Shih, Ya‐Chen Tina

11064 Background: Local and state paid sick leave (PSL) mandates have been associated with increased use of preventive services, including cancer screening, and reduced emergency care utilization. Little research has examined PSL policies and cancer after diagnosis. This study evaluates associations of PSL policies and timely treatment initiation among women with early-stage breast cancer. Methods: We identified patients aged 18-64 years newly diagnosed with stages 0-II breast cancer from the National Cancer Database in 2004-2023 and linked then to area-level policy implementation, measured as the month and year the state, county, or city required PSL. Primary outcomes were receipt of definitive surgery or neoadjuvant therapy within 30, 60, and 90 days of diagnosis. We compared changes in treatment initiation between patients residing in areas with and without PSL policies during and after the month and year of diagnosis. We used a staggered difference-in-differences design to account for differential timing of implementation, adjusting for patient demographics, clinical, and area-level characteristics. Random errors were clustered at the state level. Supplemental analyses stratified by county-level unemployment rates and individual insurance coverage. Results: The study included 1,891,411 patients, of whom 249,313 (13.2%) resided in an area with PSL policies at diagnosis. Patients in areas with PSL policies were more likely to have Medicaid, reside in higher-income and metropolitan areas. PSL policy implementation was significantly associated with a 3.4 percentage point (PPT) increase in the probability of treatment ≤30 days (95% CI: 1.1-5.7), 3.0 PPT increase ≤60 days (95% CI: 1.6-4.3), 1.3 PPT increase ≤90 days (95% CI: 0.5-2.1) (Table). Improvements in timely treatment were concentrated in low-unemployment areas; no significant improvements were observed in high-unemployment areas. The magnitude of associations between PSL policies and treatment initiation was stronger among patients without private insurance than with private insurance. Conclusions: Local and state PSL mandates were associated with earlier treatment initiation among working-age patients with early-stage breast cancer, especially in areas with low female unemployment rate. Findings suggest that PSL policies play an important role in timely cancer treatment for employed women who may not have PSL through their jobs. Association of paid sick leave policies and breast cancer treatment initiation. Time from diagnosis to surgery/neoadjuvant therapy (days) DID PPT Lower 95% CI Upper 95% CI P-value Overall ≤30 3.4 1.1 5.7 0.004 ≤60 3.0 1.6 4.3 0.000 ≤90 1.3 0.5 2.1 0.001 Low female unemployment ≤30 2.2 -0.7 5.0 0.136 ≤60 3.8 1.8 5.8 0.000 ≤90 1.2 0.3 2.2 0.012 High female unemployment ≤30 1.8 -0.6 4.2 0.145 ≤60 -1.2 -4.5 2.0 0.459 ≤90 -1.4 -3.7 0.9 0.232

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

AbstractThe legitimacy of findings from cancer health economics research depends on study design and methods. A breakout session, Methods and Study Design for Cancer Health Economics Research, was convened at the Future of Cancer Health Economics Research Conference to discuss 2 commonly used analytic tools for cancer health economics research: observational studies and decision-analytic modeling. Observational studies include analysis of data collected with the primary purpose of supporting economic evaluation or secondary use of data collected for another purpose. Modeling studies develop a parametrized structure, such as a decision tree, to estimate hypothetical impact. Whereas observational studies focus on what has happened and why, modeling studies address what may happen. We summarize the discussion at this breakout session, focusing on 3 key elements of high-quality cancer health economics research: study design, analytical methods, and addressing uncertainty.