Health equity audits: a systematic review of the effectiveness
Bibliographic record
Abstract
OBJECTIVES: The purpose of this systematic review is to explore whether health equity audits (HEAs) are effective in improving the equity of service provision and reducing health inequalities. DESIGN: Three databases (Ovid Medline, Embase, Web of Science) and grey literature (Opengrey, Google Scholar) were systematically searched for articles published after 2000, reporting on the effectiveness of HEA. Title and abstracts were screened according to an eligibility criteria to identify studies which included a full audit cycle (eg, initial equity analysis, service changes and review). Data were extracted from studies meeting the eligibility criteria after full text review and risk of bias assessed using the ROBINS-I tool. RESULTS: The search strategy identified 596 articles. Fifteen records were reviewed in full text and three records were included in final review. An additional HEA report was identified through contact with an author. Three different HEAs were included from one peer-reviewed journal article, two published reports and one unpublished report (n=4 records on n=3 HEAs). This included 102 851 participants and over 148 practices/pharmacies (information was not recorded for all records). One study reviewed health equity impacts of HEA implementation in key indicators for coronary heart disease, type 2 diabetes and chronic obstructive pulmonary disease. Two HEAs explored Stop Smoking Services on programme access and equity. All reported some degree of reduction in health inequalities compared with prior HEA implementation. However, impact of HEA implementation compared with other concurrent programmes and initiatives was unclear. All included studies were judged to have moderate to serious risk of bias. CONCLUSIONS: There is an urgent need to identify effective interventions to address health inequalities. While HEAs are recommended, we only identified limited weak evidence to support their use. More evidence is needed to explore whether HEA implementation can reduce inequalities and which factors are influencing effectiveness. TRIAL REGISTRATION NUMBER: The study was registered prior to its conduction in PROSPERO (CRD 42020218642).
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 | 0.071 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".