The Economic Burden of Chronic Psychotic Disorders: An Incidence-based Cost-of-Illness Approach.
Bibliographic record
Abstract
BACKGROUND: The economic burden of chronic psychotic disorders is substantial. However, few studies have employed an incidence based approach to estimate the economic burden of chronic psychotic disorders. Furthermore, the existing work has mainly used models populated with data obtained from published literature, making several assumptions to estimate incidence-based costs. AIMS OF THE STUDY: The objective of this study was to estimate the direct cumulative mean health care costs of chronic psychotic disorders, using an incidence-based, cost-of-illness approach and real-world data from a single-payer health care system. METHODS: Using health records from Ontario, Canada, all individuals with a valid health card number, residing in the province, and diagnosed with a chronic psychotic disorder between the ages of 16 and 45 from April 1st, 2006, to March 31st, 2021, were included in the analysis. Using a mix of bottom-up and top-down methodologies and a robust cost estimator, cumulative mean health care costs were estimated from diagnosis to death or the end of observation period. Cumulative mean health care costs, and respective 95% confidence intervals (CIs), were estimated for the 1-year period (i.e., first year post-diagnosis), overall, by sex, age groups and health service, and for the 5-, 10- and 15-periods, overall and by sex. RESULTS: One-, 5-, 10- and 15-year total discounted cumulative mean health care costs were estimated at USD 24,441.16, 95% CI (USD 24,166.13, USD 24,716.19), USD 70,754.69, 95% CI (USD 69,827.48-USD 71,681.89), USD 117,136.88, 95% CI (USD 115,370.40-USD 118,903.35), and USD 157,829.01 95% CI (USD 155,599.32.-USD 160,058.70), respectively. Total mean 1-year costs post-diagnosis were higher for younger individuals. Although females had higher 1-year costs, males had higher 5-, 10- and 15-year costs. Psychiatric hospitalisations made up the largest component of total costs across all cost estimates. DISCUSSION: These results suggest that the costs of chronic psychotic disorders are high in the year of diagnosis and then increase at a decreasing rate thereafter. Compared to previous work, the cost estimates from the present study suggest that the use of real-world data produces lower estimates of cumulative costs, albeit likely more accurate ones. However, these estimates do not account for costs of care provided in community-based agencies. IMPLICATIONS FOR HEALTH POLICIES: These estimates will serve as important inputs for policymakers looking to make decisions around resource allocation. IMPLICATIONS FOR FUTURE RESEARCH: Future research should seek to follow incident cases in administrative data over a longer time period to obtain cumulative costs of longer duration.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".