Analysis of Medical Claims Data

Author(s):  
Robert D. Gibbons
Keyword(s):  
Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Monica R Shah ◽  
Tanya F Partridge ◽  
Xiaoqing Xue ◽  
Justin L Gregg

Introduction: Regional studies have reported a decline in cardiovascular (CV) hospitalizations and procedures with the onset of the coronavirus disease-2019 (COVID-19) pandemic. Factors may include patient reluctance to seek care and de-prioritization of approvals for CV admissions by hospitals. We wanted to assess these observations at a national level. Hypothesis: To examine national trends in CV hospitalizations for acute myocardial infarction (AMI), unstable angina (USA), and heart failure (HF), as well as left heart catheterizations (LHC), using US medical claims data. Methods: We interrogated IQVIA US Claims data, a verified source, from Jan 2019 to May 2020 (214 million patients; 76% private insurance claims, 19% Medicare claims, 5% Medicaid claims). Since confirmed COVID-19 cases in the US began rising in Mar 2020, this was used as reference point to identify cohorts for comparison. Trends in volumes of hospitalizations for key CV events (AMI, USA, and HF) and LHC were compared from Mar 1 to May 8, 2020 to the equivalent time period in 2019. We used a Bayesian hierarchical model to assess trends. Results: From Mar to May 2020, compared to 2019, there were significantly fewer hospitalizations for: key CV events (1,110,492 vs. 1,487,558; p=0.0016); AMI (277,615 vs. 412,235; p=0.0002); USA (1,007 vs. 1,688, p=0.1245); and, HF (831,870 vs. 1,073,635; p=0.0036). There were significantly fewer LHC (118,393 vs. 221,701; p=0.0002). As shown in the Figure, there was a significant decline in CV hospitalizations in 2020 compared to 2019. Conclusions: During the COVID-19 pandemic, CV hospitalizations have declined significantly in the US. We observed an ~25% drop in CV hospitalizations and an ~50% drop in LHC. To the best of our knowledge, this is the first national evaluation of trends in CV care during COVID-19 and validate concerns that acute CV care in the US has been delayed or deferred, potentially foreshadowing a surge of CV complications in the future.


2019 ◽  
Vol 54 (6) ◽  
pp. 1255-1262
Author(s):  
Leah L. Zullig ◽  
Shelley A. Jazowski ◽  
Tracy Y. Wang ◽  
Anne Hellkamp ◽  
Daniel Wojdyla ◽  
...  

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Luke Keele ◽  
Catherine E. Sharoky ◽  
Morgan M. Sellers ◽  
Chris J. Wirtalla ◽  
Rachel R. Kelz

Abstract Confounding by indication is a critical challenge in evaluating the effectiveness of surgical interventions using observational data. The threat from confounding is compounded when using medical claims data due to the inability to measure risk severity. If there are unobserved differences in risk severity across patients, treatment effect estimates based on methods such a multivariate regression may be biased in an unknown direction. A research design based on instrumental variables offers one possibility for reducing bias from unobserved confounding compared to risk adjustment with observed confounders. This study investigates whether a physician’s preference for operative care is a valid instrumental variable for studying the effect of emergency surgery. We review the plausibility of the necessary causal assumptions in an investigation of the effect of emergency general surgery (EGS) on inpatient mortality among adults using medical claims data from Florida, Pennsylvania, and New York in 2012–2013. In a departure from the extant literature, we use the framework of stochastic monotonicity which is more plausible in the context of a preference-based instrument. We compare estimates from an instrumental variables design to estimates from a design based on matching that assumes all confounders are observed. Estimates from matching show lower mortality rates for patients that undergo EGS compared to estimates based in the instrumental variables framework. Results vary substantially by condition type. We also present sensitivity analyses as well as bounds for the population level average treatment effect. We conclude with a discussion of the interpretation of estimates from both approaches.


2019 ◽  
Vol 28 (3) ◽  
pp. 354-361
Author(s):  
Alys Havard ◽  
Peter Straka ◽  
Grant Sara ◽  
Sanja Lujic ◽  
Duong T. Tran ◽  
...  

2018 ◽  
Vol 166 ◽  
pp. 529-536 ◽  
Author(s):  
Christine L. Gray ◽  
Danelle T. Lobdell ◽  
Kristen M. Rappazzo ◽  
Yun Jian ◽  
Jyotsna S. Jagai ◽  
...  

1999 ◽  
Vol 2 (5) ◽  
pp. 406
Author(s):  
K Andrews ◽  
J Boscarino ◽  
M Sokol ◽  
J Yao ◽  
Z Zhao ◽  
...  

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