Nancy Donelan-McCall, PhD

Theresa Eilertsen, BS

Ron Fish, MBA

Andrew Kramer, MD

The Division of Health Care

Policy and Research

University of Colorado at Denver 

and Health Sciences Center

13611 East Colfax Avenue

Suite 100

Aurora, CO 80045-5701

MedPAC

601 New Jersey Avenue, NW

Suite 9000

Washington, DC 20001

(202) 220-3700

Fax: (202) 220-3759

www.medpac.gov

•

•

The views expressed in this report

are those of the authors.

No endorsement by MedPAC

is intended or should be inferred.

Small Patient Population

and Low Frequency

Event Effects on the Stability

A study conducted by staff from 

the Division of Health Care Policy and Research

University of Colorado at Denver and 

Health Sciences Center (UCDHSC) for the

Medicare Payment Advisory Commission

September 2006 • No. 06–5

of SNF Quality Measures
Small Patient Population and 

Low Frequency Event Effects 

on the Stability of SNF 

Quality Measures 

Contract E4015951 

Submitted to: 

Medicare Payment Advisory Commission 

Final Report 

Submitted by: 

Nancy Donelan-McCall, PhD 

Theresa Eilertsen, BS 

Ron Fish, MBA 

Andrew Kramer, MD 

 

The Division of Health Care Policy and Research 

University of Colorado at Denver and 

Health Sciences Center (UCDHSC) 

13611 East Colfax Avenue, Suite 100 

Aurora, CO 80045-5701 

June 2, 2006 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO i 

Small Patient Population and Low Frequency Event Effects on the Stability of 

SNF Quality Measures: Final Report 

Table of Contents 

Section Page

1.0 Introduction ......................................................................................................................................... 1 

2.0 Methods ................................................................................................................................................ 3 

3.0 Results .................................................................................................................................................. 7 

4.0 Discussion ............................................................................................................................................ 10 

Tables and Figures 

 Table 1: Facility characteristics of 2004 SNF admissions .................................................................. 15 

 Table 2: Resident outcomes of 2004 SNF admissions........................................................................ 16 

 Figure 1: Graph of population simulation: Community discharge (CD) and rehospitalization 

 for any five conditions (RH5)............................................................................................... 17 

 Figure 2: Graph of facility bootstrap: Community discharge (CD) and rehospitalization for 

any five conditions (RH5)..................................................................................................... 18 

 Figure 3: Graph of theoretically-derived variance for a hypothetical outcome from the normal 

distribution with a national rate of .20 .................................................................................. 19 

 Table 3: Cumulative percent of facilities by number of stays with calculable value: Community 

discharge and rehospitalization for any five specific conditions .......................................... 20 

 Table 4: Facilities lost by reporting period length for potential outcome measure............................. 21 

 Table 5: Unadjusted and adjusted facility rates of proposed SNF outcome measures........................ 22 

 Table 6: Change in adjusted facility rates of proposed outcome measures for 2000 - 2004 SNF 

 admissions............................................................................................................................. 23 

 Table 7: Stability of proposed SNF outcome measures over time (30 days [top] and 

 100 days [bottom])................................................................................................................ 24 

Reference List ................................................................................................................................................ 25 

Appendix .................................................................................................................................................. A1 

 Table A: Analysis file information for 2004..................................................................................... A2 

 Table B: Resident characteristics of 2004 SNF admissions ............................................................. A3 

 Table C1: Resident regressions for 30-day models using 90% validation sample ............................. A5 

 Table C2: Resident regressions for 100-day models using 90% validation sample ........................... A6 

 Table D: Comparison of statistical approaches to determine minimum sample size........................ A7 

 Table E: Adjusted facility rates of potential outcome measures for 2004 SNF admissions............. A8 

 Table F: Spearman’s rank order correlations of adjusted facility rates of potential outcome 

 measures at 30 days (top) and 100 days (bottom) for 2004 SNF admissions.................... A9 

 Table G: Correlations of individual adjusted rehospitalization rates over time (30 days [top] and 

 100 days [bottom]).............................................................................................................A10 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 1 

1.0 INTRODUCTION 

In 2004, Medicare beneficiaries experienced approximately 2.5 million admissions to 

post-acute skilled care in nearly 15,000 Medicare-certified skilled nursing facilities 

(SNFs) nationally, requiring Medicare expenditures of $16 billion (Centers for Medicare 

& Medicaid Services, 2004; Medicare Payment Advisory Commission, 2005b; Medicare 

Payment Advisory Commission, 2006b). In November 2002, the Centers for Medicare & 

Medicaid Services (CMS) implemented the Nursing Home Quality Initiative (NHQI) 

involving public reporting of quality measures on the Nursing Home Compare Web site 

(http://www.medicare.gov/nhcompare/home.asp) to increase nursing home accountability 

and release quality information to the general public (Harris & Clauser, 2002). Of the 15 

publicly reported quality measures, only three pertain to post-acute care relevant to 

Medicare SNF patients: delirium, moderate to severe pain, and pressure ulcers. 

Furthermore, the three short-stay quality measures require the 14-day Minimum Data Set 

(MDS) assessment (as well as the 5-day MDS assessment in the case of pressure ulcers) 

be present in order to calculate the measure. Because only 45 to 52 percent of SNF 

patients remain in the facility long enough to have a 14-day assessment completed (the 

remainder are discharged to the community, rehospitalized, transferred, or deceased 

before the 14-day assessment occurs), the patient population on which the measures are 

calculated is systematically biased (DataPRO Team, 2002; Medicare Payment Advisory 

Commission, 2005c). For these reasons, the Medicare Payment Advisory Commission 

(MedPAC) has endorsed the addition of nursing home quality measures that are specific 

to short-stay patients in SNFs, including rehospitalization and community discharge rates 

(Medicare Payment Advisory Commission, 2005b; Medicare Payment Advisory 

Commission, 2006b). 

For many patients admitted to SNFs, the primary treatment goal is the stabilization of 

medical or post-surgical problems following an acute hospitalization. Measures of 

rehospitalization therefore capture an important quality domain. For many other SNF 

patients, the major goal of SNF care is rehabilitation, either for traditional rehabilitation 

conditions such as stroke or fractures or for functional losses following extensive medical 

or surgical problems (“deconditioned” individuals). The primary goal of rehabilitative 

therapy is often discharge to community, and therefore community discharge is another 

important quality domain. In fact, 78 percent of SNF patients received rehabilitation 

services in 2003 (Medicare Payment Advisory Commission, 2005a) and 43 were percent 

expected to be discharged within 90 days1

. This study focuses on measures of 

rehospitalization and community discharge as facility quality indicators. 

Rehospitalization as a measure of SNF quality has face validity—patients would prefer to 

avoid it whenever possible—and has been used as a quality marker in other settings. A 

growing body of literature exists on hospitalizations for ambulatory care sensitive 

conditions in the ambulatory care setting (Fleming, 1995; Caminal, Starfield, Sanchez, 

Casanova, & Morales, 2004; Laditka, 2003; Solbert et al., 1990; Weissman, Gatsonis, & 

Epstein, 1992; Bindman et al., 1995; Intrator, Castle, & Mor, 1999), and hospitalization is 

 

1

 Author's calculations from DataPRO SNF Stay File MDS item Q1a for admissions in 2003. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 2 

a publicly reported quality measure for home health care (Agency for Healthcare 

Research and Quality, 2002; Centers for Medicare & Medicaid Services, 2006; National 

Quality Forum, 2005). In the nursing home setting, studies have shown that 

hospitalization of nursing home residents are frequently avoidable (Gillick & Steel, 1983; 

Gabow et al., 1985; Saliba et al., 2000; Intrator, Zinn, & Mor, 2004). Studies have 

demonstrated associations between rehospitalization and nurse staffing ratios and 

between rehospitalization and the usage of advanced practice nurses (physician 

extenders), further supporting the validity of rehospitalization measures as indicators of 

facility quality (Kramer & Fish, 2001; Intrator et al., 1999). 

Researchers commonly examine overall rates of rehospitalization without considering the 

reason for hospitalization. However, when assessing facility quality of care, we believe it 

is critical to focus on only those conditions for which hospitalization may be avoided 

with high quality nursing care. Through a combination of clinical considerations and 

analyses of rehospitalization rates, Kramer and colleagues at UCDHSC identified 

five conditions for which rehospitalization is potentially avoidable in nursing homes 

(Kramer, Eilertsen, Lin, & Hutt, 2000b; Hutt, Lin, & Kramer, 2000; Kramer, Eilertsen, 

Lin, Martau, & Hutt, 2000a; Kramer et al., 2001). These five conditions are congestive 

heart failure (CHF), respiratory infection, urinary tract infection (UTI), sepsis, and 

electrolyte imbalance. Not all hospitalizations for these conditions are preventable; 

however, rates of hospitalization for these conditions were significantly lower in facilities 

with higher nurse’s aide and licensed staff levels as well as in facilities with higher staff 

retention, after adjusting for facility case mix (Kramer et al., 2001). 

For each of these conditions, reducing hospitalization requires the use of preventive 

measures to avoid declining health, the early detection of signs and symptoms of 

worsening health, and prompt intervention by nursing staff and the physician when 

needed. For CHF, hospitalization might be reduced by proper medication administration, 

adherence to any fluid or dietary restrictions, and early recognition of increased shortness 

of breath or edema. Respiratory infections may be reduced by following proper 

precautions, appropriate positioning of residents with swallowing problems to avoid food 

aspiration that could lead to pneumonia, and administering pneumonia and influenza 

vaccinations. Hospitalization for electrolyte imbalance often results from dehydration or 

poor nutrition, which may be prevented with careful monitoring of patient fluid and 

nutrient intake. UTI can be reduced by weaning patients from urinary catheters (Wald, 

Epstein, & Kramer, 2005), following sterile procedures, and the early recognition of the 

signs and symptoms of UTI. Sepsis, a blood-borne bacterial infection, can be avoided if 

infections are identified and treated before becoming systemic. However, infections can 

present in unusual ways in older adults (e.g., confusion) and staff must be alert and 

respond promptly when any symptoms of infection are identified. 

Community discharge has clear implications for both patient quality of life and cost of 

care, and is frequently used as a construct for evaluating rehabilitation (Kramer et al., 

1997; Kramer et al., 2000c; Coleman et al., 2000; Hutt et al., 2001; Murry, Singer, 

Dawson, Thomas, & Cebul, 2003; Braun, 1991; Fitzgerald, Moore, & Dittus, 1988; 

Fitzgerald & Dittus, 1990; Jette, Warren, & Wirtalla, 2004). Community discharge, 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 3 

while itself a gross marker of functional improvement, has been associated with 

significant functional recovery across a range of specific functional measures. In 

four studies, community discharge was also associated with processes of SNF care 

(Braun, 1991; Murry et al., 2003; Jette et al., 2004; Kramer et al., 2006). Community 

discharge is clearly an important indicator of quality of care. 

The development of a SNF stay file linking claims and MDS assessment data made it 

possible to calculate rates of rehospitalization for potentially avoidable causes and rates 

of community discharge (DataPRO Team, 2002). However, important questions remain 

if these measures are to be used in public reporting. First, optimal methods for risk 

adjustment need to be developed so that facilities are not at a disadvantage by admitting 

sicker patients for whom it is more difficult to achieve the outcomes. Second, the 

minimum number of SNF admissions per facility must be determined so that facility 

measures are accurate and stable, and unbiased comparisons between facilities can be 

made. If the minimum number of stays is set too low, then measures can be misleading 

in small facilities, but if the minimum is set too high, then many facilities will be 

excluded from the measures. Third, the duration of the measurement period should be 

assessed to determine whether there are advantages of focusing on shorter stays such as 

30 days, or whether the entire SNF stay (up to 100 days) is the more appropriate focus. 

The purpose of this study was to investigate these measurement issues for the 

rehospitalization and community discharge measures. 

2.0 METHODS 

2.1 Data source 

The national DataPRO SNF Stay File (DataPRO) for calendar years 1999 through 2004 

was used for all analyses. Data for calendar years 1999 through 2004 were selected as 

they represent SNF stays that occurred following the introduction of the SNF prospective 

payment system. The file contains claims data (Medicare Standard Analytic Files Part A) 

corresponding to the qualifying hospital stay, SNF stay, and rehospitalizations following 

the SNF stay in addition to selected MDS items for each SNF admission. Each DataPRO 

record contains facility level characteristics from the Online Survey Certification and 

Reporting (OSCAR) system. Resident level variables include demographics; ICD-9-CM 

diagnosis codes for the qualifying hospital stay, SNF admission, and any 

rehospitalizations; MDS physiologic, functional and cognitive performance measures; 

SNF and hospital stay charges and Medicare payment; length of SNF and hospital stays; 

admission and discharge location; and mortality. Facility characteristics include type of 

ownership, number of beds, and facility structure (e.g., hospital-based vs. freestanding). 

Facility MSA was obtained from CMS to determine urban versus rural setting as this data 

item is not contained in the DataPRO file. Documentation including variable 

specification for the DataPRO file is available (Malitz & DataPRO Project Investigators, 

2002). 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 4 

2.2 Sample 

A total of 13,146,278 stays for calendar years 1999 through 2004 were extracted from the 

DataPRO file. Approximately 10 percent (1,301,444) of stays were excluded from 

subsequent analyses for reasons related to payer (e.g., no Medicare payment), facility 

type (e.g., swing beds), or data quality (e.g., SNF stays without a qualifying hospital 

claim). The resulting analysis file contained 11,844,834 valid stays averaging 

approximately two million stays and approximately 14,700 facilities per calendar year. 

Analysis file exclusions and detailed analysis file information for the calendar year 2004 

file are enumerated in Appendix Table A. Resident characteristics for SNF stays 

beginning in calendar year 2004 are presented in Appendix Table B. 

2.3 Outcome measures 

Measures of community discharge and potentially avoidable hospitalization for five 

conditions – electrolyte imbalance, respiratory infection, congestive heart failure, sepsis, 

and urinary tract infection – were calculated within 30 and within 100 days of SNF 

admission. In addition, measures of rehospitalization for any of the five conditions 

within 30 and within 100 days were calculated. All measures excluded residents who 

died during the outcome measure period (i.e., for the 30 day outcome measures, any 

resident who died within 30 days of his/her SNF admission was excluded from the 

measure). 

2.3.1 Community discharge definition: Community discharge was defined as the 

number of residents discharged directly to the community from the SNF. However, if a 

resident was rehospitalized within one day of the community discharge the stay was 

reclassified as a rehospitalization and the resident was not classified as discharged to the 

community. 

2.3.2 Rehospitalization definitions: The rehospitalization outcomes were defined as 

rehospitalizations at either an acute-care or critical access hospital, excluding any 

hospitalizations with an intervening Medicare claim between the SNF stay and 

rehospitalization. The measure included rehospitalizations that occurred within one day 

of the end of the SNF stay (regardless of discharge location). Condition-specific 

rehospitalization measures were defined as follows: 

• electrolyte imbalance 

• respiratory infection included pneumonia that may be either bacterial or 

viral, and upper airway infections like bronchitis 

• congestive heart failure 

• sepsis - included infection of the bloodstream from any bacteria 

• urinary tract infection included infections of the bladder, kidney, prostate, 

urethra, or any other part of the urinary tract. 

A composite measure of rehospitalization for any of the above five conditions was also 

created. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 5 

2.4 Analysis

2.4.1 Risk adjustment: Some SNFs admit more people who have higher illness 

burdens, who are frailer, and who have less functional reserve. Residents with these 

characteristics are less likely to be discharged to the community and more likely to be 

rehospitalized. Evaluating SNF performance must take into account the mix of 

characteristics of the individuals receiving SNF care. To evaluate outcomes for residents 

from different SNFs, each outcome measure was risk adjusted using logistic regression. 

Selection of covariates: The first step in the development of risk models for each of 

the outcome measures entailed conceptually and clinically selecting candidate variables 

that might influence the outcome under consideration. Candidate variables that were 

deemed subjective (e.g., MDS item Q1c - discharge expected within 30 days), related to 

incentives (e.g., payment), or that had questionable data quality (e.g., MDS Section I – 

diagnoses) were excluded. Additionally, composite measures (e.g., Barthel Index) were 

selected instead of individual data items (e.g., transferring) to increase parsimony of the 

resulting models. Covariates derived from MDS items used the five-day MDS 

assessment to capture baseline status. Covariates for facility characteristics such as 

hospital-based SNFs were also included. 

Weighted comorbidity indices were created based on the approaches of Charlson et al., 

1987; Deyo et al., 1992; and Romano et al., 1993 using the qualifying hospital stay 

diagnoses. Using a 3 percent sample of 2004 data, regression models of hospitalization 

for each dependent measure were developed using 17 diagnostic categories. The beta 

coefficients derived from the regression models were used as weights for the entire 2004 

sample, and the comorbidity indices were calculated by summing the beta coefficients 

with p<.05. 

Development and validation of risk adjustment models: Bivariate measures of 

association (Pearson correlation) and stepwise logistic regression were conducted to 

address the relationship between candidate risk factors and outcome measures under 

consideration. To estimate and then validate each logistic regression model, the entire 

pool of SNF stays for 2004 was randomly split into two groups, a developmental sample 

comprised of 10 percent of the stays and an independent validation sample comprised of 

the remaining 90 percent of SNF stays. Using the developmental sample, a logistic 

regression model was estimated for each outcome, and the coefficients and odds ratios for 

each risk factor were examined. Risk factors with questionable or insignificant 

coefficients were eliminated. Stepwise addition of covariates continued until the model c 

statistic (concordance rate) improved less than .01 or when the c statistic decreased by 

more than .01 when a variable was removed. 

Upon completion of the developmental models, the stability of the model was tested 

using the validation sample. The developmental model was fit to the validation sample 

and the coefficients and concordance rates between the two models were compared. The 

coefficients and concordance rates between the two models were nearly identical, 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 6 

indicating a good model fit. Model covariates, odds ratios, and fit statistics for all 

fourteen models can be found in Appendix Tables C1 and C2. 

2.4.2 Calculating facility level adjusted rates: For each outcome, resident-level 

risk-adjusted scores reflecting the resident’s probability of experiencing the outcome 

were calculated based on the risk adjustment models. Facility-level scores for each 

outcome were then calculated by averaging the resident-level scores for all residents 

within the facility. Risk-adjusted facility-level scores were calculated using the 

methodology for calculating quality measures for Nursing Home Compare (Abt 

Associates Inc, 2004). The risk-adjusted score is adjusted for the specific risk for that 

measure in the nursing facility, and can be thought of as an estimate of what the facility’s 

score would be if the facility had residents with average risk. The facility-level riskadjusted score is calculated using the facility observed outcome rate, the facility expected 

outcome rate (the facility-level score calculated by averaging the resident scores), and the 

national average resident observed outcome rate. 

2.4.3 Examination of composite measures: To examine the potential value of using 

a composite measure for potentially avoidable rehospitalization for any of five conditions 

versus condition-specific rehospitalization measures, analyses of measure properties 

(including associations between the condition-specific measures) were conducted. 

Methods for examining measure properties and associations between the conditionspecific rehospitalization measures included Spearmans rank order correlations, principal 

components analysis, continuity of covariates retained for the logistic regression models, 

and stability of the individual measures over time using Pearsons correlation coefficient. 

Principal components analysis is a factor analysis method used to discover if the 

observed variables (e.g., the condition-specific rehospitalization measures) can be 

explained largely or entirely in terms of a much smaller number of variables called 

factors. 

2.4.4 Analysis of minimum sample size: In order to determine the percent of 

facilities with insufficient resident stays for the community discharge and 

rehospitalization outcome measures, the impact of facility volume on outcome measure 

variability was examined using three methods. (A more detailed description of the three 

methods is presented in Appendix Table D.) 

Population simulation outcome measure variance: Using 2004 data, residents were 

pooled across the entire population of low-volume facilities (N≤ 50) and then randomly 

selected without replacement to create 500 new samples for each outcome measure at 

each facility volume of interest (e.g., 10, 15, 20, ..., 50). The average standard deviation 

of the simulated facility adjusted rates across the 500 samples was computed for each 

measure. 

Bootstrap of outcome measure variance within facility: Using 2004 data, for each 

low-volume facility (facility stays ≤ 50) residents were randomly selected with

replacement from within the facility to create 500 new facility samples of equal size (e.g., 

a facility with 25 stays would have 500 samples of 25 stays). The average standard 

deviation of the facility outcome rates across all 500 samples within a facility was 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 7 

calculated. For each facility volume (e.g., 10 stays) the mean of the facility average 

standard deviation was computed. 

Theoretical outcome measure variance: A theoretical distribution of the variance 

for a hypothetical outcome measure was created using assumptions of a normal 

distribution (mean=0.5, standard deviation=1) truncated at 0 and 1 and with a national 

rate of .20. 

Using each of these three approaches, the relationship between measure variance and 

facility sample size were graphed and the point at which variance ceased to decrease 

appreciably was determined. In addition to examining the decrease in measure variance, 

the determination of a minimum stay threshold also was based on the distribution of 

facility sample size. This information was used to identify the number of facilities that 

might be lost at any given facility stay threshold (e.g., number and percent of facilities 

lost with a minimum stay threshold of 20 versus 25). This process was repeated for all 

measures independently. 

2.4.5 Impact of longer reporting period on facility sample size: An examination of 

the percent of facilities lost due to inadequate sample size was used to determine the 

effect of increasing the reporting period on the number of facilities with valid data. To 

examine the impact of longer reporting periods (e.g., 18 months, 24 months) on the 

ability of facilities to reach the minimum sample size of 25, a combined calendar year 

2003-2004 file was used to calculate facility-level adjusted scores for a range of reporting 

periods. 

2.4.6 Analysis of trends in community discharge and rehospitalization rates from 

2000 through 2004: Repeated measures ANOVA was used to compare adjusted facility 

rates of community discharge and rehospitalization for any of five conditions at 30 and 

100 days for 2000 through 2004. Contrasts were conducted between consecutive years 

(e.g., 2000-2001, 2001-2002), and between 2004 and each year (e.g., 2000-2004, 2001-

2004). Within facility stability over time was examined using Pearsons correlation 

coefficients. 

 

3.0 RESULTS 

3.1 SNF facility characteristics 

Characteristics of skilled nursing facilities based on calendar year 2004 data are presented 

in Table 1. In 2004, 67 percent of facilities, representing 78.5 percent of SNF stays, were 

located in designated urban areas. Less than nine percent of facilities were hospitalbased, (over 90 percent were freestanding), yet these facilities accounted for more than 

15 percent of SNF stays. Low volume facilities with less than 25 stays during calendar 

year 2004 represented 7.9 percent of SNFs but less than 1 percent of SNF stays. Large 

volume facilities with more than 200 stays represent 23.5 percent of SNFs, but accounted 

for more than 54 percent of SNF stays in 2004. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 8 

3.2 Resident-level outcome rates for 2004 

Thirty-day (30-day) and 100-day outcome measures for community discharge, 

rehospitalization for five conditions (congestive heart failure, electrolyte imbalance, 

respiratory infection, sepsis, and urinary tract infection), and rehospitalization for any of 

the five conditions were calculated for all 2004 SNF residents who did not die during the 

outcome measure period (e.g., 30-day measures exclude those residents who died 

anytime within 30 days of SNF admission). Resident-level rates for each outcome 

measure for 2004 are presented in Table 2. As expected, resident level rates increased as 

the outcome measure period increased (100-day rates are higher than 30-day rates). 

Thirty-eight percent of SNF stays ended in a community discharge in 2004, and 

22 percent ended in a rehospitalization. 

Rehospitalizations for any of the five conditions represented 78 percent of all SNF 

rehospitalizations at 30 and 100 days. Although these conditions accounted for the 

majority of SNF rehospitalizations, the facility rates (presented in Appendix Table E) for 

each of the condition-specific rehospitalization measures were quite low (2.0 percent for 

sepsis at 30 days to 8.1 percent for CHF at 100 days). Furthermore, across the conditionspecific measures, the percent of facilities with a rate of zero (the event never occurred 

during 2004 within the facility) for any given rehospitalization measure ranges from 

7.3 percent for CHF at 100 days to 29.9 percent for sepsis at 30 days (see 

Appendix Table E). 

Due to the low incidence rates for the condition-specific rehospitalization measures, we 

examined the feasibility of a composite measure defined as rehospitalization for any of 

the five conditions. First, an examination of correlations among the condition-specific 

rehospitalization measures indicated moderate to strong associations (see Appendix 

Table F). Second, principal components analysis of the five condition-specific 

rehospitalization measures resulted in a single factor with all five condition-specific 

measures retained with factor loadings greater than .45. This finding suggests that the 

five condition-specific measures could be adequately represented through a composite 

measure. Third, as shown in Appendix Table C, the covariates retained in the risk 

adjustment models for the condition-specific outcome measures are nearly identical. 

Finally, the composite measure demonstrates greater stability overtime than the 

condition-specific measures (see Table 7 and Appendix Table G). Based on these 

findings, we recommend that the composite measure of rehospitalization for any of the 

five conditions be retained as the single rehospitalization outcome measure. All 

subsequent analyses will be conducted on the community discharge measure and 

composite rehospitalization measure. 

3.3 Stability of outcome measures based on facility volume 

In order to determine the percent of facilities with insufficient resident stays for the 

community discharge and rehospitalization outcome measures, a necessary first step was 

to determine a minimum for number of stays per facility (facility volume) necessary to 

validly compute the outcome measures. We examined the impact of facility volume on 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 9 

outcome measure variability using three methods; population simulation outcome 

measure variance, bootstrap of outcome measure variance within facility, and theoretical 

outcome measure variance. A detailed description of the three simulation methods is 

presented in the Methods section and in Appendix Table D. 

Results from the three variance simulation methods were graphed separately and the 

three graphs were examined to determine where the slope of the curve began to level off; 

indicating the number of stays needed for stability in the variance of the measure. Graphs 

representing the results of the three simulation methods are presented in Figures 1, 2, 

and 3. Across all three simulation methods, the steepest area of the slope occurs with 

stays less than 20. The slope continues to level off between 20 and 30 stays. Table 3 

presents the cumulative percent of facilities that would be lost at various stay thresholds. 

Taking into consideration the percent of facilities that would be excluded from the 

measure at five stay increments it was determined that a minimum of 25 stays would 

result in acceptable measure stability while retaining more than 90 percent of SNF 

facilities. Although approximately 10 percent of facilities will be excluded with a 

minimum of 25 stays, these facilities account for less than 1 percent of SNF stays. 

Facilities with less than 25 stays are less likely to be owned by a chain, more likely to be 

hospital-based, and are more likely to be classified as rural compared to those facilities 

with 25 or more stays. 

3.4 Facilities lost by reporting period 

The effect of modifying the reporting period length on number of stays was examined. 

Rates presented in Table 4 represent the percent of facilities lost at 6, 12, 15, 18, 21, and 

24 months. (The 12-month rates reported in this table differ slightly from those presented 

previously in Table 3 because these rates include loss due to deaths which was an 

exclusion already made in previous analyses.) A marked difference exists in the number 

of facilities retained between the 6- and 12-month reporting periods. However, very little 

is gained with regard to facility retention between 12 and 24 months (2 to 4 percent of 

facilities). The disadvantage of a longer reporting period, namely the inability to improve 

on the quality of care that occurred up to two years ago (for a 24-month reporting period), 

outweighs the advantage of a 2 to 4 percent increase in the retention of facilities. 

Therefore, we recommend a 12-month reporting period. 

3.5 Unadjusted and adjusted facility rates of proposed SNF outcome measures 

Having established the recommended measures (community discharge and 

rehospitalization for any of five specific conditions), the reporting period (12 months), 

and the facility stay minimum (25 stays) for computing outcome measures, the remainder 

of the analyses examined the rates for 2004, and the mean differences and stability of the 

rates over time. Descriptive statistics for unadjusted and adjusted rates for each of the 

outcome measures at 30 and 100 days are shown in Table 5. The adjusted rate for 

community discharge within 30 days is 23.9 percent and within 100 days is 32.8 percent. 

Rehospitalization for any five conditions within 30 days is 13.4 percent and within 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 10 

100 days is 17.1 percent. The unadjusted rates are less normally distributed than the 

adjusted rates, where the mean and median were extremely close. For community 

discharge the adjustment broadened the distribution but for rehospitalization, which had a 

small percentage of outlier facilities, the adjustment narrowed the distribution. 

3.6 Change in facility rates from 2000 to 2004 

Table 6 presents the average facility adjusted rates for each year, the difference between 

years, and the relative difference between years. A comparison of adjusted rates of 

community discharge and rehospitalization for any of five conditions at 30 and 100 days 

from 2000 through 2004 was conducted using repeated measures ANOVA. Findings for 

each of the models demonstrated a significant overall effect of time. Community 

discharge rates decreased steadily from 2000 through 2004. The relative decrease for 

community discharge within 30 days from 2000 to 2004 was –13.46 percent whereas the 

relative decrease for the 100-day measure was –2.84 percent. These findings suggest that 

over time fewer residents were discharged to the community within 30 days, however the 

change in rates of community discharge by the end of the SNF stay, although statistically 

significant, was slight. 

Rehospitalization measures within 30 and 100 days increased markedly from 2000 

through 2004. There was a relative increase of 50.5 percent in rehospitalization rates for 

any of five conditions within 30 days and a relative increase of 45 percent within 

100 days. 

Most contrasts between consecutive years (e.g., 2000 and 2001, 2001 and 2002, etc.) and 

between 2004 and all other years (e.g., 2004 and 2000, 2004 and 2001, etc.) were 

statistically significant at p<.005. Two exceptions were community discharge in 30 days 

between years 2003 and 2004, and community discharge in 100 days between years 2002 

and 2003. 

3.7 Stability of proposed SNF outcome measures from 2000 to 2004 

In order to examine stability within facility over time, correlations between the 2000, 

2001, 2002, 2003, and 2004 measures were examined (see Table 7). Strong to moderate 

associations were found for each of the outcome measures at each time point. 

Correlations were stronger between measures at consecutive years and for the measures 

calculated within 100 days versus 30 days. 

4.0 DISCUSSION 

A useful and valid measure of nursing home quality for post acute residents should 

represent care provided to the majority of residents and reflect care across a range of 

conditions and throughout the entire SNF stay. Additionally, publicly reported measures 

should be adequately adjusted for differences in resident case mix between facilities so 

that facilities are not at a disadvantage by caring for sicker residents for whom it is more 

difficult to achieve desirable outcomes. Currently, the three publicly reported measures 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 11 

for post-acute care require 14-day MDS assessments, resulting in a loss of 45 percent of 

residents that do not remain in a SNF long enough to have 14-day MDS assessments 

completed (Medicare Payment Advisory Commission, 2006b). This measurement 

limitation has resulted in a set of quality measures that significantly under-represent the 

SNF population. Furthermore, only one of the three post-acute care measures (percent of 

short-stay residents with pressure sores) is adjusted for resident risk2 (Abt Associates Inc, 

2004). In light of these concerns, the current project examines alternative measures of 

SNF quality for post-acute SNF residents. 

4.1 Summary of findings 

The project activities leading up to this report resulted in: 1) risk-adjusted measures of 

community discharge and rehospitalization for any of five conditions; 2) an evaluation of 

the minimum number of stays a facility must have to validly and reliably compute these 

measures as well as an evaluation of the impact on the number facilities lost due to the 

minimum stay threshold; 3) an examination of the duration of the measurement period to 

determine whether there are advantages of focusing on shorter stays such as 30 days or 

the entire SNF stay (up to 100 days); and 4) a preliminary examination of trends in 

community discharge and rehospitalization rates from 2000 through 2004 using the new 

measures developed during the project. 

4.2 Risk-adjusted measures of community discharge and rehospitalization 

As discussed previously, community discharge and potentially avoidable 

rehospitalization for any of five conditions are outcome measures that demonstrate both 

face and construct validity and are meaningful outcomes for residents, care providers, and 

payers. Rehospitalizations are costly, represent substantial declines in the resident's 

health, and impact resident quality of life. Rehospitalizations for the conditions 

examined in this project (CHF, electrolyte imbalance, respiratory infections, UTI, and 

sepsis) while not always avoidable represent markers of SNF quality because they can be 

reduced with better quality of care and improved staffing. Community discharge is the 

goal of many SNF residents and marks the ultimate success of multidisciplinary 

rehabilitation. 

The measures of community discharge and rehospitalization for any of five conditions 

examined in this study can be computed on nearly all SNF patients. These outcome 

measures do require a 5-day MDS assessment to adjust for resident risk. Based on 2004 

data, 9.3 to 10.3 percent of stays were lost for the outcome measures (depending on the 

specific measures) examined in this study due to deaths and missing 5-day MDS 

assessments, far less than the 45 percent of residents stays lost due to missing 14-day 

MDS assessments. 

 

2

 The measure of percent of short-stay residents with delirium is adjusted for no prior residential history 

when possible. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 12 

Although the five condition-specific rehospitalization measures have been evaluated as 

discrete measures in previous studies (Kramer et al., 2000b; Hutt et al., 2000; Kramer et 

al., 2000a; Kramer et al., 2001), one goal of this study was to examine the value of a 

composite measure of rehospitalization. An examination of the condition-specific 

measure properties and associations between measures indicated that a measure of 

rehospitalization for any of the five conditions would be preferable to the conditionspecific measures. First, several of the condition-specific measures occur rather 

infrequently and therefore are often not evident in small- to medium-volume facilities. 

Second, the five condition-specific measures are highly related suggesting that these 

measures reflect care as a broad construct rather than care for specific residents or 

specific conditions. Third, the measure of rehospitalization for any of the five conditions 

demonstrates greater stability over time and the measure approximates a more normal 

distribution than the condition-specific measures. For these reasons, we recommend that 

a composite measure of rehospitalization for any of five conditions be used instead of 

condition-specific rehospitalization measures. 

4.3 Minimum stay size recommendation 

Results from three methods examining measure variability as a function of facility size 

suggest that with a minimum of 25 stays, community discharge and rehospitalization for 

any of five conditions result in acceptable measure stability while retaining more than 90 

percent of SNF facilities and more than 99 percent of SNF stays. Facilities with less than 

25 stays are less likely to be owned by a chain, more likely to be hospital-based, and are 

more likely to be classified as rural compared to those facilities with 25 or more stays. 

Although the measures evaluated in this project can be calculated for most facilities and 

represent the vast majority of SNF stays, an examination of potential quality measures for 

very small facilities warrants consideration. 

After determining the minimum number of stays required to calculate measures, we 

examined the impact of various reporting period lengths on the number of facilities 

excluded. For example, would measures be available for significantly greater numbers of 

facilities if the reporting period were increased from 12 to 18 months? There is 

relatively little gain in the percent of facilities retained when the reporting period is 

increased beyond 12 months. Furthermore, a longer reporting period has the 

disadvantage of reflecting care that occurred so far in the past that the ability of SNFs to 

examine the care provided to their residents and address areas of concern is inhibited. 

Additionally, consumers may find it difficult to interpret measures that reflect care that 

occurred up to two years in the past. It should be noted that between 6 and 12 months a 

substantial number of facilities are retained and therefore we recommend that a 12-month 

reporting period be used for calculating community discharge and rehospitalization 

measures. 

Our examination of reporting period lengths indicates that when the proposed measures 

are calculated based on a 6-month reporting period approximately 25 percent of facilities 

are lost using a minimum of 25 stays. The number of facilities lost due to missing 14-day 

MDS assessments and the 6 month reporting period specified for the current post-acute 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 13 

measures is far greater than the 10 percent that would be lost for the proposed measures. 

Therefore, the proposed measures of community discharge and rehospitalization would 

be reported on significantly more facilities than the current post-acute measures. 

4.4 Measure duration 

Although the MDS post-acute measures correspond to the initial 14 days of the SNF stay, 

and previous studies have restricted the post-acute care hospitalization or community 

discharge measures to the first 30 days of the SNF stay (Kramer et al., 2001; DataPRO 

Team, 2002), findings from the current investigation suggest that measures examining 

care within 100 days of SNF admission (the covered benefit) may be more desirable. 

First, the covariates used to risk adjust the 30- and 100-day measures are nearly identical 

suggesting that these two populations are quite similar. Second, the 100-day measures 

have several empirical and conceptual advantages over the 30-day measures. They 

demonstrate greater stability over time, are more normally distributed, and have fewer 

facilities with an observed rate of zero (the event never occurred). Conceptually, 100-day 

measures represent care provided throughout the SNF stay and do not create incentives to 

discharge residents at 30 days and/or delay hospitalizations until after 30 days. For these 

reasons, we recommend that community discharge and rehospitalization at 100 days be 

used as quality measures of post acute care. 

4.5 Trends in the rates of community discharge and rehospitalization 

Having established measures of community discharge and potentially avoidable 

rehospitalization, we examined the trends in facility risk-adjusted rates over time. 

Results from this analysis demonstrated a marked increase in potentially avoidable 

rehospitalization rates within 100 days of SNF admission from 11.8 percent in 2000 to 

17.0 percent in 2004, a relative increase of 45 percent. Multiple explanations may exist 

for this alarming trend, which has implications related to the quality of care, quality of 

life, and cost of care for Medicare SNF patients. 

One issue that may influence the increase in rehospitalization rates is the ongoing decline 

in acute hospital lengths of stay (Mardis & Brownson, 2003; Medicare Payment Advisory 

Commission, 2006a), causing SNFs to admit patients who are not sufficiently stable for 

them to treat. A second potential factor is that nursing home staffing is associated with 

rehospitalization for potentially avoidable causes (Kramer et al., 2000b; Hutt et al., 2000; 

Kramer et al., 2000a; Kramer et al., 2001). These increased rehospitalization rates may 

be associated with growing staffing shortages and increases in staff turnover. These 

studies found that the skill level of staff (e.g., RN, LPN, CNA), not total staffing levels, 

and staff turnover/retention were related to avoidable rehospitalization rates. Other issues 

relate to incentives. Physicians, who ultimately make the decision about whether to 

hospitalize a resident, have incentives to hospitalize residents because providing acute 

care in the nursing home setting often requires extra visits at relatively low 

reimbursement. It has been argued that the SNF PPS does not adequately cover the costs 

of treating higher acuity patients in nursing homes, in relation to respiratory therapy and 

medications, for example. Furthermore, SNF PPS also encourages SNFs to discharge 

patients to the hospital when they become acutely ill. The decline in hospital-based SNFs 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 14 

may also contribute to the increasing rehospitalization rate. Hospital-based SNFs on 

average have lower rehospitalization rates than freestanding SNFs, possibly due to more 

skilled staffing, greater physician availability, and proximity to the acute services, or 

possibly due to unmeasured differences in resident characteristics. The reasons 

underlying the increasing trend in potential avoidable rehospitalizations require further 

exploration. 

Rehospitalization for potentially avoidable causes is not a quality measure for public 

reporting nor is it monitored in the current nursing home survey and certification process. 

Use of the current post-acute care quality measures in fact creates an incentive to 

hospitalize patients who are in pain, have delirium, or have severe pressure sores before 

14 days in order to have better quality measure scores. Two demonstration initiatives 

underway use rehospitalization as a quality measure including the nursing home qualitybased purchasing demonstration that will be conducted over the next several years and 

the five-state demonstration of the revised nursing home survey termed the Quality 

Indicator Study. Even with these demonstrations underway, however, there is no reason 

not to move expeditiously with a rehospitalization quality measure for public reporting. 

We also found a significant decrease in 100-day community discharge rates from 

33.8 percent in 2000 to 32.8 percent in 2004, a 2.8 percent relative decrease, with a larger 

decrease in community discharge rates within 30 days from 27.6 percent to 23.9 percent, 

a relative decrease of 13.5 percent. This suggests that residents are remaining in the SNF 

for longer periods of time before being discharged to the community and fewer are being 

discharged by the end of the SNF benefit. The same reasons discussed above for 

hospitalizations may contribute to this trend in community discharge. That is, shorter 

hospital stays for those who can go home. Under SNF PPS, the rehabilitation groups 

have tended to be the most profitable, particularly in the moderate intensity categories. 

Thus, there is little incentive for SNFs to discharge rehabilitation patients back to the 

community at the earliest point. 

No marker of community discharge is included in the publicly reported quality measures 

or emphasized in the current survey process, despite a significant portion of SNF care 

devoted to rehabilitation. Community discharge has been validated repeatedly as a 

marker of rehabilitation quality, and based on this evidence, is included in the revised 

Quality Indicator Survey as a quality of care measure for post-acute residents. However, 

community discharge is not included in the quality-based purchasing demonstration and 

is not being considered for public reporting. 

As the measures of the community discharge and rehospitalization examined in this 

project can be calculated for the majority of SNF residents and reported for the majority 

facilities, reflect care across a range of conditions and throughout the entire SNF stay, 

and have been adequately adjusted for differences in resident case mix between facilities, 

it is recommended that these measures be added to CMS's set of publicly reported quality 

measures. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 15 

TABLE 1: Facility characteristics of 2004 SNF admissions 

Facility Characteristic 

 Percent of Facilities 

(n=14,914) 

 Percent of Stays 

(n=2,228,507) 

 

Urban 67.0 78.5 

Hospital-based 8.9 15.1 

For-profit 67.3 65.0 

Nursing facility chain 54.9 55.9 

 

Total beds: 

 1-50 13.2 12.5 

 51-100 36.1 22.3 

 101-150 31.7 34.3 

 151 or more 19.0 30.9 

 

Number of stays per facility: 

 1-10 2.7 0.1 

 11-24 5.2 0.6 

 25-50 13.5 3.5 

 51-100 25.0 12.4 

 101-200 30.1 28.9 

 201 or more 23.5 54.5 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 16 

TABLE 2: Resident outcomes of 2004 SNF admissions 

Outcome1

 30-Day Outcomes 

(N=2,128,978) 

 100-Day Outcomes 

(N=2,107,333) 

 

 Discharged to community 28.8 38.2 

 Rehospitalized for: 

 Any reason 17.7 22.4 

 Any of five conditions below: 13.9 17.5 

 CHF 7.9 8.7 

 Electrolyte imbalance 6.8 8.6 

 Respiratory infection 4.6 5.8 

 Sepsis 2.4 3.0 

 UTI 4.7 6.2 

 

1

 Community discharge within 30 (100) days is derived from the discharge status code from the SNF claim and from 

the discharge status code from MDS assessments. Rehospitalization within 30 (100) days is determined by the 

presence of an acute or critical care access hospital claim within 30 (100) days of the SNF admission date, with no 

intervening HHA, hospice, or SNF claim. Stays for residents who are discharged to community within 30 (100) days 

and then rehospitalized within 3 days of the discharge are not considered discharged to community in 30 (100) days, 

but are instead considered a rehospitalization within 30 (100) days. Rehospitalization for a given diagnosis is derived 

from the primary and secondary diagnoses listed on the first hospital claim for that hospitalization. Residents who 

died prior to the end point of the outcome period (e.g., 30 days) were excluded from the measure. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 17 

FIGURE 1: Graph of population simulation: Community discharge (CD) and 

rehospitalization for any five conditions (RH5) 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

 10 15 20 25 30 35 40 45 50 55

Facility Stays

Mean Std. Dev.

CD - 30 Days CD - 100 Days RH5-30 Days RH5-100 Days
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 18 

FIGURE 2: Graph of facility bootstrap: Community discharge (CD) and 

rehospitalization for any five conditions (RH5) 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

10 15 20 25 30 35 40 45 50

Number of Stays

Mean Std. Dev.

CD - 30 Days CD - 100 Days RH5-30 Day RH5-100 Days
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 19 

FIGURE 3: Graph of theoretically-derived variance for a hypothetical outcome 

from the normal distribution with a national rate of 0.20 

Standard Deviation 

Number of Stays 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 20 

TABLE 3: Cumulative percent of facilities by number of stays with calculable 

value: Community discharge and rehospitalization for any five specific 

conditions 

 Community Discharge 

Rehospitalization for Any Five 

Specific Conditions 

Facility Stays 30 Days 100 Days 30 Days 100 Days 

5 1.44 1.45 1.47 1.49 

10 3.19 3.24 3.24 3.28 

15 5.09 5.15 5.16 5.22 

20 7.28 7.38 7.34 7.44 

25 9.72 9.89 9.84 9.98 

30 12.46 12.65 12.49 12.70 

35 15.46 15.67 15.47 15.68 

40 18.31 18.65 18.36 18.67 

45 21.34 21.68 21.39 21.70 

50 24.47 24.89 24.46 24.91 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 21 

TABLE 4: Facilities lost by reporting period length for potential outcome measure 

 6 

Months

12 

Months

15 

Months

18 

Months

21 

Months

24 

Months

 

30-Day Measures 

 Community discharge 25.0% 10.3% 8.4% 7.2% 6.7% 6.1% 

 

 Rehospitalized for any 

of five conditions 25.0% 10.4% 8.5% 7.3% 6.7% 6.1% 

100-Day Measures 

 Community discharge 25.5% 10.5% 8.5% 7.3% 6.7% 6.2% 

 

 Rehospitalized for any 

of five conditions 25.5% 10.6% 8.6% 7.4% 6.7% 6.2% 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 22 

TABLE 5: Unadjusted and adjusted facility rates of proposed SNF outcome 

measures 

Outcome Variable Mean (SD) Min 25% 50% 75% Max

 

Community Discharge 

 30 days unadjusted 21.9% (17.4) 0 9.3 17.6 29.3 92.3 

 30 days adjusted 23.9% (13.3) 0 14.2 22.8 32.3 95.4 

 100 days unadjusted 30.9% (19.5) 0 15.8 27.8 42.9 96.9 

 100 days adjusted 32.8% (15.6) 0 22.2 32.7 45.7 98.6 

 

Rehospitalized for Any of Five 

Conditions 

 

 30 days unadjusted 13.4% (6.2) 0 9.0 12.9 17.3 57.1 

 30 days adjusted 13.4% (5.0) 0 10.0 13.1 16.4 44.7 

 100 days unadjusted 17.5% (8.0) 0 11.7 17.0 22.7 68.8 

 100 days adjusted 17.1% (6.1) 0 13.0 16.9 20.9 58.4 

 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 23 

TABLE 6: Change in adjusted facility rates of proposed outcome measures for 

2000 - 2004 SNF admissions1 

 2000 2001 2002 2003 2004 Total2

 

Community Discharge 

 30 days 27.57% 25.91% 24.96% 23.88% 23.86% 

 -1.66 -0.95 -1.08 -0.02 -3.71 

 -6.02 -3.65 -4.34 -0.07 -13.46 

 

 100 days 33.75% 32.49% 32.38% 32.20% 32.79% 

 -1.27 -0.10 -0.18 0.58 -.96 

 -3.75 -0.32 -0.57 1.82 -2.84 

Rehospitalized for Any 

of Five Conditions

 

 30 days 9.54% 11.02% 11.93% 13.08% 13.36% 

 1.47 0.92 1.15 0.28 4.82 

 15.43 8.33 9.60 2.13 50.52 

 

 100 days 11.76% 13.69% 14.98% 16.56% 17.05% 

 1.93 1.29 1.57 0.50 5.29 

 16.41 9.44 10.50 3.02 44.98 

1

 Table entries show facility-adjusted rate on the top row, change from previous year in the middle row, and relative 

change from previous year in the bottom row 2

 Repeated measures ANOVA for all outcomes measures demonstrated a statistically significant effect (p<.0001) of 

time in all four models. In addition, most contrasts between consecutive years (e.g., 2000 and 2001, 2001 and 2002, 

etc.) and between 2004 and all other years (e.g., 2004 and 2000, 2004 and 2001, etc.) were statistically significant at 

p<.005. Two exceptions were community discharge within 30 days between years 2003 and 2004, and community 

discharge within 100 days between years 2002 and 2003. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 24 

TABLE 7: Stability of proposed SNF outcome measures over time (30 days [top] 

and 100 days [bottom])1 

 2000 2001 2002 2003 2004

Community Discharge 

2000 - 

2001 .790 

.801 

- 

2002 .717 

.732 

.798 

.819 

- 

2003 .667 

.680 

.726 

.748 

.803 

.826 

- 

2004 .617 

.630 

.672 

.689 

.727 

.747 

.811 

.831 

- 

 

Rehospitalization for Any of 

Five Conditions 

 

2000 - 

2001 .367 

.472 

- 

2002 .323 

.421 

.377 

.476 

- 

2003 .297 

.382 

.330 

.417 

.381 

.489 

- 

2004 .273 

.353 

.292 

.381 

.344 

.431 

.388 

.486 

- 

___________________ 

1 All correlations are non-zero with p-values <0.0001 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO 25 

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Division of Health Care Policy and Research, UCDHSC, Aurora, CO A1 

APPENDIX 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A2 

TABLE A: Analysis file information for 2004 

Characteristic Count 

 

Beginning number of stays 2,457,348 

Stays excluded1 228,841 

 

Final number of stays 2,228,507 

Final number of residents 1,548,714 

Final number of facilities 14,914 ____________________ 

1

 The file was first restricted to SNF admissions that occurred in calendar year 2004. The following records were then excluded

from all analyses: 136,137 swing bed stays; 426 non-PPS stays; 6,907 managed care SNF (6,904) or qualifying hospitalization 

(3) stays; 40,694 stays with no Medicare payment for SNF (35,062), qualifying hospitalization (167), or rehospitalization 

(5,465) stay; 1,771 stays with 0 Medicare-covered SNF days; 37,572 stays without a qualifying hospitalization claim; and 

5,334 stays with various inconsistent dates. An additional requirement specified that the SNF be Medicare-certified, but no 

records met that exclusion criteria. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A3 

TABLE B: Resident characteristics of 2004 SNF admissions (n = 2,227,507 stays) 

Resident Characteristics Percent of Stays

Age at admission: 

 Younger than 65 7.0 

 65-69 7.1 

 70-79 28.8 

 80-89 42.6 

 90 or older 14.5 

Female 65.3 

Caucasian 86.6 

Married 28.4 

Do not resuscitate orders 35.4 

Do not hospitalize orders 1.8 

Died in SNF within 30 days of SNF admission date 4.5 

Died in SNF within 100 days of SNF admission date 5.4 

Number of stays per resident: 

 1 stay 70.5 

 2 stays 19.9 

 3 stays 6.4 

 4 or more stays 3.2 

Qualifying Hospitalization (QH) Characteristics Percent of Stays

Admitted to QH from SNF/NF 4.4 

Bone fracture 14.1 

Cardiac arrhythmia 28.5 

COPD 24.3 

Dementia 22.0 

Fluid/Electrolyte disorder 32.1 

Genito-urinary disease 37.8 

Hypertension, uncomplicated 43.2 

Musculoskeletal disease 31.4 

Nervous system disease 24.6 

Respiratory disease (Excluding COPD) 27.0 

Skin disease 13.5 

Valvular disease 9.5 

Weight loss 6.3 

Average comorbidity score (-3.46 to 0.18) for “Community Discharge in 30 days”1

 -0.47 score 

Average comorbidity score (0 to 2.51) for “Rehospitalization for Any of Five 

Conditions in 30 Days”1 0.40 score 

Average comorbidity score (-3.26 to 0.22) for “Community Discharge in 100 

days”1 -0.48 score 

Average comorbidity score (0 to 2.31) for “Rehospitalization for Any of Five 

Conditions in 100 Days”1 0.41 score 

Average length of stay 8.9 days 
TABLE B: Resident characteristics of 2004 SNF admissions (n = 2,227,507 stays) (cont'd) 

Division of Health Care Policy and Research, UCDHSC, Aurora, CO A4 

SNF Stay Characteristics Percent of Stays

Catheter 25.1 

Feeding tube 7.1 

Parenteral/IV feeding 12.8 

Pressure ulcer 25.0 

Cognitive Performance Scale score (0-6): 1.8 (avg. score) 

 Intact (0) 36.4 

 Borderline impairment 13.1 

 Mild impairment 14.8 

 Moderate impairment 20.5 

 Moderately severe impairment 6.1 

 Severe impairment 3.4 

 Very severe impairment (6) 5.6 

5-Day Barthel score (0 poor - 90 good) (n=2,119,173) 36.9 

14-Day Barthel score (0 poor - 90 good) (n=1,328,687) 38.6 

30-Day Barthel score (0 poor - 90 good) (n=689,793) 37.6 

60-Day Barthel score (0 poor - 90 good) (n=215,123) 35.4 

90-Day Barthel score (0 poor - 90 good) (n=83,090) 33.3 

5-Day RUG hierarchies (n=2,133,009): 

 Rehabilitation 78.5 

 Extensive services 14.1 

 Special care 3.6 

 Clinically complex 2.4 

 Cognitively impaired 0.4 

 Behavior problems only 0.1 

 Reduced physical function 0.9 

Payment Mean at Stay End

 Average length of stay2

 26.7 days 

 Medicare payment $ 7,228 

 Total charges $11,636 

1

 Comorbidity scores were created for each outcome in which the outcome was the dependent variable and the independent 

variables were seventeen disease conditions based on ICD-9 and procedure codes specified by Romano (Dartmouth/Manitoba). 

The primary, eight secondary diagnoses, and six procedure codes from the qualifying hospital last claims record for the current

stay and one previous stay, if present, up to 365 days prior to the current stay, were used to determine the incidence of the 

seventeen disease conditions for each stay. Each disease condition had a value of one if present and zero if not. Using logistic 

regression, the coefficients for each of the seventeen disease conditions were calculated and summed to generate the 

comorbidity score. Only those coefficients with a p value less than or equal to .05 were included in the summation. The 

logistic regressions were run on a 3% random sample of stays for calendar year 2004 where all stays that ended in death were 

removed. 2

 Medicare covered days. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A5

TABLE C1: Resident regressions for 30-day models using 90% validation sample 

Community Discharge

Any of Five 

Conditions 

Rehospitalization 

CHF 

Rehospitalization 

Electrolyte Imbalance

Hospitalization 

Respiratory 

Rehospitalization 

Seps

Hospitali

Covariate 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Od

(95% 

Age (18 to 114) 0.99 (0.99 Female (1,0) 0.87 (0.86 - 0.88) 0.85 (0.84 - 0.87) 0.65 (0.64 - 0.66) 0.76 (0.74 Do Not Resuscitate (1,0) 0.63 (0.63 - 0.64) 0.67 (0.67 - 0.68) 0.71 (0.71 - 0.72) 0.68 (0.67 - 0.69) 0.74 (0.73 - 0.75) 0.57 (0.56 Do Not Rehospitalize (1,0) 0.45 (0.43 - 0.47) 0.49 (0.46 - 0.52) 0.47 (0.44 - 0.50) 0.44 (0.41 - 0.48) 0.47 (0.42 Barthel Score (0-90) 1.03 (1.03 - 1.03) 0.98 (0.98 - 0.98) 0.99 (0.99 - 0.99) 0.98 (0.98 - 0.98) 0.98 (0.98 - 0.98) 0.97 (0.97 Cognitive Performance Score (0-6) 0.80 (0.79 - 0.80) Catheter (1,0) 1.30 (1.29 - 1.31) 1.21 (1.20 - 1.23) 1.28 (1.26 - 1.30) 1.21 (1.19 - 1.23) 1.58 (1.55 Feeding Tube (1,0) 1.41 (1.39 - 1.43) 1.32 (1.30 - 1.35) 1.91 (1.87 - 1.95) 1.75 (1.70 Parenteral_IV_Feeding (1,0) 1.17 (1.15 - 1.18) 1.20 (1.17 - 1.22) Pressure Ulcer (1,0) 0.78 (0.77 - 0.79) 1.28 (1.26 - 1.29) 1.25 (1.24 - 1.27) 1.23 (1.21 - 1.24) 1.32 (1.30 - 1.34) 1.64 (1.61 Rehabilitation (1,0) 2.12 (2.09 - 2.14) Dartmouth/M Score (-3.5 to 3.3) 1.50 (1.49 - 1.52) 2.44 (2.41 - 2.46) 2.63 (2.61 - 2.65) 2.60 (2.54 - 2.67) 1.60 (1.55 - 1.64) 1.95 (1.89 Bone Fracture (1,0) 0.80 (0.80 - 0.81) 0.68 (0.66 Cardiac Arrhythmias (1,0) 1.22 (1.21 - 1.23) 1.44 (1.43 - 1.46) 1.19 (1.17 - 1.21) COPD (1,0) 1.26 (1.23 - 1.28) Dementia (1,0) 0.83 (0.82 - 0.84) Fluid/Electrolyte Disorders (1,0) 1.22 (1.21 - 1.23) 1.57 (1.55 - 1.59) 1.17 (1.15 - 1.19) Genito-Urinary Disease (1,0) Hypertension, Uncomplicated (1,0) 0.77 (0.75 Musculoskeletal Disease (1,0) 1.29 (1.28 - 1.30) Nervous System Disease (1,0) 0.75 (0.73 Respiratory Disease (1,0) 1.24 (1.23 - 1.25) 1.22 (1.21 - 1.24) 2.16 (2.13 - 2.19) Valvular Disease (1,0) 1.32 (1.30 - 1.34) Weight Loss (1,0) 1.25 (1.23 - 1.28) Hospital-based Facility (1,0) 3.81 (3.78 - 3.85) 0.52 (0.51 - 0.53) 0.59 (0.57 - 0.60) 0.50 (0.49 - 0.51) 0.57 (0.55 - 0.58) 0.60 (0.58 C-Statistic 0.807 0.709 0.783 0.695 0.750 0.76
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A6

TABLE C2: Resident regressions for 100-day models using 90% validation sample 

Community 

Discharge 

Any of Five 

Conditions 

Rehospitalization 

CHF 

Rehospitalization 

Electrolyte Imbalance 

Hospitalization 

Respiratory 

Rehospitalization Sepsis Hospi

Covariate 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Odds Ratio 

(95% CI) 

Adjusted Od

(95% 

Age (18 to 114) 0.99 (0.99 Female (1,0) 0.86 (0.86 - 0.87) 0.87 (0.86 - 0.88) 0.66 (0.65 - 0.67) 0.75 (0.74 Do Not Resuscitate (1,0) 1.02 (1.02 - 1.02) 0.73 (0.72 - 0.74) 0.79 (0.78 - 0.80) 0.73 (0.72 - 0.74) 0.81 (0.80 - 0.82) 0.63 (0.61 Do Not Rehospitalize (1,0) 0.50 (0.48 - 0.52) 0.55 (0.52 - 0.58) 0.52 (0.50 - 0.55) 0.50 (0.47 - 0.54) 0.54 (0.50 Barthel Score (0-90) 0.65 (0.65 - 0.66) 0.98 (0.98 - 0.98) 0.99 (0.99 - 0.99) 0.98 (0.98 - 0.98) 0.98 (0.98 - 0.98) 0.97 (0.97 Bowel Incontinence (1,0) 0.92 (0.91 - 0.92) Cognitive Performance Score (0-6) 0.84 (0.84 - 0.85) Catheter (1,0) 1.33 (1.32 - 1.34) 1.29 (1.27 - 1.31) 1.21 (1.20 - 1.23) 1.59 (1.56 Feeding Tube (1,0) 1.54 (1.51 - 1.56) 1.40 (1.37 - 1.42) 2.05 (2.01 - 2.09) 1.85 (1.81 Pressure Ulcer (1,0) 0.78 (0.78 - 0.78) 1.34 (1.33 - 1.36) 1.32 (1.30 - 1.34) 1.29 (1.28 - 1.31) 1.35 (1.33 - 1.37) 1.60 (1.57 Rehabilitation (1,0) 2.58 (2.56 - 2.61) Dartmouth/M Score (-3.3 to 3.1) 1.60 (1.59 - 1.62) 2.46 (2.43 - 2.48) 2.60 (2.58 - 2.61) 2.32 (2.26 - 2.37) 1.57 (1.53 - 1.60) 2.03 (1.98 Bone Fracture (1,0) 0.75 (0.73 Cardiac Arrhythmias (1,0) 1.22 (1.21 - 1.23) 1.46 (1.45 - 1.48) 1.17 (1.16 - 1.19) COPD (1,0) 1.31 (1.28 - 1.33) Dementia (1,0) 0.82 (0.81 - 0.83) 0.82 (0.80 - 0.83) Fluid/Electrolyte Disorders (1,0) 1.22 (1.21 - 1.23) 1.56 (1.54 - 1.58) 1.16 (1.14 - 1.17) 1.20 (1.18 Genito-Urinary Disease (1,0) Musculoskeletal Disease (1,0) 1.27 (1.26 - 1.28) Nervous System Disease (1,0) 0.84 (0.82 - 0.85) 0.77 (0.75 Respiratory Disease (1,0) 1.24 (1.23 - 1.25) 1.20 (1.19 - 1.22) 2.07 (2.04 - 2.10) Skin Disease (1,0) 1.30 (1.28 Weight Loss (1,0) 1.22 (1.21 - 1.24) 1.29 (1.26 - 1.31) Hospital-based Facility (1,0) 2.56 (2.54 - 2.59) 0.43 (0.43 - 0.44) 0.50 (0.49 - 0.51) 0.43 (0.42 - 0.44) 0.49 (0.48 - 0.50) 0.53 (0.51 C-Statistic 0.784 0.719 0.786 0.702 0.750 0.77
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A7

TABLE D: Comparison of statistical approaches to determine minimum sample size 

Approach Procedure Advantages and Disadvantages 

 

Theoretical 

Derivation 

• Compute the variance1

 for a hypothetical 

outcome measure with a national rate of .20 

using assumptions of a normal distribution 

(mean=0.5, standard deviation=1) truncated 

at 0 and 1. 

• Plot the variance by facility size (e.g. n=1, 

2, ..., 100). 

• Determine where the slope of the curve 

levels off (i.e., facility sample size where 

the variance begins to stabilize). 

 Advantages: Does not require 

significant computation time or 

real data. 

Disadvantages: Distributions of 

probabilities are not based on real 

data and therefore the type and 

shape of the distribution may not 

represent those found in the actual 

population. 

 

Bootstrap • For each “small” (N≤ 50) facility, 

randomly select patients with replacement 

from within the facility to create 500 new 

facility samples of equal size (e.g., a 

facility with 25 stays would have 500 

samples of 25 stays; a facility with 35 stays 

would have 500 samples of 35 stays). 

• For each measure, calculate the adjusted 

rate for each of the 500 within facility 

samples and then compute the variance of 

the adjusted rate for each facility. Each 

facility will have a bootstrap variance for 

each measure. 

• Plot the variance of the adjusted rate by 

facility sample size and determine where 

the slope of the curve levels off. 

 Disadvantages: Several studies 

have demonstrated that the 

bootstrap method overestimates 

sample variance for small sample 

sizes and therefore thresholds for 

facility sample size may be set 

higher than necessary. 

Comment: No distributional 

assumptions necessary other than 

that a finite variance exists. 

 

Simulation of 

Population 

Variance 

• Pool patients across the entire population 

of small facilities (N≤ 50). 

• Randomly select patients without

replacement from the pooled sample to 

create 500 new samples for each measure 

at each facility sample size of interest (e.g., 

10, 15, 20, ..., 50). 

• Calculate the adjusted rate for each 

simulated facility and then compute the 

variance of the adjusted rates within each 

facility size. Each facility size will have 

an adjusted rate variance for each measure. 

• Plot the variance of the adjusted rate by 

facility sample size and determine where 

the slope of the curve levels off. 

 Advantages: Provides a good 

approximation of the true variance 

of the adjusted rate with true 

random sampling for small n. 

Comment: Assumes the dataset 

contains the population of SNFs 

not a sample. 

1

 Variance for a Bernoulli distribution is (1-p)/(p) where p is the probability of outcome. 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A8

TABLE E: Adjusted facility rates of potential outcome measures for 2004 SNF admissions 

Facility Rate1 Mean 

Percent of Facilities 

with Rate=0 

 

Discharged to community in 30 days 23.1 6.2 

Rehospitalized in 30 days for: 

 Any of five conditions below 13.0 4.2 

 CHF 6.4 9.2 

 Electrolyte imbalance 6.3 9.2 

 Respiratory infection 4.3 13.3 

 Sepsis 2.0 29.9 

 UTI 4.3 14.1 

 

Discharged to community in 100 days 31.7 4.6 

Rehospitalized in 100 days for: 

 Any of five conditions below 16.6 3.2 

 CHF 8.1 7.3 

 Electrolyte imbalance 8.0 7.4 

 Respiratory infection 5.5 10.4 

 Sepsis 2.6 25.6 

 UTI 5.7 11.2 

1

 Excludes stays and facilities for which a measure could not be calculated due to missing values or stays ending in death (small 

facilities with less than 25 stays are not excluded). For 30 day measures, on average 207,547 (9.3%) stays and 169 (1.1%) 

facilities were excluded (varies slightly by each outcome). For 100 day measures, on average 228,147 (10.2%) stays and 169 

(1.1%) facilities were excluded (varies slightly by each outcome). 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A9

TABLE F: Spearman’s rank order correlations1 of adjusted facility rates2 of potential 

outcome measures at 30 days (top) and 100 days (bottom) for 2004 SNF 

admissions 

 Community 

Discharge 

Any of Five 

Conditions CHF 

Electrolyte 

Imbalance 

Respiratory 

Infection Sepsis UTI 

Community 

Discharge 

- 

Any of Five 

Conditions 

-0.168 

-0.245 

- 

CHF -0.107 

-0.163 

0.731 

0.744 

- 

Electrolyte Imbalance -0.119 

-0.172 

0.738 

0.758 

0.454 

0.487 

- 

Respiratory Infection -0.091 

-0.135 

0.617 

0.630 

0.442 

0.460 

0.421 

0.445 

- 

Sepsis -0.045 

-0.071 

0.416 

0.433 

0.270 

0.276 

0.351 

0.370 

0.291 

0.312 

- 

UTI -0.138 

-0.179 

0.660 

0.693 

0.412 

0.453 

0.515 

0.552 

0.348 

0.382 

0.365 

0.388 

- 

1

 All correlations are non-zero with p-values <0.0001. 

2

 Excludes stays and facilities for which a quality measure could not be calculated due to missing values or stays ending in death 

and small facilities with less than 25 stays. For 30 day measures, on average 226,329 (10.2%) stays and 1,550 (10.4%) 

facilities were excluded (varies slightly by each outcome). For 100 day measures, on average 247,206 (10.2%) stays and 169 

(1.1%) facilities were excluded (varies slightly by each outcome). 
Division of Health Care Policy and Research, UCDHSC, Aurora, CO A10 

TABLE G: Correlations1 of individual adjusted rehospitalization rates2 over time 

(30 days [top] and 100 days [bottom]) 

 2000 2001 2002 2003 2004

CHF 

 2000 - 

 2001 .228 

.298 

- 

 2002 .197 

.266 

.206 

.276 

- 

 2003 .185 

.232 

.186 

.260 

.226 

.300 

- 

 2004 .169 

.216 

.172 

.225 

.207 

.263 

.253 

.330 

- 

 

Electrolyte Imbalance 

2000 - 

2001 .280 

.367 

- 

2002 .252 

.329 

.309 

.385 

- 

2003 .242 

.303 

.263 

.329 

.305 

.384 

- 

2004 .233 

.295 

.238 

.299 

.276 

.351 

.297 

.380 

- 

 

Respiratory Disease 

 2000 - 

 2001 .202 

.282 

- 

 2002 .202 

.258 

.215 

.281 

- 

 2003 .163 

.221 

.190 

.259 

.217 

.296 

- 

 2004 .161 

.213 

.181 

.228 

.192 

.252 

.219 

.277 

- 

 

Sepsis 

 2000 - 

 2001 .211 

.250 

- 

 2002 .183 

.228 

.212 

.260 

- 

 2003 .180 

.220 

.227 

.260 

.235 

.294 

- 

 2004 .153 

.189 

.202 

.230 

.218 

.275 

.263 

.308 

- 

 
TABLE G: Correlations1

 of individual adjusted rehospitalization rates2 over time 

(30 days [top] and 100 days [bottom]) (cont'd) 

Division of Health Care Policy and Research, UCDHSC, Aurora, CO A11

 2000 2001 2002 2003 2004

UTI 

 2000 - 

 2001 .268 

.347 

- 

 2002 .250 

.326 

.288 

.368 

- 

 2003 .225 

.301 

.259 

.328 

.291 

.372 

- 

 2004 .197 

.256 

.218 

.285 

.261 

.323 

.292 

.376 

- 

1

 All correlations are non-zero with p-values <0.0001. 

2

 Excludes stays and facilities for which a measure could not be calculated due to missing values or stays ending in 

death and small facilities with less than 25 stays. For 30 day measures, on average 226,329 (10.2%) stays and 1,550 

(10.4%) facilities were excluded (varies slightly by each outcome). For 100 day measures, on average 247,206 

(10.2%) stays and 169 (1.1%) facilities were excluded (varies slightly by each outcome).