How Much Does Emergency Department Use Affect the Cost of ...

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Nov 13, 2007 - to be an important factor in the cost of Medicaid programs. These concerns may be warranted. Medicaid enrollees do use the ED at a higher ...
HEALTH POLICY AND CLINICAL PRACTICE/ORIGINAL RESEARCH

How Much Does Emergency Department Use Affect the Cost of Medicaid Programs? Daniel A. Handel, MD, MPH K. John McConnell, PhD Neal Wallace, PhD Charles Gallia, PhD

From the Center for Policy and Research in Emergency Medicine, Oregon Health & Science University, Portland, OR (Handel, McConnell); Portland State University, Portland, OR (Wallace); and the Division of Medical Assistance Programs, State of Oregon, Salem, OR (Gallia).

Study objective: Use of the emergency department (ED) is often assumed to be an important component of health care expenditures for Medicaid enrollees. We seek to quantify the absolute and percentage of total Medicaid expenditures associated with outpatient ED visits. Methods: This retrospective study used 2002 data from Oregon’s Medicaid program. ED expenditures were defined to include hospital, physician, and ancillary services associated with any ED visit not resulting in an inpatient admission. We estimated average monthly ED expenditures in absolute values and as a percentage of total medical expenditures. Multivariate models were used to assess the effect of demographic factors and eligibility status on ED spending and use. Results: We analyzed expenditures for 544,729 individuals enrolled in the Oregon Medicaid program in 2002. Monthly ED-associated expenditures averaged $12.63 (95% confidence interval $12.50 to $12.77) per member, representing 6.8% of total medical expenditures. Ancillary services (laboratory tests and diagnostic imaging) accounted for 35% of ED spending. Spending for ED services was skewed; 50% of all ED expenditures could be attributed to 3.0% of enrollees who made multiple ED visits. Conclusion: ED expenses are a relatively small percentage of total medical spending by Medicaid enrollees. An aggressive policy to cut ED expenditures by 25% would reduce Medicaid expenditures by less than 2% per year. Actual savings would be even smaller if reduced ED utilization were offset by increased spending at the primary care level. Because the majority of Medicaid patients do not use the ED in a given year, efforts to reduce ED expenditures may be best accomplished through targeting selected enrollees who have high ED expenditures, rather than attempting to decrease overall ED use. [Ann Emerg Med. 2008;51:614-621.] 0196-0644/$-see front matter Copyright © 2008 by the American College of Emergency Physicians. doi:10.1016/j.annemergmed.2007.09.002

INTRODUCTION Background There has been a longstanding concern that Medicaid enrollees use emergency departments (EDs) for nonurgent and primary care issues, with the implication that such use is likely to be an important factor in the cost of Medicaid programs. These concerns may be warranted. Medicaid enrollees do use the ED at a higher rate than other individuals. For example, the National Hospital Ambulatory Medical Care Survey estimates the visit rate for Medicaid enrollees at 80.3 visits per 100 persons, approximately twice the rate of individuals covered by Medicare (47.1 per 100 persons) or without insurance (44.6 per 100 persons) or 4 times the rate of those with private insurance (20.3 per 100 persons with private insurance).1 On the other 614 Annals of Emergency Medicine

hand, Medicaid enrollees are often sicker than uninsured individuals or individuals with other forms of insurance, and it is not clear whether their use of the ED is disproportionate relative to their overall use of health care.2 The ED is a high-profile site of care. As states struggle for ways to control the cost of their Medicaid programs, it is likely that they will seek ways to reduce the cost of ED use and expenditures according to the perception that reducing ED use will generate large cost savings. In particular, the federal Deficit Reduction Act of 2005 provides states with greater flexibility in managing their Medicaid programs, and states may use this new flexibility to apply new policies (such as copayments for ED visits) aimed at reducing ED use in their Medicaid populations.3 This legislation has enabled states to permit Volume , .  : May 

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Emergency Department Use and Medical Program Costs Editor’s Capsule Summary

What is already known on this topic It is believed by some that Medicaid beneficiaries often use emergency departments (EDs) for their primary care needs, which increases the cost of such programs. What question this study addressed This study measured the proportion of Medicaid expenditures associated with outpatient ED visits. What this study adds to our knowledge In 2002, ED use not resulting in hospital admission accounted for less than 7% of Medicaid acute care spending for the 500,000-plus members enrolled in Oregon’s Medicaid program. Fifty percent of all ED expenditures could be attributed to 3% of enrollees. How this might change clinical practice In Oregon, reducing outpatient ED care could not dramatically change total Medicaid expenditures. If cost containment is attempted, it should be focused on the small percentage of patients who consume most of the ED care.

hospitals to impose cost sharing for ED care that is deemed nonemergency if alternative nonemergency service providers are available and accessible. With such alternatives, hospitals are allowed to require payment before providing these services, although some populations are exempt. These exempt populations are those below 100% of the federal poverty level, except in cases of nonemergency care in EDs and for prescribed drugs.4 As of January 1, 2007, federal grants that provide $50 million during 4 years were made available to establish these nonemergency providers or networks of such providers.5 However, despite the potential for intensified interest in methods aimed at reducing ED utilization for Medicaid patients, there has been little effort to quantify the savings— or lack of savings—that could be expected from such efforts. Importance State budgetary concerns about the high cost of Medicaid programs are often translated into a focus on reducing ED use as a method of cutting Medicaid expenditures. If potential savings from reduced ED use are large, then these efforts are warranted. On the other hand, if potential savings from reduced ED use are small, then policies that focus solely on ED use are questionable. Goals of This Investigation The goal of this study was to quantify the absolute amount and percentage of Medicaid expenditures associated with ED visits that result in the patient being discharged to home, according to data from the Oregon Medicaid program. These Volume , .  : May 

estimates provide an important benchmark for determining the potential savings that states could expect from policies aimed at reducing ED utilization.

MATERIALS AND METHODS Study Design This was a retrospective analysis of expenditures (ie, payments to providers) for ED use, using data from the 2002 Oregon Medicaid program, known as the Oregon Health Plan. Setting We included adults and children enrolled in the Oregon Health Plan in 2002, with the following exclusions. We excluded enrollees who were dually eligible for Medicare and Medicaid because data are incomplete for these patients (ie, claims may be sent to Medicare and not recorded in the Medicaid data). We also excluded a small group of enrollees who were missing data on their eligibility status (ie, whether they were eligible for Medicaid because they were pregnant or part of the Temporary Assistance to Needy Families Program). Finally, although we had complete pharmacy claims data for all patients enrolled in the fee-for-service component of the Oregon Health Plan, we were unable to obtain pharmacy claims for some individuals enrolled in managed care. Individuals with incomplete pharmacy data were also excluded from the study. We used data from 2002. Oregon Health Plan underwent a severe contraction in 2003 (which led to disenrollment of more than 50,000 individuals during the course of 3 months) that led to program and spending instability for 2003 through 2005. Thus, 2002 represented the most recent stable period for which mature claims data were available. Methods of Measurement Our primary measure of interest was expenditures associated with any ED visit that did not result in a hospital admission. We included expenditures for the hospital component of the ED visit (the “facility” payment), the emergency physician (the “professional” payment), and any payments made to ancillary services. We defined ancillary payments as any additional outpatient or provider payment made on the same day as the ED visit. The process of developing these expenditures involved 3 steps: (1) identifying ED visits, (2) linking ancillary services to the date of the ED visit, and (3) computing expenditures for the hospital, emergency physician, and ancillary services. Step 1 involved identifying ED visits. ED visits that did not lead to an admission were defined as follows: ● An ED visit that was billed at any Current Procedural Terminology (CPT) level (CPT codes 99281 through 99285) or a visit that resulted in advanced life support (CPT code 99288). ● An ED visit that uses an ED revenue code (between 450 and 459 or 981). In certain circumstances, individuals may be directed to the ED to use the facilities for procedures provided by other Annals of Emergency Medicine 615

Emergency Department Use and Medical Program Costs specialties. For example, a primary care physician whose clinic has closed for the evening may direct patients to the ED to receive blood drawings for laboratory work. We excluded these “laboratory only” claims from our population of ED visits, excluding any claim with an ED revenue code and the following CPT codes: ● Venipuncture for blood drawing (CPT codes 36400 through 36415 and G0001). ● Upper gastrointestinal endoscopy (CPT codes 43200 through 43272). ● Lower gastrointestinal endoscopy (CPT codes 45300 through 45387). ● Radiology imaging and interpretation (CPT codes 70010 through 79999). ● Laboratory and pathology diagnostic tests (CPT codes 80048 through 89399). ● ECGs (CPT codes 93000 through 93278). Step 2 involved linking ancillary services. According to the dates of ED visits, we included additional physician and outpatient (eg, laboratory/radiograph) claims that occurred on the same day. This approach represents a conservative (large) approach to estimating ancillary services associated with ED visits because it is likely that a small portion of these services was not related to the ED visit itself but carried out in a separate setting before or after the actual ED event. Step 3 involved computing the expenditures for these services. For fee-for-service claims, this is a straightforward process because the payments are recorded in the claims data. However, managed care organization encounter data do not include expenditure data, because managed care organizations use capitated contracts with the state Medicaid agency. To assign expenditures to these encounters, we used fee-for-service data to generate average “prices” for every diagnosis related group and CPT. These imputed prices were then used to value each managed care organization claim. Because there were no patient identifiers in these data, the study was approved under expedited review by our institutional review board. In addition to expenditures for ED visits, we also calculated total expenditures for each enrollee. These expenditures include payments for all other outpatient clinic visits and all hospital inpatient services, pharmaceuticals, and other miscellaneous services, including behavioral health care and durable medical equipment, such as wheelchairs and dialysis equipment. Our estimates focus on acute care only. Thus, we did not include expenditures for long-term care or any payments related to the Medicaid Disproportionate Share Hospital program. A secondary goal of our analysis was to identify differences in ED expenditures that might be explained by demographic and enrollment characteristics. Individuals typically become eligible for Medicaid because of specific eligibility categories. For the purposes of our analyses, we identified 5 eligibility categories: children, pregnant women, enrollees eligible through the Temporary Assistance to Needy Families criteria, members of the Oregon Health Plan “expansion” population, and other 616 Annals of Emergency Medicine

Handel et al miscellaneous eligibility categories (including general assistance, citizen or alien worker emergency services, or populations denoted as “medically needy” as part of the Oregon Health Plan). Children were defined as any low-income individual younger than 19 years who was not pregnant. Pregnant women were defined as those at or below 133% of the federal poverty level.6 The Oregon Health Plan Standard population consists of individuals who are poor (incomes less than 100% of the federal poverty level) but otherwise not eligible for Medicaid under the traditional eligibility categories (eg, families with children, pregnant women, and the disabled). The Standard program has since been closed to new enrollees. To have initially qualified for Standard, the individual must have been 19 years of age or older, have been uninsured for at least 6 months, have had an income below 100% of the federal poverty level, have had less than $2,000 in resources, and have had no outstanding Oregon Health Plan premium payments.7 Outcome Measures Our primary outcome measure was average monthly Medicaid expenditures associated with ED visits not resulting in hospital admission. To estimate the percentage of spending that was accounted for by the ED, we divided ED expenditures by total expenditures for all services, including inpatient, outpatient, pharmaceutical, professional/physician, and other miscellaneous services, such as durable medical equipment or substance abuse treatment. Primary Data Analysis Our primary analysis was a univariate estimate of the mean monthly expenses associated with ED visits. We estimated the mean in absolute terms (dollars per enrollee), as well as a percentage of an enrollee’s total expenditures. We provide several univariate analyses to identify mean expenditures by eligibility category, fee-for-service versus managed care organization enrollment, and urban versus rural location, where location was defined by zip codes that have been classified urban or rural by the Oregon Office of Rural Health.8 Because enrollment length varied among individuals in our study, we present weighted means. These means were weighted by the number of months an enrollee spent in Medicaid during this year. Furthermore, because ED expenditures, like many health care expenditures, are not normally distributed, we used bootstrapping with 500 repetitions to generate 95% biascorrected confidence intervals (CIs). Our analytic method accounted for 2 phenomena that are common in medical expenditure data. First, in any year, enrollees may not use the ED and have zero ED expenditures. Second, among individuals with expenditures, the expenditures may be nonnormal and skewed to the right, with a small proportion of individuals having large ED expenditures in any given year. We followed an approach that is common in health economics and used a 2-part model.9 In the first part of the model, we model the use of services in any given year by using a Volume , .  : May 

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Table 1. Enrollee characteristics. Category Eligibility category Overall Children Pregnant women Temporary Assistance to Needy Families Standard Other Type of insurance Managed care organization Fee-For-Service Location Rural Urban

No. (%)

Age, y, mean (SD)

Sex, Men, %

Ethnicity, % Nonwhite, %

Months enrolled, mean (SD)

Urban, %

544,729 243,582 (44.7) 21,070 (3.9) 53,458 (9.8) 181,634 (33.3) 44,985 (8.3)

22.8 (16.0) 8.5 (5.3) 24.1 (5.8) 30.4 (8.9) 35.9 (11.7) 38.3 (12.8)

44.1 50.7 0.0 23.1 47.4 41.0

21.9 27.2 15.4 13.5 13.0 41.5

8.3 (3.6) 8.6 (3.6) 7.6 (3.5) 8.9 (3.5) 7.5 (3.6) 9.1 (3.7)

46.9 46.4 49.2 45.5 46.8 50.7

309,112 (56.8) 235,617 (43.3)

22.9 (16.2) 22.7 (15.6)

44.4 43.8

17.9 27.1

8.6 (3.5) 7.8 (3.7)

48.0 45.5

289,072 (53.1) 255,657 (46.9)

23.1 (16.3) 22.6 (15.6)

44.3 43.9

18.4 25.8

8.3 (3.6) 8.3 (3.7)

logistic regression. In the second part of the model, we focus on individuals with nonzero expenditures and transform the outcome variable by taking the natural logarithm. To translate our results in terms of the original metric (ie, dollars as opposed to ln(dollars)), we used a nonparametric retransformation method.10 Because interpretation of coefficients from this model is not straightforward, we present estimates of the average change in expenditures associated with each independent variable. We used bootstrapping with 500 repetitions to generate 95% bias-corrected CIs. We used Stata, version 9.2 (StataCorp, College Station, TX) for all analyses. (see Appendix E1, available at http://www.annemergmed.com)

RESULTS Characteristics of Study Subjects There were 679,883 Medicaid Enrollees in 2002. Of these, 84,615 were excluded because of dual eligibility. Another 1,604 were excluded because their categorical eligibility could not be determined, with 48,935 excluded because of missing drug data. This left 544,729 Medicaid enrollees included in our study, for a total of 4,528,200 months of enrollment. Those included in the sample were younger compared with those excluded (22.8 versus 45.8 years; P⬍.0001), more likely to be men (44.1% versus 41.8%; P⬍.0001), more likely to be nonwhite (21.9% versus 16.6%; P⬍.0001) and enrolled for a shorter duration during the year (8.3 versus 9.5 months, P⬍.0001), and less likely to live in an urban area (46.9% versus 58.4%, P⬍.0001). Table 1 describes demographic and enrollment characteristics for these enrollees. A large proportion of enrollees (44.7%) were children. Oregon’s Medicaid population has a relatively high proportion of white enrollees (78.1%), enrollees covered by managed care organizations (56.8%), and enrollees living in rural areas (53.1%). Main Results Figure 1 describes average monthly expenditures for ED visits. Monthly expenditures associated with ED visits averaged $12.63 (95% CI $12.50 to $12.77), representing 6.8% of total Volume , .  : May 

monthly expenditures. Expenditures for enrollees who were children ranked the lowest in terms of absolute dollars ($7.25); expenditures for enrollees who were pregnant women ranked the lowest in terms of percentage of total medical expenditures (3.7%). Temporary Assistance to Needy Families enrollees had the highest expenditures: $17.93 per member per month, composing 9.0% of overall costs. There was relatively little variation among rural versus urban enrollees or those covered through fee for service versus those enrolled in a managed care organization. We also investigated the relative shares of spending for ED services (Figure 2). Ancillary services (laboratory tests and diagnostic imaging) accounted for approximately 35% of ED spending. The hospital component consisted of 43.7%, and payments to emergency physicians consisted of the remaining 21.3%. Distribution of ED costs for the overall population is demonstrated in Figure 3. In any given year, only 23.9% of Medicaid enrollees actually use the ED. Moreover, among those who do use the ED, use tends to be concentrated among a small proportion of individuals who have fairly high utilization and expenditures. In our sample, 50% of all ED expenditures could be attributed to 3.0% of enrollees, approximately 16,000 people. The 2,000 individuals with the highest ED expenditures accounted for 16% of total ED spending. Results from our multivariate analysis using the 2-part model are shown in Table 2. Our explanatory variables in this model included age and dummy variables for rural residence (versus urban), female sex (versus male), fee-for-service enrollment (versus managed care organization), and the Oregon Health Plan enrollment categories Temporary Assistance to Needy Families, Pregnant, and Standard (versus other enrollment categories). Numbers displayed in Table 2 represent backtransformed numbers, using the 2-part model described above. For example, after adjusting for other characteristics, children enrolled in fee-for-service spent $1.00 more (95% CI $0.77 to $1.24) per month on ED services than those enrolled in Annals of Emergency Medicine 617

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Figure 1. Mean cost per patient: stratified as ED versus other for individuals by eligibility category, insurance type, and location.

Figure 2. Mean professional, facility, and ancillary services costs for individuals by eligibility category, insurance type, and location.

managed care organizations. No single variable contributes disproportionately to ED expenses.

LIMITATIONS This observational study had several limitations. The data are drawn from a single state’s Medicaid program. Oregon may not be representative of other states. In particular, the state is more rural and has greater managed care organization enrollment 618 Annals of Emergency Medicine

than many other states. However, our analyses did not suggest that either of these factors had a strong influence on ED expenditures. Additionally, our analysis was limited to 1 year of data (2002). Utilization patterns by this population may have changed during that time. Another limitation of our study was the focus on expenditures for patients who were not admitted to the hospital. We focused on these expenditures because our primary interest Volume , .  : May 

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Figure 3. Distribution of average monthly ED expenditures for Oregon Medicaid enrollees. Table 2. Multivariate analyses: marginal effect of selected variables on monthly ED costs.* Variable

Children

Adults

Age 0.12 (0.10 to 0.14) ⫺0.04 (⫺0.06 to ⫺0.02) Rural (vs 1.09 (0.87 to 1.32) ⫺2.07 (⫺2.48 to ⫺1.62) urban) Female ⫺0.44 (⫺0.70 to ⫺0.22) 0.91 (0.51 to 1.33) Nonwhite 0.38 (0.06 to 0.73) ⫺2.25 (⫺2.83 to ⫺1.66) Fee-for-service 1.00 (0.77 to 1.24) 0.08 (⫺0.29 to 0.61) OHP Temporary — ⫺5.84 (⫺6.45 to ⫺5.32) Assistance to Needy Families OHP Pregnant — ⫺8.94 (⫺9.49 to ⫺8.30) OHP Standard — ⫺10.45 (⫺11.53 to ⫺9.42) OHP, Oregon Health Plan; —, children excluded from these categories. *In US dollars with bootstrapped 95% CIs. Numbers displayed in this table are back-transformed from the 2-part model.

was in understanding the extent to which decreased ED use could reduce total Medicaid expenditures. Presumably, the majority of patients admitted to the hospital had conditions that were sufficiently urgent that they necessitated a visit to the ED and subsequent hospital admission. However, if an error in diagnosis of a discharged patient later resulted in hospital admission, the costs of the subsequent admission would not be captured in this study. Our estimates included expenses for ancillary services, defined as any non-ED expense occurring on the day that the ED visit was recorded. However, we did not capture ancillary services that may have been related to the ED visit but were billed on subsequent days or that occurred during multiday ED visits. Thus, although our estimate of ancillary service expenses may be an overestimate by its inclusion of any expense recorded on the same day of the ED visit (even if the bill was for an Volume , .  : May 

unrelated event), it may underestimate these expenses to the extent that not all ancillary services are billed at the initial ED visit. Costs of emergency transport were also not included in this study, because they were not hospital-generated claims. Furthermore, because of the inclusion criteria for our study population, our findings may not be completely generalizable to the entire Medicaid population. In particular, we excluded individuals who were older than 65 years (because they were dually eligible for Medicare), and thus our findings may not apply to older patient populations. Overall, in comparison to the study population, excluded beneficiaries were significantly older and more likely to be women, white, enrolled for a longer duration, and residing in an urban area. Finally, our analysis was limited by the data available from the state Medicaid agency, which did not contain full claims from those Medicaid individuals who were dually eligible for Medicaid and Medicare. These individuals are typically blind, disabled, or elderly and thus are likely to have high medical expenditures.

DISCUSSION We find that ED expenditures account for a relatively small percentage of total Oregon Health Plan expenditures: 6.8% for the total population, with a range of 3.7% for pregnant women enrolled in Medicaid and as high as 9.0% for individuals enrolled through the Temporary Assistance to Needy Families program. These estimates capture a broad range of EDassociated expenditures, including payments to the hospital, the physician, and ancillary services. According to these figures, what savings could states expect through policies that reduced ED utilization? For example, what is the extent of savings that could be generated from introducing copayments for ED visits? Copayments have been used successfully in commercially insured populations. For example, a recent study Annals of Emergency Medicine 619

Emergency Department Use and Medical Program Costs by Hsu et al11 found that, among commercially insured subjects in a managed-care population, ED visits decreased 12% with a $20 to $35 copayment and 23% with a $50 to $100 copayment compared with no copayment. However, the effect was less pronounced among Medicare enrollees, with ED visits decreasing by 4% (95% CI 3% to 6%) with a $20 to $50 copayment.11 Other studies have found a similar response rate to copayments. For example, the RAND Health Insurance Experiment study found a reduction of ED visits by 14%,12 whereas a reduction of 15% was found in another HMO population.13 These results are difficult to extrapolate to the Medicaid population. On the one hand, Medicaid patients have lower incomes and thus may be more price sensitive. On the other hand, Medicaid patients are typically sicker than the average commercially insured patient, and thus they may be less likely to curtail ED use. Furthermore, the role of the Emergency Medical Treatment and Labor Act (EMTALA) is important in attempting to understand the effectiveness of copayments for Medicaid patients. EMTALA requires EDs to provide all patients with a medical screening examination,14-16 regardless of the patients’ ability to pay, and discourages collecting copayments before treatment. It is possible that difficulty in the retroactive collection of copayments further reduces the effectiveness of such a method in reducing ED use. Thus, 25% represents a high-end estimate of the possible reduction in ED use that could be attained through copayments applied to a Medicaid population. According to our estimates, a 25% reduction in utilization would lead to less than a 2% reduction in total Medicaid expenditures. This estimate also assumes that the diagnostic tests, procedures, and treatments that typically take place in the ED would not take place in another ambulatory setting. Without this assumption, cost savings may be closer to 1% or less. Furthermore, there may be additional unintended costs associated with policies aimed at reducing ED use. Patients may delay care until their condition becomes more severe, leading to expensive hospitalizations. There has been no evidence of this in commercially insured populations, but it is unclear whether these findings generalize to a Medicaid population. Individuals in commercial insurance plans differ from Medicaid patients in important ways. Patients with private insurance can expect better access to care because insurance plans negotiate payment rates with physicians, and these physicians have incentives to satisfy patients and see that they receive preventive care. Conversely, Medicaid patients, whose insurance is characterized by substantially lower reimbursements, may have more difficulty accessing their physician and are likely to have limited ability to change providers.17 Thus, discouraging ED use with copayments may be inappropriate if the patients have difficulty accessing basic primary care. The availability of follow-up care for patients leaving the ED has been shown to be substantially worse for Medicaid enrollees than for patients with commercial insurance.18 An important finding of our study is that the ED is not used by most Medicaid enrollees in any given year. In our study, approximately 24% of beneficiaries used the ED for ambulatory 620 Annals of Emergency Medicine

Handel et al visits. Furthermore, approximately 50% of ED expenses can be attributed to 3% of the population (approximately 16,000 individuals). This phenomenon—a small number of individuals representing a disproportionate share of expenditures in any given year—is represented in the Medicaid population at large, with approximately 72% of Medicaid spending attributable to about 10% of Medicaid beneficiaries in the country.19 Other studies have demonstrated that frequent ED users are more likely to be in poor health, regardless of their insurance status.20,21 Our data suggest that the high-cost problems of Medicaid programs are probably best addressed through policies that target specific high-cost cases, rather than attempts to reduce utilization for the population as a whole; for example, looking at the 2,000 enrollees with the highest ED utilization (0.4% of the total Oregon Medicaid population) accounts for 15.8% of all ED expenditures. In other words, targeted case management may be a more effective method of controlling costs than copayments. The former also has the potential for improving the quality of care, rather than simply reducing utilization. Medicaid expenditures for patients seen in the ED and subsequently discharged comprise a relatively small component of total Medicaid acute care spending. Moreover, total Medicaid spending also includes other programs, such as long-term care. For example, in 2006, acute and long-term care were responsible for 59% and 39% of Medicaid costs, respectively.22 Thus, although ED spending accounts for less than 7% of Medicaid acute care spending, its share of total Medicaid spending (including long-term care) is even smaller. As evidenced by our 2-part multivariate analysis, there are no sociodemographic variables that are strong predictors of ED costs. In fact, many of these were counterintuitive: increasing age and nonwhite ethnicity were associated with a decrease in ED spending. However, although the associations are statistically significant, these factors are not associated with changes in ED spending that would be large enough to affect the main finding of our study: that ED spending is a relatively small proportion of total Medicaid spending. In other words, it would be difficult for policymakers to focus on one particular sociodemographic subset of the population to maximize cost savings for ED visits during which the patient was not admitted. A number of states have demonstrated that improvements in primary care can reduce ED use. For example, Delaware reduced its ED visits for Medicaid patients through improvements in referral to primary care physicians for patients who visited the ED.23 In North Carolina, a policy that guaranteed in-person or telephone access to a primary care provider 24 hours a day was associated with a 24% reduction in ED utilization for children.24 Instead of focusing policy efforts at reducing ED utilization by Medicaid patients through cost-sharing efforts, there may be more cost-effective ways of delivering health care. A legislative focus to improve overall access to care and the quality of this care is more likely to have a positive impact in this regard. In summary, our study finds that expenditures for ED visits are a small component (6.8%) of Medicaid expenditures. Nonetheless, Volume , .  : May 

Handel et al it is likely that the ED will continue to be a site that is targeted for reduced utilization or expenditures by Medicaid programs. Emergency providers can respond most appropriately to this scrutiny by acknowledging the high cost of Medicaid programs to states but by pointing out that reductions in ED use will generate relatively little savings. In particular, policies that attempt to reduce ED use among all Medicaid enrollees are unlikely to generate savings greater than 1% to 2% of total Medicaid expenditures. However, policies that target high-cost cases have the promise of leading to greater savings, as well as improving the quality of care for the Medicaid population. We thank Robert A. Lowe, MD, MPH, for his guidance in identifying ED visits. Supervising editor: David J. Magid, MD, MPH Author contributions: DAH and KJM conceived the study design. KJM and NW obtained research funding. CG supervised data collection and quality control. DAH and KJM analyzed the data. DAH and KJM drafted the article, and all authors contributed substantially to its revision. DAH takes responsibility for the paper as a whole. Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article, that might create any potential conflict of interest. See the Manuscript Submission Agreement in this issue for examples of specific conflicts covered by this statement. Drs. McConnell and Wallace were supported by a grant from the Robert Wood Johnson Foundation’s Changes in Health Care Financing and Organization initiative. Publication dates: Received for publication April 6, 2007. Revision received August 15, 2007. Accepted for publication August 27, 2007. Available online November 13, 2007. Presented at the 2007 Society for Academic Emergency Medicine annual meeting, May 2007, Chicago, IL. Reprints not available from the authors. Address for correspondence: Daniel A. Handel, MD, MPH, 3181 SW Sam Jackson Park Rd, Mail Code: CR-114, Portland, OR 97239; 503-494-9587, fax 503-494-4640; E-mail: [email protected].

REFERENCES 1. McCaig LF, Nawar EW. National Hospital Ambulatory Medical Care Survey: 2004 emergency department summary. Adv Data. 2006; 372:1-29. 2. Hadley J, Holahan J. Is health care spending higher under Medicaid or private insurance? Inquiry. 2003;40:323-342. 3. The Kaiser Commission on Medicaid and the Uninsured. Deficit Reduction Act of 2005: Implications for Medicaid. Available at: http://www.kff.org/medicaid/upload/7465.pdf. Accessed October 1, 2007.

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Emergency Department Use and Medical Program Costs 4. United States Senate Committee on Finance. Medicare and other health provisions—United States Senate Committee on Finance. Available at: http://finance.senate.gov/press/Gpress/2005/ prg121406.pdf. Accessed March 12, 2007. 5. Centers for Medicare & Medicaid Services. Grants for alternative non-emergency services. Available at: http://www.cms.hhs.gov/ GrantsAlternaNonEmergServ/. Accessed March 6, 2007. 6. Schneider AER, Garfield R, Rosseau D, et al. The Medicaid Resource Book. Washington, DC: Kaiser Commission on Medicaid and the Uninsured; 2002. 7. Oregon Department of Human Services. Oregon Health Plan, Department of Human Services. Available at: http://www. oregon.gov/DHS/about_us.shtml. Accessed March 5, 2007. 8. Oregon Office of Rural Health. Rural definitions. Available at: http://www.ohsu.edu/oregonruralhealth/. Accessed March 3, 2007. 9. Duan N, Manning W, Morris C, et al. A comparison of alternative models for the demand for medical care. J Business Econ Stat. 1983;1:115-126. 10. Ai C, Norton EC. Standard errors for the retransformation problem with heteroscedasticity. J Health Econ. 2000;19:697-718. 11. Hsu J, Price M, Brand R, et al. Cost-sharing for emergency care and unfavorable clinical events: findings from the Safety and Financial Ramifications of ED Copayments Study. Health Serv Res. 2006;41:1801-1820. 12. O’Grady KF, Manning WG, Newhouse JP, et al. The impact of cost sharing on emergency department use. N Engl J Med. 1985;313: 484-490. 13. Selby JV, Fireman BH, Swain BE. Effect of a copayment on use of the emergency department in a health maintenance organization. N Engl J Med. 1996;334:635-641. 14. Frew SA. COBRA/EMTALA—new risks for emergency medicine and managed care (part I). Emerg Physician Legal Bull. 1994; 5:1-8. 15. Frew SA. COBRA/EMTALA—new risks for emergency medicine and managed care (part II). Emerg Physician Legal Bull. 1994; 5:1-8. 16. Selbst SM. Emergency Medical Treatment and Active Labor Act: legal concerns about private or managed care patients in the emergency department. Curr Opin Pediatr. 1997;9:465-469. 17. Medicaid Access Study Group. Access of Medicaid recipients to outpatient care. N Engl J Med. 1994;330:1426-1430. 18. Asplin BR, Rhodes KV, Levy H, et al. Insurance status and access to urgent ambulatory care follow-up appointments. JAMA. 2005;294:1248-1254. 19. Schneider ALJ, Lambrew J, Shenouda Y. Medicaid Cost Containment: The Reality of High-Cost Cases. Washington, DC: Center for American Progress; 2005. 20. Hunt KA, Weber EJ, Showstack JA, et al. Characteristics of frequent users of emergency departments. Ann Emerg Med. 2006;48:1-8. 21. Fuda KK, Immekus R. Frequent users of Massachusetts emergency departments: a statewide analysis. Ann Emerg Med. 2006;48:9-16. 22. Henry J. Kaiser Family Foundation. statehealthfacts.org. Available at: http://www.statehealthfacts.org/. Accessed May 28, 2007. 23. Gill JM, Diamond JJ. Effect of primary care referral on emergency department use: evaluation of a statewide Medicaid program. Fam Med. 1996;28:178-182. 24. Piehl MD, Clemens CJ, Joines JD. ”Narrowing the Gap”: decreasing emergency department use by children enrolled in the Medicaid program by improving access to primary care. Arch Pediatr Adolesc Med. 2000;154:791-795.

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APPENDIX E1. Retransformation of the Outcome Variable for ED Expenditures Because ED expenditures are skewed to the right, we transformed our outcome variable by taking the natural log and estimating the following equation: ln(y) ⫽ X␤ ⫹ ␧ (1) where y is ED expenditures and X is the matrix of explanatory variables. We used Duan’s nonparametric smearing estimator to retransform the variable to its original metric, dollars. We assumed that the error term in equation 1 was heteroscedastic and followed a form that was estimated with the following equation: (2) ^ ␧2 ⫽ X␣ ⫹ ␷ where are squared-fitted residuals from equation (1). Following Ai and Norton,9 we define the vector gi as (ln(yi) ⫺ xi␤) gi(x, ␤, ␣) ⫽ exp x␤ ⫹ 兹x␣ (3)  x ␣ 兹i where yi and xi denote the ith element of y and the matrix X (ie, they represent a specific observation). The expected expenditures for a given vector of x values, xk is 1 N ^) gi(xk, ␤ˆ , ␣ (4) E(y|xk) ⫽ N i⫽1 where and are the fitted values from equations (1) and (2).

冉 冋

册 冊

兺

The 2-Part Model Because some individuals experienced no ED visits, we modeled the expected expenditures with a 2-part model. The first part

621.e1 Annals of Emergency Medicine

of the model is represented by a logistic regression, where the outcome variable takes on a value of 0 if y⫽0 and a value of 1 if y⬎0, where y is ED expenditures. Thus, the probability that any visit to the ED occurs is represented by P(y ⬎ 0) ⫽ Fx␤) (5) where x is explanatory and F is the cumulative logistic distribution. The expected ED expenditures, conditional on the event that a visit to the ED actually happens, are represented by (6) E(y|y ⬎ 0, x) ⫽ x␣ The change in expenditures that can be attributed to a specific independent variable (eg, sex is male) is E(y|xi,xMALE⫽1)⫺E(y|xi,x ⫽0)⫽F(xi, x ⫽1␤ˆ )· MALE

xi, x

^⫺F(x ␣ i, x

MALE⫽1

␤ˆ )·xi, x

IMALE⫽0

MALE

^ ␣

MALE⫽0

(7)

where represents the fitted values from the logistic regression of the probability of an ED visit on all x variables for every individual; represents the fitted values from the linear regression of y on all x variables for individuals where y⬎0, is a vector of x values taken at the baseline values, with the dummy variable indicating the individual is male, and is the vector of x values taken at the baseline values but with the dummy variable indicating the individual is female. A similar process is conducted to evaluate the change in spending attributable to other demographic factors. CIs are acquired through bootstrapping. Stata code to implement these routines is available from the authors by request.

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