Provision of preventive health care in systemic lupus erythematosus: data from a large observational cohort study
Bibliographic record
Abstract
INTRODUCTION: Cancer and infections are leading causes of mortality in systemic lupus erythematosus (SLE) after diseases of the circulatory system, and therefore preventing these complications is important. In this study, we examined two categories of preventive services in SLE: cancer surveillance (cervical, breast, and colon) and immunizations (influenza and pneumococcal). We compared the receipt of these services in SLE to the general population, and identified subgroups of patients who were less likely to receive these services. METHODS: We compared preventive services reported by insured women with SLE enrolled in the University of California, San Francisco Lupus Outcomes Study (n=685) to two representative samples derived from a statewide health interview survey, a general population sample (n=18,013) and a sample with non-rheumatic chronic conditions (n=4,515). In addition, using data from the cohort in both men and women (n=742), we applied multivariate regression analyses to determine whether characteristics of individuals (for example, sociodemographic and disease factors), health systems (for example, number of visits, involvement of generalists or rheumatologists in care, type of health insurance) or neighborhoods (neighborhood poverty) influenced the receipt of services. RESULTS: The receipt of preventive care in SLE was similar to both comparison samples. For cancer surveillance, 70% of eligible respondents reported receipt of cervical cancer screening and mammography, and 62% reported colon cancer screening. For immunizations, 59% of eligible respondents reported influenza immunization, and 60% reported pneumococcal immunization. In multivariate regression analyses, several factors were associated with a lower likelihood of receiving preventive services, including younger age and lower educational attainment. We did not observe any effects by neighborhood poverty. A higher number of physician visits and involvement of generalist providers in care was associated with a higher likelihood of receiving most services. CONCLUSIONS: Although receipt of cancer screening procedures and immunizations in our cohort was comparable to the general population, we observed significant variability by sociodemographic factors such as age and educational attainment. Further research is needed to identify the physician, patient or health system factors contributing to this observed variation in order to develop effective quality improvement interventions.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".