Because managed care is increasingly prevalent in health care finance and delivery, it is important to ascertain its effects on health care quality relative to that of fee-for-service plans. Some stakeholders are concerned that basing gatekeeping, provider selection, and utilization management on cost may lower quality of care. To date, research on this topic has been inconclusive, largely because of variation in research methods and covariates. Patient age has been the only consistently evaluated outcome predictor. This study provides a comprehensive assessment of the association between managed care and inpatient mortality for Medicare and privately insured patients.
Methods
A cross-sectional design was used to examine the association between managed care and inpatient mortality for four common inpatient conditions. Data from the 2009 Healthcare Cost and Utilization Project State Inpatient Databases for 11 states were linked to data from the American Hospital Association Annual Survey Database. Hospital discharges were categorized as managed care or fee for service. A phased approach to multivariate logistic modeling examined the likelihood of inpatient mortality when adjusting for individual patient and hospital characteristics and for county fixed effects.
Results
Results showed different effects of managed care for Medicare and privately insured patients. Privately insured patients in managed care had an advantage over their fee-for-service counterparts in inpatient mortality for acute myocardial infarction, stroke, pneumonia, and congestive heart failure; no such advantage was found for the Medicare managed care population. To the extent that the study showed a protective effect of privately insured managed care, it was driven by individuals aged 65 years and older, who had consistently better outcomes than their non-managed care counterparts.
Conclusions
Privately insured patients in managed care plans, especially older adults, had better outcomes than those in fee-for-service plans. Patients in Medicare managed care had outcomes similar to those in Medicare FFS. Additional research is needed to understand the role of patient selection, hospital quality, and differences among county populations in the decreased odds of inpatient mortality among patients in private managed care and to determine why this result does not hold for Medicare.
Abkürzungen
AHA
American Hospital Association
AMI
Acute myocardial infarction
APR-DRG
All Patient Refined Diagnosis-Related Group
CHF
Congestive heart failure
HCUP
Healthcare Cost and Utilization Project
HMO
Health maintenance organization
IQI
Inpatient Quality Indicator
SID
State Inpatient Databases
Background
The emergence of managed care in health care finance and delivery has created a need to evaluate whether it improves or erodes health care quality compared with fee-for-service plans and to establish which factors contribute to any differences in outcomes. Some stakeholders have been concerned that implementation of gatekeeping, constraints on provider selection, and utilization management based on cost might contribute to reduced quality of care. Unfortunately, it is difficult to draw conclusions about differential outcomes in managed care versus fee-for-service plans from the literature. Direct comparisons are problematic because individual investigations vary in research methods and covariates. Additionally, effects may be masked if managed care attracts healthier patients who accept less personal control over specific provider and service choices in exchange for lower premiums.
An additional layer of contention in the managed care debate involves the health care outcomes of those insured by Medicare versus private insurance. Overall, inpatient mortality has steadily decreased over time [1‐3]. One recent study of observed rates of inpatient mortality suggested that mortality may be declining more rapidly for Medicare patients compared with privately insured patients for acute myocardial infarction (AMI), stroke, pneumonia, and congestive heart failure (CHF) [3].
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Research findings on the association between managed care and inpatient mortality for Medicare and privately insured patients have been mixed. Two studies that compared Medicare beneficiaries in managed care and fee-for-service settings found no differences in inpatient mortality [4, 5]. However, these studies examined patients hospitalized for only one medical condition. In a study of Medicare beneficiaries only, Afendulis and colleagues [6] found that patients in Medicare Advantage had fewer hospitalizations and lower mortality than those in traditional Medicare, but they concluded that these differences may be attributable to higher payment rates for more services. Additional studies included all payers and found that patients in managed care had lower inpatient mortality rates compared with patients in fee-for-service plans [7, 8]. However, one of these studies was limited to intensive care unit data in a single state, and the other study examined a single diagnosis-related group.
Although authors have cited results from studies with similar findings to strengthen the discussion of their own work, the research designs have not always been comparable. Studies have reported that patient characteristics such as age, sex, payer, and severity of illness influence the association between managed care and inpatient mortality [5, 7, 8]. Fewer studies have evaluated the contribution of hospital characteristics to this relationship [8]. With the exception of age, no patient or hospital predictor has been included consistently across the studies. Thus, questions remain regarding the effects of patient and hospital characteristics on the inpatient mortality of patients in managed care.
The purpose of this study was to provide a comprehensive assessment of the association between managed care and inpatient mortality among Medicare and privately insured patients with four common inpatient conditions. We made adjustments for patient characteristics, hospital characteristics, and unobserved county effects. We used recent data from a population of patients from 11 states. Further, we examined managed care within the context of Medicare and private insurance environments to determine whether expected primary payer modifies this relationship.
Methods
Data source
We used the 2009 Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID). HCUP is a family of health care databases developed through a voluntary federal-state-industry partnership sponsored by the Agency for Healthcare Research and Quality. The SID include a census of hospitals for states with a summary record for each discharge, regardless of payer. This analysis included inpatient discharges for both Medicare and privately insured patients aged 18 years and older from nonfederal, community, nonrehabilitation hospitals. Patients who were transferred out to another acute care hospital were excluded from the analysis, whereas patients who were transferred in to the hospital were included. Eleven states reported expected primary payer categories that distinguished between managed care and non-managed care plans: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, Nevada, New Hampshire, New York, Ohio, and Pennsylvania. These states captured 36% of total adult (18 years and older) U.S. discharges and 38% of the adult U.S. population in 2009. We linked SID data to the American Hospital Association (AHA) Annual Survey Database to identify hospital characteristics. The HCUP databases are consistent with the definition of limited data sets under the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule and contain no direct patient identifiers. The use of HCUP data is not considered human subjects research by the Agency for Healthcare Research and Quality institutional review board.
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Data categorization
We categorized each discharge as managed care or fee-for-service on the basis of the expected primary payer coding. Six of the 11 states reported categories coded as health maintenance organization (HMO); the other states reported either a managed care category or an HMO and managed care category. For the purpose of this study, we categorized discharges coded as HMO, managed care, or HMO and managed care by states as managed care. This broad term reflects the heterogeneity in reporting among states. We categorized as fee-for-service all discharges not explicitly identified in the state data as managed care as defined above. We further stratified managed care categories by Medicare and private insurance to discern any modifying effects of these distinctive groups.
Outcome measures
Inpatient mortality
The primary outcome for this analysis was in-hospital mortality for four high-volume conditions: AMI, stroke, pneumonia, and CHF. We selected these conditions because of their prevalence among hospital discharges, which boosts statistical power to detect small differences. The mortality outcome for the regressions was defined dichotomously—whether a patient died in the hospital (Yes or No) based on the discharge disposition.
Patient and hospital characteristics
We linked patient data elements from the SID to hospital elements from the AHA database to describe the study population and to evaluate the characteristics as covariates or modifiers in the regression model. Patient characteristics included age, sex, All Patient Refined Diagnosis-Related Group (APR-DRG) and the associated risk of mortality subclass, and median household income of the patient’s residential ZIP Code (in quartiles). Consistent with other studies of inpatient mortality [9], we included this variable as the best available proxy of the patient’s income and purchasing choices. Hospital characteristics included the number of hospital beds, teaching status, ownership, and urban/rural location. We classified urban/rural locations of hospitals on the basis of the scheme for U.S. counties developed for the National Center for Health Statistics (NCHS) [10]. We excluded managed care penetration as a covariate in the analysis on the basis of findings of previous studies that ruled out its role as a predictor of the outcome of interest [7].
Hospital fixed effects
To better understand the impact of unobservable hospital-level factors related to quality of care, we examined hospital fixed effects as covariates in a separate model including patient characteristics and county fixed effects. We included dummy variables for individual hospitals visited by patients.
Geographic fixed effects
We also examined county fixed effects as covariates. Dowd and colleagues [11] found that estimated overall mortality differences between managed care and fee-for-service patients were sensitive to geographic fixed effects. Although we did not expect inpatient mortality to be strongly affected by county characteristics (as would be expected with rates of population mortality that may be driven by underlying county-level characteristics, such as availability of resources), we included dummy variables for the county locations of the patients’ residences. These inclusions controlled for other “unobservable” factors that could not be measured directly.
Data analyses
We used SAS (SAS Institute, Inc; Cary, NC) statistical software Version 9.2 to perform statistical analyses. We identified patients treated for AMI, stroke, pneumonia, and CHF on the basis of specifications of the denominator in corresponding Inpatient Quality Indicators (IQIs) [12]. The IQIs are measures of inpatient quality endorsed by the National Quality Forum that use readily available administrative data. We then used multivariate logistic modeling to examine the likelihood of dying in the hospital, adjusting for patient, hospital, and county factors. For each condition, we performed separate logistic regressions for Medicare and private insurance.
We used a phased approach to examine the contributions of patient and hospital characteristics to the relationship between managed care status and inpatient mortality. We began with an unadjusted model of the association between managed care status and mortality. In subsequent models, we added patient characteristics followed by patient characteristics plus hospital characteristics. We then ran separate models that included individual patient characteristics plus hospital fixed effects to adjust for unobservable hospital characteristics. Lastly, we ran models that included patient characteristics, hospital characteristics, and county fixed effects. Several of the models with either hospital fixed effects or county fixed effects did not converge. Detailed tables with the results of full multivariate models are included in the Appendix.
Sensitivity analysis
Our categorization of managed care is based on codes used by statewide data organizations, and these codes are not consistently defined. This variation in coding could create some bias. In our groupings of managed care versus fee-for-service, we assumed that a limited number of categories encompassed managed care on the basis of the labeling provided by states. It is possible that some managed care groups were included as fee-for-service and vice versa. Although we used the most stringent classification approach available, some of this bias is unavoidable because of the nature of the data and collection methods. Consequently, a lack of distinction between these groups could dilute any potential differences between individuals in managed care versus fee-for-service. We address this limitation in a sensitivity analysis of fewer states with more stringently defined HMO categories.
Results
Demographic characteristics
Table 1 contains the demographic characteristics of patients with AMI, stroke, pneumonia, and CHF in all plan types and the facilities from which they were discharged. Compared with Medicare patients in non-managed care, patients in Medicare managed care were slightly older, resided in higher median income ZIP Code areas, and were more likely to have been discharged from hospitals in large central metropolitan areas, teaching hospitals, and hospitals with 300 or more beds. The Medicare managed care population also was less likely than their non-managed care counterparts to have congestive heart failure, chronic pulmonary disease, diabetes with complications, and depression.
Table 1
Demographic and hospital characteristics of populations in Medicare and private insurance, 2009
Characteristica,b
Medicare managed care (n = 168,700)
Medicare fee for service (n = 562,610)
Private managed care (n = 84,170)
Private fee for service (n = 115,244)
Mean, %
SE
Mean, %
SE
p
Mean, %
SE
Mean, %
SE
p
Age in years, mean
78.04
0.02
77.43
0.02
*
57.98
0.05
57.96
0.04
Sex, %
Female
52.33
0.12
53.51
0.07
*
41.39
0.17
39.78
0.15
*
Median household income by ZIP Code, %
Lowest (<$39,999)
22.61
0.10
22.70
0.06
18.30
0.13
19.06
0.12
*
Low ($40,000-$49,999)
24.10
0.10
26.42
0.06
*
21.93
0.14
26.58
0.13
*
Moderate ($50,000-$65,999)
26.41
0.11
26.03
0.06
*
28.20
0.16
27.08
0.13
*
High (>$66,000)
26.88
0.11
24.85
0.06
*
31.56
0.16
27.28
0.13
*
Comorbidities
Congestive heart failure
10.82
0.08
11.89
0.04
*
5.19
0.08
4.90
0.06
*
Chronic pulmonary disease
32.14
0.11
34.52
0.06
*
24.07
0.15
24.89
0.13
*
Hypertension
70.49
0.11
67.61
0.06
*
59.03
0.17
56.11
0.15
*
Peripheral vascular disease
11.44
0.08
10.18
0.04
*
6.00
0.08
5.64
0.07
*
Diabetes with chronic complications
25.99
0.11
28.17
0.06
*
23.20
0.15
23.67
0.13
*
Diabetes without chronic complications
10.21
0.07
7.17
0.03
*
7.89
0.09
5.15
0.07
*
Hypothyroidism
15.40
0.09
15.80
0.05
*
8.52
0.10
8.82
0.08
*
Renal failure
27.76
0.11
27.78
0.06
14.44
0.12
12.28
0.10
*
Fluid and electrolyte disorders
24.49
0.11
27.87
0.06
*
21.71
0.14
22.30
0.12
*
Obesity
8.07
0.07
8.10
0.04
15.61
0.13
14.20
0.10
*
Deficiency anemias
23.24
0.10
24.99
0.06
*
16.52
0.13
14.09
0.10
*
Depression
8.01
0.07
9.49
0.04
*
8.40
0.10
8.51
0.08
Hospital location, %
Large central metropolitan
53.77
0.12
37.87
0.07
*
57.58
0.17
36.78
0.14
*
Large fringe metropolitan
19.88
0.10
19.34
0.05
*
17.90
0.13
20.44
0.12
*
Medium metropolitan
18.47
0.10
23.81
0.06
*
18.34
0.13
25.83
0.13
*
Small metropolitan
3.15
0.04
6.96
0.03
*
1.97
0.05
6.46
0.07
*
Micropolitan
3.78
0.05
9.42
0.04
*
3.14
0.06
8.67
0.08
*
Not metropolitan or micropolitan
0.95
0.02
2.60
0.02
*
1.08
0.04
1.82
0.04
*
Hospital ownership, %
Government
6.13
0.06
7.25
0.03
*
5.85
0.08
7.05
0.08
*
Private, not-for-profit
87.55
0.08
86.07
0.05
*
86.26
0.12
87.93
0.10
*
Private, for-profit
6.32
0.06
6.68
0.03
*
7.89
0.09
5.01
0.06
*
Hospital teaching, %
Teaching
46.25
0.12
37.35
0.07
*
46.47
0.17
43.48
0.15
*
Number of hospital beds, %
< 100
6.58
0.06
11.76
0.04
*
6.33
0.08
8.79
0.08
*
100-299
37.97
0.12
38.75
0.07
*
35.44
0.17
36.18
0.14
*
300-499
32.91
0.12
28.28
0.06
*
33.13
0.16
28.69
0.13
*
500+
22.54
0.10
21.21
0.05
*
25.10
0.15
26.34
0.13
*
Abbreviation: SE, standard error
aPatient characteristics were age, sex, community income, and All Patient Refined-Diagnosis Related Group (APR-DRG)
bHospital characteristics were urban/rural location, ownership, teaching status, and bed size
*p < 0.05
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Patients in private managed care were similar in age to their counterparts in non-managed care, but the private managed care group had a greater percentage of women and individuals residing in ZIP Codes with median household incomes greater than $50,000. In addition, compared with their non-managed care counterparts, a greater percentage of patients in private managed care were discharged from hospitals in large central metropolitan areas, private for-profit hospitals, teaching hospitals, and hospitals with 300 to 499 beds.
Observed rates of inpatient mortality by insurance type
Figure 1 displays observed rates of inpatient mortality for each of the four conditions of interest by insurance type. Compared with private insurance, patients with Medicare had higher rates of inpatient mortality for all four conditions. For AMI, the Medicare inpatient mortality rate was nearly three times that of the privately insured—the largest difference in rates across conditions.
Fig. 1
Observed inpatient mortality rates for AMI, stroke, pneumonia, and CHF for patients in Medicare and private insurance, 2009. Legend: Blue bars indicate Medicare patients; green bars indicate private insured patients. Abbreviations: AMI, acute myocardial infarction; CHF, congestive heart failure. Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
×
Controlling for patient, hospital, and county characteristics
Table 2 shows results from models of inpatient mortality for patients with Medicare and private insurance, comparing managed care with fee-for-service plans. Although patients in Medicare managed care plans had lower odds of inpatient death for stroke and CHF in models controlling for patient characteristics, these differences disappeared when hospital characteristics or hospital fixed effects were added to the model, and they remained insignificant when county fixed effects were added (Table 2).
Table 2
Inpatient mortality for patients with Medicare and private insurance, comparing managed care to fee-for-service plans, 2009
Measure
Sample size for managed care and FFS
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristics + hospital fixed effects
Patient + hospital characteristics + county fixed effects
OR
95% CI
Differencec
OR
95% CI
Differencec
OR
95% CI
Differencec
OR
95% CI
Differencec
Medicare managed care vs. Medicare FFS
AMI
112,623
0.97
0.92, 1.02
0.98
0.93, 1.04
0.98
0.92, 1.04
0.98
0.93, 1.04
Stroke
122,525
0.93
0.89, 0.98
↓
0.98
0.93, 1.03
0.97
0.91, 1.03
0.98
0.93, 1.03
Pneumonia
211,921
1.03
0.98, 1.09
1.07
1.02, 1.13
↑
0.99
0.93, 1.05
1.05
0.99, 1.11
CHF
284,241
0.95
0.90, 0.99
↓
0.98
0.93, 1.03
<did not converge>
0.95
0.90, 1.00
Private managed care vs. private FFS
AMI
53,444
0.87
0.77, 0.97
↓
0.88
0.78, 0.98
↓
<did not converge>
0.86
0.76, 0.98
↓
Stroke
38,241
0.76
0.69, 0.83
↓
0.80
0.73, 0.87
↓
0.84
0.75, 0.94
↓
0.79
0.71, 0.87
↓
Pneumonia
64,683
0.90
0.82, 1.00
0.89
0.80, 0.99
↓
0.83
0.72, 0.95
↓
0.88
0.78, 0.98
↓
CHF
43,046
0.62
0.55, 0.70
↓
0.64
0.57, 0.73
↓
<did not converge>
<did not converge>
Abbreviations: AMI, acute myocardial infarction; CHF, congestive heart failure; CI, confidence interval; FFS, fee for service; OR, odds ratio
aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income
bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
cA down arrow indicates the mortality rate for managed care is significantly lower than FFS at p < 0.05. An up arrow indicates the mortality rate for managed care is significantly higher than FFS at p < 0.05
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
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Among privately insured patients, the association between managed care and inpatient mortality was consistently negative and typically statistically significant across conditions. Patients in private managed care plans had lower odds of inpatient mortality for all four conditions when controlling for patient and hospital characteristics. Managed care was particularly protective among patients with private insurance and CHF (36% lower odds of mortality) or stroke (20% lower odds of mortality). The addition of county fixed effects to the models strengthened the managed care effects for AMI, stroke, and pneumonia.
To assess potential modifying effects of age among the privately insured, we ran additional logistic models for individuals younger than 65 years and for those 65 years and older (Table 3).
Table 3
Inpatient mortality for patients with private insurance, comparing managed care to fee-for-service plans, by patient age, 2009
Measure
Sample size for managed care and FFS
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristics + hospital fixed effects
Patient + hospital characteristics + county fixed effects
OR
95% CI
Differencec
OR
95% CI
Differencec
OR
95% CI
Differencec
OR
95% CI
Differencec
Private managed care vs. private FFS, age <65 years
AMI
44,580
0.91
0.78, 1.05
0.91
0.79, 1.06
0.89
0.75, 1.06
0.89
0.75, 1.05
Stroke
28,713
0.87
0.77, 0.97
↓
0.90
0.80, 1.01
0.89
0.78, 1.01
0.87
0.77, 0.99
↓
Pneumonia
51,636
1.05
0.92, 1.20
1.02
0.90, 1.17
1.00
0.85, 1.17
1.01
0.88, 1.17
CHF
26,980
0.84
0.69, 1.03
0.81
0.66, 0.99
↓
0.83
0.66, 1.04
0.75
0.60, 0.94
↓
Private managed care vs. private FFS, age ≥65 years
AMI
8,864
0.80
0.67, 0.95
↓
0.82
0.69, 0.98
↓
<did not converge>
<did not converge>
Stroke
9,528
0.64
0.55, 0.73
↓
0.70
0.60, 0.81
↓
<did not converge>
<did not converge>
Pneumonia
13,047
0.73
0.62, 0.86
↓
0.73
0.62, 0.86
↓
<did not converge>
<did not converge>
CHF
16,066
0.52
0.45, 0.61
↓
0.56
0.47, 0.66
↓
<did not converge>
<did not converge>
Abbreviations: AMI, acute myocardial infarction; CHF, congestive heart failure; CI, confidence interval; FFS, fee for service; OR, odds ratio
aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income
bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
cA down arrow indicates the mortality rate for managed care is significantly lower than FFS at p < 0.05. An up arrow indicates the mortality rate for managed care is significantly higher than FFS at p < 0.05
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
In the privately insured population aged 65 years and older, managed care was negatively associated with inpatient mortality for all four conditions when controlling for patient and hospital characteristics. The models including either hospital fixed effects or county fixed effects failed to converge, likely because of the small sample size of the group aged 65 years and older relative to the large number of possible hospitals and counties represented. Patients who were privately insured and younger than 65 years demonstrated inconsistent results across conditions. There were no differences in inpatient mortality for younger patients with AMI or pneumonia in private managed care and fee-for-service plans, but outcomes favored managed care for stroke and CHF when controlling for patient characteristics, hospital characteristics, and county fixed effects.
To assess how a stricter definition would affect our findings, we performed a sensitivity analysis using three states (California, New York, and Pennsylvania) with managed care defined by primary payer categories that were explicitly named HMO (Table 4). Compared with the main analysis, this sensitivity analysis has much smaller sample sizes and less geographic diversity.
Table 4
Inpatient mortality for patients with Medicare and private insurance, comparing managed care to fee-for-service plans using a stringent definition of health maintenance organization, 2009
Measure
Sample size for managed care and FFS
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristics + hospital fixed effects
Patient + hospital characteristics + county fixed effects
OR
95% CI
Differencec
OR
95% CI
Differencec
OR
95% CI
Differencec
OR
95% CI
Differencec
Medicare managed care vs. Medicare FFS
AMI
61,159
0.97
0.91, 1.04
0.98
0.91, 1.04
1.01
0.94, 1.09
1.00
0.94, 1.08
Stroke
69,803
0.91
0.86, 0.97
↓
0.96
0.90, 1.03
0.99
0.92, 1.06
0.98
0.92, 1.05
Pneumonia
114,515
0.99
0.94, 1.06
1.03
0.97, 1.09
0.99
0.92, 1.06
1.05
0.98, 1.12
CHF
157,794
0.90
0.84, 0.95
↓
0.91
0.86, 0.97
↓
<did not converge>
0.93
0.87, 0.99
↓
Private managed care vs. private FFS
AMI
27,577
0.86
0.74, 1.00
0.88
0.75, 1.02
<did not converge>
0.88
0.74, 1.05
Stroke
21,510
0.87
0.78, 0.98
↓
0.88
0.78, 0.98
↓
1.02
0.88, 1.18
0.93
0.82, 1.07
Pneumonia
33,573
0.95
0.83, 1.08
0.92
0.80, 1.05
0.96
0.80, 1.14
0.93
0.80, 1.08
CHF
22,926
0.66
0.56, 0.78
↓
0.67
0.56, 0.79
↓
<did not converge>
<did not converge>
Abbreviations: AMI, acute myocardial infarction; CHF, congestive heart failure; CI, confidence interval; FFS, fee for service; OR, odds ratio
aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income
bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
cA down arrow indicates the mortality rate for managed care is significantly lower than FFS at p < 0.05. An up arrow indicates the mortality rate for managed care is significantly higher than FFS at p < 0.05
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 3 states: California, New York, and Pennsylvania
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We found similar results favoring managed care among privately insured patients with stroke and CHF when controlling for patient and hospital characteristics, but there were no differences in outcomes between patients with AMI and pneumonia in managed care versus fee-for-service plans. Patients with Medicare managed care had lower odds of inpatient mortality for CHF than did patients with Medicare fee-for-service plans.
Discussion
For Medicare beneficiaries, outcomes differed by condition, particularly when hospital characteristics were taken into account. These results confirm those of Carlisle and colleagues [4] and Smith and colleagues [5], who also found that Medicare managed care was not related to AMI and stroke mortality outcomes. Moreover, the phased approach of this analysis revealed the unique contributions of hospital characteristics to mortality outcomes among patients in Medicare managed care. For example, although there were no differences in the outcomes of patients with pneumonia in managed care and fee-for-service Medicare when controlling for patient characteristics, a closer look at the detailed hospital model (Appendix Table 9) revealed that Medicare patients with pneumonia who were admitted to specific types of hospitals—those that were government-owned, had smaller bed sizes, and were in nonmetropolitan areas—demonstrated higher odds of mortality than similar patients admitted to larger, urban, privately owned hospitals. A previous study revealed that the Medicare Advantage population was treated more often in facilities with lower resource cost and higher risk-adjusted mortality relative to patients in fee-for-service plans [13]. Limited resources associated with hospitals in smaller geographic areas [14] may affect health care quality and outcomes for patients with pneumonia in Medicare who are treated in these types of facilities.
Among privately insured patients, those in managed care demonstrated lower rates of inpatient mortality for all four conditions after adjusting for other patient and hospital characteristics. Older age and the severity of the patient’s condition are powerful predictors of inpatient mortality, but they do not explain why managed care is associated with lower odds of inpatient mortality in this population. Despite the adjustments for patient characteristics and clinical factors (including APR-DRG severity of disease and associated risk of mortality subclass), the privately insured managed care population had lower odds of inpatient mortality. Interestingly, patients in privately insured managed care plans also demonstrated higher rates of certain common comorbidities (i.e., CHF, diabetes without chronic complications, renal failure, and obesity) than their fee-for-service counterparts. Similar to the experience of Medicare patients, hospital characteristics were strong predictors of inpatient mortality among privately insured patients. Whether patients in privately insured managed care plans systematically visit better quality hospitals than their fee-for-service counterparts is a topic worthy of future study. Furthermore, the study of the interactions between managed care and hospital characteristics as predictors could illuminate the mechanism through which managed care influences inpatient mortality.
An additional contribution of this work is the detailed examination of mortality outcomes among patients with private managed care; previous studies have focused on Medicare [4, 5]. We found that the privately insured population aged 65 years and older drove favorable managed care outcomes across the conditions studied. Although the sample sizes precluded our analysis of county fixed effects for this group, patients aged 65 years and older in managed care demonstrated lower rates of inpatient mortality compared with their fee-for-service counterparts for all four conditions. The protective effect of managed care was stronger for patients aged 65 years and older with private insurance than for their younger counterparts. There was no such age effect for Medicare outcomes when comparing beneficiaries aged 65 years and older to those younger than 65 years (data not shown). One explanation could be that privately insured individuals aged 65 years and older often are still employed or may have more wealth than those for whom Medicare is the primary payer. Either of these factors could be associated with better baseline health status, which could confound the likelihood of death from any of these conditions. Our data indicate that a higher share of patients in private managed care than in Medicare managed care were in the higher income quartiles. However, counter to this possible explanation, Appendix Tables 5–12 show that income was not a statistically significant contributor among models in this study. Therefore, additional investigation is needed to understand the potentially protective effect of managed care in the private sector for those aged 65 years and older, and the interpretation of these findings should be treated cautiously.
Anzeige
Variations in outcomes between patients in Medicare and private managed care relative to their fee-for-service counterparts bring into question differences in managed care experiences by payer. Are patients who are in private managed care treated in better hospitals than patients in Medicare managed care? Our limited descriptive information regarding hospitals from which these two groups were discharged showed similar distributions with regard to ownership, teaching status, and bed size. However, these characteristics do not fully capture the quality of care delivered. Selective contracting with hospitals, or the practice of contracting with certain providers to ensure quality or to contain costs, has previously been studied as influencing managed care and patient outcomes. This practice is not likely to be the primary driver of differences between the outcomes of privately insured managed care and fee-for-service populations [15]. However, the ways in which selective contracting or other managed care mechanisms might favor private insurance over Medicare are not known. Analysis of hospital fixed effects using an indicator for each hospital demonstrated results similar to the models that controlled for individual hospital factors. Future research should continue to explore the quality of care delivered at hospitals chosen by patients in private managed care and those to which they are referred, especially for individuals aged 65 years and older. In addition, future studies should explore the association of managed care status with outcomes by severity class of condition to discern whether there is an insurance effect.
The findings of this study should be interpreted within the context of a few limitations. First, the cross-sectional approach of this study prohibited investigators from capturing the full episode of care preceding the inpatient admission. The lack of data on past medical history limits the risk adjustment for clinical factors included in the models to conditions reported on the current discharge record only. Therefore, we cannot discern whether inpatient death was more related to the current discharge or some previous care. Second, the HCUP SID only include information on in-hospital mortality. Therefore, post-discharge deaths are not included, leading to an underestimation of overall mortality for these conditions.
Conclusions
We used hospital administrative data to examine the association between managed care and inpatient mortality, controlling for patient and hospital characteristics and county fixed effects. Although patients in private managed care had lower rates of inpatient mortality for AMI, stroke, pneumonia, and CHF compared with fee-for-service beneficiaries with hospitalizations for these conditions, patients in Medicare managed care did not experience decreased odds of mortality relative to their fee-for-service counterparts once hospital factors were controlled. Furthermore, although the advantage among patients in private managed care remained after controlling for patient and hospital characteristics as well as county fixed effects of the patient’s residence, the private managed care population aged 65 years and older drove the findings of protective effects of managed care with respect to inpatient mortality. Results of the hospital fixed effects models suggest that other unmeasured hospital factors may play a role in predicting inpatient mortality. Could the location of hospitals and availability of community resources drive these results across privately insured and Medicare patients under managed care? More research is needed to understand the relative roles of patient selection, hospital quality, and differences among county populations in decreased odds of inpatient mortality among patients in private managed care and the absence of that result among patients covered by Medicare.
Acknowledgements
The authors would like to acknowledge Rosanna Coffey, PhD, and Linda Lee, PhD, for editorial review. The authors also acknowledge the data contributions of the following organizations: Arizona Department of Health Services, California Office of Statewide Health Planning and Development, Connecticut Hospital Association, Massachusetts Center for Health Information and Analysis, Michigan Health & Hospital Association, Minnesota Hospital Association, New Hampshire Department of Health & Human Services, Nevada Department of Health and Human Services, New York State Department of Health, Ohio Hospital Association, and Pennsylvania Health Care Cost Containment Council.
Funding
This study was funded by the Agency for Healthcare Research and Quality (AHRQ) under a contract with Truven Health Analytics to develop and support the Healthcare Cost and Utilization Project (HCUP) (Contract No. HHSA-290-2013-00002-C). The views expressed in this article are those of the authors and do not necessarily reflect those of the Agency for Healthcare Research and Quality, the National Center for Health Statistics, or the U.S. Department of Health and Human Services.
Conception and design of the study: AH, SR, MB, EM, RA. Data analysis and interpretation of findings: AH, SR, MB, EM, RA. Draft manuscript: AH. Critical review and revision of manuscript: SR, MB, EM, RA. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
The HCUP databases are consistent with the definition of limited data sets under the Health Insurance Portability and Accountability Act Privacy Rule and contain no direct patient identifiers. The Agency for Healthcare Research and Quality Institutional Review Board considers research using HCUP data to have exempt status.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Association between Medicare managed care and inpatient mortality for acute myocardial infarction
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
0.969
0.919
1.021
0.982
0.931
1.036
0.979
0.922
1.039
0.983
0.929
1.040
Age 18–64 years
1.012
0.908
1.129
1.018
0.913
1.135
1.030
0.922
1.151
1.025
0.918
1.145
Age 65–74 years (REF)
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Age 75–84 years
1.198
1.125
1.276
1.196
1.123
1.273
1.194
1.120
1.272
1.190
1.117
1.268
Age 85+ years
1.362
1.276
1.453
1.352
1.267
1.444
1.354
1.266
1.447
1.339
1.253
1.431
Male
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Female
0.959
0.916
1.003
0.958
0.916
1.003
0.957
0.914
1.002
0.963
0.920
1.008
APRMORT_165002
0.332
0.144
0.768
0.339
0.147
0.783
0.343
0.148
0.793
0.334
0.144
0.771
APRMORT_165003
1.673
1.004
2.787
1.706
1.024
2.843
1.795
1.076
2.994
1.721
1.032
2.869
APRMORT_165004
16.335
10.790
24.728
16.742
11.058
25.347
18.402
12.135
27.905
17.412
11.487
26.392
APRMORT_174001
0.251
0.145
0.433
0.255
0.147
0.440
0.258
0.149
0.445
0.254
0.147
0.438
APRMORT_174002
0.823
0.532
1.274
0.837
0.541
1.295
0.862
0.557
1.335
0.838
0.541
1.297
APRMORT_174003
3.123
2.060
4.733
3.179
2.097
4.818
3.347
2.206
5.077
3.278
2.161
4.971
APRMORT_174004
33.770
22.759
50.108
34.426
23.199
51.087
38.038
25.605
56.509
36.594
24.639
54.349
APRMORT_190002
3.350
2.231
5.031
3.333
2.219
5.005
3.328
2.215
5.000
3.355
2.233
5.040
APRMORT_190003
8.975
6.061
13.290
8.898
6.009
13.177
9.044
6.103
13.401
9.090
6.135
13.468
APRMORT_190004
41.875
28.301
61.960
42.068
28.430
62.247
45.052
30.424
66.714
44.977
30.376
66.596
APRMORT_OTHER
12.857
8.627
19.162
13.113
8.797
19.545
13.865
9.292
20.689
13.436
9.008
20.041
Lowest income
1.021
0.956
1.089
1.003
0.937
1.073
0.991
0.913
1.075
1.010
0.928
1.099
Low income
1.049
0.986
1.115
1.031
0.967
1.100
1.037
0.961
1.119
1.060
0.980
1.146
Moderate income
0.997
0.938
1.061
0.998
0.938
1.062
0.982
0.915
1.054
0.997
0.929
1.069
High income
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
0-99 beds
1.168
1.062
1.284
1.185
1.067
1.315
100-299 beds
REF
REF
REF
REF
REF
REF
300-499 beds
1.009
0.952
1.070
1.019
0.953
1.090
500+ beds
1.029
0.954
1.109
1.066
0.979
1.160
Nonteaching
REF
REF
REF
REF
REF
REF
Teaching
0.931
0.878
0.988
0.908
0.846
0.975
Governmental
1.279
1.173
1.396
1.207
1.094
1.332
Not-for-profit
REF
REF
REF
REF
REF
REF
For-profit
0.995
0.908
1.092
0.971
0.873
1.081
Large metropolitan
REF
REF
REF
Medium and small metropolitan
0.993
0.944
1.046
Nonmetropolitan
1.104
1.008
1.209
Abbreviation: REF, reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Table 6
Association between private managed care and inpatient mortality for acute myocardial infarction
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
0.865
0.774
0.967
0.875
0.781
0.980
Failed to converge
0.861
0.758
0.979
Age 18–44 years
0.759
0.577
0.999
0.749
0.569
0.987
0.778
0.584
1.036
Age 45–64 years
REF
REF
REF
REF
REF
REF
REF
REF
REF
Age 65+ years
1.199
1.064
1.352
1.177
1.043
1.33
1.151
1.01
1.31
Male
REF
REF
REF
REF
REF
REF
REF
REF
REF
Female
1.087
0.969
1.221
1.081
0.963
1.214
1.122
0.994
1.266
APRMORT_165002
0.352
0.113
1.1
0.352
0.113
1.099
0.362
0.115
1.137
APRMORT_165003
3.616
1.958
6.677
3.62
1.96
6.686
3.554
1.9
6.648
APRMORT_165004
18.445
10.623
32.03
18.668
10.748
32.426
20.617
11.762
36.138
APRMORT_174001
0.095
0.04
0.221
0.094
0.04
0.221
0.091
0.039
0.214
APRMORT_174002
0.675
0.372
1.226
0.674
0.371
1.225
0.68
0.373
1.237
APRMORT_174003
5.988
3.495
10.256
5.994
3.499
10.27
6.221
3.612
10.712
APRMORT_174004
55.13
34.324
88.547
55.374
34.469
88.957
61.919
38.356
99.959
APRMORT_190002
4.869
2.899
8.178
4.847
2.886
8.141
5.085
3.016
8.572
APRMORT_190003
21.942
13.585
35.439
21.726
13.448
35.099
23.972
14.77
38.905
APRMORT_190004
122.158
76.067
196.178
122.164
76.063
196.208
144.677
89.571
233.684
APRMORT_OTHER
17.874
10.931
29.228
17.913
10.949
29.308
19.103
11.619
31.407
Lowest income
0.996
0.847
1.17
1.003
0.848
1.185
1.106
0.897
1.363
Low income
0.989
0.853
1.147
0.997
0.855
1.161
1.054
0.873
1.273
Moderate income
0.927
0.801
1.072
0.933
0.805
1.081
0.976
0.823
1.156
High income
REF
REF
REF
REF
REF
REF
REF
REF
REF
0-99 beds
1.061
0.796
1.414
1.11
0.792
1.555
100-299 beds
REF
REF
REF
REF
REF
REF
300-499 beds
1.079
0.934
1.245
1.063
0.899
1.256
500+ beds
1.224
1.017
1.474
1.243
1.001
1.544
Nonteaching
REF
REF
REF
REF
REF
REF
Teaching
0.802
0.693
0.929
0.776
0.648
0.928
Governmental
1.165
0.935
1.453
1.242
0.967
1.594
Not-for-profit
REF
REF
REF
REF
REF
REF
For-profit
0.801
0.637
1.007
0.705
0.541
0.92
Large metropolitan
REF
REF
REF
Medium and small metropolitan
0.94
0.827
1.068
Nonmetropolitan
1.072
0.837
1.372
Abbreviation: REF indicates reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Table 7
Association between Medicare managed care and inpatient mortality for stroke
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
0.931
0.885
0.98
0.978
0.929
1.029
0.969
0.914
1.028
0.979
0.927
1.034
Age 18–64 years
1.126
1.019
1.244
1.144
1.035
1.264
1.134
1.023
1.257
1.144
1.033
1.267
Age 65–74 years
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Age 75–84 years
1.182
1.114
1.254
1.167
1.1
1.239
1.180
1.111
1.255
1.171
1.102
1.243
Age 85+ years
1.614
1.518
1.717
1.574
1.48
1.675
1.589
1.490
1.693
1.561
1.466
1.663
Male
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Female
1.119
1.07
1.169
1.122
1.074
1.173
1.119
1.069
1.171
1.113
1.064
1.165
APRMORT_45002
4.166
3.283
5.286
4.184
3.297
5.31
4.207
3.313
5.341
4.219
3.324
5.356
APRMORT_45003
14.724
11.615
18.665
15.111
11.919
19.157
15.899
12.532
20.172
15.686
12.368
19.895
APRMORT_45004
98.22
77.642
124.25
103.991
82.182
131.59
117.459
92.720
148.798
112.299
88.691
142.192
APRMORT_44001
17.46
13.369
22.805
18.18
13.915
23.751
18.735
14.315
24.520
18.412
14.078
24.081
APRMORT_44002
25.718
20.201
32.743
26.697
20.964
33.998
26.618
20.881
33.932
26.749
20.992
34.085
APRMORT_44003
39.076
30.638
49.837
41.516
32.54
52.969
43.630
34.150
55.743
43.058
33.722
54.979
APRMORT_44004
378.362
298.54
479.52
409.913
323.26
519.8
485.194
381.913
616.405
453.1
356.958
575.137
APRMORT_21XXX
50.851
39.994
64.654
55.519
43.633
70.641
58.799
46.128
74.952
57.445
45.108
73.155
APRMORT_OTHER
22.104
17.3
28.241
23.958
18.742
30.626
25.232
19.713
32.297
24.529
19.177
31.374
Lowest income
0.854
0.802
0.91
0.803
0.753
0.857
0.883
0.816
0.957
0.94
0.868
1.019
Low income
0.883
0.833
0.937
0.815
0.766
0.867
0.922
0.857
0.993
0.953
0.884
1.026
Moderate income
0.944
0.891
1.000
0.916
0.864
0.971
1.000
0.935
1.069
1.025
0.959
1.094
High income
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
0-99 beds
1.251
1.134
1.38
1.348
1.212
1.499
100-299 beds
REF
REF
REF
REF
REF
REF
300-499 beds
0.961
0.907
1.019
1.022
0.957
1.091
500+ beds
1.054
0.982
1.131
1.026
0.948
1.11
Nonteaching
REF
REF
REF
REF
REF
REF
Teaching
0.862
0.814
0.912
0.86
0.804
0.92
Governmental
1.242
1.149
1.343
1.143
1.048
1.248
Not-for-profit
REF
REF
REF
REF
REF
REF
For-profit
0.799
0.725
0.88
0.776
0.693
0.868
Large metropolitan
REF
REF
REF
Medium and small metropolitan
1.115
1.06
1.172
Nonmetropolitan
1.504
1.366
1.656
Abbreviation: REF, reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Table 8
Association between private managed care and inpatient mortality for stroke
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
0.758
0.694
0.829
0.797
0.728
0.874
0.843
0.754
0.942
0.79
0.714
0.874
Age 18–44 years
0.801
0.688
0.933
0.802
0.689
0.934
0.776
0.661
0.911
0.806
0.688
0.943
Age 45–64 years
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Age 65+ years
1.884
1.708
2.078
1.828
1.655
2.019
1.857
1.662
2.075
1.871
1.684
2.08
Male
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Female
1.192
1.093
1.301
1.188
1.089
1.296
1.213
1.106
1.331
1.204
1.1
1.319
APRMORT_45002
17.472
11.606
26.303
17.306
11.494
26.059
18.513
12.194
28.107
17.421
11.539
26.301
APRMORT_45003
37.647
24.751
57.261
37.876
24.895
57.626
43.941
28.546
67.640
38.88
25.466
59.36
APRMORT_45004
286.246
190.76
429.527
297.971
198.442
447.421
429.277
281.752
654.044
327.875
217.389
494.516
APRMORT_44001
48.124
30.516
75.892
50.302
31.866
79.403
58.461
36.453
93.756
52.76
33.217
83.801
APRMORT_44002
31.378
20.633
47.719
32.77
21.531
49.876
38.932
25.279
59.958
33.019
21.609
50.454
APRMORT_44003
125.356
82.532
190.4
130.734
85.984
198.776
172.875
112.064
266.685
141.576
92.638
216.366
APRMORT_44004
>999.999
832.177
>999.999
>999.999
873.693
>999.999
>999.999
>999.999
>999.999
>999.999
>999.999
>999.999
APRMORT_21XXX
134.17
90.207
199.559
142.957
95.953
212.986
195.398
129.375
295.113
154.649
103.417
231.263
APRMORT_OTHER
49.024
32.526
73.89
51.902
34.385
78.342
68.715
44.962
105.018
53.797
35.508
81.505
Lowest income
0.973
0.855
1.107
0.925
0.811
1.055
0.929
0.791
1.092
0.996
0.846
1.174
Low income
1.028
0.912
1.158
0.959
0.848
1.084
0.993
0.859
1.148
1.044
0.9
1.212
Moderate income
1.041
0.93
1.166
1.01
0.901
1.132
1.063
0.932
1.212
1.065
0.934
1.214
High income
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
0-99 beds
1.417
1.118
1.796
1.35
1.032
1.766
100-299 beds
REF
REF
REF
REF
REF
REF
300-499 beds
0.899
0.792
1.021
0.885
0.767
1.021
500+ beds
0.96
0.829
1.111
0.885
0.749
1.045
Nonteaching
REF
REF
REF
REF
REF
REF
Teaching
0.983
0.872
1.108
1.003
0.87
1.156
Governmental
1.506
1.299
1.746
1.352
1.139
1.603
Not-for-profit
REF
REF
REF
REF
REF
REF
For-profit
0.832
0.678
1.022
0.976
0.764
1.247
Large metropolitan
REF
REF
REF
Medium and small metropolitan
1.221
1.101
1.354
Nonmetropolitan
1.423
1.141
1.774
Abbreviation: REF, reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Table 9
Association between Medicare managed care and inpatient mortality for pneumonia
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
1.032
0.982
1.085
1.072
1.019
1.128
0.989
0.932
1.050
1.047
0.992
1.105
Age 18–64 years
0.64
0.585
0.701
0.648
0.592
0.71
0.654
0.596
0.717
0.667
0.609
0.731
Age 65–74 years
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Age 75–84 years
1.245
1.175
1.319
1.239
1.169
1.314
1.238
1.167
1.314
1.228
1.158
1.303
Age 85+ years
1.859
1.754
1.969
1.845
1.742
1.956
1.816
1.711
1.928
1.802
1.699
1.912
Male
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Female
0.964
0.925
1.004
0.967
0.928
1.007
0.974
0.934
1.015
0.968
0.929
1.009
APRMORT_137xxx
24.16
18.166
32.131
24.55
18.459
32.652
28.237
21.203
37.605
27.485
20.65
36.58
APRMORT_139002
4.943
3.72
6.569
4.984
3.75
6.624
5.066
3.810
6.737
5.1
3.836
6.781
APRMORT_139003
19.988
15.087
26.481
20.675
15.605
27.394
22.743
17.150
30.161
22.143
16.703
29.35
APRMORT_139004
89.745
67.731
118.92
94.095
71.003
124.7
113.230
85.336
150.241
105.916
79.868
140.5
APRMORT_OTHER
118.202
89.115
156.78
126.055
95.007
167.25
139.748
105.195
185.649
135.944
102.388
180.5
Lowest income
0.963
0.907
1.022
0.913
0.858
0.972
0.920
0.847
0.999
0.956
0.884
1.034
Low income
0.97
0.917
1.026
0.908
0.857
0.963
0.978
0.908
1.054
0.984
0.916
1.058
Moderate income
0.942
0.891
0.996
0.923
0.872
0.976
0.999
0.934
1.068
0.989
0.928
1.054
High income
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
0-99 beds
1.15
1.075
1.23
1.269
1.174
1.372
100-299 beds
REF
REF
REF
REF
REF
REF
300-499 beds
0.942
0.892
0.995
0.968
0.909
1.03
500+ beds
0.987
0.917
1.062
0.948
0.873
1.03
Nonteaching
REF
REF
REF
REF
REF
REF
Teaching
0.882
0.834
0.933
0.903
0.844
0.967
Governmental
1.215
1.125
1.311
1.067
0.974
1.169
Not-for-profit
REF
REF
REF
REF
REF
REF
For-profit
1.051
0.973
1.135
1.048
0.956
1.148
Large metropolitan
REF
REF
REF
Medium and small metropolitan
1.042
0.993
1.093
Nonmetropolitan
1.17
1.085
1.263
Abbreviation: REF, reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Table 10
Association between private managed care and inpatient mortality for pneumonia
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
0.904
0.817
1.00
0.889
0.802
0.985
0.828
0.724
0.947
0.875
0.78
0.98
Age 18–44 years
0.396
0.33
0.476
0.393
0.328
0.472
0.374
0.308
0.454
0.393
0.326
0.474
Age 45–64 years
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Age 65+ years
1.57
1.408
1.751
1.573
1.409
1.757
1.654
1.457
1.878
1.54
1.371
1.731
Male
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
Female
0.988
0.894
1.093
0.992
0.897
1.097
1.040
0.933
1.158
1.012
0.912
1.123
APRMORT_137xxx
88.389
46.656
167.452
88.596
46.759
167.866
109.154
57.155
208.459
101.242
53.279
192.383
APRMORT_139002
31.018
16.5
58.31
30.906
16.44
58.103
32.666
17.294
61.701
31.239
16.585
58.841
APRMORT_139003
140.387
75.185
262.133
140.697
75.342
262.744
172.269
91.576
324.066
155.064
82.832
290.284
APRMORT_139004
570.545
304.621
>999.999
576.589
307.753
>999.999
851.446
450.071
>999.999
687.987
365.93
>999.999
APRMORT_OTHER
517.875
277.751
965.593
518.855
278.119
967.97
669.828
355.990
>999.999
606.831
324.27
>999.999
Lowest income
0.811
0.697
0.943
0.832
0.712
0.972
0.870
0.714
1.060
0.937
0.775
1.134
Low income
0.798
0.696
0.914
0.822
0.713
0.947
0.917
0.769
1.093
0.89
0.751
1.055
Moderate income
0.911
0.801
1.035
0.931
0.818
1.06
1.014
0.870
1.181
1.009
0.872
1.168
High income
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
REF
0-99 beds
1.214
1.018
1.447
1.2
0.971
1.483
100-299 beds
REF
REF
REF
REF
REF
REF
300-499 beds
1.012
0.883
1.159
1.026
0.879
1.198
500+ beds
0.892
0.749
1.062
0.851
0.699
1.035
Nonteaching
REF
REF
REF
REF
REF
REF
Teaching
1.221
1.063
1.402
1.204
1.021
1.419
Governmental
1.323
1.103
1.587
1.305
1.052
1.62
Not-for-profit
REF
REF
REF
REF
REF
REF
For-profit
0.868
0.699
1.079
0.87
0.68
1.113
Large metropolitan
REF
REF
REF
Medium and small metropolitan
0.844
0.747
0.953
Nonmetropolitan
0.879
0.718
1.077
Abbreviation: REF, reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Table 11
Association between Medicare managed care and inpatient mortality for congestive heart failure
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
0.950
0.904
0.998
0.981
0.933
1.031
Failed to converge
0.946
0.898
0.998
Age 18–64 years
1.009
0.908
1.122
1.028
0.925
1.143
1.058
0.951
1.177
Age 65–74 years
REF
REF
REF
REF
REF
REF
REF
REF
REF
Age 75–84 years
1.327
1.247
1.413
1.317
1.237
1.402
1.303
1.223
1.387
Age 85+ years
2.022
1.902
2.15
1.996
1.877
2.122
1.935
1.819
2.059
Male
REF
REF
REF
REF
REF
REF
REF
REF
REF
Female
0.896
0.86
0.933
0.898
0.861
0.935
0.89
0.854
0.928
APRMORT_161xxx
2.388
1.702
3.351
2.56
1.824
3.593
2.694
1.918
3.785
APRMORT_191xxx
7.093
5.329
9.442
7.573
5.687
10.084
8.173
6.133
10.892
APRMORT_194002
2.071
1.583
2.709
2.083
1.592
2.725
2.182
1.667
2.855
APRMORT_194003
7.415
5.69
9.663
7.613
5.842
9.922
8.285
6.354
10.803
APRMORT_194004
37.648
28.903
49.037
39.386
30.232
51.31
45.335
34.775
59.101
APRMORT_OTHER
15.041
11.432
19.789
16.007
12.162
21.066
17.347
13.171
22.846
Lowest income
0.881
0.83
0.935
0.832
0.782
0.885
0.828
0.766
0.894
Low income
0.971
0.919
1.027
0.91
0.859
0.965
0.942
0.878
1.012
Moderate income
0.96
0.908
1.014
0.943
0.891
0.997
1.008
0.946
1.074
High income
REF
REF
REF
REF
REF
REF
REF
REF
REF
0-99 beds
1.22
1.133
1.314
1.343
1.232
1.464
100-299 beds
REF
REF
REF
REF
REF
REF
300-499 beds
0.955
0.904
1.008
0.941
0.885
1.001
500+ beds
1.058
0.987
1.134
1.061
0.981
1.148
Nonteaching
REF
REF
REF
REF
REF
REF
Teaching
0.931
0.881
0.983
0.918
0.859
0.98
Governmental
1.137
1.046
1.237
1.012
0.919
1.115
Not-for-profit
REF
REF
REF
REF
REF
REF
For-profit
0.921
0.844
1.004
0.929
0.842
1.026
Large metropolitan
REF
REF
REF
Medium and small metropolitan
1.005
0.958
1.054
Nonmetropolitan
1.318
1.218
1.427
Abbreviation: REF, reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
Table 12
Association between private managed care and inpatient mortality for congestive heart failure
Characteristic
Patient characteristicsa
Patient + hospital characteristicsb
Patient characteristic + hospital fixed effects
Patient + hospital characteristics + county fixed effects
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Point estimate
95% Wald confidence limits
Managed care
0.621
0.549
0.704
0.643
0.567
0.729
Failed to converge
Failed to converge
Age 18–44 years
1.012
0.74
1.386
0.989
0.722
1.354
Age 45–64 years
REF
REF
REF
REF
REF
REF
Age 65+ years
2.036
1.785
2.322
2.131
1.865
2.435
Male
REF
REF
REF
REF
REF
REF
Female
1.165
1.035
1.312
1.172
1.041
1.32
APRMORT_161xxx
2.649
1.686
4.162
2.498
1.586
3.932
APRMORT_191xxx
2.739
1.803
4.162
2.612
1.716
3.975
APRMORT_194002
2.32
1.613
3.337
2.32
1.613
3.337
APRMORT_194003
5.392
3.775
7.703
5.366
3.756
7.666
APRMORT_194004
28.071
19.641
40.12
28.013
19.592
40.053
APRMORT_OTHER
9.41
6.374
13.894
8.99
6.078
13.298
Lowest income
0.736
0.619
0.876
0.713
0.596
0.853
Low income
0.82
0.699
0.961
0.829
0.703
0.977
Moderate income
0.859
0.736
1.004
0.854
0.73
1.000
High income
REF
REF
REF
REF
REF
REF
0-99 beds
1.009
0.803
1.266
100-299 beds
REF
REF
REF
300-499 beds
0.983
0.834
1.158
500+ beds
1.089
0.885
1.34
Nonteaching
REF
REF
REF
Teaching
1.144
0.967
1.354
Governmental
1.601
1.289
1.988
Not-for-profit
REF
REF
REF
For-profit
0.716
0.53
0.966
Large metropolitan
REF
REF
REF
Medium and small metropolitan
1.121
0.975
1.289
Nonmetropolitan
0.804
0.618
1.045
Abbreviation: REF, reference group
Notes: aPatient characteristics were age, sex, All Patient Refined-Diagnosis Related Group (APR-DRG), and community income. bHospital characteristics were bed size, ownership, teaching status, and urban/rural location
Source: Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project, State Inpatient Databases, 2009, from the following 11 states: Arizona, California, Connecticut, Massachusetts, Michigan, Minnesota, New Hampshire, Nevada, New York, Ohio, and Pennsylvania
