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Health Serv Res. 2003 December; 38(6 Pt 1): 1487–1508.
PMCID: PMC1360960

Measuring Hospital Quality: Can Medicare Data Substitute for All-Payer Data?

Abstract

Objectives

To assess whether adverse outcomes in Medicare patients can be used as a surrogate for measures from all patients in quality-of-care research using administrative datasets.

Data Sources

Patient discharge abstracts from state data systems for 799 hospitals in 11 states. National MedPAR discharge data for Medicare patients from 3,357 hospitals. State hospital staffing surveys or financial reports. American Hospital Association Annual Survey.

Study Design

We calculate rates for 10 adverse patient outcomes, examine the correlation between all-patient and Medicare rates, and conduct negative binomial regressions of counts of adverse outcomes on expected counts, hospital nurse staffing, and other variables to compare results using all-patient and Medicare patient data.

Data Collection/Extraction

Coding rules were established for eight adverse outcomes applicable to medical and surgical patients plus two outcomes applicable only to surgical patients. The presence of these outcomes was coded for 3 samples: all patients in the 11-state sample, Medicare patients in the 11-state sample, and Medicare patients in the national Medicare MedPAR sample. Logistic regression models were used to construct estimates of expected counts of the outcomes for each hospital. Variables for teaching, metropolitan status, and bed size were obtained from the AHA Annual Survey.

Principal Findings

For medical patients, Medicare rates were consistently higher than all-patient rates, but the two were highly correlated. Results from regression analysis were consistent across the 11-state all-patient, 11-state Medicare, and national Medicare samples. For surgery patients, Medicare rates were generally higher than all-patient rates, but correlations of Medicare and all-patient rates were lower, and regression results less consistent.

Conclusions

Analyses of quality of care for medical patients using Medicare-only and all-patient data are likely to have similar findings. Measures applied to surgery patients must be used with more caution, as those tested only in Medicare patients may not provide results comparable to those from all-patient samples or across different samples of Medicare patients.

Keywords: Quality of care, reproducibility of results, Medicare, nursing care

Monitoring quality of care across institutions and over time and examining the correlates of quality are critical to pursuing effective policy to improve quality (Institute of Medicine Committee on Quality of Health Care in America 2001; Kohn et al. 2000). Medical record abstraction has been the “gold standard” for constructing quality measures but the costs associated with abstracting data from the patient's chart makes its use infeasible in monitoring quality and overall health system performance and examining the factors that influence quality. Researchers wishing to conduct analysis on large samples of hospitals have turned to less expensive and more readily available administrative data, primarily patient discharge abstracts, to construct measures of hospital quality (Agency for Healthcare Research and Quality 2000; Ball et al. 1998; Geraci 2000; Iezzoni, Daley, Heeren, Foley, Hughes et al. 1994; Iezzoni, Daley, Heeren, Foley, Fisher et al. 1994; Johantgen et al. 1998; Kuykendall et al. 1995; Silber and Rosenbaum 1997).

Over 40 states now collect discharge abstracts on all hospitalized patients in acute care hospitals. These data vary in completeness, number of primary and secondary diagnoses and procedures reported, presence of other patient information, such as race/ethnicity and insurer, years available, and cost. The Centers for Medicare and Medicaid Services' (CMS) MedPAR system contains information on hospital discharges for all Medicare patients. These data are relatively inexpensive, consistently coded, available for virtually all acute care hospitals in the United States, and have been used in many studies (Lawthers et al. 2000; Romano et al. 1994; Romano et al. 1995; Weingart et al. 2000). There are, however, important differences between the state and national discharge data on Medicare patients. For example, the public-use MedPAR data do not include information on dates for procedures and have fewer coded secondary diagnoses and procedures than most state datasets. Nevertheless, the quality of patient care based on Medicare data is often regarded as a surrogate measure of the quality of care for all hospitalized patients.

Although the data on Medicare patients contained within all-patient state datasets are generally consistent with information on Medicare patients in the CMS Medicare data (Medstat Group Research and Policy Division 2000), and there is some evidence that hospital admission patterns for all patients can be predicted from the admission patterns of Medicare patients (Radany and Luft 1993), it is an empirical question whether Medicare data can be used as a close substitute for all-patient data for hospital quality studies.

Quality measures have been used in studies to assess quality in specific hospitals and in studies of hospital characteristics associated with quality care. We focus on the second type of study and assess whether all-patient and Medicare data provide the same results in regression-based studies of correlates of quality across a range of measures. We analyze data from a sample of all-patient discharge abstracts for hospitals in 11 states, and patients in a national sample drawn from MedPAR data, examining three samples of patients: the 11-state all-patient sample, the Medicare patients in the 11-state data (11-state Medicare sample), and national MedPAR sample. Our quality indicators were developed and tested in a larger study that examined the association of patient outcomes and nurse staffing in acute care hospitals (Needleman et al. 2002; Needleman et al. 2001).

Our analytic strategy is first to compare rates of adverse outcomes and results from regression analysis of outcomes on nurse staffing in the 11-state all-patient sample to those from analysis of the 11-state Medicare sample. This comparison allows us to draw conclusions on how closely Medicare patients are a surrogate for all patients in the same sample of hospitals using consistently coded discharge data and identical measures of nurse staffing. Because researchers working with Medicare data will likely use data from the CMS national MedPAR files, and because less information is available on these abstracts than in most state discharge abstracts, we compare adverse outcome rates and regression results in the national MedPAR sample with those from our 11-state Medicare and 11-state all-patient samples to determine if the results from the MedPAR and state data are consistent. We find some differences in regression results between the MedPAR and two 11-state samples and conduct additional analyses to determine the source of these differences.

Methods

Study Population

All-patient hospital discharge abstract data and nurse staffing data were obtained from 11 states that collect both sets of information: Arizona, California, Maryland, Massachusetts, Missouri, Nevada, New York, South Carolina, Virginia, West Virginia, and Wisconsin. We estimated calendar year 1997 staffing as the weighted average of staffing in hospital fiscal years 1997 and 1998, except for Virginia, for which only fiscal 1997 data were available. We obtained discharges for calendar year 1997 (for Virginia, the four calendar quarters matching each hospital's fiscal year). The initial sample was 1,041 hospitals. We then excluded hospitals with an average daily census below 20, occupancy rate below 20 percent, missing staffing data, or reporting extremely low (below the 7.5 percentile) or high (above the 92.5 percentile) staffing per patient day. The final sample included 799 hospitals, which together had 26 percent of the 1997 discharges for nonfederal hospitals in the United States.

To construct a second analytic sample (the national MedPAR sample), we used the CMS public use Medicare MedPAR discharge data file for 1997, and the American Hospital Association (AHA) Annual Survey data for 1997 and 1998. The AHA data were used to apply the same exclusions to the MedPAR sample as used in the 11-state sample. The final sample size for the MedPAR sample was 3,357 hospitals.

Constructing Measures of Adverse Patient Outcomes

Based upon an extensive review of research reported in the literature and unpublished evidence (Blegen, Goode, and Reed 1998; Bryan et al. 1998; Czaplinski and Diers 1998; Iezzoni, Daley, Heeren, Foley, Hughes et al. 1994; Iezzoni, Daley, Heeren, Foley, Fisher et al. 1994; Karon 1999; Keyes 2000; Kovner and Gergen 1998; Lichtig, Knauf, and Milholland 1999; Palmer et al. 1996; Silber, Sainfort, and Zimmerman 1992; U.S. Department of Health and Human Services Agency for Healthcare Research and Policy 2001; Wan and Shukla 1987), we identified measures of adverse outcomes or hospital-acquired complications that could be coded from hospital discharge abstracts. In this study, we use eight measures applicable to both medical patients and major surgery patients: adjusted length of stay, urinary tract infections (UTIs), skin pressure ulcer, pneumonia, shock/cardiac arrest, upper gastrointestinal (UGI) bleeding, sepsis, failure to rescue (defined as death among patients with hospital-acquired pneumonia, shock/cardiac arrest, UGI bleeding, sepsis, or deep vein thrombosis). Two outcomes applicable to surgical patients only are also examined: surgical wound infection and metabolic derangement (such as diabetic ketoacidosis, postoperative hypovolemia, oliguria, and anuria). Building upon coding rules developed in the Complication Screening Program (Iezzoni, Daley, Heeren, Foley, Hughes et al. 1994; Iezzoni et al. 1992) and in the Agency for Healthcare Research and Quality Hospital Cost and Utilization Project (Agency for Healthcare Research and Quality 2000), detailed coding rules for constructing these 10 outcome measures were developed and are discussed elsewhere (Mattke et al. 2002; Needleman et al. 2001).

Customary practice in studying complications in surgical patients using administrative data is to restrict the sample of patients to those who had their surgical procedure on the first or second day of admission. The underlying assumption is that sicker and unstable patients are more likely to develop complications or those who enter the hospital with complications will be operated on after an initial stabilization period (Ball et al. 1995; Iezzoni et al. 1992). This restriction is intended to create a more homogeneous group of patients admitted for elective procedures. We applied this restriction in coding outcomes in the 11-state all-patient and Medicare samples (except for patients from 33 hospitals in Nevada and West Virginia, for whom day of procedure was not available). Because the public use MedPAR data does not have information on day of procedure, we do not impose this restriction in analysis using these data.

Nurse Staffing Measures

We obtained data to construct measures of nursing personnel for hospitals in the 11-state sample from state hospital financial reports or staffing surveys. Nursing personnel were assigned to the categories RN, LPN, and nursing aide, assistant, or orderly (aide). For states that reported staffing as full time equivalents, we adopted a standard year of 2,080 hours.

For analysis of the MedPAR sample, we used staffing data on RNs and LPNs from the AHA annual surveys for 1997 and 1998, constructing a weighted average for calendar 1997. Unlike the state-level data, the national AHA survey does not contain information on aides. For hospitals in the 11-state sample, state data on RN and LPN staffing and AHA survey data were generally consistent.

With the exception of California, the state and AHA staffing data are reported for the hospital as a whole, and therefore adjustments must be made to take a hospital's outpatient volume into account to estimate inpatient staffing. We have found, using detailed staffing data from California, that the standard approach for adjusting total hours to reflect both inpatient and outpatient hospital volume (“adjusted patient days” as defined by the AHA [American Hospital Association 1998]) underestimates inpatient staffing in hospitals that have large outpatient volume. To correct the undercounting of inpatient nursing personnel, a correction factor based on analysis of the California data was applied to all hospitals in the 11-state sample and those in the AHA sample for the MedPAR analysis. A description of this adjustment is described elsewhere (Needleman et al. 2002).

To better compare nurse staffing across hospitals, we adjusted nursing hours per day for differences in the nursing care needed by each hospital's patients. We used estimates of the relative nursing care needed for patients in each diagnosis related group (Ballard et al. 1993; Lichtig, Knauf, and Milholland 1999) to construct a nursing case-mix index for each hospital. We divided nursing hours per inpatient day by this index to calculate nursing case-mix-adjusted hours per day. In the MedPAR analysis, acuity was estimated using only Medicare discharges.

Risk Adjustment and Hospital Characteristics

When patient outcomes are influenced by both hospital-level and patient-level variables, to observe the true relationship between the outcomes and hospital-level variables, it is necessary control for differences among hospitals in the risk profile of their patients (Silber, Rosenbaum, and Ross 1995; Silber and Rosenbaum 1997; Silber et al. 1992). We did this by first estimating a patient-level logit regression of the risk for each outcome. Patient-level variables in these regressions included the rate for the outcome in the patient's diagnosis related group (DRG), state, age, sex, primary health insurer, whether an emergency admission, and presence or absence of 13 chronic diseases (cancer with poor prognosis, metastatic cancer, AIDS, coronary artery disease, congestive heart failure, peripheral vascular disease, severe chronic liver disease, chronic renal failure, dementia, functional impairment, diabetes with end organ damage, chronic pulmonary disease, and nutritional deficiencies). The regressions also included interactions of the specific rate of each outcome in each DRG with all the other variables, and interactions between the age and chronic disease variables. (Many DRGs are paired, with the same diagnosis and treatment, but with patients separated by whether or not they have complications or comorbidities. Following the approach being used to construct AHRQ's Quality Indicators, we pooled patients in these paired DRGs into a single group.) We summed the probabilities predicted by the logit model for patients in each hospital to obtain the expected number of patients in that hospital who would experience each outcome. These same variables were used in an ordinary least squares regression to estimate expected length of stay. We obtained other hospital characteristics (bed size, teaching status, state, and metropolitan location) from the AHA Annual Survey of Hospitals for 1997 and 1998. Separate regressions were run for the three samples: 11-state all-patient, 11-state Medicare, and national MedPAR sample.

Statistical Analysis

With the hospital as the unit of analysis, we calculated rates of adverse outcomes and length of stay, and the proportion of hospitals in each sample with no reported adverse outcomes. The correlations between rates in the 11-state all-patient and 11-state Medicare samples were calculated to provide an indication of the similarity between these two measures of adverse outcomes at the same hospital.

To assess whether analysis using all-patient and Medicare-only samples of patients comes to the same conclusions, we examined the relationship between patient outcomes and staffing while controlling for patient risk and hospital characteristics. In these regressions, we used the actual count for each outcome as the dependent variable in a negative binomial count model regression (Greene 2000) using STATA, release 6 (Stata Corporation 1999). Expected counts were included as the measure of exposure. We analyzed length of stay using ordinary least squares regression with expected length of stay included as an independent variable. Independent variables in each regression included nurse staffing variables and categorical variables for state, hospital bed size, teaching status, and location. We tested each coefficient for statistical significance using z-statistics in the negative binomial regressions and t-tests in the OLS regressions.

We estimate incidence risk ratios for the nursing variables in separate regressions for the 11-state all-patient sample, 11-state Medicare sample, and the national MedPAR sample. Similar to other studies that have used staffing data with information only on RNs and LPNs (Kovner and Gergen 1998; Lichtig, Knauf, and Milholland 1999; Network 2000), the staffing variables used in the analysis of the national MedPAR sample included: total licensed (RN plus LPN) nursing hours and the proportion of licensed hours provided by RNs. The staffing specification used in the analysis of the 11-state data, for which data on aides were available, included those in the MedPAR specification, plus aide hours per patient day.

We hypothesize that the incidence risk ratios associated with nurse staffing variables will be less than one. That is, as staffing increases, the risk of the outcome goes down. Across the regressions in the three samples, we compare the statistical significance of corresponding staffing variables and the magnitudes of the incidence risk ratio. We also test whether the differences in the incidence risk ratios across regression models are statistically significant.

Results

Rates of Adverse Patient Outcomes and Length of Stay

In Table 1, we compare the length of stay and rates of adverse outcomes in the 11-state Medicare sample to those in the 11-state all-patient sample, and length of stay and outcome rates in the national MedPAR sample to those in the 11-state Medicare sample. We also examine the correlation of rates in the 11-state all-patient and 11-state Medicare samples, and examine the frequency with which hospitals report no adverse outcomes. These are compared separately for medical and surgical patients.

Table 1
Comparison of 11-State All Patient, 11-State Medicare Only, and National Medicare Length of Stay and Rates for 11 Patient Complications

For both medical and surgical patients in the 11-state samples, length of stay is approximately 25 percent higher for Medicare patients compared with that for all patients. The rates for each outcome in the 11-state Medicare sample are also higher than in the 11-state all-patient sample. Medicare rates are 10 to 70 percent higher for medical and surgical patients. All differences are statistically significant.

In the national MedPAR sample, among the medical patients, all outcomes rates are slightly lower than in the 11-state Medicare sample. Although the differences are statistically significant for all but three outcomes, the values in the two samples are close, differing on average by 6 percent. Among surgical patients, however, the rates in the national MedPAR sample are substantially higher than in the 11-state Medicare sample, on average 40 percent higher, with the differences statistically significant for all but three outcomes. The higher rates in the national MedPAR sample may be due to the inability to restrict the analysis to patients operated on the first or second day of hospitalization.

Correlation of All-Patient and Medicare Rates

The correlation between hospital rates for the 11-state all-patient and the 11-state Medicare samples is quite high in the medical pool, consistently above .8, but lower in the major surgery pool, ranging from .6 to .9. These results suggest that Medicare-patient experience may be a better proxy for all-patient experience among medical patients.

Frequency with Which Hospitals Report No Adverse Outcomes

Given the low rates for some of the outcomes, it is possible that some smaller hospitals will have no patients with adverse outcomes. Zero rates are difficult to interpret in descriptive analysis, since they may result from high quality care, a small cohort of patients at risk, or poor coding. The count models used in the regressions of these outcomes on nurse staffing appropriately take into account zero rates due to the first two causes, but the power to detect statistically significant effects is diminished as the proportion of hospitals with rates of zero increase. Among surgical patients, the proportion of hospitals with zero rates can be quite high; for 4 of the 10 outcomes, more than 20 percent of hospitals in some samples have no events. For both medical and surgical patients, the proportion of hospitals with zero rates is always higher among Medicare patients, a smaller pool, than all patients.

Comparing rates in the two Medicare samples, for medical patients, for all outcomes except pneumonia, the proportion of hospitals with no events in the national MedPAR sample is always higher than in the 11-state Medicare sample. Among surgical patients, by contrast, except for metabolic derangement, the proportion of hospitals with no events in the national MedPAR sample is always lower than in the 11-state Medicare sample. This is consistent with the higher rates of complications in the national MedPAR sample for surgical patients.

Relationship between Patient Outcomes and Nurse Staffing

Means and standard deviations for the nursing variables used in the regression analysis are presented in Table 2. Average licensed hours of nursing staff hours and the mean proportion of hours provided by RNs were greater in the 11-state sample than in the MedPAR sample (Table 2).

Table 2
Mean and Standard Deviation of Hospital Inpatient Nurse Staffing Measures, 1997

To assess whether using outcomes for Medicare patients leads to the same conclusions as those based on all patients, we regressed counts of outcomes for each of these samples of patients on nurse staffing and other hospital variables. We examined whether the results were comparable by assessing whether regression results were in the same direction and statistical significance, and whether the magnitude of the estimated effects were statistically equivalent in regressions where a statistically significant association was found. We first compared results in the 11-state all-patient sample to those in the 11-state Medicare sample, and then compared results in the national MedPAR sample to those in these two 11-state samples. We examined medical and surgical patients separately, and in reporting results, present results only for the two measures of nurse staffing—the proportion of licensed hours from RNs and licensed hours per day. Full regression results are in Appendix 1 (online version, which is available at http://www.blackwellpublishing.com/products/journals/suppmat/HESR/HESR02025/HESR02025sm.htm).

Medical Patients

Among the eight measures for medical patients (Table 3), there is complete agreement in the results in the 11-state all-patient and 11-state Medicare samples for four measures for which an association with nurse staffing is observed—length of stay, urinary tract infection, pneumonia, and shock/cardiac arrest. There is also complete agreement in the analyses for two measures in which no effect is observed—pressure ulcers and sepsis.

Table 3
Regression of Length of Stay and Patient Complications on Nurse Staffing Variables, Medical Patients in 11-State All-Patient, 11-State Medicare Only, and National MedPAR Samples

There is disagreement for two measures. With respect to failure to rescue, in the 11-state all-patient sample, there is an association with the proportion of hours provided by RNs. The incidence risk ratio (IRR) on RN proportion for the 11-state Medicare sample is similar in magnitude to that for the all-patient sample, and the p-value for the IRR is .056, indicating that the results are very close. For UGI bleeding, we find a significant association in the 11-state all-patient sample for RN proportion, but not for licensed hours per day, while in the 11-state Medicare sample, there is no significant association with RN proportion but there is for licensed hours. However the IRRs for RN proportion and licensed hours are similar in magnitude across these two models, and the p-value for RN proportion in the 11-state Medicare sample is .052, and the p-value for licensed hours in the 11-state all-patient sample is .075. Thus, across all eight measures, the two models generate results that are similar even though not totally concordant.

Similarly, there is a high degree of concordance between the results of the national MedPAR analysis and the 11-state Medicare sample for medical patients. For five outcomes—pneumonia, shock/cardiac arrest, pressure ulcer, sepsis, and failure to rescue—the results agree completely. For two outcomes for which an association with RN proportion is found in the 11-state Medicare sample—length of stay and urinary tract infections—a statistically significant association is also observed in the national MedPAR sample, although the magnitude of the IRR is significantly closer to one in the national MedPAR sample. For another measure, UGI bleeding, a measure in which the results differed somewhat between the 11-state all-patient and 11-state Medicare samples, there is no observed association between nurse staffing in the national MedPAR sample.

Surgical Patients

Results show complete agreement for surgical patients (Table 4) in the 11-state all-patient and 11-state Medicare samples for three measures in which an association with nurse staffing is observed—pneumonia, failure to rescue, and metabolic derangement—and three measures in which no effect is observed—length of stay, sepsis, and wound infection.

Table 4
Regression of Length of Stay and Patient Complications on Nurse Staffing Variables, Surgical Patients in 11-State All-Patient, 11-State Medicare Only, and National MedPAR Samples

There is disagreement in four measures. For one—urinary tract infections—an association is observed with RN proportion in the 11-state all-patient sample, but not the 11-state Medicare sample. The magnitude of the IRRs are close, however, and the p-value on RN proportion in the 11-state Medicare sample is .062. For two measures—pressure ulcer and shock and cardiac arrest—we observe an association of licensed hours or RN proportion in the 11-state Medicare sample but not the 11-state all-patient sample. Here, too, the magnitude of the IRRs across the models is similar and the p-values on the corresponding IRRs in the all-patient sample are below .10 (pressure ulcer: p=.081; UGI bleeding: p=.077). For shock/cardiac arrest, however, the results differ substantially. An association is observed with RN proportion in the 11-state Medicare sample but not in the 11-state all-patient sample. The IRRs, while not statistically different, are much further apart than for other outcomes, and the p-value in the 11-state all-patient sample is greater than .10. While there is a high degree of concordance in results for surgical patients between the 11-state all-patient and 11-state Medicare samples, it is lower than that for medical patients.

There is substantial discordance in the results between the 11-state Medicare sample and national MedPAR sample because only two measures are consistent—pressure ulcer and shock/cardiac arrest. These are the two measures for which no statistically significant association was observed in the 11-state all-patient sample, suggesting that these may be more sensitive measures for Medicare surgical patients than patients in general.

For four measures, an association of at least one nursing variable and the outcome is found in the national MedPAR sample but not the 11-state Medicare sample—length of stay, UTI, sepsis, and wound infections. While the coefficients in the length of stay analysis and IRRs for the other three measures are not statistically different, only for sepsis are they the same magnitude and in the predicted direction. For length of stay, the coefficient on licensed hours is three times larger in the national MedPAR sample and the p-value for this variable in the 11-state Medicare sample is .73. For urinary tract infection, the IRR on licensed hours, significant with a value of .991 in the national MedPAR sample, is over one in the 11-state Medicare sample. For wound infection, the statistically significant association of RN proportion in the national MedPAR sample is not in the predicted direction.

For one outcome—metabolic derangement—we observe a statistically significant association of RN proportion in the 11-state Medicare sample but not the national MedPAR sample. The IRR in the national MedPAR sample, while not statistically significant, is over one, that is, not in the expected direction.

For the remaining three outcomes—pneumonia, UGI bleeding, and failure to rescue—we find statistically significant associations with one of the two nurse staffing variables in both the 11-state Medicare sample and national MedPAR sample, but the variable that is statistically significant differs across the two models. Only in the case of upper GI bleeding are the IRRs of comparable magnitude statistically equal and in the predicted direction for the two samples.

As noted above, there are substantial differences between the data for outcomes and nurse staffing used in the national MedPAR and 11-state Medicare sample. To determine whether differences in the datasets produced different results across the Medicare samples, we reran the regression analysis in the 11-state Medicare sample so that it more closely matched the national MedPAR analysis. Specifically, we dropped the day of surgery restriction originally imposed in the 11-state analysis, reestimated the counts of expected cases based on the less restricted definition, and, using the AHA staffing data used in the national MedPAR analysis, reran the count model regressions. Results from the 11-state Medicare sample analyzed using national staffing data and less restrictively coded complications do not match those from national MedPAR sample, and we conclude that the differences in staffing and outcome definitions in the two Medicare samples do not explain the differences observed (results not shown).

Conclusions

Assessing quality over time and across a large number of health care institutions are important to achieving improved quality in U.S. hospitals, and administrative datasets are important in this effort. The overall question motivating this study was whether measures of hospital quality can be constructed from data on Medicare beneficiaries alone, or whether data on all patients are required when examining correlates of quality using administrative data. We addressed this question in two ways. We examined the correlation between rates of adverse outcomes for the same hospitals for their Medicare patients and all patients at the hospital and found that correlations were high, although lower for major surgery patients. The lower correlation is likely due to the smaller pools of Medicare surgical patients and the larger number of hospitals with no cases of the adverse outcomes in the Medicare pool.

We also applied an operational test of whether comparable conclusions would be drawn from regression analysis involving Medicare-only samples and all-patient data. Comparing regressions of outcomes on measures of hospital nurse staffing, we found that results in an 11-state all-patient sample, an 11-state Medicare sample, and a national MedPAR sample were generally consistent for medical patients, but less consistent for surgical patients. Also among surgical patients, there were only two outcomes among the ten studied in which results in the 11-state Medicare and national MedPAR analyses agreed. Recoding the outcomes in the 11-state Medicare sample and using the same staffing data to make the analysis in the two Medicare samples more comparable did not resolve this conflict.

Overall, we conclude that outcome measures applied to medical patients that are implemented in Medicare-only datasets are likely to yield comparable results to those that would be observed in analyses using all-patient data. Thus, using national Medicare data from medical patients in studies of hospital quality is justified.

We would urge caution, however, in using quality measures in surgical patients in Medicare-only data; these measures may not provide results comparable to those from all-patient samples or across different samples of Medicare patients. The reasons for the differences across Medicare samples are not clear. The inability to implement day-of-procedure restrictions from public use data does not explain the differences. A more likely explanation is that the smaller size of the surgical pool of patients, their lower risk for many complications, and the higher proportion of hospitals with no reported complications among surgical patients make it harder to obtain consistent results in regression-based studies of surgical patients using administrative data. The three-sample approach to cross-validating measures presented here is one way to test the usefulness of Medicare-only analysis in these patients.

This paper assesses the ability of Medicare data to substitute for all-patient data in studies of correlates of quality using regression-based techniques. A second potential use of Medicare data as a substitute for all-patient data is in studies that assess quality in specific hospitals. The high correlation of the all-patient and Medicare measures presented in Table 1 suggests that Medicare data might be usable for studies of hospital-specific quality. To fully assess this potential, additional analysis is required. This would include: examining the degree of agreement in ranking hospitals by rates of complications when using each dataset; determining whether observed disagreements are associated with specific hospital characteristics, especially the relative and absolute size of the hospital's Medicare patient population; and assessing how stable rates are for quality measures for hospitals with small numbers of patients.

In conducting the comparisons reported here, we encountered many challenges that arose principally from weaknesses of currently available data, particularly the well-known problems associated with using discharge data to construct quality measures (Geraci 2000; Geraci et al. 1997; Lawthers et al. 2000; Weingart et al. 2000). Because there is no reliable coding of “present on admission” status for secondary diagnoses reported on discharge abstracts, constructing coding and exclusion rules for each adverse outcome requires considerable clinical judgment and technical skill. Complications and adverse outcomes are likely to be underreported, and underreporting may be higher where staffing is low.

Despite these difficulties, we believe that administrative datasets offer a valuable tool for understanding factors influencing quality across hospitals. While more states are making available all patient discharge datasets, these are not universally available. Moreover, creating consistent data across many states can be both time-consuming and expensive. As a consequence of this, Medicare MedPAR data will remain a major data source for analyzing hospital quality. The CMS should take steps to improve the usefulness of these data, including adding day-of-procedure codes to public use datasets. The CMS, the Agency for Healthcare Research and Quality through its Healthcare Cost and Utilization Project (HCUP), and individual states should take additional actions to improve the usefulness of their discharge data for studying quality. They should require consistent and accurate coding of present-on-admission status for secondary diagnoses and identify a set of “must code” secondary diagnoses that are hospital acquired and related to quality. With these changes to discharge abstracts, the ability to monitor quality of care, whether using all-patient or Medicare data, will be enhanced considerably.

Acknowledgments

We thank Carole Gassert, Evelyn Moses, Judy Goldfarb, Tim Cuerdon, Cheryl Jones, Peter Gergen, Carole Hudgings, Pamela Mitchell, Donna Diers, Chris Kovner, Mary Blegen, Margaret Sovie, Nancy Donaldson, Ann Minnick, Lisa Iezzoni, Leo Lichtig, Robert Knauf, Alan Zaslavsky, Lucian Leape, Sheila Burke, Barbara Berney, and Gabrielle Hermann-Camara for their advice. We also gratefully acknowledge the California Office of Statewide Health Planning and Development and State of Maryland for contributing their data for this study, and the staffs of the agencies in each state from which we obtained data, for their assistance. We also thank the anonymous reviewers of this manuscript. The opinions expressed are those of the authors and not necessarily those of the funding or data agencies, or others acknowledged.

Appendices

(Available online only: http://www.blackwellpublishing.com/products/journals/suppmat/HESR/HESR02200/HESR02200sm.htm)

Appendix T.1: Regression of Outcomes on Nursing and Other Variables, Medical Pool, 11-State All-Patient, 11-State Medicare, MedPAR Samples Table A1, Table A2, Table A3

Appendix T.2: Regression of Outcomes on Nursing and Other Variables, Surgical Pool, 11-State All-Patient, 11-State Medicare, MedPAR Samples

Appendix T.3: Regression of Selected Outcomes, 11-State Medicare Sample with AHA Staffing Data and without Procedure Date

Table A1

Regression of Outcomes on Nursing and Other Variables, Medical Pool, 11-State All-Patient, 11-State Medicare, MedPAR Samples

UTIPressure UlcerPneumoniaDVT




11-State All Patient

RobustRobustRobustRobust
IRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>z
LICENSED Hours per Patient Day0.9980.0040.6830.9970.0080.6700.9990.0060.8171.0090.0090.337
AIDE Hours per Patient Day0.9980.0070.7331.0200.0130.1141.0010.0080.9391.0020.0140.858
RN Hours/LICENSED Hours0.4890.0600.0000.7650.1570.1920.6060.0930.0011.3830.2980.132
New York0.9770.0260.3950.8970.0420.0210.9980.0310.9440.8840.0440.013
Massachusetts1.0140.0320.6611.0710.0630.2401.0120.0730.8680.9000.0680.161
Maryland1.0150.0310.6290.9850.0510.7741.0800.0510.1030.9110.0570.135
Virginia0.9760.0360.5001.0570.0610.3321.0330.0440.4521.0180.0670.783
West Virginia0.9910.0540.8731.1070.0930.2250.9930.0610.9081.1490.0980.102
South Carolina1.0620.0560.2471.3190.1110.0011.0880.0650.1581.0130.0720.852
Wisconsin1.0640.0370.0721.1770.0760.0121.0270.0420.5100.9990.0770.993
Missouri0.9880.0310.7061.0690.0620.2561.0740.0450.0881.0050.0720.945
Arizona0.9940.0480.9050.9490.0890.5761.0510.0600.3820.9930.0740.928
Nevada0.9880.0780.8741.2840.2620.2201.0700.0750.3310.9680.1120.780
Major Teaching Hospital1.0930.0390.0131.1040.0530.0371.2040.0620.0001.1450.0820.059
Other Teaching Hospital1.0580.0220.0070.9980.0350.9521.0330.0260.1980.9900.0410.811
Large Metropolitan1.0650.0230.0031.2420.0450.0001.0010.0270.9661.0760.0470.095
Non-Metropolitan0.9610.0290.1871.1150.0630.0540.9780.0370.5630.9660.0590.566
Less Than 100 Beds0.9980.0250.9490.9740.0480.5961.0840.0340.0100.8870.0470.023
250–499 Beds0.9940.0200.7651.0590.0390.1190.9820.0260.4891.0570.0410.145
500 or More1.0330.0370.3711.0210.0530.6910.9400.0400.1481.2850.1120.004
Number of Obs799.000799.000799.000799.000
Wald chi2(20)64.08074.81043.760116.330
Prob>chi20.0000.0000.0020.000
Pseudo R20.0080.0120.0080.016
11-State Medicare

LICENSED Hours per Patient Day1.0000.0040.9890.9910.0080.2591.0010.0070.8831.0040.0100.668
AIDE Hours per Patient Day0.9940.0070.3811.0210.0140.1260.9990.0090.8841.0010.0160.958
RN Hours/LICENSED Hours0.5370.0650.0000.7060.1580.1210.5860.1000.0021.1280.2780.624
New York0.9660.0260.1930.9080.0460.0590.9870.0330.6830.8770.0480.016
Massachusetts1.0090.0320.7741.0100.0680.8840.9820.0770.8180.9090.0720.229
Maryland1.0270.0320.3861.0360.0580.5301.0790.0560.1420.9290.0670.307
Virginia0.9800.0350.5801.0740.0660.2491.0130.0460.7751.0200.0730.780
West Virginia0.9990.0540.9851.1160.0940.1951.0350.0670.5991.1050.1130.332
South Carolina1.0200.0490.6801.3210.1220.0021.0310.0650.6301.0040.0870.968
Wisconsin1.0870.0380.0171.1470.0790.0471.0060.0440.8951.0100.0890.907
Missouri0.9940.0300.8311.1080.0700.1061.0500.0460.2630.9900.0750.894
Arizona0.9440.0470.2460.9630.1040.7270.9760.0620.7030.9670.0880.715
Nevada0.9740.0770.7361.2530.2660.2891.0190.0940.8371.0300.1270.813
Major Teaching Hospital1.1330.0400.0001.1490.0610.0091.2190.0650.0001.1230.0830.115
Other Teaching Hospital1.0640.0220.0030.9740.0380.4971.0200.0280.4630.9790.0440.640
Large Metropolitan1.0520.0220.0151.2510.0500.0001.0100.0300.7471.0900.0540.082
Non-Metropolitan0.9480.0280.0651.1100.0660.0810.9710.0390.4680.9770.0690.739
Less Than 100 Beds1.0010.0250.9520.9560.0500.3911.0830.0360.0170.8500.0510.007
250–499 Beds0.9920.0200.6861.0890.0440.0330.9920.0290.7771.1080.0460.013
500 or More1.0160.0350.6461.0320.0580.5700.9420.0420.1761.2980.1200.005

Number of Obs799.000797.000799.000799.000
Wald chi2(20)66.50078.37037.63098.940
Prob>chi20.0000.0000.0100.000
Pseudo R20.0080.0130.0070.016
National MedPAR

LICENSED Hours per Patient Day0.9960.0020.0250.9940.0030.0730.9990.0030.8141.0000.0040.940
RN Hours/LICENSED Hours0.7590.0470.0000.8920.0990.3050.8330.0670.0241.2700.1470.038
Alabama1.0490.0350.1521.2710.0690.0000.9860.0450.7561.0110.0720.874
Alaska0.9620.0930.6911.1020.0780.1710.7250.1130.0400.8990.1880.610
Arizona1.0570.0470.2101.0270.0840.7411.0050.0510.9240.9900.0720.890
Arkansas1.0690.0470.1271.3730.1050.0001.0120.0600.8421.0660.1110.540
Colorado1.0030.0380.9291.1540.1090.1280.9320.0520.2130.9620.0780.629
Connecticut0.9930.0460.8741.1150.1010.2290.9930.0500.8860.9410.0820.483
Delaware1.0240.0780.7590.9170.1480.5931.0600.1030.5530.7670.1700.231
District of Columbia0.9540.0620.4740.8830.0720.1291.0370.1020.7140.8950.1210.414
Florida1.0390.0250.1141.0990.0460.0231.0140.0340.6800.9630.0460.431
Georgia1.0120.0280.6601.1810.0610.0010.9970.0400.9510.9870.0610.830
Hawaii0.9810.0400.6421.2220.1060.0210.9360.1050.5551.0660.1160.559
Idaho1.1990.0800.0071.6040.2160.0000.9430.0850.5160.9120.1750.632
Illinois1.0070.0260.8021.0110.0530.8351.0060.0400.8830.8810.0520.031
Indiana1.0340.0360.3381.2010.0720.0020.9750.0440.5741.0140.0820.867
Iowa1.0530.0400.1741.2180.1160.0390.9730.0560.6401.0220.1020.825
Kansas1.0530.0420.1901.1840.1120.0730.9510.0570.4021.0320.1060.763
Kentucky1.0560.0360.1081.2320.0760.0010.9460.0450.2380.9910.0590.883
Louisiana1.0200.0380.5911.0900.0550.0910.9450.0400.1761.0560.0660.379
Maine1.1200.0610.0371.3020.1670.0400.9910.0600.8881.3030.1710.043
Maryland1.0130.0320.6721.0460.0540.3880.9950.0500.9220.8930.0660.127
Massachusetts0.9800.0320.5361.0980.0580.0780.9530.0380.2190.8530.0570.018
Michigan0.9610.0290.1790.9360.0470.1910.9510.0340.1530.8200.0460.000
Minnesota1.0140.0380.7151.0530.0950.5680.9360.0430.1520.9670.0710.652
Mississippi1.0480.0370.1851.2590.0850.0010.9130.0560.1391.1150.1040.247
Missouri1.0000.0290.9941.1020.0650.1011.0180.0420.6670.9460.0650.421
Montana1.0410.0820.6141.1170.1080.2530.9570.0880.6331.2090.1440.113
Nebraska1.0990.0690.1331.3440.1520.0090.9480.0790.5211.0540.0920.549
Nevada1.0260.0560.6331.0630.1360.6350.9980.0640.9750.9470.1540.736
New Hampshire1.0930.0530.0651.2580.2650.2750.9760.0780.7640.8550.1460.358
New Jersey1.0510.0320.1041.0910.0570.0971.0750.0450.0850.9330.0490.189
New Mexico1.0320.0540.5471.2280.1180.0320.9850.0630.8200.9910.0890.923
New York0.9760.0240.3290.9100.0390.0290.9790.0300.4880.8410.0430.001
North Carolina1.0160.0300.6051.1280.0620.0300.9930.0680.9220.9100.0540.110
North Dakota1.1300.0720.0551.2480.0810.0010.9990.1080.9901.1850.1150.082
Ohio0.9970.0250.9121.0570.0530.2640.9710.0350.4170.9070.0460.055
Oklahoma1.0490.0460.2731.2650.1040.0040.9280.0500.1711.0480.0690.474
Oregon1.0190.0530.7201.2130.1070.0290.9640.0560.5231.0630.1140.565
Pennsylvania0.9880.0260.6531.0320.0450.4781.0010.0310.9830.9110.0470.068
Rhode Island0.8640.0580.0290.8910.1010.3080.8590.0560.0200.7960.0760.017
South Carolina1.0310.0440.4661.3520.0930.0000.9740.0450.5671.0350.0790.653
South Dakota1.0400.0810.6111.2590.1370.0330.9920.1730.9641.0620.1530.678
Tennessee1.0480.0340.1521.1380.0750.0500.9950.0430.9011.0640.0870.446
Texas1.0280.0280.3021.1240.0470.0050.9390.0270.0311.0140.0500.771
Utah1.0150.0890.8681.0850.1490.5510.8850.0600.0710.9580.1090.706
Vermont0.9890.0530.8361.2140.2370.3200.9020.1140.4131.0710.1500.627
Virginia1.0180.0360.6091.1620.0620.0051.0010.0440.9800.9890.0600.854
Washington1.0350.0400.3801.1420.0830.0680.9640.0450.4370.9990.0850.993
West Virginia1.0340.0520.5041.3570.1160.0000.9420.0540.2951.0800.0940.376
Wisconsin1.0390.0330.2261.1920.0750.0050.9690.0390.4420.9730.0770.731
Wyoming1.1660.1410.2051.5670.1620.0000.9500.1700.7761.2370.1160.024
Major Teaching Hospital1.1770.0210.0001.3850.0430.0001.1340.0270.0001.2080.0480.000
Other Teaching Hospital1.0390.0120.0011.0840.0210.0000.9850.0140.2910.9820.0210.403
Large Metropolitan1.0470.0130.0001.1810.0240.0001.0240.0160.1351.0660.0250.007
Non-Metropolitan0.9410.0130.0000.9360.0240.0091.0070.0200.7280.9320.0260.011
Less Than 100 Beds1.0580.0140.0000.9990.0270.9801.0980.0210.0000.8820.0250.000
250–499 Beds0.9780.0110.0530.9890.0190.5701.0110.0150.4471.1580.0260.000
500 or More0.9690.0170.0790.9580.0270.1300.9650.0210.1081.2450.0500.000

Number of Obs3,356.0003,314.0003,355.0003,357.000
Wald chi2(20)193.360317.060118.650430.220
Prob>chi20.0000.0000.0000.000
Pseudo R20.0050.0150.0040.019
MortalityFailure to RescueGI BleedingCNS




11-State All-Patient

RobustRobustRobustRobust
IRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>z
LICENSED Hours per Patient Day1.0000.0040.9801.0030.0040.5250.9880.0070.0751.0130.0120.252
AIDE Hours per Patient Day1.0100.0060.1041.0150.0070.0200.9990.0110.9021.0280.0190.151
RN Hours/LICENSED Hours0.9000.0910.2940.8020.0840.0340.6580.1260.0291.7440.5180.061
New York0.9940.0210.7810.9770.0220.3091.0530.0470.2441.0330.0690.623
Massachusetts1.0270.0320.3871.0140.0350.6981.0790.0610.1761.0230.0840.778
Maryland1.0200.0260.4321.0310.0280.2621.0260.0470.5790.9410.1050.584
Virginia1.0180.0320.5620.9990.0300.9781.0330.0600.5771.0320.0940.732
West Virginia0.9800.0500.6951.0100.0730.8931.1310.0870.1121.0450.1550.769
South Carolina0.9240.0380.0550.9820.0410.6731.1530.0860.0560.8660.1080.248
Wisconsin1.0330.0370.3591.0410.0420.3151.2010.0740.0030.9440.0750.465
Missouri1.0480.0260.0661.0120.0320.7121.1350.0620.0200.9710.0860.743
Arizona1.0900.0570.1031.0460.0510.3531.1640.0780.0230.8770.0820.162
Nevada0.9380.0510.2411.0170.0340.6190.9820.1330.8940.8230.1010.113
Major Teaching Hospital1.0130.0290.6551.0630.0290.0240.9870.0530.8000.8620.0680.059
Other Teaching Hospital1.0070.0180.7001.0050.0190.8101.0530.0350.1170.9550.0570.435
Large Metropolitan0.9590.0170.0211.0090.0210.6691.0680.0400.0780.9660.0560.555
Non-Metropolitan0.9380.0210.0040.9890.0270.6820.9380.0450.1831.2170.0960.012
Less Than 100 Beds0.9510.0180.0060.9480.0240.0370.9880.0420.7690.7370.0520.000
250–499 Beds1.0210.0190.2671.0360.0200.0651.0400.0320.2041.0810.0540.122
500 or More1.0380.0280.1681.0660.0260.0080.9410.0520.2720.9600.0740.594

Number of Obs799.000799.000798.000799.000
Wald chi2(20)44.49059.44036.07045.740
Prob>chi20.0010.0000.0150.001
Pseudo R20.0050.0080.0050.009

11-State Medicare


LICENSED Hours per Patient Day0.9980.0040.6101.0030.0050.5820.9850.0070.0331.0160.0130.220
AIDE Hours per Patient Day1.0110.0060.0741.0140.0070.0360.9970.0120.7841.0290.0210.165
RN Hours/LICENSED Hours0.8570.0820.1070.8110.0890.0560.6840.1340.0521.8980.6180.049
New York1.0000.0210.9980.9870.0250.5921.0540.0480.2471.0480.0740.507
Massachusetts1.0160.0320.6191.0070.0370.8441.0530.0610.3721.0200.0980.833
Maryland1.0290.0260.2651.0330.0310.2711.0150.0460.7480.9350.1110.571
Virginia0.9890.0280.6840.9910.0300.7771.0370.0610.5391.0170.1020.867
West Virginia1.0140.0480.7671.0190.0750.7961.1480.0970.1031.0500.1640.754
South Carolina0.9020.0390.0170.9610.0420.3551.0830.0890.3340.8780.1240.356
Wisconsin1.0170.0320.5831.0290.0390.4511.1710.0720.0100.9670.0820.695
Missouri1.0310.0270.2491.0020.0320.9511.1240.0630.0370.9800.0950.830
Arizona1.0340.0470.4601.0540.0560.3231.0940.0840.2390.8140.0960.081
Nevada0.8850.0490.0280.9460.0370.1540.9570.1330.7520.8660.1350.356
Major Teaching Hospital0.9720.0270.2961.0450.0310.1411.0360.0570.5240.8500.0740.064
Other Teaching Hospital0.9900.0180.5790.9950.0190.8001.0500.0350.1410.9240.0610.235
Large Metropolitan0.9280.0170.0000.9670.0210.1271.0570.0410.1540.9610.0600.528
Non-Metropolitan0.9430.0220.0110.9950.0280.8550.9350.0460.1761.2350.1030.011
Less Than 100 Beds0.9620.0180.0370.9520.0250.0650.9680.0430.4630.7650.0600.001
250–499 Beds1.0380.0190.0361.0400.0210.0491.0490.0340.1401.0880.0600.127
500 or More1.0500.0270.0531.0640.0270.0160.9300.0530.2030.9460.0820.517

Number of Obs799.000791.000798.000799.000
Wald chi2(20)52.23039.84035.76040.430
Prob>chi20.0000.0050.0160.004
Pseudo R20.0060.0060.0050.009

National MedPAR

LICENSED Hours per Patient Day1.0020.0020.3141.0010.0020.6740.9940.0030.0501.0030.0050.558
RN Hours/LICENSED Hours0.9850.0470.7520.9000.0520.0680.9570.0960.6591.8580.3250.000
Alabama0.9640.0250.1530.9870.0310.6731.1000.0670.1170.9930.0910.940
Alaska1.0740.1490.6071.0350.1110.7500.9460.1000.5991.4220.2900.084
Arizona0.9860.0310.6401.0030.0410.9421.0470.0650.4580.9560.0980.664
Arkansas0.9750.0340.4690.9980.0380.9601.0260.0570.6451.0680.1330.596
Colorado1.0020.0320.9391.0290.0400.4571.0120.1000.9050.9730.1270.833
Connecticut0.9980.0330.9410.9960.0400.9131.0320.0580.5800.9410.0990.559
Delaware0.8950.0260.0000.9030.0590.1211.1630.0760.0201.0400.1720.813
District of Columbia1.0510.0550.3401.0240.0720.7421.0140.1160.9040.8710.1680.474
Florida0.9650.0170.0480.9770.0240.3451.0130.0400.7380.9760.0650.710
Georgia1.0030.0260.9161.0080.0340.8031.0300.0530.5681.0420.0950.653
Hawaii0.9920.0490.8741.0210.0580.7130.9730.1110.8120.9050.2060.660
Idaho0.9870.0450.7761.0430.0800.5781.0830.1240.4831.1300.2120.515
Illinois1.0020.0220.9271.0070.0250.7930.9970.0420.9370.9100.0670.200
Indiana0.9870.0270.6310.9950.0340.8841.0330.0470.4741.0350.0900.695
Iowa0.9980.0370.9471.0460.0520.3661.0390.0700.5721.0410.1290.746
Kansas1.0290.0460.5131.0600.0500.2161.0660.0740.3551.1030.1300.406
Kentucky0.9900.0300.7321.0140.0410.7331.0340.0540.5241.0170.0950.860
Louisiana0.9240.0250.0040.9320.0330.0491.0060.0510.9071.0230.0820.778
Maine1.0360.0370.3141.0130.0610.8251.1110.1050.2661.1990.1790.223
Maryland1.0020.0340.9511.0150.0370.6790.9970.0450.9500.9620.1140.745
Massachusetts0.9850.0270.5981.0190.0360.5921.0120.0570.8371.0010.0970.990
Michigan0.9860.0220.5411.0000.0280.9880.9790.0460.6500.9910.0760.909
Minnesota1.0220.0290.4341.0240.0350.4871.0930.0800.2251.0260.1040.799
Mississippi0.9620.0340.2740.9780.0400.5941.0830.1010.3931.1130.1630.466
Missouri1.0350.0270.1881.0140.0330.6751.0580.0570.2911.0410.0930.654
Montana1.0110.0560.8451.0400.0940.6611.0250.1000.7991.0240.3230.939
Nebraska0.9870.0380.7310.9750.0610.6821.1260.1190.2610.9760.1830.895
Nevada0.9380.0500.2310.9500.0500.3290.9110.0990.3900.9580.1250.742
New Hampshire1.0330.0440.4411.0570.0610.3350.9880.1030.9070.8670.1520.416
New Jersey1.0200.0240.3961.0090.0280.7561.0370.0550.4920.9280.0740.354
New Mexico0.9980.0560.9711.0210.0690.7541.0730.0840.3720.8730.1520.436
New York0.9740.0200.1820.9910.0220.6910.9850.0390.7061.0160.0700.821
North Carolina0.9500.0260.0600.9760.0310.4350.9940.0490.9051.0720.1020.464
North Dakota0.9730.0560.6281.0450.0640.4681.0620.1350.6381.2500.3140.374
Ohio0.9930.0190.7261.0060.0240.7851.0170.0390.6590.9300.0650.299
Oklahoma0.9470.0300.0820.9650.0390.3741.0430.0630.4821.0180.1740.915
Oregon1.0030.0380.9451.0340.0500.4921.0160.0850.8461.1100.1360.392
Pennsylvania1.0180.0200.3431.0170.0230.4460.9900.0360.7841.0060.0710.928
Rhode Island1.0190.0280.4941.0160.0520.7570.9710.1050.7870.9230.1030.472
South Carolina0.9360.0380.1060.9780.0380.5701.0280.0810.7250.9870.1150.908
South Dakota1.0030.0400.9381.1040.0890.2201.0740.1070.4731.0590.2080.771
Tennessee0.9550.0270.1050.9620.0310.2211.0400.0530.4361.0040.0920.963
Texas0.9440.0190.0030.9680.0230.1661.0170.0430.6971.0660.0710.336
Utah1.0080.0480.8701.0620.0810.4311.0540.1040.5921.0510.1440.719
Vermont1.0080.0570.8900.9880.0830.8841.0880.1770.6061.0600.3380.855
Virginia0.9910.0260.7171.0090.0310.7710.9990.0530.9841.0060.0920.945
Washington0.9930.0300.8280.9960.0390.9101.0130.0740.8580.9760.0900.792
West Virginia0.9760.0390.5500.9870.0480.7861.0490.0670.4561.0070.1240.956
Wisconsin0.9970.0270.9121.0340.0360.3401.0880.0570.1100.9610.0770.620
Wyoming1.1340.0730.0521.1340.1200.2360.9700.1320.8251.2000.2740.424
Major Teaching Hospital0.9620.0160.0171.0130.0180.4511.0050.0310.8690.8580.0420.002
Other Teaching Hospital0.9750.0080.0020.9980.0100.8771.0090.0170.6090.9460.0280.059
Large Metropolitan0.9390.0090.0000.9610.0110.0001.0390.0190.0411.0030.0320.918
Non-Metropolitan0.9670.0110.0030.9790.0140.1351.0070.0220.7400.9780.0390.585
Less Than 100 Beds0.9470.0110.0000.9420.0140.0000.9870.0230.5590.8900.0360.004
250–499 Beds1.0450.0090.0001.0360.0110.0011.0280.0180.1111.0610.0330.054
500 or More1.0680.0160.0001.0640.0170.0000.9350.0270.0191.0230.0490.628
Number of Obs3,357.0003,336.0003,357.0003,354.000
Wald chi2(20)194.360100.92039.82081.890
Prob>chi20.0000.0010.9740.026
Pseudo R20.0060.0050.0020.004
SepsisShock/Cardiac Arrest


11-State All Patient

RobustRobust
IRRStd. Err.P>zIRRStd. Err.P>z
LICENSED Hours per Patient Day0.9930.0070.3120.9960.0120.708
AIDE Hours per Patient Day1.0070.0130.6081.0150.0170.384
RN Hours/LICENSED Hours1.3940.2790.0970.4860.1430.014
New York0.9080.0380.0210.9530.0550.407
Massachusetts0.9690.0610.6221.0200.1390.885
Maryland0.9380.0500.2291.0360.0690.597
Virginia0.9640.0670.6040.9760.0930.796
West Virginia1.0740.1050.4620.9880.1400.930
South Carolina1.1070.0770.1431.0320.0930.728
Wisconsin1.0080.0610.8961.1280.0850.110
Missouri1.0360.0520.4791.0580.0830.475
Arizona1.0820.0740.2491.1970.1310.100
Nevada1.0610.1200.6041.1790.1360.152
Major Teaching Hospital1.2070.0570.0001.1820.0840.019
Other Teaching Hospital1.0420.0340.2110.9990.0480.976
Large Metropolitan1.0750.0360.0281.1330.0580.014
Non-Metropolitan0.9280.0460.1321.0590.0700.383
Less Than 100 Beds0.8390.0370.0000.9490.0620.425
250–499 Beds1.0710.0350.0371.0830.0520.096
500 or More1.1000.0520.0431.0030.0720.966

Number of Obs799.000798.000
Wald chi2(20)166.15033.510
Prob>chi20.0000.030
Pseudo R20.0220.006

11-State Medicare

LICENSED Hours per Patient Day0.9890.0080.1700.9950.0110.651
AIDE Hours per Patient Day1.0120.0140.3921.0260.0180.159
RN Hours/LICENSED Hours1.1050.2330.6360.4030.1140.001
New York0.9220.0410.0640.9260.0570.210
Massachusetts1.0430.0710.5311.0780.1630.621
Maryland1.0040.0580.9441.0160.0740.828
Virginia0.9430.0670.4100.8990.0840.251
West Virginia1.2540.1300.0300.9700.1560.851
South Carolina1.1080.0850.1830.9720.0880.754
Wisconsin1.0070.0700.9171.0890.0890.295
Missouri1.0960.0620.1041.0160.0890.853
Arizona1.0490.0820.5431.2160.1430.096
Nevada1.0170.1570.9130.8770.1130.307
Major Teaching Hospital1.2460.0640.0001.1970.0960.024
Other Teaching Hospital1.0690.0390.0650.9660.0490.498
Large Metropolitan1.0650.0370.0721.0570.0560.295
Non-Metropolitan0.9080.0510.0821.1000.0770.169
Less Than 100 Beds0.8330.0410.0000.9670.0600.583
250–499 Beds1.0920.0390.0141.1710.0580.001
500 or More1.1300.0580.0171.0420.0840.608

Number of Obs799.000798.000
Wald chi2(20)157.22045.610
Prob>chi20.0000.001
Pseudo R20.0250.009

National MedPAR

LICENSED Hours per Patient Day0.9940.0030.0941.0010.0040.868
RN Hours/LICENSED Hours1.2370.1370.0560.6630.0930.004
Alabama1.1340.0700.0410.9310.0700.342
Alaska1.2480.2620.2911.0510.2780.850
Arizona1.0110.0690.8681.0350.0930.707
Arkansas1.1620.0940.0641.0070.0970.941
Colorado1.0000.0680.9951.0110.1000.914
Connecticut0.9880.0800.8800.9820.1010.863
Delaware0.8970.1080.3690.8990.2050.639
District of Columbia0.8820.0820.1761.1080.2500.649
Florida0.9810.0360.5920.9620.0540.496
Georgia1.0300.0530.5681.0030.0660.959
Hawaii1.1090.1340.3890.9280.1350.606
Idaho1.2070.1540.1391.1240.2050.520
Illinois0.9710.0420.4941.0070.0570.906
Indiana1.0160.0560.7730.9610.0740.609
Iowa1.0220.0690.7491.0560.0930.536
Kansas1.2110.1030.0250.9850.1120.896
Kentucky1.0430.0680.5150.9900.0880.908
Louisiana1.0360.0550.5010.9610.0690.578
Maine1.2360.1080.0151.0900.1640.567
Maryland0.9650.0580.5520.9990.0750.988
Massachusetts0.9420.0510.2690.9570.0760.583
Michigan0.8750.0400.0040.9590.0620.517
Minnesota0.9760.0740.7490.9240.0610.229
Mississippi1.1240.0900.1440.9670.0910.718
Missouri0.9870.0530.8141.0340.0780.661
Montana1.2060.1270.0761.0280.1410.839
Nebraska1.1990.1460.1360.9660.1150.772
Nevada0.8940.0720.1640.9330.1380.638
New Hampshire1.2490.1310.0341.0540.1690.743
New Jersey1.0030.0560.9611.0420.0740.559
New Mexico1.2430.1030.0080.9780.1610.894
New York0.8680.0330.0000.9710.0480.551
North Carolina0.9550.0510.3870.9810.0640.764
North Dakota1.1090.1110.3030.9940.1650.973
Ohio0.9820.0430.6770.9420.0500.255
Oklahoma1.0310.0880.7211.0110.1100.920
Oregon1.1310.1030.1741.0640.1090.543
Pennsylvania0.9780.0400.5760.9950.0530.920
Rhode Island0.7890.0970.0540.9190.0860.369
South Carolina1.0450.0650.4770.9170.0720.265
South Dakota1.2370.1510.0811.2740.2270.173
Tennessee1.0370.0670.5670.9030.0670.169
Texas1.0190.0430.6510.9690.0470.509
Utah0.9670.0910.7240.9540.1490.761
Vermont1.1650.1400.2030.9230.1410.599
Virginia1.0510.0590.3830.9640.0710.622
Washington1.0410.0730.5670.9970.0960.974
West Virginia1.1170.1100.2650.9760.0950.803
Wisconsin1.0610.0650.3331.0370.0780.624
Wyoming1.6500.4110.0441.2380.3600.464
Major Teaching Hospital1.2310.0350.0001.1070.0450.012
Other Teaching Hospital1.0380.0190.0370.9880.0230.618
Large Metropolitan1.0670.0200.0011.0020.0250.933
Non-Metropolitan0.8680.0220.0000.9510.0290.103
Less Than 100 Beds0.8760.0240.0000.9810.0320.555
250–499 Beds1.1290.0210.0001.0500.0250.044
500 or More1.2070.0310.0001.0330.0410.415

Number of Obs3,351.0003,355.000
Wald chi2(20)644.57037.790
Prob>chi20.0000.986
Pseudo R20.0270.002
Length of Stay Medical

11-state

Robust
Coef.Std. Err.P>t
LICENSED Hours per Patient Day−0.0940.0190.00
AIDE Hours per Patient Day0.0660.0270.015
RN Hours/LICENSED Hours−1.4100.4350.001
New York0.1560.1230.204
Massachusetts0.0950.1000.341
Maryland0.0650.0850.443
Virginia0.4790.1190.000
West Virginia0.5840.2620.026
South Carolina0.2980.1040.004
Wisconsin0.1090.1150.345
Missouri0.2670.1330.044
Arizona−0.2590.1490.081
Nevada−0.2550.3170.422
Major Teaching Hospital0.2500.1120.026
Other Teaching Hospital−0.0220.0710.759
Large Metropolitan0.1670.0740.024
Non-Metropolitan−0.3340.0900.000
Less Than 100 Beds−0.2410.0890.007
250–499 Beds0.1810.0710.010
500 or More0.2370.1150.039
e2losmed0.8730.0570.000
Constant2.1750.5610.000
Number of Obs797.000
F(21, 775)55.310
Prob>F0.000
R-squared0.690
Root MSE0.828

Medicare

LICENSED Hours per Patient Day−0.1410.0240.000
AIDE Hours per Patient Day0.0970.0370.009
RN Hours/LICENSED Hours−2.1760.5850.000
New York0.3160.1780.076
Massachusetts−0.4160.1400.003
Maryland−0.1580.1330.234
Virginia0.3560.1510.018
West Virginia0.5720.2460.020
South Carolina0.1600.1590.315
Wisconsin−0.0310.1300.810
Missouri0.1700.1680.311
Arizona−0.9720.2340.000
Nevada−0.5630.3610.118
Major Teaching Hospital0.5730.1990.004
Other Teaching Hospital0.0570.1000.565
Large Metropolitan0.2790.0960.004
Non-Metropolitan−0.4240.1200.000
Less Than 100 Beds−0.4660.1130.000
250–499 Beds0.2300.0990.020
500 or More0.1120.1880.550
e2losmed0.8000.0650.000
Constant3.9550.7040.000

Number of Obs797.000
F(21, 775)53.210
Prob>F0.000
R-squared0.636
Root MSE1.129

Medpar

LICENSED Hours per Patient Day−0.0540.0100.000
RN Hours/LICENSED Hours−0.8180.2480.001
Alabama−0.0330.1430.815
Alaska−0.1660.4150.689
Arizona−0.2670.1230.030
Arkansas0.1050.1670.528
Colorado−0.0830.1490.578
Connecticut−0.0480.2530.850
Delaware−1.0300.9040.255
District of Columbia−0.4670.3970.239
Florida−0.2900.1090.008
Georgia0.1960.2180.368
Hawaii−0.5130.4830.289
Idaho0.3980.2160.065
Illinois−0.0820.1120.460
Indiana−0.0590.1190.623
Iowa0.3970.1640.016
Kansas0.2330.1890.217
Kentucky−0.0690.1380.619
Louisiana−0.5040.1740.004
Maine0.3490.2060.089
Maryland−0.4390.1380.002
Massachusetts−1.1740.1840.000
Michigan−0.1500.1240.226
Minnesota−0.3090.1310.018
Mississippi0.3120.1790.081
Missouri0.1670.1370.223
Montana0.4040.2470.102
Nebraska−0.1760.1770.320
Nevada−0.8010.1770.000
New Hampshire−0.1330.1990.506
New Jersey−0.2720.2520.280
New Mexico0.1600.1480.280
New York−0.6620.2380.005
North Carolina−0.0550.1300.674
North Dakota−0.1930.1950.324
Ohio0.0310.1120.784
Oklahoma−0.2180.1710.201
Oregon0.3350.1340.012
Pennsylvania−0.2050.1430.153
Rhode Island−0.5970.2670.026
South Carolina−0.2720.1570.083
South Dakota0.5990.2050.004
Tennessee−0.0330.1850.858
Texas−0.2610.2180.232
Utah0.0780.1900.681
Vermont0.1870.2410.440
Virginia−0.0290.1310.824
Washington−0.5360.1110.000
West Virginia−0.1770.1320.180
Wisconsin0.1580.1080.146
Wyoming0.5920.2210.007
Major Teaching Hospital0.3400.1000.001
Other Teaching Hospital−0.0310.0470.516
Large Metropolitan0.1770.0630.005
Non-Metropolitan−0.2490.0770.001
Less Than 100 Beds−0.2810.0780.000
250–499 Beds0.2140.0480.000
500 or More0.3150.1020.002
e2losmed0.9400.0490.000
Constant1.2960.3820.001

Number of Obs3,354.000
F(21, 775)70.020
Prob>F0.000
R-squared0.535
Root MSE1.242

Table A2

Regression of Outcomes on Nursing and Other Variables, Surgical Pool, 11-State All-Patient, 11-State Medicare, MedPAR Samples

UTIPressure UlcerPneumoniaDVT




11-State All Patient

RobustRobustRobustRobust
IRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>z
LICENSED Hours per Patient Day1.0060.0080.4800.9810.0110.0811.0180.0120.1411.0320.0140.019
AIDE Hours per Patient Day1.0000.0120.9841.0340.0200.0851.0030.0180.8731.0290.0270.283
RN Hours/LICENSED Hours0.5890.1190.0090.8280.2990.6000.5220.1640.0381.6870.6250.158
New York0.0400.0550.9740.0610.6740.9470.0670.4390.8170.0750.028
Massachusetts0.9560.0560.4481.1450.1540.3140.8000.0820.0300.7410.1020.029
Maryland0.9140.0630.1940.9780.0780.7861.0170.0890.8470.8900.1060.327
Virginia0.8850.0560.0530.9870.0970.8920.9570.0750.5730.8550.0880.130
West Virginia0.9080.0770.2541.0090.1360.9461.0610.1150.5851.0530.1660.746
South Carolina0.9410.0710.4221.0600.1500.6831.0040.1240.9731.0470.1300.711
Wisconsin0.9440.0620.3811.0260.1110.8121.0540.1060.6030.9030.0930.325
Missouri0.9120.0570.1451.0090.1020.9321.0630.0980.5040.8790.0900.210
Arizona0.9530.0750.5411.0670.1230.5740.9020.0840.2700.7800.1330.144
Nevada1.0700.0910.4271.1920.2290.3601.1730.1760.2881.1720.2200.396
Major Teaching Hospital1.1600.0660.0101.0190.0690.7801.3410.1000.0001.4200.2140.020
Other Teaching Hospital1.0440.0390.2471.0500.0550.3511.0580.0570.2911.0600.0600.305
Large Metropolitan1.0380.0380.3061.0840.0600.1471.1040.0610.0721.1510.0720.025
Non-Metropolitan1.0610.0530.2391.0710.0990.4570.9850.0810.8531.2400.1080.013
Less Than 100 Beds1.0880.0480.0560.9130.0900.3551.0240.0690.7270.7840.0680.005
250–499 Beds1.0130.0390.7381.0570.0590.3221.0660.0540.2101.1020.0660.108
500 or More1.0190.0570.7380.9700.0760.6990.9870.0780.8651.2080.1280.074
Number of Obs795.000788.000796.000796.000
Wald chi2(20)34.09014.81037.75077.110
Prob>chi20.0260.7870.0100.000
Pseudo R20.0050.0050.0080.028

11-State Medicare
LICENSED Hours per Patient Day1.0030.0080.6560.9670.0120.0091.0150.0130.2681.0200.0150.183
AIDE Hours per Patient Day1.0030.0120.7951.0420.0230.0641.0090.0200.6720.9980.0240.920
RN Hours/LICENSED Hours0.6750.1420.0620.8110.3450.6240.3560.1210.0021.2650.5200.566
New York0.9290.0420.1050.9900.0690.8840.9320.0670.3280.8480.0750.062
Massachusetts0.9300.0580.2471.1340.1810.4310.6940.0800.0010.6860.0920.005
Maryland0.9120.0610.1671.0420.1050.6800.9900.0880.9100.8720.0990.227
Virginia0.9210.0570.1840.9520.1070.6610.9650.0820.6710.8530.1020.181
West Virginia0.9430.0770.4721.0610.1430.6590.9760.1080.8250.9630.1760.838
South Carolina0.9610.0730.6061.1230.1550.4020.9510.1200.6871.1230.1540.398
Wisconsin0.9900.0660.8741.0910.1270.4531.0040.1090.9741.0200.1010.839
Missouri0.9040.0590.1181.0090.1200.9400.9910.0980.9240.9090.0960.363
Arizona0.9290.0820.4061.2130.1360.0860.8660.0840.1380.8030.1550.257
Nevada1.0480.0850.5611.4470.3220.0971.0510.1240.6741.1770.2150.371
Major Teaching Hospital1.2010.0690.0011.0540.0870.5241.3360.1130.0011.3770.1400.002
Other Teaching Hospital1.0870.0410.0281.1020.0700.1251.0430.0600.4671.0450.0670.490
Large Metropolitan1.0260.0360.4761.1110.0680.0861.1350.0650.0261.2230.0920.008
Non-Metropolitan1.0200.0510.6941.1610.1170.1380.9700.0860.7361.2970.1280.008
Less Than 100 Beds1.1340.0490.0040.9240.1010.4731.0660.0770.3770.8760.0860.176
250–499 Beds0.9900.0390.8041.0540.0670.4071.0250.0570.6641.0780.0720.258
500 or More0.9520.0500.3561.0820.0980.3810.9260.0750.3411.2210.1100.027

Number of Obs789.000781.000793.000794.000
Wald chi2(20)33.65023.61038.75056.920
Prob>chi20.0290.2600.0070.000
Pseudo R20.0060.0090.0090.022

National MedPAR

LICENSED Hours per Patient Day0.9910.0030.0040.9860.0040.0010.9890.0030.0010.9950.0050.326
RN Hours/LICENSED Hours0.8770.0840.1690.9020.1110.4040.9360.1010.5401.5160.2390.008
Alabama1.0470.0520.3561.1720.0730.0111.0880.0690.1831.0650.0890.450
Alaska0.9370.1860.7431.3490.1820.0260.6120.1230.0151.2050.3350.501
Arizona1.0430.0660.5131.0490.0910.5820.9740.0660.7030.9110.1080.431
Arkansas1.0860.0670.1781.1820.1070.0641.0640.0820.4191.0520.1270.678
Colorado0.9820.0650.7781.0250.0800.7500.9900.0580.8660.9500.1220.692
Connecticut0.9710.0740.7031.1090.1150.3231.0030.0880.9771.0480.1250.697
Delaware0.9240.0960.4481.2370.0510.0001.2120.3390.4920.7760.1040.059
District of Columbia0.9640.0850.6760.8790.0900.2061.0830.1720.6170.8750.1310.372
Florida0.9920.0330.8011.0560.0490.2460.9890.0400.7900.9970.0590.961
Georgia1.0070.0460.8791.1240.0670.0511.0670.0570.2201.0520.0860.534
Hawaii0.9920.0530.8861.1270.1280.2930.9520.0800.5571.0690.2160.741
Idaho1.0540.0930.5541.0740.2230.7301.1170.1710.4721.0760.2440.748
Illinois0.9860.0390.7121.0130.0510.8020.9900.0430.8070.8970.0570.088
Indiana0.9880.0590.8361.0660.0640.2891.0170.0660.7891.0300.0970.756
Iowa1.0010.0760.9931.0900.0950.3211.1010.1310.4171.0600.1380.654
Kansas0.9990.0630.9921.1930.1420.1380.9810.0810.8141.1740.1280.142
Kentucky1.0090.0470.8431.1540.0880.0591.0660.0650.2951.0140.0870.871
Louisiana0.9720.0520.5941.0530.0660.4081.0570.0620.3401.0600.0910.494
Maine1.0520.1060.6131.1630.1920.3600.9950.0860.9571.1130.1990.551
Maryland0.9700.0520.5650.9930.0550.8940.9960.0590.9430.9060.0760.242
Massachusetts0.9450.0500.2860.9920.0750.9140.9400.0580.3160.8690.0750.104
Michigan0.8940.0380.0080.9870.0520.8040.9640.0530.5060.8590.0650.046
Minnesota0.9470.0570.3641.0850.0870.3090.8790.0640.0770.9770.0820.784
Mississippi1.0860.0660.1771.1890.0840.0141.1000.0960.2751.0740.1100.484
Missouri0.9550.0490.3771.0440.0860.5961.0260.0570.6430.8820.0700.113
Montana1.0250.1100.8161.0990.1590.5111.0060.1090.9531.3670.3550.228
Nebraska1.0270.0890.7621.1760.1130.0931.0070.1040.9481.1600.2480.488
Nevada1.0370.0780.6281.2320.2180.2381.0280.0860.7401.0540.1760.752
New Hampshire1.0090.0890.9151.0710.1700.6681.0100.1510.9481.0670.1860.709
New Jersey0.9650.0420.4031.0020.0490.9651.0470.0570.4030.8820.0680.102
New Mexico1.0320.0790.6761.2160.1960.2261.0220.1150.8481.0700.2070.725
New York0.9360.0310.0500.9050.0420.0310.9650.0390.3860.7990.0470.000
North Carolina0.9970.0450.9541.1150.0650.0641.0400.0620.5070.9890.0780.883
North Dakota1.0110.0750.8831.1960.1520.1600.9520.0890.5971.1980.3060.479
Ohio0.9520.0370.2041.0590.0520.2440.9750.0410.5380.8690.0570.033
Oklahoma1.0470.0730.5091.1830.1250.1101.0490.0960.6021.0950.1010.326
Oregon1.0110.0700.8701.1120.0960.2191.0010.0910.9901.0380.1210.748
Pennsylvania0.9250.0370.0540.9870.0490.7841.0220.0450.6140.9260.0610.248
Rhode Island0.8590.0810.1070.9780.1450.8790.8480.0980.1510.8730.1410.399
South Carolina0.9700.0600.6261.1380.0850.0841.0190.0680.7811.0320.0970.736
South Dakota0.9770.0990.8201.2110.1600.1461.0730.2110.7200.9790.2350.931
Tennessee1.0430.0560.4301.1040.0690.1111.0570.0700.4051.0140.0850.867
Texas1.0310.0380.4101.0940.0530.0631.0480.0420.2421.0020.0640.977
Utah0.9010.1090.3901.0380.0960.6890.9710.0840.7351.0010.1910.994
Vermont0.9140.0930.3791.1160.1680.4650.8600.1980.5130.8360.2180.493
Virginia0.9690.0460.5011.0650.0580.2471.0390.0520.4430.9800.0850.811
Washington0.9880.0670.8571.0500.1090.6370.9620.0830.6561.0100.1560.951
West Virginia0.9240.0700.3021.1500.0970.0981.1100.0830.1631.1040.1080.309
Wisconsin0.9720.0520.5941.0950.0710.1611.0790.0850.3370.9860.0730.853
Wyoming0.9120.1870.6521.3250.2050.0690.8410.1400.3001.0640.1970.737
Major Teaching Hospital1.2160.0320.0001.2230.0390.0001.1350.0340.0001.2170.0540.000
Other Teaching Hospital1.0720.0180.0001.0810.0220.0001.0300.0200.1260.9590.0260.121
Large Metropolitan1.0380.0180.0291.1840.0260.0001.0870.0220.0001.1070.0310.000
Non-Metropolitan0.9570.0190.0320.9840.0290.5871.0110.0270.6770.9810.0370.616
Less Than 100 Beds1.0980.0230.0001.1010.0370.0041.0740.0300.0110.9080.0400.028
250–499 Beds0.9680.0160.0520.9630.0200.0740.9450.0190.0041.1400.0320.000
500 or More0.9400.0230.0110.9070.0260.0010.8760.0240.0001.2130.0470.000
Number of Obs3,288.0003,217.0003,293.0003,297.000
Wald chi2(20)118.960202.510102.930231.480
Prob>chi20.0000.0000.0000.000
Pseudo R20.0050.0110.0050.016
MortalityFailure to RescueGI BleedingCNS
11-State All Patient
RobustRobustRobustRobust
IRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>z
LICENSED Hours per Patient Day1.0000.0070.9920.9820.0080.0220.9890.0130.3801.0330.0170.051
AIDE Hours per Patient Day1.0080.0120.4961.0170.0140.1941.0070.0200.7071.0340.0260.180
RN Hours/LICENSED Hours1.1240.2020.5170.7150.1490.1070.5280.1910.0772.0980.9680.108
New York0.9030.0350.0090.9450.0440.2241.1030.0790.1700.9460.0950.581
Massachusetts0.9090.0610.1571.0140.1330.9161.1600.1130.1271.0550.1140.618
Maryland0.9480.0520.3350.9990.0680.9841.0370.0910.6750.9120.1730.628
Virginia0.8940.0510.0481.0210.0630.7351.0040.1070.9700.9500.1320.711
West Virginia1.0070.0700.9230.9630.0760.6331.0360.1360.7901.0860.2070.666
South Carolina0.9530.0730.5331.0080.0650.9031.1100.1380.4030.9030.1530.546
Wisconsin0.9810.0510.7070.9990.0720.9931.1100.0990.2450.8220.0940.087
Missouri0.9920.0490.8670.9760.0590.6891.0370.0820.6471.0000.1170.999
Arizona0.9340.0740.3831.0500.0890.5631.0250.1500.8640.8790.1290.380
Nevada1.0810.0850.3200.9890.0860.9021.0530.1670.7420.7720.2060.332
Major Teaching Hospital1.1670.0540.0011.0310.0520.5410.9810.0830.8210.9900.1020.923
Other Teaching Hospital1.0220.0310.4791.0110.0350.7581.0790.0560.1440.9280.0750.358
Large Metropolitan0.9860.0300.6340.9780.0360.5401.1010.0610.0801.0210.0900.815
Non-Metropolitan0.9770.0480.6290.9290.0550.2131.0000.0850.9971.1340.1270.264
Less Than 100 Beds0.9460.0460.2550.9380.0550.2750.9460.0770.4920.7650.0900.023
250–499 Beds1.1100.0350.0011.0080.0360.8351.0830.0570.1291.1720.0880.035
500 or More1.1630.0500.0001.0970.0570.0710.8480.0690.0441.0800.1240.500
Number of Obs796.000785.000797.000796.000
Wald chi2(20)75.08017.99030.65028.930
Prob>chi20.0000.5880.0600.089
Pseudo R20.0150.0060.0080.010
11-State Medicare
LICENSED Hours per Patient Day0.9930.0080.4030.9780.0090.0130.9910.0140.5171.0330.0200.106
AIDE Hours per Patient Day1.0010.0130.9551.0140.0160.3690.9990.0240.9621.0140.0320.647
RN Hours/LICENSED Hours0.9290.1890.7180.6980.1680.1350.4040.1710.0322.0631.0950.172
New York0.9420.0420.1790.9480.0460.2641.1260.0920.1471.0620.1260.615
Massachusetts0.8540.0710.0571.0280.1300.8261.0630.1260.6081.0870.1450.533
Maryland0.9380.0510.2391.0120.0680.8570.9790.0950.8300.9320.2030.747
Virginia0.9240.0530.1721.0690.0690.3011.1180.1350.3521.0340.1740.842
West Virginia0.9320.0670.3210.8780.0830.1660.9810.1380.8910.9700.2350.901
South Carolina0.9800.0780.7941.0100.0830.9021.1450.1610.3360.9990.2260.998
Wisconsin0.9690.0550.5751.0010.0820.9951.0690.1050.4970.8990.1160.409
Missouri0.9750.0550.6560.9910.0670.8891.0790.0970.4011.0350.1440.807
Arizona0.9340.0750.3921.0750.0940.4071.0130.1840.9440.8550.1430.346
Nevada1.0580.0840.4741.0350.1180.7601.0840.2470.7220.8010.2600.495
Major Teaching Hospital1.1430.0590.0101.0140.0530.7980.9620.0960.6991.0190.1340.884
Other Teaching Hospital1.0160.0320.6101.0240.0430.5801.1060.0670.0990.9190.0860.365
Large Metropolitan0.9770.0350.5180.9610.0430.3651.1630.0730.0171.0000.1020.999
Non-Metropolitan0.9900.0510.8390.9270.0570.2211.0350.0990.7211.1030.1460.456
Less Than 100 Beds1.0160.0530.7600.9470.0580.3781.0090.0900.9210.8590.1190.273
250–499 Beds1.0970.0370.0071.0290.0440.5031.0620.0650.3301.0770.0940.393
500 or More1.0640.0500.1901.0950.0620.1080.8160.0790.0351.0210.1410.878

Number of Obs794.000767.000794.000794.000
Wald chi2(20)27.56019.18033.20012.980
Prob>chi20.1200.5100.0320.878
Pseudo R20.0070.0060.0100.004
MortalityFailure to RescueGI BleedingCNS
National MedPAR

RobustRobustRobustRobust




IRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>z
LICENSED Hours per Patient Day0.9990.0020.6600.9990.0030.7630.9870.0040.0010.9910.0070.189
RN Hours/LICENSED Hours0.8730.0690.0880.8130.0660.0110.7690.1090.0631.6280.3810.037
Alabama0.9500.0390.2170.9730.0390.4951.0470.0810.5500.9960.1150.969
Alaska0.9890.1750.9480.9860.2550.9581.0070.3760.9851.1290.4300.750
Arizona0.9990.0420.9861.0210.0390.5871.0190.0950.8390.8830.1070.307
Arkansas0.9770.0560.6880.9770.0610.7091.0690.1010.4850.9590.2080.848
Colorado0.9860.0560.8060.9940.0670.9251.0010.1080.9931.0090.1680.958
Connecticut1.0020.0500.9641.0010.0390.9811.0890.0990.3460.8780.1110.302
Delaware0.8970.0540.0720.9480.0610.4021.2570.1330.0300.9110.3170.788
District of Columbia0.9590.0950.6710.9970.0840.9681.0190.1660.9090.8250.2170.465
Florida0.9570.0260.0980.9790.0300.4921.0050.0540.9230.9780.0760.775
Georgia0.9690.0370.4081.0000.0410.9981.0600.0720.3900.9660.1170.773
Hawaii0.9910.0760.9040.9810.0710.7930.9310.1470.6511.1570.3530.632
Idaho1.0260.0830.7551.0440.1070.6751.0910.1530.5321.0260.2930.929
Illinois0.9790.0330.5221.0190.0380.6041.0480.0550.3730.9710.0960.766
Indiana0.9870.0380.7291.0130.0500.7981.0710.0870.3950.9810.1190.875
Iowa0.9520.0560.4021.0080.0500.8661.0390.1000.6960.9360.1950.753
Kansas0.9900.0710.8841.0010.0800.9860.9790.0890.8180.9620.1570.812
Kentucky0.9670.0480.5021.0060.0410.8811.0090.0800.9070.9130.1190.484
Louisiana0.9480.0390.1960.9610.0490.4280.9880.0710.8641.0790.1230.506
Maine1.0360.0970.7041.0030.1120.9791.1320.1140.2191.2480.2480.266
Maryland0.9860.0500.7801.0240.0590.6861.0310.0720.6610.8910.1440.476
Massachusetts0.9880.0450.7930.9810.0460.6811.0560.0850.4971.0040.1160.971
Michigan0.9470.0350.1360.9920.0330.8121.0130.0630.8390.9350.0980.521
Minnesota1.0360.0670.5801.0170.0710.8041.0010.0910.9891.1010.1310.421
Mississippi0.9750.0450.5840.9920.0660.9071.0750.1280.5450.9830.1920.932
Missouri1.0130.0400.7431.0160.0520.7531.0440.0790.5660.9590.1080.712
Montana1.0390.1060.7061.0460.0990.6361.0390.2450.8720.8520.3290.677
Nebraska0.9380.0780.4390.9400.1080.5950.9990.1560.9930.8510.1860.458
Nevada0.9690.0730.6750.9550.0640.4890.9760.1400.8661.0470.2560.850
New Hampshire0.9940.0780.9411.0170.0880.8471.0380.1570.8070.9670.1910.865
New Jersey0.9940.0380.8660.9920.0360.8201.0350.0670.5930.9160.0960.403
New Mexico1.0160.0850.8531.0220.1570.8890.9550.1130.6991.0990.2570.686
New York0.9720.0290.3441.0040.0290.8930.9850.0540.7770.8880.0700.131
North Carolina0.9700.0340.3831.0030.0370.9301.0750.0830.3501.0290.1370.827
North Dakota0.9700.0670.6600.9890.0700.8791.0340.1600.8271.0780.3100.794
Ohio0.9560.0290.1280.9890.0330.7401.0480.0630.4290.9530.0920.618
Oklahoma0.9430.0540.3040.9780.0510.6731.0370.0870.6670.8070.2150.420
Oregon1.0100.0550.8541.0440.0820.5811.0700.1150.5330.9600.1230.751
Pennsylvania0.9760.0300.4230.9960.0300.8851.0100.0520.8520.9260.0790.367
Rhode Island0.9240.0960.4481.0150.1020.8850.9100.1430.5490.8020.1700.298
South Carolina0.9260.0430.1010.9700.0390.4591.0110.0860.8990.9880.1640.943
South Dakota0.9930.0680.9220.9960.0660.9521.1900.2230.3540.9990.2340.998
Tennessee0.9550.0380.2570.9850.0390.7091.0660.0760.3721.0020.1100.986
Texas0.9590.0270.1400.9850.0310.6241.0230.0530.6620.9890.0880.900
Utah0.9750.0590.6791.0360.0820.6531.0390.1340.7681.1360.2260.522
Vermont0.9190.1160.5021.0310.0970.7471.1060.1710.5151.0240.4060.952
Virginia0.9600.0390.3160.9910.0410.8250.9990.0820.9900.9720.1190.815
Washington0.9770.0620.7161.0170.0480.7230.9980.0970.9800.9230.1280.563
West Virginia0.9350.0460.1770.9760.0480.6151.0640.1020.5211.0370.2020.853
Wisconsin0.9890.0440.8051.0060.0470.9021.0690.0830.3940.8820.1080.308
Wyoming1.0470.1410.7321.0810.2260.7101.0900.2180.6670.8420.4570.752
Major Teaching Hospital1.0490.0220.0211.0310.0210.1260.9750.0400.5490.9570.0610.486
Other Teaching Hospital1.0200.0130.1341.0300.0140.0331.0220.0240.3530.9500.0370.196
Large Metropolitan0.9670.0130.0160.9590.0140.0041.0450.0260.0841.0060.0430.892
Non-Metropolitan0.9570.0170.0140.9670.0200.1080.9790.0320.5190.9920.0550.881
Less Than 100 Beds0.9540.0210.0340.9630.0250.1441.0480.0370.1830.8310.0490.002
250–499 Beds1.0690.0140.0001.0170.0150.2551.0200.0240.3970.9960.0400.917
500 or More1.0750.0190.0001.0290.0190.1260.8740.0320.0001.0430.0680.516
Number of Obs3,297.0003,184.0003,297.0003,294.000
Wald chi2(20)101.00037.57058.68045.420
Prob>chi20.0010.9870.4870.903
Pseudo R20.0050.0020.0030.004

11-State All Patient

LICENSED Hours per Patient Day1.0060.0110.5790.9980.0130.8960.9970.0100.7421.0000.0121.000
AIDE Hours per Patient Day0.9970.0170.8470.9900.0230.6741.0160.0170.3311.0140.0190.456
RN Hours/LICENSED Hours1.1070.3250.7280.5950.2050.1321.3270.4070.3561.2020.3660.545
New York0.8940.0550.0660.8530.0680.0450.9190.0550.1540.9640.0630.579
Massachusetts0.8650.1060.2360.9120.1670.6151.0520.0820.5190.9170.0940.401
Maryland0.9860.0810.8640.9530.0970.6390.9450.0690.4391.0090.1050.933
Virginia0.8910.0790.1910.8300.0850.0700.9520.0830.5740.9610.1070.721
West Virginia1.0900.1360.4890.8240.1260.2041.0770.1680.6331.2430.2010.178
South Carolina1.1550.1470.2600.9500.1320.7121.0090.1130.9361.0910.1220.434
Wisconsin1.0290.0850.7321.0030.1150.9760.9950.0800.9481.0100.0830.899
Missouri0.9580.0920.6580.9350.1120.5751.0040.0890.9681.0600.0690.369
Arizona1.0500.1420.7211.0670.1620.6711.1320.1210.2450.8890.1050.320
Nevada1.1100.1040.2631.0280.1840.8761.1800.1900.3051.3210.2020.068
Major Teaching Hospital1.1920.0950.0281.1310.1060.1901.1600.0710.0151.0040.0720.953
Other Teaching Hospital1.0450.0510.3641.0550.0650.3871.0190.0490.6960.9760.0550.666
Large Metropolitan1.2020.0600.0001.0300.0660.6421.0450.0520.3681.1150.0670.069
Non-Metropolitan1.0780.0870.3491.0920.1130.3981.1780.0850.0230.7750.0630.002
Less Than 100 Beds0.9320.0670.3240.9140.0830.3250.8700.0580.0370.8520.0610.025
250–499 Beds1.0590.0550.2681.0380.0610.5321.0960.0520.0571.1250.0630.035
500 or More1.0990.0820.2040.9810.0940.8431.0930.0760.1981.0930.0820.238
Number of Obs796.000796.000796.000796.000
Wald chi2(20)54.19013.40038.04091.450
Prob>chi20.0000.8600.0090.000
Pseudo R20.0120.0040.0090.017
11-State Medicare
LICENSED Hours per Patient Day0.9860.0120.2471.0020.0140.8831.0100.0120.4170.9940.0120.630
AIDE Hours per Patient Day1.0050.0200.7861.0060.0260.8301.0000.0210.9951.0190.0200.356
RN Hours/LICENSED Hours0.8590.2860.6490.4200.1610.0230.9290.3510.8450.8920.2840.719
New York0.8560.0610.0300.8850.0830.1941.0220.0720.7600.9880.0670.856
Massachusetts0.7760.1100.0730.9070.1830.6280.9290.0860.4280.8670.1010.221
Maryland0.9590.0890.6511.0220.1120.8431.0370.0970.7021.0450.1220.707
Virginia0.8930.0810.2130.8640.0990.2040.9470.1010.6080.9440.1010.586
West Virginia1.0120.1420.9290.6890.1170.0280.9140.2160.7041.1200.1950.517
South Carolina1.1230.1580.4111.0150.1460.9180.9680.1500.8321.1640.1450.221
Wisconsin0.9910.0990.9301.0060.1230.9581.0690.1020.4851.0170.0960.857
Missouri0.9700.1030.7740.9190.1130.4910.9700.1000.7691.0580.0750.428
Arizona1.0330.1220.7821.0600.1700.7161.0980.1420.4710.8940.1250.426
Nevada1.0580.1460.6831.1480.1890.4020.9120.1880.6531.0740.1110.491
Major Teaching Hospital1.3470.1400.0041.1180.1320.3461.1870.0970.0371.0150.0870.862
Other Teaching Hospital1.0290.0610.6311.0610.0760.4081.0000.0600.9970.9820.0580.754
Large Metropolitan1.1730.0680.0061.0050.0720.9450.9790.0570.7101.0800.0690.230
Non-Metropolitan1.0310.0940.7421.1110.1250.3491.1930.1080.0510.7400.0650.001
Less Than 100 Beds0.8810.0790.1550.9700.0980.7660.9080.0780.2600.9050.0720.212
250–499 Beds1.0130.0580.8271.0660.0730.3481.0900.0640.1441.1490.0670.018
500 or More0.9850.0870.8670.9510.1060.6491.0480.0920.5921.0720.0880.395
Number of Obs791.000794.000794.000794.000
Wald chi2(20)37.29016.35021.53062.520
Prob>chi20.0110.6950.3670.000
Pseudo R20.0120.0060.0060.016
National MedPAR
LICENSED Hours per Patient Day0.9850.0030.0000.9950.0050.3131.0070.0040.1200.9950.0030.108
RN Hours/LICENSED Hours1.0990.1260.4100.5890.0860.0001.9080.2920.0001.2060.1240.068
Alabama1.0550.0720.4360.9420.0810.4881.1380.1070.1680.9950.0570.928
Alaska1.0550.3870.8830.8900.3300.7530.6200.1440.0401.0030.0900.974
Arizona1.0460.0780.5440.9800.0870.8191.0740.0980.4351.0430.0640.495
Arkansas1.0770.0860.3510.9080.1000.3821.1630.1460.2301.1200.0840.130
Colorado1.0220.0730.7611.0110.1200.9240.9980.0990.9810.9930.0980.941
Connecticut1.0390.0830.6290.9920.0870.9231.0000.0840.9981.0370.0630.546
Delaware0.9830.0500.7370.9860.1920.9420.7740.1970.3151.1200.1560.416
District of Columbia1.0330.1300.7971.1510.3240.6170.9360.1130.5831.0210.1240.865
Florida0.9940.0430.8840.9680.0510.5300.9950.0600.9290.9930.0370.855
Georgia1.0130.0610.8230.9710.0700.6861.0220.0870.8001.0360.0530.495
Hawaii1.0780.1310.5360.9920.1320.9521.0060.1430.9640.9920.0580.896
Idaho1.1720.1360.1721.0600.1880.7441.0470.1290.7071.1800.1580.218
Illinois0.9700.0470.5281.0210.0640.7450.9550.0640.4930.9840.0390.685
Indiana0.9950.0690.9460.9850.0860.8630.8980.0810.2321.0160.0520.753
Iowa1.0130.0680.8431.0200.1160.8610.9180.0990.4261.0880.0770.235
Kansas1.1360.0960.1300.9980.1370.9880.9220.0760.3231.0750.0880.380
Kentucky1.0560.0600.3440.9600.0640.5430.9260.0820.3851.0630.0670.335
Louisiana1.0240.0620.6960.9250.0730.3231.1320.0970.1471.0290.0500.550
Maine1.1400.1190.2110.9260.1500.6361.0050.1320.9711.2060.1320.087
Maryland0.9690.0650.6341.0100.0840.9050.9620.0750.6221.0020.0750.981
Massachusetts0.9620.0640.5611.0000.0900.9980.9980.0790.9840.9660.0630.593
Michigan0.9190.0480.1110.9860.0690.8410.9490.0620.4210.9940.0430.892
Minnesota0.9670.0660.6250.9780.0780.7770.8960.0740.1820.9940.0720.931
Mississippi1.0260.0780.7360.9280.1120.5351.0380.0980.6901.1290.0850.107
Missouri0.9830.0640.7881.0250.0870.7710.9640.0780.6491.0620.0540.235
Montana1.0210.1830.9060.8830.0920.2340.9970.1550.9841.2610.1310.026
Nebraska0.9930.1030.9450.9110.1270.5061.1090.1390.4081.0880.1270.468
Nevada1.0130.0930.8890.9570.1160.7181.0050.1140.9631.0420.0880.625
New Hampshire1.0830.1790.6281.1170.1770.4830.9740.1400.8551.0480.0880.580
New Jersey0.9730.0540.6151.0350.0770.6451.0050.0820.9460.9430.0410.176
New Mexico1.0870.1150.4300.9430.1780.7540.9670.1280.7991.0950.1010.326
New York0.8940.0390.0100.9970.0520.9490.9150.0460.0770.9850.0350.679
North Carolina0.9790.0540.6981.0240.0790.7600.9470.0610.3961.0070.0550.896
North Dakota1.0790.1530.5890.8960.1580.5361.0150.1630.9271.0500.1030.622
Ohio0.9780.0480.6450.9980.0610.9800.9310.0570.2410.9920.0430.860
Oklahoma1.0190.0880.8280.9510.0950.6111.0660.1290.5981.0530.0810.498
Oregon1.0640.1000.5081.0150.1100.8930.9780.1190.8531.0720.0930.420
Pennsylvania0.9920.0440.8500.9870.0590.8220.9450.0570.3450.9770.0370.542
Rhode Island0.8430.0850.0890.9870.1950.9480.9410.1570.7160.9470.0870.558
South Carolina1.0350.0840.6750.9760.1030.8181.0340.1060.7411.0720.0730.307
South Dakota1.1060.1710.5141.0170.2160.9371.0710.2010.7131.1480.1490.288
Tennessee0.9970.0680.9700.9970.0720.9711.0860.0880.3111.0560.0540.292
Texas1.0300.0450.4880.9750.0510.6251.0480.0560.3871.0040.0370.915
Utah1.0470.0870.5780.9650.1360.7990.9520.1310.7220.9880.1020.909
Vermont1.0680.1210.5620.9120.1960.6680.8380.1580.3481.1240.1410.349
Virginia0.9940.0590.9250.9830.0690.8110.9670.0780.6761.0030.0670.965
Washington0.9910.0740.9030.9900.1030.9271.0060.0950.9531.0490.0960.604
West Virginia1.0340.1020.7320.8920.1000.3080.9480.1020.6201.0910.0960.325
Wisconsin1.0140.0710.8441.0510.0890.5550.9420.0730.4390.9740.0540.630
Wyoming1.1250.3010.6591.1730.2930.5221.0660.3870.8601.0620.1350.636
Major Teaching Hospital1.1220.0350.0001.0350.0400.3731.1130.0430.0061.0000.0260.992
Other Teaching Hospital1.0360.0200.0730.9900.0250.7011.0360.0270.1721.0310.0180.090
Large Metropolitan1.0930.0220.0000.9850.0260.5621.0080.0270.7601.0300.0200.122
Non-Metropolitan0.9410.0260.0280.9690.0350.3941.0410.0370.2550.8210.0210.000
Less Than 100 Beds0.9610.0310.2230.9640.0410.3811.0600.0430.1440.9250.0270.008
250–499 Beds0.9820.0190.3500.9910.0250.7081.0420.0270.1191.0330.0180.064
500 or More1.0140.0270.6010.9560.0360.2251.1160.0430.0040.9920.0250.751

Number of Obs3,289.0003,295.0003,297.0003,294.000
Wald chi2(20)136.04027.43085.520195.710
Prob>chi20.0001.0000.0140.000
Pseudo R20.0080.0020.0050.012
Metabolic Derangement

11-State All Patient

Robust
IRRStd. Err.P>z
LICENSED Hours per Patient Day0.9790.0120.091
AIDE Hours per Patient Day1.0170.0200.409
RN Hours/LICENSED Hours0.4920.1620.031
New York0.9940.0670.932
Massachusetts1.1410.1180.204
Maryland0.9370.0850.474
Virginia1.0560.1200.631
West Virginia1.0820.0980.384
South Carolina1.2930.2010.099
Wisconsin1.1030.0950.252
Missouri1.0450.0880.604
Arizona1.1140.1520.431
Nevada1.4600.1610.001
Major Teaching Hospital0.9590.0850.639
Other Teaching Hospital0.9790.0510.693
Large Metropolitan1.1920.0690.002
Non-Metropolitan0.9780.0740.766
Less Than 100 Beds0.9920.0630.894
250–499 Beds1.0340.0600.564
500 or More0.9920.0880.925
Number of Obs797.000
Wald chi2(20)28.370
Prob>chi20.101
Pseudo R20.004
11-State Medicare
LICENSED Hours per Patient Day0.9850.0110.160
AIDE Hours per Patient Day1.0070.0180.702
RN Hours/LICENSED Hours0.5380.1610.039
New York1.0680.0670.294
Massachusetts1.0260.1060.802
Maryland0.9220.0740.314
Virginia1.0860.1120.426
West Virginia1.0610.0920.497
South Carolina1.2530.1810.118
Wisconsin1.0950.0920.280
Missouri1.1160.0900.173
Arizona1.0580.1370.661
Nevada1.2710.1040.003
Major Teaching Hospital1.0380.0830.642
Other Teaching Hospital1.0350.0520.492
Large Metropolitan1.0980.0580.079
Non-Metropolitan0.9490.0680.464
Less Than 100 Beds1.0220.0630.724
250–499 Beds1.0230.0550.671
500 or More0.8990.0690.167
Number of Obs790.000
Wald chi2(20)24.650
Prob>chi20.215
Pseudo R20.004
National MedPAR
LICENSED Hours per Patient Day0.9940.0040.134
RN Hours/LICENSED Hours1.0450.1220.706
Alabama1.0220.0710.757
Alaska0.9470.1710.761
Arizona0.9480.0600.396
Arkansas1.0210.0810.791
Colorado0.9890.0740.885
Connecticut1.1080.0980.247
Delaware1.0400.1100.711
District of Columbia1.0310.1630.849
Florida1.0370.0470.425
Georgia1.0430.0560.428
Hawaii1.0240.1600.879
Idaho1.1100.1860.535
Illinois1.0040.0490.933
Indiana1.0090.0640.884
Iowa0.8860.0760.158
Kansas1.0840.0790.265
Kentucky1.0310.0720.659
Louisiana1.0580.0620.335
Maine1.0310.1190.795
Maryland0.9310.0600.267
Massachusetts0.9920.0630.894
Michigan1.0100.0660.880
Minnesota0.9900.1000.921
Mississippi1.1380.1000.142
Missouri1.0240.0650.707
Montana0.9450.1150.641
Nebraska0.9780.1100.843
Nevada1.0880.0800.248
New Hampshire0.9080.1290.497
New Jersey1.0090.0620.882
New Mexico1.0860.1030.379
New York0.9460.0400.192
North Carolina1.0000.0730.998
North Dakota1.0190.1480.896
Ohio1.0010.0540.990
Oklahoma1.0090.0720.902
Oregon1.1160.1190.306
Pennsylvania0.9530.0500.365
Rhode Island1.1090.1630.481
South Carolina1.0630.0980.508
South Dakota0.9720.1480.853
Tennessee0.9880.0550.824
Texas1.0230.0430.592
Utah0.9450.0900.556
Vermont0.9350.2010.756
Virginia0.9580.0620.510
Washington0.9920.0550.887
West Virginia1.0390.0870.647
Wisconsin0.9840.0640.799
Wyoming1.0110.2010.958
Major Teaching Hospital1.0570.0350.097
Other Teaching Hospital0.9980.0210.937
Large Metropolitan1.0740.0230.001
Non-Metropolitan1.0170.0280.537
Less Than 100 Beds1.0350.0270.193
250–499 Beds0.9580.0200.040
500 or More0.9160.0300.007
Number of Obs3,283.000
Wald chi2(20)50.480
Prob>chi20.778
Pseudo R20.002
Length of Stay
11-State All Patient
Robust
Coef.Std. Err.P>t
LICENSED Hours per Patient Day−0.0150.0150.312
AIDE Hours per Patient Day0.0170.0230.461
RN Hours/LICENSED Hours−0.5530.4350.204
New York0.0220.0720.758
Massachusetts−0.0860.0640.182
Maryland−0.0080.0790.924
Virginia0.0750.0980.441
West Virginia−0.6910.5990.249
South Carolina0.0420.0800.601
Wisconsin0.1780.0700.011
Missouri0.0400.0940.674
Arizona0.0880.1120.434
Nevada0.4160.1380.003
Major Teaching Hospital0.2930.0790.000
Other Teaching Hospital−0.0190.0530.723
Large Metropolitan0.1520.0670.024
Non-Metropolitan0.0220.0850.796
Less Than 100 Beds−0.1360.0800.087
250–499 Beds0.1310.0640.040
500 or More0.0460.0830.577
e2losmaj1.0080.0520.000
Constant0.3530.3840.358
Number of obs796.000
F(21, 775)85.660
Prob>F0.000
R-squared0.646
Root MSE0.809
11-State Medicare
LICENSED Hours per Patient Day−0.0100.0300.733
AIDE Hours per Patient Day−0.0190.0390.625
RN Hours/LICENSED Hours−0.7850.5650.165
New York−0.1410.3830.712
Massachusetts−0.4430.1730.010
Maryland−0.1870.1240.133
Virginia−0.1230.1980.536
West Virginia−1.4870.8610.085
South Carolina−0.2450.2180.262
Wisconsin0.0120.1370.928
Missouri0.0620.1890.742
Arizona0.0390.1970.844
Nevada−0.0820.4070.840
Major Teaching Hospital0.6320.2520.012
Other Teaching Hospital0.0910.0930.332
Large Metropolitan0.1540.0820.061
Non-Metropolitan0.0350.1050.740
Less Than 100 Beds−0.2960.0920.001
250–499 Beds0.1000.1150.381
500 or More−0.1900.1860.308
e2losmaj1.1180.2200.000
Constant0.2151.3810.876
Number of Obs793.000
F (21, 775)39.440
Prob>F0.000
R-squared0.630
Root MSE1.072
National MedPAR
LICENSED Hours per Patient Day−0.0330.0160.042
RN Hours/LICENSED Hours−0.0630.3130.839
Alabama−0.1090.1730.530
Alaska−0.2620.8500.758
Arizona−0.1670.1350.214
Arkansas0.0420.2330.856
Colorado−0.2520.1630.122
Connecticut0.1100.2230.621
Delaware−0.7310.2030.000
District of Columbia0.4771.0590.653
Florida−0.3680.1200.002
Georgia−0.1370.1760.434
Hawaii−0.6430.9510.499
Idaho0.1310.2390.585
Illinois−0.2260.1400.106
Indiana−0.0380.1280.765
Iowa−0.0050.1630.973
Kansas0.3630.2150.092
Kentucky−0.1620.2200.461
Louisiana−0.1650.2050.420
Maine0.3290.1910.084
Maryland−0.2940.1480.047
Massachusetts−0.2060.1600.197
Michigan−0.3830.1410.007
Minnesota−0.0810.1490.585
Mississippi−0.3940.2630.134
Missouri0.1830.2260.417
Montana0.4120.2190.060
Nebraska0.0830.2320.721
Nevada−0.3210.2060.119
New Hampshire0.5280.3230.102
New Jersey0.2950.3750.432
New Mexico−0.2380.3430.488
New York−0.4590.3260.159
North Carolina−0.4030.1730.020
North Dakota−0.9580.6700.153
Ohio−0.1330.1450.358
Oklahoma0.0780.2140.715
Oregon0.1390.1420.325
Pennsylvania−0.1650.1500.272
Rhode Island−0.6850.2350.004
South Carolina−0.5620.2360.017
South Dakota0.3350.2320.150
Tennessee−0.4750.1860.011
Texas−0.1960.1290.130
Utah−0.1740.1770.325
Vermont0.0310.3520.931
Virginia−0.0930.1880.622
Washington−0.0130.1410.928
West Virginia−0.2090.2470.396
Wisconsin0.1180.1370.388
Wyoming0.2620.3890.501
Major Teaching Hospital0.5820.1620.000
Other Teaching Hospital0.0420.0620.494
Large Metropolitan0.1060.0680.116
Non-Metropolitan−0.1410.0670.036
Less Than 100 Beds−0.4770.0650.000
250–499 Beds0.3150.0750.000
500 or More0.2660.1270.037
e2losmaj1.0370.0580.000
Constant0.0660.5750.908
Number of Obs3,296.000
F(21, 775)68.380
Prob>F0.000
R-squared0.708
Root MSE1.467

Table A3

Regressions of Selected Outcomes, 11-State Medicare Sample with AHA Staffing Data and without Procedure Date

PneumoniaSepsisMetabolic Derangement
RobustRobustRobust
IRRStd. Err.P>zIRRStd. Err.P>zIRRStd. Err.P>z
LICENSED Hours per Patient Day0.9950.0080.4901.0010.0070.8580.9950.0070.515
RN Hours/LICENSED Hours0.4340.1080.0010.5790.1440.0280.5060.1210.004
New York0.9230.0460.1090.9440.0500.2710.9750.0510.625
Massachusetts0.8930.0650.1220.9090.0630.1650.0700.0770.352
Maryland0.9900.0600.8731.0100.0620.8770.9160.0600.180
Virginia0.9740.0600.6670.8440.0600.0170.9820.0740.810
West Virginia1.0180.1120.8740.9130.1340.5340.9840.0780.837
South Carolina1.0260.1050.8001.1370.1320.2691.1040.1290.398
Wisconsin1.0450.0870.5991.0280.0760.7111.0690.0730.324
Missouri0.9840.0670.8150.8960.0640.1271.0620.0710.367
Arizona0.9170.0980.4210.9290.0990.4910.9630.1200.763
Nevada0.8420.1000.1460.8960.1240.4301.0580.0710.400
Major Teaching Hospital1.3160.0850.0001.1730.0770.0151.0530.0650.404
Other Teaching Hospital1.0610.0430.1421.0270.0430.5231.0150.0410.706
Large Metropolitan1.1090.0460.0131.1200.0460.0061.0830.0470.068
Non-Metropolitan0.9600.0590.0590.9400.0610.3420.9670.0560.565
Less Than 100 Beds1.0610.0540.2420.8770.0510.0251.0170.0490.723
250–499 Beds0.9970.0400.9371.0130.0420.7581.0160.0450.713
500 or More0.9020.0550.0941.0110.0620.8560.9090.0580.136
Number of Obs849.000849.000846.000
Wald chi2(20)47.36058.73023.180
Prob>chi20.0000.0000.230
Pseudo R20.0090.0120.004
Length of Stay
Robust
CoefficientStd. Err.P>t
LICENSED Hours per Patient Day−0.0300.0210.156
RN Hours/LICENSED Hours−1.0790.7720.162
New York−0.1690.6270.787
Massachusetts−0.9110.2480.000
Maryland−0.5430.2290.018
Virginia−0.1220.2290.595
West Virginia−2.7800.6900.000
South Carolina−0.2980.3060.330
Wisconsin0.1690.1680.315
Missouri0.0200.2620.939
Arizona−0.0320.2990.915
Nevada−0.4980.2500.047
Major Teaching Hospital1.1050.3390.001
Other Teaching Hospital0.2350.1360.085
Large Metropolitan0.3690.1440.011
Non-Metropolitan−0.0330.1470.821
Less Than 100 Beds−0.4820.1280.000
250–499 Beds0.2280.1440.115
500 or More−0.1340.2680.616
e2losmaj1.0290.1650.000
_cons0.8751.4220.539
Number of Obs763.000
F(21,775)63.370
Prob>F0.000
R-squared0.786
Root MSE1.487

Footnotes

This research was carried out under contract no. 230-99-0021 with the U.S. Department of Health and Human Services, Health Resources and Services Administration, with funding from the Health Resources and Services Administration, Agency for Healthcare Research and Quality, Centers for Medicare and Medicaid Services, and the National Institute of Nursing Research. Additional analysis was supported by grant no. HS09958 from the Agency for Healthcare Research and Quality. The preparation of this manuscript was supported in part by a Dissemination and Development Grant from Abt Associates Inc. to Dr. Mattke. At the time the research was conducted, Dr. Needleman and Ms. Stewart were at the Harvard School of Public Health and Dr. Mattke was at the Harvard School of Public Health and Abt Associates.

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