Disparities in Access to Early Psychosis Intervention Services: Comparison of Service Users and Nonusers in Health Administrative Data
Bibliographic record
Abstract
OBJECTIVE: There is a dearth of information on people with first-episode psychosis who do not access specialized early psychosis intervention (EPI) services. We sought to estimate the proportion of incident cases of nonaffective psychosis that do not access these services and to examine factors associated with EPI admission. METHODS: Using health administrative data, we constructed a retrospective cohort of incident cases of nonaffective psychosis in the catchment area of the Prevention and Early Intervention Program for Psychoses (PEPP) in London, Ontario, between 1997 and 2013. This cohort was linked to primary data from PEPP to identify EPI users. We used multivariate logistic regression to model sociodemographic and service factors associated with EPI admission. RESULTS: Over 50% of suspected cases of nonaffective psychosis did not have contact with EPI services for screening or admission. EPI users were significantly younger, more likely to be male (odds ratio [OR] 1.58; 95% confidence interval [CI] 1.24 to 2.01), and less likely to live in areas of socioeconomic deprivation (OR 0.51; 95% CI 0.36 to 0.73). EPI users also had higher odds of psychiatrist involvement at the index diagnosis (OR 7.35; 95% CI 5.43 to 10.00), had lower odds of receiving the index diagnosis in an outpatient setting (OR 0.50; 95% CI 0.38 to 0.65), and had lower odds of prior alcohol-related (OR 0.42; 95% CI 0.28 to 0.63) and substance-related (OR 0.68; 95% CI 0.50 to 0.93) disorders. CONCLUSIONS: We need a greater consideration of patients with first-episode psychosis who are not accessing EPI services. Our findings suggest that this group is sizable, and there may be sociodemographic and clinical disparities in access. Nonpsychiatric health professionals could be targeted with interventions aimed at increasing detection and referral rates.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".