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Other Articles The Effects of Managed Care and Prospective Payment on the Demand for Hospital Nurses: Evidence from California Joanne Spetz Objective. To examine the effects of managed care and the prospective payment system on the hospital employment of registered nurses (RNs), licensed practical nurses (LPNs), and aides. Data Sources. Hospital-level data from California's Office of Statewide Health Planning and Development (OSHPD) HospitalDisclosure Reports from 1976/1977 through 1994/1995. Additional information is extracted from OSHPD Patient Discharge Data. Study Design. Multivariate regression equations are used to estimate demand for nurses as a function of wages, hospital output, technology level, and ownership. Separate equations are estimated for RNs, LPNs, and aides for all daily services and for medical-surgical units. Instrumental variables are used to correct for the endogeneity of wages, and fixed effects are included to control for unobserved differences across hospitals. Principal Findings. HMOs are associated with a lower use of LPNs and aides, and HMOs do not have a statistically significant effect on the demand for RNs. Managed care has a smaller effect on nurse staffing in medical-surgical units than in daily service units as a whole. The prospective payment system does not have a statistically significant effect on nurse staffing. Conclusions. HMOs have affected nursing employment both because HMOs have reduced the number of discharges and because of a direct relationship between HMO penetration and the demand for LPNs and aides. Contrary to press reports, LPNs and aides have been affected more by HMOs than have registered nurses. Key Words. Nurses, hospitals, managed care, prospective payment

The effect of health maintenance organizations (HMOs) and preferred provider organizations (PPOs) on the staffing of nursing personnel in hospitals is causing growing concern. Recent newspaper articles report that hospitals reduced their use of registered nurses (RNs) by replacing RNs with unlicensed assistive personnel in response to cost-cutting pressures caused by the growth

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HSR: Health Services Research 34:5 Part I (December 1999)

of HMOs (Rosenthal 1996; Kunen 1996; Shuit 1996). Patient advocates, nursing unions, and other observers argue that these staffing changes are reducing the quality of care provided by hospitals (Rosenthal 1996). In response to these claims of reduced nurse staffing and quality of care, state legislatures are attempting to regulate hospital employment of nursing personnel. Legislation has been introduced in several states, including Massachusetts, Nevada, California, and Florida, to establish minimum staffing levels for RNs and other staff. For example, California's Assembly Bill 394 in the current session would mandate staffing levels in most units of hospitals; similar legislation was vetoed by Governor Wilson after being passed by the state assembly and senate in 1998. This and similar legislation typically are supported by labor groups and unions and are opposed by hospitals and their associations. Unions and hospitals have engaged in heated debates about whether nurse staffing levels have been reduced and whether such reductions affect quality of care. Contract disputes serve as a forum to draw public attention to the effect of nurse staffing changes on the quality of care at the hospitals (Herscher 1997). For example, during a contract dispute, the California Nurses Association (CNA) alleged that Kaiser Permanente hospitals reduced staffing to dangerous levels. The CNA received public support despite a lack of documentation of the staffing cuts that the CNA claimed had occurred

(Hall 1998). Little research has been published on whether significant changes have occurred in the staffing of RNs, licensed practical nurses (LPNs, called licensed vocational nurses in California), and aides (and other unlicensed assistive personnel), and the research that has been done has found contradictory results. Some researchers have found a decline in the fulltime equivalent employment of hospital nurses per patient day (Aiken, Sochalski, and Anderson 1996), while others have found an increase in hours worked (Anderson and Kohn 1996; Spetz 1998).1 A cross-state comparison of RN employment and HMO penetration identified slower rates of RN employment growth in states with higher HMO penetration, but did not control for other factors that might affect hospital demand for nursing personnel (Buerhaus and Staiger 1996). Financial support was provided by the National Science Foundation, the Lynde and Harry Bradley Foundation, and the Public Policy Institute of California. Address correspondence and requests for reprints tojoanne Spetz, Ph.D., Research Fellow, Public Policy Institute of California, 500 Washington Street, Suite 800, San Francisco, CA 94111. This

article, submitted to Health Services Research on March 6, 1998, was revised and accepted for publication on November 24, 1998.

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Because future regulations of hospitals are likely to be based on the perception that HMOs are causing reductions in nurse staffing, it is important to determine whether reductions actually are occurring and whether managed care is associated with any such changes. This article answers the question of whether reductions actually are occurring and measures the effect of managed care on such reductions by estimating labor demand equations for RNs, LPNs, and aides in California's short-term general hospitals from 1976 through 1994. The findings have implications for future hospital regulation and provide policymakers and healthcare professionals with much-needed information so that they can make informed statements about nurse staffing.

CHANGING FINANCIAL INCENTIVES AND NURSE STAFFING The healthcare system in the United States underwent significant changes during the 1980s. Laws permitting selective contracting between insurers and healthcare providers resulted in growing enrollments in HMOs and PPOs; the first such law was implemented in California in 1983. Also in 1983, the federal government implemented the prospective payment system (PPS) for Medicare enrollees. Under PPS, as with many HMOs, hospitals earn a profit if their costs for treatment are below the predetermined payment level. PPS and selective contracting legislation gave hospitals a strong incentive to reduce the cost of patient care. Selective contracting legislation and the introduction of PPS led to many changes in hospital care, some of which have been documented extensively: fewer inpatient admissions, lower average lengths of hospital stays, and a more severely ill patient population (Dranove, Shanley, and White 1993; Guterman, Eggers, Riley, et al. 1988; Hodgkin and McGuire 1994; Melnick and Zwanziger 1988). PPS and the growth of managed care also may have affected the acquisition of technology (Cutler and Sheiner 1997). Hospitals should have responded to PPS and selective contracting by adjusting nurse staffing in a cost-reducing fashion, especially since expenditures on nurse wages are the largest single item in a hospital's operating budget (Anderson and Wootton 1991). However, the best way for a hospital to alter nursing employment and thus to reduce expenditures is not clear. Early research of state prospective reimbursement programs found evidence that hospitals reduced employment and payroll expenditures per patient day, but changes in skill mix or wages that might have contributed to these drops in staffing costs could not be identified (Kidder and Sullivan 1982).

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Data from California show little recent change in nursing expenditures after more than a decade of virtually consistent growth. Average nursing expenditures increased 115 percent between 1976 and 1994 (real 1994 dollars; see Table 1; data source described further on), with small declines between 1976 and 1977 and between 1983 and 1984. A 0.6 percent decline in average hours worked by RNs occurred between 1992 and 1994, after continuous increases in average RN hours through 1992. Average hours worked by LPNs dropped 5.4 percent and aide hours rose 6 percent over these same years. Average wages dropped slightly for RNs, LPNs, and aides. The declines in RN hours and wages between 1992 and 1994 were the first experienced by this group in nearly 20 years. These changes in nursing employment may have been the result of changing wages, reductions in the quantity of care provided by hospitals, the development of technology, or the new financial incentives created by managed care and PPS. To determine the effect of changes in healthcare financing on nursing employment, it is necessary to control for other factors in the healthcare environment that affect the demand for nurses.

DATA AND MODELING California Data California's hospital industry is ideal for studying the effects of selective contracting and prospective payment for several reasons. First, California is a leader in the development of competitive changes in healthcare financing (Dranove, Shanley, and White 1993; Gruber 1994; Zwanziger and Melnick 1988). Second, California's hospitals are located in a variety of markets with a range of competitive and managed care environments. Finally, California's Office of Statewide Health Planning and Development (OSHPD) collects annual hospital data, enabling researchers to conduct detailed analyses of hospital functioning. OSHPD's Hospital Disclosure Report is a survey of service provision, finances, and resource utilization in a hospital's fiscal year. It includes information about capital acquisition, labor staffing, and the provision of medical care in each revenue unit of the hospital (e.g., intensive care units, laboratory services, medical-surgical units). OSHPD's Patient Discharge Data contain abstracts of every inpatient discharge in a calendar year. OSHPD audits survey responses for consistency, and many hospitals use accounting systems that automatically produce reports for OSHPD at

Demand for Hospital Nurses Table 1: Summary Statistics for California Hospitals, Selected Years 1976 (s.c.) 1980 (s.c.)

Nursing Expenditures

1985 (s.c.)

1990 (s.c.)

1992 (s.c)

997

1994 (s.c.)

$4,764,395 $4,628,569 $6,573,083 $8,828,568 $10,144,600 $10,220,900 (12,917,700) (5,195,780) (7,368,409) (9,494,401) (10,716,700) (10,455,600)

RN Hours (all daily services) LVN Hours (all daily services) Aide Hours (all daily services)

104,746

119,488

175,022

(113,939)

(140,539)

(204,040)

44,804

52,189

47,437

(48,033)

(56,400)

(49,801)

74,154 (63,879)

65,773 (75,355)

(57,722)

RN Wage (1994$) $16.97 (all daily services) (2.41) LVN Wage (1994$) $12.10 (all daily services) (1.70) Aide Wage (1994$) $10.28 (all daily services) (5.80)

$17.55 (2.71) $12.06 (1.73) $9.46 (1.63)

$19.69 (2.55) $13.42 (2.04) $10.38 (1.86)

Discharges 5860 (all daily services) (4643) Patient Days 37,865 (all daily services) (33,671) Case-mix Index 0.894 (0.145)

39,420

201,621 (230,717) 41,359 (42,662) 51,442 (77,303)

213,938 (237,291) 40,506 (41,571) 59,595 (76,774)

212,736 (226,127) 38,329 (36,824) 63,144 (69,112)

$23.42

$24.65 (3.55) $15.41 (4.66) $10.28 (2.06)

$24.23 (3.93) $14.94 (2.16) $10.07 (2.20)

(3.16) $14.79 (2.18) $10.30 (2.13)

6583

7122

8010

7848

7158

(6647)

(7886) 39,784 (43,647)

(8492) 43,297 (45,375)

(7980) 42,513 (43,248)

(6645) 39,108 (36,621)

40,194

(42,449) 0.889

0.927

0.941

0.938

0.951

(0.140)

(0.159)

(0.200)

(0.197)

(0.191)

5.26 (3.00)

6.06 (3.26)

6.60 (3.38)

6.73

6.45

(2.95)

(3.36)

(3.42)

For-profit Owner 35.5% Non-profit Owner 42.9%h Government Owner 19.1°/

31.5% 44.1% 23.0%

24.9% 49.6% 23.7%

29.2% 46.8% 21.5%

25.9°h 49.6%

25.5% 48.8%

21.6%

21.5%

HMO Penetration

0.025 (0.018)

0.023 (0.019)

0.144 (0.070)

0.163

0.191

(0.075)

(0.081)

0.000 (0)

0.000 (0)

0.031 (0.024) 0.010 (0.052)

0.154 (0.085)

0.179 (0.082)

0.186 (0.081)

361

447

337

363

347

330

Technology Level

(non-Kaiser) PPS Biteshare (non-Kaiser) N Observations

5.44

998


the end of the fiscal year. Not every hospital is observed over the entire time period; some hospitals closed, some opened, and others changed their reporting calendar resulting in a missed year. Kaiser Foundation Hospitals do not report labor data and thus are not included in the study sample.2 Federal hospitals, which also do not report to OSHPD, are also excluded. My sample consists of short-term general hospitals that responded to the Hospital Disclosure Report in at least two years, from the 1976/1977 fiscal year through the 1994/1995 fiscal year. This sample contains about 81 percent of the nonfederal hospitals in California per year (between 389 and 488 hospitals). The "1976" data provide information for hospitals with fiscal years that ended betweenJuly 1, 1976 andJune 30, 1977. The regression equations reported in the next section contain between 330 and 465 observations per year as a result of nonresponse to certain survey questions. Summary statistics for selected years are presented in Table 1.

Statistical Analysis To examine the effects of selective contracting and PPS, I estimate demand equations for hospital nursing personnel. In these equations, nursing demand is a function of wages, hospital output, technology, and other characteristics (e.g., whether the owner is a for-profit corporation or a government entity). The labor demand equations are in a log-linear specification and are estimated using multivariate least squares regression. In the equations I include the percentage of county non-Kaiser hospital discharges insured by HMOs and a measure of the impact of the prospective payment system (described further on). These two variables measure the effects of changes in healthcare financing on the demand for nurses. All demand equations are estimated with fixed effects to control for differences across hospitals that do not vary over time and with yearly dummy variables to control for changes that uniformly affect all hospitals over time. Other factors could be included in the nursing demand equations, such as the labor of physicians, residents, and other hospital staff; however, the OSHPD data do not include information about residents' wages or about physician labor. Because hospitals do not directly employ most physicians, they have limited ability to substitute nurse labor for physician labor.3 Nursing employment is measured as the number of "productive hours" worked by RNs, LPNs, and aides. Productive hours include all paid hours except vacation and sick time. Although discrepancies may appear in hospital reports of the number of hours worked by nursing personnel, the main concern is whether and how registry and other temporary nurses are reported.


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Because it is impossible to know which (if any) hospitals report registry nurses differently, it is impossible to determine whether a systematic bias is present. If hospitals consistently misreport registry and temporary nurses over time, the fixed effects will measure this difference and thus will partially correct this error. Several issues must be considered in estimating the demand equations. First is the question of how to measure hospital output. Researchers have used the numbers of patient days and discharges to measure output in previous work. It is not clear whether output is best measured as a discharge or a patient day. Personnel must be available for patients during each day in which those patients are in the hospital; thus, one would expect hospitals to base their staffing on patient days. However, the average number of days spent in a hospital per patient discharge (length of stay) declined dramatically in the mid- and late 1980s, drawing into question whether a day of care should be the standard measure. If a hospital treats its patients intensively for a short period of time, its demand for personnel might be the same as that of a hospital providing less intensive care for more days per patient. I use inpatient discharges to measure output in the equations presented in this article. Equations that control for patient days produce similar results. The number of discharges does not fully describe hospitals' output. The cases treated by hospitals vary in severity; care for acute myocardial infarction is more complex than treatment for appendicitis. I computed the average case mix for each hospital using the PatientDischarge Data.4 In the creation of these case-mix indexes, each diagnosis is weighted by its HCFA diagnosis-related group (DRG) weight. I use both the number of hospital discharges and the case-mix index to control for output changes in this study. The second issue to address is which hospital units should be included in the analysis of demand for nursing personnel. Total hospital employment includes daily services units (intensive care, acute care, intermediate care, etc.), ancillary services, and outpatient units. The output measures just discussed do not include ancillary and outpatient services provided by the hospital; thus, I estimate demand for nursing personnel in daily service units. I also evaluate the effects of selective contracting and PPS on the demand for nurses in medical-surgical units, as these are the largest units in most hospitals. Third, I must develop measures of hospital technology. Including a measure of technological change is crucial in determining the demand for nursing labor because technology affects the demand for skilled labor relative to unskilled labor (Berman, Bound, and Griliches 1994). Prior studies of nursing employment have not included a measure of technological change.

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In this analysis I use a Saidin index to control for changes in the technologies used by hospitals (Spetz 1995; Spetz and Baker 1999). This index is a weighted sum of indicators for various technologies and services, with the weights representing the percentage of hospitals that do not possess the technology or service. Rare technologies-rare because they are new, expensive, or difficult to implement-receive higher weights. Common technologies such as operating rooms receive low weights. A Saidin index was developed using weights from the 1981 data.5 The fourth concern is measuring the impact of selective contracting. There is no reliable source for city-level HMO and PPO enrollment data before the late 1980s. However, one can determine the percentage of hospital discharges for which the expected source of payment is an HMO from the OSHPD Patient Discharge Data.6 I calculate the share of county non-Kaiser discharges insured by HMOs to measure the penetration of HMOs in local markets.7 Although the effect on nursing employment of the share of an individual hospital's patients insured by HMOs is of interest, the relationship between these is probably endogenous. It is less likely that regional HMO penetration is endogenous with an individual hospital's staffing decisions. The share of discharges reimbursed by HMOs is likely to be a lower bound for HMO enrollment in a county because HMO patients typically have fewer hospital stays than non-HMO patients (Luft 1981). Before 1983's selective contracting legislation, there were virtually no non-Kaiser HMO enrollees; thus, I assume that the percentage of discharges reimbursed by HMOs was constant from 1976 through 1983. HMO penetration does not explicitly measure the financial pressure placed on hospitals by managed care. HMOs bargain with hospitals individually or with multihospital corporations. Each contract between an HMO and a hospital produces a different level of financial pressure for that hospital. Thus, heterogeneity in the financial pressure caused by HMOs, either across hospitals or over time, is not examined in this study. No explicit way exists to measure the growth of PPOs, even though they might be placing significant financial pressure on hospitals. To the extent that markets in which HMOs have greater influence are more conducive to competitive healthcare financing, HMO penetration might proxy for PPO penetration. The yearly dummy variables included in the demand equations also might measure some of the effect of both HMO and PPO growth. Measuring the impact of the prospective payment system is my fifth concern. A measure of the financial pressure placed on hospitals by the prospective payment system is used to identify the effect of PPS. "Biteshare" is


1001

the expected reduction in revenue caused by PPS as a percentage of projected pre-PPS revenue. Gruber (1994) and Staiger and Gaumer (1992) used similar measures to evaluate the effects ofPPS in studies of charity care and Medicare mortality. PPS biteshare may not fully capture PPS's impact. PPS could have had a "base" impact on hospitals in addition to the effect of reimbursement variation. For example, physicians who have admitting privileges at the hospital usually determine admissions. When PPS was implemented, many hospitals established utilization review programs that worked with physicians to eliminate unprofitable admissions. Once a physician has adjusted his or her other admissions practices, that physician might apply these new standards to all patients, not just those insured by Medicare. The coefficient of biteshare captures only the change in hospital staffing caused by a change in PPS's reimbursement formula or rates; any general effect of PPS will be measured by the yearly dummy variables. Finally, the issue of potential endogeneity of wages must be addressed. The wages of aides are likely to be exogenous, since aides have no special training and thus hospitals are more likely to pay a prevailing market wage. It also might be safe to assume that individual hospitals in large urban markets have no control over market wages, even if hospitals in rural areas behave monopsonistically. I estimated the nursing demand equations using an abridged sample containing hospitals in large metropolitan areas in 1981, 1986, and 1989 to assess whether the endogeneity of wages might be a problem. The regression coefficients for this sample are similar to those estimated for all hospitals. Although the similarity between the urban-only findings and the fullsample results suggests that endogeneity of wages is not a large problem, I also estimate demand equations for RNs using the county unemployment rate as an instrumental variable. Numerous studies have found that the wages of a nurse's spouse are an important determinant of nurses' labor supply (Link and Settle 1980; Bognanno, Hixson, andJeffers 1974). As the unemployment rate in a community rises, the probability that a nurse's spouse becomes unemployed rises. RNs generally have ample employment opportunities, even during recessions, as demonstrated by ongoing reports of RN shortages. Thus, as the unemployment rate rises, the supply of nursing labor should rise. The county unemployment rate appears to be a good instrument for RN wages, as shown in Table 2. As expected, higher county unemployment rates are associated with lower RN wages. While the unemployment rate might also serve as an instrument for LPN wages in theory, it did not perform

1002


well in first-stage equations; thus, the LPN equations are estimated without an instrument for wage.

RESULTS Tables 3 and 4 present estimates of employment equations for RNs, LPNs, and aides for the full sample of hospitals. All equations are estimated with fixed Table 2: Fixed-effects Estimates of RN Wages Using the Unemployment Rate as an Instrument Daily Services Units (se.) log (Unemployment) log (LVN Wage) log (Aide Wage) log (Technology) log (Discharges) log (Case Mix) For-profit Government HMO Penetration PPS Biteshare 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

R-squared N *p < .05.

-0.025* 0.364* 0.189* 0.009 0.012* 0.027 -0.019 -0.008 -0.050 -0.030 0.007 0.006 0.009 0.030* 0.061* 0.099* 0.097* 0.082* 0.088* 0.097* 0.115* 0.150* 0.188* 0.215* 0.257* 0.270* 0.282* 0.269*

(0.009) (0.043) (0.028) (0.006) (0.006) (0.022) (0.011) (0.016) (0.045) (0.029) (0.005) (0.006) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.008) (0.009) (0.010) (0.011) (0.012) (0.013) (0.013) (0.014) (0.015) (0.015) 0.823 7246

Medical-Surgical Units (s.c.) -0.025* (0.009) 0.364* (0.043) 0.189* (0.028) 0.010 (0.006) 0.009 (0.005) (0.022) 0.015 -0.019 (0.011) -0.008 (0.016) -0.043 (0.045) -0.029 (0.029) 0.007 (0.005) 0.007 (0.006) 0.009 (0.007) 0.032* (0.007) 0.064* (0.007) 0.101* (0.007) 0.099* (0.007) 0.085* (0.007) 0.091* (0.008) 0.100* (0.010) 0.118* (0.010) 0.153* (0.011) 0.191* (0.012) 0.219* (0.013) 0.260* (0.013) 0.273* (0.014) 0.284* (0.015) 0.271* (0.016) 0.800 7229

Demandrfor Hospital Nurses

1003

effects; F-tests indicate thatjointly these fixed effects are significantly different from zero. Heteroskedasticity- and autocorrelation-consistent standard errors are produced using the method of Newey and West (1987). The equations estimating the number of hours worked by nursing personnel in daily services units, holding discharges constant, are in Table 3. HMO penetration has a statistically significant negative effect on hours Table 3: Fixed-effects Estimates of Hours Worked by RN, LVN, and Aides in Daily Services Units of Califomia Hospitals, Holding Discharges Constant Aides (s.c.) RNs-IV (s.e.) LVNs (s.c.) RNs-OLS (s.c) log (Aide Wage) -0.057 (0.060) 0.019 (0.059) log (LVN Wage) log (RN Wage) -0.194* (0.078) log (Technology) 0.011 (0.025) 0.627* (0.028) log (Discharges) 0.440* (0.078) log (Case Mix) -0.088* (0.041) For-profit Government 0.019 (0.054) HMO Penetraion -0.124 (0.141) PPS Biteshare -0.059 (0.119) 1977 0.034* (0.016) 1978 0.073* (0.016) 1979 0.068* (0.019) 1980 0.080* (0.019) 1981 0.162* (0.020) 1982 0.264* (0.021) 1983 0.367* (0.021) 1984 0.393* (0.020) 1985 0.416* (0.023) 1986 0.445* (0.024) 1987 0.491* (0.027) 1988 0.489* (0.029) 1989 0.533* (0.034) 1990 0.513* (0.038) 1991 0.552* (0.040) 1992 0.596* (0.044) 1993 0.661* (0.046) 1994 0.636* (0.048) 0.954 R-squared N 7350 *p