2022
DOI: 10.3386/w30553
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Mortality Effects of Healthcare Supply Shocks: Evidence Using Linked Deaths and Electronic Health Records

Abstract: NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Cited by 8 publications
(8 citation statements)
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“…Because the Datavant mortality data do not contain state identifiers, we are unable to assess data completeness in our individual study states of Florida and Ohio. During the COVID-19 pandemic, Datavant mortality data have been used in other peer-reviewed 15 and publicly available 16 research on excess mortality. The Yale University Institutional Review Board exempted the study from review because the data were deidentified, and reporting adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.…”
Section: Data Sourcementioning
confidence: 99%
“…Because the Datavant mortality data do not contain state identifiers, we are unable to assess data completeness in our individual study states of Florida and Ohio. During the COVID-19 pandemic, Datavant mortality data have been used in other peer-reviewed 15 and publicly available 16 research on excess mortality. The Yale University Institutional Review Board exempted the study from review because the data were deidentified, and reporting adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.…”
Section: Data Sourcementioning
confidence: 99%
“…First, it is not contaminated by measurement error in the choice of what to label a "COVID" death, which could systematically vary across groups. Second, it allows us to capture not only direct effects of the pandemic on mortality but also potential indirect effects that might occur, for example, due to the declines in economic activity (Ruhm 2000;2005;Stevens et al 2015), effects of individual job loss (Sullivan and Von Wachter 2009), avoidance of medical care (Zhang 2021;Ziedan et al 2022), or changes in health behaviors such as drug use (Friedman and Akre 2021). 7 We use mortality records from January 2011 through February 2021 from the Census Bureau's version of the Social Security Administration's Numerical Identification (Census Numident) database.…”
Section: All-cause Mortalitymentioning
confidence: 99%
“…Locations with an older population may have larger reductions in COVID‐19 deaths from SIP policies, while locations with younger populations may be more impacted by non‐COVID causes of excess death (e.g., deaths of despair). At the same time, older populations are more likely to be impacted by delayed non‐COVID medical care (Ziedan et al., 2022).…”
Section: Robustness Testsmentioning
confidence: 99%