Nancy Nicosia

Elaine Reardon

Karl Lorenz

Joanne Lynn

Melinda Beeuwkes Buntin

RAND Health

1200 South Hayes Street

Arlington, VA 22202-5050

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.

The Medicare Hospice

Payment System: A

Preliminary Consideration

A study conducted by staff from Rand Health for the

Medicare Payment Advisory Commission

June 2006 • No. 06–4

of Potential Refinements
The Medicare Hospice

Payment System: A 

Preliminary Consideration 

of Potential Refinements 

NANCY NICOSIA 

ELAINE REARDON 

KARL LORENZ 

JOANNE LYNN 

MELINDA BEEUWKES BUNTIN

WR-401-MedPAC 

June 2006 

Prepared for Medicare Payment Advisory Commission 

WORKING 

P A P E R

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 is a registered trademark. 
 1 

Abstract 

Recent changes in the patient population and provision of care have generated interest in 

re-evaluating Medicare’s per diem payment system for hospice. Given the limitations of the 

Medicare data with respect to patient-level resource utilization, we rely on visit data from a large 

national chain of hospice providers. The findings suggest that the hospice payment system 

reflects resource use and that available case mix adjusters would not substantially improve the 

system’s performance. However, we also find that the per diem system could be adjusted to 

more accurately capture variation in costs within stays. Future data collection and analysis 

efforts should focus on more detailed and comprehensive measures of patient-level utilization 

and on patient characteristics that relate directly to necessary care. 

Introduction 

The Medicare payment system for hospice care has remained largely unchanged since per 

diem rates were instituted in 1983. The payment system was developed based on the results of a 

Health Care Financing Administration demonstration project conducted from 1980 to 1982. The 

demonstration included 26 hospices providing care to Medicare patients with terminal cancer. 

The resulting per diem rates vary based on four categories of care: routine home care, continuous 

home care, inpatient respite care, and general inpatient care. Payments are also adjusted using a 

local wage index. 

During the past two decades, payment levels have been updated for inflation based on a 

market basket, but otherwise the payment methodology has remained largely unchanged. Unlike 

other Medicare payment systems, hospice payments are not adjusted for case mix, urban/rural 

location (apart from the wage index), costly outliers or other factors that could affect costs. 

There is increasing concern that the payment methodology no longer accurately reflects costs 

because of changes in provider, patient, and service characteristics (Huskamp, Buntin et al. 

2001). This concern is compounded by the rapid expansion in the number of hospice providers, 

hospice use, and associated Medicare expenditures. Since 1992, the number of active providers 

has doubled, the number of beneficiaries has quadrupled, and Medicare expenditures have 

increased five-fold (MedPAC 2002; GAO 2004; MedPAC 2005). These developments have 

raised interest in potential adjustments to the current payment methodology (MedPAC 2002, 

MedPAC 2004). However, the limitations of Medicare data have undermined efforts to examine 

patient-level service utilization and costs and to understand how these factors, in turn, impact the 

appropriateness of the payment structure. Despite calls for additional data collection to support 

research efforts (GAO 2004), there is little evidence on what data might be useful in analyzing 

and adjusting the payment system. 

Given the current per diem payment structure and the change in the hospice population 

over time, we focus on three specific questions related to potential refinements to the hospice 

payment system. These questions had been raised in earlier literature and in the Commission’s 

June 2004 report (Buntin 2002; MedPAC 2004). 

1. How well does the per diem system reflect current hospice resource use? 

2. Should case mix adjustments be considered? 

3. Are the beginnings and ends of hospice stays more intensive? 
 2 

Background 

Two recent developments – the increase in the share of non-cancer diagnoses among the 

hospice population and changes in care for cancer patients – raise concerns that the payment 

system may not track resources used to care for patients (GAO 2000a and b; GAO 2004). The 

share of hospice patients with cancer diagnoses has fallen considerably. In 1992, cancer patients 

comprised 75% of hospice patients (NCHS 2003). Ten years later, the share of hospice patients 

with cancer diagnoses declined to only 43%, while neurodegenerative, cerebrovascular, and 

cardiovascular diagnoses increased in prevalence (see Table 1). Research indicates that noncancer hospice patients – such as those diagnosed with congestive heart failure, chronic 

obstructive pulmonary disease, Alzheimer’s or stroke – differ in the intensity of services used, 

types of services, and length of stay (GAO 2002a and b; Campbell, Lynn et al. 2004). Patients 

with non-cancer diagnoses may also have different locations of care – such as differential use of 

nursing homes. Finally, these patients may also be older, implying an increased number of 

comorbidities and lower probability of a living partner. These factors can impact their service 

utilization and the costs of providing their care. Evidence suggests increasing use of more 

expensive elements of hospice care such as nursing services, social services, and durable medical 

equipment relative to less expensive elements such as home health aides (GAO 2004). 

The second concern regarding the payment structure arises from changes in the treatment 

of cancer patients due to advances in medical technology such as new palliative drugs, 

chemotherapy, and radiation (GAO 2004). Advances in cancer treatments may alter the timing 

of entry into hospice as patients pursue curative treatment. Such changes can affect service 

utilization and the appropriateness of the payment structure by shortening the length of stay and 

increasing the average intensity of care during an episode. GAO (2004) found that the average 

length of stay decreased for cancer patients and for the four major non-cancer categories (e.g., 

stroke, congestive heart failure, chronic obstructive pulmonary disease, and Alzheimer’s) 

between the 1980-82 demonstration and 2001. 

While these issues have been recognized in the literature, Medicare data cannot provide 

complete information about their impact on service utilization and costs. Medicare claims record 

the number and type of days billed, but do not record the number of visits received, the length of 

those visits, the disciplines of the staff-members providing care, and the timing of visits during 

the patient’s stay. Medicare data from facility cost reports record expenditures by category (e.g., 

staff, drugs), but these costs cannot be allocated to individual patients. Consequently, 

differences in service utilization and costs across diagnosis categories and variations in the 

intensity of care during a stay cannot be examined directly for the Medicare population. 

Policymakers have called for improved data collection to address these issues, but such efforts 

have not yet been undertaken (GAO 2004). Given these limitations, our analyses rely on a more 

detailed dataset collected by a large chain of freestanding hospice providers. 

Data 

The data for this study consist of patient-level demographics and diagnoses as well as 

visit-level data on service utilization from a major national chain hospice provider. The analyses 

focus on Medicare hospice patients who were admitted in 2002 and 2003. The resulting sample 

consists of 68,725 patients or about 6% of the Medicare hospice population. The dataset 

includes substantial detail on service utilization and patient demographics that are not available 
 3 

in the Medicare data. Key service utilization variables include the exact date of each visit, the 

discipline of staff-members involved in each visit, the length of visits, and receipt of routine 

home care at a nursing home. Key demographics and case factors include primary diagnosis, 

age, race/ethnicity, gender, marital status/living arrangement, and discharge status. 

The detailed data allow the construction of two measures of service utilization. The 

number of visits received by each patient is calculated by counting the number of visits received 

during a stay.1

 The second measure, visit labor costs, are the wages associated with providing 

the visits and are based on the number of visits, the length of visits, and the discipline of staffmembers involved. These factors are converted into dollars using average hourly earnings for 

each discipline from the Bureau of Labor Statistics and adjusted for geographic location using 

the Medicare wage index. Visit labor costs capture only the direct wage costs of staff-member 

time spent with each patient. We estimate that these costs reflect approximately one-fifth of total 

facility costs per patient. The measure does not capture transportation time, administrative 

overhead, employee benefits, and the non-labor costs of providing care (e.g., drugs and medical 

supplies). While facility-level data were available on these elements of costs, it was not possible 

to allocate shares to particular patients based on individual resource use. A more comprehensive 

measure of costs would thus be useful, but visit labor costs are likely representative of the major 

component of variable costs because labor accounts for the majority of routine costs, indirect 

labor costs parallel direct costs to a sizable extent, and visit labor costs reflect patient needs and 

severity. 

Analyses 

The chain provider’s patient population differs from the Medicare hospice population 

overall and also differs from the population in freestanding hospices (see Table 1).2

 The chain 

provider has significantly fewer cancer patients. Only one-third of Medicare patients at these 

facilities had a primary diagnosis of cancer compared to 43% among all hospices and 40% 

among freestanding hospices. In addition, the chain provider had a greater share of 

neurodegenerative, cerebrovascular and cardiovascular patients. The patients are also 

significantly older, which is driven in part by the differences in diagnoses. They are also more 

likely to be non-white, but this is a function of the geographic location of the chain provider’s 

facilities. Finally, the provider uses inpatient care to a greater extent than the average hospice 

and does not use respite care. 

Due to the limitations in the Medicare data, it is not possible to compare how these 

sample differences translate into differences in service utilization except with respect to the 

number of days enrolled. Overall, the mean patient at the chain provider was enrolled in hospice 

care for a greater number of days, but median patient for fewer days (Tables 2.1 and 2.2). This 

implies that the chain provider has a greater incidence of longer stays than the average hospice. 

The longer stays are driven in part by the differences in case mix and in part by much longer 

stays among cardiovascular, cerebrovascular, ill-defined debility, neurodegenerative, and 

respiratory patients. The vast majority of days billed are routine home care days for both the 

chain provider (90%) and the Medicare population (93%). But the chain provider uses inpatient 

care to a greater extent than the average hospice and does not use any respite care. For the chain 

provider, more than two-fifths (43%) of routine home care days are provided in nursing homes. 

 

1

 This measure excludes visits by volunteers. 2

 Limiting the comparison to only freestanding hospice providers yielded similar results. 
 4 

Ill-defined debility, neurodegenerative, and cerebrovascular patients received a majority of their 

routine home care days in a nursing home. Nursing home residence is not recorded in the 

Medicare data. 

Tables 2.1 and 2.2 also show that there is substantial variation across diagnoses in the 

number of days enrolled. The average stay for cancer patients is shorter than for patients with 

cardiovascular, cerebrovascular, ill-defined debility and neurodegenerative diagnoses. The 

variation is evident in both the chain provider sample and in the Medicare population. Table 3 

shows that this variation is also evident in other measures of service utilization. Cancer patients 

in the chain provider sample received fewer visits and incurred lower visit labor costs than 

patients with ill-defined debility and neurodegenerative disease, in part because of their shorter 

length of stay. 

How well does the per diem system reflect current hospice resource use? 

We estimated a standard (ordinary least squares or OLS) regression to examine how well 

the per diem payment categories explained hospice visits and visit labor costs. Tables 4.1 and 

4.2 show the proportion of variation in the number of visits and in visit labor costs explained by 

the number and type of day billed for each patient. The adjusted R-squared is approximately 

90% for both the number of visits and visit labor costs indicating these resource use measures are 

well explained by the per diem payment system. All three regressors are statistically significant 

predictors.3

There are two possible explanations for this result. First, dying patients may have similar 

clinical needs within the four levels of care corresponding to the per diem categories. Two 

physicians on our team specializing in hospice and palliative care felt that this was a plausible 

explanation for this finding. Alternatively, the regression results may simply reflect that the 

chain provider responded to the financial incentives of the current per diem system. It is not 

possible to distinguish between these alternative explanations. 

Should case mix adjustment be considered? 

The chain provider data contain a rich set of patient-level characteristics that may be 

useful in explaining patient costs and informing potential adjustments to the current payment 

system. Specifically, the dataset includes primary ICD-9 diagnosis codes, race/ethnicity, marital 

status, age, receipt of care in a nursing home, discharge status, and geographic location. We 

aggregated the individual, clinically similar ICD-9 codes into nine cancer and seven non-cancer 

diagnosis categories hypothesized to have similar resource use for the purpose of the analyses 

described below. 

We tested whether these variables were predictors of resource use both on their own and 

in conjunction with the per diem category variables. Specification 2 of Tables 4.1 and 4.2 show 

the results of regressions on our service utilization measures when only the patient-level 

demographics and diagnoses are included. The regressions include primary diagnosis4

, age, 

race/ethnicity, marital status/living arrangement, discharge status and geographic location. 

 

3

 We include only three types of days because the chain provider did not utilize inpatient respite care. 

4

 The results shown are based on 16 diagnoses categories. We also estimated regression with a larger number of 

disaggregated categories. The results were qualitatively similar. 
 5 

Service utilization was higher for patients who were younger, African-American, female, 

currently or formerly married (or living with a partner), and those who were discharged alive.5

 

Cancer and HIV patients had significantly fewer visits and lower visit labor costs, while 

neurodegenerative and ill-defined debility patients had more visits and higher costs. Although 

many of the demographic variables and diagnosis categories were statistically significant, these 

factors explained only 11% and 8% of the variation in the number of visits and visit labor costs, 

respectively.6

When added to the model that contains the days by type variables, the demographic and 

diagnoses variables add little explanatory power, as shown in the third specification (see column 

3 of Tables 4.1 and 4.2). Indeed, in a statistical sense they do not add any explanatory power 

when the number of variables added to the model is taken into consideration. Although many of 

these disease categories are statistically significant predictors of visits and visit labor costs their 

magnitudes are very small: the average prediction error between the two models differs by 

approximately 1⁄2 of a percent of the average episode visit labor costs. 

The final specification includes an indicator variable for whether the patient received any 

routine home care in a nursing home as well as the interaction of nursing home care and age 

category. Again, the additional regressors are statistically significant, but fail to increase the 

explanatory power of the current per diem system. 

Are the beginnings and ends of hospice stays more intensive? 

The changes in the types of patients seen by hospices and the decline in length of stay 

since the benefit was implemented has raised concerns about a payment system that reimburses 

providers using a set of flat per diem rates that do not vary throughout a stay. Because the data 

from the chain provider record the admission date, discharge date, and the date of each visit, we 

were able to examine measures of the distribution of visits across each patient’s stay to assess 

how well a constant per diem rate reflects the resource use throughout a hospice stay. 

The data show that the first and last three days of stays are more intensive than days 

falling into the middle of a hospice stay. The median length of stay in the sample is 13 days and 

the median number of visits received is 18; the median number of visits received per day of 1.5. 

Table 5 shows the relative number of visits at the beginning, middle, and ends of hospice stays. 

Given that the median length of stay is less than two weeks, stays were broken into first three, 

last three, and middle days of each stay separately. Visits during stays of three days or less are 

allocated to the last three days: visits during stays of six days or less are allocated to first to the 

last three days and then to the first three days. At the median, patients receive twice as many 

visits during the last three days as they do in the “middle” days. Resource use is also higher at 

the beginning of the stay relative to the “middle” days. Because the beginning and ends of stays 

are relatively more expensive, a constant per diem rate may create incentives for providers to 

seek patients with longer lengths of stay. The flat per diem system may cause shorter stay 

patients, for whom it is more difficult to recoup these higher initial and final costs, to be less 

profitable than longer-stay patients. 

 

5

 Patients can be discharged alive due to a change in diagnosis/prognosis, change in provider, or change of location 

of residence. 

6

 The demographic characteristics and diagnosis categories explain only slightly more of the variation (13%) in days 

enrolled (not shown). 
 6 

Implications for Policy 

Policymakers have called for an analysis of potential refinements to Medicare’s hospice 

payment system given how much the patient mix and treatment patterns have changed since the 

system was implemented. Using proprietary data from a large chain provider, we assessed 

whether the current payment system reflects hospice resource use, whether case mix adjustments 

could improve the payment structure, and whether the intensity of care varies within stays. 

While our analysis sample is not representative of the general Medicare population, our sample 

size and set of potential case mix adjusters are both large. Likewise, detailed data on visits 

provide the opportunity to examine individual service utilization patterns. 

Our analyses show that the payment system variables, namely the number of days of each 

type billed for a patient, predict much of the variation in the number of visits and labor visit costs 

at the patient level. This indicates that the payment system still reflects our measures of resource 

use. It is not possible, however, to distinguish whether the strong relationship follows from the 

appropriateness of the payment structure or from the hospice provider responding to the system’s 

financial incentives. 

A related issue is whether patients’ individual demographic characteristics and diagnoses 

can be used to improve the accuracy of the payment system. The proprietary dataset analyzed 

here provides an extensive sample of potential case mix adjusters. However, our regression 

analyses indicate that the available case mix adjusters, including diagnoses, were not powerful 

predictors of service utilization in this sample. These results do not preclude the utility of 

alternative adjusters that may be more closely related to service utilization, such as measures of 

functional limitations or comorbidities, but they do support our finding that the per diem 

categories reflect our measures of resource use well. 

Our analyses reveal substantial variation in the intensity of care during a stay. Patients 

receive a greater number of visits and incur greater visit labor costs at the beginning and end of 

their stays. Because the beginning and end of stays are more intensive and thus more expensive 

than the intervening days, longer stay patients would have a lower average cost all else equal. 

Higher service utilization at the beginning and end of stays combined with a constant per diem 

payment system might now create incentives for providers to lower their average daily costs by 

seeking patients with longer lengths of stay. 

These analyses suggest that the current payment system continues to reflect hospice 

patient costs and that diagnoses likely have limited potential as case mix adjusters. They do, 

however, suggest that adjusting per diem amounts to reflect greater resource use at the 

beginnings and ends of stays could be warranted. The findings also speak to the data 

requirements necessary to make informed policy decisions regarding actual payment 

adjustments. Previous studies have called for data collection efforts, but there was limited 

information on what variables should be collected. Our analyses of visit labor costs for over 

68,000 patients with a diverse set of diagnoses, showed that basic demographics and diagnoses 

were not powerful explanatory factors upon which to base payment adjustments. But given that 

these patients received care from a single chain, the analyses would be strengthened by the 

examination of a more representative sample of hospices and patients. Factors more directly 
 7 

related to necessary care – such as functional limitations and comorbidities – should also be 

investigated. The results also indicate that the collection of more detailed information on service 

utilization such as the number of visits, timing of visits, length of visits, site of visit, and type of 

discipline would be useful in understanding how well payments reflect costs in an era of 

changing patient populations and length of stay. Our analyses confirm that such data can be 

useful in examining how service utilization varies throughout a stay. Finally, a more 

comprehensive measure of costs that includes the individual resource use of non-labor inputs 

such as drugs and medical equipment would improve our understanding of hospice resource use. 
 8 

References 

Beeuwkes Buntin, Melinda and Haiden Huskamp, “What Is Known About the Economics of 

End-of-Life Care for Medicare Beneficiaries?” The Gerontologist, Vol. 42, Special Issue 

III, 2002, 40-48. 

Campbell, Diane E., Joanne Lynn, Tom A. Louis, and Lisa R. Shugarman. “Medicare Program 

Expenditures Associated with Hospice Use,” Annals of Internal Medicine Vol. 140, No. 

4, February 17, 2004, 269-277. 

Huskamp Haiden A., Melinda Beeuwkes Buntin, Virginia Wang, Joseph P. Newhouse, 

“Providing Care At The End Of Life: Do Medicare Rules Impede Good Care?” Health 

Affairs. Vol. 20, No.3, 204-211. 

Medicare Payment Advisory Commission., “Hospice Care in Medicare: Recent Trends and a 

Review of the Issues,” in Report to Congress: New Approaches in Medicare. June 2004, 

139-152. Online at 

http://www.medpac.gov/publications%5Ccongressional_reports%5CJune04_ch6.pdf. 

Medicare Payment Advisory Commission, Other Services: Dialysis, Hospice, Durable Medical 

Equipment in Data Book: Healthcare Spending and the Medicare Program, June 2005, 

189. Online at http://www.medpac.gov/publications/chapters/Jun05DataBookSec12.pdf. 

Medicare Payment Advisory Commission, Report to Congress: Medicare Beneficiaries’ Access 

to Hospice, May 2002. Online at 

http://www.medpac.gov/publications/congressional_reports/may2002_HospiceAccess.pd

f. 

National Center for Health Statistics, “New Report Profiles Hospice Care in the United States 

Shows Drop in Length of Care,” Press Release, August 21, 2003. Online at 

 http://www.cdc.gov/nchs/pressroom/03facts/hospicecare.htm. 

U.S. Government Accountability Office. Medicare Hospice Care: Modifications to Payment 

Methodology May Be Warranted, Washington, D.C., GAO-05-42, October 2004. 

U.S. General Accounting Office. Medicare: More Beneficiaries Use Hospice but for Fewer 

Days of Care, Washington, D.C., GAO-HEHS-00-182, September 2000a. 

U.S. General Accounting Office. Medicare: More Beneficiaries Use Hospice; Many Factors 

Contribute to Shorter Periods of Use. GAO-HEHS-00-201, September 2000b. 
 9 

TABLE 1: SAMPLE STATISTICS (2002-2003) 

 Chain Provider 

Medicare 

(Freestanding 

Only) 

Medicare 

(All Claims) 

Category Frequency Percent Frequency Percent Frequency Percent

Age Category 

Under 65 2820 4.10 155634 20.45 60251 5.10 

65 to 74 12221 17.78 286669 37.66 249263 21.10 

75 to 84 25405 36.97 279476 36.71 447308 37.87 

85 & over 28279 41.15 39433 5.18 424434 35.93 

Marital Status 

Divorced/separated/widow 40183 58.47 n.a. n.a. n.a. n.a. 

Married, living together 23000 33.47 n.a. n.a. n.a. n.a. 

Single 5542 8.06 n.a. n.a. n.a. n.a. 

Race 

Asian 691 1.00 4589 0.60 6688 0.57 

Black 7960 11.58 66299 8.74 90425 7.68 

Hispanic 7807 11.36 11345 1.50 15541 1.32 

Other 422 0.61 7827 1.03 11417 0.97 

White 51846 75.44 668453 88.13 1053159 89.46 

Sex 

Female 41077 59.77 442914 58.19 680877 57.64 

Male 27648 40.23 318298 41.81 500379 42.36 

Nursing Home 

Routine Home Care in Nursing 

Home 19746 28.73 n.a. n.a. n.a. n.a. 

Discharge Status 

Died 62355 90.73 629740 82.73 978371 82.82 

Diagnoses 

 Cancer - Breast 1672 2.43 18377 2.46 30248 2.61 

 Cancer – Colorectal 2720 3.96 20659 2.76 34191 2.95 

 Cancer – Gynecological 1105 1.61 10651 1.42 17988 1.55 

 Cancer – Hematological 1721 2.50 11867 1.59 19284 1.66 

 Cancer – Kidney, Bladder 1254 1.82 13581 1.82 23012 1.99 

 Cancer – Lung, larynx, pleura 6652 9.68 81352 10.88 135228 11.67 

 Cancer – Other Gastroint. 3866 5.63 40972 5.48 67417 5.82 

 Cancer – Other 2749 4.00 16148 2.16 25601 2.21 

 Cancer – Prostate 1648 2.40 20749 2.78 34194 2.95 

 Cancer (Medicare only) n.a. n.a. 62179 8.32 105523 9.11 

 Cardiovascular 9768 14.21 100156 13.40 150538 12.99 

 Cerebrovascular 5880 8.56 47943 6.41 68981 5.95 

 HIV 415 0.60 3892 0.52 5656 0.49 

 Ill-Defined Debility 6197 9.02 85756 11.47 124469 10.74 

 Neurodegenerative 13602 19.79 115989 15.51 163547 14.12 

 Other Diagnosis 4369 6.36 39356 5.26 63392 5.47 

 Respiratory 5106 7.43 58072 7.77 89163 7.70 

NOTES: 

1. Age and racial differences between the chain provider and Medicare samples are significant at the 0.01 level. 

2. The differences between the chain provider and Medicare-Freestanding samples are statistically significant at the 

0.01 level except for breast and gynecological cancers (which are significantly different at the 0.05 level) and kidney 

cancer, other gastrointestinal cancer and respiratory, which are not significantly different. 

3. The differences between the chain provider and Medicare-All Claims samples are statistically significant at the 

0.01 level except for other gastrointestinal cancer (which is significantly different at the 0.05 level), and breast and 

kidney cancers and respiratory, which are not significantly different. 
 

10 

TABLE 2.1: DAYS BY DIAGNOSIS CATEGORY (CHAIN PROVIDER)

Total Days Continuous 

Care Days 

General 

Inpatient 

Days 

Routine 

Home Days 

Routine 

Home Care 

at Home 

Routine 

Home Care 

at Nursing 

Home 

Disease Category

Mean Med Mean Med Mean Med Mean Med Mean Mean 

Cancer - Breast 54.5 17 3.6 0 2.8 0 48.1 10 34.3 13.9 

Cancer – Colorectal 53.2 19 3.2 0 3.1 0 46.9 13 37.2 9.7 

Cancer – Gynecological 44.7 17 3.6 0 3.6 0 37.4 9 27.6 9.8 

Cancer – Hematological 32.4 9 2.3 0 2.4 0 27.7 4 20.5 7.1 

Cancer – Kidney, Bladder 43.3 14 3.0 0 2.8 0 37.5 8 30.0 7.5 

Cancer – Lung, Larynx, Pleura 41.4 14 2.7 0 2.8 0 35.8 9 29.2 6.7 

Cancer – Other Gastrointestinal 39.4 14 3.0 0 2.8 0 33.6 8 28.5 5.1 

Cancer – Other 40.2 15 3.5 0 3.3 0 33.4 9 26.3 7.1 

Cancer – Prostate 48.3 16 3.1 0 3.0 0 42.2 11 32.3 9.8 

Cardiovascular 62.5 11 3.2 0 2.7 0 56.5 5 37.7 18.8 

Cerebrovascular 48.5 8 2.9 0 3.6 1 41.9 0 17.5 24.5 

HIV 33.3 8 2.1 0 5.7 3 37.4 0 16.5 9.0 

Ill-Defined Debility 94.7 23 3.9 1 1.6 0 89.2 17 34.9 54.4 

Neurodegenerative 88.8 16 3.8 1 2.2 0 82.8 10 34.0 48.8 

Other Diagnosis 26.8 6 1.9 0 2.8 0 21.9 0 13.8 8.1 

Respiratory 62.0 9 3.0 0 3.1 1 55.8 3 41.9 14.0 

Average 60.4 13 3.2 0 2.8 0 54.5 7 30.9 23.6 

Percent of Total 

By Type of Day 

100% 5.3% 4.5% 90.2% 51.1% 39.0% 
 

11 

TABLE 2.2: DAYS BY DISEASE (MEDICARE CLAIMS) 

Total Days Continuous Care 

Days 

General 

Inpatient 

Days 

Routine 

Home Days 

Inpatient 

Respite 

Days Disease Category

Mean Med Mean Med Mean Med Mean Med Mean Med

Cancer - Breast 48.5 22 2.0 0 0.1 0 45.0 19 1.3 0 

Cancer – Colorectal 48.3 24 2.4 0 0.1 0 44.0 20 1.4 0 

Cancer – Gynecological 43.0 22 2.0 0 0.1 0 39.0 19 1.5 0 

Cancer – Hematological 34.3 13 2.0 0 0.1 0 30.7 10 1.2 0 

Cancer – Kidney 40.5 18 1.9 0 0.1 0 36.9 15 1.3 0 

Cancer – Lung, Larynx, 

Pleura 40.1 19 1.8 0 0.1 0 36.6 16 1.2 0 

Cancer – Other 

Gastrointestinal 37.3 19 2.0 0 0.1 0 33.6 16 1.2 0 

Cancer – Other 43.7 22 2.7 0 0.1 0 39.1 18 1.4 0 

Cancer – Prostate 48.5 23 1.9 0 0.1 0 44.9 20 1.2 0 

Cancer (Medicare Only) 40.6 19 1.2 0 0.1 0 37.8 17 1.2 0 

Cardiovascular 52.7 17 2.0 0 0.1 0 49.7 15 1.2 0 

Cerebrovascular 35.4 9 1.9 0 0.0 0 31.3 5 1.9 0 

HIV 27.7 8 1.6 0 0.0 0 22.8 3 2.7 0 

Ill-Defined Debility 54.5 18 1.6 0 0.1 0 52.3 16 0.9 0 

Neurodegenerative 61.3 20 2.4 0 0.1 0 58.7 17 1.0 0 

Other Diagnosis 28.5 9 1.6 0 0.0 0 25.0 6 1.5 0 

Respiratory 50.4 15 2.0 0 0.1 0 45.0 19 1.3 0 

Average 46.5 17 1.9 0 0.1 0 43.2 14 1.3 0 

Percent of Total Days By 

Type 100.0% 4.1% 0.2% 93.0% 2.7% 
 

12 

TABLE 3: AGE, VISITS AND VISIT LABOR COSTS BY DISEASE (Chain Provider) 

Age Number of Visits Visit Labor Cost 

Disease Category Mean Med Mean Med Mean Med 

Cancer - Breast 77.6 79 46.4 21 1853.1 765.8

Cancer – Colorectal 78.4 79 41.8 22 1676.2 746.2

Cancer – Gynecological 76.7 78 40.4 22 1734.5 802.3

Cancer – Hematological 78.5 79 29.1 13 1172.0 458.9

Cancer – Kidney, Bladder 78.7 79 36.7 19 1482.2 686.6

Cancer – Lung, Larynx, Pleura 76.1 76 33.9 18 1395.4 623.1

Cancer – Other Gastrointestinal 77.5 78 34.3 18 1468.9 628.6

Cancer – Other 76.8 78 38.0 19 1688.6 705.6

Cancer – Prostate 80.2 81 40.8 21 1599.1 738.0

Cardiovascular 84.7 86 49.6 17 1746.5 606.7

Cerebrovascular 83.3 84 42.7 12 1488.1 414.5

HIV 60.6 56 29.9 11 1220.5 347.7

Ill-Defined Debility 87.1 88 69.0 27 2218.5 955.9

Neurodegenerative 84.8 86 70.2 23 2233.1 906.5

Other Diagnosis 78.2 80 24.5 10 973.3 319.1

Respiratory 79.7 80 46.8 14 1680.9 494.6

Average 81.4 82 48.6 18 1731.1 645.9 
 

13 

Table 4.1: SERVICE UTILIZATION REGRESSIONS – NUMBER OF VISITS 

Model (1) Model (2) Model (3) Model (4) 

Days Billed Continuous Care 3.96*** --- 3.82*** 3.81*** 

 General Inpatient Care 1.13*** --- 0.99*** 0.99*** 

 Routine Home Care 0.57*** --- 0.57*** 0.57*** 

 Total --- --- --- --- 

Age 65-74 --- -9.83*** 0.76 1.61** 

 75-84 --- -8.39*** 1.13* 2.37*** 

 85 & over --- -12.00*** 0.42 2.06*** 

Race Asian --- -2.42 -2.59** -2.59** 

 Black --- 4.48*** 1.09*** 1.09*** 

 Hispanic --- 0.93 1.44*** 1.45*** 

 Other --- 6.06 0.52 0.51 

Marital Status Married --- 4.96*** 0.72 0.85* 

 Widowed, Div., Sep. --- 3.50*** 0.30 0.37 

Gender Female --- 9.73*** 0.36 0.42* 

Nursing Home RHC in NH --- 34.52*** 7.22*** 13.36*** 

 RHC in NH*Age 65-74 --- --- --- -3.96*** 

 RHC in NH*Age 75-84 --- --- --- -6.12*** 

 RHC in NH*Age 85+ --- --- --- -7.17*** 

Discharge Alive (Not extended) --- 8.55*** -3.86*** -3.89*** 

 Prognosis Extended --- 113.13*** -10.47*** -10.55*** 

Diagnosis Cancer – Breast --- -3.06 1.66** 1.68** 

 Cancer – Colorectal --- -2.07 -0.71 -0.64 

 Cancer – Gynecological --- -6.97*** 1.21 1.29 

 Cancer – Hematological --- -13.31*** 1.57** 1.59** 

 Cancer – Kidney, bladder --- -5.47** 0.68 0.74 

 Cancer – Lung, larynx, pleura --- -7.69*** -0.18 -0.11 

 Cancer – Other Gastroint. --- -7.33*** 0.79 0.88 

 Cancer – Other --- -6.52*** 1.73*** 1.75*** 

 Cancer – Prostate --- 0.40 1.33 1.38* 

 Cardiovascular --- 0.35 1.13** 1.11** 

 Cerebrovascular --- -9.02*** 2.35*** 2.30*** 

 HIV --- -21.89*** 0.32 0.33 

 Ill-defined/Debility --- 10.18*** 1.23** 1.30** 

 Neurodegenerative --- 11.56*** 4.06*** 4.06*** 

 Other Diagnosis --- -21.52*** 0.93 0.95 

Intercept Constant No Yes Yes Yes 

Year FE Year Indicator Variables No Yes Yes Yes 

Facility FE Facility Indicator Variables No Yes Yes Yes 

R-squared 0.914 0.111 0.891 0.891 

Sample 68725 68725 68725 68725 
 

14 

TABLE 4.2: SERVICE UTILIZATION REGRESSIONS – VISIT LABOR COSTS 

Model (1) Model (2) Model (3) Model (4) 

Days Billed Continuous Care 300.21*** --- 295.27*** 295.17*** 

 General Inpatient Care 34.52*** --- 27.32*** 27.23*** 

 Routine Home Care 10.91*** --- 10.79*** 10.73*** 

 Total --- --- --- --- 

Age 65-74 --- -463.31*** -46.59** -10.85 

 75-84 --- -459.19*** -58.03*** -15.92 

 85 & over --- -640.21*** -95.72*** -40.07 

Race Asian --- -76.47 -52.28 -52.16 

 Black --- 141.24*** 17.35 17.66 

 Hispanic --- 61.67 15.78 16.01 

 Other --- 226.94 35.92 35.61 

Marital Status Married --- 232.17*** 27.02* 30.82* 

 Widowed, Div., Sep. --- 166.52*** 15.32 17.37 

Gender Female --- 351.13*** 26.03*** 27.76*** 

Nursing Home RHC in NH --- 1038.56*** 148.29*** 362.79*** 

 RHC in NH*Age 65-74 --- --- --- -171.00*** 

 RHC in NH*Age 75-84 --- --- --- -208.33*** 

 RHC in NH*Age 85+ --- --- --- -245.85*** 

Discharge Alive (Not extended) --- -267.34*** -101.31*** -102.17*** 

 Prognosis Extended --- 1507.03*** -355.99*** -358.88*** 

Diagnosis Cancer – Breast --- 73.16 58.21** 59.09** 

 Cancer – Colorectal --- 85.09 22.63 24.88 

 Cancer – Gynecological --- -4.22 41.07** 43.51 

 Cancer – Hematological --- -368.37*** 6.52 7.25 

 Cancer – Kidney, bladder --- -54.54 17.52 19.10 

 Cancer – Lung, larynx, pleura --- -134.07** 7.17 9.23 

 Cancer – Other Gastroint. --- -58.35 28.37 30.92 

 Cancer – Other --- 55.23 80.39*** 81.05*** 

 Cancer – Prostate --- 148.94* 36.06 37.40 

 Cardiovascular --- 14.94 -4.17 -4.87 

 Cerebrovascular --- -321.77*** -44.34** -45.85** 

 HIV --- -730.16*** 2.83 3.09 

 Ill-defined/Debility --- 289.98*** -45.75** -43.58** 

 Neurodegenerative --- 229.94*** 10.48 10.60 

 Other Diagnosis --- -697.37*** -31.79 -30.93 

Intercept Constant No Yes Yes Yes 

Year FE Year Indicator Variables No Yes Yes Yes 

Facility FE Facility Indicator Variables No Yes Yes Yes 

R-squared 0.906 0.075 0.879 0.879 

Sample 68725 68725 68725 68725 

Note: Reference (excluded) group is white males aged under 65 with respiratory diagnosis that died in 2003. 

Significant at 1% (***), 5% (**), and 10% (*) level. 
 

15 

TABLE 5: TIMING OF VISITS AND VISIT LABOR COSTS 

Mean Median 

Days Enrolled 60.4 13 

Number of Visits 48.6 18 

Number of Visits per Day 1.5 1.1 

Number of Visits per Day– First 3 Days 1.3 1 

Number of Visits per Day– First 3 Days (including preadmission visits) 1.4 1 

Number of Visits per Day– Middle Days 1.1 0.8 

Number of Visits per Day– Last 3 Days 2.0 1.7 

Number of Visits per Day– Last 3 Days (excludes those 

discharged alive) 2.1 1.7 

Visit Labor Costs $1731.06 $645.93 

Visit Labor Costs per Day $69.28 $37.20 

Visit Labor Costs per Day– First 3 Days $53.84 $33.80 

Visit Labor Costs per Day– First 3 Days (including preadmission visits) $57.20 $36.72 

Visit Labor Costs per Day– Middle Days $54.56 $20.11 

Visit Labor Costs per Day– Last 3 Days $113.86 $47.89 

Visit Labor Costs per Day– Last 3 Days (excludes those 

discharged alive) $122.60 $54.37