Hellinger, F. J. (1976). The effect of certificate-of-need legislation on hospital investment. Inquiry, 13(2), 187–193.
CON legislation induced hospitals to increase investments before CON took effect. He interprets this as a bad result. We code it as positive since it did increase access (in the short run).
Salkever, D. S., & Bice, T. W. (1976). The impact of certificate-of need controls on hospital investment. The Milbank Memorial Fund Quarterly. Health and Society, 54(2),185–214.
CON does not decrease investment but does change its composition.
Salkever, D. S., & Bice, T. W. (1979). Hospital certificate-of-need controls: Impact on investment, costs, and use. American Enterprise Institute. https://www.aei.org/research-products/book/hospital-certificate-of-need-controls-impact-on-investment-costs-and-use/.
They assess the effect of CON on a cross section, time series data set that covers all states from 1968 to 1972. They find that CON is associated with: 1) At best, a modest reduction in total spending per capita; 2) A small increase in average inpatient cost per inpatient day; and 3) Reduced inpatient days per capita.
Joskow, P. L. (1980). The effects of competition and regulation on hospital bed supply and the reservation quality of the hospital. The Bell Journal of Economics, 11(2),421–447.
He assesses the effects of regulations on bed supply and the probability that a hospital will turn away patients. He finds that CON reduces bed supply by about 6% and makes it more likely that a hospital will turn away patients.
Sloan, F. A., & Steinwald, B. (1980). Effects of regulation on hospital costs and input use. The Journal of Law and Economics, 23(1), 81–109.
Comprehensive CON programs have no effect on hospital expenditures per patient day, while noncomprehensive programs increase hospital expenditures by 5% per patient day.
Sloan, F. A. (1981). Regulation and the rising cost of hospital care. The Review of Economics and Statistics, 63(4),479–487. https://doi.org/10.2307/1935842.
He studies the effects of both mature and new CON regulations on hospital costs and profits. His data is drawn from the 48 contiguous states, plus DC, over the years 1963-1978. His measures of cost are total hospital expense per admission, per adjusted admission, per patient day, and per adjusted patient day. His measure of profit is the ratio of total revenue to total expense. He finds: 1) Total expense per admission was lower in the years after CON was implemented for part of the period studied; 2) Expense per adjusted admission was not statistically significantly different after CON was implemented; 3) Expense per patient day was not statistically significantly different after CON was implemented; 4) Expense per adjusted patient day was not statistically significantly different after CON was implemented; and 5) Profits were lower after CON was implemented.
Coelen, C., & Sullivan, D. (1981). An analysis of the effects of prospective reimbursement programs on hospital expenditures. Health Care Financing Review, 2(3), 1–40.
They use data from a sample of approximately 2700 community hospitals in the U.S. from 1969 to 1978 to estimate the effects of prospective reimbursement programs on hospital expenditures per patient day, per admission, and per capita. Though their primary interest is in prospective reimbursement programs, they also included CON as a covariate. They find no evidence that CON reduces spending per patient day, per admission, or per capita and some evidence that it increases expenditures. And in about half the states they find evidence that it is associated with higher spending per patient day, per admission, and per capita.
Cromwell, J., & Kanak, J. R. (1982). The effects of prospective reimbursement programs on hospital adoption and service sharing. Health Care Financing Review, 4(2), 67–88. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4191291/.
Their primary focus is on prospective reimbursement programs and their effect on the diffusion of services, but they use CON as a control variable and find that it has no effect on the diffusion of services.
Eastaugh, S. R. (1982). The effectiveness of community-based hospital planning: some recent evidence. Applied Economics, 14(5), 475–490. https://doi.org/10.1080/00036848200000043.
He assesses the effects of CON on change in plant assets, change in beds, and change in plant assets per bed during the 1975-1979 period. His data are from 50 states, and his measure of CON is the percentage of the 1975-1979 period in which a CON program was in effect in each state. He finds CON has: 1) A marginally significant, positive effect on change in plant assets (percentage and log), which he interprets as a negative result; 2) No statistically significant effect on change in beds (percentage and log), which he interprets as a negative result; and 3) Significant, positive effect on change in plant assets per bed (percentage and log), which he interprets as a negative result.
Sloan, F. A. (1983). Rate regulation as a strategy for hospital cost control: evidence from the last decade. The Milbank Memorial Fund Quarterly. Health and Society, 61(2),195–221. https://doi.org/10.2307/3349905.
His primary interest is the effect of rate regulation on hospital costs, but he includes CON as a control. His data is drawn from the 48 contiguous states, plus DC, over the years 1963-1980. His measures of spending are total hospital expense per admission, per “adjusted” admission (adjusted for hospital outpatient activity), per patient day, per adjusted patient day, and per length of stay. He finds no evidence that CON reduces spending per patient.
Lee, A. J., Birnbaum, H., & Bishop, C. (1983). How nursing homes behave: A multi-equation model of nursing home behavior. Social Science & Medicine, 17(23), 1897–1906. https://doi.org/10.1016/0277-9536(83)90167-3.
The paper assesses the effect of various policies on nursing home behavior using the 1973 National Nursing Home Survey. Of relevance here, they find that CON is associated with: 1) Higher operating costs per patient day and 2) Higher average annual occupancy.
Ashby J. L., Jr. (1984). The impact of hospital regulatory programs on per capita costs, utilization, and capital investment. Inquiry, 21(1), 45–59.
He assesses the effect of CON and other regulatory programs on five outcomes. His unit of analysis is each state in each year from 1971- 1977. He finds that: 1) CON is associated with statistically significant positive growth in hospital costs per capita; 2) CON has no statistically significant effect on percentage change in average length of stay; 3) CON has no statistically significant effect on percentage change in total admissions per capita; and 4) CON has no statistically significant effect on percentage change in plant assets.
Gertler, Paul J., (October 1985). A Latent Variable Model of Quality Determination. (Working Paper). Nat’l Bureau of Economic Research. https://www.nber.org/papers/w1750.
He finds that under a binding CON capacity constraint, increases in Medicaid rates are associated with lower quality in New York state nursing home facilities.
Anderson, K. B., & Kass, D. I. (1986). Certificate of need regulation of Entry Into Home Health Care: A Multi-product Cost Function Analysis, an Economic Policy Analysis. Federal Trade Commission. https://www.ftc.gov/sites/default/files/documents/reports/certificate-need-regulation-entry-home-health-care/231954.pdf.
They examined the effect of CON on economies of scale and cost in the home health care industry. They find: 1) Costs were 2% higher in CON states relative to non-CON states; 2) No substantial economies of scale in the home health industry overall; and 3) No difference in economies of scale in CON and non-CON states.
Noether, M. (1988) Competition among hospitals, Journal of Health Economics 7(3), 259–284.
CON increases the average price and expense for several disease categories including: 1) Diabetes mellitus; 2) Cataract surgery; 3) Acute myocardial infarction; 4) Congestive heart failure; 5) Acute, cerebrovascular disease; 6) Pneumonia; 7) Other respiratory system disease; 8) Inguinal hernia; 9) Diverticula of intestine; 10) Hyperplasia of prostate; and 11) Fracture of neck and femur.
Sherman, D. (1988). The effect of state certificate-of-need laws on hospital costs: An economic policy analysis. Bureau of Economics, Federal Trade Commission. https://www.ftc.gov/reports/effect-state-certificate-need-laws-hospital-costs-economic-policy-analysis.
He estimates the effects of CON on cost functions using a sample of 3708 hospitals using 1983-1984 data. Though he uses the term costs, he is actually measuring operating expenditures. He finds that spending would fall by 1.4% if states relaxed CON by raising the thresholds at which it is applied.
Shortell, S. M., & Hughes, E. F. (1988). The effects of regulation, competition, and ownership on mortality rates among hospital inpatients. New England Journal of Medicine, 318(17), 1100–1107. https://doi.org/10.1056/NEJM198804283181705.
They examined the effect of CON (among other factors) on hospital quality, finding that the ratio of actual to predicted mortality rates among Medicare patients were 5 to 6% higher in state with stringent CON regulation.
Mayo, J. W., & McFarland, D. A. (1989). Regulation, market structure, and hospital costs. Southern Economic Journal, 55(3),559–569. https://doi.org/10.2307/1059572.
They study the effect of variation in CON approval in different service areas of Tennessee on the number of beds, finding it is associated with fewer beds. They also find that larger hospital size is associated with more spending and infer that CON is associated with lower average spending per patient day, though they don’t directly measure it.
Anderson, K. B. (1991). Regulation, market structure, and hospital costs: comment. Southern Economic Journal, 58(2),528–534. https://doi.org/10.2307/1060194.
This is a reply to Mayo and McFarland’s 1989 paper. Anderson estimates the effects of CON (and the number of years CON has been in effect) on average variable costs among 2,069 general acute hospitals with 100 or more beds. He uses CON age as a measure of CON stringency under the theory that “the effect should increase the longer the regulation has been around.” He applies the equation linearly and multiplied by the number of beds to see if CON has a different effect on large hospitals. He finds: 1) CON is associated with 10% higher variable costs and 2) CON is associated with greater probability of a hospital having 100 or fewer beds.
Eakin, B. K. (1991). Allocative inefficiency in the production of hospital services. Southern Economic Journal, 58(1), 240–248. https://doi.org/10.2307/1060045.
CON hospitals are less efficient than non-CON hospitals.
Lanning, J. A., Morrisey, M. A., & Ohsfeldt, R. L. (1991). Endogenous hospital regulation and its effects on hospital and non-hospital expenditures. Journal of Regulatory Economics, 3, 137–154. https://doi.org/10.1007/BF00140955.
They measure the effect of CON on hospital expenditures, finding that it is associated with 20.6% higher spending per capita.
Mayo, J. W., & McFarland, D. A. (1991). Regulation, market structure, and hospital costs: reply. Southern Economic Journal, 58(2),535-538. https://doi.org/10.2307/1060195.
This is a reply to Anderson’s (1991) critique of their 1989 paper. Anderson worried CON might constrain hospitals on one dimension (say beds), but then cause them to substitute into other areas of spending (say labor). They tested this possibility and found mixed results. In a larger panel dataset, they found support for Anderson’s concern (CON increases spending), while in a 1984 cross-section they found support for their initial (implied) conclusion (CON decreases spending).
Swan, J., & Harrington, C. (1991). Certificate of need and nursing home bed capacity in states. Journal of Health and Social Policy, 2(2), 87–105. https://doi.org/10.1300/j045v02n02_06.
They assess the effect of nursing home CONs on nursing home bed stock using cross-section, time-series data (1981–1984). They find: 1) Nursing home CONs constrain the bed stock and 2) The greater the dollar amount of CON approvals per aged population (a measure of CON stringency), the greater the bed stock.
Campbell, E. S., & Ahern, M. W. (1993). Have procompetitive changes altered hospital provision of indigent care?. Health Economics, 2(3), 281–289. https://doi.org/10.1002/hec.4730020311.
Private nonprofit hospitals that are more profitable offer more uncompensated care. This suggests the possibility of a quid pro quo, but they do not actually test CON.
Campbell, E. S., & Fournier, G. M. (1993). Certificate-of-need deregulation and indigent hospital care. Journal of Health Politics, Policy and Law, 18(4), 905–925. https://doi.org/10.1215/03616878-18-4-905.
Not a direct test of CON, they find that CONs are more likely to be awarded to hospitals that provide more indigent care.
Ford, J. M., & Kaserman, D. L. (1993). Certificate-of-need regulation and entry: Evidence from the dialysis industry. Southern Economic Journal, 59(4),783–791. https://doi.org/10.2307/1059739.
They assess the effect of CON on the number of dialysis clinics and stations, finding that it has limited new firm entry and total capacity.
Mendelson, D. N., & Arnold, J. (1993). Certificate of need revisited. Spectrum (Lexington, Ky.), 66(1), 36-44.
They find that Ohio denied CON applications that could have had adverse effects on the financial viability of safety net hospitals, but it was not a direct test of CON.
Nyman, J. A. (1994). The effects of market concentration and excess demand on the price of nursing home care. The Journal of Industrial Economics, 42(2),193–204. https://doi.org/10.2307/2950490.
He doesn’t directly test CON, but rather tests the effect of market concentration and excess demand on nursing home prices. Since CON is likely to make both matters worse, he concludes that CON likely undermines its goals.
Zinn, J. S. (1994). Market competition and the quality of nursing home care. Journal of Health Politics, Policy and Law, 19(3), 555–582. https://doi.org/10.1215/03616878-19-3-555.
She examined the determinants of nursing home quality. One of her explanatory variables was nursing home construction moratoria. She finds these to be associated with lower RN staffing ratios and greater use of physical restraint.
Antel, J. J., Ohsfeldt, R. L., & Becker, E. R. (1995). State regulation and hospital costs. The Review of Economics and Statistics, 77(3), 416–422. https://doi.org/10.2307/2109904.
They find that CON increases per-day and per-admission hospital expenditures but has no relationship to per capita hospital expenditures.
Caudill, S. B., Ford, J. M., & Kaserman, D. L. (1995). Certificate‐of‐need regulation and the diffusion of innovations: a random coefficient model. Journal of Applied Econometrics, 10(1), 73–78.
They examine the effect of CON on the diffusion of hemodialysis an effective and practical treatment for chronic renal failure. Their data span 50 states and 14 years. They find that CON regulation slows the spread of hemodialysis.
Fournier, G. M., & Campbell, E. S. (1997). Indigent care as quid pro quo in hospital regulation. Review of Economics and Statistics, 79(4), 669–673. https://doi.org/10.1162/003465397557088.
They find that Florida awarded CON licenses to hospitals providing more care to the poor, though they don’t directly test whether CON increases indigent care.
Harrington, et al. (1997). The effect of certificate of need and moratoria policy on change in nursing home beds in the United States. Medical Care, 35(8),574–588.
In a two-stage least squares regression, they assess the effect of CON and/or moratoria on the growth of nursing home beds and Medicaid nursing home reimbursement rates. They find: 1) CON had no effect on Medicaid nursing home reimbursement rates and 2) CON reduced growth of beds.
Conover, C. J., & Sloan, F. A. (1998). Does removing certificate-of-need regulations lead to a surge in health care spending?. Journal of Health Politics, Policy and Law, 23(3), 455–481. https://doi.org/10.1215/03616878-23-3-455.
CON has no effect on total per capita health expenditures; there is no evidence of a surge in spending after repeal.
D’aunno, T., Succi, M., & Alexander, J. A. (2000). The role of institutional and market forces in divergent organizational change. Administrative Science Quarterly, 45(4), 679–703. https://doi.org/10.2307/2667016.
They study the market and institutional determinants of radical organizational change in rural hospitals. In particular, they study the factors that make a rural hospital likely to change to provide other types of services. They find that stronger CON regulation makes a rural hospital 8% less likely to change.
Robinson, J. L., et al. (2001). Certificate of need and the quality of cardiac surgery. American Journal of Medical Quality, 16(5), 155–160.
They examined the effect of CON elimination in PA (comparing it with NJ, which maintained CON) on: 1) The number of open-heart surgery programs, which increased 25% following elimination of CON; 2) The total volume of CABG surgeries, which were unchanged following repeal; 3) Provider volume, which shifted from programs that had been established before CON repeal to programs that were established after CON repeal; and 4) Mortality rate, which was unchanged following repeal.
Miller, N. A., Harrington, C., & Goldstein, E. (2002). Access to community-based long-term care: Medicaid’s role. Journal of Aging and Health, 14(1), 138–159. https://doi.org/10.1177/089826430201400108.
They find that CON increases per capita Medicaid community-based care expenditures.
Vaughan-Sarrazin, M. S., et al. (2002). Mortality in Medicare beneficiaries following coronary artery bypass graft surgery in states with and without certificate of need regulation. JAMA, 288(15), 1859–1866.
They assess the effect of CON on coronary artery bypass graft (CABG) surgery, finding: 1) Mean annual hospital volume is lower in states without CON; 2) More patients undergo CABG surgery in low-volume hospitals in states without CON; and 3) Mortality following CABG is higher in states without CON.
Grabowski, D. C., Ohsfeldt, R. L., & Morrisey, M. A. (2003). The effects of CON repeal on Medicaid nursing home and long-term care expenditures. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 40(2), 146–157. https://doi.org/10.5034/inquiryjrnl_40.2.146.
CON repeal: 1) Has no statistically significant effect on per diem Medicaid nursing home charges; 2) No effect on per diem Medicaid long-term-care charges; and 3) No effect on days.
Gulley, O. D., & Santerre, R. E. (2003). The Effect of Public Policies on Nursing Home Care in the United States. Eastern Economic Journal, 29(1), 93–104.
They look at the effects of several public policies on nursing home residents and nursing home beds per person 65 years old and older. Their data are from a cross-section of counties in 1991. Their measure of CON is the number of years in which a CON law has been in effect. They find that in states where CON has been in effect for longer: 1) There are fewer nursing home beds per persons 65 years old and older, but the effect is not statistically significant and 2) There are fewer nursing home patients per persons 65 and older, but this effect is also statistically insignificant.
Conover, C. J., & Sloan, F. A. (2003). Evaluation of Certificate of Need in Michigan. Volume II: Technical appendices. Raleigh, NC:Duke University Center for Health Policy, Law and Management.
Repealing CON has 0% effect on all expenditures.
Teske, P., & Chard, R. (2004). Hospital Certificates-of-Need. Regulation in the States, Brookings Institute, 125–132. https://epdf.pub/regulation-in-the-states.html.
This study examines several political factors to determine the likelihood of a state retaining CON regulation. They find that the following factors are associated with CON regulation: 1) Democrats in upper and lower houses; 2) Higher hospital costs; 3) More affluent and better-educated citizens; 4) Fewer physicians; and 5) A variable measuring hospital interests: the number of hospital industry–related interest groups active in a particular state multiplied by their average political action committee spending: They find this to be significantly associated with retention of CON, but legislative party makeup is more important.
Ho, V. (2004). Certificate of need, volume, and percutaneous transluminal coronary angioplasty outcomes. American Heart Journal, 147(3), 442–448.
She compares Florida, where there is a CON for percutaneous transluminal coronary angioplasty (PTCA) with California, where there is no such CON. She finds: 1) CON is associated with higher in-hospital volume for PTCA and 2) There is a positive relationship between PTCA volume and mortality outcomes (though note that she does not directly study the relationship between CON and PTCA mortality outcomes).
Chen, C. C. (2005). Estimating nursing home cost and production functions: Application of stochastic frontier models for the analysis of efficiency [Doctoral dissertation, Tulane University], ProQuest Dissertations & Theses Global. http://www.proquest.com/docview/305399421/abstract/F9AE5D67757C4ACAPQ/1.
Nursing home CONs are associated with greater cost efficiency but diminished technical efficiency.
Bates, L. J., Mukherjee, K., & Santerre, R. E. (2006). Market structure and technical efficiency in the hospital services industry: a DEA approach. Medical Care Research and Review, 63(4), 499–524. https://doi.org/10.1177/1077558706288842.
CON hospitals are not any less efficient than non-CON hospitals.
Custer, W. S., et al. (2006). Report of Data Analyses to the Georgia Commission on the Efficacy of the CON Program. https://scholarworks.gsu.edu/cgi/viewcontent.cgi?article=1017&context=ghpc_reports.
They use a cross-border design to study the effect of CON in hospital markets. This allows them to control for unobservable factors. They also used interviews and public information to develop an index measuring CON rigor based on fees, administrative requirements, reviewability, appeals, and administrative complexity. They assess the effects of CON on acute care, long term care, and home health markets. They find: 1) CON is associated with higher private inpatient acute care costs; 2) Acute care costs rise with the rigor of the CON program for the most resource-intensive acute care diagnoses; 3) Some evidence that CON is associated with higher Medicaid costs for home health services; 4) Weak evidence that CON is associated with higher private long term care costs; 5) Weak evidence that CON is associated higher Medicaid long term care costs; 6) Some evidence that CON is associated with higher per-capita costs for home health services; 7) CON is associated with fewer hospitals; 8) CON is associated with fewer hospital beds; 9) CON is associated with fewer home health agencies per 1000 residents; 10) CON is associated with fewer Medicare beneficiaries receiving home health services; 11) There is no significant relationship between the percentage of hospital admissions that are self-pay, though when controlling for the number of uninsured and family income, CON is positively related to self-pay admission per uninsured; 12) There is no apparent difference in acute care quality in CON and non-CON markets; 13) In long term care, CON is associated with better quality on two measures but worse quality on six measures; 14) In home health markets, they find no evidence that CON affects any of 10 outcome measures of quality; 15) They find that acute care markets are less competitive when CON is rigorous; and 16) CON is associated with lower levels of competition in home health agency markets.
DiSesa, V. J., et al. (2006). Contemporary impact of state certificate-of-need regulations for cardiac surgery: an analysis using the Society of Thoracic Surgeons’ National Cardiac Surgery Database. Circulation, 114(20), 2122–2129.
They study CON, volume, and mortality in coronary artery bypass grafting (CABG). They find: 1) CON is positively associated with CABG volume within hospitals and 2) There is no direct relationship between CON and mortality.
Ho, V. (2006). Does certificate of need affect cardiac outcomes and costs?. International Journal of Health Care Finance and Economics, 6, 300–324.
The study assesses the effect of CON on cardiac costs and outcomes. She finds: 1) While CON is associated with lower average costs per patient, it also seems to be associated with more procedures and this is enough to offset the savings from lower average costs; 2) CON is associated with greater volume within hospitals; and 3) CON does not seem to be related to inpatient mortality.
Popescu, I., Vaughan-Sarrazin, M. S., & Rosenthal, G. E. (2006). Certificate of need regulations and use of coronary revascularization after acute myocardial infarction. Jama, 295(18), 2141–2147.
They study access and quality outcomes in revascularization. They find that patients in CON states: 1) Were less likely to be admitted to hospitals offering revascularization; 2) Were less likely to undergo revascularization; and 3) Had no difference in 30-day mortality rates relative to patients in non-CON states.
Dobson, A., et al. (2007). An Evaluation of Illinois’ Certificate of Need Program. https://cgfa.ilga.gov/Upload/LewinGroupEvalCertOfNeed.pdf.
They find that safety-net hospitals in non-CON states had higher margins than those in CON states.
Ho, V., et al. (2007). Cardiac certificate of need regulations and the availability and use of revascularization services. American Heart Journal, 154(4), 767–775.
They study the association between cardiac CON regulations, availability of revascularization facilities, and revascularization rates, focusing on differences between the general population and the elderly and on differences between procedures (coronary artery bypass graft surgery (CABG) or a percutaneous coronary intervention (PCI)). They find: 1) CON is associated with fewer hospitals offering CABG and PCI; 2) CON has no effect on overall CABG utilization; and 3) CON is associated with 19.2% fewer PCIs per 1,000 elderly.
Rivers, P. A., Fottler, M. D., & Younis, M. Z. (2007). Does certificate of need really contain hospital costs in the United States?. Health Education Journal, 66(3), 229–244. https://doi.org/10.1177/0017896907080127.
They find CON laws increase hospital expenditures per adjusted admission.
Ross, J. S., et al. (2007). Certificate of need regulation and cardiac catheterization appropriateness after acute myocardial infarction. Circulation, 115(8), 1012–1019.
They examine the effect of CON on the volume of cardiac catheterization after admission for acute myocardial infarction. In particular, however, they were interested in procedural volume under different levels of appropriateness (strongly, equivocally, or weakly indicated). While CON did not seem to decrease the volume of strongly-indicated catheterization, it did reduce the volume of equivocally and weakly indicated catheterization. Because their interest is both overall volume and rates of catheterization when it is not warranted, I categorize in both the volume and the quality sections.
Taylor Jr, D. H., et al.(2007). What length of hospice use maximizes reduction in medical expenditures near death in the US Medicare program?. Social Science & Medicine, 65(7), 1466–1478. https://doi.org/10.1016/j.socscimed.2007.05.028.
Hospices are associated with savings of about $2,309 per user. Conover and Bailey use this to figure that “each hospice foregone in a market area represents $230,000 in potential annual savings lost.” See Conover, C. J., & Bailey, J. (2020). Certificate of need laws: a systematic review and cost-effectiveness analysis. BMC Health Services Research, 20, 748. https://doi.org/10.1186/s12913-020-05563-1.
Short, M. N., Aloia, T. A., & Ho, V. (2008). Certificate of need regulations and the availability and use of cancer resections. Annals of surgical oncology, 15, 1837–1845.
They study Medicare data on beneficiaries treated with one of six cancer resections and an associated cancer diagnosis from 1989 to 2002. They find: 1) CON is associated with fewer hospitals per cancer incident for colectomy, rectal resection, and pulmonary lobectomy; 2) CON has no effect on the number of procedures per cancer incident; and 3) CON was associated with greater hospital volume.
Zhang, L. (2008). Uncompensated care provision and the economic behavior of hospitals: The influence of the regulatory environment. Georgia Institute of Technology and Georgia State University. http://scholarworks.gsu.edu/pmap_diss/19.
He examined the effect of three regulatory policies—CON laws, uncompensated care pools, and community benefit requirement laws. CON is associated with small increases in uninsured admissions, though the results were small (0.07%) and not statistically significant when he attempted to control for endogeneity. Furthermore, he finds that in the presence of all three policies, the number of uninsured admissions by nonprofit hospitals fell.
Cantor, J. C., et al. (2009). Reducing racial disparities in coronary angiography. Health Affairs, 28(5), 1521–1531. https://doi.org/10.1377/hlthaff.28.5.1521.
The authors study a 1996 New Jersey reform that created a pilot program to license additional hospitals to perform coronary angiography. They find that a large black-white disparity disappeared after the reform.
DeLia, D., et al. (2009). Effects of regulation and competition on health care disparities: the case of cardiac angiography in New Jersey. Journal of Health Politics, Policy and Law, 34(1), 63–91. https://doi.org/10.1215/03616878-2008-992.
This builds off of the authors’ previous study, confirming the result (the reforms eliminated the black-white disparity) using additional techniques (weighting ZIP codes by the number of black and white residents). They also study the mechanism by which the disparity was eliminated, finding that incumbent hospitals served more black patients as new entrants cut into their market share for white patients.
Garmon, C. (2009). Hospital competition and charity care. Forum for Health Economics & Policy, 12(1). https://doi.org/10.2202/1558-9544.1130.
This is not a direct test of CON. Instead, he tests whether hospital competition is associated with more or less charity care. He finds no evidence that increased competition reduces charity care. Furthermore, he finds some evidence that reduced competition leads to higher prices for uninsured patients.
Hellinger, F. J. (2009). The effect of certificate-of-need laws on hospital beds and healthcare expenditures: an empirical analysis. American Journal of Managed Care, 15(10), 737–744.
CON is associated with fewer hospital beds, which in turn are associated with slower growth in aggregate health expenditures per capita. There is no direct relationship between CON and health expenditures per capita.
Ho, V., Ku‐Goto, M. H., & Jollis, J. G. (2009). Certificate of need (CON) for cardiac care: controversy over the contributions of CON. Health services research, 4(2p1), 483–500. https://doi.org/10.1111/j.1475-6773.2008.00933.x.
They use difference-in-difference regression analysis to compare states that dropped CON during the sample period with states that kept the regulation. They focused on coronary artery bypass graft surgery (CABG) and percutaneous coronary interventions (PCI). They find that in states that dropped CON: 1) The number of hospitals in the state performing CABG and PCI went up following repeal; 2) Statewide procedural volume for CABG and PCI were unchanged; 3) Mean hospital volume declined for both procedures; and 4) Procedural CABG mortality declined after repeal, though the difference was not permanent.
Kolstad, J. T. (2009). Essays on information, competition and quality in health care provider markets. Harvard University. https://healthpolicy.fas.harvard.edu/people/jonathan-kolstad.
He examined how the 1996 repeal of CON legislation in Pennsylvania affected the market for coronary artery bypass graft (CABG) surgery in the state, finding: 1) The number of CABG facilities increased 46% and 2) Surgeries were more likely to be performed by high-quality surgeons.
Tynan, A., et al. (2009). General hospitals, specialty hospitals and financially vulnerable patients. Center for Studying Health System Change.
Not a direct test of CON, they find that general hospitals are able to cope with the entry of specialty hospitals with little change in the provision of care for financially vulnerable patients. The analysis was done in three markets with established specialty hospitals—Indianapolis, IN, Phoenix, AZ, and Little Rock, AR.
Carlson, M. D., et al. (2010). Geographic access to hospice in the United States. Journal of Palliative Medicine,13(11), 1331–1338. https://doi.org/10.1089/jpm.2010.0209.
This is a cross-sectional study of geographic access to U.S. hospices using multivariate logistic regression to identify gaps in hospice availability (measured by distance to hospice facilities) by community characteristics. CON was associated with longer travel distance to hospice care.
Cutler, D. M., Huckman, R. S., & Kolstad, J. T. (2010). Input constraints and the efficiency of entry: Lessons from cardiac surgery. American Economic Journal: Economic Policy, 2(1), 51–76.
They assess the 1996 repeal of CON in Pennsylvania on coronary artery bypass graft. They find: 1) Repeal of CON reduced travel distance by 9%; 2) There was no statistically significant effect on total volume following CON repeal; 3) There were mixed results on scale; following CON repeal, fewer surgeries were performed by high-volume hospitals, but more were performed by high-volume surgeons; 4) CON repeal led to a shift from standard quality to high-quality surgeons; and 5) Incumbent hospital margins initially fell following repeal but these hospitals had regained profitability and were the most profitable by 2002.
Ferrier, G. D., Leleu, H., & Valdmanis, V. G. (2010). The impact of CON regulation on hospital efficiency. Health Care Management Science, 13, 84–100.
CON hospitals are more efficient than non-CON hospitals.
Rivers, P. A., Fottler, M. D., & Frimpong, J. A. (2010). The Effects of Certificate of Need Regulation on Hospital Costs. Journal of Health Care Finance, 36(4), 1–16.
They find that stringent CON programs increase hospital expenditures per admission.
Shamji, E. C., & Shamji, M. F. (2010). Effect of US State Certificate of Need regulation of operating rooms on surgical resident training. Clinical and Investigative Medicine, E78–E84.
They evaluate the mean per capita rates of 26 diverse surgical procedures in 21 CON and five non-CON states between 2004 and 2006. The proportion of procedures performed in teaching facilities was also assessed. They find no significant difference in procedural rates between CON and non-CON states.
Vaughan Sarrazin, M. S., Bayman, L., & Cram, P. (2010). Trends during 1993-2004 in the availability and use of revascularization after acute myocardial infarction in markets affected by certificate of need regulations. Medical care research and review, 67(2), 213–231. https://doi.org/10.1177/1077558709346565.
In a study design that exploits the fact that some markets cross boundaries between CON and non-CON states, they find: 1) A greater increase in coronary artery bypass graft surgery programs in states that reduced CON regulation and 2) No change in percutaneous coronary intervention programs in states that reduced CON.
Eichmann, T. L., & Santerre, R. E. (2011). Do hospital chief executive officers extract rents from Certificate of Need laws?. Journal of Health Care Finance, 37(4), 1–14.
They study the effects of CON on access and rents. They find CON is associated with: 1) 12% fewer beds per capita; 2) 48% fewer hospitals per capita; and 3) $91,000 more in urban hospital CEO pay.
Granderson, G. (2011). The impacts of hospital alliance membership, alliance size, and repealing certificate of need regulation, on the cost efficiency of non‐profit hospitals. Managerial and Decision Economics, 32(3), 159–173. https://doi.org/10.1002/mde.1524.
He studies the effect of hospital alliance membership, alliance size, and CON on hospital cost efficiency among 144 urban Midwest hospitals from 1996 to 1999. He finds that repeal of CON resulted in greater hospital efficiency, as measured by a stochastic cost frontier.
Jacobs, B. L., et al. (2012). Certificate of Need Regulations and the Diffusion of Intensity-Modulated Radiotherapy. Urology, 80(5), 1015–1020. https://doi.org/10.1016/j.urology.2012.07.042.
They examine whether CON reduces the use of a questionably-warranted procedure, radiotherapy, for prostate cancer. They find no difference in the use of the procedure in CON and non-CON health service areas. In fact, in HSAs with high-stringency CONs, they find greater use of the procedure.
Lorch, S. A., Maheshwari, P., & Even-Shoshan, O. (2012). The impact of certificate of need programs on neonatal intensive care units. Journal of Perinatology, 32(1), 39–44.
They study NICU CONs. They find: 1) CON is associated with fewer units; 2) CON is associated with fewer beds; 3) CON was unrelated to very low birth weight, infant mortality, and low birth weight infant mortality; and 4) CON is associated with lower rates of all-infant mortality in states with a large metropolitan area.
Nelson, C., et al. (2012). Certificate of Need (CON) Status and its Impact on Overtreatment of Low-risk Prostate Cancer in the Elderly. International Journal of Radiation Oncology, Biology, Physics, 84(3), S123–S124. https://doi.org/10.1016/j.ijrobp.2012.07.121.
They examine whether CON reduces the use of a questionably-warranted procedure, definitive intensity modulated radiation therapy (IMRT), among 155,379 men between 2004 and 2007. They find no evidence that limits the use of the procedure.
Delamater, P. L., et al. (2013). Do more hospital beds lead to higher hospitalization rates? A spatial examination of Roemer’s law. PloS One, 8(2), e54900. https://doi.org/10.1371/journal.pone.0054900.
Not a direct test of CON, this is an assessment of Roemer’s Law—the idea that a built hospital bed tends to be filled. They find evidence for the theory.
Ho, V., & Ku-Goto, M. H. (2013). State deregulation and Medicare costs for acute cardiac care. Medical Care Research and Review, 70(2), 185–205. https://doi.org/10.1177/1077558712459681.
Removing CON decreases the cost of coronary artery bypass grafts, but not for percutaneous coronary intervention. In Ohio, reimbursements fell 2.8% following repeal of CON and in Pennsylvania, they fell 8.8% following repeal.
Jacobs, B. L., et al. (2013). Certificate of need legislation and the dissemination of robotic surgery for prostate cancer. The Journal of Urology, 189(1), 80–85. https://doi.org/10.1016/j.juro.2012.08.185.
They study whether CON restrains the use of a questionable procedure—robotic prostatectomy. They find that CON stringency had no effect on the use of the procedure.
Lu-Yao, G. L., et al. (2013). Certificate of need and use of IMRT in elderly patients with low-risk prostate cancer, Journal of Clinical Oncology, 31(6, Suppl.), S204. https://doi.org/10.1200/jco.2013.31.6_suppl.204.
They study whether CON limits the use of IMRT (intensity modulated radiation therapy) in a population that would likely benefit from it the least: older or debilitated men with low-risk prostate cancer. They find that CON laws actually encourage the procedure.
Khanna, A., et al. (2013). Certificate of need programs, intensity modulated radiation therapy use and the cost of prostate cancer care. The Journal of Urology, 189(1), 75–79. https://doi.org/10.1016/j.juro.2012.08.181.
The authors focus on intensity modulated radiation therapy. They find: 1) CON is not associated with any difference in cost growth and 2) CON is associated with greater growth in intensity modulated radiation therapy, which is an expensive and no more effective treatment, so they interpret this as a negative quality result.
Paul, J. A., Ni, H., & Bagchi, A. (2014). Effect of certificate of need law on emergency department length of stay. The Journal of Emergency Medicine, 47(4), 453–461. https://doi.org/10.1016/j.jemermed.2014.04.027.
They study the effect of CON and CON stringency on the length of stay in emergency room departments. They find that CON is associated with shorter ED stays, which they interpret as quality-enhancing, though it is not clear why this is definitely a good thing. More stringent CON regulation undermines the effect.
Polsky, D., et al. (2014). The effect of entry regulation in the health care sector: The case of home health. Journal of Public Economics, 110, 1–14. https://doi.org/10.1016/j.jpubeco.2013.11.003.
They assess the effect of CON on home health agencies, using a research design that focuses on markets that straddle CON and non-CON states. They find: 1) Medicare expenditures are not statistically significantly different between CON and non-CON states; 2) Non-CON states have roughly twice as many home health agencies per Medicare beneficiary; 3) CON states have 13.7% fewer home health admissions from hospitals; 4) 60 day (total) readmission rates are 5% higher in CON states than in non-CON states, though the effect is not sustained; 5) 60 day preventable readmission rates are 13% higher in CON states than in non-CON states, though the effect is not sustained; 6) In CON states there are fewer home health visits, fewer visits per week, and a lower proportion of visits by skilled nurses, but the effects are small and not statistically significant; and 7) The Herfindahl Index in the home health market is approximately 1,000 points lower in non-CON states.
Rosko, M. D., & Mutter, R. L. (2014). The association of hospital cost-inefficiency with certificate-of-need regulation. Medical Care Research and Review, 71(3), 280–298. https://doi.org/10.1177/1077558713519167.
CON hospitals are more efficient than non-CON hospitals.
Stratmann, T., & Russ, J. (July 2014). Do Certificate-of-Need Laws Increase Indigent Care?.(Working Paper No. 14-20). Mercatus Center at George Mason University. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3211637.
They study the effects of CON on the supply of services and provision of services to indigent populations. They find: 1) CON programs are associated with 99 fewer hospital beds per 100,000 people; 2) Bed-specific CONs are associated with 131 fewer beds per 100,000 people; 3) There are 4.7 fewer beds per 100,000 persons for each additional service covered by CON; 4) CON programs reduce the number of hospitals with MRI machines by 1 to 2 hospitals per 500,000 people; and 5) CON programs that require charitable care are uncorrelated with uncompensated care.
Chui, P. W., et al. (2015). Association of state certificate of need regulations with the appropriateness of PCI procedures. Circulation, 132(Suppl_3), A18805. https://doi.org/10.1161/circ.132.suppl_3.18805.
To see if CON limits the use of inappropriate percutaneous coronary interventions, they looked at the share of procedures considered appropriate, uncertain, or inappropriate in CON and non-CON states. They find that states with CON have a lower proportion of inappropriate PCIs, but the differences were small.
Falchook, A. D., & Chen, R. C. (2015). Association between certificate of need legislation and radiation therapy use among elderly patients with early cancers. International Journal of Radiation Oncology, Biology, Physics, 91(2), 448–450. https://doi.org/10.1016/j.ijrobp.2014.10.033.
They examined utilization of radiation therapy when it is not warranted in CON and non-CON states, concluding that in CON states there is greater use of this treatment on elderly patients who may not need it.
Horwitz, J. R., & Polsky, D. (2015). Cross border effects of state health technology regulation. American Journal of Health Economics, 1(1), 101–123. https://doi.org/10.1162/ajhe_a_00005.
They use a cross-border design to estimate the effect of CON on MRI machines. They find that in a CON county that borders a non-CON county there are 6.4 fewer MRIs per million people.
Li, S., & Dor, A. (2015). How do hospitals respond to market entry? Evidence from a deregulated market for cardiac revascularization. Health economics, 24(8), 990–1008. https://doi.org/10.1002/hec.3079.
Removal of CON is associated with: 1) A substantial increase in the number of hospitals performing cardiac revascularization procedures; 2) An overall downward trend in coronary artery bypass graft (CABG) and an overall upward trend in the alternative procedure, percutaneous coronary intervention (PCI); 3) Entry led to a significant increase in the likelihood of CABG, relative to trend, but it did not contribute to the increase in PCI after adjusting for patient traits, market characteristics, and area-specific trends; 4) The probability of receiving PCI specifically at incumbent hospitals decreased with market entry, suggesting a volume shift from incumbents to entrants; 5) Entry shifted a disproportionate volume of low-severity patients from incumbent hospitals to entrants; and 6) Entry by new cardiac surgery centers tended to sort high-severity patients into the more invasive CABG procedure and low-severity patients into the less invasive PCI procedures, potentially improving quality of care.
Bailey, J. B. (August 2016). Can Health Spending Be Reined In through Supply Constraints? An Evaluation of Certificate-of-Need Laws. (Working Paper). Mercatus Center at George Mason University. https://mercatus.org/research/working-papers/can-health-spending-be-reined-through-supply-constraints-evaluation.
Removing CON reduces hospital charges by 5.5% five years after repeal.
Bailey, J., Hamami, T., & McCorry, D. (2016). Certificate of need laws and health care prices. Journal of Health Care Finance, 43(4).
They find that prices are higher in CON states relative to non-CON states, but the difference isn’t statistically significant.
Kim, S., et al. (2016). Does certificate of need minimize intensity modulated radiation therapy use in patients with low risk prostate cancer?. Urology Practice, 3(5), 342–348. https://doi.org/10.1016/j.urpr.2015.09.001.
They study the effect of CON laws on the use of intensity modulated radiation therapy when it is not warranted. They find that the therapy was actually used more often in CON states than in non-CON states, concluding that it failed to achieve its goal.
Rahman, M., et al (2016). The Impact of Certificate-of-Need Laws on Nursing Home and Home Health Care Expenditures. Medical Care Research and Review, 73(1), 85–105.
CON increases the growth in Medicare and Medicaid expenditures on nursing home care but decreases growth in home healthcare expenditures.
Stratmann, T., & Koopman, C. (February 2016). Entry Regulation and Rural Health Care: Certificate-of-Need Laws, Ambulatory Surgical Centers, and Community Hospitals. (Working Paper). Mercatus Center at George Mason University.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3191476.
They study the effect of CON on overall supply of services as well as rural supply of services. In particular, they find: 1) CON programs are associated with 30% fewer hospitals per 100,000 residents across the entire state; 2) Ambulatory surgical center (ASC)-specific CONs are correlated with 14% fewer total ASCs per 100,000 residents; 3) CON programs are associated with 30% fewer rural hospitals per 100,000 rural residents; 4) ASC-specific CONs are correlated with 13% fewer rural ASCs per 100,000 rural residents.
Ni, H., Paul, J. A., & Bagchi, A. (2017). Effect of certificate of need law on the intensity of competition: the market for emergency care. Socio-Economic Planning Sciences, 60, 34–48. https://doi.org/10.1016/j.seps.2017.02.002.
They assess the effect of CON on market concentration (as measured by the Herfindahl–Hirschman Index (HHI)). They measure CON two ways—using a simple binary measure and a stringency measure based on the dollar threshold at which investments are subject to review. They use two-stage least square regression to address concerns of endogeneity. Their (somewhat dubious) IVs in the binary tests are an index of science and technology and the unemployment rate, and in the stringency model, they are the CPI and the unemployment rate. They find that CON laws are associated with greater competition, concluding that they serve as a sort of anti-trust tool.
Perry, B. J. (2017). Certificate of Need Regulation and Hospital Behavior: Evidence from MRIs in North Carolina. Available at SSRN 3225741. https://doi.org/10.2139/ssrn.3225741.
Service areas in North Carolina are allocated a new machine when the number of MRI procedures performed in the area crosses a predetermined threshold. He compares service areas that are just below the threshold to areas just above the threshold to see the effect of a binding CON constraint. He finds: 1) By limiting the use of scanners, CON laws reduce spending on patients with low back pain by about $400 in the first month of diagnosis; 2) CON limits the number of MRI scanners in an area—when an area is allowed to obtain a scanner, they almost always do; 3) Providers get around this constraint, to some degree, by utilizing unregulated mobile scanners; 4) Patients in a region constrained by CON receive 34% fewer scans in the first month after diagnosis; 5) Medicare patients are disproportionately crowded out by CON; their fraction of MRIs performed jumps 10 percentage points after CON approval; and 6) CON seems to limit cancer patient access to scans, but not musculoskeletal disorder patient access to scans.
Stratmann, T., & Monaghan, S. (August 2017). The effect of interest group pressure on favorable regulatory decisions: the case of certificate-of-need laws. (Working Paper) Mercatus Center at George Mason University. https://www.mercatus.org/research/working-papers/effect-interest-group-pressure-favorable-regulatory-decisions.
They examine the link between PAC contributions by applicants and the likelihood of CON approval in three states. They find: 1) The approval rate in Georgia is 57%, the approval rate in Michigan is 77%, and the approval rate in Virginia is 51% and 2) A 1% increase in contributions by an applicant firm increases the odds of approval by 6.7% in Georgia, 1.8% in Michigan, and 3.6% in Virginia.
Bailey, J. (2018). The effect of certificate of need laws on all‐cause mortality. Health Services Research, 53(1), 49–62. https://doi.org/10.1111/1475-6773.12619.
He uses fixed‐ and random‐effects regressions to test how the scope of state CON laws affects all‐cause mortality within US counties. Though he finds a positive relationship between CON laws and all-cause mortality, the results are not statistically significant.
Browne, J. A., et al. (2018). Certificate-of-need state laws and total knee arthroplasty. The Journal of Arthroplasty, 33(7), 2020–2024. https://doi.org/10.1016/j.arth.2018.01.063.
They examined the effect of CON on total knee arthroplasty (TKA) by comparing states with and without CON programs. They look at four factors and find: 1) Average Medicare reimbursements were 5% to 10% lower in non-CON states; 2) CON was associated with lower TKA utilization per capita, but faster growth in utilization per capita; 3) CON was associated with TKA in higher-volume hospitals; and 4) Examination of adverse events rates did not reveal any strong associations between any adverse outcome and CON status.
Noh, S., & Brown, C. H. (2018). Factors associated with the number of substance abuse nonprofits in the US states: focusing on Medicaid expansion, certificate of need, and ownership. Nonprofit Policy Forum, 9(2), 20170010. https://doi.org/10.1515/npf-2017-0010.
They study the effects of CON on substance abuse facilities, finding: 1) CON laws are negatively associated with the number of nonprofit substance abuse facilities and 2) In states with both CON laws and Medicaid expansion, the number of nonprofit substance abuse facilities tended to increase.
Ohsfeldt, R. L., & Li, P. (2018). State entry regulation and home health agency quality ratings. Journal of Regulatory Economics, 53, 1–19. https://doi.org/10.1007/s11149-018-9351-4.
They examine the effect of CON on home health agency (HHA) quality ratings from the Centers for Medicare and Medicaid Services. They find: 1) HHAs in CON states were about 58% less likely to be rated as high quality ( p < .01) and 2) HHAs in CON states also were about 30% more likely to be rated as medium quality compared to HHAs in states without CON for HHAs.
Bailey, J. (2019). Can health spending be reined in through supply restraints? An evaluation of certificate-of-need laws. Journal of Public Health, 27, 755–760. https://doi.org/10.1007/s10389-018-0998-1.
States that eliminate CON experience 4% reductions in real per capita health care spending.
Casp, A. J., et al. (2019). Certificate-of-need State Laws and Total Hip Arthroplasty. The Journal of Arthroplasty, 34(3), 401–407. https://doi.org/10.1016/j.arth.2018.11.038.
They study the effect of CON on total hip arthroplasty. They find CON is associated with: 1) Lower volume of total hip arthroplasty; 2) Care in high-volume hospitals; and 3) No difference in postoperative complications between CON and non-CON states.
Paul, J. A., Ni, H., & Bagchi, A. (2019). A Study of the Effects of Certificate of Need Law on Inpatient Occupancy Rates. Service Science, 11(1), 1–15. https://doi.org/10.1287/serv.2018.0228.
States with CON laws have lower bed occupancy rates. The authors speculate that while CON reduces the number of beds, it may also shorten the length of patient stay and the net effect is to reduce the occupancy rate. Note that this is the opposite of the intention (which was to reduce unused capacity).
Paul, J. A., Ni, H., & Bagchi, A. (2019). Does certificate of need law enhance competition in inpatient care market? An empirical analysis. Health Economics, Policy and Law, 14(3), 400–420. https://doi.org/10.1017/S1744133117000184.
They study the effect of CON on market concentration, as measured by a normalized Herfindahl–Hirschman Index (HHI) built using inpatient volume data of acute care hospitals in each health referral region (HRR). They find that CON is associated with less market concentration.
Wu, B., et al. (2019). Entry regulation and the effect of public reporting: Evidence from Home Health Compare. Health Economics, 28(4), 492–516.
They assess the effect of CON regulation on several measures of quality in home health care, using a cross-border design to control for endogeneity. They find that CON is uniformly associated with worse outcomes including: 1) Patients perform worse on functional improvement measures (bathing, ambulating, transferring to bed, managing oral medication, and less pain interfering with activity); 2) Patients are more likely to be admitted to the ER; and 3) Patients are more likely to be admitted to an acute care hospital.
Averett, S. L., Terrizzi, S., & Wang, Y. (2019). Taking the CON out of Pennsylvania: Did hip/knee replacement patients benefit? A retrospective analysis. Health Policy and Technology, 8(4), 349–355. https://doi.org/10.1016/j.hlpt.2019.09.006.
They analyze the effects of the expiration of Pennsylvania’s CON law on hip and knee replacement surgeries. They assess the effect of deregulation on one measure of cost per service (charges) and four measures of quality. They find that deregulation had: 1) No effect on total charges; 2) Increased the length of stay; 3) No effect on hospital acquired infections; and 4) Decreased mortality.
Chui, P. W., et al. (2019). Association of statewide certificate of need regulations with percutaneous coronary intervention appropriateness and outcomes. Journal of the American Heart Association, 8(2), e010373. https://doi.org/10.1161/JAHA.118.010373.
Like their 2015 paper, this one assesses whether CON limits inappropriate percutaneous coronary interventions. Again, they find a small but economically insignificant effect.
Malik, A. T., et al.(2019). Certificate-of-Need state laws and elective posterior lumbar fusions: A Medicare trends, costs and outcomes analysis. The Spine Journal, 19(9), S45. https://doi.org/10.1016/j.spinee.2019.05.106.
The examined the effect of CON on elective posterior lumbar fusions (PLFs) from 2005 to 2014, finding: 1) Average 90-day reimbursements were slightly higher (1.4% higher) in non-CON states ($22,115 vs. $21,802) 2) CON laws are associated with lower per capita utilization of PLFs; 3) CON laws are associated with more high-volume facilities; 4) CON laws are not associated with significant reduction in 90-day readmissions; 5) CON laws are not associated with significant reduction in 90-day complications.
Cancienne, J. M, et al. (2020). Certificate-of-Need Programs Are Associated with a Reduced Incidence, Expenditure, and Rate of Complications with Respect to Knee Arthroscopy in the Medicare Population. The Musculoskeletal Journal of Hospital for Special Surgery, 16(2 Suppl.), 264–271. https://doi.org/10.1007/s11420-019-09693-z.
They examine the effect of CON on knee arthroscopy, assessing its effect on: 1) Charges and reimbursements: in t-tests without controls they find that charges (which are the prices set before any negotiation) were lower in CON states, while reimbursements (which are actual payments) were not statistically significantly different; 2) Total volume: total volume and growth in total volume was lower in CON states than in non-CON states; 3) Volume within facilities: CON is associated with the presence of more high-volume facilities; and 4) Quality: There were more ER visits within 30 days of operation and more infections within 6 months of operation in CON than in non-CON states; there were no differences in in-hospital deaths or readmissions within 30 days of the operation between CON and non-CON states.
Ettner, S. L., et al. (2020). Certificate of need and the cost of competition in home healthcare markets. Home Health Care Services Quarterly, 39(2), 51–64. https://doi.org/10.1080/01621424.2020.1728464.
They examine the effects of home health agency (HHA) CONs and nursing home CONs on home health agencies. They find that in states with HHA CONs there are: 1) Lower per patient expenditures (they don’t know if this is due to skimping or to economies of scale); 2) Higher expenditures per agency; 3) Higher expenditures per resident; 4) Slightly fewer home health agencies per capita; and 5) Higher caseloads (volume) within agencies (this is what drives the higher expenditures per agency).
Fayissa, B., et al. (2020). Certificate-of-need Regulation and Healthcare Service Quality: Evidence from the Nursing Home Industry. Healthcare 8(4), 423. https://doi.org/10.3390/healthcare8040423.
In an IV study, they find that CON is associated with: 1) 18 to 24% lower nursing home survey scores computed by healthcare professionals and 2) The substitution of lower-quality certified nursing assistance care for higher-quality licensed practical nurse care.
Mitchell, M. D., Stratmann, T., & Bailey, J. B. (April 2020). Raising the Bar: ICU Beds and Certificates of Need. (Policy Brief) Mercatus Center at George Mason University.https://www.mercatus.org/publications/covid-19-crisis-response/raising-bar-icu-beds-and-certificates-need.
They study the relationship between CON and projected ICU bed shortages over the course of the COVID-19 pandemic. They find that compared with non-CON states, in CON states, expected shortages were more than twice as likely and the shortages were about nine times greater in per capita terms.
Myers, M. S., & Sheehan, K. M. (2020). The impact of certificate of need laws on emergency department wait times. Journal of Private Enterprise, 35(1), 59–75.
They examine the effect of CON laws on wait times. They find CON programs increase: 1) Median wait times for medical examinations; 2) Wait times for pain medication administration; 3) Wait times for hospital admittance; and 4) Wait times for hospital discharge.
Sridharan, M., et al. (2020). Certificate-of-Need State Laws and Elective Posterior Lumbar Fusions: Is It Time to Repeal the Mandate?. World Neurosurgery,144, e495–e499. https://doi.org/10.1016/j.wneu.2020.08.201.
They examined the effect of CON on elective posterior lumbar fusions (PLFs) from 2005 to 2014, finding: 1) Average 90-day reimbursements were slightly higher (1.4% higher) in non-CON states ($22,115 vs $21,802); 2) CON laws are associated with lower per capita utilization of PLFs; 3) CON laws are associated with more high-volume facilities; 4) CON laws are not associated with significant reduction in 90-day readmissions; and 5) CON laws are not associated with significant reduction in 90-day complications.
Stratmann, T., & Baker, M. (July 2020). Examining Certificate-of-Need Laws in the Context of the Rural Health Crisis. (Working Paper) Mercatus Center at George Mason University. https://www.mercatus.org/publications/healthcare/examining-certificate-need-laws-context-rural-health-crisis.
They examine the effect of CON on two measures of spending and two measures of quality (all four are indicators of “overutilization or waste”): 1) Medicare spending per rural beneficiary (they find this was $295 higher in CON states than in non-CON states); 2) Ambulance spending per beneficiary ($2.54 higher in CON states); 3) Hospital readmission rates (1.2 percentage points higher in CON states); and 4) Emergency room visits per 1,000 beneficiaries (35.1 more emergency department visits per 1,000 beneficiaries in CON states).
Yuce, T. K., et al. (2020). Association of state certificate of need regulation with procedural volume, market share, and outcomes among Medicare beneficiaries. JAMA, 24(20), 2058–2068. https://doi.org/10.1001/jama.2020.21115.
They assess the effect of CON on measures of volume and of quality. They find: 1) No significant difference between CON and non-CON states in county-level procedures per 10,000 persons; 2) No significant difference between CON and non-CON states for hospital procedural volume; 3) No difference in hospital market share; 4) No difference in risk-adjusted 30-day postoperative mortality; 5) No difference in surgical cite infection; and 6) No difference in readmission.
Ziino, C., Bala, A., & Cheng, I. (2020). Does ACDF Utilization and Reimbursement Change Based on Certificate of Need Status?. Clinical Spine Surgery, 33(3), E92–E95. https://doi.org/10.1097/BSD.0000000000000914.
The paper looks at reimbursements for spinal surgery in CON and non-CON states, finding that reimbursements fell the most in non-CON outpatient settings (-11% compound annual growth) in non-CON states.
Ziino, C., Bala, A., & Cheng, I. (2020). Does certificate-of-need status impact lumbar microdecompression reimbursement and utilization? A retrospective database review. Current Orthopaedic Practice, 31(1), 85–89. https://doi.org/10.1097/BCO.0000000000000828.
They examined the effect of CON in lumbar microdecompressions in both in-patient and out-patient settings, focusing on growth in utilization of the procedure over time and changes in reimbursement over time. These were simple comparisons, not regressions with controls. They find: 1) CON status did not affect overall reimbursement rates (“The ability of outpatient surgery to lower costs may, in fact, be more powerful than CON programs.”) and 2) Utilization of the procedure increased more in CON states than in non-CON states.
Bailey, J., & Lewin, E. (2021). Certificate of Need and Inpatient Psychiatric Services. The Journal of Mental Health Policy and Economics, 24(4), 117 –124.
They examine the effect of psychiatric service CONs. They find that psychiatric service CONs: 1) Reduce the number of psychiatric hospitals by 20%; 2) Reduce the likelihood that a hospital will accept Medicare by 5.35 percentage points; and 3) Reduce the number of psychiatric clients per capita by 56%.
Baker, M. C., & Stratmann, T. (2021). Barriers to entry in the healthcare markets: Winners and Losers from Certificate-of-Need Laws. Socio-Economic Planning Sciences, 77, 101007. https://doi.org/10.1016/j.seps.2020.101007.
They examine the effect of medical imaging CONs on medical imaging providers. They find: 1) CON laws are associated with 20 to 33% fewer providers; 2) Residents of CON states are 3.4 to 5.3 percentage points more likely to travel out of state to obtain these services; and 3) CON laws are associated with 27 to 53% fewer scans by nonhospital providers per beneficiary; 4) CON laws are associated with 23 to 70% fewer scans by new hospitals; and 5) CON laws are associated with 6 to 21% more scans by older hospitals.
Chiu, K. (2021). The impact of certificate of need laws on heart attack mortality: Evidence from county borders. Journal of Health Economics, 79, 102518. https://doi.org/10.2139/ssrn.3678714.
He uses a cross-border discontinuity design to study the effect of CON on heart attack mortality. He finds that it is associated with 6 to 10% higher mortality three years after enactment.
Herb, J. N., et al. (2021). Travel time to radiation oncology facilities in the United States and the influence of certificate of need policies. International Journal of Radiation Oncology Biology Physics, 109(2), 344–351. https://doi.org/10.1016/j.ijrobp.2020.08.059.
They measure the effect of CON on travel time to radiation oncology facilities, breaking down the effect by region. They find CON: 1) Has no association with prolonged travel in the West; 2) Is associated with lower odds of prolonged travel in both urban and rural tracts in the South; and 3) Is associated with increased odds of prolonged travel in both urban and rural tracts in the Midwest and Northeast.
Mitchell, M., & Stratmann, T. (2021). The Economics of a Bed Shortage: Certificate-of-Need Regulation and Hospital Bed Utilization during the COVID-19 Pandemic. Journal of Risk and Financial Management, 15(1), 10. https://doi.org/10.3390/jrfm15010010.
They examine the effect of bed CON on statewide bed utilization rates and on individual hospital shortages. They find: 1) States that require CONs for beds had 12% higher bed utilization rates; 2) Those states had 58% more days with more than 70% of their beds in use; 3) Hospitals in these states were 27% more likely to run out of beds; and 4) States that relaxed these rules for COVID saw no difference in utilization rates or shortages.
Schultz, O. A., Shi, L., & Lee, M. (2021). Assessing the Efficacy of Certificate of Need Laws Through Total Joint Arthroplasty. The Journal for Healthcare Quality, 43(1), e1–e7. https://doi.org/10.1097/JHQ.0000000000000286.
They examined the effect of CON on total knee (TKA), hip (THA), and shoulder arthroplasty (TSA), finding: 1) TKA and TSA costs were higher in CON states than in non-CON states (and these results were statistically significant); 2) THA costs were lower in CON states but these results were not statistically significant; 3) CON is associated with a lower volume of TKA and TSA procedures, though it was not statistically significant for THA; and 4) CON has no statistically significant effect on complications (deep vein thrombosis and pulmonary embolism).
Ziino, C., Bala, A., & Cheng, I. (2021). Utilization and Reimbursement Trends Based on Certificate of Need in Single-Level Cervical Discectomy. Journal of the American Academy of Orthopaedic Surgeons, 29(10), e518-e522.
They study inpatient cervical discectomy in CON and non-CON states in inpatient and outpatient settings. It appears that they did not use any controls, however. Regarding reimbursements, they find: 1) In the inpatient setting, reimbursement was lower in non-CON states ($1,128.40) than in the CON states ($1,223.56), but reimbursements in the CON states were falling faster over time; 2) In the outpatient setting reimbursement was higher in non-CON states ($4,237.01) than in CON states ($3,859.31) and reimbursements were growing in non-CON states but falling in the CON states. Regarding access: 3) In the inpatient setting, there were more patients in the CON setting than in the non-CON setting (657 compared with 231) and utilization of the procedure was growing faster in CON than in non-CON states but this does not appear to control for the larger population of CON states than non-CON states; and 4) Similarly, in the outpatient setting, there were more patients in the CON setting than in the non-CON setting (435 compared with 257) and utilization of the procedure was growing faster in CON than in non-CON states (again this does not appear to control for the larger population of CON states than non-CON states).
Bailey, J. B., Lu, T., & Vogt, P. (2022). Certificate-of-need laws and substance use treatment. Substance Abuse Treatment Prevention and Policy, 17, 38. https://doi.org/10.1186/s13011-022-00469-z.
They measure how CON affects the number of substance abuse facilities and beds per capita in a state, and the effect of CON on the forms of payment that treatment facilities accept. They find that CON reduces the acceptance of private insurance but has no statistically significant effect on the number of facilities, beds, or clients and no significant effect on the acceptance of Medicare or Medicaid.
Choudhury, A., Ghosh, S., & Plemmons, A. (2022). Certificate of Need Laws and Health Care Use during the COVID-19 pandemic. Journal of Risk and Financial Management, 15(2), 76. https://doi.org/10.3390/jrfm15020076.
They examined the relationship between CON and mortality associated with illnesses that require similar medical equipment as COVID-19. They find: 1) There are higher mortality rates in CON states than in non-CON states and 2) States with high healthcare utilization that reformed their CON laws during the pandemic saw lower mortality rates resulting from natural death, septicemia, diabetes, chronic lower respiratory disease, influenza or pneumonia, Alzheimer’s, and COVID-19.
Stratmann, T. (2022). The Effects of Certificate-of-Need Laws on the Quality of Hospital Medical Services. Journal of Risk and Financial Management, 15(6), 272. https://doi.org/10.3390/jrfm15060272.
He studies the effect of CON on nine measures of hospital quality: 1) Death among surgical inpatients with serious treatable complications; 2) Postoperative pulmonary embolism or deep vein thrombosis; 3) Percent of patients giving their hospital a 9 or 10 overall rating; 4) Pneumonia readmission rate; 5) Pneumonia mortality rate; 6) Heart failure readmission rate; 7) Heart failure mortality rate; 8) Heart attack readmission rate; and 9) Heart attack mortality rate. Hospitals in CON states performed worse than those in non-CON states in eight of the nine categories, the exception being postoperative pulmonary embolism.
Bailey, J., & Hamami, T. (2023). Competition and health‐care spending: Theory and application to Certificate of Need laws. Contemporary Economic Policy, 41(1), 128–145. https://doi.org/10.1111/coep.12584.
CON causes spending on those with less than excellent health to be as much as 20% higher, though it has no statistically significant effect on spending on those in good health.
Gaines, A. G., & Cagle, J. G. (2023). Associations Between Certificate of Need Policies and Hospice Quality Outcomes. American Journal of Hospice and Palliative Medicine, 10499091231180613. Advance online publication. https://doi.org/10.1177/10499091231180613.
They study the effects of CON laws in a cross-sectional analysis of hospice quality outcomes using the hospice item set metric (HIS) developed by the Centers for Medicare and Medicaid Services. Controlling for ownership and size, they find hospice CON states had higher HIS ratings than those from non-CON states along four dimensions: 1) Beliefs and values addressed (â = .05, P = .009); 2) Pain assessment (â = .05, P = .009); 3) Dyspnea treatment (â = .08, P < .001); and 4) The composite measure (â = .09, P < .001). They also find that along four additional measures the differences were statistically insignificant (P > .05); 1) Treatment preferences; 2) Pain screening; 3) Dyspnea screening; and 4) Opioid bowel treatment.
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