Daniel Price

Experimental Ethnography Online: The asthma files

This essay describes The Asthma Files, an experimental, digital ethnography project structured to support a collaborative research process and new ways of presenting academic research. While examining ways in which asthma is understood, cared for and governed in varied settings, the project also examines how digital tools can be used to support new research practices, new ways of expressing ethnographic analyses and new ways of drawing readers to ethnographic work. The Asthma Files is an experiment in ethnography, and in science, health and environmental communication. The project responds to dramatic increases in asthma incidence in the USA and globally in recent decades, and to wide acknowledgement that new forms of asthma knowledges are needed. The project aims to advance understanding of the way asthma and other complex conditions can be productively engaged, leveraging ethnography, deep play with interdisciplinarity and deep respect for different kinds and forms of knowledges.

Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining

OBJECTIVES Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures. METHODS We present an ARM-based methodology that produces rules associated with relevant odds ratios and limits the number of final rules even at very low support levels (0.5%), thanks to post-pruning criteria that limit rule redundancy and control for statistical significance. The methodology has been applied to a case-crossover study to explore the effects of multiple air pollutants on risk of asthma in pediatric subjects. RESULTS We identified 27 rules with interesting odds ratio among more than 10,000 having the required support. The only rule including only one chemical is exposure to ozone on the previous day of the reported asthma attack (OR=1.14). 26 combinatory rules highlight the limitations of air quality policies based on single pollutant thresholds and suggest that exposure to mixtures of chemicals is more harmful, with odds ratio as high as 1.54 (associated with the combination day0 SO2, day0 NO, day0 NO2, day1 PM). CONCLUSIONS The proposed method can be used to analyze risk variations caused by single and multiple exposures. The method is reliable and requires fewer assumptions on the data than parametric approaches. Rules including more than one pollutant highlight interactions that deserve further investigation, while helping to limit the search field.

Air Quality Simulations Using Big Data Programming Models

Forecasts of daily pollutant levels have become a standard part of weather predictions in television, on-line, and in newspapers. Research groups also need to analyze larger timeframes across more locations to correlate long term developments for different pollutants with multiple serious health effects such as asthma. This paper presents a comparison of the Hadoop MapReduce and Spark programing models for air quality simulations, guiding future code development for the research groups interested in these analyses. Two use cases have been used, namely (i) calculating the eight hour rolling average of pollutants in a restricted region, (ii) identifying clusters of sensors showing similar patterns in pollutant concentration over multiple years in the state of Texas. The data set used in this analysis is air pollution data collected over fifteen years at 179 monitor sites across the state of Texas for a variety of pollutants. Our results reveal 20-25% performance benefits for the Spark solutions over MapReduce. Furthermore, it documents performance benefits of the Spark MLlib machine learning library over the Mahout library which is based on the MapReduce programing model.

Investigation of Primary Factors Affecting the Variation of Modeled Oak Pollen Concentrations: A Case Study for Southeast Texas in 2010

Oak pollen concentrations over the Houston-Galveston-Brazoria (HGB) area in southeastern Texas were modeled and evaluated against in-situ data. We modified the Community Multi-scale Air Quality (CMAQ) model to include oak pollen emission, dispersion, and deposition. The Oak Pollen Emission Model (OPEM) calculated gridded oak pollen emissions, which are based on a parameterized equation considering a plant-specific factor (Ce), surface characteristics, and meteorology. The simulation period was chosen to be February 21 to April 30 in the spring of 2010, when the observed monthly mean oak pollen concentrations were the highest in six years (2009-2014). The results indicated Ce and meteorology played an important role in the calculation of oak pollen emissions. While Ce was critical in determining the magnitude of oak pollen emissions, meteorology determined their variability. In particular, the contribution of the meteorology to the variation in oak pollen emissions increased with the oak pollen emission rate. The evaluation results using in-situ surface data revealed that the model underestimated pollen concentrations and was unable to accurately reproduce the peak pollen episodes. The model error was likely due to uncertainty in climatology-based Ce used for the estimation of oak pollen emissions and inaccuracy in the wind fields from the Weather Research and Forecast (WRF) model.

An exploratory decision tree analysis to predict physical activity compliance rates in breast cancer survivors

ABSTRACT Background: The study of physical activity in cancer survivors has been limited to one cause, one effect relationships. In this exploratory study, we used recursive partitioning to examine multiple correlates that influence physical activity compliance rates in cancer survivors. Methods: African American breast cancer survivors (N = 267, Mean age = 54 years) participated in an online survey that examined correlates of physical activity. Recursive partitioning (RP) was used to examine complex and nonlinear associations between sociodemographic, medical, cancer-related, theoretical, and quality of life indicators. Results: Recursive partitioning revealed five distinct groups. Compliance with physical activity guidelines was highest (82% met guidelines) among survivors who reported higher mean action planning scores (P < 0.001) and lower mean barriers to physical activity (P = 0.035). Compliance with physical activity guidelines was lowest (9% met guidelines) among survivors who reported lower mean action and coping (P = 0.002) planning scores. Similarly, lower mean action planning scores and poor advanced lower functioning (P = 0.034), even in the context of higher coping planning scores, resulted in low physical activity compliance rates (13% met guidelines). Subsequent analyses revealed that body mass index (P = 0.019) and number of comorbidities (P = 0.003) were lowest in those with the highest compliance rates. Conclusion: Our findings support the notion that multiple factors determine physical activity compliance rates in African American breast cancer survivors. Interventions that encourage action and coping planning and reduce barriers in the context of addressing function limitations may increase physical activity compliance rates.

God Turns a Blind Eye: Terrifying Angels before the Apocalypse

Thomas J. J. Altizers apocalyptic voice defies standard academic discourse because it sets itself against the demands of merely understanding what is and insists on living in the demand to sweep away being and all its nostalgic comforts. This paper attempts to forefront the consequences of that apocalyptic voice, beyond post-modern criticisms of both apocalyptic tones and the idea of voice itself, in the refiguring of the sacred amid the difference between ‘space’ and ‘place’. Rilkes ‘terrifying angels’ from the Duino Elegies serve as a case study for understanding the shape of an individual response to the apocalyptic demand, and help to make sense of the type of sacred engagement Altizers apocalyptic voice calls forth.