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Characteristics of EDs serving high volumes of safety-net populations Catharine W. Burt, Ed.D. Chief, Ambulatory Care Statistics Branch July 13, 2004 Data.

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Presentation on theme: "Characteristics of EDs serving high volumes of safety-net populations Catharine W. Burt, Ed.D. Chief, Ambulatory Care Statistics Branch July 13, 2004 Data."— Presentation transcript:

1 Characteristics of EDs serving high volumes of safety-net populations Catharine W. Burt, Ed.D. Chief, Ambulatory Care Statistics Branch July 13, 2004 Data Users Conference U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics

2 Topics Why do we care? What is the NHAMCS? What other data sources were used? What are the findings? So what?

3 “ Rising numbers of uninsured Americans, an increasing price-driven health care marketplace, and rapid growth in enrollment of Medicaid beneficiaries in managed care plans may have critical implications for the future viability of American’s health care safety net that serves a large portion of low-income and uninsured Americans.” America’s Health Care Safety Net...Institute of Medicine, 2000

4 What is the health care safety net? Emergency departments Public hospital systems Community health centers Rural health clinics Other clinics run by local health departments

5 What are the vulnerable populations? Uninsured persons Low income, under-insured persons Medicaid beneficiaries SCHIP beneficiaries Persons with special health care needs

6 Why are safety-net hospitals so concerned? Provide large amount of uncompensated care Increased Medicaid managed care increases their risk of under- compensation Decreased revenue from Medicare and private insurance Decreased Medicaid Disproportionate Share Hospital (DSH) payments from States Many hospitals and EDs closed

7 What are DSH payments? Federal matching to State giving History of creative funding 1997 BBA reduced size of DSH payments 2000 BIPA modified the DSH criteria MIUR > +1 sd in state > 1% MIUR (optional) >25% LIUR

8 Current study goals Use NHAMCS ED data to identify high-burden EDs. Use NHAMCS ED visit data, hospital information, and community factors to describe high-burden EDs in comparison to low-burden EDs. Describe which factors are most associated with high burden.

9 National Hospital Ambulatory Medical Care Survey (NHAMCS) Conducted annually since 1992 Endorsed by emergency medicine associations Census Bureau — personal interview w/ medical record abstraction: 93% response Complex sample of 600 non-Federal, general & short stay hospitals Patient and visit characteristics for 25,611 ED encounters in 2000 Hospital characteristics for 376 EDs

10 Other data sources HRSA’s Area Resource File State and county level data CMS’s Medicaid DSH payments for 2000

11 High safety-net ED definition If the ED met one or more of the following criteria >30% Medicaid patient visits >30% uninsured patient visits >40% combined Medicaid and uninsured

12 Distribution of hospital EDs by safety-net criteria: United States, High Medicaid/low uninsured High Uninsured/low Medicaid High both High combined Not Safety net Percent of hospital EDs

13 Distribution of hospital EDs by percent combined Medicaid & uninsured visits grouped by safety-net criteria: United States, Percent of visits Percent of hospital EDs Met 2+ criteria for safety net Met 1 criteria for safety net Met no criteria for safety net

14 Domains of ED characteristics studied Hospital Community Patient mix Diagnosis mix (Billings’ ACS algorithm) Visit severity, content, and outcome

15 Hospital: Located in the South Any Medicaid DSH payment Medicaid DSH amount Annual ED volume Public owned Medical school affiliation Located in a non-metro area Correlation coefficient

16 Probability that an ED has high safety- net burden by geographic region SOURCE: CDC/NCHS Northeast.25 South.61 West.24 Midwest.16

17 Distribution of EDs by geographic region according to safety-net status South 23% South 65% Low safety net High safety net 0%20%40%60%80%100% Percent of EDs Northeast Midwest South West

18 Community: Percent in poverty Unemployment rate State's DSH payment ratio ED visit rate HMO penetration rate Income per capita Primary care docs per pop Percent age Correlation coefficient

19 Patient Mix: % Medicaid % Uninsured % Black or African American % Child % Medicaid risk plan % Medicare % Senior Correlation coefficient

20 Diagnosis mix: % nonurgent % emergent, primary care treatable % mental health % unclassified % alcohol % injury % emergent, avoidable % emergent, not avoidable Correlation coefficient

21 Comparison of diagnosis mix using Billings' algorithm by safety-net status Nonurgent * Emergent, PC treatable * Emergent, avoidable Emergent, unavoidable * Injury * Mental health Other diagnosis Percent of visits Low safety netHigh safety net * Difference is significant at p<.05

22 Visit severity, content, and outcome: % of admits that are Medicaid % Left before being seen % of admits that are uninsured Mean waiting time to see physician % Resident/intern seen % Triaged as emergent or urgent % No follow-up planned Mean drug mention rate % of Medicaid patients admitted % Transfer to another facility % arrive via ambulance % of uninsured patients admitted % IV fluids administered % Admitted to hospital Correlation coefficient % of admits that are Medicaid % Left before being seen % of admits that are uninsured Mean waiting time to see physician % Resident/intern seen % Triaged as emergent or urgent % No follow-up planned Mean drug mention rate % of Medicaid patients admitted % Transfer to another facility % arrive via ambulance % of uninsured patients admitted % IV fluids administered % Admitted to hospital

23 Plot of bivariate correlation coefficients between ED characteristics and sizes of the Medicaid and uninsured burdens & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & Corr w. % Medicaid Corr w. % Uninsured

24 Co-distribution of linear associations between ED characteristics and size of Medicaid and uninsured burdens Characteristics in the + or - association cells are based on correlation coefficients that are significantly different from zero (p<.01) Medicaid burden Uninsured burden % senior % Medicare % admit to hospital % pop 65+ Primary care doc per pop ACS % emergent, not avoidable ACS % emergent, avoidable % transfer Located in non-MSA area ACS % nonurgent Located in South State's DSH payment ratio % black or Afican American ED visit rate % resident/intern Any DSH payment ACS % emergnet, primary care treatable Unemployment rate % child Percent in poverty % of admits that are Medicaid % of admits that are uninsured Mean waiting time % left before being seen % IV fluids Income per capita % injury % arrive via ambulance remaining 12 characteristics

25 Role of DSH payments 41% of high-burden EDs receive payments compared with 25% of low- burden EDs State generosity is the highest determinant of whether a hospital receives a DSH payment State mean as standard puts hospitals in heavy-demand States at risk for no financial supplements

26 Probability that an ED received a Medicaid DSH payment by safety-net status and region SOURCE: CDC/NCHS Northeast LSN=.50 HSN=.64 South LSN=.28 HSN=.33 West LSN=.06 HSN=.47 Midwest LSN=.20 HSN=.56

27 Regional variation in rates of uninsured and Medicaid persons under age 65: United States, NortheastMidwestSouthWest Rate per 100 persons UninsuredMedicaid SOURCE: A Shared Destiny (IOM 2003)

28 Comparison of percent distributions Uninsured persons Medicaid enrollees High safety-net EDs 0%20%40%60%80%100% NortheastMidwestSouthWest

29 Adjusted odds ratio for ED status of high safety net

30 State Medicaid DSH payment per Medicaid enrollee or uninsured person: United States, 2000 SOURCE: CDC/NCHS DSH per person $400+ $ $ $50-99 <$50 Missing

31 So what? One-third of EDs carry a large burden of Medicaid or uninsured care, rarely both. Hospitals in the southern states are at greatest risk of having high safety-net EDs DSH payments help but vary widely across Nation. More Federal funding may be needed to distribute help more widely.

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