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ObjectiveThe 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.MethodsWe 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.ResultsThe 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.ConclusionThe Patient-Reported Outcomes Measurement Information System Self-Efficacy measure is valid, reliable, and responsive to change for persons with systemic sclerosis.

ObjectiveTo evaluate the extent to which uterine fibroids are associated with antimüllerian hormone (AMH) concentrations.DesignCross-sectional study.SettingBaseline 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.ExposureFibroid 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.

BackgroundThe 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.MethodsChildren 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.ResultsInfection 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.ConclusionsIn 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.

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.

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.

BackgroundExtreme 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.ObjectiveWe 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.MethodsWe 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).ResultsUnsupervised 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.DiscussionHVIs 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.

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.

ObjectiveInitiatives 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 methodsWe 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.ResultsSignificant, 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).DiscussionTweet-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.ConclusionsTwitter 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.

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.

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.

OBJECTIVE:In a pilot study, our group showed that an internet-based self-management program improves self-efficacy in systemic sclerosis (SSc). The objective of the current study was to compare an internet-based self-management program to a patient-focused educational book developed to assess measures of self-efficacy and other patient-reported outcomes in patients with SSc. METHODS:We conducted a 16-week randomized, controlled trial. RESULTS:Of the 267 participants who completed baseline questionnaires and were randomized to the intervention (internet: www.selfmanagescleroderma.com) or control (book) group, 123 participants (93%) in the internet group and 124 participants (94%) in the control group completed the 16-week randomized controlled trial (RCT). The mean ± SD age of all participants was 53.7 ± 11.7 years, 91% were women, and 79.4% had some college or a higher degree. The mean ± SD disease duration after diagnosis of SSc was 8.97 ± 8.50 years. There were no statistical differences between the 2 groups for the primary outcome measure (Patient-Reported Outcomes Measurement Information System Self-Efficacy for Managing Symptoms: mean change of 0.35 in the internet group versus 0.94 in the control group; P = 0.47) and secondary outcome measures, except the EuroQol 5-domain instrument visual analog scale score (P = 0.05). Internet group participants agreed that the self-management modules were of importance to them, the information was presented clearly, and the website was easy to use and at an appropriate reading level. CONCLUSION:Our RCT showed that the internet-based self-management website was not statistically superior to an educational patient-focused book in improving self-efficacy and other measures. The participants were enthusiastic about the content and presentation of the self-management website.

Small-scale farming may have large impacts on the selection and spread of antimicrobial resistance to humans. We conducted an observational study to evaluate antibiotic-resistant Escherichia coli populations from poultry and humans in rural northwestern Esmeraldas, Ecuador. Our study site is a remote region with historically low resistance levels of third-generation antibiotics such cefotaxime (CTX), a clinically relevant antibiotic, in both poultry and humans. Our study revealed 1) high CTX resistance (66.1%) in farmed broiler chickens, 2) an increase in CTX resistance over time in backyard chicken not fed antibiotics (2.3-17.9%), and 3) identical bla CTX-M sequences from human and chicken bacteria, suggesting a spillover event. These findings provide evidence that small-scale meat production operations have direct impacts on the spread and selection of clinically important antibiotics among underdeveloped settings.

ObjectiveTo examine the feasibility of using social media to assess the consumer nutrition environment by comparing sentiment expressed in Yelp reviews with information obtained from a direct observation audit instrument for grocery stores.DesignTrained raters used the Nutrition Environment Measures Survey in Stores (NEMS-S) in 100 grocery stores from July 2015 to March 2016. Yelp reviews were available for sixty-nine of these stores and were retrieved in February 2017 using the Yelp Application Program Interface. A sentiment analysis was conducted to quantify the perceptions of the consumer nutrition environment in the review text. Pearson correlation coefficients (ρ) were used to compare NEMS-S scores with Yelp review text on food availability, quality, price and shopping experience.SettingDetroit, Michigan, USA.ParticipantsNone.ResultsYelp reviews contained more comments about food availability and the overall shopping experience than food price and food quality. Negative sentiment about food prices in Yelp review text and the number of dollar signs on Yelp were positively correlated with observed food prices in stores (ρ=0·413 and 0·462, respectively). Stores with greater food availability were rated as more expensive on Yelp. Other aspects of the food store environment (e.g. overall quality and shopping experience) were captured only in Yelp.ConclusionsWhile Yelp cannot replace in-person audits for collecting detailed information on the availability, quality and cost of specific food items, Yelp holds promise as a cost-effective means to gather information on the overall cost, quality and experience of food stores, which may be relevant for nutrition outcomes.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on a health outcome of interest such as mortality and morbidity. Most previous DLM approaches only consider one pollutant at a time. In this article, we propose distributed lag interaction model (DLIM) to characterize the joint lagged effect of two pollutants. One natural way to model the interaction surface is by assuming that the underlying basis functions are tensor products of the basis functions that generate the main-effect distributed lag functions. We extend Tukey's one-degree-of-freedom interaction structure to the two-dimensional DLM context. We also consider shrinkage versions of the two to allow departure from the specified Tukey's interaction structure and achieve bias-variance tradeoff. We derive the marginal lag effects of one pollutant when the other pollutant is fixed at certain quantiles. In a simulation study, we show that the shrinkage methods have better average performance in terms of mean squared error (MSE) across different scenarios. We illustrate the proposed methods by using the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) data to model the joint effects of PM10 and O3 on mortality count in Chicago, Illinois, from 1987 to 2000.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on an outcome of interest such as mortality or cardiovascular events. Generally speaking, DLMs can be applied to time-series data where the current measure of an independent variable and its lagged measures collectively affect the current measure of a dependent variable. The corresponding distributed lag (DL) function represents the relationship between the lags and the coefficients of the lagged exposure variables. Common choices include polynomials and splines. On one hand, such a constrained DLM specifies the coefficients as a function of lags and reduces the number of parameters to be estimated; hence, higher efficiency can be achieved. On the other hand, under violation of the assumption about the DL function, effect estimates can be severely biased. In this article, we propose a general framework for shrinking coefficient estimates from an unconstrained DLM, that are unbiased but potentially inefficient, toward the coefficient estimates from a constrained DLM to achieve a bias-variance trade-off. The amount of shrinkage can be determined in various ways, and we explore several such methods: empirical Bayes-type shrinkage, a hierarchical Bayes approach, and generalized ridge regression. We also consider a two-stage shrinkage approach that enforces the effect estimates to approach zero as lags increase. We contrast the various methods via an extensive simulation study and show that the shrinkage methods have better average performance across different scenarios in terms of mean squared error (MSE).We illustrate the methods by using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to explore the association between PM$_{10}$, O$_3$, and SO$_2$ on three types of disease event counts in Chicago, IL, from 1987 to 2000.

An established body of research has used secondary data sources (such as proprietary business databases) to demonstrate the importance of the neighborhood food environment for multiple health outcomes. However, documenting food availability using secondary sources in low-income urban neighborhoods can be particularly challenging since small businesses play a crucial role in food availability. These small businesses are typically underrepresented in national databases, which rely on secondary sources to develop data for marketing purposes. Using social media and other crowdsourced data to account for these smaller businesses holds promise, but the quality of these data remains unknown. This paper compares the quality of full-line grocery store information from Yelp, a crowdsourced content service, to a "ground truth" data set (Detroit Food Map) and a commercially-available dataset (Reference USA) for the greater Detroit area. Results suggest that Yelp is more accurate than Reference USA in identifying healthy food stores in urban areas. Researchers investigating the relationship between the nutrition environment and health may consider Yelp as a reliable and valid source for identifying sources of healthy food in urban environments.

ObjectiveTo determine the inter/intraobserver reliability of the tender and swollen joint counts (TJC, SJC) and the modified Rodnan Skin Score (mRSS) in diffuse cutaneous systemic sclerosis (dcSSc) and to assess content validity of the TJC/SJC.MethodsTen rheumatologists completed the SJC, TJC, and mRSS on 7 patients. Musculoskeletal ultrasound (MSUS) was performed.ResultsInterobserver and intraobserver reliability for the TJC was 0.97 and 0.99, for the SJC was 0.24 and 0.71, and for the mRSS was 0.81 and 0.94, respectively. MSUS abnormalities did not correspond with SJC/TJC.ConclusionWe demonstrate excellent inter- and intraobserver reliability for the mRSS and TJC in dcSSc. However, the SJC and TJC did not correspond to MSUS.

Spatial smoothing is an essential step in the analysis of functional magnetic resonance imaging (fMRI) data. One standard smoothing method is to convolve the image data with a three-dimensional Gaussian kernel that applies a fixed amount of smoothing to the entire image. In pre-surgical brain image analysis where spatial accuracy is paramount, this method, however, is not reasonable as it can blur the boundaries between activated and deactivated regions of the brain. Moreover, while in a standard fMRI analysis strict false positive control is desired, for pre-surgical planning false negatives are of greater concern. To this end, we propose a novel spatially adaptive conditionally autoregressive model with variances in the full conditional of the means that are proportional to error variances, allowing the degree of smoothing to vary across the brain. Additionally, we present a new loss function that allows for the asymmetric treatment of false positives and false negatives. We compare our proposed model with two existing spatially adaptive conditionally autoregressive models. Simulation studies show that our model outperforms these other models; as a real model application, we apply the proposed model to the pre-surgical fMRI data of two patients to assess peri- and intra-tumoral brain activity.

ObjectiveEarly diffuse cutaneous systemic sclerosis (dcSSc) is characterized by rapid changes in the skin and internal organs. The objective of this study was to develop a composite response index in dcSSc (CRISS) for use in randomized controlled trials (RCTs).MethodsWe developed 150 paper patient profiles with standardized clinical outcome elements (core set items) using patients with dcSSc. Forty scleroderma experts rated 20 patient profiles each and assessed whether each patient had improved or not improved over a period of 1 year. Using the profiles for which raters had reached a consensus on whether the patients were improved versus not improved (79% of the profiles examined), we fit logistic regression models in which the binary outcome referred to whether the patient was improved or not, and the changes in the core set items from baseline to followup were entered as covariates. We tested the final index in a previously completed RCT.ResultsSixteen of 31 core items were included in the patient profiles after a consensus meeting and review of test characteristics of patient-level data. In the logistic regression model in which the included core set items were change over 1 year in the modified Rodnan skin thickness score, the forced vital capacity, the patient and physician global assessments, and the Health Assessment Questionnaire disability index, sensitivity was 0.982 (95% confidence interval 0.982-0.983) and specificity was 0.931 (95% confidence interval 0.930-0.932), and the model with these 5 items had the highest face validity. Subjects with a significant worsening of renal or cardiopulmonary involvement were classified as not improved, regardless of improvements in other core items. With use of the index, the effect of methotrexate could be differentiated from the effect of placebo in a 1-year RCT (P = 0.02).ConclusionWe have developed a CRISS that is appropriate for use as an outcome assessment in RCTs of early dcSSc.

Built environment factors constrain individual level behaviors and choices, and thus are receiving increasing attention to assess their influence on health. Traditional regression methods have been widely used to examine associations between built environment measures and health outcomes, where a fixed, prespecified spatial scale (e.g., 1 mile buffer) is used to construct environment measures. However, the spatial scale for these associations remains largely unknown and misspecifying it introduces bias. We propose the use of distributed lag models (DLMs) to describe the association between built environment features and health as a function of distance from the locations of interest and circumvent a-priori selection of a spatial scale. Based on simulation studies, we demonstrate that traditional regression models produce associations biased away from the null when there is spatial correlation among the built environment features. Inference based on DLMs is robust under a range of scenarios of the built environment. We use this innovative application of DLMs to examine the association between the availability of convenience stores near California public schools, which may affect children's dietary choices both through direct access to junk food and exposure to advertisement, and children's body mass index z scores.

ObjectiveDyspnoea is a common, multifactorial source of functional impairment among patients with dcSSc. Our objective was to assess the reliability, construct validity and responsiveness to change of the Saint George's Respiratory Questionnaire (SGRQ) in patients with early dcSSc participating in a multicentre prospective study.MethodsAt enrolment and 1 year, patients completed the SGRQ (a multi-item instrument with four scales: symptoms, activity, impact and total), a visual analogue scale (VAS) for breathing and the HAQ Disability Index (HAQ-DI) and underwent 6 min walk distance and pulmonary function tests, physician and patient global health assessments and high-resolution CT (HRCT). We assessed internal consistency reliability using Cronbach's α. For validity we examined the ability of the SGRQ to differentiate the presence vs absence of interstitial lung disease (ILD) on HRCT or restrictive lung disease and evaluated the 1 year responsiveness to change using pulmonary function tests and patient- and physician-reported anchors. Correlation coefficients of 0.24-0.36 were considered moderate and >0.37 was considered large.ResultsA total of 177 patients were evaluated. Reliability was satisfactory for all SGRQ scales (0.70-0.93). All scales showed large correlations with the VAS for breathing and diffusing capacity of the lung for carbon monoxide in the overall cohort and in the subgroup with ILD. Three of the four scales in the overall cohort and the total scale in the ILD subgroup showed moderate to large correlation with the HAQ-DI and the predicted forced vital capacity (r = 0.33-0.44). Each scale discriminated between the presence and absence of ILD and restrictive lung disease (P ≤ 0.0001-0.03). At follow-up, all scales were responsive to change using different anchors.ConclusionThe SGRQ has acceptable reliability, construct validity and responsiveness to change for use in a dcSSc population and differentiates between patients with and without ILD.

ObjectivesWe examined how individual and area socio-demographic characteristics independently modified the extreme heat (EH)-mortality association among elderly residents of 8 Michigan cities, May-September, 1990-2007.MethodsIn a time-stratified case-crossover design, we regressed cause-specific mortality against EH (indicator for 4-day mean, minimum, maximum or apparent temperature above 97th or 99 th percentiles). We examined effect modification with interactions between EH and personal marital status, age, race, sex and education and ZIP-code percent "non-green space" (National Land Cover Dataset), age, race, income, education, living alone, and housing age (U.S. Census).ResultsIn models including multiple effect modifiers, the odds of cardiovascular mortality during EH (99 th percentile threshold) vs. non-EH were higher among non-married individuals (1.21, 95% CI=1.14-1.28 vs. 0.98, 95% CI=0.90-1.07 among married individuals) and individuals in ZIP codes with high (91%) non-green space (1.17, 95% CI=1.06-1.29 vs. 0.98, 95% CI=0.89-1.07 among individuals in ZIP codes with low (39%) non-green space). Results suggested that housing age may also be an effect modifier. For the EH-respiratory mortality association, the results were inconsistent between temperature metrics and percentile thresholds of EH but largely insignificant.ConclusionsGreen space, housing and social isolation may independently enhance elderly peoples' heat-related cardiovascular mortality vulnerability. Local adaptation efforts should target areas and populations at greater risk.

ObjectiveSkin and musculoskeletal involvement are frequently present early in diffuse cutaneous systemic sclerosis (dcSSc). The current study examined the correlates for skin and musculoskeletal measures in a 1-year longitudinal observational study.MethodsPatients with dcSSc were recruited at 4 US centers and enrolled in a 1-year study. Prespecified and standardized measures included physician and patient assessments of skin involvement, modified Rodnan skin score (MRSS), durometer score, Health Assessment Questionnaire disability index, serum creatine phosphokinase, tender joint counts, and presence/absence of tendon friction rubs, small joint contractures, and large joint contractures. Additionally, physician and patient global health assessments and health-related quality of life assessments were recorded. Correlations were computed among the baseline global assessments, skin variables, and musculoskeletal variables. Using the followup physician and patient anchors, effect sizes were calculated.ResultsA total of 200 patients were studied: 75% were women, mean ± SD age was 50.0 ± 11.9 years, and mean ± SD disease duration from first non-Raynaud's phenomenon symptom was 1.6 ± 1.4 years. Physician global health assessment had large correlations with MRSS (r = 0.60) and physician-reported skin involvement visual analog scale in the last month (r = 0.74), whereas patient global assessment had large correlations with MRSS, the Short Form 36 health survey physical component scale, skin interference, and skin involvement in the last month (r = 0.37-0.72). Four of 9 skin variables had moderate to large effect sizes (0.51-1.09).ConclusionPhysician and patient global assessments have larger correlations with skin measures compared to musculoskeletal measures. From a clinical trial perspective, skin variables were more responsive to change than musculoskeletal variables over a 1-year period, although both provide complementary information.

Mosquito management within households remains central to the control of dengue virus transmission. An important factor in these management decisions is the spatial clustering of Aedes aegypti. We measured spatial clustering of Ae. aegypti in the town of Borbón, Ecuador and assessed what characteristics of breeding containers influenced the clustering. We used logistic regression to assess the spatial extent of that clustering. We found strong evidence for juvenile mosquito clustering within 20 m and for adult mosquito clustering within 10 m, and stronger clustering associations for containers ≥ 40 L than those < 40 L. Aedes aegypti clusters persisted after adjusting for various container characteristics, suggesting that patterns are likely attributable to short dispersal distances rather than shared characteristics of containers in cluster areas. These findings have implications for targeting Ae. aegypti control efforts.

ObjectiveTo evaluate routinely collected non-invasive tests from 2 systemic sclerosis (SSc) cohorts to determine their predictive value alone and in combination versus right heart catheterization (RHC)-confirmed pulmonary arterial hypertension (PAH).MethodsWe evaluated 2 cohorts of patients who were at risk or with incident PAH: (1) The Pulmonary Hypertension Assessment and Recognition Outcomes in Scleroderma (PHAROS) cohort and (2) an inception SSc cohort at Cochin Hospital, Paris, France. Estimated right ventricular systolic pressure (eRVSP) as determined by transthoracic echocardiogram (TTE) and pulmonary function test (PFT) measures was evaluated, and the predictive values determined. We then evaluated patients with PAH missed on TTE cutoffs that were subsequently identified by a PFT measure.ResultsIn the PHAROS cohort (n = 206), 59 (29%) had RHC-defined PAH. An eRVSP threshold of 35-50 mm Hg failed to diagnose PAH in 7% to 31% of patients, 50% to 70% of which (n = 2-13) were captured by PFT measures. In the Cochin cohort (n = 141), 10 (7%) patients had RHC confirmed PAH. An eRVSP threshold of 35-50 mm Hg missed 0% to 70% (n = 0-7) of patients, of which 0% to 68% (n = 0-6) were met by PFT measures. The combination of TTE and PFT improved the negative predictive value for diagnosing PAH.ConclusionIn 2 large SSc cohorts, screening with TTE and PFT captured a majority of patients with PAH. TTE and PFT complement each other for the diagnosis of PAH.

BackgroundMobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or mobile phones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices. ObjectiveIn this study, we compared the data quality of location information collected from two mobile devices that determine location in different ways—a global positioning system (GPS) watch and a mobile phone with Google’s Location History feature enabled. MethodsA total of 21 chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A mobile phone–based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at 4 random times throughout each day. Participants also took part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent mobile phone and watch trace data to compare the devices’ trace data with their memory of their activities on those days. Trace data from the devices were compared on the basis of (1) missing data days, (2) reasons for missing data, (3) distance between the route data collected for matching day and the associated EMA survey locations, and (4) activity space total area and density surfaces. ResultsThe watch resulted in a much higher proportion of missing data days (P