Association of social determinants of health with first antiseizure medication prescription for patients with newly diagnosed epilepsy: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To assess whether social determinants of health (SDOHs) are associated with the first antiseizure medication (ASM) prescribed for newly diagnosed epilepsy. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed, and the protocol registered (CRD42023448998). Embase, Medline, and Web of Science were searched up to July 31, 2023. Two reviewers independently screened studies and reached mutual consensus for inclusion. Studies reporting the first ASM prescribed for patients with new epilepsy in all age groups, countries, and languages were eligible for inclusion. Review articles, conference abstracts, and studies with fewer than 15 participants were not eligible for inclusion. Studies were meta-analyzed using fixed-effects models. Quality assessment was performed using the Newcastle-Ottawa Scale. RESULTS: Thirteen studies (total participants = 380,785) contained SDOH data and their association with the first ASM prescription after epilepsy diagnosis. Meta-analysis of studies with compatible data revealed that Black (pooled odds ratio [OR] .94, 95% confidence interval [CI] .90-.98) and Hispanic (pooled OR .89, 95% CI .82-.97) patients with U.S. Medicare/Medicaid had a lower odds of receiving a newer ASM compared to White patients. Three studies revealed that rural epilepsy patients had a lower odds of receiving new ASMs compared to urban patients (pooled OR .84, 95% CI .80-.89). The relationship between income levels and ASM prescription patterns differed across countries, highlighting inconsistencies that warrant further investigation. Among studies identified for inclusion, relatively few had combinable data, thereby limiting the scope of our meta-analysis to two SDOHs. SIGNIFICANCE: Significant disparities exist in first-line ASM prescription for non-White and rural persons with epilepsy. There exist few data on other SDOHs including gender identity and socioeconomic background. Future work leveraging large data sets may reveal additional ASM prescription inequities. Developing care pathways to rectify known prescribing disparities may improve health equity among PWE.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.046 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".