Instructions-Assignment SAHIE & HCUP UMBC (3)

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Health Economics: ECON 467-01 (13351) & ECON 652-01(13431) Aug 27-Dec 8 Dept. of Economics, UMBC/Fall 2020 Step-by-step weekly Assignments to build up a paper Eight Individual Assignments each 4 points: Step1- Sep. 13-Data analyses of SAHIE Interactive Data Tool (4%) Step 2-Sep 27-Abstract: Formulate a public health economic research question using PICO elements and SAHIE data (4%) Step 3-Oct. 11-Send the First Draft of the paper to your reviewer (4%) Step 4-Oct. 25-Reviewer send constructive feedback to the authors (4%) Step 5-Nov. 8-Send the First Draft of the paper to the instructor (4%) Step 6-Nov. 29-Send Final Draft of the paper to the reviewer (4%) Step 7-Dec. 6-Reviewer send final constructive feedback to the authors (4%) Step 8-Dec. 13-Send the Final revised version of the Draft will be sent to the instructor (4%) 1

Instructions for Step1- Sep. 13-Data analyses of SAHIE Interactive Data Tool (4%) The Analyses in Health Disparities: Gaps in Access, Quality, and Affordability of Medical Care 1. These assignments' primary objective is to show the disparity in access to care and hospitalization utilization by different sociodemographic characteristics such as: by State, county, race, age, gender, income, etc. First, choose your key variables, and second, let SAHIE and HCUP create your tables and graphs to show disparities, and third, write a few paragraphs to explain your data analysis, findings. 2. Please choose similar regions and years to compare for both datasets SAHIE and HCUP. I-The Small Area Health Insurance Estimates ( SAHIE) shows the disparity in access to care by region, age, race, sex, income over the years (e.g., before and after the affordable care act, 2009 and 2017). Interactive Data Tool https://www.census.gov/data-tools/demo/sahie/#/ Data Tool : https://www.census.gov/programs-surveys/sahie/data/datasets.html II-Healthcare Cost and Utilization Project ( HCUP ), shows the disparity in healthcare utilization (hospitalization and length of stay in hospital) by regions , type of Insurance, age, sex, income , type of disease (e.g., Opioid) in two years between 2008 – 2017 (before and after ACA). HCUP Fast Stats - State Trends in Inpatient Stays by Payer https://www.hcup-us.ahrq.gov/faststats/StatePayerServlet? state1=MD&type1=PY00&combo1=s&state2=OH&type2=PY00&combo2=s&expansionInfoStat e=hide&dataTablesState=hide&definitionsState=hide&exportState=hide III-HCUP Fast Stats - Opioid-Related Hospital Use https :// www . hcup -us. ahrq .gov/ faststats / OpioidUseServlet ?radio- 3=on&location1=US&characteristic1=01&setting1=ED&location2=MD&characteristic2=01&setting2=I P& expansionInfoState =hide& dataTablesState =hide& definitionsState =hide& exportState =hide HCUP User Support (HCUP-US): www.hcup-us.ahrq.gov HCUPnet: https://hcupnet.ahrq.gov/#setup HCUP Statistical Briefs: www.hcup-us.ahrq.gov/reports/statbriefs/statbriefs.jsp Center on Budget and Policy Priorities . Chart Boo k: The Far-Reaching Benefits of the Affordable Care Act’s Medicaid Expansion, October 2, 2018 https://www.cbpp.org/research/health/chart-book-the-far-reaching-benefits-of-the-affordable-care-acts- medicaid 3. You need to submit your assignment on Bb in word document using SafeAssign Here are some sample questions to explore in your short essay. But you are not limited to explore only the following questions. a. Use SAHIE Interactive Data Tool to compare the number of insured/Uninsured in two States or two counties (e.g., Baltimore and Montgomery counties in Maryland) in the years 2006 and 2017. b. Download your table in an excel spreadsheet and make a nice table in the word document. c. In a brief essay (1-2 pages), please discuss what region had the highest uninsured rates than the other areas In 2006 and 2017? d. What county in your selected State has the highest rate of uninsured in 2006 and 2017? Check to see if this is related to their income level or age? e. Compare your tables with the charts from “ Center on Budget and Policy Priorities ” and discuss whether your selected states are one of the those with Medicaid Expansion or not. f. Write a one-page summary of your comparison by referring to the tables and graphs . Detailed information about Step1- Sep. 13-Data analyses of SAHIE Interactive Data Tool (4%) 2

Data analysis: Both SAHIE and HCUP data provide a structured, graphical analysis of the information on the level of access to health care and the utilization of hospitals and health care services. Both the SAHIE and HCUP are excellent data sources to show disparity within health care visually. They are also instrumental in establishing the impact of the Affordable Health Care Act. Using both SAHIE and HCUP data, we can compare access to care and utilization by different states and counties over the years (e.g., before and after ACA) and by Sociodemographic factors such as Regions (e.g., States, counties), Race, Gender, Age, and Income. SAHIE breaks down the different insurance coverage (including uninsured status) in each region/state/demographic group and income groups. SAHIE doesn't have a type of insurance. While HCUP shows us what proportion of hospital utilization is covered by different insurers, region/state/demographic group, and income groups, HCUP doesn't give you specific counties. HCUP is the health care cost and utilization project that helps find patient hospital stays and patients' different service visits. HCUP looks at the State rather than the county specifically. Additionally, it also provides age, sex, income, patient location (metropolitan size), and income. Healthcare Cost and Utilization Project (HCUP) By Focusing on Cancer or Opioid The Healthcare Cost and Utilization Project (HCUP) family of health care databases and related software tools and products is made possible by a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ). Using HCUP data: Please use the Healthcare Cost and Utilization Project (HCUP) statistical data to show the Trends and Disparities in Delivery Hospitalizations by the following choices by:  Two States or two counties (e.g., Maryland and Ohio) or  Two points of time (e.g., 2006 and 2015),  Specific disease (E.g., opioid-related hospitalizations and emergency department visits)  Sociodemographic characteristics such as age, sex, States, income, Payer (Type of insurance), and hospitalization types for Cancer or opioids during 2007 and 2016. An example for Step1- Sep. 13-Data analyses of SAHIE Interactive Data Tool (4%) 3

The Analyses in Health Disparities: Gaps in Access, Quality, and Affordability of Medical Care A sample of work from my ex-student: Sean Fleming Disparities in Health Insurance Coverage: Maryland and Virginia, 2006-2017 Objective : To show the disparity in access to healthcare between Maryland and Virginia in 2006 and 2017. In 2006, 15.6% of Maryland's population was uninsured, which was very close to Virginia with 15.5%. By 2017, after the Affordable Care Act (ACA), both states experienced decreases in the percent of uninsured populations. Still, there was a more considerable drop observed in Maryland. The percent of uninsured Maryland residents fell by more than half over this period to 7.0% in 2017. In contrast, the percent of uninsured Virginia residents shrank to 10.2% in 2017. Health Insurance Coverage by Race In 2006, 10.7% of Marylanders and 11.7% of Virginians of the White non-Hispanic race were uninsured. By 2017, 4.3% of Marylanders and 7.6% of Virginians of the White non-Hispanic race were uninsured. In 2006, 19.2% of Marylanders and 19.3% of Virginians of Black non-Hispanic race were uninsured. By 2017, 6.8% of Marylanders and 11.6% of Virginians of Black non-Hispanic race were uninsured. This clearly shows that the percentage of Black non-Hispanic without insurance in Virginia is becoming more extensive than in Maryland (11.6% vs. 6.8%). In 2006, 37.8% of Marylanders and 37.3% of Virginians of Hispanic origin were uninsured. By 2017, 20.4% of Marylanders and 23.5% of Virginians of Hispanic origin were uninsured. 4

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