Measuring the Benefits of Healthcare: DALYs and QALYs – Does the Choice of Measure Matter? A Case Study of Two Preventive Interventions

Background The measurement of health benefits is a key issue in health economic evaluations. There is very scarce empirical literature exploring the differences of using quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs) as benefit metrics and their potential impact in decision-making. Methods Two previously published models delivering outputs in QALYs, were adapted to estimate DALYs: a Markov model for human papilloma virus (HPV) vaccination, and a pneumococcal vaccination deterministic model (PNEUMO). Argentina, Chile, and the United Kingdom studies were used, where local EQ-5D social value weights were available to provide local QALY weights. A primary study with descriptive vignettes was done (n = 73) to obtain EQ-5D data for all health states included in both models. Several scenario analyses were carried-out to evaluate the relative importance of using different metrics (DALYS or QALYs) to estimate health benefits on these economic evaluations. Results QALY gains were larger than DALYs avoided in all countries for HPV, leading to more favorable decisions using the former. With discounting and age-weighting – scenario with greatest differences in all countries – incremental DALYs avoided represented the 75%, 68%, and 43% of the QALYs gained in Argentina, Chile, and United Kingdom respectively. Differences using QALYs or DALYs were less consistent and sometimes in the opposite direction for PNEUMO. These differences, similar to other widely used assumptions, could directly influence decision-making using usual gross domestic products (GDPs) per capita per DALY or QALY thresholds. Conclusion We did not find evidence that contradicts current practice of many researchers and decision-makers of using QALYs or DALYs interchangeably. Differences attributed to the choice of metric could influence final decisions, but similarly to other frequently used assumptions

Lipoprotein(a) and the Risk for Recurrent Atherosclerotic Cardiovascular Events Among Adults With CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study

Rationale & Objective: Many adults with chronic kidney disease (CKD) and atherosclerotic cardiovascular disease (ASCVD) have high lipoprotein(a) levels. It is unclear whether high lipoprotein(a) levels confer an increased risk for recurrent ASCVD events in this population. We estimated the risk for recurrent ASCVD events associated with lipoprotein(a) in adults with CKD and prevalent ASCVD. Study Design: Observational cohort study. Setting & Participants: We included 1,439 adults with CKD and prevalent ASCVD not on dialysis enrolled in the Chronic Renal Insufficiency Cohort study between 2003 and 2008. Exposure: Baseline lipoprotein(a) mass concentration, measured using a latex-enhanced immunoturbidimetric assay. Outcomes: Recurrent ASCVD events (primary outcome), kidney failure, and death (exploratory outcomes) through 2019. Analytical Approach: We used Cox proportional-hazards regression models to estimate adjusted HR (aHRs) and 95% CIs. Results: Among participants included in the current analysis (mean age 61.6 years, median lipoprotein(a) 29.4 mg/dL [25th-75th percentiles 9.9-70.9 mg/dL]), 641 had a recurrent ASCVD event, 510 developed kidney failure, and 845 died over a median follow-up of 6.6 years. The aHR for ASCVD events associated with 1 standard deviation (SD) higher log-transformed lipoprotein(a) was 1.04 (95% CI, 0.95-1.15). In subgroup analyses, 1 SD higher log-lipoprotein(a) was associated with an increased risk for ASCVD events in participants without diabetes (aHR, 1.23; 95% CI, 1.02-1.48), but there was no evidence of an association among those with diabetes (aHR, 0.99; 95% CI, 0.88-1.10, P comparing aHRs = 0.031). The aHR associated with 1 SD higher log-lipoprotein(a) in the overall study population was 1.16 (95% CI, 1.04-1.28) for kidney failure and 1.02 (95% CI, 0.94-1.11) for death. Limitations: Lipoprotein(a) was not available in molar concentration. Conclusions: Lipoprotein(a) was not associated with the risk for recurrent ASCVD events in adults with CKD, although it was associated with a risk for kidney failure

Evidence-based assessment of lipoprotein(a) as a risk biomarker for cardiovascular diseases – some answers and still many questions

The present article is aimed to outline the current state of knowledge regarding the effects of lipoprotein(a) (Lp(a)) on cardiovascular disease (CVD) risk by summarizing the recent results of studies, meta-analyses and systematic reviews. The literature supports the predictive value of Lp(a) on CVD outcomes, although the effect size is modest. Lp(a) would also appear to have an effect on cerebrovascular outcomes, with the effect appearing even smaller than that for CVD outcomes. Consideration of apolipoprotein apolipoprotein(a) (apo (a)) isoforms and LPA genetics in relation to the simple assessment of Lp(a) concentration may enhance improving clinical practice in vascular medicine. We also describe recent advances in Lp(a) research (including therapies) and highlight areas where further research is needed such as the measurement of Lp(a) and its involvement in additional pathophysiological processes

Effect of eplerenone on parathyroid hormone levels in patients with primary hyperparathyroidism: a randomized, double-blind, placebo-controlled trial

<p>Abstract</p> <p>Background</p> <p>Increasing evidence suggests the bidirectional interplay between parathyroid hormone and aldosterone as an important mechanism behind the increased risk of cardiovascular damage and bone disease observed in primary hyperparathyroidism. Our primary object is to assess the efficacy of the mineralocorticoid receptor-blocker eplerenone to reduce parathyroid hormone secretion in patients with parathyroid hormone excess.</p> <p>Methods/design</p> <p>Overall, 110 adult male and female patients with primary hyperparathyroidism will be randomly assigned to eplerenone (25 mg once daily for 4 weeks and 4 weeks with 50 mg once daily after dose titration] or placebo, over eight weeks. Each participant will undergo detailed clinical assessment, including anthropometric evaluation, 24-h ambulatory arterial blood pressure monitoring, echocardiography, kidney function and detailed laboratory determination of biomarkers of bone metabolism and cardiovascular disease.</p> <p>The study comprises the following exploratory endpoints: mean change from baseline to week eight in (1) parathyroid hormone(1–84) as the primary endpoint and (2) 24-h systolic and diastolic ambulatory blood pressure levels, NT-pro-BNP, biomarkers of bone metabolism, 24-h urinary protein/albumin excretion and echocardiographic parameters reflecting systolic and diastolic function as well as cardiac dimensions, as secondary endpoints.</p> <p>Discussion</p> <p>In view of the reciprocal interaction between aldosterone and parathyroid hormone and the potentially ensuing target organ damage, the EPATH trial is designed to determine whether eplerenone, compared to placebo, will effectively impact on parathyroid hormone secretion and improve cardiovascular, renal and bone health in patients with primary hyperparathyroidism.</p> <p>Trial registration</p> <p>ISRCTN33941607</p

Lipid profile, cardiovascular disease and mortality in a Mediterranean high-risk population: the ESCARVAL-RISK study

The potential impact of targeting different components of an adverse lipid profile in populations with multiple cardiovascular risk factors is not completely clear. This study aims to assess the association between different components of the standard lipid profile with all cause mortality and hospitalization due to cardiovascular events in a high-risk population. Methods This prospective registry included high risk adults over 30 years old free of cardiovascular disease (2008±2012). Diagnosis of hypertension, dyslipidemia or diabetes mellitus was inclusion criterion. Lipid biomarkers were evaluated. Primary endpoints were all-cause mortality and hospital admission due to coronary heart disease or stroke. We estimated adjusted rate ratios (aRR), absolute risk differences and population attributable risk associated with adverse lipid profiles. Results 51,462 subjects were included with a mean age of 62.6 years (47.6% men). During an average follow-up of 3.2 years, 919 deaths, 1666 hospitalizations for coronary heart disease and 1510 hospitalizations for stroke were recorded. The parameters that showed an increased rate for total mortality, coronary heart disease and stroke hospitalization were, respectively, low HDL-Cholesterol: aRR 1.25, 1.29 and 1.23; high Total/HDL-Cholesterol: aRR 1.22, 1.38 and 1.25; and high Triglycerides/HDL-Cholesterol: aRR 1.21, 1.30, 1.09. The parameters that showed highest population attributable risk (%) were, respectively, low HDL-Cholesterol: 7.70, 11.42, 8.40; high Total/HDL-Cholesterol: 6.55, 12.47, 8.73; and high Triglycerides/ HDL-Cholesterol: 8.94, 15.09, 6.92. Conclusions In a population with cardiovascular risk factors, HDL-cholesterol, Total/HDL-cholesterol and triglycerides/HDL-cholesterol ratios were associated with a higher population attributable risk for cardiovascular disease compared to other common biomarkers

Sex differences in high-intensity statin use following myocardial infarction in the United States

Background Historically, women have been less likely than men to receive guideline-recommended statin therapy for the secondary prevention of myocardial infarction (MI). Objectives The authors examined contemporary sex differences in prescription fills for high-intensity statin therapy following an MI, overall and across population subgroups, and assessed whether sex differences were attenuated following recent efforts to reduce sex disparities in the use of cardiovascular disease preventive therapies. Methods The authors studied 16,898 (26% women) U.S. adults &lt;65 years of age with commercial health insurance in the MarketScan database, and 71,358 (49% women) U.S. adults ≥66 years of age with government health insurance through Medicare who filled statin prescriptions within 30 days after hospital discharge for MI in 2014 to 2015. The authors calculated adjusted women-to-men risk ratios and 95% confidence intervals (CIs) for filling a high-intensity statin prescription (i.e., atorvastatin 40 to 80 mg, and rosuvastatin 20 to 40 mg) following hospital discharge for MI. Results In 2014 to 2015, 56% of men and 47% of women filled a high-intensity statin following hospital discharge for MI. Adjusted risk ratios for filling a high-intensity statin comparing women with men were 0.91 (95% CI: 0.90 to 0.92) in the total population, 0.91 (95% CI: 0.89 to 0.92) among those with no prior statin use, and 0.87 (95% CI: 0.85 to 0.90) and 0.98 (95% CI: 0.97 to 1.00) for those taking low/moderate-intensity and high-intensity statins prior to their MI, respectively. Women were less likely than men to fill high-intensity statins within all subgroups analyzed, and the disparity was largest in the youngest and oldest adults and for those without prevalent comorbid conditions. Conclusions Despite recent efforts to reduce sex differences in guideline-recommended therapy, women continue to be less likely than men to fill a prescription for high-intensity statins following hospitalization for MI

Development of algorithms for identifying fatal cardiovascular disease in Medicare claims

Background Cause of death is often not available in administrative claims data. Objective To develop claims-based algorithms to identify deaths due to fatal cardiovascular disease (CVD; i.e., fatal coronary heart disease [CHD] or stroke), CHD, and stroke. Methods Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data were linked with Medicare claims to develop the algorithms. Events adjudicated by REGARDS study investigators were used as the gold standard. Stepwise selection was used to choose predictors from Medicare data for inclusion in the algorithms. C-index, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were used to assess algorithm performance. Net reclassification index (NRI) was used to compare the algorithms to an approach of classifying all deaths within 28 days following hospitalization for myocardial infarction and stroke to be fatal CVD. Results Data from 2,685 REGARDS participants with linkage to Medicare, who died between 2003 and 2013, were analyzed. The C-index for discriminating fatal CVD from other causes of death was 0.87. Using a cut-point that provided the closest observed-to-predicted number of fatal CVD events, the sensitivity was 0.64, specificity 0.90, PPV 0.65 and NPV 0.90. The algorithms resulted in a positive NRIs compared with using deaths within 28 days following hospitalization for myocardial infarction and stroke. Claims-based algorithms for discriminating fatal CHD and fatal stroke performed similarly to fatal CVD. Conclusion The claims-based algorithms developed to discriminate fatal CVD events from other causes of death performed better than the method of using hospital discharge diagnosis codes

Development of algorithms for identifying fatal cardiovascular disease in Medicare claims

Background Cause of death is often not available in administrative claims data. Objective To develop claims-based algorithms to identify deaths due to fatal cardiovascular disease (CVD; i.e., fatal coronary heart disease [CHD] or stroke), CHD, and stroke. Methods Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data were linked with Medicare claims to develop the algorithms. Events adjudicated by REGARDS study investigators were used as the gold standard. Stepwise selection was used to choose predictors from Medicare data for inclusion in the algorithms. C-index, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were used to assess algorithm performance. Net reclassification index (NRI) was used to compare the algorithms to an approach of classifying all deaths within 28 days following hospitalization for myocardial infarction and stroke to be fatal CVD. Results Data from 2,685 REGARDS participants with linkage to Medicare, who died between 2003 and 2013, were analyzed. The C-index for discriminating fatal CVD from other causes of death was 0.87. Using a cut-point that provided the closest observed-to-predicted number of fatal CVD events, the sensitivity was 0.64, specificity 0.90, PPV 0.65 and NPV 0.90. The algorithms resulted in a positive NRIs compared with using deaths within 28 days following hospitalization for myocardial infarction and stroke. Claims-based algorithms for discriminating fatal CHD and fatal stroke performed similarly to fatal CVD. Conclusion The claims-based algorithms developed to discriminate fatal CVD events from other causes of death performed better than the method of using hospital discharge diagnosis codes