with moderate or severe symptoms of anxiety and depression
Bibliographic record
Abstract
BACKGROUND: Effective mental healthcare coverage (EMHC) is an important health system performance indicator of a population's mental healthcare needs. This study aims to assess the factors and healthcare costs associated with the receipt of EMHC for anxiety and depression. METHODS: This study draws on data from participants from Alberta's Tomorrow Project with moderate or severe symptoms of anxiety and depression during the first wave of the COVID-19 pandemic (2020) with available medico-administrative and complete data [n = 720]. EMHC was assessed during the eighteen months as of March 1, 2020, and defined as adequate pharmacotherapy (i.e., antidepressant dispensed, with ≥ 80% proportion of days covered and 4 follow-up medical visits) and/or adequate psychotherapy (≥ 8 physician consultations for psychotherapy) depending on the severity of symptoms. Logistic regression analysis was used to study EMHC as a function of study variables. Regressions with augmented inverse probability weighting were used to estimate the total healthcare costs attributable to receipt of EMHC during the first 18-month period of the pandemic, controlling for confounders. Mean adjusted differences with 95% bias-corrected bootstrap confidence intervals (CIs) are presented. RESULTS: The proportion receiving EMHC was 26.7%. Individuals with worse self-rated mental health after the pandemic than before were less likely to receive EMHC. Those with a lifetime diagnosis of depression and anxiety were more likely to receive EMHC. The overall mean adjusted total healthcare costs attributable to receipt of EMHC during the pandemic was $2601 [ - $247, $5694]. The mean adjusted outpatient costs attributable to EMHC was significantly higher and reached $1613 [$873, $2577]. CONCLUSION: The study's findings highlight the existence of health inequalities and potential unmet mental health needs in individuals with worsening mental health during the pandemic. The receipt of EMHC during the pandemic was not significantly associated with increased total healthcare costs. These findings underscore the need for mental health policies that are aimed at improving timely access to EMHC to address population unmet mental health service needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".