Search

Calls-to-action in health research have described a need to improve research on race, ethnicity, and structural racism. Well-established cohort studies typically lack access to novel structural and social determinants of health (SSDOH) or precise race and ethnicity categorization, contributing to a loss of rigor to conduct informative analyses and a gap in prospective evidence on the role of structural racism in health outcomes. We propose and implement methods that prospective cohort studies can use to begin to rectify this, using the Women's Health Initiative (WHI) cohort as a case study. To do so, we evaluated the quality, precision, and representativeness of race, ethnicity, and SSDOH data compared with the target US population and operationalized methods to quantify structural determinants in cohort studies. Harmonizing racial and ethnic categorization to the current standards set by the Office of Management and Budget improved measurement precision, aligned with published recommendations, disaggregated groups, decreased missing data, and decreased participants reporting "some other race". Disaggregation revealed sub-group disparities in SSDOH, including a greater proportion of Black-Latina (35.2%) and AIAN-Latina (33.3%) WHI participants with income below the US median compared with White-Latina (42.5%) participants. We found similarities in the racial and ethnic patterning of SSDOH disparities between WHI and US women but less disparity overall in WHI. Despite higher individual-level advantage in WHI, racial disparities in neighborhood resources were similar to the US, reflecting structural racism. Median neighborhood income was comparable between Black WHI ($39,000) and US ($34,700) women. WHI SSDOH-associated outcomes may be generalizable on the basis of comparing across race and ethnicity but may quantitatively (but not qualitatively) underestimate US effect sizes. This paper takes steps towards data justice by implementing methods to make visible hidden health disparity groups and operationalizing structural-level determinants in prospective cohort studies, a first step to establishing causality in health disparities research.

PurposeWe describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties.MethodsUsing an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance.ResultsWe enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata.ConclusionsOur stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections.

BackgroundUrinary incontinence affects >40% of women in the United States, with an annual societal cost of >$12 billion and demonstrated associations with depressive symptoms, social isolation, and loss of work productivity. Weight has been established as an exposure that increases urinary incontinence risk and certain dietary components have been associated with urinary incontinence symptoms. We hypothesized that diet plays a key role in the association between weight and urinary incontinence in US women.ObjectiveThis study aimed to examine the effect of a low-fat diet on urinary incontinence in postmenopausal women as a post hoc analysis of a randomized controlled trial of diet modification.Study designThis was a post hoc analysis of the Women's Health Initiative Dietary Modification randomized controlled trial of 48,835 postmenopausal women from 40 US centers assigned to a dietary intervention (20% energy from fat, 5 fruits or vegetable servings, and 6 whole grain servings daily and an intensive behavioral modification program) or to the usual diet comparison group. The outcome was urinary incontinence at 1 year.ResultsOf the participants, 60% were randomized to the usual diet comparison group and 40% to the dietary modification intervention. After adjusting for weight change, women assigned to the dietary modification intervention were less likely to report urinary incontinence (odds ratio, 0.94; 95% confidence interval, 0.90-0.98; P=.003), more likely to report urinary incontinence resolution (odds ratio, 1.11; 95% confidence interval, 1.03-1.19; P=.01), and less likely to develop urinary incontinence (odds ratio, 0.92; 95% confidence interval, 0.87-0.98; P=.01) in adjusted models.ConclusionDietary modification may be a reasonable treatment for postmenopausal women with incontinence and also a urinary incontinence prevention strategy for continent women. Our results provide evidence to support a randomized clinical trial to determine whether a reduced fat-intake dietary modification is an effective intervention for the prevention and treatment of urinary incontinence. In addition to providing further insights into mechanisms of lower urinary tract symptoms, these findings may have a substantial impact on public health based on the evidence that diet seems to be a modifiable risk factor for urinary incontinence.

BackgroundThe great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients.MethodsLeveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial.ResultsWe identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset.ConclusionsEarly immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models.FundingSupport for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford's Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

ObjectiveThe aim of the study was to develop a web-based calculator that predicts the likelihood of experiencing multiple, competing outcomes prospectively over 5, 10, and 15 years.MethodsBaseline demographic and medical data from a healthy and racially and ethnically diverse cohort of 161,808 postmenopausal women, aged 50 to 79 at study baseline, who participated in the Women's Health Initiative (WHI), were used to develop and evaluate a risk-prediction calculator designed to predict individual risk for morbidity and mortality outcomes. Women were enrolled from 40 sites arranged in four regions of the United States. The calculator predicts all-cause mortality, adjudicated outcomes of health events (ie, myocardial infarction [MI], stroke, and hip fracture), and disease (lung, breast, and colorectal cancer). A proportional subdistribution hazards regression model was used to develop the calculator in a training dataset using data from three regions. The calculator was evaluated using the C-statistic in a test dataset with data from the fourth region.ResultsThe predictive validity of our calculator measured by the C-statistic in the test dataset for a first event at 5 and 15 years was as follows: MI 0.77, 0.61, stroke 0.77, 0.72, lung cancer 0.82, 0.79, breast cancer 0.60, 0.59, colorectal cancer 0.67, 0.60, hip fracture 0.79, 0.76, and death 0.74, 0.72.ConclusionThis study represents the first large-scale study to develop a risk prediction calculator that yields health risk prediction for several outcomes simultaneously. Development of this tool is a first step toward enabling women to prioritize interventions that may decrease these risks. : Video Summary:http://links.lww.com/MENO/A463.

BackgroundOptical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.MethodsParticipants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.ResultsWe recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.ConclusionsThe probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).

Pregnancy, menses and menopause are related to fluctuations in endogenous sex hormones in women, which cumulatively may alter cardiac electrical conduction. Therefore, we sought to study the association between number of pregnancies and reproductive period duration (RD, time from menarche to menopause) with ECG intervals in the Women's Health Initiative Clinical Trials.Secondary analysis of multicentre clinical trial.USA.ECGintervals: PR interval, P-wave duration, P-wave dispersion, QTc interval.n=40 687 women (mean age=62 years) participating in the Women's Health Initiative Clinical Trials. 82.5% were white, 9.3% black, 4% Hispanic and 2.7% Asian.In primary analysis, we employed multivariable linear regression models relating number of pregnancies and RD with millisecond changes in intervals from enrolment ECG. We studied effect modification by hormone therapy use.Among participants, 5+ live births versus 0 prior pregnancies was associated with a 1.32 ms increase in PR interval (95% CI 0.25 to 2.38), with a graded association with longer QTc interval (ms) (none (prior pregnancy, no live births)=0.66 (-0.56 to 1.88), 1=0.15 (-0.71 to 1.02), 2-4=0.25 (-0.43 to 0.94) and 5+ live births=1.15 (0.33 to 1.98), p=0.008). RD was associated with longer PR interval and maximum P-wave duration (but not P-wave dispersion) among never users of hormone therapy: (PR (ms) per additional RD year: 0.10 (0.04 to 0.16); higher P-wave duration (ms): 0.09 (0.06 to 0.12)). For every year increase in reproductive period, QTc decreased by 0.04 ms (-0.07 to -0.01).An increasing number of live births is related to increased and RD to decreased ventricular repolarisation time. Both grand multiparity and longer RD are related to increased atrial conduction time. Reproductive factors that alter midlife cardiac electrical conduction system remodelling in women may modestly influence cardiovascular disease risk in later life.NCT00000611; Post-results.

Physical activity has been associated with lower lung cancer incidence and mortality in several populations. We investigated these relationships in the Women's Health Initiative Observational Study (WHI-OS) and Clinical Trial (WHI-CT) prospective cohort of postmenopausal women. The WHI study enrolled 161,808 women aged 50-79 years between 1993 and 1998 at 40 U.S. clinical centers; 129,401 were eligible for these analyses. Cox proportional hazards models were used to assess the association of baseline physical activity levels [metabolic equivalent (MET)-min/week: none

BackgroundKidney disease disproportionately affects minority populations, including African Americans and Hispanics; therefore, understanding the relationship of kidney function to cardiovascular (CV) outcomes within different racial/ethnic groups is of considerable interest. We investigated the relationship between kidney function and CV events and assessed effect modification by race/ethnicity in the Women's Health Initiative.Study designProspective cohort study.Setting & participantsBaseline serum creatinine concentrations (assay traceable to isotope-dilution mass spectrometry standard) of 19,411 postmenopausal women aged 50 to 79 years who self-identified as either non-Hispanic white (n=8,921), African American (n=7,436), or Hispanic (n=3,054) were used to calculate estimated glomerular filtration rates (eGFRs).PredictorsCategories of eGFR (exposure); race/ethnicity (effect modifier).OutcomesThe primary outcome was the composite of 3 physician-adjudicated CV events: myocardial infarction, stroke, or CV-related death.MeasurementsWe evaluated the multivariable-adjusted associations between categories of eGFR and CV events using proportional hazards regression and formally tested for effect modification by race/ethnicity.ResultsDuring a mean follow-up of 7.6 years, 1,424 CV events (653 myocardial infarctions, 627 strokes, and 297 CV-related deaths) were observed. The association between eGFR and CV events was curvilinear; however, the association of eGFR with CV outcomes differed by race (P=0.006). In stratified analyses, we observed that the U-shaped association was present in non-Hispanic whites, whereas African American participants had a rather curvilinear relationship, with lower eGFR being associated with higher CV risk, and higher eGFR, with reduced CV risk. Analyses among Hispanic women were inconclusive owing to few Hispanic women having very low or high eGFRs and very few events occurring in these categories.LimitationsLack of urinary albumin measurements; residual confounding by unmeasured or imprecisely measured characteristics.ConclusionsIn postmenopausal women, the patterns of association between eGFR and CV risk differed between non-Hispanic whites and African American women.

BackgroundGenome-wide association studies have identified polymorphisms linked to both smoking exposure and risk of lung cancer. The degree to which lung cancer risk is driven by increased smoking, genetics, or gene-environment interactions is not well understood.MethodsWe analyzed associations between 28 single nucleotide polymorphisms (SNPs) previously associated with smoking quantity and lung cancer in 7156 African-American females in the Women's Health Initiative (WHI), then analyzed main effects of top nominally significant SNPs and interactions between SNPs, cigarettes per day (CPD) and pack-years for lung cancer in an independent, multi-center case-control study of African-American females and males (1078 lung cancer cases and 822 controls).FindingsNine nominally significant SNPs for CPD in WHI were associated with incident lung cancer (corrected p-values from 0.027 to 6.09 × 10(-5)). CPD was found to be a nominally significant effect modifier between SNP and lung cancer for six SNPs, including CHRNA5 rs2036527[A](betaSNP*CPD = - 0.017, p = 0.0061, corrected p = 0.054), which was associated with CPD in a previous genome-wide meta-analysis of African-Americans.InterpretationThese results suggest that chromosome 15q25.1 variants are robustly associated with CPD and lung cancer in African-Americans and that the allelic dose effect of these polymorphisms on lung cancer risk is most pronounced in lighter smokers.

BackgroundWe examined the social and economic factors associated with nursing home (NH) admission in older women, overall and poststroke.MethodsThe Women's Health Initiative (WHI) included women aged 50-79 years at enrollment (1993-1998). In the WHI Extension Study (2005-2010), participants annually reported any NH admission in the preceding year. Separate multivariate logistic regression models analyzed social and economic factors associated with long-term NH admission, defined as an admission on 2 or more questionnaires, overall and poststroke.ResultsOf 103,237 participants, 8904 (8.6%) reported NH admission (2005-2010); 534 of 2225 (24.0%) women with incident stroke reported poststroke NH admission. Decreased likelihoods of NH admission overall were demonstrated for Asian, Black, and Hispanic women (versus whites, adjusted odds ratio [aOR] = .35-.44, P < .001) and women with higher income (aOR = .75, 95% confidence interval [CI] = .63-.90), whereas increased likelihoods of NH admission overall were seen for women with lower social support (aOR = 1.34, 95% CI = 1.16-1.54) and with incident stroke (aOR = 2.59, 95% CI = 2.15-3.12). Increased odds of NH admission after stroke were demonstrated for women with moderate disability after stroke (aOR = 2.76, 95% CI = 1.73-4.42). Further adjustment for stroke severity eliminated the association found for race/ethnicity, income, and social support.ConclusionsThe level of care needed after a disabling stroke may overwhelm social and economic structures in place that might otherwise enable avoidance of NH admission. We need to identify ways to provide care consistent with patients' preferences, even after a disabling stroke.

BackgroundIt is unclear whether obesity unaccompanied by metabolic abnormalities is associated with increased cardiovascular disease risk across racial and ethnic subgroups.Methods and resultsWe identified 14 364 postmenopausal women from the Women's Health Initiative who had data on fasting serum lipids and serum glucose and no history of cardiovascular disease or diabetes at baseline. We categorized women by body mass index (in kg/m(2)) as normal weight (body mass index 18.5 to