Faculty Bibliography

AIMS/OBJECTIVE:The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. METHODS:In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS:In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION/CONCLUSIONS:Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

To understand the impact of COVID-19-related disruptions on PrEP services, we reviewed PrEP prescriptions at NYC Health + Hospitals/Bellevue from July 2019 through July 2021. PrEP prescriptions were examined as PrEP person-equivalents (PrEP PE) in order to account for the variable time of refill duration (i.e., 1-3 months). To assess "PrEP coverage", we calculated PrEP medication possession ratios (MPR) while patients were under study observation. Pre-clinic closure, mean PrEP PE = 244.2 (IQR 189.2, 287.5; median = 252.5) were observed. Across levels of clinic closures, mean PrEP PE = 247.3, (IQR 215.5, 265.4; median = 219.9) during 100% clinic closure, 255.4 (IQR 224, 284.3; median = 249.0) during 80% closure, and 274.6 (IQR 273.0, 281.0; median = 277.2) during 50% closure were observed. Among patients continuously prescribed PrEP pre-COVID-19, the mean MPR mean declined from 83% (IQR 72-100%; median = 100%) to 63% (IQR 35-97%; median = 66%) after the onset of COVID-19. For patients newly initiated on PrEP after the onset of COVID-19, the mean MPR was 73% (IQR 41-100%; median = 100%). Our ability to sustain PrEP provisions, as measured by both PrEP PE and MPR, can likely be attributed to our pre-COVID-19 system for PrEP delivery, which emphasizes navigation, same-day initiation, and primary care integration. In the era of COVID-19 as well as future unforeseen healthcare disruptions, PrEP programs must be robust and flexible in order to sustain PrEP delivery.

Smoke-free housing policies are intended to reduce the deleterious health effects of secondhand smoke (SHS) exposure, but there is limited evidence regarding their health impacts. We examined associations between implementation of a federal smoke-free housing rule by the New York City Housing Authority (NYCHA) and pediatric Medicaid claims for asthma, lower respiratory infections (LRIs), and upper respiratory infections (URIs) in the early post-policy period. We used geocoded address data to match children living in tax lots with NYCHA buildings (exposed to policy) to children living in lots with other subsidized housing (unexposed to policy). We constructed longitudinal difference-in-differences models to assess relative changes in monthly rates of claims between November 1, 2015 and December 31, 2019 (policy introduction was July 30, 2018). We also examined effect modification by baseline age group (0-2, 3-6, 7-15). In NYC, introduction of a smoke-free policy was not associated with lower rates of Medicaid claims for any outcomes in the early post-policy period. Exposure to the smoke-free policy was associated with slightly higher than expected rates of outpatient URI claims (IRR=1.05, 95% CI=1.01, 1.08), a result most pronounced among children ages 3-6. Ongoing monitoring is essential to understanding long-term health impacts of smoke-free housing policies.

BACKGROUND:The financial hardships and social isolation experienced during the COVID-19 pandemic have been found to adversely affect children's developmental outcomes. While many studies thus far have focused on school-aged children and the pandemic-related impacts on their academic skills and behavior problems, relatively less is known about pandemic hardships and associations with children's development during their early years. Using a racially and economically diverse sample, we examined whether hardships experienced during the pandemic were associated with children's development with a particular focus on communication and socioemotional development. METHODS:Participants from eight cohorts of the Environmental influences on Child Health Outcomes program provided data on pandemic-related financial and social hardships as well as child developmental outcomes. Financial hardship was defined as at least one parent experiencing job loss or change, and social hardship was defined as families' quarantining from household members or extended family and friends. The development of children under 4 was assessed longitudinally, before and during the pandemic (N = 684), using the Ages and Stages Questionnaire (ASQ). The Generalized Estimating Equations, which accounted for within-child correlation, were used for analysis. RESULTS:s = 0.000). Pandemic-related hardships in the social and financial areas did not explain within-individual changes in children's developmental outcomes. CONCLUSION/CONCLUSIONS:Negative developmental changes from pre- to during-pandemic were found in boys, yet we did not find any associations between increased experience of pandemic-related hardships and children's development. E how pandemic hardships affect development using a larger sample size and with longer follow-up is warranted.

INTRODUCTION/BACKGROUND:The COVID-19 pandemic disrupted cancer screening and treatment delivery, but COVID-19's impact on tobacco cessation treatment for cancer patients who smoke has not been widely explored. METHODS:We conducted a sequential cross-sectional analysis of data collected from 34 NCI-designated cancer centers participating in NCI's Cancer Center Cessation Initiative (C3I), across three reporting periods: one prior to COVID-19 (January-June 2019) and two during the pandemic (January-June 2020, January-June 2021). Using McNemar's Test of Homogeneity, we assessed changes in services offered and implementation activities over time. RESULTS:The proportion of centers offering remote treatment services increased each year for Quitline referrals (56%, 68%, and 91%; p=.000), telephone counseling (59%, 79%, and 94%; p=.002), and referrals to Smokefree TXT (27%, 47%, and 56%; p=.006). Centers offering video-based counseling increased from 2020 to 2021 (18% to 59%; p=.006), Fewer than 10% of centers reported laying off tobacco treatment staff. Compared to early 2020, in 2021 C3I centers reported improvements in their ability to maintain staff and clinician morale, refer to external treatment services, train providers to deliver tobacco treatment, and modify clinical workflows. DISCUSSION/CONCLUSIONS:The COVID-19 pandemic necessitated rapid transition to new telehealth program delivery of tobacco treatment for patients with cancer. C3I cancer centers adjusted rapidly to challenges presented by the pandemic, with improvements reported in staff morale and ability to train providers, refer patients to tobacco treatment, and modify clinical workflows. These factors enabled C3I centers to sustain evidence-based tobacco treatment implementation during and beyond the COVID-19 pandemic. IMPLICATIONS/CONCLUSIONS:This work describes how NCI-designated cancer centers participating in the Cancer Center Cessation Initiative (C3I) adapted to challenges to sustain evidence-based tobacco use treatment programs during the COVID-19 pandemic. This work offers a model for resilience and rapid transition to remote tobacco treatment services delivery and proposes a policy and research agenda for telehealth services as an approach to sustaining evidence-based tobacco treatment programs.

BACKGROUND:Air pollution is both a sensory blight and a threat to human health. Inhaled environmental pollutants can be naturally occurring or human-made, and include traffic-related air pollution (TRAP), ozone, particulate matter (PM) and volatile organic compounds, among other substances, including those from secondhand smoking. Studies of air pollution on reproductive and endocrine systems have reported associations of TRAP, secondhand smoke (SHS), organic solvents and biomass fueled-cooking with adverse birth outcomes. While some evidence suggests that air pollution contributes to infertility, the extant literature is mixed, and varying effects of pollutants have been reported. OBJECTIVE AND RATIONALE/OBJECTIVE:Although some reviews have studied the association between common outdoor air pollutants and time to pregnancy (TTP), there are no comprehensive reviews that also include exposure to indoor inhaled pollutants, such as airborne occupational toxicants and SHS. The current systematic review summarizes the strength of evidence for associations of outdoor air pollution, SHS and indoor inhaled air pollution with couple fecundability and identifies gaps and limitations in the literature to inform policy decisions and future research. SEARCH METHODS/METHODS:We performed an electronic search of six databases for original research articles in English published since 1990 on TTP or fecundability and a number of chemicals in the context of air pollution, inhalation and aerosolization. Standardized forms for screening, data extraction and study quality were developed using DistillerSR software and completed in duplicate. We used the Newcastle-Ottawa Scale to assess risk of bias and devised additional quality metrics based on specific methodological features of both air pollution and fecundability studies. OUTCOMES/RESULTS:The search returned 5200 articles, 4994 of which were excluded at the level of title and abstract screening. After full-text screening, 35 papers remained for data extraction and synthesis. An additional 3 papers were identified independently that fit criteria, and 5 papers involving multiple routes of exposure were removed, yielding 33 articles from 28 studies for analysis. There were 8 papers that examined outdoor air quality, while 6 papers examined SHS exposure and 19 papers examined indoor air quality. The results indicated an association between outdoor air pollution and reduced fecundability, including TRAP and specifically nitrogen oxides and PM with a diameter of ≤2.5 µm, as well as exposure to SHS and formaldehyde. However, exposure windows differed greatly between studies as did the method of exposure assessment. There was little evidence that exposure to volatile solvents is associated with reduced fecundability. WIDER IMPLICATIONS/CONCLUSIONS:The evidence suggests that exposure to outdoor air pollutants, SHS and some occupational inhaled pollutants may reduce fecundability. Future studies of SHS should use indoor air monitors and biomarkers to improve exposure assessment. Air monitors that capture real-time exposure can provide valuable insight about the role of indoor air pollution and are helpful in assessing the short-term acute effects of pollutants on TTP.

BACKGROUND:The plasma proteome can be quantified using different types of highly multiplexed technologies, including aptamer-based and proximity-extension immunoassay methods. There has been limited characterization of how these protein measurements correlate across platforms and with absolute measures from targeted immunoassays. METHODS:We assessed the comparability of (a) highly multiplexed aptamer-based (SomaScan v4; Somalogic) and proximity-extension immunoassay (OLINK Proseek® v5003; Olink) methods in 427 Atherosclerosis Risk in Communities (ARIC) Study participants (Visit 5, 2011-2013), and (b) 18 of the SomaScan protein measurements against targeted immunoassays in 110 participants (55 cardiovascular disease cases, 55 controls). We calculated Spearman correlations (r) between the different measurements and compared associations with case-control status. RESULTS:There were 417 protein comparisons (366 unique proteins) between the SomaScan and Olink platforms. The average correlation was r = 0.46 (range: -0.21 to 0.97; 79 [19%] with r ≥ 0.8). For the comparison of SomaScan and targeted immunoassays, 6 of 18 assays (growth differentiation factor 15 [GDF15], interleukin-1 receptor-like 1 [ST2], interstitial collagenase [MMP1], adiponectin, leptin, and resistin) had good correlations (r ≥ 0.8), 2 had modest correlations (0.5 ≤ r < 0.8; osteopontin and interleukin-6 [IL6]), and 10 were poorly correlated (r < 0.5; metalloproteinase inhibitor 1 [TIMP1], stromelysin-1 [MMP3], matrilysin [MMP7], C-C motif chemokine 2 [MCP1], interleukin-10 [IL10], vascular cell adhesion protein 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], interleukin-18 [IL18], tumor necrosis factor [TNFα], and visfatin) overall. Correlations for SomaScan and targeted immunoassays were similar according to case status. CONCLUSIONS:There is variation in the quantitative measurements for many proteins across aptamer-based and proximity-extension immunoassays (approximately 1/2 showing good or modest correlation and approximately 1/2 poor correlation) and also for correlations of these highly multiplexed technologies with targeted immunoassays. Design and interpretation of protein quantification studies should be informed by the variation across measurement techniques for each protein.

Objective: We determined the interactive associations of apolipoprotein e4 (APOE-e4), and obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease and examined for racial/ethnic differences of this association. Methods: We used data from the National Alzheimer's Coordinating Center Uniform Dataset (NACC UDS). All participants undergo annual observations, including demographic survey, battery of neuropsychological tests, blood draw (with genotyping), and a clinical evaluation with medical and cognitive/dementia status assessment, while a subset of participants have cerebrospinal fluid (CSF) biomarkers and neuroimaging data. Biomarkers of AD were characterized as the presence of abnormally low amyloid in CSF, via validated Aβ42 cut off protocols, and total segmented hippocampal volume, and volume of white matter hyper intensities (WMH). While clinical markers (to preview cognitive relationships) were characterized via the Montreal Cognitive Assessment (MOCA). Results: Biomarker and clinical marker data were derived from 1,387 participants at baseline (mean age = 69.73 � 8.32; 58.6% female; 13.7% Black/African American), 18.4% of the sample had sleep apnea, and 37.9% were APOE-e4 carriers. Our results confirmed previous reports that OSA and APOE-e4 were independently associated with AD through abnormal levels of amyloid (F(1,306) = 4.27; p = 0.040; F(1,285) = 60.88; p < 0.000, respectively), WMH volume (F(1,306) = 4.27; p = 0.040; F(1,285) = 60.88; p < 0.000, respectively), and MOCA scores (F(1,306) = 4.27; p = 0.040; F(1,285) = 60.88; p < 0.000, respectively). No significant interaction between OSA and APOE-e4 relative to amyloid emerged, however, race stratified analyses indicated the interaction of OSA and APOE-e4 and was significantly associated with WMH and hippocampal volume in Black/African American, but not white participants. Conclusion: OSA and APOE-e4 are interactively associated with WHM in Black/African Americans. This interaction may partially explicate increased levels of risk in this population.