Association of Area Socioeconomic Status and Breast, Cervical, and Colorectal Cancer Screening: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Although numerous studies have examined the association of area socioeconomic status (SES) and cancer screening after controlling for individual SES, findings have been inconsistent. A systematic review of existing studies is timely to identify conceptual and methodologic limitations and to provide a basis for future research directions and policy. OBJECTIVE: The objectives were to (a) describe the study designs, constructs, methods, and measures; (b) describe the independent association of area SES and cancer screening; and (c) identify neglected areas of research. METHODS: We searched six electronic databases and manually searched cited and citing articles. Eligible studies were published before 2008 in peer-reviewed journals in English, represented primary data on individuals ages > or = 18 years from developed countries, and measured the association of area and individual SES with breast, cervical, or colorectal cancer screening. RESULTS: Of 19 eligible studies, most measured breast cancer screening. Studies varied widely in research design, definitions, and measures of SES, cancer screening behaviors, and covariates. Eight employed multilevel logistic regression, whereas the remainder analyzed data with standard single-level logistic regression. The majority measured one or two indicators of area and individual SES; common indicators at both levels were poverty, income, and education. There was no consistent pattern in the association between area SES and cancer screening. DISCUSSION: The gaps and conceptual and methodologic heterogeneity in the literature to date limit definitive conclusions about an underlying association between area SES and cancer screening. We identify five areas of research deserving greater attention in the literature.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".