HEALTH AND DEPRIVATION: Inequality and the north

https://ir.nctu.edu.tw:443/bitstream/11536/100881/1/982410H009004.PDF

國際期刊 Transportation Research-Part E---Logistics and Transportation Review 特刊編輯補助

Communicating environmental risk in multiethnic communities

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

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Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities.

A voluminous and complex amount of information — ‘big data’ — from social media such as Twitter and Flickr is now ubiquitous and of increasing interest to researchers studying human behaviour in cities. Yet the value of social-media data (SMD) for urban-sustainability research is still poorly understood. Here, we discuss key opportunities and challenges for the use of SMD by sustainability scholars in the natural and social sciences as well as by practitioners making daily decisions about urban systems. Evidence suggests that the vast scale and near-real-time observation are unique advantages of SMD and that solutions to most SMD challenges already exist. In this Review, the authors discuss challenges and opportunities for the use of social-media data in sustainability research and practice at the city level, and identify useful directions for future research.

Social-media data for urban sustainability

Background: Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. Objective: The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors. Methods: We utilized Twitter’s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics. Results: Machine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic. Conclusions: Machine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors. [JMIR Public Health Surveill 2016;2(2):e158]

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Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity.

Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity.

Wildfire evacuation triggers refer to prominent geographic features used in wildfire evacuation practices, and when a fire crosses a feature, an evacuation warning is issued to the communities or firefighters in the path of the fire. The existing wildfire trigger modeling methods consider evacuation time as an input from a decision maker and employ fire spread modeling and GIS to create a trigger buffer around a threatened asset. This paper substantially improves on previous methods by coupling fire and traffic simulation models to set triggers, which allows us to estimate evacuation time using a traffic simulation model rather than relying on expert judgment. Specifically, we propose a three-step method within a spatiotemporal GIS framework to couple these models and to evaluate the value of the generated trigger buffers. The first step uses traffic simulation to estimate the total evacuation time for a threatened community. The second step derives the cumulative probabilities for distinct evacuation times from multiple simulations and generates corresponding probability-based trigger buffers. In the last step, we evaluate the value of the generated buffers by coupling fire and traffic simulation models to examine the spatial configurations of fire perimeters and evacuation traffic. A case study of Julian, California is used to test the proposed method. The results from two evacuation scenarios with different travel demand indicate that a larger trigger buffer (more lead time) will be needed for higher levels of evacuation travel demand. For example, the time required to guarantee that 95% of the evacuating residents arrive at the safe area as a fire approaches a community is estimated at 160 min for one scenario but 292 min if the travel demand is doubled. The resulting framework advances the dynamic representation of evacuation traffic in wildfires and improves our understanding of wildfire evacuation timing and decision making. The paper concludes with a discussion of the strengths and limitations of the proposed method, as well as future research directions.

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Setting Wildfire Evacuation Triggers by Coupling Fire and Traffic Simulation Models: A Spatiotemporal GIS Approach

Determining the most effective public warnings to issue during a hazardous environmental event is a complex problem. Three primary questions need to be answered: Who should take protective action? What is the best action? and When should this action be initiated? Warning triggers provide a proactive means for emergency managers to simultaneously answer these questions by recommending that a target group take a specified protective action if a preset environmental trigger condition occurs (e.g., warn a community to evacuate if a wildfire crosses a proximal ridgeline). Triggers are used to warn the public across a wide variety of environmental hazards, and an improved understanding of their nature and role promises to: (1) advance protective action theory by unifying the natural, built, and social themes in hazards research into one framework, (2) reveal important information about emergency managers' risk perception, situational awareness, and threat assessment regarding threat behavior and public response, and (3) advance spatiotemporal models for representing the geography and timing of disaster warning and response (i.e., a coupled natural-built-social system). We provide an overview and research agenda designed to advance our understanding and modeling of warning triggers.© 2016 Society for Risk Analysis. Language: en

Warning Triggers in Environmental Hazards: Who Should Be Warned to Do What and When?

Climate variability and trends have significant environmental and socioeconomic impacts. Global challenges such as food security, biodiversity loss, water scarcity and human health are affected by reference evapotranspiration, temperature, solar radiation, and precipitation together, but nonlinear dynamics of these four climatic factors have not been assessed simultaneously at the national scale. This leads to unclear climatic dynamics and limited applications. To address this knowledge gap, we analyzed the daily variability and trends of four climatic factors (reference evapotranspiration, temperature, solar radiation, and precipitation) in China simultaneously using high spatial resolution data from 1960 to 2013. The results indicate that the daily variability of climate system dynamics (quantified by multiplying fractal dimensions of the four climatic factors) in north China was higher than that in south China. For example, the climate system dynamics were more chaotic and with higher nonlinear variation in north China, most notably in Heilongjiang Province, the major grain base of China, posing threats to food security in the context of growing national population. Spatial distribution of variability varies among different climatic factors. Our study highlights the need for a more holistic study of climate variability and trends in other countries with multiple climate types to address challenges of sustainable development.

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Dapeng Li

Papers

Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity.

Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity.

Setting Wildfire Evacuation Triggers by Coupling Fire and Traffic Simulation Models: A Spatiotemporal GIS Approach

Warning Triggers in Environmental Hazards: Who Should Be Warned to Do What and When?

Climate variability and trends at a national scale.