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BackgroundClinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences. ObjectiveThis tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application. MethodsWe mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools. ResultsOverall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients’ attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (−0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of −0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied. ConclusionsThis report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results.

BACKGROUND: Hurricane Harvey made landfall in August 2017 and resulted in catastrophic flooding in Houston, Texas. Prior studies of hurricanes and preterm birth have found conflicting results. We tested the hypotheses that exposure to Hurricane Harvey was associated with a higher risk of spontaneous pre- and early-term birth and assessed vulnerable subpopulations. METHODS: We conducted a retrospective study of singleton births using administrative birth records in the nine-county greater Houston area from 2015 to 2019. We estimated the likelihood of pre- and early-term births using logistic regression, comparing births occurring during or within 1, 2, or 4 weeks of Hurricane Harvey to unexposed reference periods encompassing the same dates 2 years prior and after. Stratified models assessed effect modification by degree of flooding, birth parent age, high- vs. low-risk pregnancy, race/ethnicity, and prenatal care. RESULTS: Among 15,564 births, we found no association between exposure to Hurricane Harvey and spontaneous preterm birth within 1 week adjusted (odds ratio [OR], 1.06; 95% confidence interval [CI] = 0.91, 1.25) but a 14% higher odds of spontaneous early-term birth (OR, 1.14; 95% CI = 1.04, 1.25). The odds of early-term birth were even higher in neighborhoods with severe flooding (OR, 1.21; 95% CI = 1.05, 1.38), segregated neighborhoods (OR, 1.23; 95% CI = 1.03, 1.47), and among foreign-born Hispanics (OR, 1.21; 95% CI = 1.04, 1.53) and pregnant people receiving no prenatal care (OR, 1.37; 95% CI = 1.03, 1.82). Effect estimates were attenuated or null when considering 2-week or 4-week lags to define exposure. CONCLUSIONS: Hurricane Harvey was associated with higher odds of spontaneous early-term birth up to 1 week later, especially among socially marginalized populations.

BACKGROUND: The gut microbiome has been linked to many diseases with sex bias including autoimmune, metabolic, neurological, and reproductive disorders. While numerous studies report sex differences in fecal microbial communities, the role of the reproductive axis in this differentiation is unclear and it is unknown how sex differentiation affects microbial diversity in specific regions of the small and large intestine. METHODS: We used a genetic hypogonadal mouse model that does not produce sex steroids or go through puberty to investigate how sex and the reproductive axis impact bacterial diversity within the intestine. Using 16S rRNA gene sequencing, we analyzed alpha and beta diversity and taxonomic composition of fecal and intestinal communities from the lumen and mucosa of the duodenum, ileum, and cecum from adult female (n = 20) and male (n = 20) wild-type mice and female (n = 17) and male (n = 20) hypogonadal mice. RESULTS: Both sex and reproductive axis inactivation altered bacterial composition in an intestinal section and niche-specific manner. Hypogonadism was significantly associated with bacteria from the Bacteroidaceae, Eggerthellaceae, Muribaculaceae, and Rikenellaceae families, which have genes for bile acid metabolism and mucin degradation. Microbial balances between males and females and between hypogonadal and wild-type mice were also intestinal section-specific. In addition, we identified 3 bacterial genera (Escherichia Shigella, Lachnoclostridium, and Eggerthellaceae genus) with higher abundance in wild-type female mice throughout the intestinal tract compared to both wild-type male and hypogonadal female mice, indicating that activation of the reproductive axis leads to female-specific differentiation of the gut microbiome. Our results also implicated factors independent of the reproductive axis (i.e., sex chromosomes) in shaping sex differences in intestinal communities. Additionally, our detailed profile of intestinal communities showed that fecal samples do not reflect bacterial diversity in the small intestine. CONCLUSIONS: Our results indicate that sex differences in the gut microbiome are intestinal niche-specific and that sampling feces or the large intestine may miss significant sex effects in the small intestine. These results strongly support the need to consider both sex and reproductive status when studying the gut microbiome and while developing microbial-based therapies.

Knowledge translation (KT) is key to the scientific process, yet academic work produced often does not reach the public. In this article a unique undergraduate KT term assignment is presented where students summarize a large body of research into an artifact for public use. This assignment model may increase student engagement and learning outcomes while filling the knowledge-to-action translation gap. The option dovetails students' and employers' need for demonstrative performance prior to hiring, the academic push for publicly and freely available scientific knowledge (e.g., open access), and challenges involved with engagement with advanced material in both in-person and remote environments. The assignment is presented in the context of benefitting effective KT and enhancing open access to information; it is then analyzed using the American Psychological Association's (APA) goals for undergraduates alongside Bloom's Taxonomy of Learning. Various APA goals accomplished through the assignment, benefits, limitations, and future directions are highlighted.

BackgroundReproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear.MethodsAnalyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided.ResultsCompared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like.ConclusionsThis large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.

Only two-thirds of Americans meet the recommended 7 hours of sleep nightly. Insufficient sleep and circadian disruption have been associated with adverse health outcomes, including diabetes and cardiovascular disease. Several environmental disruptors of sleep have been reported, such as artificial light at night (ALAN) and noise. These studies tended to evaluate exposures individually. We evaluated several spatially derived environmental exposures (ALAN, noise, green space, and air pollution) and self-reported sleep outcomes obtained in 2012-2015 in a large cohort of 51,562 women in the California Teachers Study. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for sleep duration and latency. After adjusting for age, race/ethnicity, chronotype, use of sleep medication, and self-reported trouble sleeping, ALAN (per 5 millicandela (mcd)/m2 luminance, OR = 1.13, 95% CI: 1.07, 1.20) and air pollution (per 5 μg/m3 PM2.5, OR = 1.06, 95% CI: 1.04, 1.09) were associated with shorter sleep duration (15 minutes) (per 10 decibels, OR = 1.05, 95% CI: 1.01, 1.10). Green space was associated with increased duration (per 0.1 units, OR = 0.41, 95% CI: 0.28, 0.60) and decreased latency (per 0.1 units, OR = 0.55, 95% CI: 0.39, 0.78). Further research is necessary to understand how these and other exposures (e.g., diet) perturb an individuals' inherited sleep patterns and contribute to downstream health outcomes.

IntroductionCoronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term.MethodsThis article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions.ResultsSuccessful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe.DiscussionThe Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection.Key pointsThe following review describes what is known so far in terms of molecular and epidemiological links among COVID-19, the brain, neurological symptoms, and AD and related dementias (ADRD)The primary objective of this large-scale collaboration is to clarify the pathogenesis of ADRD and to advance our understanding of the impact of a neurotropic virus on the long-term risk of cognitive decline and other CNS sequelae. No available evidence supports the notion that cognitive impairment after SARS-CoV-2 infection is a form of dementia (ADRD or otherwise). The longitudinal methodologies espoused by the consortium are intended to provide data to answer this question as clearly as possible controlling for possible confounders. Our specific hypothesis is that SARS-CoV-2 triggers ADRD-like pathology following the extended olfactory cortical network (EOCN) in older individuals with specific genetic susceptibility.The proposed harmonization strategies and flexible study designs offer the possibility to include large samples of under-represented racial and ethnic groups, creating a rich set of harmonized cohorts for future studies of the pathophysiology, determinants, long-term consequences, and trends in cognitive aging, ADRD, and vascular disease.We provide a framework for current and future studies to be carried out within the Consortium. and offers a "green paper" to the research community with a very broad, global base of support, on tools suitable for low- and middle-income countries aimed to compare and combine future longitudinal data on the topic.The Consortium proposes a combination of design and statistical methods as a means of approaching causal inference of the COVID-19 neuropsychiatric sequelae. We expect that deep phenotyping of neuropsychiatric sequelae may provide a series of candidate syndromes with phenomenological and biological characterization that can be further explored. By generating high-quality harmonized data across sites we aim to capture both descriptive and, where possible, causal associations.

BackgroundThere is growing evidence that exposure to low-grade inflammation may be associated with adverse health outcomes.MethodsWe conducted a cross-sectional study within the California Teachers Study prospective cohort, among female participants who had completed a questionnaire that asked about their health behaviors (e.g., diabetes, physical activity, body mass index, medication use) and who had donated blood within a year of their questionnaire. 822 women with stored serum were evaluated for 16 immune biomarkers. In addition, four immune pathways were constructed: Th1, pro-inflammatory/macrophage activation, B-cell activation, and T-cell activation. Odds ratios (ORs) and 95% confidence intervals (CI) for the association between host characteristics and immune biomarkers were assessed using logistic regression models.ResultCompared to women of a normal BMI, obese women (>30 kg/m2) were positively associated with sTNFR2, CD27, IL6, CXCL13, sIL-2Rα, and IL6Ra levels above the median, with odds ratios ranging from 1.5 to 6.0. The pro-inflammatory/macrophage activation pathway was positively associated with diabetes (OR = 2.12, 95% CI = 1.14-3.95), fueled by individual associations between diabetes and sTNF-R2, TNFα and sCD27. Physical activity was inversely associated with sTNF-R2, TNFα, CXCL13, IL6, IL10, and IFN-γ levels, particularly for the highest category of activity (5.88+ hours/week) (ORs = 0.32-0.69). In pathway-based analyses, the Th1 pathway which includes decreased levels of IL4 and IL10 was positively associated with elevated physical activity (OR = 1.5). In contrast, the pro-inflammatory, B- and T-cell activation pathways were positively associated with higher BMI (OR ranging from 1.6 to 3) and inversely associated with increasing levels of physical activity.ConclusionsSeveral host characteristics were associated with circulating levels of immune biomarkers, including markers of inflammation. Further understanding of associations between immune marker profiles with human disease are warranted.

BackgroundThe debate continues among medical professionals regarding the frequency, starting age, and stopping age for mammography screening. Some experts suggest tailoring recommendations based on individuals' personal breast cancer risk. Previous studies have not compared the impact of annual versus biennial mammography stratified by age group and risk category. The purpose of this study was to examine the relationship between mammography frequency and mortality by age group and risk category in the California Teachers Study.MethodsUsing data from study questionnaires from 93,438 women between the ages of 40 and 85 and linkages to the California Cancer Registry and other indices, overall and breast cancer-specific mortality by mammography frequency were estimated using multivariable Cox proportional hazards models, stratified by age group and risk category at baseline as determined by the Gail breast cancer risk model.ResultsDuring the follow-up period of 20 years, overall mortality risk was lower in women who had annual or biennial mammography compared to less frequent or no mammography in all age groups. Annual mammography was associated with lower overall mortality risk compared to biennial mammography among women age 50-85. This difference was especially apparent in women age 60-74, regardless of estimated Gail risk category at baseline. Breast cancer-specific mortality was lower among women who had annual mammography compared to biennial or less frequent mammography among women age 60-74, regardless of their baseline risk.ConclusionsOur findings suggest that at least biennial mammography is beneficial to most women age 40-85 and that annual mammography is more beneficial than biennial mammography to most women age 50-85 in terms of overall mortality.

BackgroundLarge-scale cancer epidemiology cohorts (CEC) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more findable, accessible, interoperable, and reusable, or FAIR. How CECs should approach this transformation is unclear.MethodsThe California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a data warehouse to integrate and manage data and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses.ResultsOur new CTS infrastructure includes a data warehouse and data marts, which are focused subsets from the data warehouse designed for efficiency. The secure CTS workspace utilizes a remote desktop service that operates within a Health Insurance Portability and Accountability Act (HIPAA)- and Federal Information Security Management Act (FISMA)-compliant platform. Our infrastructure offers broad access to CTS data, includes statistical analysis and data visualization software and tools, flexibly manages other key data activities (e.g., cleaning, updates, and data sharing), and will continue to evolve to advance FAIR principles.ConclusionsOur scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework.ImpactThe CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.See all articles in this CEBP Focus section, "Modernizing Population Science."

BackgroundLike other cancer epidemiologic cohorts, the California Teachers Study (CTS) has experienced declining participation to follow-up questionnaires; neither the reasons for these declines nor the steps that could be taken to mitigate these trends are fully understood.MethodsThe CTS offered their 6th study questionnaire (Q6) in the fall of 2017 using an integrated, online system. The team delivered a Web and mobile-adaptive questionnaire to 45,239 participants via e-mail using marketing automation technology. The study's integrated platform captured data on recruitment activities that may influence overall response, including the date and time invitations and reminders were e-mailed and the date and time questionnaires were started and submitted.ResultsThe overall response rate was 43%. Participants ages 65 to 69 were 25% more likely to participate than their younger counterparts (OR = 1.25; 95% CI, 1.18-1.32) and nonwhite participants were 28% less likely to participate than non-Hispanic white cohort members (OR = 0.72; 95% CI, 0.68-0.76). Previous questionnaire participation was strongly associated with response (OR = 6.07; 95% CI, 5.50-6.70). Invitations sent after 2 pm had the highest response (OR = 1.75; 95% CI, 1.65-1.84), as did invitations sent on Saturdays (OR = 1.48; 95% CI, 1.36-1.60).ConclusionsAn integrated system that captures paradata about questionnaire recruitment and response can enable studies to quantify the engagement patterns and communication desires of cohort members.ImpactAs cohorts continue to collect scientific data, it is imperative to collect and analyze information on how participants engage with the study.See all articles in this CEBP Focus section, "Modernizing Population Science."