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

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

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

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

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

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

A 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.

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.

Using social media to assess the consumer nutrition environment: comparing Yelp reviews with a direct observation audit instrument for grocery stores.

OBJECTIVE: To 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. DESIGN: Trained 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. SETTING: Detroit, Michigan, USA.ParticipantsNone. RESULTS: Yelp 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. CONCLUSIONS: While 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.

User acceptance of location-tracking technologies in health research: Implications for study design and data quality.

Research regarding place and health has undergone a revolution due to the availability of consumer-focused location-tracking devices that reveal fine-grained details of human mobility. Such research requires that participants accept such devices enough to use them in their daily lives. There is a need for a theoretically grounded understanding of acceptance of different location-tracking technology options, and its research implications. Guided by an extended Unified Theory of Acceptance and Use of Technology (UTAUT), we conducted a 28-day field study comparing 21 chronically ill people's acceptance of two leading, consumer-focused location-tracking technologies deployed for research purposes: (1) a location-enabled smartphone, and (2) a GPS watch/activity tracker. Participants used both, and completed two surveys and qualitative interviews. Findings revealed that all participants exerted effort to facilitate data capture, such as by incorporating devices into daily routines and developing workarounds to keep devices functioning. Nevertheless, the smartphone was perceived to be significantly easier and posed fewer usability challenges for participants than the watch. Older participants found the watch significantly more difficult to use. For both devices, effort expectancy was significantly associated with future willingness to participate in research although prosocial motivations overcame some concerns. Social influence, performance expectancy and use behavior were significantly associated with intentions to use the devices in participants' personal lives. Data gathered via the smartphone was significantly more complete than data gathered via the watch, primarily due to usability challenges. To make longer-term participation in location tracking research a reality, and to achieve complete data capture, researchers must minimize the effort involved in participation; this requires usable devices. For long-term location-tracking studies using similar devices, findings indicate that only smartphone-based tracking is up to the challenge.

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.

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.

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.

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.

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.

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 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.

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.

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

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

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

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