Use of a Financial Incentive Code for In-Home COVID-19 Vaccination of Homebound Older Adults.

This study evaluated the uptake of Healthcare Common Procedure Coding System code M0201 after initial implementation to inform future policy related to in-home preventive care.

Time From Authorization by the US Food and Drug Administration to Medicare Coverage for Novel Technologies.

Importance: A wide variety of novel medical diagnostics and devices are determined safe and effective by the US Food and Drug Administration (FDA) each year, but to our knowledge the literature lacks evidence documenting how long it takes to establish new Medicare coverage for these technologies. Objective: To measure time from FDA authorization to at least nominal Medicare coverage for technologies requiring a new reimbursement pathway. Design, Setting, and Participants: In this cross-sectional study, public databases were used to associate each technology to billing codes, determine the effective date of each code and Medicare coverage decisions, and stratify by the maturity of the Medicare coverage. At least nominal coverage was defined as achievement of explicit coverage milestones through a national coverage determination, local coverage determinations by Medicare administrative contractors, or by implicit coverage aligned to a new billing code. Characterization by product type (acute treatment, chronic or ongoing treatment, diagnostic assay, and diagnostic device), manufacturer size, and evidence level were assessed for association with coverage achievement. The study included new product applications authorized by the FDA through the premarket approval pathway, the de novo pathway, or with breakthrough designation in the 510(k) pathway from January 1, 2016, to December 31, 2019. Data analysis took place between May 1, 2022, and December 31, 2022. Main Outcome Measurement: Time from FDA authorization to the first coverage milestone. Results: Among 281 identified technologies in the total sample, 64 (23%) were deemed novel technologies based on the absence of coverage determinations and/or the use of temporary or miscellaneous billing codes. Twenty-eight of 64 technologies (44%) successfully achieved explicit or implicit coverage following FDA authorization. The median time to at least nominal coverage for the analysis cohort was 5.7 years (90% CI, 4.4-NA years). Analysis of time-to-coverage data highlighted company size (log-rank P<.001) and product type (log-rank P = .01) as significant covariates associated with coverage achievement. No association was observed for technologies with level 1 evidence at FDA authorization and subsequent coverage milestone achievement (log-rank P = .40). Conclusions and Relevance: In this cross-sectional study of 64 novel technologies, only 28 (44%) achieved coverage milestones over the study timeline. The several-year period observed to establish at least nominal coverage suggests existing coverage processes may affect timely reimbursement of new technologies.

A comparison of survival and disease-specific survival in surgically resected, lymph node-positive bronchioloalveolar carcinoma versus nonsmall cell lung cancer: implications for adjuvant therapy.

BACKGROUND: The objective of this study was to assess whether disease-specific survival (DSS) and overall survival (OS) differed among patients who had N1 and N2 bronchioloalveolar carcinoma (BAC) compared with patients who had non-BAC nonsmall cell lung cancer (NSCLC). METHODS: The Surveillance, Epidemiology, and End Results (SEER) Program database from 1992 to 2002 contained 684 patients with BAC and 9809 patients with non-BAC NSCLC who had N1/N2 tumors and who underwent a definitive surgical procedure. OS and DSS rates were compared according to potential prognostic factors, including the use of a matched-pair analysis. RESULTS: The BAC patients with either pathologic N1 or N2 lymph node status were significantly more likely to be women, and nonblack/nonwhite race, but significantly less likely to have poorly differentiated or undifferentiated tumors than patients with non-BAC cancers with comparable lymph node status. The median follow-up of all patients was 29 months. There was a significant difference in DSS between patients with N2 BAC and non-BAC cancers, but not for patients with N1 disease. There was a nonsignificant trend toward longer OS for patients with N2 BAC compared with non-BAC cancers. CONCLUSIONS: Patients with lymph node-positive BAC had distinctly different patient and tumor characteristics than patients with lymph node-positive non-BAC NSCLC. Because DSS appears to be better for patients with N2 BAC, they may not benefit as much from adjuvant therapy as patients with non-BAC NSCLC.

An informative Bayesian structural equation model to assess source-specific health effects of air pollution.

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.