Bridging diagnostic safety and mental health: a systematic review highlighting inequities in autism spectrum disorder diagnosis
Bibliographic record
Abstract
INTRODUCTION: There is increased recognition that diagnostic errors disproportionately affect marginalised and underserved patient populations in the USA. However, evidence on diagnostic inequities in mental disorders is sparse and not well integrated into the overall diagnostic safety literature. OBJECTIVE: We systematically reviewed and narratively synthesised evidence on inequities in diagnosis of mental disorders, guided by the Diagnostic Process Framework developed by The National Academies of Sciences, Engineering, and Medicine. METHODS: We conducted a systematic review and a narrative synthesis. Medline, Embase, PsycInfo and CINAHL were searched for studies published between 2015 and 2024. Studies were eligible if they reported on inequities in the diagnosis of mental disorders and applied a quantitative, qualitative or mixed-methods design. Studies had to be peer reviewed, US based and published in English. The Mixed-Methods Appraisal Tool was used for quality appraisal. Data were analysed with a descriptive intent, and inequities were mapped into the diagnostic process. RESULTS: 20 studies of varying methodological quality were included. Though not the initial focus, autism spectrum disorder (ASD) emerged as the most studied mental disorder (n=17). Of the diagnostic errors identified, most fell into the category of delayed diagnosis. 11 factors emerged as contributors to diagnostic inequities. Limited health literacy among patients and caregivers was the leading cause of diagnostic error in symptom recognition. Insurance coverage issues delayed patient engagement with the healthcare system. Provider bias during clinical history-taking and interviewing was seen as a key cause of delays and misdiagnoses. Within diagnostic testing and interpretation, culturally inequivalent assessment measures might cause misdiagnosis and delayed diagnosis for Black/African American and Hispanic/Latino patients. The use of medical jargon and lack of qualified language interpreters during communicating the diagnosis were associated with diagnostic errors impacting patients with limited health literacy and low English language proficiency. CONCLUSIONS: Diagnostic inequities in ASD and other mental disorders persist across US patient populations. Multiple factors such as parental health literacy, provider bias and limited access interact and impact the diagnostic process. Addressing these interconnected barriers is essential to ensure timely, accurate and equitable care. PROSPERO REGISTRATION NUMBER: CRD42024581271.
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 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 | 0.028 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".