Mayur M. Desai

Quality of preventive medical care for patients with mental disorders

Background/Objectives. This study compares quality of preventive services between persons with and without mental/substance use disorders for a national sample of medical outpatients. Research Design. Cross‐sectional study. Subjects. A total of 113,505 veterans with chronic conditions and at least three general medical visits to Veterans Health Administration medical providers during 1998 to 1999. Measures. Chart‐derived rates of eight preventive services: two measures of immunization, four measures of cancer screening, and two of tobacco screening and counseling. Multivariable‐generalized estimating equations compared rates of each preventive service among veterans with psychiatric disorders, substance use disorders, both, and neither, adjusting for demographic, health status, and facility‐level characteristics. Results. On average, persons in the sample obtained 64% of the eight preventive procedures for which they were eligible. Overall rates of currency with preventive services were 58% for patients with combined psychiatric/substance use disorders, 60% and 65% for those with psychiatric and substance use disorders alone, and 66% for those with neither psychiatric nor substance use disorders. Each difference remained statistically significant in multivariable models. Conclusions. In this sample of patients in active medical treatment, rates of preventive services were higher than rates reported for population‐based, private‐sector samples. Despite these high‐baseline rates, persons with psychiatric disorders, particularly with comorbid substance use, were at risk for lower rate of receipt of preventive services.

Restricted activity among community-living older persons: incidence, precipitants, and health care utilization.

Restricted activity, defined as staying in bed for at least half a day or cutting down on ones usual activities because of an illness or injury (1), has high face validity as a measure of health and functional status, especially for older persons, who often value quality of life over longevity (2). The importance of restricted activity was recognized more than 20 years ago in the U.S. Surgeon Generals original Healthy People Report (3), which identified reduction of restricted activity as one of its two major goals for older persons. Subsequently, several clinical trials of preventive interventions have included restricted activity as a key outcome measure (4-7). Despite this attention, relatively little is known about the epidemiology of restricted activity among older persons. Previous studies, based largely on one-time assessments, have suggested that only a minority of community-living older persons experience restricted activity in the course of 1 year (8, 9). The factors precipitating restricted activity, moreover, have not been well defined. Finally, whether older persons seek medical attention in the setting of restricted activity has not been studied. Those who do not seek attention may consider restricted activity to be a normal part of aging and may miss a chance for successful evaluation and intervention. In this prospective cohort study, we sought to better elucidate the epidemiology of restricted activity in community-living older persons. Our goals were to more accurately estimate the rate of restricted activity, identify the health-related and non-health-related problems leading to restricted activity, and determine whether restricted activity is associated with increased health care utilization. Methods Study Sample The study sample comprised the 754 participants of the Precipitating Events Project, a longitudinal study of nondisabled, community-living persons 70 years of age or older. Participants in the Precipitating Events Project were identified from a computerized list of 3157 age-eligible members of a large health plan in New Haven, Connecticut. Members were eligible if they were communityliving, English-speaking, and nondisabled (that is, required no personal assistance) in four key activities of daily livingbathing, walking, dressing, and transferring from a chair. Plan members were excluded on the basis of three criteria: diagnosis of a terminal illness with a life expectancy less than 12 months, plans to move out of the New Haven area during the next 12 months, and significant cognitive impairment with no available proxy. Enrollment To minimize potential selection effects, a computerized randomization program was used to assign each age-eligible health plan member a unique number. Screening for eligibility and enrollment proceeded sequentially from March 1998 to October 1999. Potential participants were sent a letter that briefly described the study and explained that they would be contacted by phone. During the phone interview, eligibility was assessed, and a home visit was scheduled among consenting eligible persons. During the home visit, eligibility was verified, informed consent was obtained, and a comprehensive baseline assessment was completed. On the basis of gait speed, cognitive status, and age, participants were categorized into one of three risk groups for disability by using a model developed and validated in an earlier study (Table 1) (10). To ensure that enough participants were included in each risk group, participants were enrolled in a 4:2:1 ratio for low, intermediate, and high risk for disability, respectively. Table 1. Risk Model for Disability and Number of Participants Enrolled, according to Phase Assembly of the Precipitating Events Project cohort is shown in the Figure. We applied our stratified sampling strategy in three phases. In phase 1, all eligible and consenting persons were enrolled. In phase 2, persons were excluded from the study if they indicated during the screening telephone interview that they had walked 0.5 mile or for 30 minutes continuously without stopping within the past month. In phase 3, persons who were eligible based on the screening telephone interview were excluded from the study if they were found to have low risk for disability during the home visit. The enrollment procedures in phases 2 and 3 were otherwise identical to those in phase 1. Figure. Assembly of Precipitating Events Project cohort. The number of participants enrolled in each of the three phases is shown in Table 1. During phase 1, 77% of the participants had low risk for disability. Phase 2 was designed to decrease this percentage by excluding persons who were likely to have low risk for disability. The sensitivity and specificity of the screening question used during phase 2 were 66% and 76%, respectively, for low disability risk (based on gold standard data from the first 282 participants enrolled during phase 1). Other candidate screening questions, alone or in combination, had a lower sensitivity or specificity (or both). As shown in the Figure, only 4.6% (126 of 2735) of the health plan members who could be contacted declined to complete the screening telephone interview, and 75.2% (754 of 1002) of the eligible members agreed to participate in the study. Persons who declined to participate did not differ significantly from those who were enrolled in terms of age or sex. Baseline Data Collection Trained research nurses used standard instruments to perform baseline interviews and assessments. Clinical data included 13 self-reported, physician-diagnosed chronic conditions: hypertension; myocardial infarction; congestive heart failure; stroke; diabetes; arthritis; hip fracture; fracture of wrist, arm, or spine since 50 years of age; amputation of leg; chronic lung disease; cirrhosis or liver disease; cancer (other than minor skin cancers); and Parkinson disease. Cognitive status was assessed by using the Folstein Mini-Mental State Examination (11). Timed rapid gait was assessed by having the participants walk back and forth over a 10-foot course as quickly and safely as possible (10). Follow-up Data Collection The occurrence of restricted activity and health-related and non-health-related problems leading to restricted activity were ascertained during monthly telephone interviews by using a standardized, four-step protocol. First, participants were asked two questions related to restricted activity: Since we last talked on [date of last interview], have you stayed in bed for at least half a day due to an illness, injury, or other problem? and Since we last talked on [date of last interview], have you cut down on your usual activities due to an illness, injury, or other problem? Second, if participants had restricted activity (that is, answered yes to either question), they were asked sequentially whether they had had any of 24 prespecified problems since we last talked on [date of last interview]. To develop our list of potential problems, we identified common physical and mental health symptoms that community-living older persons had reported in previous studies (12-14), and we supplemented these symptoms with other events that we deemed important on the basis of our own clinical and research experience (15). Third, immediately after each yes response to a specific problem, participants were asked, Did this problem cause you to stay in bed for at least half a day or to cut down on your usual activities? (that is, did it lead to restricted activity). Finally, participants with restricted activity were asked to specify any other reasons why they stayed in bed for at least half a day or cut down on their usual activities. Participants without restricted activity were not asked about the specific problems. During pilot testing, we found that the test-retest reliability of this four-step protocol was high, with a value of 0.90 for the presence or absence of restricted activity and a value of 0.75 or greater for the presence or absence of 20 of the 24 problems leading to restricted activity (mean time between assessments, 4.1 days among 20 persons). The value was less than 0.6 for only 3 of the problems (swelling in feet or ankles, fear of falling, and frequent or painful urination). During the monthly telephone interviews, participants were also asked whether they had stayed at least overnight in a hospital and whether they had seen a physician in the office or emergency department since their last interview. The research protocol was approved by the Yale University School of Medicine Institutional Review Board. Statistical Analysis We calculated the rate of restricted activity for the overall cohort and for subgroups defined by sex and risk for disability by dividing the number of months in which participants reported staying in bed for at least half a day or cutting down on their usual activities by the total person-months of follow-up. These analyses were repeated for staying in bed for at least half a day and for cutting down on ones usual activities alone (that is, without staying in bed for at least half a day). We then calculated the overall and stratified rates for each of the prespecified problems leading to restricted activity by using person-months with restricted activity as the denominator. The mean number of problems per episode of restricted activity was also calculated. Finally, the rates of health care utilization, including physician office visits, emergency department visits, and hospital admissions, were calculated for months with and months without restricted activity. The events of interest in this study were potentially recurrent in nature; that is, participants may have experienced restricted activity or used health care services in more than one month. Because standard statistical approaches based on the binomial or Poisson distributions assume independence among events, we used alternative methods, designed specifically for recurrent events, t

Trends in Comorbidity, Disability, and Polypharmacy in Heart Failure

BACKGROUND Comorbidity, disability, and polypharmacy commonly complicate the care of patients with heart failure. These factors can change biological response to therapy, reduce patient ability to adhere to recommendations, and alter patient preference for treatment and outcome. Yet, a comprehensive understanding of the complexity of patients with heart failure is lacking. Our objective was to assess trends in demographics, comorbidity, physical function, and medication use in a nationally representative, community-based heart failure population. METHODS Using data from the National Health and Nutrition Examination Survey, we analyzed trends across 3 survey periods (1988-1994, 1999-2002, 2003-2008). RESULTS We identified 1395 participants with self-reported heart failure (n=581 in 1988-1994, n=280 in 1999-2002, n=534 in 2003-2008). The proportion of patients with heart failure who were ≥80 years old increased from 13.3% in 1988-1994 to 22.4% in 2003-2008 (P <.01). The proportion of patients with heart failure who had 5 or more comorbid chronic conditions increased from 42.1% to 58.0% (P <.01). The mean number of prescription medications increased from 4.1 to 6.4 prescriptions (P <.01). The prevalence of disability did not increase but was substantial across all years. CONCLUSION The phenotype of patients with heart failure changed substantially over the last 2 decades. Most notably, more recent patients have a higher percentage of very old individuals, and the number of comorbidities and medications increased markedly. Functional disability is prevalent, although it has not changed. These changes suggest a need for new research and practice strategies that accommodate the increasing complexity of this population.

An Administrative Claims Measure Suitable for Profiling Hospital Performance Based on 30-Day All-Cause Readmission Rates Among Patients With Acute Myocardial Infarction

Background— National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction. Methods and Results— We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points). Conclusions— This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.

National Trends in US Hospital Admissions for Hyperglycemia and Hypoglycemia Among Medicare Beneficiaries, 1999 to 2011

IMPORTANCE The increasing intensity of diabetes mellitus management over the past decade may have resulted in lower rates of hyperglycemic emergencies but higher rates of hospital admissions for hypoglycemia among older adults. Trends in these hospitalizations and subsequent outcomes are not known. OBJECTIVE To characterize changes in hyperglycemia and hypoglycemia hospitalization rates and subsequent mortality and readmission rates among older adults in the United States over a 12-year period, and to compare these results according to age, sex, and race. DESIGN, SETTING, AND PATIENTS Retrospective observational study using data from 33,952,331 Medicare fee-for-service beneficiaries 65 years or older from 1999 to 2011. MAIN OUTCOMES AND MEASURES Hospitalization rates for hyperglycemia and hypoglycemia, 30-day and 1-year mortality rates, and 30-day readmission rates. RESULTS A total of 279,937 patients experienced 302,095 hospitalizations for hyperglycemia, and 404,467 patients experienced 429,850 hospitalizations for hypoglycemia between 1999 and 2011. During this time, rates of admissions for hyperglycemia declined by 38.6% (from 114 to 70 admissions per 100,000 person-years), while admissions for hypoglycemia increased by 11.7% (from 94 to 105 admissions per 100,000 person-years). In analyses designed to account for changing diabetes mellitus prevalence, admissions for hyperglycemia and hypoglycemia declined by 55.2% and 9.5%, respectively. Trends were similar across age, sex, and racial subgroups, but hypoglycemia rates were 2-fold higher for older patients (≥75 years) when compared with younger patients (65-74 years), and admission rates for both hyperglycemia and hypoglycemia were 4-fold higher for black patients compared with white patients. The 30-day and 1-year mortality and 30-day readmission rates improved during the study period and were similar after an index hospitalization for either hyperglycemia or hypoglycemia (5.4%, 17.1%, and 15.3%, respectively, after hyperglycemia hospitalizations in 2010; 4.4%, 19.9%, and 16.3% after hypoglycemia hospitalizations). CONCLUSIONS AND RELEVANCE Hospital admission rates for hypoglycemia now exceed those for hyperglycemia among older adults. Although admissions for hypoglycemia have declined modestly since 2007, rates among black Medicare beneficiaries and those older than 75 years remain high. Hospital admissions for severe hypoglycemia seem to pose a greater health threat than those for hyperglycemia, suggesting new opportunities for improvement in care of persons with diabetes mellitus.

National Patterns of Risk-Standardized Mortality and Readmission for Acute Myocardial Infarction and Heart Failure: Update on Publicly Reported Outcomes Measures Based on the 2010 Release

Background—Patient outcomes provide a critical perspective on quality of care. The Centers for Medicare and Medicaid Services (CMS) is publicly reporting hospital 30-day risk-standardized mortality rates (RSMRs) and risk-standardized readmission rates (RSRRs) for patients hospitalized with acute myocardial infarction (AMI) and heart failure (HF). We provide a national perspective on hospital performance for the 2010 release of these measures. Methods and Results—The hospital RSMRs and RSRRs are calculated from Medicare claims data for fee-for-service Medicare beneficiaries, 65 years or older, hospitalized with AMI or HF between July 1, 2006, and June 30, 2009. The rates are calculated using hierarchical logistic modeling to account for patient clustering, and are risk-adjusted for age, sex, and patient comorbidities. The median RSMR for AMI was 16.0% and for HF was 10.8%. Both measures had a wide range of hospital performance with an absolute 5.2% difference between hospitals in the 5th versus 95th percentile for AMI and 5.0% for HF. The median RSRR for AMI was 19.9% and for HF was 24.5% (3.9% range for 5th to 95th percentile for AMI, 6.7% for HF). Distinct regional patterns were evident for both measures and both conditions. Conclusions—High RSRRs persist for AMI and HF and clinically meaningful variation exists for RSMRs and RSRRs for both conditions. Our results suggest continued opportunities for improvement in patient outcomes for HF and AMI.

Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia

BACKGROUND Readmission following hospital discharge has become an important target of quality improvement. OBJECTIVE To describe the development, validation, and results of a risk-standardized measure of hospital readmission rates among elderly patients with pneumonia employed in federal quality measurement and efficiency initiatives. DESIGN A retrospective cohort study using hospital and outpatient Medicare claims from 2005 and 2006. SETTING A total of 4675 hospitals in the United States. PATIENTS Medicare beneficiaries aged >65 years with a principal discharge diagnosis of pneumonia. INTERVENTION None. MEASUREMENTS Hospital-specific, risk-standardized 30-day readmission rates calculated as the ratio of predicted-to-expected readmissions, multiplied by the national unadjusted rate. Comparison of the areas under the receiver operating curve (ROC) and measurement of correlation coefficient in development and validation samples. RESULTS The development sample consisted of 226,545 hospitalizations at 4675 hospitals, with an overall unadjusted 30-day readmission rate of 17.4%. The median risk-standardized hospital readmission rate was 17.3%, and the odds of readmission for a hospital one standard deviation above average was 1.4 times that of a hospital one standard deviation below average. Performance of the medical record and administrative models was similar (areas under the ROC curve 0.59 and 0.63, respectively) and the correlation coefficient of estimated state-specific standardized readmission rates from the administrative and medical record models was 0.96. CONCLUSIONS Rehospitalization within 30 days of treatment for pneumonia is common, and rates vary across hospitals. A risk-standardized measure of hospital readmission rates derived from administrative claims has similar performance characteristics to one based on medical record review.

Statistical Models and Patient Predictors of Readmission for Acute Myocardial Infarction A Systematic Review

Background—Readmission after acute myocardial infarction (AMI) has been targeted for public reporting because it is a common, costly, and often preventable outcome. To assist in ongoing efforts to risk-stratify patients and profile hospitals through public reporting of performance measures, we conducted a systematic review to identify models designed to compare hospital rates of readmission or predict patients’ risk of readmission after AMI and to identify studies evaluating patient characteristics associated with AMI readmission. Methods and Results—We identified relevant English-language studies published between 1950 and 2007 by searching MEDLINE, Scopus, PsycINFO, and all 4 Ovid Evidence-Based Medicine Reviews. Eligible publications reported on readmission up to 1 year after AMI hospitalization among adults. From 751 potentially relevant articles, 35 met our predefined inclusion/exclusion criteria. Overall, none developed models to compare readmission rates among hospitals or models to predict patients’ risk of readmission. All 35 examined patient characteristics associated with AMI readmission. However, studies varied in methods for case and outcome identification, used multiple types of data sources, examined differing outcomes (often either readmission alone or a composite outcome of readmission or death) over varying follow-up periods (from 30 days to 1 year), and found few patient characteristics consistently associated with readmission. Conclusions—Patient characteristics may be important predictors of AMI readmission; however, few variables were consistently identified. Thus, clinically, patient risk stratification is challenging. From a policy perspective, a validated risk-standardized model to profile hospitals using AMI readmission rates is currently unavailable in the literature.

Trends in Aortic Valve Replacement for Elderly Patients in the United States, 1999-2011

IMPORTANCE There is a need to describe contemporary outcomes of surgical aortic valve replacement (AVR) as the population ages and transcatheter options emerge. OBJECTIVE To assess procedure rates and outcomes of surgical AVR over time. DESIGN, SETTING, AND PARTICIPANTS A serial cross-sectional cohort study of 82,755,924 Medicare fee-for-service beneficiaries undergoing AVR in the United States between 1999 and 2011. MAIN OUTCOMES AND MEASURES Procedure rates for surgical AVR alone and with coronary artery bypass graft (CABG) surgery, 30-day and 1-year mortality, and 30-day readmission rates. RESULTS The AVR procedure rate increased by 19 (95% CI, 19-20) procedures per 100,000 person-years over the 12-year period (P<.001), with an age-, sex-, and race-adjusted rate increase of 1.6% (95% CI, 1.0%-1.8%) per year. Mortality decreased at 30 days (absolute decrease, 3.4%; 95% CI, 3.0%-3.8%; adjusted annual decrease, 4.1%; 95% CI, 3.7%- 4.4%) per year and at 1 year (absolute decrease, 2.6%; 95% CI, 2.1%-3.2%; adjusted annual decrease, 2.5%; 95% CI, 2.3%-2.8%). Thirty-day all-cause readmission also decreased by 1.1% (95% CI, 0.9%-1.3%) per year. Aortic valve replacement with CABG surgery decreased, women and black patients had lower procedure and higher mortality rates, and mechanical prosethetic implants decreased, but 23.9% of patients 85 years and older continued to receive a mechanical prosthesis in 2011. CONCLUSIONS AND RELEVANCE Between 1999 and 2011, the rate of surgical AVR for elderly patients in the United States increased and outcomes improved substantially. Medicare data preclude the identification of the causes of the findings and the trends in procedure rates and outcomes cannot be causally linked. Nevertheless, the findings may be a useful benchmark for outcomes with surgical AVR for older patients eligible for surgery considering newer transcatheter treatments.

Burden of Illness Score for Elderly Persons: Risk adjustment incorporating the cumulative impact of diseases, physiologic abnormalities, and functional impairments

Background/Objectives. To develop and validate a new risk adjustment index–the Burden of Illness Score for Elderly Persons (BISEP)–which integrates multiple domains, including diseases, physiologic abnormalities, and functional impairments. Research Design Subjects. The index was developed in a prospective cohort of 525 patients aged ≥70 years from the medicine service of a university hospital. The index was validated in a cohort of 1246 patients aged ≥65 years from 27 hospitals. The outcome was 1-year mortality. Results. Five risk factors were selected from diagnosis, laboratory, and functional status axes: high-risk diagnoses, albumin ≤3.5 mg/dL, creatinine >1.5 mg/dL, dementia, and walking impairment. The BISEP score (range 0–7) created four groups of increasing risk: group I (score 0–1), group II (2), group III (3), and group IV (≥4). In the development cohort, where overall mortality was 154/525 (29%), 1-year mortality rates increased significantly across each risk group, from 8% to 24%, 51%, and 74%, in groups I to IV respectively (&khgr;2 trend, P = 0.001)—an overall 17-fold increased risk by hazard ratio. The c-statistic for the final model was 0.83. Corresponding rates in the validation cohort, where overall mortality was 488/1246 (39%), were 5%, 17%, 33%, and 61% in groups I to IV, respectively (&khgr;2 trend, P = 0.001)—an overall 18-fold increased risk by hazard ratio. The c-statistic for the final model was 0.77. In each cohort, sequential addition of variables from different sources (eg, administrative, laboratory, and chart) substantially improved model fit and predictive accuracy. BISEP had significantly superior mortality prediction compared with five widely used measures. Conclusions. BISEP provides a useful new risk adjustment system for hospitalized older persons. Although index performance using different data sources has been evaluated, the full BISEP model, incorporating disease, laboratory, and functional impairment information, demonstrates the best performance.