Mapping the evidence on health equity considerations in economic evaluations of health interventions: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Equity in health has become an important policy agenda around the world, prompting health economists to advance methods to enable the inclusion of equity in economic evaluations. Among the methods that have been proposed to explicitly include equity are the weighting analysis, equity impact analysis, and equity trade-off analysis. This is a new development and a comprehensive overview of trends and concepts of health equity in economic evaluations is lacking. Thus, our objective is to map the current state of the literature with respect to how health equity is considered in economic evaluations of health interventions reported in the academic and gray literature. METHODS: We will conduct a scoping review to identify and map evidence on how health equity is considered in economic evaluations of health interventions. We will search relevant electronic, gray literature and key journals. We developed a search strategy using text words and Medical Subject Headings terms related to health equity and economic evaluations of health interventions. Articles retrieved will be uploaded to reference manager software for screening and data extraction. Two reviewers will independently screen the articles based on their titles and abstracts for inclusion, and then will independently screen a full text to ascertain final inclusion. A simple numerical count will be used to quantify the data and a content analysis will be conducted to present the narrative; that is, a thematic summary of the data collected. DISCUSSION: The results of this scoping review will provide a comprehensive overview of the current evidence on how health equity is considered in economic evaluations of health interventions and its research gaps. It will also provide key information to decision-makers and policy-makers to understand ways to include health equity into the prioritization of health interventions when aiming for a more equitable distribution of health resources. SYSTEMATIC REVIEW REGISTRATION: This protocol was registered with Open Science Framework (OSF) Registry on August 14, 2019 (https://osf.io/9my2z/registrations).
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 | 0.186 | 0.192 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.077 | 0.022 |
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".