Geography, generalisability, and susceptibility in clinical trials

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Putting Co-Exposures on Equal Footing: An Ecological Analysis of Same-Scale Measures of Air Pollution and Social Factors on Cardiovascular Disease in New York City

Epidemiologic evidence consistently links urban air pollution exposures to health, even after adjustment for potential spatial confounding by socioeconomic position (SEP), given concerns that air pollution sources may be clustered in and around lower-SEP communities. SEP, however, is often measured with less spatial and temporal resolution than are air pollution exposures (i.e., census-tract socio-demographics vs. fine-scale spatio-temporal air pollution models). Although many questions remain regarding the most appropriate, meaningful scales for the measurement and evaluation of each type of exposure, we aimed to compare associations for multiple air pollutants and social factors against cardiovascular disease (CVD) event rates, with each exposure measured at equal spatial and temporal resolution. We found that, in multivariable census-tract-level models including both types of exposures, most pollutant–CVD associations were non-significant, while most social factors retained significance. Similarly, the magnitude of association was higher for an IQR-range difference in the social factors than in pollutant concentrations. We found that when offered equal spatial and temporal resolution, CVD was more strongly associated with social factors than with air pollutant exposures in census-tract-level analyses in New York City

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates

Interactions between a Sap Beetle, Sabal Palm, Scale Insect, Filamentous Fungi and Yeast, with Discovery of Potential Antifungal Compounds

The multi-trophic relationship between insects, yeast, and filamentous fungi is reported on sabal palm (Sabal palmetto (Walter) Lodd. ex Schult. & Schult. f.). Gut content analyses and observations of adult and larval feeding of the sap beetle Brachypeplus glaber LeConte indicate that niche partitioning of fungal food substrata occurs between adults and larvae. This is the first report of specific mycophagous niche partitioning among beetle life stages based on gut content analyses. Fungi isolated from the beetle gut of adults, larvae, and pupae include species of Fusarium Link, Cladosporium Link, and Penicillium Link, which were differentially ingested by larvae and adults; Fusarium solani and Penicillium species in larvae, whereas F. oxysoproum, F. verticillioides, and Cladosporium in adults. These data indicate the first species-level host data for Brachypeplus Erichson species. Fusarium proliferatum (Matsush.) Nirenberg was the most commonly occurring fungal gut component, being isolated from the palm as well as gut of larvae, pupae, and adults; representing a commonly shared food resource. One species of yeast, Meyerozyma caribbica (Vaughan-Mart. et al.) Kurtzman & Suzuki (basionym = Pichia caribbica), was isolated from all life stages and is likely responsible for anti-fungal properties observed in the pupae and represents a promising source of antifungal compounds; rearing and diagnostic protocols are provided to aid biomedical researchers. Feeding and cleaning behaviors are documented using time-lapse video-micrography, and discussed in a behavioral and functional morphological context. Adults spent long periods feeding, often >1/3 of the two-hour observation period. A generic adult body posture was observed during feeding, and included substrate antennation before and after ingestion. Adult grooming behaviors were manifested in distinct antennal and tarsal cleaning mechanisms. Larval behaviors were different from adults, and larvae feeding on Fusarium fungi immediately ceased all subsequent feeding. This is the first ethogram for any adult or larval sap beetle

Identification of africanized honey bees (Hymenoptera: Apidae) incorporating morphometrics and an improved polymerase chain reaction mitotyping procedure

We propose an Africanized honey bee identification strategy using morphometrics and an improved polymerase chain reaction (PCR)-based mitotyping procedure that distinguishes between feral and commercial bees maternally descendent from 4 racial groups-Eastern European (Apis mellifera ligustica, caucasica, and carnica), Western European (A. m. mellifera), Egyptian (A. m. lamarckii), and other African origins. Mitochondrial genotype is highly correlated with morphology. Ninety-five percent of morphometrically determined Africanized feral colonies collected in Texas, Arizona, California, and Mexico also contained African mitochondria. Sixty-two percent of colonies from commercial or minimally managed apiaries in Mexico and Central America, with Africanized forewing lengths and 17% of colonies with intermediate forewing lengths, had African mitochondria. The strong correlation between non-European morphology and African mitotype, as well as the speed and accuracy of mitotype determination, suggest a 3-step Africanized bee identification procedure. This identification procedure first examines forewing length. Bees with lengths above a given threshold (9.12 mm) have a very high probability of being pure European in origin and are not examined further. Those bees with wing lengths below the threshold are subjected to mitochondrial analysis (mitotyping). Samples having African mitochondria are not examined further. Those bees with small forewing lengths, but European mitotypes, are then identified using detailed morphometric discriminant function analysis. By performing these steps in sequence, the number of bees requiring full morphometric analysis is reduced, saving time and improving the accuracy of Africanized honey bee identification

First Record of a Tachinid Fly Parasitoid (Diptera: Tachinidae) on a Dragonfly (Odonata: Calopterygidae)

During a biodiversity survey in the forest of central Guyana, an adult male of the damselfly Hetaerina caja dominula Hagen in Selys was found parasitized by a tachinid larva. This constitutes the first record of a parasitoid on an adult odonate, and of an odonate as host of a tachinid larva. CO1 DNA sequencing of the larva placed it closest to the tachinid genera Actinodoria Townsend, Euhalidaya Walton, and Cryptomeigenia Brauer & Bergenstamm in the tribe Blondeliini (subfamily Exoristinae). Pictures are provided of the third instar fly larva protruding from the host, of its posterior spiracles, and of the first and second instar cephaloskeletons

First Record of a Tachinid Fly Parasitoid (Diptera: Tachinidae) on a Dragonfly (Odonata: Calopterygidae)

During a biodiversity survey in the forest of central Guyana, an adult male of the damselfly Hetaerina caja dominula Hagen in Selys was found parasitized by a tachinid larva. This constitutes the first record of a parasitoid on an adult odonate, and of an odonate as host of a tachinid larva. CO1 DNA sequencing of the larva placed it closest to the tachinid genera Actinodoria Townsend, Euhalidaya Walton, and Cryptomeigenia Brauer & Bergenstamm in the tribe Blondeliini (subfamily Exoristinae). Pictures are provided of the third instar fly larva protruding from the host, of its posterior spiracles, and of the first and second instar cephaloskeletons

Fine-Scale Source Apportionment Including Diesel-Related Elemental and Organic Constituents of PM2.5 across Downtown Pittsburgh

Health effects of fine particulate matter (PM2.5) may vary by composition, and the characterization of constituents may help to identify key PM2.5 sources, such as diesel, distributed across an urban area. The composition of diesel particulate matter (DPM) is complicated, and elemental and organic carbon are often used as surrogates. Examining multiple elemental and organic constituents across urban sites, however, may better capture variation in diesel-related impacts, and help to more clearly separate diesel from other sources. We designed a “super-saturation” monitoring campaign of 36 sites to capture spatial variance in PM2.5 and elemental and organic constituents across the downtown Pittsburgh core (~2.8 km2). Elemental composition was assessed via inductively-coupled plasma mass spectrometry (ICP-MS), organic and elemental carbon via thermal-optical reflectance, and organic compounds via thermal desorption gas-chromatography mass-spectrometry (TD-GCMS). Factor analysis was performed including all constituents—both stratified by, and merged across, seasons. Spatial patterning in the resultant factors was examined using land use regression (LUR) modelling to corroborate factor interpretations. We identified diesel-related factors in both seasons; for winter, we identified a five-factor solution, describing a bus and truck-related factor [black carbon (BC), fluoranthene, nitrogen dioxide (NO2), pyrene, total carbon] and a fuel oil combustion factor (nickel, vanadium). For summer, we identified a nine-factor solution, which included a bus-related factor (benzo[ghi]fluoranthene, chromium, chrysene, fluoranthene, manganese, pyrene, total carbon, total elemental carbon, zinc) and a truck-related factor (benz[a]anthracene, BC, hopanes, NO2, total PAHs, total steranes). Geographic information system (GIS)-based emissions source covariates identified via LUR modelling roughly corroborated factor interpretations

Spatial Patterns in Rush-Hour vs. Work-Week Diesel-Related Pollution across a Downtown Core

Despite advances in monitoring and modelling of intra-urban variation in multiple pollutants, few studies have attempted to separate spatial patterns by time of day, or incorporated organic tracers into spatial monitoring studies. Due to varying emissions sources from diesel and gasoline vehicular traffic, as well as within-day temporal variation in source mix and intensity (e.g., rush-hours vs. full-day measures), accurately assessing diesel-related air pollution within an urban core can be challenging. We allocated 24 sampling sites across downtown Pittsburgh, Pennsylvania (2.8 km2) to capture fine-scale variation in diesel-related pollutants, and to compare these patterns by sampling interval (i.e., “rush-hours” vs. “work-week” concentrations), and by season. Using geographic information system (GIS)-based methods, we allocated sampling sites to capture spatial variation in key traffic-related pollution sources (i.e., truck, bus, overall traffic densities). Programmable monitors were used to collect integrated work-week and rush-hour samples of fine particulate matter (PM2.5), black carbon (BC), trace elements, and diesel-related organics (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), in summer and winter 2014. Land use regression (LUR) models were created for PM2.5, BC, total elemental carbon (EC), total organic carbon (OC), elemental (Al, Ca, Fe), and organic constituents (total PAHs, total hopanes), and compared by sampling interval and season. We hypothesized higher pollution concentrations and greater spatial contrast in rush-hour, compared to full work-week samples, with variation by season and pollutant. Rush-hour sampling produced slightly higher total PM2.5 and BC concentrations in both seasons, compared to work-week sampling, but no evident difference in spatial patterns. We also found substantial spatial variability in most trace elements and organic compounds, with comparable spatial patterns using both sampling paradigms. Overall, we found higher concentrations of traffic-related trace elements and organic compounds in rush-hour samples, and higher concentrations of coal-related elements (e.g., As, Se) in work-week samples. Mean bus density was the strongest LUR predictor in most models, in both seasons, under each sampling paradigm. Within each season and constituent, the bus-related terms explained similar proportions of variance in the rush-hour and work-week samples. Rush-hour and work-week LUR models explained similar proportions of spatial variation in pollutants, suggesting that the majority of emissions may be produced during rush-hour traffic across downtown. Results suggest that rush-hour emissions may predominantly shape overall spatial variance in diesel-related pollutants