ULV: A robust statistical method for clustered data, with applications to multi-subject, single-cell omics data.

Molecular and genomic technological advancements have greatly enhanced our understanding of biological processes by allowing us to quantify key biological variables such as gene expression, protein levels, and microbiome compositions. These breakthroughs have enabled us to achieve increasingly higher levels of resolution in our measurements, exemplified by our ability to comprehensively profile biological information at the single-cell level. However, the analysis of such data faces several critical challenges: limited number of individuals, non-normality, potential dropouts, outliers, and repeated measurements from the same individual. In this article, we propose a novel method, which we call U-statistic based latent variable (ULV). Our proposed method takes advantage of the robustness of rank-based statistics and exploits the statistical efficiency of parametric methods for small sample sizes. It is a computationally feasible framework that addresses all the issues mentioned above simultaneously. We show that our method controls false positives at desired significance levels. An additional advantage of ULV is its flexibility in modeling various types of single-cell data, including both RNA and protein abundance. The usefulness of our method is demonstrated in two studies: a single-cell proteomics study of acute myelogenous leukemia (AML) and a single-cell RNA study of COVID-19 symptoms. In the AML study, ULV successfully identified differentially expressed proteins that would have been missed by the pseudobulk version of the Wilcoxon rank-sum test. In the COVID-19 study, ULV identified genes associated with covariates such as age and gender, and genes that would be missed without adjusting for covariates. The differentially expressed genes identified by our method are less biased toward genes with high expression levels. Furthermore, ULV identified additional gene pathways likely contributing to the mechanisms of COVID-19 severity.

Social Acceptability of Health Behavior Posts on Social Media: An Experiment.

INTRODUCTION: Social media sites like Twitter (now X) are increasingly used to create health behavior metrics for public health surveillance. Yet little is known about social norms that may bias the content of posts about health behaviors. Social norms for posts about four health behaviors (smoking tobacco, drinking alcohol, physical activity, eating food) on Twitter/X were evaluated. METHODS: This was a randomized experiment delivered via web-based survey to adult, English-speaking Twitter/X users in three Michigan, USA, counties from 2020 to 2022 (n=559). Each participant viewed 24 posts presenting experimental manipulations regarding four health behaviors and answered questions about each post's social acceptability. Principal component analysis was used to combine survey responses into one perceived social acceptability measure. Linear mixed models with the Benjamini-Hochberg correction were implemented to test seven study hypotheses in 2023. RESULTS: Supporting six hypotheses, posts presenting healthier (CI: 0.028, 0.454), less stigmatized behaviors (CI: 0.552, 0.157) were more socially acceptable than posts regarding unhealthier, stigmatized behaviors. Unhealthy (CI: -0.268, -0.109) and stigmatized behavior (CI: -0.261, -0.103) posts were less acceptable for more educated participants. Posts about collocated activities (CI: 0.410, 0.573) and accompanied by expressions of liking (CI: 0.906, 1.11) were more acceptable than activities undertaken alone or disliked. Contrary to one hypothesis, posts reporting unusual activities were less acceptable than usual ones (CI: -0.472, 0.312). CONCLUSIONS: Perceived social acceptability may be associated with the frequency and content of health behavior posts. Users of Twitter/X and other social media platform posts to estimate health behavior prevalence should account for potential estimation biases from perceived social acceptability of posts.

Associations of reproductive and breastfeeding history with anti-Müllerian hormone concentration among African-American women of reproductive age.

RESEARCH QUESTION: Are gravidity, parity and breastfeeding history associated with anti-Müllerian hormone concentration among African-American women of reproductive age? DESIGN: This study included baseline data from the Study of the Environment, Lifestyle and Fibroids, a 5-year longitudinal study of African-American women. Within this community cohort, data from 1392 women aged 25-35 years were analysed. The primary outcome was serum anti-Müllerian hormone concentration measured using the Ansh Labs picoAMH assay, an enzyme-linked immunosorbent assay. Multivariable linear regression models were used to estimate mean differences in anti-Müllerian hormone concentration (ß) and 95% CI by self-reported gravidity, parity and breastfeeding history, with adjustment for potential confounders. RESULTS: Of the 1392 participants, 1063 had a history of gravidity (76.4%). Of these, 891 (83.8%) were parous and 564 had breastfed. Multivariable-adjusted regression analyses found no appreciable difference in anti-Müllerian hormone concentration between nulligravid participants and those with a history of gravidity (ß = -0.025, 95% CI -0.145 to 0.094). Among participants with a history of gravidity, there was little difference in anti-Müllerian hormone concentration between parous and nulliparous participants (ß = 0.085, 95% CI -0.062 to 0.232). There was also little association between anti-Müllerian hormone concentration and breastfeeding history (ever versus never: ß = 0.009, 95% CI -0.093 to 0.111) or duration of breastfeeding (per 1-month increase: ß = -0.002, 95% CI -0.010 to 0.006). CONCLUSIONS: Gravidity, parity and breastfeeding history were not meaningfully associated with anti-Müllerian hormone concentration in this large sample of the Study of the Environment, Lifestyle and Fibroids cohort.

Reliability, validity, and responsiveness to change of the Patient-Reported Outcomes Measurement Information System self-efficacy for managing chronic conditions measure in systemic sclerosis.

Objective: The aim of this study is to examine validity, reliability, and responsiveness to change of Patient-Reported Outcomes Measurement Information System Self-Efficacy for Managing Chronic Conditions in persons with systemic sclerosis. Methods: We conducted a post hoc analysis of the Patient-Reported Outcomes Measurement Information System Self-Efficacy measure and other quality-of-life measures from systemic sclerosis participants from a 16-week randomized control trial. The trial compared an Internet-based self-management program to a control condition where participants were provided an educational book. All participants completed outcome measures at baseline and following the 16-week trial period. Results: The mean age of participants was 53.7 years, 91% were female and systemic sclerosis subtype included 44.9% limited/sine and 43.1% diffuse; mean disease duration was 9.0 years. All self-efficacy subscales (Managing Emotions, Symptoms, Daily Activities, Social Interactions, and Medications/Treatment) demonstrated good internal consistency (.92-.96). All subscales showed statistically significant correlations with other validated measures of depressive symptoms and quality of life (.20-.86) but were not associated with satisfaction nor with appearance. The subscales appropriately discriminated between those with and without depressive symptoms and demonstrated responsiveness to change over the 16-week period for those who had a corresponding increase in reported quality of life. Conclusion: The Patient-Reported Outcomes Measurement Information System Self-Efficacy measure is valid, reliable, and responsive to change for persons with systemic sclerosis.

Association between uterine fibroids and antimüllerian hormone concentrations among African American women.

OBJECTIVE: To evaluate the extent to which uterine fibroids are associated with antimüllerian hormone (AMH) concentrations. DESIGN: Cross-sectional study. SETTING: Baseline data from the Study of the Environment, Lifestyle, and Fibroids, which is a 5-year longitudinal study of African American women. PATIENT(S): A total of 1,643 women aged 23-35 years without a known history of fibroids. EXPOSURE: Fibroid presence. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): The primary outcome was percent difference in the mean AMH concentration between participants with fibroids and those without fibroids. The secondary outcomes were percent differences in the mean AMH concentrations in participants with different numbers, sizes, types, and positions of fibroids and the percent difference in the mean AMH concentration in participants with different uterine volumes. RESULT(S): At least 1 fibroid was identified on ultrasound in 362 (22%) participants. There was a small difference in the mean AMH concentrations in participants with fibroids (age-adjusted model: -4.6%, 95% confidence interval (CI): -14.5% to 6.5%; multivariable model: -4.6%, 95% CI: -14.4% to 6.3%). The mean AMH concentrations were found to decrease with increasing fibroid number. Although differences in AMH concentrations were not statistically significant, compared with no fibroids, the mean percent differences in AMH concentrations for 1, 2-3, and ≥4 fibroids were -1.2% (95% CI: -13.2% to 12.5%), -7.1% (95% CI: -23.3% to 12.5%), and -17.5% (95% CI: -38.2% to 10.0%), respectively. There were no consistent associations between AMH concentrations and fibroid location, size, or uterine volume. CONCLUSION(S): The presence of fibroids was not materially associated with AMH concentrations. Other than a monotonic inverse relationship between fibroid number and AMH concentrations, no other fibroid characteristics were consistently or appreciably associated, although associations were imprecise.

Identifying regions of inhomogeneities in spatial processes via an M-RA and mixture priors.

Soils have been heralded as a hidden resource that can be leveraged to mitigate and address some of the major global environmental challenges. Specifically, the organic carbon stored in soils, called soil organic carbon (SOC), can, through proper soil management, help offset fuel emissions, increase food productivity, and improve water quality. As collecting data on SOC are costly and time-consuming, not much data on SOC are available, although understanding the spatial variability in SOC is of fundamental importance for effective soil management. In this manuscript, we propose a modeling framework that can be used to gain a better understanding of the dependence structure of a spatial process by identifying regions within a spatial domain where the process displays the same spatial correlation range. To achieve this goal, we propose a generalization of the multiresolution approximation (M-RA) modeling framework of Katzfuss originally introduced as a strategy to reduce the computational burden encountered when analyzing massive spatial datasets. To allow for the possibility that the correlation of a spatial process might be characterized by a different range in different subregions of a spatial domain, we provide the M-RA basis functions weights with a two-component mixture prior with one of the mixture components a shrinking prior. We call our approach the mixture M-RA. Application of the mixture M-RA model to both stationary and nonstationary data show that the mixture M-RA model can handle both types of data, can correctly establish the type of spatial dependence structure in the data (e.g., stationary versus not), and can identify regions of local stationarity.

An urban-to-rural continuum of malaria risk: new analytic approaches characterize patterns in Malawi.

BACKGROUND: The urban-rural designation has been an important risk factor in infectious disease epidemiology. Many studies rely on a politically determined dichotomization of rural versus urban spaces, which fails to capture the complex mosaic of infrastructural, social and environmental factors driving risk. Such evaluation is especially important for Plasmodium transmission and malaria disease. To improve targeting of anti-malarial interventions, a continuous composite measure of urbanicity using spatially-referenced data was developed to evaluate household-level malaria risk from a house-to-house survey of children in Malawi. METHODS: Children from 7564 households from eight districts throughout Malawi were tested for presence of Plasmodium parasites through finger-prick blood sampling and slide microscopy. A survey questionnaire was administered and latitude and longitude coordinates were recorded for each household. Distances from households to features associated with high and low levels of development (health facilities, roads, rivers, lakes) and population density were used to produce a principal component analysis (PCA)-based composite measure for all centroid locations of a fine geo-spatial grid covering Malawi. Regression methods were used to test associations of the urbanicity measure against Plasmodium infection status and to predict parasitaemia risk for all locations in Malawi. RESULTS: Infection probability declined with increasing urbanicity. The new urbanicity metric was more predictive than either a governmentally defined rural/urban dichotomous variable or a population density variable. One reason for this was that 23% of cells within politically defined rural areas exhibited lower risk, more like those normally associated with "urban" locations. CONCLUSIONS: In addition to increasing predictive power, the new continuous urbanicity metric provided a clearer mechanistic understanding than the dichotomous urban/rural designations. Such designations often ignore urban-like, low-risk pockets within traditionally rural areas, as were found in Malawi, along with rural-like, potentially high-risk environments within urban areas. This method of characterizing urbanicity can be applied to other infectious disease processes in rapidly urbanizing contexts.

A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping.

Dengue is recognized as a major health issue in large urban tropical cities but is also observed in rural areas. In these environments, physical characteristics of the landscape and sociodemographic factors may influence vector populations at small geographic scales, while prior immunity to the four dengue virus serotypes affects incidence. In 2019, a rural northwestern Ecuadorian community, only accessible by river, experienced a dengue outbreak. The village is 2-3 hours by boat away from the nearest population center and comprises both Afro-Ecuadorian and Indigenous Chachi households. We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases. We mapped visible water containers seen in UAV images and calculated both the green-red vegetation index (GRVI) and household proximity to public spaces like schools and meeting areas. To identify risk factors for symptomatic dengue infection, we used mixed-effect logistic regression models to account for the clustering of symptomatic cases within households. We identified 55 dengue cases (9.5% of the population) from 37 households. Cases peaked in June and continued through October. Rural spatial organization helped to explain disease risk. Afro-Ecuadorian (versus Indigenous) households experience more symptomatic dengue (OR = 3.0, 95%CI: 1.3, 6.9). This association was explained by differences in vegetation (measured by GRVI) near the household (OR: 11.3 95% 0.38, 38.0) and proximity to the football field (OR: 13.9, 95% 4.0, 48.4). The integration of UAV mapping with other data streams adds to our understanding of these dynamics.

Lymphocyte subset abnormalities in early diffuse cutaneous systemic sclerosis.

BACKGROUND: Abnormalities in lymphocyte surface markers and functions have been described in systemic sclerosis (SSc), but conflicting results abound, and these studies often examined patients with heterogeneous disease duration, severity, clinical phenotype, and concurrent immunosuppressive agents. We studied a clinically homogeneous group of early diffuse cutaneous SSc patients not exposed to immunosuppressive drugs who were enrolled in a clinical trial and compared their immune parameters to healthy control subjects. METHODS: Lymphocyte subsets were enumerated by multi-parameter flow cytometry of peripheral blood mononuclear cells at baseline visit. Production of the cytokines IL-4 and IL-17 was measured by intracellular flow cytometry following T cell activation. RESULTS: SSc patients had increased percentages of CD4+ T cells but lower percentages of CD8+ T cells versus controls. The CD28-negative population was expanded in SSc, in the CD4 subset. Striking expansion of CD319+ T cells was noted among the CD4+ cells, in which they were barely detectable in healthy subjects. Frequencies of IL-4 producing cells did not differ between SSc and controls, but expansion of IL-17 producing cells was observed in SSc. A higher proportion of CD319+ cells produced cytokines, compared to other CD4+ cells. Numbers of activated T cells, regulatory T cells, and B cells were similar in SSc and control groups. Circulating follicular helper but not peripheral helper T cells were slightly expanded in SSc. CONCLUSION: In this carefully selected group of early diffuse cutaneous SSc patients, analysis of immune cell parameters has identified abnormalities that likely reflect disease pathogenesis and that are candidate biomarkers for sub-classification and targeted treatment. The CD4+CD319+ (SLAM-F7+) cells are cytotoxic and oligoclonal, were recently shown to be a dominant T cell population in perivascular lymphocytic infiltrates in SSc skin, actively secrete cytokines, and are emerging as a target for novel treatments of SSc.

Examining PM2.5 concentrations and exposure using multiple models.

Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 µg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 µg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 µg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 µg m-3 in 2011 and PM2.5 improvements of about 2 µg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis.

BACKGROUND: Extreme heat poses current and future risks to human health. Heat vulnerability indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to identify populations vulnerable to extreme heat. Few studies critically assess implications of analytic choices made when employing this methodology for fine-scale vulnerability mapping. OBJECTIVE: We investigated sensitivity of HVIs created by applying PCA to input variables and whether training input variables on heat-health data produced HVIs with similar spatial vulnerability patterns for Detroit, Michigan, USA. METHODS: We acquired 2010 Census tract and block group level data, land cover data, daily ambient apparent temperature, and all-cause mortality during May-September, 2000-2009. We used PCA to construct HVIs using: a) "unsupervised"-PCA applied to variables selected a priori as risk factors for heat-related health outcomes; b) "supervised"-PCA applied only to variables significantly correlated with proportion of all-cause mortality occurring on extreme heat days (i.e., days with 2-d mean apparent temperature above month-specific 95th percentiles). RESULTS: Unsupervised and supervised HVIs yielded differing spatial vulnerability patterns, depending on selected land cover input variables. Supervised PCA explained 62% of variance in the input variables and was applied on half the variables used in the unsupervised method. Census tract-level supervised HVI values were positively associated with increased proportion of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data. Unsupervised HVI values were not associated with extreme heat mortality for either tracts or block groups. DISCUSSION: HVIs calculated using PCA are sensitive to input data and scale. Supervised HVIs may provide marginally more specific indicators of heat vulnerability than unsupervised HVIs. PCA-derived HVIs address correlation among vulnerability indicators, although the resulting output requires careful contextual interpretation beyond generating epidemiological research questions. Methods with reliably stable outputs should be leveraged for prioritizing heat interventions. https://doi.org/10.1289/EHP4030.

Spatial Exposure of Agricultural Antimicrobial Resistance in Relation to Free-Ranging Domestic Chicken Movement Patterns among Agricultural Communities in Ecuador.

The use of antimicrobial growth promoters in chicken farming has been commonly associated with high levels of antimicrobial resistance (AMR) in humans. Most of this work, however, has been focused on intensive large-scale operations. Intensive small-scale farming that regularly uses antibiotics is increasing worldwide and has different exposure pathways compared with large-scale farming, most notably the spatial connection between chickens and households. In these communities, free-ranging backyard chickens (not fed antibiotics) can roam freely, whereas broiler chickens (fed antibiotics) are reared in the same husbandry environment but confined to coops. We conducted an observational field study to better understand the spatial distribution of AMR in communities that conduct small-scale farming in northwestern Ecuador. We analyzed phenotypic resistance of Escherichia coli sampled from humans and backyard chickens to 12 antibiotics in relation to the distance to the nearest small-scale farming operation within their community. We did not find a statistically significant relationship between the distance of a household to small-scale farming and antibiotic-resistant E. coli isolated from chicken or human samples. To help explain this result, we monitored the movement of backyard chickens and found they were on average 17 m (min-max: 0-59 m) from their household at any given time. These backyard chickens on average ranged further than the average distance from any study household to its closest neighbor. This level of connectivity provides a viable mechanism for the spread of antimicrobial-resistant bacteria and genes throughout the community.

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration.

A typical challenge in air pollution epidemiology is to perform detailed exposure assessment for individuals for which health data are available. To address this problem, in the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at fine spatial and temporal scales (daily, usually) with complete coverage. However, it is not clear how much the predicted exposures yielded by the various methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance. Using PM2.5 in year 2011 over the continental U.S. as a case study, we generate national maps of ambient PM2.5 concentration using: (i) ordinary least squares and inverse distance weighting; (ii) kriging; (iii) statistical downscaling models, that is, spatial statistical models that use the information contained in air quality model outputs; (iv) land use regression, that is, linear regression modeling approaches that leverage the information in Geographical Information System (GIS) covariates; and (v) machine learning methods, such as neural networks, random forests and support vector regression. We examine the various methods' predictive performance via cross-validation using Root Mean Squared Error, Mean Absolute Deviation, Pearson correlation, and Mean Spatial Pearson Correlation. Additionally, we evaluated whether factors such as, season, urbanicty, and levels of PM2.5 concentration (low, medium or high) affected the performance of the different methods. Overall, statistical methods that explicitly modeled the spatial correlation, e.g. universal kriging and the downscaler model, outperform all the other exposure assessment approaches regardless of season, urbanicity and PM2.5 concentration level. We posit that the better predictive performance of spatial statistical models over machine learning methods is due to the fact that they explicitly account for spatial dependence, thus borrowing information from neighboring observations. In light of our findings, we suggest that future exposure assessment methods for regional PM2.5 incorporate information from neighboring sites when deriving predictions at unsampled locations or attempt to account for spatial dependence.

Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey.

Household-level information on central air conditioning (cenAC) and room air conditioning (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information from the American Housing Survey for 2003-2013 and 140 US core-based statistical areas employing variables that would be present in publicly available parcel records. We present random-intercept logistic regression models with either cenAC, rmAC or "home was uncomfortably cold for 24 h or more" (tooCold) as outcome variables and housing value, rented vs. owned, age, and multi- vs. single-family, each interacted with cooling- or heating-degree days as predictors. The out-of-sample predicted probabilities for years 2015-2017 were compared with corresponding American Housing Survey values (0 or 1). Using a 0.5 probability threshold, the model had 63% specificity (true negative rate), and 91% sensitivity (true positive rate) for cenAC, while specificity and sensitivity for rmAC were 94% and 34%, respectively. Area-specific sensitivities and specificities varied widely. For tooCold, the overall sensitivity was effectively 0%. Future epidemiologic studies, heat vulnerability maps, and intervention screenings may reliably use these or similar AC models with parcel-level data to improve understanding of health risk and the spatial patterning of homes without AC.

Abatacept in Early Diffuse Cutaneous Systemic Sclerosis: Results of a Phase II Investigator-Initiated, Multicenter, Double-Blind, Randomized, Placebo-Controlled Trial.

OBJECTIVE: T cells play a key role in the pathogenesis of early systemic sclerosis. This study was undertaken to assess the safety and efficacy of abatacept in patients with diffuse cutaneous systemic sclerosis (dcSSc). METHODS: In this 12-month, randomized, double-blind, placebo-controlled trial, participants were randomized 1:1 to receive either subcutaneous abatacept 125 mg or matching placebo, stratified by duration of dcSSc. Escape therapy was allowed at 6 months for worsening disease. The coprimary end points were change in the modified Rodnan skin thickness score (MRSS) compared to baseline and safety over 12 months. Differences in longitudinal outcomes were assessed according to treatment using linear mixed models, with outcomes censored after initiation of escape therapy. Skin tissue obtained from participants at baseline was classified into intrinsic gene expression subsets. RESULTS: Among 88 participants, the adjusted mean change in the MRSS at 12 months was -6.24 units for those receiving abatacept and -4.49 units for those receiving placebo, with an adjusted mean treatment difference of -1.75 units (P = 0.28). Outcomes for 2 secondary measures (Health Assessment Questionnaire disability index and a composite measure) were clinically and statistically significantly better with abatacept. The proportion of subjects in whom escape therapy was needed was higher in the placebo group relative to the abatacept group (36% versus 16%). In the inflammatory and normal-like skin gene expression subsets, decline in the MRSS over 12 months was clinically and significantly greater in the abatacept group versus the placebo group (P < 0.001 and P = 0.03, respectively). In the abatacept group, adverse events occurred in 35 participants versus 40 participants in the placebo group, including 2 deaths and 1 death, respectively. CONCLUSION: In this phase II trial, abatacept was well-tolerated, but change in the MRSS was not statistically significant. Secondary outcome measures, including gene expression subsets, showed evidence in support of abatacept. These data should be confirmed in a phase III trial.

Development and Pilot Testing of MyGoutCare: A Novel Web-Based Platform to Educate Patients With Gout.

BACKGROUND: Management guidelines have identified unmet educational needs in gout patients. Our objective was to develop and pilot test MyGoutCare (MGC©)-a web-based, interactive educational resource for gout patients, tailored to improve knowledge. METHODS: The website was developed with input from patients and experts. A health informatics expert tailored content areas so the patient could walk through a journey to learn various aspects of gout. During the pilot study, patients completed baseline demographics and a 10-item validated gout knowledge questionnaire. After reviewing the website, patients completed a post-survey within 2 weeks of their physician visit. Data were analyzed using paired t-tests and effect size (ES) was calculated for the changed scores. RESULTS: Gout patients and experts agreed on these content areas-triggers of flares, comorbidities, pharmacologic and non-pharmacologic treatment, healthy gout diet, and lifestyle choices. In the pilot study, 50 patients (mean age of 54 years, mean disease duration of 9.5 years, and mean 3-5 flares/year) were recruited. Their post-survey scores (0-10) on knowledge questions improved significantly when compared to pre-survey scores with mean (SD) of 1.95 (1.76) p < 0.0001, ES = 0.95. Patients identified actionable changes moving forward after reviewing the website-decision to continue lifelong urate-lowering therapy, complying with periodic monitoring of serum urate, and making dietary changes. CONCLUSIONS: Web-based platforms that offer patient-focused materials can serve as a practical tool to address ongoing educational needs of gout patients. Additional studies are needed to evaluate if the website can improve patient-physician communication and lead to better long-term outcomes.

Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics.

OBJECTIVE: Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes. MATERIALS AND METHODS: We analyzed the "healthiness" quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes. We analyzed keywords driving the differences in food healthiness between the most and least-affluent tracts, and qualitatively analyzed contents of a random sample of tweets. RESULTS: Significant, albeit weak, correlations existed between healthiness and sentiment in food-related tweets and tract-level measures of affluence, disadvantage, race, age, U.S. density, and mortality from conditions associated with obesity. Analyses of keywords driving the differences in food healthiness revealed foods high in saturated fat (eg, pizza, bacon, fries) were mentioned more frequently in less-affluent tracts. Food-related discussion referred to activities (eating, drinking, cooking), locations where food was consumed, and positive (affection, cravings, enjoyment) and negative attitudes (dislike, personal struggles, complaints). DISCUSSION: Tweet-based healthiness scores largely correlated with offline phenomena in the expected directions. Social media offer less resource-intensive data collection methods than traditional surveys do. Twitter may assist in informing local health programs that focus on drivers of food consumption and could inform interventions focused on attitudes and the food environment. CONCLUSIONS: Twitter provided weak but significant signals concerning food-related behavior and attitudes at the neighborhood level, suggesting its potential usefulness for informing local health disparity reduction efforts.

Evaluation of Scleroderma Clinical Trials Consortium training recommendations on modified Rodnan skin score assessment in scleroderma.

AIM: The modified Rodnan skin score (mRSS) is a validated outcome measure for skin thickness in systemic sclerosis (SSc). Training has been shown to reduce variability in the measurement of mRSS. Our objective was to assess the inter- and intra-observer variability of mRSS scoring using the proposed recommendations for training by the Scleroderma Clinical Trials Consortium (SCTC) and World Scleroderma Foundation (WSF). METHOD: Fifty-two trainees and eight adult SSc patients participated in the SSc skin scoring workshop that was conducted in two sessions by four teachers. Each session, attended by 26 trainees, had a teaching and evaluation phase. The teaching phase comprised of: (a) lecture on mRSS scoring; (b) video demonstration of mRSS scoring; and (c) live demonstration of mRSS on one SSc patient. In the evaluation phase, each trainee independently assessed the mRSS in four SSc patients. For intra-observer reliability, 14 trainees re-assessed the mRSS of two SSc patients whom they had previously examined. We computed the inter- and intra-observer variability using a linear mixed model. RESULTS: For the evaluation phase, 34 (65.4%) trainees were within five units of the established teachers' score in 3 out of 4 patients. Overall, the whole group had acceptable inter-observer variability (intra-class correlation coefficient [ICC] = 0.71, mean = 8.64 and within-patient standard deviation [SD] = 4.25). The intra-observer ICC was 0.85 and within-patient SD was 2.73. CONCLUSION: There was good inter-observer and excellent intra-observer reliability. This is the first study examining the training of assessors using the SCTC/WSF recommendations and our results support the importance of standardized training for skin scoring.

Determinants of Latrine Use Behavior: The Psychosocial Proxies of Individual-Level Defecation Practices in Rural Coastal Ecuador.

There is increasing appreciation that latrine access does not imply use-many individuals who own latrines do not consistently use them. Little is known, however, about the determinants of latrine use, particularly among those with variable defecation behaviors. Using the integrated behavior model of water, sanitation, and hygiene framework, we sought to characterize determinants of latrine use in rural Ecuador. We interviewed 197 adults living in three communities with a survey consisting of 70 psychosocial defecation-related questions. Questions were excluded from analysis if responses lacked variability or at least 10% of respondents did not provide a definitive answer. All interviewed individuals had access to a privately owned or shared latrine. We then applied adaptive elastic nets (ENET) and supervised principal component analysis (SPCA) to a reduced dataset of 45 questions among 154 individuals with complete data to select determinants that predict self-reported latrine use. Latrine use was common, but not universal, in the sample (76%). The SPCA model identified six determinants and adaptive ENET selected five determinants. Three indicators were represented in both models-latrine users were more likely to report that their latrine is clean enough to use and also more likely to report daily latrine use; while those reporting that elderly men were not latrine users were less likely to use latrines themselves. Our findings suggest that social norms are important predictors of latrine use, whereas knowledge of the health benefits of sanitation may not be as important. These determinants are informative for promotion of latrine adoption.