Life Expectancy and Years of Life Lost in Chronic Obstructive Pulmonary Disease: Findings from the NHANES III Follow-Up Study

RATIONALE: Previous studies have demonstrated that chronic obstructive pulmonary disease (COPD) causes increased mortality in the general population. But life expectancy and the years of life lost have not been reported. OBJECTIVES: To quantify mortality, examine how it varies with age, sex, and other risk factors, and determine how life expectancy is affected. METHODS: We constructed mortality models using the Third National Health and Nutrition Examination Survey, adjusting for age, sex, race, and major medical conditions. We used these to compute life expectancy and the years of life lost. MEASUREMENTS AND MAIN RESULTS: Pulmonary function testing classified patients as having Global Initiative on Obstructive Lung Disease (GOLD) stage 0, 1, 2, 3 or 4 COPD or restriction. COPD is associated with only a modest reduction in life expectancy for never smokers, but with a very large reduction for current and former smokers. At age 65, the reductions in male life expectancy for stage 1, stage 2, and stages 3 or 4 disease in current smokers are 0.3 years, 2.2 years, and 5.8 years. These are in addition to the 3.5 years lost due to smoking. In former smokers the reductions are 1.4 years and 5.6 years for stage 2 and stages 3 or 4 disease, and in never smokers they are 0.7 and 1.3 years. CONCLUSIONS: Persons with COPD have an increased risk of mortality compared to those who do not, with consequent reduction in life expectancy. The effect is most marked in current smokers, and this is further reason for smokers to quit

Clinical utility of 2.8MM probe-CMA

Data gathered in a clinical trial of physician behavior regarding the diagnosis and treatment of simulated patients with rare genetic disorders

Clinical Utility of Definitive Drug–Drug Interaction Testing in Primary Care

Drug⁻drug interactions (DDIs) are a leading cause of morbidity and mortality. New tools are needed to improve identification and treatment of DDIs. We conducted a randomized controlled trial to assess the clinical utility of a new test to identify DDIs and improve their management. Primary care physicians (PCPs) cared for simulated patients presenting with DDI symptoms from commonly prescribed medications and other ingestants. All physicians, in either control or one of two intervention groups, cared for six patients over two rounds of assessment. Intervention physicians were educated on the DDI test and given access to these test reports when caring for their patients in the second round. At baseline, we saw no significant differences in making the DDI diagnosis (p = 0.071) or DDI-related treatment (p = 0.640) between control and intervention arms. By round two, providers who accessed the DDI test performed significantly better in making the DDI diagnosis (+41.6%) and performing DDI-specific treatment (+12.2%) than in the previous round, and were 9.8 and 20.4 times more likely to diagnose and identify the DDI (p < 0.001 for all). The introduction of a definitive DDI test significantly increased identification, appropriate management, and counseling of DDIs among PCPs, which has the potential to improve clinical care

Clinical Variation in the Treatment Practices for Patients With Type 2 Diabetes: A Cross‐Sectional Patient Simulation Study Among Primary Care Physicians and Cardiologists

Background Cardiovascular disease risk stratification is necessary and critically important in patients with type 2 diabetes. Despite its known benefits to guide treatment and prevention, we hypothesized that providers do not routinely incorporate this into their diagnostic and treatment decisions. Methods and Results The QuiCER DM (QURE CVD Evaluation of Risk in Diabetes Mellitus) study enrolled 161 primary care physicians and 80 cardiologists. Between March 2022 and June 2022, we measured the care variation in risk determination among these providers caring for simulated patients with type 2 diabetes. We found a wide variation in the overall assessment of cardiovascular disease in patients with type 2 diabetes. Participants performed half of the necessary care items with quality‐of‐care scores, ranging between 13% and 84%, averaging 49.4±12.6%. Participants did not assess cardiovascular risk in 18.3% of cases and incorrectly stratified risk in 42.8% of cases. Only 38.9% of participants arrived at the correct cardiovascular risk stratification. Those who correctly identified a cardiovascular risk score were significantly more likely to order nonpharmacologic treatments, advising on their patients' nutrition (38.8% versus 29.9%, P=0.013) and the correct glycated hemoglobin target (37.7% versus 15.6%, P<0.001). Pharmacologic treatments, however, did not vary between those who correctly specified risk and those who did not. Conclusions Physician participants struggled to determine the correct cardiovascular disease risk and specify the appropriate pharmacologic interventions in simulated patients with type 2 diabetes. Additionally, there was a wide variation in the quality of care regardless of risk level, indicating opportunities to improve risk stratification

Comparative effectiveness of two disparate policies on child health: experimental evidence from the Philippines.

BACKGROUND: Should health systems invest more in access to care by expanding insurance coverage or in health care services including improving the quality of care? Comparing these options experimentally would shed light on the impact and cost-effectiveness of these strategies. METHODS: The Quality Improvement Demonstration Study (QIDS) was a randomized policy experiment conducted across 30 districts in the Philippines. The study had a control group and two policy intervention groups intended to improve the health of young children. The demand-side intervention in QIDS was universal health insurance coverage (UHC) for children aged 5 years or younger, and a supply-side intervention, a pay-for-performance (P4P) bonus for all providers who met pre-determined quality levels. In this paper, we compare the impacts of these policies from the QIDS experiment on childhood wasting by calculating DALYs averted per US$spent. RESULTS: The direct per capita costs to implement UHC and P4P are US$4.08 and US$1.98 higher, respectively, compared to control. DALYs due to wasting were reduced by 334,862 in UHC and 1,073,185 in P4P. When adjustments are made for the efficiency of higher quality, the DALYS averted per US$ spent is similar in the two arms, 1.56 and 1.58 for UHC and P4P, respectively. Since the P4P quality improvements touches all patients seen by qualifying providers (32% in UHC versus 100% in P4P), there is a larger reduction in DALYs. With similar programmatic costs for either intervention, in this study, each US$spent under P4P yielded 1.52 DALYs averted compared to the standard program, while UHC yielded only a 0.50 DALY reduction. CONCLUSION: P4P had a greater impact and was more cost-effective compared to UHC as measured by DALYs averted. While expanded insurance benefit ceilings affected only those who are covered, P4P incentivizes practice quality improvement regardless of whether children are insured or uninsured