Reductions in Blood Lead Level Screening During Peak COVID‐19 Restrictions and Beyond
Bibliographic record
Abstract
Background and Objectives: Among the multitude of health effects on children associated with the COVID-19 pandemic, there have been significant interruptions in the provision of routine pediatric primary care, including blood lead level (BLL) screening. We aimed to investigate trends in BLL screening before and during the pandemic era using patient-level electronic health record data extracted from CurrentCare, Rhode Island's statewide health information exchange (HIE). Methods: De-identified data were analyzed from CurrentCare for the study period January 2018 to December 2021. We utilized ATLAS, a web-based analytics platform from the Observational Health Data Sciences and Informatics (OHDSI) community, to extract and stratify BLL by variables of interest from the CurrentCare data, standardized to OHDSI's Observational Medical Outcomes Partnership common data model. Results: A decrease in BLL screening occurred in the spring of 2020, aligning with initial periods of shelter-in-place in response to the novel coronavirus outbreak; there was a 48% decrease comparing quarter 2 (April to June) of 2019 and 2020. BLL screening rebounded in the summer of 2020, however, it remained 16% lower overall in 2020 than in 2019. In 2021, BLL screening fell again to 23% lower than in 2019. Although overall numbers of BLL screenings were reduced, the proportion of abnormal BLLs was higher, particularly in the range of 3.5-5.0 µg/dL. Conclusions: Leveraging statewide HIE data, we found that significant deficiencies in BLL screening remain unresolved since the beginning of the COVID-19 pandemic. The disruption of children's lives by the COVID-19 pandemic appears to have greatly affected lead screening and exposure in Rhode Island.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".