Ambient heat exposure patterns and emergency department visits and hospitalizations among medicare beneficiaries 2008-2019.

OBJECTIVE: To assess the association between ambient heat and all-cause and cause-specific emergency department (ED) visits and acute hospitalizations among Medicare beneficiaries in the conterminous United States. DESIGN: Retrospective cohort study. SETTING: Conterminous US from 2008 and 2019. PARTICIPANTS: 2% random sample of all Medicare fee-for-service beneficiaries eligible for Parts A, B, and D. MAIN OUTCOME MEASURES: All-cause and cause-specific (cardiovascular, renal, and heat-related) ED visits and unplanned hospitalizations were identified using primary ICD-9 or ICD-10 diagnosis codes. We measured the association between ambient temperature - defined as daily mean temperature percentile of summer (June through September) - and the outcomes. Hazard ratios and their associated 95% confidence intervals were estimated using multivariable Cox proportional hazards regression, adjusting for individual level demographics, comorbidities, healthcare utilization factors and zip-code level social factors. RESULTS: Among 809,636 Medicare beneficiaries (58% female, 81% non-Hispanic White, 24% <65), older beneficiaries (aged ≥65) exposed to >95th percentile temperature had a 64% elevated adjusted risk of heat-related ED visits (HR [95% CI], 1.64 [1.46,1.85]) and a 4% higher risk of all-cause acute hospitalization (1.04 [1.01,1.06]) relative to <25th temperature percentile. Younger beneficiaries (aged <65) showed increased risk of heat-related ED visits (2.69 [2.23,3.23]) and all-cause ED visits (1.03 [1.01,1.05]). The associations with heat related events were stronger in males and individuals dually eligible for Medicare and Medicaid. No significant differences were observed by climatic region. We observed no significant relationship between temperature percentile and risk of CV-related ED visits or renal-related ED visits. CONCLUSIONS: Among Medicare beneficiaries from 2008 to 2019, exposure to daily mean temperature ≥ 95th percentile was associated with increased risk of heat-related ED visits, with stronger associations seen among beneficiaries <65, males, and patients with low socioeconomic position. Further longitudinal studies are needed to understand the impact of heat duration, intensity, and frequency on cause-specific hospitalization outcomes.

Air pollution and serious bleeding events in high-risk older adults.

IMPORTANCE: Despite biological plausibility, very few epidemiologic studies have investigated the risks of clinically significant bleeding events due to particulate air pollution. OBJECTIVE: To measure the independent and synergistic effects of PM2.5 exposure and anticoagulant use on serious bleeding events. DESIGN: Retrospective cohort study (2008-2016). SETTING: Nationwide Medicare population. PARTICIPANTS: A 50% random sample of Medicare Part D-eligible Fee-for-Service beneficiaries at high risk for cardiovascular and thromboembolic events. EXPOSURES: Fine particulate matter (PM2.5) and anticoagulant drugs (apixaban, dabigatran, edoxaban, rivaroxaban, or warfarin). MAIN OUTCOMES AND MEASURES: The outcomes were acute hospitalizations for gastrointestinal bleeding, intracranial bleeding, or epistaxis. Hazard ratios and 95% CIs for PM2.5 exposure were estimated by fitting inverse probability weighted marginal structural Cox proportional hazards models. The relative excess risk due to interaction was used to assess additive-scale interaction between PM2.5 exposure and anticoagulant use. RESULTS: The study cohort included 1.86 million high-risk older adults (mean age 77, 60% male, 87% White, 8% Black, 30% anticoagulant users, mean PM2.5 exposure 8.81 µg/m3). A 10 µg/m3 increase in PM2.5 was associated with a 48% (95% CI: 45%-52%), 58% (95% CI: 49%-68%) and 55% (95% CI: 37%-76%) increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis, respectively. Significant additive interaction between PM2.5 exposure and anticoagulant use was observed for gastrointestinal and intracranial bleeding. CONCLUSIONS: Among older adults at high risk for cardiovascular and thromboembolic events, increasing PM2.5 exposure was significantly associated with increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis. In addition, PM2.5 exposure and anticoagulant use may act together to increase risks of severe gastrointestinal and intracranial bleeding. Thus, clinicians may recommend that high-risk individuals limit their outdoor air pollution exposure during periods of increased PM2.5 concentrations. Our findings may inform environmental policies to protect the health of vulnerable populations.

Ambient Heat and Risk of Serious Hypoglycemia in Older Adults With Diabetes Using Insulin in the U.S. and Taiwan: A Cross-National Case-Crossover Study.

OBJECTIVE: To measure the association between ambient heat and hypoglycemia-related emergency department visit or hospitalization in insulin users. RESEARCH DESIGN AND METHODS: We identified cases of serious hypoglycemia among adults using insulin aged ≥65 in the U.S. (via Medicare Part A/B/D-eligible beneficiaries) and Taiwan (via National Health Insurance Database) from June to September, 2016-2019. We then estimated odds of hypoglycemia by heat index (HI) percentile categories using conditional logistic regression with a time-stratified case-crossover design. RESULTS: Among ∼2 million insulin users in the U.S. (32,461 hypoglycemia case subjects), odds ratios of hypoglycemia for HI >99th, 95-98th, 85-94th, and 75-84th percentiles compared with the 25-74th percentile were 1.38 (95% CI, 1.28-1.48), 1.14 (1.08-1.20), 1.12 (1.08-1.17), and 1.09 (1.04-1.13) respectively. Overall patterns of associations were similar for insulin users in the Taiwan sample (∼283,000 insulin users, 10,162 hypoglycemia case subjects). CONCLUSIONS: In two national samples of older insulin users, higher ambient temperature was associated with increased hypoglycemia risk.

Protocol for a pragmatic trial testing a self-directed lifestyle program targeting weight loss among patients with obstructive sleep apnea (POWER Trial).

BACKGROUND: Obesity comprises the single greatest reversible risk factor for obstructive sleep apnea (OSA). Despite the potential of lifestyle-based weight loss services to improve OSA severity and symptoms, these programs have limited reach. POWER is a pragmatic trial of a remote self-directed weight loss care among patients with OSA. METHODS: POWER randomizes 696 patients with obesity (BMI 30-45 kg/m2) and recent diagnosis or re-confirmation of OSA 1:1 to either a self-directed weight loss intervention or usual care. POWER tests whether such an intervention improves co-primary outcomes of weight and sleep-related quality of life at 12 months. Secondary outcomes include sleep symptoms, global ratings of change, and cardiovascular risk scores. Finally, consistent with a hybrid type 1 approach, the trial embeds an implementation process evaluation. We will use quantitative and qualitative methods including budget impact analyses and qualitative interviews to assess barriers to implementation. CONCLUSIONS: The results of POWER will inform population health approaches to the delivery of weight loss care. A remote self-directed program has the potential to be disseminated widely with limited health system resources and likely low-cost.

Retrospective cohort study investigating synergism of air pollution and corticosteroid exposure in promoting cardiovascular and thromboembolic events in older adults.

OBJECTIVE: To evaluate the synergistic effects created by fine particulate matter (PM2.5) and corticosteroid use on hospitalisation and mortality in older adults at high risk for cardiovascular thromboembolic events (CTEs). DESIGN AND SETTING: A retrospective cohort study using a US nationwide administrative healthcare claims database. PARTICIPANTS: A 50% random sample of participants with high-risk conditions for CTE from the 2008-2016 Medicare Fee-for-Service population. EXPOSURES: Corticosteroid therapy and seasonal-average PM2.5. MAIN OUTCOME MEASURES: Incidences of myocardial infarction or acute coronary syndrome (MI/ACS), ischaemic stroke or transient ischaemic attack, heart failure (HF), venous thromboembolism, atrial fibrillation and all-cause mortality. We assessed additive interactions between PM2.5 and corticosteroids using estimates of the relative excess risk due to interaction (RERI) obtained using marginal structural models for causal inference. RESULTS: Among the 1 936 786 individuals in the high CTE risk cohort (mean age 76.8, 40.0% male, 87.4% white), the mean PM2.5 exposure level was 8.3±2.4 µg/m3 and 37.7% had at least one prescription for a systemic corticosteroid during follow-up. For all outcomes, we observed increases in risk associated with corticosteroid use and with increasing PM2.5 exposure. PM2.5 demonstrated a non-linear relationship with some outcomes. We also observed evidence of an interaction existing between corticosteroid use and PM2.5 for some CTEs. For an increase in PM2.5 from 8 µg/m3 to 12 µg/m3 (a policy-relevant change), the RERI of corticosteroid use and PM2.5 was significant for HF (15.6%, 95% CI 4.0%, 27.3%). Increasing PM2.5 from 5 µg/m3 to 10 µg/m3 yielded significant RERIs for incidences of HF (32.4; 95% CI 14.9%, 49.9%) and MI/ACSs (29.8%; 95% CI 5.5%, 54.0%). CONCLUSION: PM2.5 and systemic corticosteroid use were independently associated with increases in CTE hospitalisations. We also found evidence of significant additive interactions between the two exposures for HF and MI/ACSs suggesting synergy between these two exposures.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Air Pollution and Cardiovascular and Thromboembolic Events in Older Adults With High-Risk Conditions.

Little epidemiologic research has focused on pollution-related risks in medically vulnerable or marginalized groups. Using a nationwide 50% random sample of 2008-2016 Medicare Part D-eligible fee-for-service participants in the United States, we identified a cohort with high-risk conditions for cardiovascular and thromboembolic events (CTEs) and linked individuals with seasonal average zip-code-level concentrations of fine particulate matter (particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5)). We assessed the relationship between seasonal PM2.5 exposure and hospitalization for each of 7 CTE-related causes using history-adjusted marginal structural models with adjustment for individual demographic and neighborhood socioeconomic variables, as well as baseline comorbidity, health behaviors, and health-service measures. We examined effect modification across geographically and demographically defined subgroups. The cohort included 1,934,453 individuals with high-risk conditions (mean age = 77 years; 60% female, 87% White). A 1-µg/m3 increase in PM2.5 exposure was significantly associated with increased risk of 6 out of 7 types of CTE hospitalization. Strong increases were observed for transient ischemic attack (hazard ratio (HR) = 1.039, 95% confidence interval (CI): 1.034, 1.044), venous thromboembolism (HR = 1.031, 95% CI: 1.027, 1.035), and heart failure (HR = 1.019, 95% CI: 1.017, 1.020). Asian Americans were found to be particularly susceptible to thromboembolic effects of PM2.5 (venous thromboembolism: HR = 1.063, 95% CI: 1.021, 1.106), while Native Americans were most vulnerable to cerebrovascular effects (transient ischemic attack: HR = 1.093, 95% CI: 1.030, 1.161).

Air Pollution and Mortality at the Intersection of Race and Social Class.

BACKGROUND: Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 µm [PM2.5]) than White Americans and may be more susceptible to its health effects. Low-income Americans may also be more susceptible to PM2.5 pollution than high-income Americans. Because information is lacking on exposure-response curves for PM2.5 exposure and mortality among marginalized subpopulations categorized according to both race and socioeconomic position, the Environmental Protection Agency lacks important evidence to inform its regulatory rulemaking for PM2.5 standards. METHODS: We analyzed 623 million person-years of Medicare data from 73 million persons 65 years of age or older from 2000 through 2016 to estimate associations between annual PM2.5 exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible). RESULTS: Lower PM2.5 exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM2.5 levels decreased. For example, the hazard ratio associated with decreasing PM2.5 from 12 µg per cubic meter to 8 µg per cubic meter for the White higher-income subpopulation was 0.963 (95% confidence interval [CI], 0.955 to 0.970), whereas equivalent hazard ratios for marginalized subpopulations were lower: 0.931 (95% CI, 0.909 to 0.953) for the Black higher-income subpopulation, 0.940 (95% CI, 0.931 to 0.948) for the White low-income subpopulation, and 0.939 (95% CI, 0.921 to 0.957) for the Black low-income subpopulation. CONCLUSIONS: Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM2.5 levels than higher-income White persons. These findings underscore the importance of considering racial identity and income together when assessing health inequities. (Funded by the National Institutes of Health and the Alfred P. Sloan Foundation.).

Power for balanced linear mixed models with complex missing data processes.

When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurements. For example, in a physiotherapy study of patients with Parkinson's disease, increasing intermittent dropout over time yielded missing measurements of physical function. In this example, we assume data are missing completely at random, since the chance that a data point was missing appears to be unrelated to either outcomes or covariates. For data missing completely at random, we propose noncentral F power approximations for the Wald test for balanced linear mixed models with Gaussian responses. The power approximations are based on moments of missing data summary statistics. The moments were derived assuming a conditional linear missingness process. The approach provides approximate power for both complete-case analyses, which include independent sampling units where all measurements are present, and observed-case analyses, which include all independent sampling units with at least one measurement. Monte Carlo simulations demonstrate the accuracy of the method in small samples. We illustrate the utility of the method by computing power for proposed replications of the Parkinson's study.

Transportability Methods for Time-to-Event Outcomes: Application in Adjuvant Colon Cancer Trials.

PURPOSE: Differences in the benefits of treatment on 5-year overall survival have been observed in 12 randomized phase III colon cancer adjuvant clinical trials from the ACCENT group. We investigated the reasons for these differences by incorporating the distribution of the observed covariates from each trial. MATERIALS AND METHODS: We applied state-of-the-art transportability methods on the basis of causal inference, and compared them with a conventional meta-analysis approach to predict the treatment effect for the target population. Prediction errors were defined to evaluate whether the identifiability conditions necessary for causal inference were satisfied among the 12 trials, and to measure the performance of each method. RESULTS: In the one-trial-at-a-time transportability analysis, the ranks of prediction errors for the target population were mostly consistent with the discrepancy in treatment effects among the 12 trials across the three models. The overall prediction errors between the leave-one-trial-out transportability method and the conventional individual participant data meta-analysis approach were very similar, and more than 40% lower than the overall prediction errors from the one-trial-at-a-time transportability method. CONCLUSION: The discrepancy in treatment effects among the 12 trials is unlikely to arise from the choice of model specification or distribution of observed covariates but from the distribution of unobserved covariates or study-level features. The ability to quantify heterogeneity among the 12 trials was greatly reduced in both the leave-one-trial-out transportability method and the conventional meta-analysis approach compared with the one-trial-at-a-time transportability method.

A calibration approach to transportability and data-fusion with observational data.

Two important considerations in clinical research studies are proper evaluations of internal and external validity. While randomized clinical trials can overcome several threats to internal validity, they may be prone to poor external validity. Conversely, large prospective observational studies sampled from a broadly generalizable population may be externally valid, yet susceptible to threats to internal validity, particularly confounding. Thus, methods that address confounding and enhance transportability of study results across populations are essential for internally and externally valid causal inference, respectively. These issues persist for another problem closely related to transportability known as data-fusion. We develop a calibration method to generate balancing weights that address confounding and sampling bias, thereby enabling valid estimation of the target population average treatment effect. We compare the calibration approach to two additional doubly robust methods that estimate the effect of an intervention on an outcome within a second, possibly unrelated target population. The proposed methodologies can be extended to resolve data-fusion problems that seek to evaluate the effects of an intervention using data from two related studies sampled from different populations. A simulation study is conducted to demonstrate the advantages and similarities of the different techniques. We also test the performance of the calibration approach in a motivating real data example comparing whether the effect of biguanides vs sulfonylureas-the two most common oral diabetes medication classes for initial treatment-on all-cause mortality described in a historical cohort applies to a contemporary cohort of US Veterans with diabetes.

Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis.

BACKGROUND: Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT). METHODS: Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups. RESULTS: A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from - 5.1% (95% CI - 8.7, - 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (- 4.2% [95% CI - 8.1, - 1.0]), intermediate (- 5.1% [95% CI - 8.7, - 1.5]), and high (- 4.3% [95% CI - 7.7, - 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone. CONCLUSIONS: This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk.

Trends in Timing of and Glycemia at Initiation of Second-line Type 2 Diabetes Treatment in U.S. Adults.

OBJECTIVE: Therapeutic inertia threatens the potential long-term benefits of achieving early glycemic control after type 2 diabetes diagnosis. We evaluated temporal trends in second-line diabetes medication initiation among individuals initially treated with metformin. RESEARCH DESIGN AND METHODS: We included data from 199,042 adults with type 2 diabetes in the U.S. Department of Veterans Affairs health care system initially treated with metformin monotherapy from 2005 to 2013. We used multivariable Cox proportional hazards and linear regression to estimate associations of year of metformin monotherapy initiation with time to second-line diabetes treatment over 5 years of follow-up (primary outcome) and with hemoglobin A1c (HbA1c) at the time of second-line diabetes treatment initiation (secondary outcome). RESULTS: The cumulative 5-year incidence of second-line medication initiation declined from 47% among metformin initiators in 2005 to 36% in 2013 counterparts (P < 0.0001) despite a gradual increase in mean HbA1c at the end of follow-up (from 6.94 ± 1.28% to 7.09 ± 1.42%, Ptrend < 0.0001). In comparisons with metformin monotherapy initiators in 2005, adjusted hazard ratios for 5-year initiation of second-line diabetes treatment ranged from 0.90 (95% CI 0.87, 0.92) for 2006 metformin initiators to 0.68 (0.66, 0.70) for 2013 counterparts. Among those receiving second-line treatment within 5 years of metformin initiation, HbA1c at second-line medication initiation increased from 7.74 ± 1.66% in 2005 metformin initiators to 8.55 ± 1.92% in 2013 counterparts (Ptrend < 0.0001). CONCLUSIONS: We observed progressive delays in diabetes treatment intensification consistent with therapeutic inertia. Process-of-care interventions early in the diabetes disease course may be needed to reverse adverse temporal trends in diabetes care.

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal forests in two similarly designed randomized trials in type II diabetes patients. Methods We evaluated published HTE of intensive versus standard glycemic control on all-cause mortality from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD) in a second trial, the Veterans Affairs Diabetes Trial (VADT). We then applied causal forests to VADT, ACCORD, and pooled data from both studies and compared variable importance and subgroup effects across samples. Results HTE in ACCORD did not replicate in similar subgroups in VADT, but variable importance was correlated between VADT and ACCORD (Kendall's tau-b 0.75). Applying causal forests to pooled individual-level data yielded seven subgroups with similar HTE across both studies, ranging from risk difference of all-cause mortality of -3.9% (95% CI -7.0, -0.8) to 4.7% (95% CI 1.8, 7.5). Conclusions Machine learning detection of HTE subgroups from randomized trials may not generalize across study samples even when variable importance is correlated. Pooling individual-level data may overcome differences in study populations and/or differences in interventions that limit HTE generalizability.

Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States.

The year 2020 brought unimaginable challenges in public health, with the confluence of the COVID-19 pandemic and wildfires across the western United States. Wildfires produce high levels of fine particulate matter (PM2.5). Recent studies reported that short-term exposure to PM2.5 is associated with increased risk of COVID-19 cases and deaths. We acquired and linked publicly available daily data on PM2.5, the number of COVID-19 cases and deaths, and other confounders for 92 western U.S. counties that were affected by the 2020 wildfires. We estimated the association between short-term exposure to PM2.5 during the wildfires and the epidemiological dynamics of COVID-19 cases and deaths. We adjusted for several time-varying confounding factors (e.g., weather, seasonality, long-term trends, mobility, and population size). We found strong evidence that wildfires amplified the effect of short-term exposure to PM2.5 on COVID-19 cases and deaths, although with substantial heterogeneity across counties.

Transporting experimental results with entropy balancing.

We show how entropy balancing can be used for transporting experimental treatment effects from a trial population onto a target population. This method is doubly robust in the sense that if either the outcome model or the probability of trial participation is correctly specified, then the estimate of the target population average treatment effect is consistent. Furthermore, we only require the sample moments of the effect modifiers drawn from the target population to consistently estimate the target population average treatment effect. We compared the finite-sample performance of entropy balancing with several alternative methods for transporting treatment effects between populations. Entropy balancing techniques are efficient and robust to violations of model misspecification. We also examine the results of our proposed method in an applied analysis of the Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial transported to a sample of US adults with diabetes taken from the National Health and Nutrition Examination Survey cohort.

Comparison of Fruit and Vegetable Intake Among Urban Low-Income US Adults Receiving a Produce Voucher in 2 Cities.

Importance: Fruit and vegetable vouchers have been implemented by cities and counties across the US to increase fruit and vegetable intake and thereby improve overall nutritional quality. Objective: To determine whether and why use of fruit and vegetable vouchers are associated with varied nutritional intake across different populations and environments. Design, Setting, and Participants: In a population-based pre-post cohort study of 671 adult participants with low income before and during (6 months after initiation) participation in a 6-month program, fruit and vegetable vouchers were distributed for redemption at local San Francisco and Los Angeles neighborhood grocery and corner stores between 2017 and 2019. A transportability analysis was performed to identify factors that may explain variation in voucher use between cities. Exposure: Receipt of $20 per month in produce vouchers for 6 months from 2017 to 2019. Main Outcomes and Measures: Change in total fruits and vegetables (as defined by the US Department of Agriculture) consumed per person per day (change in cup-equivalents between month 6 and month 0). Results: A total of 671 adults (median age, 54.9 years [interquartile range, 45.0-65.0 years]; 61.7% female; 30.9% Black; 19.7% Hispanic) were enrolled. An increase in fruit and vegetable intake of 0.22 cup-equivalents per day overall (95% CI, 0.14-0.31 cup-equivalents; P < .001) was observed. However, the observed increase was larger in Los Angeles compared with San Francisco (0.64 cup-equivalents per day; 95% CI, 0.41-0.88 cup-equivalents vs 0.10 cup-equivalents per day; 95% CI, 0.01-0.19 cup-equivalents). When the concurrently sampled San Francisco group (n = 157) was weighted in transportability analysis to demographically match the Los Angeles group (n = 155) in observed covariates, the weighted San Francisco group had an estimated increase of 0.53 fruit and vegetable cup-equivalents per day (95% CI, 0.27- 0.79 cup-equivalents, P = .03), with income being the variable needed to allow the 95% confidence intervals to overlap between the weighted San Francisco and unweighted Los Angeles populations. Conclusions and Relevance: In this study, the use of fruit and vegetable vouchers appeared to be associated with greater benefit among those with lower incomes, suggesting that further investigation of flat-rate rather than income-scaled benefits is warranted.

Risk of obstructive coronary artery disease and major adverse cardiac events in patients with noncoronary atherosclerosis: Insights from the Veterans Affairs Clinical Assessment, Reporting, and Tracking (CART) Program.

We sought to determine the risk of obstructive coronary artery disease (oCAD) associated with noncoronary atherosclerosis (cerebrovascular disease [CVD] or peripheral arterial disease [PAD]) and major adverse cardiac events following percutaneous coronary intervention (PCI). METHODS: Rates of the angiographic end point of oCAD were compared among patients with and without noncoronary atherosclerosis undergoing coronary angiography within the Veterans Health Administration between October 2007 and August 2015. The primary angiographic end point of oCAD was defined as left main stenosis ≥50% or any stenosis ≥70% in 1, 2, or 3 vessels. In patients who proceeded to PCI, the rate of the composite clinical end point of death, myocardial infarction, or stroke was compared among those with concomitant noncoronary atherosclerosis (CVD, PAD, or CVD + PAD) versus isolated CAD. RESULTS: Among 233,353 patients undergoing angiography, 9.6% had CVD, 12.4% had PAD, and 6.1% had CVD + PAD. Rates of oCAD were 57.9% for neither CVD nor PAD, 66.4% for CVD, 73.6% for PAD, and 80.9% for CVD + PAD. Compared with patients without noncoronary atherosclerosis, the adjusted risk of oCAD with CVD, PAD, or CVD + PAD was 1.03 (95% CI 1.02-1.04), 1.10 (95% CI 1.09-1.11), and 1.12 (95% CI 1.11-1.13), respectively. In patients who underwent PCI, the adjusted hazard for death, myocardial infarction, or stroke among those with CVD, PAD, or CVD + PAD was 1.36 (95% CI 1.26-1.45), 1.53 (95% CI 1.45-1.62), and 1.72 (95% CI 1.59-1.86), respectively. CONCLUSIONS: In patients undergoing coronary angiography, noncoronary atherosclerosis was associated with increased burden of oCAD and adverse events post-PCI.

Dual antiplatelet therapy in non-ST elevation acute coronary syndromes at Veterans Affairs Hospitals.

OBJECTIVE: Current guidelines recommend that patients with non-ST elevation acute coronary syndromes (NSTEACS) receive dual antiplatelet therapy (DAPT) early in hospitalisations. However, observational studies suggest that this rarely occurs. We evaluated site-specific variation and clinical outcomes associated with early DAPT among patients undergoing angiography for NSTEACS. METHODS: In this observational analysis, we identified patients undergoing angiography for NSTEACS in Veterans Affairs hospitals from 2008 to 2016 and assessed characteristics and site variation associated with early DAPT (administration <24 hours of admission). Using propensity matching, we compared time to revascularisation, recurrent myocardial infarction (MI) and mortality between those receiving early DAPT and those not receiving early DAPT (administration ≥24 hours). RESULTS: Of 45 569 patients undergoing angiography for NSTEACS, 15 084 (33%) received early DAPT. Early DAPT was more frequent in patients with non-ST elevation MI, prior surgical revascularisation and among patients undergoing revascularisation. There was a greater than twofold difference in early DAPT across sites, independent of patient characteristics (median OR 2.43, 95% CI 2.28 to 2.55). There was no difference in time topercutaneous coronary intervention (PCI) between groups, but a significant delay to surgical revascularisation with early DAPT (median 4 vs 3 days, p<0.001) without reduction in hazard of death or MI (HR 1.08, 95% CI 1.00 to 1.16) and similar results demonstrated in the subgroup of patients undergoing revascularisation (HR 1.02, 95% CI 0.91 to 1.13). CONCLUSION: Among NSTEACS patients undergoing coronary angiography, early DAPT was not associated with improvement of outcomes but was associated with delays in surgical revascularisation.

Temporal Trends, Complications, and Predictors of Outcomes Among Nonagenarians Undergoing Percutaneous Coronary Intervention: Insights From the Veterans Affairs Clinical Assessment, Reporting, and Tracking Program.

OBJECTIVES: The aim of this study was to determine temporal trends, in-laboratory complications, mortality, and predictors of mortality among nonagenarians undergoing percutaneous coronary intervention (PCI). BACKGROUND: Nonagenarians (patients 90 years of age or older) undergoing PCI are often underrepresented in clinical trials, and their management remains challenging and controversial. METHODS: All veterans undergoing PCI with data recorded in the Veterans Affairs Clinical Assessment, Reporting, and Tracking program from 2005 to 2014 were evaluated. Temporal trends in the use of PCI, occurrence of in-laboratory complications, and 30-day and 1-year mortality were assessed. Using a frailty model, predictors of 30-day and 1-year mortality in nonagenarians were evaluated. RESULTS: Among all veterans undergoing PCI (n = 67,148) between 2005 and 2014, 274 (0.4%) were nonagenarians. The proportion of nonagenarians increased from 0.25% in 2008 to 0.58% in 2014. Compared with younger patients, nonagenarians had a greater risk for acute cardiogenic shock post-procedure (0.73% vs. 0.12%; p = 0.04) and no reflow (2.9% vs. 1.0%; p = 0.02). Unadjusted (10.6% vs. 1.4%; p < 0.0001) and adjusted 30-day mortality (odds ratio: 2.14; 95% confidence interval [CI]: 1.42 to 3.22) and unadjusted (16.3% vs. 4.2%; p < 0.0001) and adjusted 1-year mortality (odds ratio: 1.82; 95% CI: 1.27 to 2.62) were higher among PCI patients who were nonagenarians. The National Cardiovascular Data Registry risk score was highly predictive of both 30-day (hazard ratio: 2.29; 95% CI: 1.86 to 2.82) and 1-year (hazard ratio: 1.43; 95% CI: 1.07 to 1.90) mortality among nonagenarians. CONCLUSIONS: Nonagenarians were a small but growing population with worse 30-day and 1-year mortality. The National Cardiovascular Data Registry risk score was a strong predictor of mortality in these patients.