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Short Term Trend Analysis of Hospital Admissions Due to Red Blood Cell Disorders: Big Data Perspective

Wang, Qin
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1428070351

Abstract Details

2015, MS, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
There are about 5000 hospitals in the United States. The Health Care Cost and Utilization Project (HCUP) has been collecting data on admissions to hospitals using some special sampling plans from 1988. The latest year for which data are available is 2012. The sampling plan is designed to get about 20% of the total number of admissions. The hospitals employ a DRG (Diagnosis Related Group) code from 001 to 999 to classify each admission. In a year, the sample consists of about 8 million admissions with information for each admission on about 250 variables. This is big data. This thesis can be viewed as an exercise how to handle big data to extract information. We focus on admissions due to red blood cell disorders (RBCDs) for the years 2006 to 2012 (a seven-year stretch). Especially, we examine incidence of RBCD admissions, total length of stay (LOS), total charges (TOTCHG), gender distribution, age distribution, and gender × age distribution. We observe a great imbalance in the gender distribution. DRG code is very broad. We also use the International Classification of Disease (ICD)-9 coding system to extract the top 5 prevalent subtypes of RBCDs. We estimated the nation-wide total discharges, LOS, and TOTCHG for the top 5 subtypes. Also calculated is the percentage of each of these top 5 subtypes over total RBCDs in terms of discharges, LOS and TOTCHG, respectively. We also examine the gender × age distribution for the 5 subtypes. We observed various gender × age distribution patterns for the top 5 prevalent RBCD subtypes.
Marepalli Rao, Ph.D. (Committee Chair)
Aimin Chen, Ph.D. (Committee Member)
57 p.
Biostatistics

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Citations

  • Wang, Q. (2015). Short Term Trend Analysis of Hospital Admissions Due to Red Blood Cell Disorders: Big Data Perspective [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1428070351

    APA Style (7th edition)

  • Wang, Qin. Short Term Trend Analysis of Hospital Admissions Due to Red Blood Cell Disorders: Big Data Perspective. 2015. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1428070351.

    MLA Style (8th edition)

  • Wang, Qin. "Short Term Trend Analysis of Hospital Admissions Due to Red Blood Cell Disorders: Big Data Perspective." Master's thesis, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1428070351

    Chicago Manual of Style (17th edition)

ucin1428070351
401
© 2015, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.