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Cancer Screening Disparities Before and After the COVID-19 Pandemic

2023· article· en· W4388835016 on OpenAlexafffundabout
Aïsha Lofters, Fangyun Wu, Eliot Frymire, Tara Kiran, Mandana Vahabi, Michael Green, Richard H. Glazier

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsToronto Metropolitan UniversityQueen's UniversityWomen's College HospitalUniversity of TorontoSt. Michael's Hospital
FundersHealth CanadaCanadian Institutes of Health ResearchCanadian Medical AssociationOntario Medical AssociationInstitute for Clinical Evaluative SciencesPfizer
KeywordsMedicinePopulationCancer screeningSigmoidoscopyPandemicCohortColonoscopyDemographyBreast cancer screeningCancerBreast cancerColorectal cancerMammographyInternal medicineCoronavirus disease 2019 (COVID-19)Environmental healthDisease

Abstract

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Importance: Breast, cervical, and colorectal cancer-screening disparities existed prior to the COVID-19 pandemic, and it is unclear whether those have changed since the pandemic. Objective: To assess whether changes in screening from before the pandemic to after the pandemic varied for immigrants and for people with limited income. Design, Setting, and Participants: This population-based, cross-sectional study, using data from March 31, 2019, and March 31, 2022, included adults in Ontario, Canada, the country's most populous province, with more than 14 million people, almost 30% of whom are immigrants. At both dates, the screening-eligible population for each cancer type was assessed. Exposures: Neighborhood income quintile, immigrant status, and primary care model type. Main Outcomes and Measures: For each cancer screening type, the main outcome was whether the screening-eligible population was up to date on screening (a binary outcome) on March 31, 2019, and March 31, 2022. Up to date on screening was defined as having had a mammogram in the previous 2 years, a Papanicolaou test in the previous 3 years, and a fecal test in the previous 2 years or a flexible sigmoidoscopy or colonoscopy in the previous 10 years. Results: The overall cohort on March 31, 2019, included 1 666 943 women (100%) eligible for breast screening (mean [SD] age, 59.9 [5.1] years), 3 918 225 women (100%) eligible for cervical screening (mean [SD] age, 45.5 [13.2] years), and 3 886 345 people eligible for colorectal screening (51.4% female; mean [SD] age, 61.8 [6.4] years). The proportion of people up to date on screening in Ontario decreased for breast, cervical, and colorectal cancers, with the largest decrease for breast screening (from 61.1% before the pandemic to 51.7% [difference, -9.4 percentage points]) and the smallest decrease for colorectal screening (from 65.9% to 62.0% [difference, -3.9 percentage points]). Preexisting disparities in screening for people living in low-income neighborhoods and for immigrants widened for breast screening and colorectal screening. For breast screening, compared with income quintile 5 (highest), the β estimate for income quintile 1 (lowest) was -1.16 (95% CI, -1.56 to -0.77); for immigrant vs nonimmigrant, the β estimate was -1.51 (95% CI, -1.84 to -1.18). For colorectal screening, compared with income quintile 5, the β estimate for quntile 1 was -1.29 (95% CI, 16 -1.53 to -1.06); for immigrant vs nonimmigrant, the β estimate was -1.41 (95% CI, -1.61 to -1.21). The lowest screening rates both before and after the COVID-19 pandemic were for people who had no identifiable family physician (eg, moving from 11.3% in 2019 to 9.6% in 2022 up to date for breast cancer). In addition, patients of interprofessional, team-based primary care models had significantly smaller reductions in β estimates for breast (2.14 [95% CI, 1.79 to 2.49]), cervical (1.72 [95% CI, 1.46 to 1.98]), and colorectal (2.15 [95% CI, 1.95 to 2.36]) postpandemic screening and higher uptake of screening in general compared with patients of other primary care models. Conclusions and Relevance: In this cross-sectional study in Ontario that included 2 time points, widening disparities before compared with after the COVID-19 pandemic were found for breast cancer and colorectal cancer screening based on income and immigrant status, but smaller declines in disparities were found among patients of interprofessional, team-based primary care models than among their counterparts. Policy makers should investigate the value of prioritizing and investing in improving access to team-based primary care for people who are immigrants and/or with limited income.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.430
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations34
Published2023
Admission routes3
Has abstractyes

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