Prevalence, incidence and economic burden of schizophrenia among Medicaid beneficiaries
Bibliographic record
Abstract
Objective To estimate the prevalence, incidence and economic burden of schizophrenia among Medicaid beneficiaries.Methods Annual prevalence and incidence of schizophrenia among adult Medicaid beneficiaries were estimated during 2012–2017, by state and across six states (IA, KS, MS, MO, NJ and WI). The pooled estimate of the economic burden of schizophrenia was obtained during 1998Q1–2018Q1 across six states; adults with ≥2 diagnoses of schizophrenia were matched 1:1 to schizophrenia-free controls. The last observed schizophrenia diagnosis (schizophrenia cohort) or the last service claim (control cohort) with ≥12 months of continuous Medicaid enrollment before/after it defined the index date. Healthcare resource utilization (HRU) and costs ($2018 USD) incurred 12 months post-index were compared between cohorts. The economic burden of schizophrenia was also evaluated among young adults (18–34 years).Results Annual prevalence of schizophrenia ranged between 2.30% and 2.71% and annual incidence between 0.31% and 0.39% during 2012–2016. In 2017, only states with the highest incidence and prevalence rates (KS, MS, MO) had data, resulting in higher prevalence (4.01%) and incidence (0.52%). For the economic burden, adults with schizophrenia (N = 158,763) had higher HRU and incurred $14,087 higher healthcare costs versus controls (mean: $28,644 vs. $14,557), driven by $4677 higher long-term care costs (all p < .001). Young adults with schizophrenia incurred $14,945 higher healthcare costs versus controls, driven by $3473 higher inpatient costs (p < 0.001).Conclusions Annual prevalence and incidence of schizophrenia varied by state but remained stable over time. Adults with schizophrenia incurred greater HRU and costs relative to adults without schizophrenia; the burden appeared comparable among young adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".