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 (β) 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 (β = -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 (β = 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: β = 0.009, 95% CI -0.093 to 0.111) or duration of breastfeeding (per 1-month increase: β = -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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03950-5.

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.

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.

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.

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.

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.

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.

Enhancing Models and Measurements of Traffic-Related Air Pollutants for Health Studies Using Dispersion Modeling and Bayesian Data Fusion

INTRODUCTION

The adverse health effects associated with exposure to traffic-related air pollutants (TRAPs) remain a key public health issue. Often, exposure assessments have not represented the small-scale variation and elevated concentrations found near major roads and in urban settings. This research explores approaches aimed at improving exposure estimates of TRAPs that can reduce exposure measurement error when used in health studies. We consider dispersion models designed specifically for the near-road environment, as well as spatiotemporal and data fusion models. These approaches are implemented and evaluated utilizing data collected in recent modeling, monitoring, and epidemiological studies conducted in Detroit, Michigan.

APPROACH

Dispersion models, which estimate near-road pollutant concentrations and individual exposures based on first principles - and in particular, high fidelity models - can provide great flexibility and theoretical strength. They can represent the spatial variability of TRAP concentrations at locations not measured by conventional and spatially sparse air quality monitoring networks. A number of enhancements to dispersion modeling and mobile on-road emissions inventories were considered, including the representation of link-based road networks and updated estimates of temporal allocation of traffic activity, emission factors, and meteorological inputs. The recently developed Research LINE-source model (RLINE), a Gaussian line-source dispersion model specifically designed for the near-road environment, was used in an operational evaluation that compared predicted concentrations of nitrogen oxides (NOx), carbon monoxide (CO), and PM2.5 (particulate matter ≤ 2.5 µm in aerodynamic diameter) with observed concentrations at air quality monitoring stations located near high-traffic roads. Spatiotemporal and data fusion models provided additional and complementary approaches for estimating TRAP exposures. We formulated both nonstationary universal kriging models that exploit the spatial correlation in the monitoring data, and data fusion models that leverage the information contained in both the monitoring data and the output of numerical models, specifically RLINE. These models were evaluated using observations of nitric oxide (NO), NOx, black carbon (BC), and PM2.5 monitored along transects crossing major roads in Detroit. We also examined model assumptions, including the appropriateness of the covariance functions, errors in RLINE outputs, and the effects of jointly modeling two pollutants and using an updated emission inventory.

RESULTS

For CO and NOx, dispersion model performance was best when monitoring sites were close to major roads, during downwind conditions, during weekdays, and during certain seasons. The ability to discern local and particularly the traffic-related portion of PM2.5 was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain sources (e.g., area, fugitive) and some processes (e.g., formation of secondary aerosols). Sensitivity analyses of alternative meteorological inputs and updated emission factors showed some performance gain when using local (on-site) meteorological data and updated inventories. Overall, the operational evaluation suggested RLINE's usefulness for estimating spatially and temporally resolved exposure estimates. The application of the universal kriging models confirmed that wind speed and direction are important drivers of nonstationarity in pollutant concentrations, and that these models can predict exposure estimates that have lower prediction errors than do stationary model counterparts. The application of the Bayesian data fusion models suggested that the RLINE output had a spatially varying additive bias for NOx and PM2.5 and provided little additional information for NOx, besides what is already contained in traffic and geographical information system (GIS) covariates, but had improved estimates of PM2.5 concentrations. Results of the nonstationary Bayesian data fusion model that used RLINE output across a field spanning the measurement sites were similar to a regression-based Bayesian data fusion approach that used only RLINE output at the monitoring locations, with the latter being computationally less burdensome. Using the regression-based Bayesian data fusion model, we found that RLINE with the updated emission inventory provided results that were more useful for estimating NOx concentration at unmonitored sites, but the updated emission inventory did not improve predictions of PM2.5 concentrations. Joint modeling of NOx and PM2.5 was not useful, a result of differences in RLINE's utility in predicting PM2.5 and NOx - useful for the former, but not for the latter - and differences in the spatial dependence structures of the two pollutants. Overall, information provided by RLINE was shown to have the potential to improve spatiotemporal estimates of TRAP concentrations.

CONCLUSIONS

The study results should be interpreted and generalized cautiously given the limitations of the data used. Similar analyses in other settings are recommended for confirming and extending our findings. Still, the study highlights considerations that are relevant for exposure estimates used in health studies. The ability of a dispersion model to accurately reproduce and predict a pollutant depends on the pollutant as well as on spatial and temporal factors, such as the distance and direction from the road, time-of-day, and day-of-week. The nature and source of exposure measurement errors should be taken into consideration, particularly in health studies that take advantage of time- activity information that describes where and when individuals are exposed to pollution. Efforts to refine model inputs and improve model performance can be helpful; meteorological inputs may be the most critical. For both dispersion and spatiotemporal statistical models, sufficient and high-quality monitoring data are essential for developing and evaluating these models. Our analyses using Bayesian data fusion models confirm the presence of spatially varying errors in dispersion model outputs and allow quantification of both the magnitude and the spatial nature of these errors. This valuable information can be leveraged in health studies examining air pollution exposure as well as in studies informing regulatory responses.

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.

High Prevalence of Extended-Spectrum Beta-Lactamase CTX-M-Producing Escherichia coli in Small-Scale Poultry Farming in Rural Ecuador

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.

Impacts of small-scale chicken farming activity on antimicrobial-resistant Escherichia coli carriage in backyard chickens and children in rural Ecuador

The emergence, spread, and persistence of antimicrobial resistance (AMR) remains a pressing global concern. Increased promotion of commercial small-scale agriculture within low-resource settings has facilitated an increased use in antimicrobials as growth promoters globally, creating antimicrobial-resistant animal reservoirs. We conducted a longitudinal field study in rural Ecuador to monitor the AMR of Escherichia coli populations from backyard chickens and children at three sample periods with approximately 2-month intervals (February, April, and June 2017). We assessed AMR to 12 antibiotics using generalized linear mixed effects models (GLMM). We also sampled and assessed AMR to the same 12 antibiotics in one-day-old broiler chickens purchased from local venders. One-day-old broiler chickens showed lower AMR at sample period 1 compared to sample period 2 (for 9 of the 12 antibiotics tested); increases in AMR between sample periods 2 and 3 were minimal. Two months prior to the first sample period (December 2016) there was no broiler farming activity due to a regional collapse followed by a peak in annual farming in February 2017. Between sample periods 1 and 2, we observed significant increases in AMR to 6 of the 12 antibiotics in children and to 4 of the 12 antibiotics in backyard chickens. These findings suggest that the recent increase in farming, and the observed increase of AMR in the one-day old broilers, may have caused the increase in AMR in backyard chickens and children. Small-scale farming dynamics could play an important role in the spread of AMR in low- and middle-income countries.

Robust distributed lag models using data adaptive shrinkage

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.

A mixed-effects, spatially varying coefficients model with application to multi-resolution functional magnetic resonance imaging data

Spatial resolution plays an important role in functional magnetic resonance imaging studies as the signal-to-noise ratio increases linearly with voxel volume. In scientific studies, where functional magnetic resonance imaging is widely used, the standard spatial resolution typically used is relatively low which ensures a relatively high signal-to-noise ratio. However, for pre-surgical functional magnetic resonance imaging analysis, where spatial accuracy is paramount, high-resolution functional magnetic resonance imaging may play an important role with its greater spatial resolution. High spatial resolution comes at the cost of a smaller signal-to-noise ratio. This begs the question as to whether we can leverage the higher signal-to-noise ratio of a standard functional magnetic resonance imaging study with the greater spatial accuracy of a high-resolution functional magnetic resonance imaging study in a pre-operative patient. To answer this question, we propose to regress the statistic image from a high resolution scan onto the statistic image obtained from a standard resolution scan using a mixed-effects model with spatially varying coefficients. We evaluate our model via simulation studies and we compare its performance with a recently proposed model that operates at a single spatial resolution. We apply and compare the two models on data from a patient awaiting tumor resection. Both simulation study results and the real data analysis demonstrate that our newly proposed model indeed leverages the larger signal-to-noise ratio of the standard spatial resolution scan while maintaining the advantages of the high spatial resolution scan.

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.

Randomized Controlled Trial to Evaluate an Internet-Based Self-Management Program in Systemic Sclerosis

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.

Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study

Background

Mobile 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.

Objective

In 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.

Methods

A 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.

Results

The watch resulted in a much higher proportion of missing data days (P<.001), with missing data explained by technical differences between the devices as well as participant behaviors. The mobile phone was significantly more accurate in detecting home locations (P=.004) and marginally more accurate (P=.07) for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation.

Conclusions

The most suitable mobile device for location-based health research depends on the particular study objectives. Furthermore, data generated from mobile devices, such as GPS phones and smartwatches, require careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from the use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or those that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the greater precision demonstrated by the mobile phone in recording indoor activities.

Distributed Lag Interaction Models with Two Pollutants

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.

Using Social Media to Identify Sources of Healthy Food in Urban Neighborhoods

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.

Reliability and Validity of the Tender and Swollen Joint Counts and the Modified Rodnan Skin Score in Early Diffuse Cutaneous Systemic Sclerosis: Analysis from the Prospective Registry of Early Systemic Sclerosis Cohort

OBJECTIVE

To 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.

METHODS

Ten rheumatologists completed the SJC, TJC, and mRSS on 7 patients. Musculoskeletal ultrasound (MSUS) was performed.

RESULTS

Interobserver 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.

CONCLUSION

We demonstrate excellent inter- and intraobserver reliability for the mRSS and TJC in dcSSc. However, the SJC and TJC did not correspond to MSUS.

The American College of Rheumatology Provisional Composite Response Index for Clinical Trials in Early Diffuse Cutaneous Systemic Sclerosis

OBJECTIVE

Early 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).

METHODS

We 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.

RESULTS

Sixteen 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).

CONCLUSION

We have developed a CRISS that is appropriate for use as an outcome assessment in RCTs of early dcSSc.

Pre-Surgical fMRI Data Analysis Using a Spatially Adaptive Conditionally Autoregressive Model

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.

The American College of Rheumatology Provisional Composite Response Index for Clinical Trials in Early Diffuse Cutaneous Systemic Sclerosis

OBJECTIVE

Early 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).

METHODS

We 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.

RESULTS

Sixteen 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).

CONCLUSION

We have developed a CRISS that is appropriate for use as an outcome assessment in RCTs of early dcSSc.

Non-stationary spatio-temporal modeling of traffic-related pollutants in near-road environments

A problem often encountered in environmental epidemiological studies assessing the health effects associated with ambient exposure to air pollution is the spatial misalignment between monitors' locations and subjects' actual residential locations. Several strategies have been adopted to circumvent this problem and estimate pollutants concentrations at unsampled sites, including spatial statistical or geostatistical models that rely on the assumption of stationarity to model the spatial dependence in pollution levels. Although computationally convenient, the assumption of stationarity is often untenable for pollutants concentration, particularly in the near-road environment. Building upon the work of Fuentes (2001) and Schmidt et al. (2011), in this paper we present a non-stationary spatio-temporal model for three traffic-related pollutants in a localized near-road environment. Modeling each pollutant separately and independently, we express each pollutant's concentration as a mixture of two independent spatial processes, each equipped with a non-stationary covariance function with covariates driving the non-stationarity and the mixture weights.

Distributed Lag Models: Examining Associations Between the Built Environment and Health

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.

Reliability, validity and responsiveness to change of the Saint George's Respiratory Questionnaire in early diffuse cutaneous systemic sclerosis

OBJECTIVE

Dyspnoea 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.

METHODS

At 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.

RESULTS

A 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.

CONCLUSION

The SGRQ has acceptable reliability, construct validity and responsiveness to change for use in a dcSSc population and differentiates between patients with and without ILD.

Vulnerability to extreme heat by socio-demographic characteristics and area green space among the elderly in Michigan, 1990-2007

OBJECTIVES

We 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.

METHODS

In 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).

RESULTS

In 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.

CONCLUSIONS

Green 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.

Correlates and responsiveness to change of measures of skin and musculoskeletal disease in early diffuse systemic sclerosis

OBJECTIVE

Skin 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.

METHODS

Patients 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.

RESULTS

A 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).

CONCLUSION

Physician 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.

Spatial clustering of Aedes aegypti related to breeding container characteristics in Coastal Ecuador: implications for dengue control

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.

Combination of echocardiographic and pulmonary function test measures improves sensitivity for diagnosis of systemic sclerosis-associated pulmonary arterial hypertension: analysis of 2 cohorts

OBJECTIVE

To 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).

METHODS

We 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.

RESULTS

In 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.

CONCLUSION

In 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.

Space-time data fusion under error in computer model output: an application to modeling air quality

We provide methods that can be used to obtain more accurate environmental exposure assessment. In particular, we propose two modeling approaches to combine monitoring data at point level with numerical model output at grid cell level, yielding improved prediction of ambient exposure at point level. Extending our earlier downscaler model (Berrocal, V. J., Gelfand, A. E., and Holland, D. M. (2010b). A spatio-temporal downscaler for outputs from numerical models. Journal of Agricultural, Biological and Environmental Statistics 15, 176-197), these new models are intended to address two potential concerns with the model output. One recognizes that there may be useful information in the outputs for grid cells that are neighbors of the one in which the location lies. The second acknowledges potential spatial misalignment between a station and its putatively associated grid cell. The first model is a Gaussian Markov random field smoothed downscaler that relates monitoring station data and computer model output via the introduction of a latent Gaussian Markov random field linked to both sources of data. The second model is a smoothed downscaler with spatially varying random weights defined through a latent Gaussian process and an exponential kernel function, that yields, at each site, a new variable on which the monitoring station data is regressed with a spatial linear model. We applied both methods to daily ozone concentration data for the Eastern US during the summer months of June, July and August 2001, obtaining, respectively, a 5% and a 15% predictive gain in overall predictive mean square error over our earlier downscaler model (Berrocal et al., 2010b). Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites.

On the use of a PM(2.5) exposure simulator to explain birthweight

In relating pollution to birth outcomes, maternal exposure has usually been described using monitoring data. Such characterization provides a misrepresentation of exposure as it (i) does not take into account the spatial misalignment between an individual's residence and monitoring sites, and (ii) it ignores the fact that individuals spend most of their time indoors and typically in more than one location. In this paper, we break with previous studies by using a stochastic simulator to describe personal exposure (to particulate matter) and then relate simulated exposures at the individual level to the health outcome (birthweight) rather than aggregating to a selected spatial unit.We propose a hierarchical model that, at the first stage, specifies a linear relationship between birthweight and personal exposure, adjusting for individual risk factors and introduces random spatial effects for the census tract of maternal residence. At the second stage, our hierarchical model specifies the distribution of each individual's personal exposure using the empirical distribution yielded by the stochastic simulator as well as a model for the spatial random effects.We have applied our framework to analyze birthweight data from 14 counties in North Carolina in years 2001 and 2002. We investigate whether there are certain aspects and time windows of exposure that are more detrimental to birthweight by building different exposure metrics which we incorporate, one by one, in our hierarchical model. To assess the difference in relating ambient exposure to birthweight versus personal exposure to birthweight, we compare estimates of the effect of air pollution obtained from hierarchical models that linearly relate ambient exposure and birthweight versus those obtained from our modeling framework.Our analysis does not show a significant effect of PM(2.5) on birthweight for reasons which we discuss. However, our modeling framework serves as a template for analyzing the relationship between personal exposure and longer term health endpoints.

A bivariate space-time downscaler under space and time misalignment

Ozone and particulate matter PM(2.5) are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce concentration surfaces over large spatial regions. In this paper, we offer a fully-model based approach for fusing these two sources of information for the pair of co-pollutants which is computationally feasible over large spatial regions and long periods of time. Due to the association between concentration levels of the two environmental contaminants, it is expected that information regarding one will help to improve prediction of the other. Misalignment is an obvious issue since the monitoring networks for the two contaminants only partly intersect and because the collection rate for PM(2.5) is typically less frequent than that for ozone.Extending previous work in Berrocal et al. (2009), we introduce a bivariate downscaler that provides a flexible class of bivariate space-time assimilation models. We discuss computational issues for model fitting and analyze a dataset for ozone and PM(2.5) for the ozone season during year 2002. We show a modest improvement in predictive performance, not surprising in a setting where we can anticipate only a small gain.

A Spatio-Temporal Downscaler for Output From Numerical Models

Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process.As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1-October 15, 2001. For the best choice, we present a summary of the analysis. Supplemental material, including color versions of Figures 4, 5, 6, 7, and 8, and MCMC diagnostic plots, are available online.