Medicaid/SCHIP cuts and hospital emergency department use

Health Affairs

Volumn 25, Issue 1, 2006, Pages 237-247

  Cunnlngham, Peter J ^a  

^a CENTER FOR STUDYING HEALTH SYSTEM CHANGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADULT; CHILD; CHILD HEALTH CARE; CONFERENCE PAPER; ECONOMICS; EMERGENCY HEALTH SERVICE; HUMAN; INFORMATION PROCESSING; INSURANCE; MEDICAID; MIDDLE AGED; POVERTY; PRESCHOOL CHILD; UNITED STATES; UTILIZATION REVIEW;

ADOLESCENT; ADULT; CHILD; CHILD HEALTH SERVICES; CHILD, PRESCHOOL; DATA COLLECTION; EMERGENCY SERVICE, HOSPITAL; HUMANS; INSURANCE COVERAGE; MEDICAID; MIDDLE AGED; POVERTY; UNITED STATES;

EID: 32044434025     PISSN: 02782715     EISSN: 02782715     Source Type: Journal    
DOI: 10.1377/hlthaff.25.1.237     Document Type: Conference Paper

References (23)

1. L.R. Brewster, L.S. Rudell, and C.S. Lesser, "Emergency Room Diversions: A Symptom of Hospitals under Stress," Issue Brief no. 38 (Washington: Center for Studying Health System Change, 2001).
2. P.J. Cunningham and J.H. May, "Insured Americans Drive Surge in Emergency Department Visits," Issue Brief no. 70 (Washington: HSC, 2003).
3. P.J. Cunningham and L.M. Nichols, "The Effects of Medicaid Reimbursement on Access to Care of Medic-aid Enrollees: A Community Perspective," Medical Care Research and Review (forthcoming);
4. and A.F. Coburn, S.H. Long, and M.S. Marquis, "Effects of Changing Medicaid Fees on Physician Participation and Enrollee Access," Inquiry 36, no. 3 (1999): 265-279.
5. Bureau of Primary Health Care, "The Uniform Data System - Data" (calendar year 2003 data), http://bphc.hrsa.gov/uds/data.htm (accessed 28 October 2005).
6. R. Strouse, B. Carlson, and J. Hall, Community Tracking Study: Household Survey Methodology Report 2000-01 (Round Three), Technical Pub. no. 46, http://www.hschange.com/CONTENT/602/602.pdf (accessed 28 October 2005).
7. The sample is restricted to low-income people so that comparisons of ED use across insurance coverage groups are made for people with roughly similar income levels. The definition of "low income" used to identify the sample for this analysis did not take into account differences across communities in the cost of living. However, sensitivity analyses show that adjustments for cost-of-living differences would have virtually no impact either on point estimates of ED use by insurance coverage or on simulating the effects of Medicaid enrollment reductions.
8. Since ED and physician visits reflect the preceding twelve months, some people with a certain type of coverage on the day of the interview could have had a different type of coverage for some or all of their ED visits (that is, changed coverage during the year). The CTS data indicate that about 80 percent of people had the same type of coverage throughout the year as they had on the day of the interview. Restricting the analysis to people who had the same type of coverage throughout the year did not change the results.
9. The details of the research methodology are described extensively in an online Research Brief, available at http://content.healthaffairs.org/cgi/ content/full/25/1/237/DC1.
10. To account for the possibility of selection bias in the analysis, a conventional two-stage least-squares model was estimated. The first-stage models predicted the probability of private insurance and Medic-aid/SCHIP coverage for each sample person, with key instrumental variables in the analysis including public program eligibility, health insurance costs, and employment characteristics. The predicted insurance coverage variables were then entered into a second-stage model of ED use. The effects of predicted insurance coverage on ED use exhibited, the same general patterns as the actual measures of insurance coverage, although the low level of statistical precision of these estimates makes them unsuitable for predicting effects on aggregate ED use.
11. The simulation involved recomputing ED visits per person for 25 percent of the weighted Medicaid/SCHIP population and adding this total to the uninsured and privately insured, visit counts. For the majority of the 25 percent sample who become uninsured, ED visits per person were computed, by subtracting the adjusted difference in ED visits per person between Medicaid/SCHIP and uninsured people (Exhibit 3) from the actual number of ED visits per person for Medicaid/SCHIP (which reflects the ED visit rate for uninsured people who have the same sample characteristics as Medicaid/SCHIP enrollees). For the small number of the 25 percent who are assumed to enroll in private coverage, ED visit rates are computed by subtracting the adjusted difference in ED use between Medicaid/SCHIP and private insurance from the actual number of ED visits for Medicaid/SCHIP.
12. D.C. Ross and L. Cox, Beneath the Surface: Barriers Threaten to Slow Progress on Expanding Health Coverage of Children and Families (Washington: Kaiser Commission on Medicaid and the Uninsured, October 2004);
13. C. Mann and S. Artiga, The Impact of Recent Changs in Health Care Coverage for Low Income People: A First Look at the Research Following Changes in Oregon's Medicaid Plan (Washington: Kaiser Commission, June 2004);
14. and L. Ku and V. Wachino, The Potential Impact of Eliminating TennCare and Reverting to Medicaid: A Preliminary Analysis (Washington: Center on Budget and Policy Priorities, 15 November 2004).
15. S. Parrott, A. Sherman, and B. Hardy, "House Budget Resolution Would Require Much Deeper Cuts in Key Low-Income Programs than Senate Budget Plan," 7 April 2005, http://www.cbpp.org/3-30-05bud.pdf (accessed 28 October 2005).
16. It is unlikely that all who have access to employer coverage would enroll in that coverage. However, there is no previous research to offer guidance on what the take-up rate would be for Medicaid/SCHIP enrollees who lose coverage, and sensitivity analysis shows that assuming a lower employer coverage takeup rate (for example, 60 percent) would have little effect on the results.
17. Cunningham and Nichols, "The Effects of Medicaid Reimbursement."
18. N. Diaz-Tena et al., "Community Tracking Study, Physician Survey Methodology Report 2000-01 (Round 3)," Technical Pub. no. 38 (Washington: HSC, 2003). Site-specific estimates from the 2000-01 survey were used for both the 2000-01 and 2003 household survey samples in this analysis, since the 2003 physician survey was not available at the time of this writing. Ideally, separate measures for specialists and general practitioners would have been constructed, since they might differ in their willingness to see Medicaid patients. However, site-specific samples of physicians are not large enough in many sites to support statistically valid estimates separately for specialists and generalists.
19. Similar measures have been linked to the CTS data in other analyses of the effects of safety-net providers on medical care access and use. See P. Cunningham and J. Hadley, "Expanding Care versus Expanding Coverage: How to Improve Access to Care," Health Affairs 23, no. 4 (2004): 234-244;
20. and J. Hadley and P. Cunningham, "Availability of Safety Net Providers and Access to Care of Uninsured Persons," Health Services Research 39, no. 5 (2004): 1527-1546. Although the sample for this analysis uses the threshold of 300 percent of poverty, the measure of CHC capacity is normalized, using 100 percent of poverty because of the availability of census data by ZIP code, and because CHCs are used disproportionately by the poor.
21. The results for the effects of Medicaid acceptance rates and CHC capacity on ED use for Medicaid/SCHIP enrollees are based on the same regression models, run separately for Medicaid/SCHIP enrollees. To view the results of this analysis, see the online Research Brief (Note 10).
22. Mann and Artiga, The Impact of Recent Changes.
23. Cunningham and. May, "Insured Americans Drive Surge."