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Abstract Background Exposure to systemic racism is linked to increased dementia burden. To assess systemic inflammation as a potential pathway linking exposure to racism and dementia disparities, we investigated the mediating role of C-reactive protein (CRP), a systemic inflammation marker, and the moderating role of the racialization process in incident dementia. Methods In the US Health and Retirement Study (n = 6,908), serum CRP was measured at baseline (2006, 2008 waves). Incident dementia was classified by cognitive tests over a six-year follow-up. Self-reported racialized categories were a proxy for exposure to the racialization process. We decomposed racialized disparities in dementia incidence (non-Hispanic Black and/or Hispanic vs. non-Hispanic white) into 1) the mediated effect of CRP, 2) the moderated portion attributable to the interaction between racialized group membership and CRP, and 3) the controlled direct effect (other pathways through which racism operates). Results The 6-year cumulative incidence of dementia is 12%. Among minoritized participants (i.e., non-Hispanic Black and/or Hispanic), high CRP levels ( ≥ 75th percentile or 4.73μg/mL) are associated with 1.26 (95%CI: 0.98, 1.62) times greater risk of incident dementia than low CRP (

Abstract Background Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. Results We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. Conclusions By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .

IntroductionLittle is known on the association between cross-shift changes in pulmonary function and personal inhalation exposure to particulate matter (PM) among informal electronic-waste (e-waste) recovery workers who have substantial occupational exposure to airborne pollutants from burning e-waste.MethodsUsing a cross-shift design, pre- and post-shift pulmonary function assessments and accompanying personal inhalation exposure to PM (sizes

Abstract Mobile health (mHealth) interventions may enhance positive health behaviors, but randomized trials evaluating their efficacy are uncommon. Our goal was to determine if a mHealth intervention augmented and extended benefits of center-based cardiac rehabilitation (CR) for physical activity levels at 6-months. We delivered a randomized clinical trial to low and moderate risk patients with a compatible smartphone enrolled in CR at two health systems. All participants received a compatible smartwatch and usual CR care. Intervention participants received a mHealth intervention that included a just-in-time-adaptive intervention (JITAI) as text messages. The primary outcome was change in remote 6-minute walk distance at 6-months stratified by device type. Here we report the results for 220 participants enrolled in the study (mean [SD]: age 59.6 [10.6] years; 67 [30.5%] women). For our primary outcome at 6 months, there is no significant difference in the change in 6 min walk distance across smartwatch types (Intervention versus control: +31.1 meters Apple Watch, −7.4 meters Fitbit; p = 0.28). Secondary outcomes show no difference in mean step counts between the first and final weeks of the study, but a change in 6 min walk distance at 3 months for Fitbit users. Amongst patients enrolled in center-based CR, a mHealth intervention did not improve 6-month outcomes but suggested differences at 3 months in some users.

[This corrects the article DOI: 10.1371/journal.pgph.0002601.].

ObjectiveTo overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.MethodsDrawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.ResultsThe current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.ConclusionBy employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Epigenetic researchers often evaluate DNA methylation as a potential mediator of the effect of social/environmental exposures on a health outcome. Modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large multi-ethnic cohort in the United States, while providing an R package for their seamless implementation and adoption. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model (BSLMM) and high-dimensional mediation analysis (HDMA); while the preferred methods for estimating the global mediation effect are high-dimensional linear mediation analysis (HILMA) and principal component mediation analysis (PCMA). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.

Abstract Despite racial disparities in diseases of aging and premature mortality, non-Hispanic Black Americans tend to have longer leukocyte telomere length (LTL), a biomarker of cellular aging, than non-Hispanic White Americans. Previous findings suggest that exposure to certain persistent organic pollutants (POPs) is both racially-patterned and associated with longer LTL. We examine whether Black/White differences in LTL are explained by differences in exposure to 15 POPs by estimating the indirect effect (IE) of self-reported race on LTL that is mediated through nine polychlorinated biphenyls (PCBs), three furans, and three dioxins, as well as their mixtures. Our study population includes 1,251 adults from the 1999–2000 and 2001–2002 cycles of the cross-sectional National Health and Nutrition Examination Survey. We characterized single-pollutant mediation effects by constructing survey-weighted linear regression models. We also implemented various approaches to quantify a global mediation effect of all POPs, including unpenalized linear regression, ridge regression, and examination of three summary exposure scores. We found support for the hypothesis that exposure to PCBs partially mediates Black/White differences in LTL. In single-pollutant models, there were significant IEs of race on LTL through six individual PCBs (118, 138, 153, 170, 180, and 187). Ridge regression (0.013, CI 0.001, 0.023; 26.0% mediated) and models examining summative exposure scores with linear combinations derived from principal components analysis (0.019, CI 0.009, 0.029; 34.8% mediated) and Toxic Equivalency Quotient (TEQ) scores (0.016, CI 0.005, 0.026; 28.8% mediated) showed significant IEs when incorporating survey weights. Exposures to individual POPs and their mixtures, which may arise from residential and occupational segregation, may help explain why Black Americans have longer LTL than their White counterparts, providing an environmental explanation for counterintuitive race differences in cellular aging.

The Epidemiologic Questionnaire (EPI-Q) was established to collect broad, uniform, self-reported health data to supplement electronic health record (EHR) and genotype information from participants in the University of Michigan (UM) Precision Health cohorts. Recruitment of EPI-Q participants, who were already enrolled in 1 of 3 ongoing UM Precision Health cohorts—the Michigan Genomics Initiative, Mental Health Biobank, and Metabolism, Endocrinology, and Diabetes cohorts—began in March 2020. Of 54,043 retrospective invitations, 5,577 individuals enrolled, representing a 10.3% response rate. Of these, 3,502 (63.7%) were female, and the average age was 56.1 years (standard deviation, 15.4). The baseline survey comprises 11 modules on topics including personal and family health history, lifestyle, and cancer screening and history. Additionally, 11 optional modules cover topics including financial toxicity, occupational exposure, and life meaning. The questions are based on standardized and validated instruments used in other cohorts, and we share resources to expedite development of similar surveys. Data are collected via the MyDataHelps platform, which enables current and future participants to share non-Michigan Medicine EHR data. Recruitment is ongoing. Cohort data are available to those with institutional review board approval; for details, contact the Data Office for Clinical and Translational Research (DataOffice@umich.edu).

Background and objectivesElevated circulating cystatin C is associated with cognitive impairment in non-Hispanic Whites, but its role in racial disparities in dementia is understudied. In a nationally representative sample of older non-Hispanic White, non-Hispanic Black, and Hispanic adults in the United States, we use mediation-interaction analysis to understand how racial disparities in the cystatin C physiological pathway may contribute to racial disparities in prevalent dementia.MethodsIn a pooled cross-sectional sample of the Health and Retirement Study (n = 9,923), we employed Poisson regression to estimate prevalence ratios and to test the relationship between elevated cystatin C (>1.24 vs. ≤1.24 mg/L) and impaired cognition, adjusted for demographics, behavioral risk factors, other biomarkers, and chronic conditions. Self-reported racialized social categories were a proxy measure for exposure to racism. We calculated additive interaction measures and conducted four-way mediation-interaction decomposition analysis to test the moderating effect of race/ethnicity and mediating effect of cystatin C on the racial disparity.ResultsOverall, elevated cystatin C was associated with dementia (prevalence ratio [PR] = 1.2; 95% CI: 1.0, 1.5). Among non-Hispanic Black relative to non-Hispanic White participants, the relative excess risk due to interaction was 0.7 (95% CI: −0.1, 2.4), the attributable proportion was 0.1 (95% CI: −0.2, 0.4), and the synergy index was 1.1 (95% CI: 0.8, 1.8) in a fully adjusted model. Elevated cystatin C was estimated to account for 2% (95% CI: −0, 4%) for the racial disparity in prevalent dementia, and the interaction accounted for 8% (95% CI: −5, 22%). Analyses for Hispanic relative to non-white participants suggested moderation by race/ethnicity, but not mediation.DiscussionElevated cystatin C was associated with dementia prevalence. Our mediation-interaction decomposition analysis suggested that the effect of elevated cystatin C on the racial disparity might be moderated by race/ethnicity, which indicates that the racialization process affects not only the distribution of circulating cystatin C across minoritized racial groups, but also the strength of association between the biomarker and dementia prevalence. These results provide evidence that cystatin C is associated with adverse brain health and this effect is larger than expected for individuals racialized as minorities had they been racialized and treated as non-Hispanic White.

The maternal epigenome may be responsive to prenatal metals exposures. We tested whether metals are associated with concurrent differential maternal whole blood DNA methylation. In the Early Autism Risk Longitudinal Investigation cohort, we measured first or second trimester maternal blood metals concentrations (cadmium, lead, mercury, manganese, and selenium) using inductively coupled plasma mass spectrometry. DNA methylation in maternal whole blood was measured on the Illumina 450 K array. A subset sample of 97 women had both measures available for analysis, all of whom did not report smoking during pregnancy. Linear regression was used to test for site-specific associations between individual metals and DNA methylation, adjusting for cell type composition and confounding variables. Discovery gene ontology analysis was conducted on the top 1,000 sites associated with each metal. We observed hypermethylation at 11 DNA methylation sites associated with lead (FDR False Discovery Rate q-value 0.86). DNA methylation sites associated with lead and manganese may be potential biomarkers of exposure or implicate downstream gene pathways.

There has been raging discussion and debate around the quality of COVID death data in South Asia. According to WHO, of the 5.5 million reported COVID-19 deaths from 2020-2021, 0.57 million (10%) were contributed by five low and middle income countries (LMIC) countries in the Global South: India, Pakistan, Bangladesh, Sri Lanka and Nepal. However, a number of excess death estimates show that the actual death toll from COVID-19 is significantly higher than the reported number of deaths. For example, the IHME and WHO both project around 14.9 million total deaths, of which 4.5-5.5 million were attributed to these five countries in 2020-2021. We focus our gaze on the COVID-19 performance of these five countries where 23.5% of the world population lives in 2020 and 2021, via a counterfactual lens and ask, to what extent the mortality of one LMIC would have been affected if it adopted the pandemic policies of another, similar country? We use a Bayesian semi-mechanistic model developed by Mishra et al. (2021) to compare both the reported and estimated total death tolls by permuting the time-varying reproduction number (Rt) across these countries over a similar time period. Our analysis shows that, in the first half of 2021, mortality in India in terms of reported deaths could have been reduced to 96 and 102 deaths per million compared to actual 170 reported deaths per million had it adopted the policies of Nepal and Pakistan respectively. In terms of total deaths, India could have averted 481 and 466 deaths per million had it adopted the policies of Bangladesh and Pakistan. On the other hand, India had a lower number of reported COVID-19 deaths per million (48 deaths per million) and a lower estimated total deaths per million (80 deaths per million) in the second half of 2021, and LMICs other than Pakistan would have lower reported mortality had they followed India's strategy. The gap between the reported and estimated total deaths highlights the varying level and extent of under-reporting of deaths across the subcontinent, and that model estimates are contingent on accuracy of the death data. Our analysis shows the importance of timely public health intervention and vaccines for lowering mortality and the need for better coverage infrastructure for the death registration system in LMICs.

The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of 'experimentation' in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of 'team science', the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a 'call to actions' for both policy-makers and researchers.

Cervical cancer is the second most common gynecologic cancer in Vietnam but based on the literature, only ~25% of Vietnamese women reported ever being screened for cervical cancer. To inform strategies to reduce the cervical cancer burden in Southern Vietnam where disease incidence is higher than the national average, this study examined behaviors, awareness, barriers, and beliefs about cervical cancer screening among rural and urban women in this geographical region. In October-November 2021, we conducted a cross-sectional study among 196 rural and 202 urban women in Southern Vietnam; participants completed a cervical cancer screening questionnaire. Descriptive analyses and rural-urban differences in screening behavior, awareness, barriers, and beliefs are presented. About half of the rural and urban participants reported ever being screened for cervical cancer. Most participants showed high perceived severity of cervical cancer and benefits of screening. Further, they reported that they would screen if it was recommended by doctors and/or friends/family. However, most women showed low awareness and perceived susceptibility to cervical cancer. Logistical and psychosocial barriers to physician-based screening methods were reported. Based on our results, the World Health Organization 2030 goals for cervical cancer screening are not currently met in Southern Vietnam. Increasing health literacy and engaging doctors and family members/social networks emerged as important avenues to improve screening. HPV (Human papillomavirus) self-sampling is also a potential approach to increase uptake of cervical cancer screening given the identified psychosocial and logistical barriers.

Background: Short telomere length (TL) and telomere attrition (TA) have been associated with age-related diseases.Objective: We assessed whether a genetic risk score for short TL (GRS-TL) combining seven TL-associated genetic variants identified in a European-ancestry genome-wide association study (GWAS) was associated with TL and TA over 10 years.Methods: Relative TL (T/S ratio) was measured by the quantitative polymerase chain reaction method for a sample of white, African American, and Hispanic participants, who attended Exam 1 and/or 5 of the Multi-Ethnic Study of Atherosclerosis (MESA). Our final sample included 1,227 participants for the TL analysis and 1,138 for the TA analysis. Participants were 45–84 years at Exam 1. We used a linear mixed effects model and adjusted for age, sex, and population structure. Models were stratified by race/ethnicity.Results: In the TL analysis, higher GRS-TL significantly predicted shorter TL (estimates = -0.18 [S.E. = 0.08], p = 0.02 for white; -0.18 [0.07], p < 0.01 for African American; and -0.13 [0.05], p = 0.02 for Hispanic) in fully adjusted models. In the TA analysis, no association between GRS-TL and TA over 10 years was found.Conclusion: Although GRS-TL was developed in European-ancestry populations, it was significantly associated with TL (but not TA) in all three race/ethnic groups examined.

Introduction: Observational studies of COVID-19 vaccines’ effectiveness can provide crucial information regarding the strength and durability of protection against SARS-CoV-2 infection and whether the protective response varies across different patient subpopulations and in the context of different SARS-CoV-2 variants. Methods: We used a test-negative study design to assess vaccine effectiveness against SARS-CoV-2 infection and severe COVID-19 resulting in hospitalization, intensive care unit admission, or death using electronic health records data of 170,741 adults who had been tested for COVID-19 at the University of Michigan Medical Center between January 1 and December 31, 2021. We estimated vaccine effectiveness by comparing the odds of vaccination between cases and controls during each 2021 calendar quarter and stratified all outcomes by vaccine type, patient demographic and clinical characteristics, and booster status. Results: Unvaccinated individuals had more than double the rate of infections (12.1% vs 4.7%) and >3 times the rate of severe COVID-19 outcomes (1.4% vs 0.4%) than vaccinated individuals. COVID-19 vaccines were 62.1% (95% CI=60.3, 63.8) effective against a new infection, with protection waning in the last 2 quarters of 2021. The vaccine effectiveness against severe disease overall was 73.7% (95% CI=69.6, 77.3) and remained high throughout 2021. Data from the last quarter of 2021 indicated that adding a booster dose augmented effectiveness against infection up to 87.3% (95% CI=85.0, 89.2) and against severe outcomes up to 94.0% (95% CI=89.5, 96.6). Pfizer-BioNTech and Moderna vaccines showed comparable performance when controlling for vaccination timing. Vaccine effectiveness was greater in more socioeconomically affluent areas and among healthcare workers; otherwise, we did not detect any significant modification of vaccine effectiveness by covariates, including gender, race, and SES. Conclusions: COVID-19 vaccines were highly protective against infection and severe COVID-19 resulting in hospitalization, intensive care unit admission, or death. Administration of a booster dose significantly increased vaccine effectiveness against both outcomes. Ongoing surveillance is required to assess the durability of these findings.

Social networks are often measured as conduits of infection. Our prior cross-sectional analyses found that denser social ties among individuals reduces transmission of acute gastrointestinal illness (AGI) in coastal Ecuador; social networks can describe both risk and protection. We extend findings to examine how social connectedness influences AGI longitudinally in Ecuador from 2007 to 2013, a time of rapid development, using a two-stage Bayesian hierarchical model to estimate multiple network effects. A larger community network of people to discuss important matters with was consistently protective against AGI over time, and a network defined by people passing time together became a stronger measure of risk, due to increasing population density and travel. These networks were interdependent: the joint effect of having a small passing time network and large important matters network reduced the odds of AGI over time (2007: OR 1.16 (95% CI: 0.94, 1.44), 2013: OR 0.56 (95% CI: 0.45, 0.71)); and synergistic: the people an individual passed time with became the people they discussed important matters with. Focus groups emphasized that with greater remoteness came greater community cohesion resulting in safer WASH practices. Social networks can enhance and reduce health differently as social infrastructure evolves, highlighting the importance of community-level factors in a period of rapid development.

Abstract Objective There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020–January 31, 2021) and part of the second wave (February 1, 2021–May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations. Results Both wave 1 and wave 2 estimates qualitatively show that there is a large degree of “covert infections” in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71–11.47) and for deaths as 3.56 (95% CrI 3.48–3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26–28.81) and to 5.77 (95% CrI 5.34–6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates.

Abstract Background Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Methods Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson’s and Lin’s correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model. Results For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63–8.80), while eSIR yields 8.35 (7.19–9.60), SAPHIRE returns 8.17 (7.90–8.52) and SEIR-fansy projects 8.51 (8.18–8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy. Conclusions In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the “total” number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

Abstract Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15–June 30, 2020, we estimate the underreporting factor for cases at 34–53 (deaths: 8–13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27–July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30–42 for cases. Together, these imply approximately 96–98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13–22 (deaths: 3–7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15–23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17–21. Together, these updated estimates imply approximately 92–96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

Abstract Lipidome-wide metabolites may be useful biomarkers of pregnancy outcomes. We sought to characterize maternal lipidomic signatures associated with preterm birth and neonatal anthropometric parameters. Plasma samples were collected 24–28 weeks gestation, and lipidomic profiling was quantified using high-performance liquid chromatography tandem mass spectrometry. Lipid metabolites were analyzed individually and as whole lipid classes and subgroups based on degree of hydrocarbon chain saturation. Associations were estimated using linear and logistic regression. After false discovery adjustment (q

Low socioeconomic status (SES) and living in a disadvantaged neighborhood are associated with poor cardiovascular health. Multiple lines of evidence have linked DNA methylation to both cardiovascular risk factors and social disadvantage indicators. However, limited research has investigated the role of DNA methylation in mediating the associations of individual- and neighborhood-level disadvantage with multiple cardiovascular risk factors in large, multi-ethnic, population-based cohorts. We examined whether disadvantage at the individual level (childhood and adult SES) and neighborhood level (summary neighborhood SES as assessed by Census data and social environment as assessed by perceptions of aesthetic quality, safety, and social cohesion) were associated with 11 cardiovascular risk factors including measures of obesity, diabetes, lipids, and hypertension in 1,154 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). For significant associations, we conducted epigenome-wide mediation analysis to identify methylation sites mediating the relationship between individual/neighborhood disadvantage and cardiovascular risk factors using the JT-Comp method that assesses sparse mediation effects under a composite null hypothesis. In models adjusting for age, sex, race/ethnicity, smoking, medication use, and genetic principal components of ancestry, epigenetic mediation was detected for the associations of adult SES with body mass index (BMI), insulin, and high-density lipoprotein cholesterol (HDL-C), as well as for the association between neighborhood socioeconomic disadvantage and HDL-C at FDR q < 0.05. The 410 CpG mediators identified for the SES-BMI association were enriched for CpGs associated with gene expression (expression quantitative trait methylation loci, or eQTMs), and corresponding genes were enriched in antigen processing and presentation pathways. For cardiovascular risk factors other than BMI, most of the epigenetic mediators lost significance after controlling for BMI. However, 43 methylation sites showed evidence of mediating the neighborhood socioeconomic disadvantage and HDL-C association after BMI adjustment. The identified mediators were enriched for eQTMs, and corresponding genes were enriched in inflammatory and apoptotic pathways. Our findings support the hypothesis that DNA methylation acts as a mediator between individual- and neighborhood-level disadvantage and cardiovascular risk factors, and shed light on the potential underlying epigenetic pathways. Future studies are needed to fully elucidate the biological mechanisms that link social disadvantage to poor cardiovascular health.

Diverse toxicological mechanisms may mediate the impact of environmental toxicants on pregnancy outcomes. In this study the authors introduce an analytical framework for multivariate mediation analysis to identify mediation pathways in the relationship between environmental toxicants and gestational age at delivery.

Abstract Fusobacterium nucleatum (Fn) is frequently found in colorectal cancers (CRCs). High loads of Fn DNA are detected in CRC tissues with microsatellite instability-high (MSI-H), or with the CpG island hypermethylation phenotype (CIMP). Fn infection is also associated with the inflammatory tumor microenvironment of CRC. A subtype of CRC exhibits inflammation-associated microsatellite alterations (IAMA), which are characterized by microsatellite instability-low (MSI-L) and/or an elevated level of microsatellite alterations at selected tetra-nucleotide repeats (EMAST). Here we describe two independent CRC cohorts in which heavy or moderate loads of Fn DNA are associated with MSI-H and L/E CRC respectively. We also show evidence that Fn produces factors that induce γ-H2AX, a hallmark of DNA double strand breaks (DSBs), in the infected cells.

Introduction: Recent media articles have suggested that women-led countries are doing better in terms of their responses to the COVID-19 pandemic. The aim of this paper is to examine an ensemble of public he- alth metrics to assess the control of COVID-19 epidemic in women- versus men-led countries worldwide based on data available up to June 3. Methods: We restrict our analysis to 159 countries with at least 100 cumulative reported cases of CO- VID-19 infections and at least 10 days of reported data as of June 3, 2020. We compared in women- versus men-led countries the trajectory of time varying reproduction number R(t) as well as most recent values of R(t) and other metrics of viral transmission such as doubling time and case-fatality rates. A two-sample bootstrap procedure-based comparison of medians and construct 95% confidence interval (95% CI) based on empirical 2.5th and 97.5th percentile of the bootstrap distributions were used. Results: The median of the distribution of median time-varying effective reproduction number for women- and men-led countries were 0.89 and 1.14 respectively with the 95% two-sample bootstrap-based confiden- ce interval for the difference (women - men) being -0.34 to 0.02. In terms of scale of testing, the median percentage of population tested were 3.28% (women), 1.59% (men) [95% CI -1.29% to 3.60%)] with test positive rates of 2.69% (women) and 4.94% (men) respectively. Conclusion: It appears that though statistically not significant, countries led by women have an edge over countries led by men in terms of public health metrics for controlling the spread of the COVID-19 pan- demic worldwide.

There has been much discussion and debate around underreporting of deaths in India in media articles and in the scientific literature. In this brief report, we aim to meta-analyze the available/inferred estimates of infection fatality rates for SARS-CoV-2 in India based on the existent literature. These estimates account for uncaptured deaths and infections. We consider empirical excess death estimates based on all-cause mortality data as well as disease transmission-based estimates that rely on assumptions regarding infection transmission and ascertainment rates in India. Through an initial systematic review (Zimmermann et al., 2021) that followed PRISMA guidelines and comprised a search of databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch) on July 3, 2021, we further extended the search verification through May 26, 2022. The screening process yielded 15 studies qualitatively analyzed, of which 9 studies with 11 quantitative estimates were included in the meta-analysis. Using a random effects meta-analysis framework, we obtain a pooled estimate of nationwide infection fatality rate (defined as the ratio of estimated deaths over estimated infections) and a corresponding confidence interval. Death underreporting from excess deaths studies varies by a factor of 6.1-13.0 with nationwide cumulative excess deaths ranging from 2.6-6.3 million, whereas the underreporting from disease transmission-based studies varies by a factor of 3.5-7.3 with SARS-CoV-2 related nationwide estimated total deaths ranging from 1.4-3.4 million, through June 2021 with some estimates extending to 31 December 2021. Underreporting of infections was found previously (Zimmermann et al., 2021) to be 24.9 (relying on the latest 4th nationwide serosurvey from 14 June-6 July 2021 prior to launch of the vaccination program). Conservatively, by considering the lower values of these available estimates, we infer that approximately 95% of infections and 71% of deaths were not accounted for in the reported figures in India. Nationwide pooled infection fatality rate estimate for India is 0.51% (95% confidence interval [CI]: 0.45%- 0.58%). We often tend to compare countries across the world in terms of total reported cases and deaths. Although the US has the highest number of reported cumulative deaths globally, after accounting for underreporting, India appears to have the highest number of cumulative total deaths (reported + unreported). However, the large number of estimated infections in India leads to a lower infection fatality rate estimate than the US, which in part is due to the younger population in India. We emphasize that the age-structure of different countries must be taken into consideration while making such comparisons. More granular data are needed to examine heterogeneities across various demographic groups to identify at-risk and underserved populations with high COVID mortality; the hope is that such disaggregated mortality data will soon be made available for India.

Since the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic, a focus of research has been to identify risk factors associated with COVID-19-related outcomes, such as testing and diagnosis, and use them to build prediction models. Existing studies have used data from digital surveys or electronic health records (EHRs), but very few have linked the two sources to build joint predictive models. In this study, we used survey data on 7,054 patients from the Michigan Genomics Initiative biorepository to evaluate how well self-reported data could be integrated with electronic records for the purpose of modeling COVID-19-related outcomes. We observed that among survey respondents, self-reported COVID-19 diagnosis captured a larger number of cases than the corresponding EHRs, suggesting that self-reported outcomes may be better than EHRs for distinguishing COVID-19 cases from controls. In the modeling context, we compared the utility of survey- and EHR-derived predictor variables in models of survey-reported COVID-19 testing and diagnosis. We found that survey-derived predictors produced uniformly stronger models than EHR-derived predictors—likely due to their specificity, temporal proximity, and breadth—and that combining predictors from both sources offered no consistent improvement compared to using survey-based predictors alone. Our results suggest that, even though general EHRs are useful in predictive models of COVID-19 outcomes, they may not be essential in those models when rich survey data are already available. The two data sources together may offer better prediction for COVID severity, but we did not have enough severe cases in the survey respondents to assess that hypothesis in in our study.

Background/Aim: The association between heavy metal exposure and adverse birth outcomes is well-established. However, there is a paucity of research identifying biomarker profiles that may improve the early detection of heavy metal-induced adverse birth outcomes. Because lipids are abundant in our body and associated with important signaling pathways, we assessed associations between maternal metals/metalloid blood levels with lipidomic profiles among 83 pregnant women in the Puerto Rico PROTECT birth cohort.Methods: We measured 10 metals/metalloid blood levels during 24–28 weeks of pregnancy. Prenatal plasma lipidomic profiles were identified by liquid chromatography–mass spectrometry-based shotgun lipidomics. We derived sums for each lipid class and sums for each lipid sub-class (saturated, monounsaturated, polyunsaturated), which were then regressed on metals/metalloid. False discovery rate (FDR) adjusted p-values (q-values) were used to account for multiple comparisons.Results: A total of 587 unique lipids from 19 lipid classes were profiled. When controlling for multiple comparisons, we observed that maternal exposure to manganese and zinc were negatively associated with plasmenyl-phosphatidylethanolamine (PLPE), particularly those containing polyunsaturated fatty acid (PUFA) chains. In contrast to manganese and zinc, arsenic and mercury were positively associated with PLPE and plasmenyl-phosphatidylcholine (PLPC).Conclusion: Certain metals were significantly associated with lipids that are responsible for the biophysical properties of the cell membrane and antioxidant defense in lipid peroxidation. This study highlighted lipid-metal associations and we anticipate that this study will open up new avenues for developing diagnostic tools.

Background: Exposure to metals and metal mixtures may influence ovarian aging. However, epidemiologic evidence of their potential impact is lacking. Objective: We prospectively examined the associations of 15 urinary metal concentrations and their mixtures with natural menopause in the Study of Women’s Health Across the Nation Multi-Pollutant Study. Methods: The study population consisted of 1082 premenopausal women from multiple racial/ethnic groups, aged 45–56 years at baseline (1999–2000), with the median follow-up of 4.1 years. Urinary concentrations of 15 metals, including arsenic, barium, cadmium, cobalt, cesium, copper, mercury, manganese, molybdenum, nickel, lead, antimony, tin, thallium, and zinc, were measured at baseline. Natural menopause was defined as the final bleeding episode prior to at least 12 months of amenorrhea, not due to surgery or hormone therapy. Cox proportional hazards models were used to examine associations between individual metal concentrations and timing of natural menopause. The associations between metal mixtures and natural menopause were evaluated using elastic net penalized Cox regression, and an environmental risk score (ERS) was computed to represent individual risks of natural menopause related to metal mixtures. Results: The median age at natural menopause was 53.2 years. Using the Cox proportional hazards models, the adjusted hazard ratio (HR) (and its 95% confidence interval (CI)) for natural menopause was 1.32 (1.03, 1.67) for arsenic and 1.36 (1.05, 1.76) for lead, comparing the highest with the lowest quartiles of metal concentrations. The predicted ages at natural menopause in the highest and lowest quartiles were 52.7 and 53.5 years for arsenic; and 52.9 and 53.8 years for lead. A significant association between ERS and menopause was also observed. Women in the highest vs. the lowest quartiles of ERS had an HR of 1.71 (1.36, 2.15), equivalent to a 1.6 year earlier median time to natural menopause. Conclusion: This study suggests that arsenic, lead, and metal mixtures are associated with earlier natural menopause, a risk factor for adverse health outcomes in later life.

Exposures to phthalate compounds have been linked to adverse birth outcomes, potentially through oxidative stress mechanisms. We explored associations between mixtures of biomarkers of phthalate and phthalate replacement metabolites and oxidative stress using lipid peroxidation biomarker 8-iso-prostaglandin-F2α (8-iso-PGF2α). As 8-iso-PGF2α can be generated via both chemical (nonenzymatic) and enzymatic lipid peroxidation pathways, we calculated the ratio of 8-iso-PGF2α/prostaglandin F2α in an attempt to distinguish the potential contributions of the two pathways. Urinary biomarker measurements were taken from 775 pregnant women in the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) longitudinal birth cohort at up to three time points during gestation (16–20, 20–24, and 24–28 weeks gestation). Adaptive elastic net with pairwise linear interaction terms (adENET-I) was used to determine individual phthalate metabolites and phthalate interactions that were predictive of lipid oxidative stress biomarkers, and to subsequently create environmental risk scores (ERS) to represent weighted sums of phthalate exposure for each individual at each study visit. Repeated ERS were then used in linear mixed effects models to test for associations between biomarkers of phthalate mixtures and biomarkers of oxidative stress. We also used Bayesian kernel machine regression (BKMR) to explore nonlinearity and interactions between phthalate metabolites within the mixture. An increase from the first to fourth quartile of phthalate ERS derived from adENET-I was associated with a 96.7% increase (95% CI: 74.0, 122) in the hypothesized chemical fraction of 8-iso-PGF2α and a 268% increase (95% CI: 139, 465) in the hypothesized enzymatic fraction of 8-iso-PGF2α. BKMR analyses also suggested strong linear associations between the phthalate mixture and biomarkers of lipid oxidative stress. Various phthalates displayed nonlinear relationships with both chemical and enzymatic fractions of 8-iso-PGF2α, and we observed some evidence of interactions between metabolites in the mixture. In conclusion, exposure to phthalate mixtures was strongly associated with linear increases in biomarkers of lipid oxidative stress, and differences observed between hypothesized chemical and enzymatic lipid peroxidation outcomes highlight the need to critically assess pathways of 8-iso-PGF2α generation in relation to environmental exposures.

BackgroundEarly delivery remains a significant public health problem that has long-lasting impacts on mother and child. Understanding biological mechanisms underlying timing of labor, including endocrine disruption, can inform prevention efforts.MethodsGestational hormones were measured among 976 women in PROTECT, a longitudinal birth cohort in Puerto Rico. We evaluated associations between hormone concentrations at 18 and 26 weeks gestation and gestational age at birth, while assessing effect modification by fetal sex. Exploratory analyses assessed binary outcomes of overall preterm birth (PTB,

Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.

Abstract Introduction Prenatal exposure to some phenols and parabens has been associated with adverse birth outcomes. Hormones may play an intermediate role between phenols and adverse outcomes. We examined the associations of phenol and paraben exposures with maternal reproductive and thyroid hormones in 602 pregnant women in Puerto Rico. Urinary triclocarban, phenol and paraben biomarkers, and serum hormones (estriol, progesterone, testosterone, sex-hormone-binding globulin (SHBG), corticotropin-releasing hormone (CRH), total triiodothyronine (T3), total thyroxine (T4), free thyroxine (FT4) and thyroid-stimulating hormone (TSH)) were measured at two visits during pregnancy. Methods Linear mixed models with a random intercept were constructed to examine the associations between hormones and urinary biomarkers. Results were additionally stratified by study visit. Results were transformed to hormone percent changes for an inter-quartile-range difference in exposure biomarker concentrations (%Δ). Results Bisphenol-S was associated with a decrease in CRH [(%Δ -11.35; 95% CI: -18.71, − 3.33), and bisphenol-F was associated with an increase in FT4 (%Δ: 2.76; 95% CI: 0.29, 5.22). Butyl-, methyl- and propylparaben were associated with decreases in SHBG [(%Δ: -5.27; 95% CI: -9.4, − 1.14); (%Δ: -3.53; 95% CI: -7.37, 0.31); (%Δ: -3.74; 95% CI: -7.76, 0.27)]. Triclocarban was positively associated with T3 (%Δ: 4.08; 95% CI: 1.18, 6.98) and T3/T4 ratio (%Δ: 4.67; 95% CI: -1.37, 6.65), and suggestively negatively associated with TSH (%Δ: -10.12; 95% CI: -19.47, 0.32). There was evidence of susceptible windows of vulnerability for some associations. At 24–28 weeks gestation, there was a positive association between 2,4-dichlorophenol and CRH (%Δ: 9.66; 95% CI: 0.67, 19.45) and between triclosan and estriol (%Δ: 13.17; 95% CI: 2.34, 25.2); and a negative association between triclocarban and SHBG (%Δ: -9.71; 95% CI:-19.1, − 0.27) and between bisphenol A and testosterone (%Δ: -17.37; 95% CI: -26.7, − 6.87). Conclusion Phenols and parabens are associated with hormone levels during pregnancy. Further studies are required to substantiate these findings.

Background: Some heavy metals (e.g., arsenic, cadmium, lead, mercury) have been associated with obesity and obesity comorbidities. The analytical approach for those associations has typically focused on individual metals. There is a growing interest in evaluating the health effects of cumulative exposure to metal mixtures. Objectives: We utilized our Environmental Risk Score (ERS), a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research, to evaluate the associations of cumulative exposure to a mixture of correlated heavy metals with obesity and its comorbidities including hypertension, and type-2 diabetes mellitus (T2DM) while accounting for high degree correlations and interactions among metal mixtures components. Methods: We examined blood and urinary markers of 18 heavy metals among 9537 adults in NHANES 2003–2014. We randomly split data into a training set for the construction of ERS (n = 6675) and a testing set for the evaluation of its statistical performance (n = 2862). ERS of heavy metal mixtures was computed for waist circumference using adaptive elastic-net (AENET) with 189 predictors including 18 main effects, 18 squared terms, and 153 pairwise interactions of heavy metals. Regression analyses with complex survey designs were performed to assess the associations of ERS with other obesity measures, hypertension and T2DM. Results: 7 main effects (blood lead, blood cadmium, blood mercury, and urinary markers of monomethylarsonic acid (MMA), barium, mercury and thallium), 4 squared terms (blood cadmium, urinary cadmium, urinary antimony and urinary tungsten), and 7 pairwise interactions (blood lead & urinary cadmium, blood lead & urinary MMA, blood lead & urinary uranium, urinary cadmium & urinary MMA, urinary dimethylarsinic acid (DMA) & urinary tungsten, urinary MMA & urinary cobalt, and urinary lead & urinary antimony) were selected by AENET for construction of ERS of waist circumference-related metal mixtures. An increase in ERS from 10th percentile to 90th percentile in the overall study population was significantly associated with 4.50 kg/m2 (95% CI: 4.06, 4.94) higher BMI, 4.16 mm (95% CI: 3.56, 4.76) higher skinfold thickness, and 4.11 kg (95% CI: 0.83, 7.40) higher total body fat, independent of age, sex, race/ethnicity, education, smoking status, physical activity and NHANES cycle (Ps

Background: Metal exposure and psychosocial stress in pregnancy have each been associated with adverse birth outcomes, including preterm birth and low birth weight, but no study has examined the potential interaction between them. Objectives: We examined the modifying effect of psychosocial stress on the association between metals and birth outcomes among pregnant women in Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) birth cohort study. Methods: In our analysis of 682 women from the PROTECT study, we measured 16 essential and non-essential metals in blood samples at two time points. We administered questionnaires to collect information on depression, perceived stress, social support, and life experience during pregnancy. Using K-means clustering, we categorized pregnant women into one of two groups: “good” and “poor” psychosocial status. We then evaluated whether the effect of blood metals (geometric average) on adverse birth outcomes (gestational age, preterm birth [overall and spontaneous], birth weight z-score, small for gestation [SGA], large for gestation [LGA]) vary between two clusters of women, adjusting for maternal age, maternal education, pre-pregnancy body mass index (BMI), and second-hand smoke exposure. Results: Blood manganese (Mn) was associated with an increased odds ratio (OR) of overall preterm birth (OR/interquartile range [IQR] = 2.76, 95% confidence interval [CI] = 1.25, 6.12) and spontaneous preterm birth (OR/IQR: 3.68, 95% CI: 1.20, 6.57) only among women with “poor” psychosocial status. The association between copper (Cu) and SGA was also statistically significant only among women having “poor” psychosocial status (OR/IQR: 2.81, 95% CI: 1.20, 6.57). We also observed associations between nickel (Ni) and preterm birth and SGA that were modified by psychosocial status during pregnancy. Conclusions: Presence of “poor” psychosocial status intensified the adverse associations between Mn and preterm birth, Cu and SGA, and protective effects of Ni on preterm. This provides evidence that prenatal psychosocial stress may modify vulnerability to metal exposure.

Abstract Background To assess whether Healing in Harmony (HiH), a form of music therapy, improved women's mental health following conflict-related trauma and sexual violence in the Democratic Republic of Congo. Methods This study used a step-wedged design and included 167 women, who completed up to two pre-tests, a post-test, and up to two follow-up interviews at 3 and 6 months after completing the program. The Hopkins Symptoms Checklist was used to measure anxiety and depression. The Harvard Trauma Questionnaire was used to measure post-traumatic stress disorder (PTSD). Generalized estimating equations with unstructured covariance were used to estimate mean change in mental health scores and relative risks (RRs) for screening positive. Results Prior to starting the HiH program, 73.9, 84.2, and 68.5% screened positive with median scores being 2.20, 2.70, and 2.06 for depression, anxiety, and PTSD, respectively. The RR for screening positive declined significantly (RR = 0.49 for depression, 0.61 for anxiety, and 0.54 for PTSD) and mean scores declined significantly by −0.54, −0.67, and −0.53 points, respectively, from the pre- to the post-test, declines that were sustained at the 3-month and 6-month follow-up interviews. Conclusion The HiH program was associated with significant improvement in women's mental health that was sustained up to 6 months post completion of the program despite instability in the region and evidence of continued experience of conflict-related trauma during the study. These data support the value of providing psychological care in the context of ongoing humanitarian crises.

COVID-19 has had a substantial impact on clinical care and lifestyles globally. The State of Michigan reports over 80,000 positive COVID-19 tests between March 1, 2020 and July 29, 2020. We surveyed 8,041 Michigan Medicine biorepository participants in late June 2020. We found that 55% of COVID-19 cases reported no known exposure to family members or to someone outside the house diagnosed with COVID-19. A significantly higher rate of COVID-19 cases were employed as essential workers (45% vs 19%, p = 9x10-12). COVID-19 cases reporting a fever were more likely to require hospitalization (categorized as severe; OR = 4.4 [95% CI: 1.6-12.5, p = 0.005]) whereas respondents reporting rhinorrhea was less likely to require hospitalization (categorized as mild-to-moderate; OR = 0.16 [95% CI: 0.04-0.73, p = 0.018]). African-Americans reported higher rates of being diagnosed with COVID-19 (OR = 4.0 [95% CI: 2.2-7.2, p = 5x10-6]), as well as higher rates of exposure to family or someone outside the household diagnosed with COVID-19, an annual household income < $40,000, living in rental housing, and chronic diseases. During the Executive Order in Michigan, African Americans, women, and the lowest income group reported worsening health behaviors and higher overall concern for the potential detrimental effects of the pandemic. The higher risk of contracting COVID-19 observed among African Americans may be due to the increased rates of working as essential employees, lower socioeconomic status, and exposure to known positive cases. Continued efforts should focus on COVID-19 prevention and mitigation strategies, as well as address the inequality gaps that result in higher risks for both short-term and long-term health outcomes.

Background: Epidemiologic studies on associations between metals and insulin resistance and β-cell dysfunction have been cross-sectional and focused on individual metals. Objective: We assessed the association of exposure to metal mixtures, based on assessment of 15 urinary metals, with both baseline levels and longitudinal changes in homeostatic model assessments for insulin resistance (HOMA-IR) and β-cell function (HOMA-β). Methods: We examined 1262 women, aged 45–56 years at baseline (1999–2000), who were followed through 2015–2016, from the Study of Women’s Health Across the Nation. Urinary concentrations of 15 metals (arsenic, barium, cadmium, cobalt, cesium, copper, mercury, manganese, molybdenum, nickel, lead, antimony, tin, thallium, and zinc) were determined at baseline. HOMA-IR and HOMA-β were repeatedly measured over 16 years of follow-up. A two-stage modeling was used to account for correlations in dependent and independent variables: In stage-1, linear mixed effects models were used to estimate the participant-specific baseline HOMA levels from random intercepts and participant-specific rates of changes from random slopes. In stage-2, adaptive elastic-net (AENET) models were fit to identify components of metal mixtures associated with participant-specific baseline levels and rates of changes in HOMA-IR and HOMA-β, respectively. An environmental risk score (ERS) was used to integrate metal mixture effects from AENET results. Results: In multivariable adjusted AENET models, urinary zinc was associated with higher HOMA-IR at baseline, whereas molybdenum was associated with lower HOMA-IR at baseline. The estimated changes in baseline HOMA-IR for one standard deviation increase in log-transformed urinary metal concentrations were 5.76% (3.05%, 8.55%) for zinc and −3.25% (−5.45%, −1.00%) for molybdenum, respectively. Urinary zinc was also associated with lower HOMA- β at baseline. Arsenic was associated with a slightly faster rate of decline in HOMA-β in the AENET model evaluating associations between metals and rate of changes. Significant associations of ERS with both HOMA-IR and HOMA-β at baseline were observed. ERS for the rate of changes was not calculated and examined in relation to rates of changes in HOMA-IR and HOMA-β because only a single metal was selected by AENET. Conclusion: Exposure to metal mixtures may be exerting effects on insulin resistance and β-cell dysfunction, which might be mechanisms by which metal exposures lead to elevated diabetes risks.

Background: In previous studies, exposures to heavy metals such as Pb and Cd have been associated with adverse birth outcomes; however, knowledge on effects at low levels of exposure and of other elements remain limited. Method: We examined individual and mixture effects of metals and metalloids on birth outcomes among 812 pregnant women in the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) cohort. We measured 16 essential and non-essential metal(loid)s in maternal blood collected at 16–20 and 24–28 weeks gestation. We used linear and logistic regression to independently examine associations between geometric mean (GM) concentrations of each metal across visits and gestational age, birthweight z-scores, preterm birth, small for gestational age (SGA), and large for gestational age (LGA). We evaluated effect modification with infant sex*metal interaction terms. To identify critical windows of susceptibility, birth outcomes were regressed on visit-specific metal concentrations. Furthermore, average metal concentrations were divided into tertiles to examine the potential for non-linear relationships. We used elastic net (ENET) regularization to construct Environmental Risk Score (ERS) as a metal risk score and Bayesian Kernel Machine Regression (BKMR) to identify individual metals most critical to each outcome, accounting for correlated exposures. Results: In adjusted models, an interquartile range (IQR) increase in GM lead (Pb) was associated with 1.63 higher odds of preterm birth (95%CI = 1.17, 2.28) and 2 days shorter gestational age (95% CI = −3.1, −0.5). Manganese (Mn) and zinc (Zn) were also associated with higher odds of preterm birth and shorter gestational age; the associations were strongest among the highest tertile for Mn and among females for Zn. Mercury (Hg) was associated with higher risk of preterm birth at the later window of pregnancy. Ni measured later in pregnancy was associated with lower odds of SGA. ENET and BKMR models selected similar metals as “important” predictors of birth outcomes. The association between ERS and preterm birth was assessed and the third tertile of ERS was significantly associated with an elevated odds ratio of 2.13 (95% CI = 1.12, 5.49) for preterm birth compared to the first tertile. Conclusion: As the PROTECT cohort has lower Pb concentrations (GM = 0.33 μg/dL) compared to the mainland US, our findings suggest that low-level prenatal lead exposure, as well as elevated Mn and Zn exposure, may adversely affect birth outcomes. Improved understanding on environmental factors contributing to preterm birth, together with sustainable technologies to remove contamination, will have a direct impact in Puerto Rico and elsewhere. Keywords: Metals, Birth outcomes, Biomarkers, Mixtures, Puerto Rico

Background: Limited information exists regarding longitudinal trends in midlife women’s exposure to perfluoroalkyl and polyfluoroalkyl substances (PFAS). Further, little is known about how patterns of exposure differ by race/ethnicity and reproductive characteristics including parity and menopause. Objective: We aimed to examine temporal variations in serum PFAS concentrations among midlife women from the Study of Women′s Health Across the Nation. Methods: Serum concentrations of 11 PFAS homologues were measured in 75 White, Black and Chinese women with blood samples collected in 1999–2000, 2002–2003, 2005–2006, and 2009–2011. Rates of changes in PFAS concentrations were calculated assuming a first-order elimination model. Associations between PFAS concentrations and race/ethnicity, menstruation and parity were evaluated with linear mixed models, adjusting for age, body mass index and study site. Results: Serum concentrations of linear-chain perfluorooctanoic acid (n-PFOA), linear- and branched-chain perfluorooctane sulfonic acid (n-PFOS and sm-PFOS) decreased significantly (-6.0%, 95% CI: −8.3%, −3.6% per year for n-PFOA; −14.8%, 95% CI: −17.3%, −12.3% per year for n-PFOS; −16.9%, 95% CI: −19.1%, −14.6% per year for sm-PFOS); whereas perfluorononanoic acid (PFNA) increased (16.0%, 95% CI: 10.6%, 21.6% per year). Detection rates of perfluorodecanoic acid (PFDeA) and perfluoroundecanoic acid (PFUA) doubled. Temporal trends varied significantly by race/ethnicity. Chinese women tended to have consistently higher PFNA concentrations at each follow-up visit, compared with White and Black women. Serum PFHxS concentrations significantly decreased in White and Black women, but not in Chinese. Menstruating women consistently had lower concentrations. Parity was associated with lower concentrations at baseline but the differences between nulliparous and parous women became smaller over time. Conclusions: Our results suggest longitudinal declines in serum concentrations of legacy PFAS and increases in serum concentrations of emerging compounds from 1999 to 2011 in midlife women. Temporal trends in PFAS concentrations are not uniform across race/ethnicity and parity groups. Keywords: Perfluoroalkyl and polyfluoroalkyl substances (PFAS), Biomonitoring, Midlife women, Racial/ethnic disparities, Menstruation, Parity

Abstract Background Few longitudinal studies examine inflammation and lung function in asthma. We sought to determine the cytokines that reduce airflow, and the influence of respiratory viral infections on these relationships. Methods Children underwent home collections of nasal lavage during scheduled surveillance periods and self-reported respiratory illnesses. We studied 53 children for one year, analyzing 392 surveillance samples and 203 samples from 85 respiratory illnesses. Generalized estimated equations were used to evaluate associations between nasal lavage biomarkers (7 mRNAs, 10 proteins), lung function and viral infection. Results As anticipated, viral infection was associated with increased cytokines and reduced FVC and FEV1. However, we found frequent and strong interactions between biomarkers and virus on lung function. For example, in the absence of viral infection, CXCL10 mRNA, MDA5 mRNA, CXCL10, IL-4, IL-13, CCL4, CCL5, CCL20 and CCL24 were negatively associated with FVC. In contrast, during infection, the opposite relationship was frequently found, with IL-4, IL-13, CCL5, CCL20 and CCL24 levels associated with less severe reductions in both FVC and FEV1. Conclusions In asthmatic children, airflow obstruction is driven by specific pro-inflammatory cytokines. In the absence of viral infection, higher cytokine levels are associated with decreasing lung function. However, with infection, there is a reversal in this relationship, with cytokine abundance associated with reduced lung function decline. While nasal samples may not reflect lower airway responses, these data suggest that some aspects of the inflammatory response may be protective against viral infection. This study may have ramifications for the treatment of viral-induced asthma exacerbations.

Abstract Background Preterm birth is a significant public health concern and exposure to phthalates has been shown to be associated with an increased odds of preterm birth. Even modest reductions in gestational age at delivery could entail morbid consequences for the neonate and analyzing data with this additional information may be useful. In the present analysis, we consider gestational age at delivery as our outcome of interest and examine associations with multiple phthalates. Methods Women were recruited early in pregnancy as part of a prospective, longitudinal birth cohort at the Brigham and Women’s Hospital in Boston, Massachusetts. Urine samples were collected at up to four time points during gestation for urinary phthalate metabolite measurement, and birth outcomes were recorded at delivery. From this population, we selected all 130 cases of preterm birth (

Abstract Background Several systemic treatments have been shown to increase survival for patients with metastatic castration-resistant prostate cancer. This study sought to characterize variation in use of the six “focus drugs” (docetaxel, abiraterone, enzalutamide, sipuleucel-T, radium-223, and cabazitaxel) that have been approved by the Food and Drug Administration for the treatment of metastatic castration-resistant prostate cancer during the years 2010–2015. We hypothesized that the use of these treatments would vary over time and by region of the country. Methods We used Clinformatics DataMart™ Database (OptumInsight, Eden Prairie, MN), a de-identified claims database from a national insurance provider. Our sample included patients with prostate cancer who received any of the six drugs. We describe changes in usage patterns over time and geographic region of the United States via detailed descriptive statistics. We explore both patterns of first line therapy and sequence of treatments in our database. Results Our final analysis included 4275 patients with a mean age of 74 years. Docetaxel was the most commonly used first-line therapy in 2010 (97%), 2011 (66%), and 2012 (49%). Abiraterone was the most commonly used first-line therapy in 2013 (56%), 2014 (46%), and 2015 (34%). Approximately 14% of our study cohort received ≥3 of the 6 drugs throughout their disease course. There was marked geographic variation in use of each of the drugs. Conclusion Variation in treatment patterns were found with respect to both time and geographic location. Prescription rates of abiraterone outpaced docetaxel as the most commonly prescribed drug after 2013 when it became widely available. However, some regions of the country still lagged behind and prescribed less than would be expected.

Abstract Background There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutant-pollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress (gamma-glutamyl transferase (GGT)), a common disease pathway linking environmental exposure and numerous health endpoints. Methods We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003–2004 to 2013–2014, n = 9664). We randomly split the data evenly into training and testing sets and constructed ERS’s of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints. Results ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS’s showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS’s showed non-significant positive associations with mortality outcomes. Conclusions ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints.

Living in a disadvantaged neighborhood is associated with poor health outcomes even after accounting for individual-level socioeconomic factors. The chronic stress of unfavorable neighborhood conditions may lead to dysregulation of the stress reactivity and inflammatory pathways, potentially mediated through epigenetic mechanisms such as DNA methylation. We used multi-level models to examine the relationship between 2 neighborhood conditions and methylation levels of 18 genes related to stress reactivity and inflammation in purified monocytes from 1,226 participants of the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based sample of US adults. Neighborhood socioeconomic disadvantage, a summary of 16 census-based metrics, was associated with DNA methylation [False discovery rate (FDR) q-value ≤ 0.1] in 2 out of 7 stress-related genes evaluated (CRF, SLC6A4) and 2 out of 11 inflammation-related genes (F8, TLR1). Neighborhood social environment, a summary measure of aesthetic quality, safety, and social cohesion, was associated with methylation in 4 of the 7 stress-related genes (AVP, BDNF, FKBP5, SLC6A4) and 7 of the 11 inflammation-related genes (CCL1, CD1D, F8, KLRG1, NLRP12, SLAMF7, TLR1). High socioeconomic disadvantage and worse social environment were primarily associated with increased methylation. In 5 genes with significant associations between neighborhood and methylation (FKBP5, CD1D, F8, KLRG1, NLRP12), methylation was associated with gene expression of at least one transcript. These results demonstrate that multiple dimensions of neighborhood context may influence methylation levels and subsequent gene expression of stress- and inflammation-related genes, even after accounting for individual socioeconomic factors. Further elucidating the molecular mechanisms underlying these relationships will be important for understanding the etiology of health disparities.

Abstract Background In China, the measles vaccine is offered for free whereas the pneumococcal vaccine is a for-fee vaccine. This difference has the potential to influence how caregivers evaluate whether a vaccine is important or necessary for their child, but it is unclear if models of health behavior, such as the Health Belief Model, reveal the same associations for different diseases. This study compares caregiver perceptions of different diseases (measles, pneumonia and meningitis); and characterizes associations between Health Belief Model constructs and both pneumococcal vaccine uptake and perceived vaccine necessity for pneumonia, measles, and meningitis. Methods Caregivers of infants and young children between 8 months and 7 years of age from Shanghai (n = 619) completed a written survey on their perceptions of measles, pneumonia, and meningitis. We used logistic regression models to assess predictors of pneumococcal vaccine uptake and vaccine necessity. Results Only 25.2% of children had received a pneumococcal vaccine, although most caregivers believed that pneumonia (80.8%) and meningitis (92.4%), as well as measles (93.2%), vaccines were serious enough to warrant a vaccine. Perceived safety was strongly associated with both pneumococcal vaccine uptake and perceived vaccine necessity, and non-locals had 1.70 times higher odds of pneumonia vaccine necessity than non-locals (95% CI: 1.01, 2.88). Conclusions Most factors had a similar relationship with vaccine necessity, regardless of disease, indicating a common mechanism for how Chinese caregivers decided which vaccines are necessary. Because more caregivers believed meningitis needed a vaccine than pneumonia, health care workers should emphasize pneumococcal vaccination’s ability to protect against meningitis.

Background To evaluate whether blood markers of lead, cadmium, and mercury can improve prediction for cardiovascular disease (CVD) mortality when added individually, jointly, or as an integrative index/Environmental Risk Score (ERS), in a model with established risk factors. Methods and Results Our study sample comprised 16 028 adults aged ≥40 years who were enrolled in the National Health and Nutrition Examination Survey 1999–2012 and followed up through December 31, 2015. The study sample was randomly split into training for the ERS construction (n=8043) and testing for the evaluation of prediction performance (n=7985). ERS was computed using elastic‐net penalized Cox's model based on the selected metal predictors predicting CVD mortality. During median follow‐up of 7.2 years, 517 died from CVD. In the training set, linear terms of cadmium and mercury, squared terms of lead and mercury, and all 3 pairwise interactions were selected by elastic‐net for ERS construction. In the testing set, the C‐statistic increased from 0.845 when only established CVD risk factors were in the model to 0.854 when the ERS was additionally added to the model. Addition of all linear, squared, and pairwise interaction terms of blood metals to the Cox's models improved C‐statistic from 0.845 to 0.857. The improvement remained significant when it was assessed by net reclassification improvement and integrated discrimination improvement. Conclusions Our findings suggest that blood markers of toxic metals can improve CVD risk prediction over the established risk factors and highlight their potential utility for CVD risk assessment, prevention, and precision health.

Background: Maternal exposure to environmental phenols is common in pregnancy and has been linked to preterm birth, preeclampsia, and reduced fetal growth. One potential mechanism may be through increased maternal oxidative stress. Objective: We examined the associations between a panel of 10 urinary phenols, including dichlorophenols, benzophenone-3, parabens, triclosan and triclocarban, and bisphenol-S, and two urinary oxidative stress biomarkers, 8-hydroxydeoxyguanosine (8-OHdG) and 8-isoprostane. All exposure and outcome biomarkers were measured at 4 time points in pregnancy. Methods: We used repeated measures models to examine the association between repeated exposure and outcome biomarkers. Additionally, we used adaptive elastic net (AENET) to identify non-null associations accounting for the correlation structure of exposures, both for phenols and urinary phthalate metabolites that were previously associated with the oxidative stress biomarkers in our study population. Results: In adjusted repeated measures models, we observed that dichlorophenols, benzophenone-3, triclosan, and some parabens were associated with increases in both oxidative stress biomarkers. The greatest effect estimates were observed for 2,5-dichlorophenol; an interquartile range (IQR) increase in this compound was associated with a 15.2% (95% confidence interval [CI] = 11.0, 19.6) increase in 8-OHdG and a 16.7% (95% CI = 9.66, 24.2) increase in 8-isoprostane. Bisphenol-S detection was associated with a clear increase in 8-isoprostane (18.5%, 95% CI = 7.68, 30.5) but a more modest increase in 8-OHdG (6.18%, 95% CI = −0.27, 13.1). However, AENET models did not consistently select any of the phenols as predictors of 8-OHdG or 8-isoprostane when phthalate metabolites were included in the model. Conclusion: Overall, urinary phenols were associated with increases in biomarkers of oxidative stress in pregnancy but either to a lesser extent, or due to correlation with, urinary phthalate metabolites. Keywords: Oxidative stress, Phenols, Parabens, Phthalates, Pregnancy, Mixtures