Objective
To measure the extent to which the provision of mammograms was impacted by the COVID‐19 pandemic and surrounding guidelines.
Data Sources
De‐identified summary data derived from medical claims and eligibility files were provided by Independence Blue Cross for women receiving mammograms.
Study Design
We used a difference‐in‐differences approach to characterize the change in mammograms performed over time and a queueing formula to estimate the time to clear the queue of missed mammograms.
Data Collection
We used data from the first 30 weeks of each year from 2018 to 2020.
Principal Findings
Over the 20 weeks following March 11, 2020, the volume of screening mammograms and diagnostic mammograms fell by 58% and 38% of expected levels, on average. Lowest volumes were observed in week 15 (April 8 to 14), when screening and diagnostic mammograms fell by 99% and 74%, respectively. Volumes began to rebound in week 19 (May), with diagnostic mammograms reaching levels to similar to previous years’ and screening mammograms remaining 14% below expectations. We estimate it will take a minimum of 22 weeks to clear the queue of missed mammograms in our study sample.
Conclusions
The provision of mammograms has been significantly disrupted due to the COVID‐19 pandemic.
Despite considerable research on nursing turnover, few studies have considered turnover among nurses working in home health care. Using novel administrative data from one of the largest home health care organizations in the United States, this study examined turnover among home health nurses, focusing on the role of schedule volatility. We estimated separation rates among full-time and part-time registered nurses and licensed practical nurses and used daily visit logs to estimate schedule volatility, which was defined as the coefficient of variation of the number of daily visits in the prior four weeks. Between 2016 and 2019, the average annual separation rate of home health nurses was over 30%, with most separations occurring voluntarily. Schedule volatility and turnover were positively associated for full-time nurses, but not for part-time nurses. These results suggest that reducing schedule volatility for full-time nurses could mitigate nursing turnover in home health care.
We draw upon newly merged administrative data sets to study the relationship between payments from medical technology firms to physicians and medical device procurement by hospitals. These payments (and the interactions that accompany them) may facilitate the transfer of valuable information to and from physicians. However, they may also influence physicians' preferences, and in turn hospital device procurement, in favor of paying firms. Payments are pervasive: 87 percent of device sales in our sample occurred at a hospital where a relevant physician received a payment from a device firm. Payments are also highly correlated with spending within a firmhospital pair: event studies suggest that a large positive increase in payments to a given hospital from a given firm ($438 per physician on average, or 112 percent of the mean) is associated with 27 percent higher expenditures on the paying firm's devices post-event. Finally, we explore how payments mediate the relationship between expertise and device procurement patterns. Hospitals affiliated with the top Academic Medical Centers (AMCs), which plausibly represent an expert benchmark, purchase a different mix of devices than other hospitals, and payments to hospitals outside the top AMCs are correlated with larger deviations from the procurement patterns of top AMC hospitals.
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