Neighborhood-level income and MRSA infection risk in the USA: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The impact of neighborhood-level income on community-associated methicillin-resistant S. aureus (CA-MRSA) risk remains poorly understood, despite established associations between MRSA risk and the social determinants of health. There are conflicting findings in the existing literature and no known systematic reviews based in the U.S. Our objective was to conduct a systematic review and meta-analysis of the association between neighborhood-level income and CA-MRSA in the U.S. METHODS: We searched MEDLINE (Ovid), MEDLINE Epub Ahead of Print, In-Process, In-Data-Review & Other Non-Indexed Citations, and Daily (Ovid), Global Health (Ovid), Embase (Elsevier), Cochrane Database of Systematic Reviews (Wiley), Cochrane Central Register of Controlled Trials (Wiley), and Web of Science Core Collection from 2017 to 10 January 2021. An updated search was completed in November 2023. Eligible studies reported stratified CA-MRSA case counts and/or effect measures by neighborhood income level, reported as a categorical or continuous variable. Relevant data were extracted using Covidence following the PRISMA guidelines. A random-effects model meta-analysis was used to estimate the pooled effect measure. Three study design-specific risk of bias assessments and a quality assessment were applied using the modified Newcastle-Ottawa Quality Assessment Scale and GRADE approach, respectively. RESULTS: 73%). Limiting to low risk of bias studies (n = 3), there was no significant relationship between low income and CA-MRSA infection (OR: 1.13, 95% CI: 0.96, 1.33) with heterogeneity of 0%. CONCLUSIONS: Evidence supports an association between lower neighborhood income and higher CA-MRSA infection risk, albeit with considerable heterogeneity. Future studies should consider evaluating neighborhood-level income as a continuous variable, and at the block-group level to avoid exposure misclassification. Furthermore, researchers should consider adjusting for covariates that could allow for a causal interpretation of the relationship between low neighborhood-level income and CA-MRSA risk.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".