Janel Hanmer

US norms for six generic health-related quality-of-life indexes from the National Health Measurement study.

Background:A number of indexes measuring self-reported generic health-related quality-of-life (HRQoL) using preference-weighted scoring are used widely in population surveys and clinical studies in the United States. Objective:To obtain age-by-gender norms for older adults on 6 generic HRQoL indexes in a cross-sectional US population survey and compare age-related trends in HRQoL. Methods:The EuroQol EQ-5D, Health Utilities Index Mark 2, Health Utilities Index Mark 3, SF-36v2™ (used to compute SF-6D), Quality of Well-being Scale self-administered form, and Health and Activities Limitations index were administered via telephone interview to each respondent in a national survey sample of 3844 noninstitutionalized adults age 35–89. Persons age 65–89 and telephone exchanges with high percentages of African Americans were oversampled. Age-by-gender means were computed using sampling and poststratification weights to adjust results to the US adult population. Results:The 6 indexes exhibit similar patterns of age-related HRQoL by gender; however, means differ significantly across indexes. Females report slightly lower HRQoL than do males across all age groups. HRQoL seems somewhat higher for persons age 65–74 compared with people in the next younger age decade, as measured by all indexes. Conclusions:Six HRQoL measures show similar but not identical trends in population norms for older US adults. Results reported here provide reference values for 6 self-reported HRQoL indexes.

Report of nationally representative values for the noninstitutionalized US adult population for 7 health-related quality-of-life scores

Background. Despite widespread use of generic health-related quality-of-life (HRQoL) scores, few have publicly published nationally representative US values. Purpose. To create current nationally representative values for 7 of the most common HRQoL scores, stratified by age and sex. Methods. The authors used data from the 2001 Medical Expenditures Panel Survey (MEPS) and the 2001 National Health Interview Survey (NHIS), nationally representative surveys of the US noninstitutionalized civilian population. The MEPS was used to calculate 6 HRQoL scores: categorical self-rated health, EuroQoL-5D with US scoring, EuroQoL-5D with UK scoring, EuroQol Visual Analog Scale, mental and physical component summaries from the SF-12, and the SF-6D. The authors estimated Quality of Well-being scale scores from the NHIS. Results. They included 22,523 subjects from MEPS 2001 and 32,472 subjects from NHIS 2001. Most age and sex categories had instrument completion rates above 85%. Females reported lower scores than males across all ages and instruments. In general, those in older age groups reported lower scores than younger age groups, with the exception of the mental component summary from the SF-12. Conclusion. This is one of the first sets of publicly available, nationally representative US values for any standardized HRQoL measure. These values are important for use in both generalized comparisons of health status and in cost-effectiveness analyses.

Association Between Palliative Care and Patient and Caregiver Outcomes: A Systematic Review and Meta-analysis

Importance The use of palliative care programs and the number of trials assessing their effectiveness have increased. Objective To determine the association of palliative care with quality of life (QOL), symptom burden, survival, and other outcomes for people with life-limiting illness and for their caregivers. Data Sources MEDLINE, EMBASE, CINAHL, and Cochrane CENTRAL to July 2016. Study Selection Randomized clinical trials of palliative care interventions in adults with life-limiting illness. Data Extraction and Synthesis Two reviewers independently extracted data. Narrative synthesis was conducted for all trials. Quality of life, symptom burden, and survival were analyzed using random-effects meta-analysis, with estimates of QOL translated to units of the Functional Assessment of Chronic Illness Therapy-palliative care scale (FACIT-Pal) instrument (range, 0-184 [worst-best]; minimal clinically important difference [MCID], 9 points); and symptom burden translated to the Edmonton Symptom Assessment Scale (ESAS) (range, 0-90 [best-worst]; MCID, 5.7 points). Main Outcomes and Measures Quality of life, symptom burden, survival, mood, advance care planning, site of death, health care satisfaction, resource utilization, and health care expenditures. Results Forty-three RCTs provided data on 12 731 patients (mean age, 67 years) and 2479 caregivers. Thirty-five trials used usual care as the control, and 14 took place in the ambulatory setting. In the meta-analysis, palliative care was associated with statistically and clinically significant improvements in patient QOL at the 1- to 3-month follow-up (standardized mean difference, 0.46; 95% CI, 0.08 to 0.83; FACIT-Pal mean difference, 11.36] and symptom burden at the 1- to 3-month follow-up (standardized mean difference, -0.66; 95% CI, -1.25 to -0.07; ESAS mean difference, -10.30). When analyses were limited to trials at low risk of bias (n = 5), the association between palliative care and QOL was attenuated but remained statistically significant (standardized mean difference, 0.20; 95% CI, 0.06 to 0.34; FACIT-Pal mean difference, 4.94), whereas the association with symptom burden was not statistically significant (standardized mean difference, -0.21; 95% CI, -0.42 to 0.00; ESAS mean difference, -3.28). There was no association between palliative care and survival (hazard ratio, 0.90; 95% CI, 0.69 to 1.17). Palliative care was associated consistently with improvements in advance care planning, patient and caregiver satisfaction, and lower health care utilization. Evidence of associations with other outcomes was mixed. Conclusions and Relevance In this meta-analysis, palliative care interventions were associated with improvements in patient QOL and symptom burden. Findings for caregiver outcomes were inconsistent. However, many associations were no longer significant when limited to trials at low risk of bias, and there was no significant association between palliative care and survival.

Mode of Administration Is Important in US National Estimates of Health-Related Quality of Life

Background:It is unknown if different national surveys that vary in mode of administration yield similar national averages for health-related quality of life (HRQoL). Purpose:Examine HRQoL scores from 4 surveys representative of the noninstitutionalized US adult population for patterns related to age, gender, and mode of administration. Methods:We use data from the Joint Canada/United States Survey of Health (JCUSH; telephone survey), 2002 Medical Expenditure Panel Survey (MEPS; mail survey), National Health Measurement Study (NHMS; telephone survey), and US Valuation of the EuroQol EQ-5D Health States Survey (USVEQ; self-administered with interviewer present). We compare estimates from the EQ-5D, Visual Analog Scale, Health Utilities Index Mark 3, and general self-rated health stratified by age and gender. Scores were also regressed on age and gender within each survey and in a pooled analysis. Results:We used 4939 subjects from JCUSH, 23,006 from MEPS, 3844 from NHMS, and 3878 from USVEQ. The majority of age and gender strata had instrument completion rates above 85%. Age- and gender-stratified estimates of HRQoL scores tended to be consistent when mode of administration (self- or interviewer-administered) was the same. Telephone administration yielded more positive HRQoL estimates than self-administration in older age groups. Older age groups and females reported lower HRQoL than younger age groups and males regardless of mode of administration. Conclusions:When choosing survey-collected HRQoL scores for comparative purposes, analysts need to take mode of administration into account.

Relative disutilities of 47 risk factors and conditions assessed with seven preference-based health status measures in a national U.S. sample : Toward consistency in cost-effectiveness analyses

Background:Preference-based health measures yield summary scores that are compatible with cost-effectiveness analyses. There is limited comparative information, however, about how different measures weight health conditions in the U.S. population. Methods:We examined data from 11,421 adults in the 2000 Medical Expenditure Panel Survey, a nationally representative sample of the U.S. general population, using information on sociodemographics (age, gender, race/ethnicity, income, and education), health status (EQ-5D, EQ-VAS, and SF-12), 4 risk factors (smoking, overweight, obesity, and lacking health insurance), and 43 conditions. From the EQ-5D, we derived summary scores using U.K. [EQ(UK)] and U.S. weights. From the SF-12 we derived SF-6D, and regression-predicted EQ-5D (U.S. and U.K. weights) and Health Utility Index scores. Each of the 7 preference measures was regressed on each of the 47 problems (risk factors and conditions) to determine the disutility associated with the problem, adjusting for socio-demographics. Results:The adjusted disutilities averaged across the 47 problems for the 7 preference measures ranged from 0.059 for the SF-6D to 0.104 for the EQ(UK). Correlations between each of the measures of the adjusted disutilities ranged from 0.85–1.0. Standardization, using linear regression, attenuated between measure differences in disutilities. Conclusions:Absolute incremental cost-effectiveness analyses of a given problem would likely vary depending on the measure used, whereas the relative ordering of incremental cost-effectiveness analyses of a series of problems would likely be similar regardless of the measure chosen, as long as the same measure is used in each series of analyses. Absolute consistency across measures may be enhanced by standardization.

Predicting an SF-6D preference-based score using MCS and PCS scores from the SF-12 or SF-36.

BACKGROUND The SF-6D preference-based scoring system was developed several years after the SF-12 and SF-36 instruments. A method to predict SF-6D scores from information in previous reports would facilitate backwards comparisons and the use of these reports in cost-effectiveness analyses. METHODS This report uses data from the 2001-2003 Medical Expenditures Panel Survey (MEPS), the Beaver Dam Health Outcomes Survey, and the National Health Measurement Study. SF-6D scores were modeled using age, sex, mental component summary (MCS) score, and physical component summary (PCS) score from the 2002 MEPS. The resulting SF-6D prediction equation was tested with the other datasets for groups of different sizes and groups stratified by age, MCS score, PCS score, sum of MCS and PCS scores, and SF-6D score. RESULTS The equation can be used to predict an average SF-6D score using average age, proportion female, average MCS score, and average PCS score. Mean differences between actual and predicted average SF-6D scores in out-of-sample tests was -0.001 (SF-12 version 1), -0.013 (SF-12 version 2), -0.007 (SF-36 version 1), and -0.010 (SF-36 version 2). Ninety-five percent credible intervals around these point estimates range from +/-0.045 for groups with 10 subjects to +/-0.008 for groups with more than 300 subjects. These results were consistent for a wide range of ages, MCS scores, PCS scores, sum of MCS and PCS scores, and SF-6D scores. SF-6D scores from the SF-36 and SF-12 from the same data set were found to be substantially different. CONCLUSIONS Simple equation predicts an average SF-6D preference-based score from widely published information.

Do implantable cardioverter defibrillators improve survival in patients with chronic kidney disease at high risk of sudden cardiac death? A meta-analysis of observational studies

AIMS Prospective randomized clinical trials show that implantable cardioverter defibrillators (ICDs) can reduce the risk of total mortality in select populations. However, data regarding patients with chronic kidney disease (CKD) are inconclusive. The aim of this study was to evaluate if ICDs affect total mortality in CKD patients at high risk of sudden cardiac death. METHODS AND RESULTS Two separate meta-analyses were performed to (i) assess the effect of ICD on all-cause mortality in CKD patients at high risk of sudden cardiac death and (ii) assess the effect of CKD on all-cause mortality in patients who already had an ICD for primary or secondary prevention purposes. Medline and EMBASE were searched from 1966 to 2013. A manual search by cross-referencing was performed. Five observational studies with 17 460 CKD patients considered at high risk of sudden cardiac death were included to evaluate the effect of ICDs on patients with severe CKD. Patients with ICD implants had a reduction in all-cause mortality (adjusted hazard ratio (HR) = 0.65, 95% confidence interval (CI) = 0.47-0.91, P < 0.05) compared with a matched control group. Based on 15 observational studies with 5233 patients as part of our second comparison that evaluated the effect of CKD on patients who received an ICD, CKD was associated with higher mortality risk (HR = 2.86, 95% CI = 1.91-4.27, P < 0.05) despite an ICD. CONCLUSION The meta-analysis indicates that for patients undergoing ICD implant, CKD is associated with greater risk of dying. However, ICD placement reduces mortality in CKD patients at high risk of sudden cardiac death.

The PROMIS of QALYs

Measuring health and health-related quality of life (HRQoL) is important for tracking the health of individuals and populations over time. Generic HRQoL measures allow for comparison across health conditions. One form of generic HRQoL measures are profile measures, which provide a description of health across several different domains (such as physical functioning, depression, and pain). Recent advances in health profile measurement include the development of measures based on item response theory. The Patient-Reported Outcomes Measurement Information System (PROMIS®) has been constructed using this theory. Another form of generic HRQoL measures are utility measures, which assess the value of health states. Multi-attribute utility theory provides a framework for valuing disparate domains of health and aggregating them into a single preference-based score. Such a score provides an overall measure of health outcomes as well as a quality of life weight for use in decision analyses and cost-effectiveness analyses. Developing a utility score for PROMIS® would allow simultaneous estimation of both health profile and utility scores using a single measure. The purpose of this paper is to provide a roadmap of the methodological steps necessary to create such a scoring system.

Insurance Status and the Transfer of Hospitalized Patients: An Observational Study

Context Despite concern for and legislation to prevent insurance status from influencing the transfer of patients between emergency departments, not much is known about interhospital transfer of already-hospitalized patients. Contribution This study of hospitalized patients in the United States found that uninsured persons were less likely to be transferred from one hospital to another than those with private insurance. Women were less likely to be transferred than men. Caution The available data could not indicate clinical variables that might influence decisions about transfer or whether requests were made or denied. Implication Insurance status may be an important factor influencing which hospitalized patients are transferred to other facilities as part of their care. The Editors Hospitals and physicians are generally expected to treat patients in need of emergent medical care without consideration of patients race, ethnicity, sex, or ability to pay for required services. Originally enacted in 1986, the Emergency Medical Treatment and Active Labor Act (EMTALA) (1) stipulates that once a patient enters the emergency department, the hospital (and staff) must provide a medical screening examination and treat and stabilize any patient identified as having an emergency medical condition until the patient is stable for discharge, regardless of his or her ability to pay for services. Notably, this act does not clearly define what constitutes an emergency medical condition, how much care must be provided, and when a patient is stable for discharge (2, 3). Although the application of EMTALA to the emergency department and the decision whether to admit an acutely ill patient is fairly well-defined, the appositeness of EMTALA to the decision to discharge or transfer a patient who has already been admitted remains an active area of debate (4, 5). In the more than 2 decades since the passage of EMTALA, there has been persistent concern that patients are often transferred between hospitals for nonmedical reasons (for example, provider convenience, patient financial status, and race), but the existing data are limited with virtually all studies focusing on the prehospital or emergency department settings. In a landmark study from 1984, Himmelstein and coworkers found evidence that patients transferred from the emergency departments of 14 private hospitals to a public hospital were predominantly uninsured (6). Schiff and colleagues and Kellermann and Hackman (79) also found evidence of patients transferred between emergency departments for economic factors. More recent reports have shown associations between an array of patient characteristics (for example, sex, age, race, or insurance status) and the likelihood of transfer of patients with trauma in the prehospital setting (1013). However, we are unaware of any contemporary analyses that have examined the relationship between insurance coverage and interhospital transfer of patients who have already been admitted. If such evidence were found, it wouldsuggest a new and previously understudied gap in EMTALA. We used 2010 data from the Nationwide Inpatient Sample (NIS) to examine the relationship between patientsinsurance coverage and whether they were transferred between hospitals. In particular, we hypothesized that uninsured patients would be more likely to be transferred by their admitting hospital to another acute care hospital, reflecting the desire of admitting hospitals to relieve themselves of less profitable patients as rapidly as possible. Methods Data Sources We used 2010 discharge data from the NIS, which are available for purchase from the Agency for Health Care Research and Quality (AHRQ) (14). Each year of the NIS contains data from approximately 8 million hospitalizations from 1051 U.S. hospitals. The NIS is generated from states participating in AHRQs Healthcare Cost and Utilization Project, and the NIS approximates a 20% sample of U.S. hospitals. It does not contain unique patient identifiers, making it impossible to track individual patients across hospitalizations or time. We therefore analyzed discharges and not individual patients. The NIS has extensive preprocessing at AHRQ to reconcile variations in coding by individual states and definitions across them and to ensure consistency of coding and variables in the final data set made available. Excluded from the NIS are short-term rehabilitation hospitals, long-term nonacute care hospitals, psychiatric hospitals, and alcoholism or chemical dependency treatment facilities. The NIS also does not include individuals hospitalized under observation status. Data contain elements summarized from the hospital discharge abstract of universal billing form 92 and are similar to other commonly used administrative data sources, such as Medicare Part A. Data elements include patient demographics; primary and secondary diagnoses as captured by International Classification of Diseases, Ninth Revision, Clinical Modification codes; insurance type (Medicare, Medicaid, private, uninsured, no charge, or other); and discharge disposition (categorized as routine, short-term hospital, skilled-nursing facility, intermediate care facility, another type of facility, home health care, against medical advice, or died). The NIS has been used extensively in previous health services research (15). Weights for the NIS are provided by AHRQ to allow researchers to calculate nationally representative estimates. We identified all discharges of adults aged 18 to 64 years at hospitalization. We excluded all patients aged 65 years and older because nearly all of them are insured by Medicare. Inclusion of this population would not add much to our efforts to evaluate the relationship between variations in insurance coverage and interhospital transfers. We excluded all patients who did not have Medicare, Medicaid, private, or uninsured as their primary payer (categorized as other in the NIS) because this group constitutes a heterogeneous group of patients with varying insurance coverage. We also excluded patients who died or left against medical advice because they did not have the chance to be transferred. We then identified condition-specific cohorts by using each patients primary admission diagnosis. Diagnoses were identified by using AHRQs clinical classification software coding (16) (Appendix). These codes amalgamate closely related International Classification of Diseases, Ninth Revision, Clinical Modification codes into clinically meaningful categories. We excluded pregnancy-related, psychiatric, and orthopedic diagnoses because of our focus on general medical conditions. We also excluded diabetes mellitus with complications, device complication, and other complication because in our experience as clinicians we believe these categories to be vague and heterogeneous. After applying these exclusions, we selected the 5 most common diagnoses for further study: biliary tract disease, chest pain, septicemia, skin and subcutaneous tissue infections (skin infection), and pneumonia. After identifying our 5 cohorts, we linked each discharge (and admitting hospital) to the hospital weights file provided by AHRQ. This file includes an array of hospital-level data, such as hospital ownership and financial status (for-profit, not-for-profit, and government-owned), teaching status, and bed size (17). These fields were complete for all but 11 of the 1051 hospitals. Variables Our outcome of interest was each patients discharge disposition, specifically whether he or she was transferred to another acute care hospital at the end of the observed admission. Patients who were transferred to long-term acute care, inpatient rehabilitation, and skilled-care facilities were not considered to have been transferred for the purposes of our analysis. Our primary independent variable of interest was each patients insurance status as captured by the primary payer field in the NIS. To make coding uniform across all states contributing to the NIS, AHRQ contractors condense several insurance subcategories into 4 general groups: Medicare includes fee-for-service and managed care Medicare patients; Medicaid includes fee-for-service and managed care Medicaid patients; private insurance includes Blue Cross, commercial carriers, and private HMOs and preferred provider organizations; and uninsured includes self-pay, charity care, and underinsured. We excluded patients whose primary payer was coded as other. Explanatory patient-level variables included each patients age in years, sex, race/ethnicity (categorized as white [non-Hispanic], black, Hispanic, and other persons), comorbid conditions, and quartile of median household income for the patients ZIP code of residence. The data set provided by AHRQ includes 29 comorbid conditions identified by using methods developed by Elixhauser and colleagues (18). We calculated the number of comorbid conditions for each patient by summing the total number of conditions at the individual-patient level. We then analyzed this calculated number to present the mean total number of conditions for each diagnosis. Explanatory hospital-level variables included hospital teaching status (major or nonteaching), hospital ownership (for-profit, not-for-profit, or government), location (rural or urban), and bed size. Bed size is a combined variable created by AHRQ that includes the total number of beds, location, and separates teaching status into 3 categories (small, medium, and large) (19). We calculated an additional hospital-level variable for our analysis, which was the percentage of all admissions that were uninsured in each hospital. Statistical Analysis First, we compared the demographic characteristics (age, sex, and race/ethnicity), presence of key comorbid conditions, mean number of comorbid conditions, and insurance coverage of patients who were not transferred to another acute care hospital by using the Rao-Scott chi-square test for a

Cardiovascular magnetic resonance characterization of left ventricular non-compaction provides independent prognostic information in patients with incident heart failure or suspected cardiomyopathy

BackgroundWith recent advances in imaging methods, detection of LVNC is increasingly common. Concomitantly, the prognostic importance of LVNC is less clear.MethodsWe followed 42 patients (63% male, age 44 ± 15 years) with incident heart failure or suspected cardiomyopathy, in whom cardiovascular magnetic resonance (CMR) yielded a diagnosis of LVNC, for 27 ± 16 months.ResultsLVNC was preferentially distributed among posterolateral segments, with apical predominance. Patients with maximum non-compacted-to-compacted thickness ratio (NC:C) < 3 improved by 0.9 ± 0.7 NYHA Class, compared to 0.3 ± 0.8 for patients with NC:C > 3 (p = 0.001). In 29 patients with baseline LVEF < 0.40, there was an inverse correlation between NC:C ratio, and the change in LVEF during follow-up. Tachyarrhythmias were observed in 42% of patients with LGE, and in 0% of patients without LGE (p = 0.02). In multivariate analysis, arrhythmia incidence was significantly higher in patients with LGE, even when adjusted for LVEF and RVEF.ConclusionsCMR assessments of myocardial morphology provide important prognostic information for patients with LVNC who present with incident heart failure or suspected cardiomyopathy.