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Quality of Medical Care and Excess Mortality in Older Patients With Mental Disorders
Benjamin G. Druss, MD, MPH;
W. David Bradford, PhD;
Robert A. Rosenheck, MD;
Martha J. Radford, MD;
Harlan M. Krumholz, MD
Arch Gen Psychiatry. 2001;58:565-572.
ABSTRACT
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Background This study investigated whether differences in quality of medical care
might explain a portion of the excess mortality associated with mental disorders
in the year after myocardial infarction.
Methods This study examined a national cohort of 88 241 Medicare patients
65 years and older who were hospitalized for clinically confirmed acute myocardial
infarction. Proportional hazard models compared the association between mental
disorders and mortality before and after adjusting 5 established quality indicators:
reperfusion, aspirin, ß-blockers, angiotensin-converting enzyme inhibitors,
and smoking cessation counseling. All models adjusted for eligibility for
each procedure, demographic characteristics, cardiac risk factors and history,
admission characteristics, left ventricular function, hospital characteristics,
and regional factors.
Results After adjusting for the potential confounding factors, presence of any
mental disorder was associated with a 19% increase in 1-year risk of mortality
(hazard ratios [HR], 1.19; 95% confidence interval [CI], 1.04-1.36). After
adding the 5 quality measures to the model, the association was no longer
significant (HR, 1.10; 95% CI, 0.96-1.26). Similarly, while schizophrenia
(HR, 1.34; 95% CI, 1.01-1.67) and major affective disorders (HR, 1.11; 95%
CI, 1.02-1.20) were each initially associated with increased mortality, after
adding the quality variables, neither schizophrenia (HR, 1.23; 95% CI, 0.86-1.60)
nor major affective disorder (HR, 1.05; 95% CI, 0.87-1.23) remained a significant
predictor.
Conclusions Deficits in quality of medical care seemed to explain a substantial
portion of the excess mortality experienced by patients with mental disorders
after myocardial infarction. The study suggests the potential importance of
improving these patients' medical care as a step toward reducing their excess
mortality.
INTRODUCTION
MUCH LITERATURE has demonstrated that patients with mental disorders
are at risk for elevated rates of cardiovascular mortality.1, 2, 3, 4, 5, 6, 7, 8
Authors have postulated that arrhythmogenic, neuroendocrine, or other direct
physiological mechanisms mediate this excess mortality,9, 10
and thus previous studies of mortality in mental disorders have generally
not examined the quality of these patients' medical care. However, patterns
of cardiac procedures after acute myocardial infarction (AMI) have been found
to differ substantially for patients with and without mental disorders,11 raising the question as to whether there may also
be a gap in quality of medical care. If quality of care does vary, such differences
might play a role in mediating excess cardiovascular mortality in patients
with mental disorders.
This study uses a national sample of older adults to examine the association
between mental disorders, quality of cardiac care, and mortality in the first
year after hospitalization for myocardial infarction. We test the hypotheses
that (1) mental disorders are associated with decreased quality of care, (2)
that these conditions predict excess mortality in the year after hospital
discharge, and (3) that differences in quality may account for a portion of
the differences in mortality.
MATERIALS AND METHODS
SAMPLE
The Cooperative Cardiovascular Project (CCP) is part of an ongoing national
Health Care Financing Administration program developed to improve the quality
of care for Medicare beneficiaries with AMI.12
The methods have been outlined in detail in previous studies.12, 13
The CCP sampled randomly selected Medicare fee-for-service beneficiaries who
were hospitalized with AMI from February 1994 through July 1995 with a primary
discharge diagnosis of AMI (International Classification
of Diseases, Ninth Revision [ICD-9] code 410)
with the exception of AMI readmissions (ICD-9 code
410.x2).14
Medical records for each sampled hospitalization were forwarded to 1
of 2 national clinical data abstraction centers and data for each hospitalization,
including patient medical history, signs and symptoms on arrival, electrocardiographic
and laboratory examination, in-hospital treatment and events, discharge treatment,
and disposition, were collected. Data quality was ensured through the use
of technicians trained in prespecified record abstraction rules using computerized
abstraction modules with embedded prompts. Abstraction reliability was monitored
by random record reabstraction;  values ranged from 0.88 to 0.95.15
A total of 166 348 patients in the CCP sample were 65 years or
older and had a confirmed AMI. The current study used the following exclusion
criteria to arrive at a final study cohort: patients who died during the index
admission (n = 31 301); whose records indicated that they were terminally
ill or had do-not-resuscitate orders (n = 35 782), as their medical care
most likely emphasized palliation rather than prolongation of life; who were
transferred to another facility or whose index admission represented a transfer
from another hospital (n = 40 120), since data on their discharge quality
of care were incomplete; and who had dementia and/or delirium (n = 6465),
since these conditions imply a medical etiology and are associated with uniquely
high rates of mortality.16 (The numbers add
to greater than 100% of excluded candidates, since some individuals may have
met more than 1 exclusion criterion). A total of 88 241 eligible individuals
who met these inclusion criteria constituted the final study sample.
INDEPENDENT VARIABLES
Mental Illness
In addition to the abstracted clinical variables, administrative elements,
including admission diagnosis codes, are part of the CCP data set. These diagnoses
are based on conditions listed by the primary attending physician for the
hospitalization. Any secondary admission diagnosis between codes 295.00 and
319.99, other than dementia and delirium, was considered a mental disorder.
To determine whether patients with particular psychiatric diagnoses showed
differing patterns of quality or mortality, separate analyses were also conducted
for the following mental disorders, comparing each group with patients without
a secondary mental diagnosis: schizophrenia (ICD-9
codes 295.00-295.99); major affective disorder (ICD-9
codes 296.00-296.99); and substance abuse and dependence disorders (ICD-9 codes 303.00-305.99).
Covariates
Table 1 outlines a series
of variables identified in the literature as clinically relevant to, or predictive
of, mortality post-AMI.17, 18, 19, 20
These variables include demographic characteristics, cardiac risk factors,
cardiac history, admission characteristics, and left ventricular function,
and were obtained from the record abstraction database. The Medicare Mortality
Predictor Score, a summary score constructed using demographic and clinical
data,21 was used to compare the overall medical
morbidity between patients with and without mental disorders.
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Table 1. Characteristics of Sample (n = 88 241)*
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Previous work has demonstrated an important link between socioeconomic
status and cardiovascular mortality,22, 23, 24
as well as an association between mental disorders and lower socioeconomic
status.25, 26, 27 Therefore,
as a proxy for socioeconomic status, county-level per capita income and educational
attainment were merged from the Area Resource File.28
A series of other regional and hospital-level covariates were also included
in all multivariate models. The following county-level data were obtained
from the Area Resource File: state, mean number of physicians in the county,
and number of hospitals in the county. Hospital-level data were obtained from
the 1994 American Hospital Association Annual Survey of Hospitals.29 Number of beds, presence of on-site catheterization,
percutaneous transluminal coronary angioplasty and open-heart surgical facilities,
academic affiliation, for-profit status, total number of physicians, nurses,
residents, and other staff.
DEPENDENT VARIABLES
Quality Indicators
For the CCP, the Health Care Financing Administration developed a set
of measures of quality for treatment of AMI, based on clinical research findings
and practice guidelines.30 Four indicators
were included in the current study based on evidence from randomized trials
of a link between them and improved mortality: reperfusion therapy,31 aspirin prescribed at hospital discharge,32 ß-blockers prescribed at hospital discharge,33 and angiotensin-converting enzyme inhibitors at hospital
discharge.34, 35 A fifth indicator,
smoking cessation counseling documented during the index hospitalization,
was also included, based on wide inclusion in clinical guidelines for cardiac
patients.36
For each indicator of quality, the Health Care Financing Administration
defined "ideal" candidates for therapy; that is, patients with clear indications
for and without contraindications for the treatment. For any particular treatment,
this cohort should have received the intervention. A second group of patients
with potential relative but without absolute contraindications for each therapy
was also identified. For instance, in the case of aspirin therapy, a patient
might be considered eligible but not ideal if there was a history of peptic
ulcer disease, renal insufficiency, or anemia. Table 2 presents the eligibility/idealness criteria for each procedure,
for patients with and without secondary mental diagnoses.
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Table 2. Eligibility for Interventions (n = 88 241)
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Mortality
Death within 1 year of discharge from the index hospitalization was
determined by linking the CCP database with the Medicare Enrollment Database
using unique patient identifiers. The Medicare Enrollment Database is derived
from Social Security information, which is used as a basis for Social Security
payments. Death data are regularly validated against the National Death Index,37 and have been demonstrated to correlate highly with
other sources of mortality information.38
MISSING DATA
For variables in which a coder could report that data were missing (as
opposed to a simple "yes/no" response), the only variable with greater than
1% missing data was left ventricular function; missing information for this
variable was treated as a separate dummy variable for multivariate analyses.
For other variables, pilot tests showed a high correlation between data collected
by medical record abstractors and gold standard physician reviewers.39
STATISTICAL METHODS
After conducting bivariate analyses on the independent and dependent
variables of interest, multivariable models were constructed to model the
association between mental disorders and the outcomes of interest (quality
indicator compliance and 1-year mortality) while taking into account patient
characteristics that are potential confounding factors. Each analysis first
used a dichotomous "any mental disorder" variable and then a separate equation
comparing the disaggregated mental disorder (schizophrenia, major affective
disorder, substance use disorder, and other disorder) variable to a group
with no mental disorder.
First, logistic regression was used to model each quality measure as
a function of presence of a mental disorder, adjusting for the demographic
and clinical variables outlined in Table
1, and the hospital and regional characteristics outlined above.
As described previously, separate analyses were conducted among ideal patients,
all of whom should have received the intervention, and eligible but not ideal
patients, who had indications for the intervention, but who also had some
potential contraindications.
Relative risk (RR) is the most clinically appropriate indicator of effect
size for associations between dichotomous variables.40
For this study, the RR represents the ratio of the likelihood of receipt of
a given quality indicator among those with mental disorders to the likelihood
among the remainder of the population. Because odds ratios, which are output
from logistic regression models, may not provide accurate estimates of RR
when an outcome of interest is relatively common (ie, greater than 10%),41 RRs were derived from adjusted odds ratios using
the method described by Zhang and Yu.42
Next, to assess the association between mental disorders and mortality,
a set of Cox proportional hazard equations modeled days until death in the
year after discharge as a function of presence of a mental disorder. Models
adjusted for the demographic, clinical, and hospital and regional factors
are outlined in Table 1. Models
also adjusted for all the eligibility/idealness parameters in Table 2 that differed significantly at a P<.05
level. Effect size for proportional hazard models is expressed as a hazard
ratio (HR), which reflects the excess risk of death over time.
Finally, to determine to what degree quality of care explained differences
in mortality for patients with mental disorders, multivariate Cox proportional
hazard equations modeled days until death in the year after hospital discharge
as a function of presence of a mental disorder, adding the 5 quality measures
to the demographic, clinical, hospital, regional, and eligibility/idealness
factors included in the previous models.
All tests of significance were 2-tailed and used a critical value of 
= .05.
RESULTS
CHARACTERISTICS AND ELIGIBILITY STATUS FOR PATIENTS WITH AND WITHOUT
MENTAL DISORDERS
Of a total of 88 241 patients in the study sample (ie, clinically
confirmed AMI and no exclusion criteria), 4664 (5.3%) had a secondary diagnosis
of a mental disorder. The "other" group was largely composed of transient
or mild depressive conditions; 2037 individuals (61.0% of that group) were
diagnosed as having depression not otherwise specified, adjustment reaction,
"neurotic" depression, or dysthymia.
The summary Medicare Mortality Predictor Score score indicated an extremely
small but significantly lower risk of mortality at baseline for patients with
mental disorders than for the rest of the population (0.14 vs 0.15) (P = .007). Other baseline demographic and clinical differences
between the groups are outlined in Table
1. Table 2 outlines
the differences in eligibility/idealness status between the groups. As previously
described, all significant differences were included as covariates in mortality
models.
QUALITY OF CARE FOR PATIENTS WITH AND WITHOUT MENTAL DISORDERS
Among ideal candidates, after adjusting for potential demographic, clinical,
hospital and regional confounding factors, presence of any secondary mental
disorder predicted a 13% decreased likelihood of reperfusion therapy (RR,
0.87; 95% confidence interval [CI], 0.79-0.95). Within this ideal subgroup,
there were no significant differences between patients with and without mental
disorders on the other 4 quality measures (Table 3).
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Table 3. Quality of Care for Patients With and Without Mental Disorders*
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Among "eligible but not ideal" candidates—those with indications
but with some potential contraindications—more pronounced differences
in patterns of care were evident between patients with and without mental
disorders. After adjusting for potential confounding factors, patients with
mental disorders were 26% less likely to have reperfusion therapy (RR, 0.74;
95% CI, 0.56-0.95), 9% less likely to receive aspirin (RR, 0.91; 95% CI, 0.82-0.99),
10% less likely to receive ß-blockers (RR, 0.90; 95% CI, 0.81-0.99),
and 12% less likely to receive angiotensin-converting enzyme inhibitors (RR,
0.88; 95% CI, 0.76-0.99).
Similar patterns were evident in multivariable models examining quality
indicators for specific psychiatric disorders. Among ideal candidates, patients
with schizophrenia were 52% as likely (RR, 0.52; 95% CI, 0.26-0.90) and those
with affective disorders 71% as likely (RR, 0.71; 95% CI, 0.45-0.99) to undergo
reperfusion therapy as those without psychiatric disorders, although differences
in the other quality measures were not significantly different.
Among those considered eligible but not ideal, specific psychiatric
disorders were consistently associated with deficits on the quality measures.
As compared with those without a psychiatric disorder, patients with schizophrenia
were less likely to have reperfusion (RR, 0.48; 95% CI, 0.30-0.72), ß-blockers
(RR, 0.75; 95% CI, 0.56-0.99), and angiotensin-converting enzyme inhibitors
(RR, 0.91; 95% CI, 0.83-0.99). Patients with affective disorders were less
likely to have reperfusion (RR, 0.69; 95% CI, 0.53-0.87) and aspirin (RR,
0.86; 95% CI, 0.75-0.98), and those with substance use disorders were less
likely to be given angiotensin-converting enzyme inhibitors (RR, 0.79; 95%
CI, 0.64-0.96).
ASSOCIATION BETWEEN QUALITY INTERVENTIONS AND MORTALITY IN PATIENTS
WITH MENTAL DISORDERS
A total of 22 118 patients (25.1% of the sample) died during the
year after hospital discharge. In proportional hazards models, adjusting for
all variables in Table 1 and Table 2, as well as the hospital and regional
characteristics outlined in the "Materials and Methods" section, patients
with mental disorders had a 19% increased likelihood of mortality as compared
with the rest of the population during the year after hospital discharge (HR,
1.19; 95% CI, 1.04-1.36). In a model comparing patients with specific psychiatric
disorders with the rest of the population, patients with schizophrenia had
a 34% increase in likelihood of mortality (HR, 1.34; 95% CI, 1.01-1.66), and
those with affective disorders had an 11% increase in likelihood of mortality
(HR, 1.11; 95% CI, 1.03-1.18).
Adding quality measures significantly improved the explanatory ability
of the proportional hazards model for 1-year mortality (difference for -2
log likelihood of models:  25 = 114, P<.001). In this model, each of the 5 quality measures was strongly
associated with reduced mortality across the entire sample: reperfusion therapy
(HR, 0.84; 95% CI, 0.81-0.88); use of aspirin (HR, 0.42; 95% CI, 0.40-0.43); ß-blockers
(HR, 0.51; 95% CI, 0.49-0.53); angiotensin-converting enzyme inhibitors (HR,
0.86; 95% CI, 0.83-0.88); and smoking cessation counseling (HR, 0.67; 95%
CI, 0.62-0.72).
In the model adjusting for quality measures as well as all covariates
in the previous model, the association between mental disorders and 1-year
mortality was no longer significant (P = .17), and
overall mortality was reduced by 9%, or almost half of the original value
for excess mortality (HR, 1.10; 95% CI, 0.96-1.26). A similar pattern was
seen in models examining mortality in specific psychiatric disorders. The
excess mortality seen in schizophrenia and affective disorders was reduced
in magnitude and became nonsignificant after adding the 5 quality measures
(schizophrenia [HR, 1.23; 95% CI, 0.86-1.60] and major affective disorders
[HR, 1.05; 95% CI, 0.87-1.23]) (Table 4).
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Table 4. Mental Disorders and Mortality
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COMMENT
Among patients with indications for cardiac treatment but for whom there
was a possible justification for not treating, a substantial gap was evident
between patients with and without mental disorders. Differences in quality
seemed to explain a substantial portion of the excess mortality associated
with mental disorders.
The results confirm previous authors' observation that practice pattern
variation is highest in situations where there is less consensus on the need
for treatment.43 Within "ideal" subgroups,
presence of practice guidelines and clear outcome data likely lead to greater
uniformity of treatment across patients with and without mental disorders.
In contrast, for patients for whom there may be some benefit, but who also
have relative contraindications that may attenuate that benefit (the eligible
but not ideal group), substantially more variation in these treatments becomes
evident. Among quality measures, the variation is greatest for reperfusion
therapy, where clinical uncertainty is compounded by higher treatment risks.
An even wider gap between patients with and without mental disorders exists
in use of cardiovascular procedures, interventions with high levels of uncertainty,
risks, and costs.11
It is reassuring that under ideal conditions, there were relatively
few differences in quality of care. However, clinical uncertainty may be more
the rule than the exception in medical care; for this study, more patients
were classified in the eligible but not ideal category than the ideal group
for most of the interventions. These findings speak to the importance of ongoing
research to better understand the use and outcomes of these treatments in
patient cohorts not yet examined in randomized controlled trials.
The study's findings indicate that excess mortality in mental disorders
(ie, the mortality that could not be explained by the measured clinical factors)
clustered in patients who did not receive the cardiac guideline–based
interventions. A growing body of literature in the general population has
demonstrated the link between process and outcomes of care after myocardial
infarction.17, 18, 19, 20, 44, 45, 46
Such a link may be mediated both by the interventions themselves and by the
fact that these quality performance measures may serve as a marker for quality
in other domains of care that also affect mortality.
The 1.19-times increase in mortality associated with mental disorders
(and 1.11-times increase associated with major affective disorders) is considerably
smaller than the 3.4-times increase in mortality in the most widely cited
study of depression after AMI,4 although similar
to the 1.09 RR 47, 48 reported
in 2 more recent reports using similar techniques. In at least 3 other studies,
associations between depressive symptoms that were significant in bivariate
models became insignificant after controlling for demographic and clinical
covariates.49, 50, 51
Such findings speak to the importance of large sample sizes and robust adjustment
for medical comorbidity in isolating the specific impact of mental disorders
on excess cardiovascular mortality.
Several limitations of the study design should be noted. First, the
method of case definition for mental conditions relied on medical provider
diagnosis, and there were no data on current psychotropic medications, treatment
status (including psychiatric consultation during the hospitalization), or
severity of mental illness. This almost certainly resulted in an underestimate
of the true prevalence of mental disorders in the population. If some patients
with psychiatric disorders in remission were included as cases, then it might
underestimate the association between mental disorders and poor outcomes;
alternatively, the misclassification of "difficult" cardiac patients as having
psychiatric disorders could lead to an overestimate of this relationship.
Second, because the medical history was also drawn from medical record reviews,
it is possible that incomplete documentation could have led to bias. Third,
our proxies for socioeconomic status were limited to county-level measures
of income and education. Finally, the observational nature of the study cannot
definitively rule out the possibility that an unmeasured factor, such as persons
with mental illness being treated by lower-quality providers or facilities,
is mediating the relationship between mental disorders and mortality. Randomized
trials would be needed to more definitively establish a causal link.
Perhaps the most important question left unanswered by the study is
whether differences in quality of care are primarily a function of patient
or provider factors. If patients' fears, cognitive limitations, or socioeconomic
disadvantage, then initiatives to improve patient education and case management
programs may be needed. If physician discomfort in treating these patients
is the main factor impeding treatment, then similar initiatives may need to
be targeted toward providers. Whatever the source, the results suggest that
improving quality of medical care may be an important step in reducing excess
mortality for this vulnerable population.
AUTHOR INFORMATION
Accepted for publication December 21, 2000.
Funded in part by grant K08 MH01556, from the National Institute of
Mental Health, and the National Association for Research in Schizophrenia
and Affective Disorders, Bethesda, Md (Dr Druss).
The analyses on which this publication is based were performed under
contract 500-96-P549, entitled "Utilization and Quality Control Peer Review
Organization for the State of Connecticut," sponsored by the Health Care Financing
Administration, Department of Health and Human Services. The content of this
publication does not necessarily reflect the views or policies of the Department
of Health and Human Services, nor does mention of trade names, commercial
products, or organizations imply endorsement by the US government. The author
assumes full responsibility for the accuracy and completeness of the ideas
presented. This article is a direct result of the Health Care Quality Improvement
Program initiated by the Health Care Financing Administration, which has encouraged
identification of quality improvement projects derived from analysis of patterns
of care, and therefore required no special funding on the part of this contractor.
Ideas and contributions to the author concerning experience in engaging with
issues presented are welcomed.
From the Departments of
Psychiatry (Dr Druss and Rosenheck) and Epidemiology and Public Health
(Drs Druss, Rosenheck, and Krumholz), Yale University School of
Medicine, VA Northeast Program Evaluation Center and the VA-Connecticut
Mental Illness Research, Education and Clinical Center (Drs Druss
and Rosenheck), Yale-New Haven Hospital Center for Outcomes Research
and Evaluation (Drs Radford and Krumholz), and the Section of
Cardiovascular Medicine, Department of Medicine, Yale University (Drs
Radford and Krumholz), New Haven, Conn; the Center for Health Care
Research, Medical University of South Carolina, Charleston (Dr
Bradford); and Qualidigm, Middletown, Conn (Drs Radford and
Krumholz).
Corresponding author: Benjamin G. Druss, MD, MPH, 950 Campbell Ave/116A,
West Haven, CT 06516 (e-mail: benjamin.druss{at}yale.edu).
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