Faculty Bibliography

In this study we investigated the concentration and composition of particulate matter (PM2.5) in the New York City subway system. Realtime measurements, at a 1-s cadence, and gravimetric measurements were performed inside train cars along 300 km of nine subway lines, as well as on 333 platforms on 287 subway stations. The mean (±SD) PM2.5 concentration on the underground platforms was 142 ± 69 μg/m³ versus 29 ± 20 μg/m³ for aboveground stations. The average concentrations inside train cars were 88 ± 14 μg/m³ when traveling through underground tunnels and platforms and 29 ± 31 μg/m³ while on aboveground tracks. The particle composition analysis of filtered samples was done using X-ray fluorescence (XRF), revealing that iron made up approximately 43% of the total PM2.5 mass on station platforms, approximately 126 times higher than the outdoor ambient iron concentration. Other trace elements include silicon, sulfur, copper, nickel, aluminum, calcium, barium, and manganese. Considering the very high iron content, the comparative analysis of the measured concentration versus the standards set by the Environmental Protection Agency (US EPA) is not appropriate since those limits are largely based on particulate matter from fossil fuel combustion. Health impact analysis of inhalation of iron-based particles is needed to contextualize the results presented here.

SIGNIFICANCE STATEMENT:Cardiovascular diseases account for 32% of deaths among kidney transplant recipients. Statin therapy is common in this population. However, its effect on mortality prevention remains unclear among kidney transplant recipients, whose clinical risk profile might be unique because of concomitant immunosuppressive therapy. In this national study of 58,264 single-kidney transplant recipients, statin use was associated with a 5% decrease in mortality. More importantly, this protective association was stronger among those who used a mammalian target of rapamycin (mTOR) inhibitor for immunosuppression (27% decrease in mTOR inhibitor users versus 5% in nonusers). Our results suggest that statin therapy may reduce mortality in kidney transplant recipients and that the strength of this protective association may vary by immunosuppression regimen. BACKGROUND:Cardiovascular diseases are the leading cause of mortality in kidney transplant (KT) recipients, accounting for 32% of deaths. Statins are widely used in KT recipients, but effectiveness for preventing mortality remains unclear in this population, especially because of interaction between statins and immunosuppressive agents. We analyzed a national cohort to assess the real-world effectiveness of statins for reducing all-cause mortality in KT recipients. METHODS:We studied statin use and mortality among 58,264 adults (18 years or older) who received single kidneys between 2006 and 2016 and had Medicare part A/B/D. Statin use was ascertained from Medicare prescription drug claims and deaths from Center for Medicare and Medicaid Services records. We estimated the association of statin use with mortality using multivariable Cox models, with statin use as a time-varying exposure and immunosuppression regimen as effect modifiers. RESULTS:Statin use increased from 45.5% at KT to 58.2% at 1-year post-KT to 70.9% at 5-year post-KT. We observed 9785 deaths over 236,944 person-years. Overall, statin use was significantly associated with lower mortality (adjusted hazard ratio [aHR], 0.95; 95% confidence interval [CI], 0.90 to 0.99). The strength of this protective association varied by calcineurin inhibitor use (among tacrolimus users, aHR, 0.97; 95% CI, 0.92 to 1.03 versus among calcineurin nonusers, aHR, 0.72; 95% CI, 0.60 to 0.87; interaction P =0.002), mammalian target of rapamycin (mTOR) inhibitor use (among mTOR inhibitor users, aHR, 0.73; 95% CI, 0.57 to 0.92 versus among nonusers, aHR, 0.95; 95% CI, 0.91 to 1.00; interaction P =0.03), and mycophenolate use (among mycophenolate users, aHR, 0.96; 95% CI, 0.91 to 1.02 versus among nonusers, aHR, 0.76; 95% CI, 0.64 to 0.89; interaction P =0.002). CONCLUSION:Real-world evidence supports statin therapy for reducing all-cause mortality in KT recipients. Effectiveness might be greater when combined with mTOR inhibitor-based immunosuppression.

Breast cancer is the most common cancer diagnosed in women, accounting for an estimated 30% of all new cancer diagnoses in women in 2022. Advances in breast cancer treatment have reduced the mortality rate over the past 25 years by up to 34% but not all groups have benefitted equally from these improvements. These disparities span the continuum of care from screening to the receipt of guideline-concordant therapy and survivorship. At the 2022 American College of Surgeons Clinical Congress, a panel session was dedicated to educating and discussing methods of addressing these disparities in a coordinated manner. While there are multilevel solutions to address these disparities, this article focuses on screening, genetic testing, reconstruction, and oncofertility.

STUDY QUESTION:How did the first two coronavirus disease 2019 (COVID-19) waves affect fertility rates in the USA? SUMMARY ANSWER:States differed widely in how their fertility rates changed following the COVID-19 outbreak and these changes were influenced more by state-level economic, racial, political, and social factors than by COVID-19 wave severity. WHAT IS KNOWN ALREADY:The outbreak of the COVID-19 pandemic contributed to already declining fertility rates in the USA, but not equally across states. Identifying drivers of differential changes in fertility rates can help explain variations in demographic shifts across states in the USA and motivate policies that support families in general, not only during crises. STUDY DESIGN, SIZE, DURATION:This is an ecological study using state-level data from 50 US states and the District of Columbia (n = 51). The study period extends from 2020 to 2021 with historical data from 2016 to 2019. We identified Wave 1 as the first apex for each state after February 2020 and Wave 2 as the second apex, during Fall/Winter 2020-2021. PARTICIPANTS/MATERIALS, SETTING, METHODS:State-level COVID-19 wave severity, defined as case acceleration during each 3-month COVID-19 wave (cases/100 000 population/month), was derived from 7-day weekly moving average COVID-19 case rates from the US Centers for Disease Control and Prevention (CDC). State-level fertility rate changes (change in average monthly fertility rate/100 000 women of reproductive age (WRA)/year) were derived from the CDC Bureau of Vital Statistics and from 2020 US Census and University of Virginia 2021 population estimates 9 months after each COVID-19 wave. We performed univariate analyses to describe national and state-level fertility rate changes following each wave, and simple and multivariable linear regression analyses to assess the relation of COVID-19 wave severity and other state-level characteristics with fertility rate changes. MAIN RESULTS AND THE ROLE OF CHANCE:Nationwide, fertility dropped by 17.5 births/month/100 000 WRA/year following Wave 1 and 9.2 births/month/100 000 WRA/year following Wave 2. The declines following Wave 1 were largest among majority-Democrat, more non-White states where people practiced greater social distancing. Greater COVID-19 wave severity was associated with steeper fertility rate decline post-Wave 1 in simple regression, but the association was attenuated when adjusted for other covariates. Adjusting for the economic impact of the pandemic (hypothesized mediator) also attenuated the effect. There was no relation between COVID-19 wave severity and fertility rate change following Wave 2. LIMITATIONS, REASONS FOR CAUTION:Our study harnesses state-level data so individual-level conclusions cannot be inferred. There may be residual confounding in our multivariable regression and we were underpowered to detect some effects. WIDER IMPLICATIONS OF THE FINDINGS:The COVID-19 pandemic initially impacted the national fertility rate but, overall, the fertility rate rebounded to the pre-pandemic level following Wave 2. Consistent with prior literature, COVID-19 wave severity did not appear to predict fertility rate change. Economic, racial, political, and social factors influenced state-specific fertility rates during the pandemic more than the severity of the outbreak alone. Future studies in other countries should also consider whether these factors account for internal heterogeneity when examining the impact of the COVID-19 pandemic and other crises on fertility. STUDY FUNDING/COMPETING INTEREST(S):L.G.K. received funding from the National Institute of Environmental Health Sciences (R00ES030403), M.C. from the National Science Foundation Graduate Research Fellowship Program (20-A0-00-1005789), and M.L. and E.S. from the National Institute of Environmental Health Sciences (R01ES032808). None of the authors have competing interests. TRIAL REGISTRATION NUMBER:N/A.

PURPOSE:The purpose of the study was to examine differences among adult patients with diabetes who receive care through a telementoring model versus care at an academic specialty clinic on guideline-recommended diabetes care and self-management behaviors. METHODS:Endocrinology-focused Extension for Community Healthcare Outcomes (ECHO Endo) patients completed surveys assessing demographics, access to care, health care quality, and self-management behaviors at enrollment and 1 year after program enrollment. Diabetes Comprehensive Care Center (DCCC) patients completed surveys at comparable time points. RESULTS:At baseline, ECHO patients were less likely than DCCC patients to identify English as their primary language, have postsecondary education, and private insurance. One year postenrollment, ECHO patients visited their usual source of diabetic care more frequently. There were no differences in A1C testing or feet checking by health care professionals, but ECHO patients were less likely to report eye exams and smoking status assessment. ECHO and DCCC patients did not differ in consumption of high-fat foods and soda, physical activity, or home feet checks. ECHO patients were less likely to space carbohydrates evenly and test glucose levels and more likely to have smoked cigarettes. CONCLUSIONS:Endo ECHO is a suitable alternative to specialty care for patients in underserved communities with restricted access to specialty care. Results support the value of the Project ECHO telementoring model in addressing barriers to high-quality care for underserved communities.

BACKGROUND:Team-based care (TBC), a team of ≥2 healthcare professionals working collaboratively toward a shared clinical goal, is a recommended strategy to manage blood pressure (BP). However, the most effective and cost-effective TBC strategy is unknown. METHODS:A meta-analysis of clinical trials in US adults (aged ≥20 years) with uncontrolled hypertension (≥140/90 mm Hg) was performed to estimate the systolic BP reduction for TBC strategies versus usual care at 12 months. TBC strategies were stratified by the inclusion of a nonphysician team member who could titrate antihypertensive medications. The validated BP Control Model-Cardiovascular Disease Policy Model was used to project the expected BP reductions out to 10 years and simulate cardiovascular disease events, direct healthcare costs, quality-adjusted life years, and cost-effectiveness of TBC with physician and nonphysician titration. RESULTS:Among 19 studies comprising 5993 participants, the 12-month systolic BP change versus usual care was -5.0 (95% CI, -7.9 to -2.2) mm Hg for TBC with physician titration and -10.5 (-16.2 to -4.8) mm Hg for TBC with nonphysician titration. Relative to usual care at 10 years, TBC with nonphysician titration was estimated to cost $95 (95% uncertainty interval, -$563 to $664) more per patient and gain 0.022 (0.003-0.042) quality-adjusted life years, costing $4400/quality-adjusted life year gained. TBC with physician titration was estimated to cost more and gain fewer quality-adjusted life years than TBC with nonphysician titration. CONCLUSIONS:TBC with nonphysician titration yields superior hypertension outcomes compared with other strategies and is a cost-effective way to reduce hypertension-related morbidity and mortality in the United States.

PURPOSE:The Asia-Pacific (APAC) region is a major focus for multinational clinical trials, although its cultural, linguistic, economic, and regulatory diversity pose significant challenges for trial conduct, particularly for academic clinical trials. METHODS:We describe our experience running the investigator-initiated phase III randomized, fully accrued, Aspirin for Dukes C and high-risk Dukes B Colorectal cancer trial (ASCOLT, ClinicalTrials.gov identifier: NCT00565708, N = 1,587), studying the benefit of aspirin in resected high-risk colorectal cancer. ASCOLT opened in 2008 and is the first large academic adjuvant trial fully conducted in the APAC region. Centrally coordinated by the Trial Management Team at the National Cancer Centre Singapore, it has involved 74 sites across 12 APAC countries/regions, including five middle-income countries. RESULTS:Challenges encountered included regulatory complexity, communication and logistical barriers, limited funding and resources, disparate experience and infrastructure across sites, recruitment holds because of changes in local laws, patient attrition, and disruptions caused by the COVID-19 pandemic. Over 100 contracts and 49 ethics board reviews were required, contributing to a lengthy prestudy preparation time of 2 years and start-up times of approximately 6 months per site. Some of the mitigating actions included engaging local cooperative groups (eg, the Australasian Gastro-Intestinal Trials Group in Australia and New Zealand) and seven contract research organizations to manage sites, regular communication with the central team, transition to electronic data management, and a centralized drug-dispensing system. CONCLUSION:To ensure an efficient and patient-centered clinical trials environment in the APAC region and sustained growth, we suggest coordinated approaches to harmonize regulatory processes, APAC academic oncology trials consortia to streamline processes and provide governance, and ongoing commitment from governments, funding agents, and industry.

Reproducible translation of transcriptomics data has been hampered by the ubiquitous presence of batch effects. Statistical methods for managing batch effects were initially developed in the setting of sample group comparison and later borrowed for other settings such as survival outcome prediction. The most notable such method is ComBat, which adjusts for batches by including it as a covariate alongside sample groups in a linear regression. In survival prediction, however, ComBat is used without definable groups for survival outcome and is done sequentially with survival regression for a potentially batch-confounded outcome. To address these issues, we propose a new method called BATch MitigAtion via stratificatioN (BatMan). It adjusts batches as strata in survival regression and uses variable selection methods such as the regularized regression to handle high dimensionality. We assess the performance of BatMan in comparison with ComBat, each used either alone or in conjunction with data normalization, in a resampling-based simulation study under various levels of predictive signal strength and patterns of batch-outcome association. Our simulations show that (1) BatMan outperforms ComBat in nearly all scenarios when there are batch effects in the data and (2) their performance can be worsened by the addition of data normalization. We further evaluate them using microRNA data for ovarian cancer from the Cancer Genome Atlas and find that BatMan outforms ComBat while the addition of data normalization worsens the prediction. Our study thus shows the advantage of BatMan and raises caution about the use of data normalization in the context of developing survival prediction models. The BatMan method and the simulation tool for performance assessment are implemented in R and publicly available at LXQin/PRECISION.survival-GitHub.