Examining Agricultural Workplace Micro and Macroclimate Data Using Decision Tree Analysis to Determine Heat Illness Risk

Mac, Valerie; Hertzberg, Vicki; McCauley, Linda

doi:10.1097/jom.0000000000001484

Cited by 12 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our finding that WS HI was significantly associated with individually experienced HI was consistent with the findings revealed in the study by Bernhard et al (2015) and Mac et al (2018) [ 14 , 15 ]. However, body fat (%) and income level, which were identified as significant factors associated with individually experienced temperatures in both urban and rural participants in the study by Bernhard et al (2015), were only identified as significant factors among outdoor workers in this study.…”

Section: Discussionsupporting

confidence: 93%

“…The study by Bernhard et al (2015) reported that it was feasible to measure individually experienced temperature through sensors clipped on the shoe in both urban and rural settings in Alabama (AL), USA [ 14 ]. Other studies used similar sensors to characterize exposure, with the sensor worn around the neck or on the waist [ 15 , 16 ], or attached to a shirt pocket, belt or bag [ 17 , 18 ]. Differences in exposure across a study area and differences between individually experienced temperature and temperature measured at regional WS have been reported [ 17 , 19 , 14 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterization of heat index experienced by individuals residing in urban and rural settings

Wang

Richardson

et al. 2021

J Expo Sci Environ Epidemiol

View full text Add to dashboard Cite

Heatwave warning systems rely on forecasts made for fixed-point weather stations (WS), which do not reflect variation in temperature and humidity experienced by individuals moving through indoor and outdoor locations. We examined whether neighborhood measurement improved the prediction of individually experienced heat index in addition to nearest WS in an urban and rural location. Participants (residents of Birmingham, Alabama [N=89] and Wilcox County, Alabama [N=88]) wore thermometers clipped to their shoe for 7 days. Shielded thermometers/hygrometers were placed outdoors within participant’s neighborhoods (N=43). Nearest WS and neighborhood thermometers were matched to participant’s home address. Heat index (HI) was estimated from participant thermometer temperature and WS humidity per person-hour (HI[individual]), or WS temperature and humidity, or neighborhood temperature and humidity. We found that neighborhood HI improved the prediction of individually experienced HI in addition to WS HI in the rural location, and neighborhood heat index alone served as a better predictor in the urban location, after accounting for individual-level factors. Overall, a 1°C increase in HI[neighborhood] was associated with 0.20°C [95%CI (0.19, 0.21)] increase in HI[individual]. After adjusting for ambient condition differences, we found higher HI[individual] in the rural location, and increased HI[individual] during non-rest time (5am-midnight) and on weekdays.

show abstract

Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Characterization of heat index experienced by individuals residing in urban and rural settings

Wang

Richardson

et al. 2021

J Expo Sci Environ Epidemiol

View full text Add to dashboard Cite

show abstract

“…With an increasing recognition of the importance of big data in nursing scholarship (Brennan & Bakken, 2015), content analysis of tweets served as a complementary method of discovery for our previous work on the health effects of heat illness (Mac, Hertzberg, & McCauley, 2019; Mac & McCauley, 2017). With hundreds of thousands of users, Twitter and other social media platforms represent vast sources of data that will allow for many robust analyses.…”

Section: Discussionmentioning

confidence: 99%

Using Twitter for Nursing Research: A Tweet Analysis on Heat Illness and Health

Smith

Mac

Hertzberg

2021

J of Nursing Scholarship

View full text Add to dashboard Cite

Aim To provide an example of a tweet analysis for nurse researchers using Twitter in their research. Design A content analysis using tweets about “heat illness + health.” Methods Tweets were pulled from Twitter’s application programming interface with premium access using Postman and the key words “heat illness + health.” All data cleaning and analysis was performed in R Version 3.5.2, and the tweet set was analyzed for term frequency, sentiment, and topic modeling. Principal R packages included LDAvis, tidytext, tm, and zyuzhet. Results 6,317 tweets were analyzed with a date range of April 6, 2009, to December 30, 2019. The most common terms in the tweets were heat (n = 4,532), illness (n = 4,085), and health (n = 2,257). Sentiment analysis showed that the majority of tweets (55%) had a negative sentiment. Topic modeling showed that there were three topics within the tweet set: increasing impact, prevention and safety, and symptoms. Conclusions Twitter can be a useful tool for nursing researchers, serving as a viable adjunct to current research methodologies. This practical example has facilitated a deeper understanding of the social media representation of heat illness and health that can be applied to other research. Clinical Relevance Twitter serves as a tool for collecting health information for multiple groups, ranging from clinicians and researchers to patients. By utilizing the plethora of data that comes from the platform, we can work towards developing theories and interventions related to numerous health conditions and phenomena.

show abstract

“…34,35 A number of tools have been developed to measure on-scene heat risk, including personal temperature loggers and wet bulbs that measure radiant heat, ambient temperature, wind, and humidity. [36][37][38][39] However, to our knowledge these have only been used to measure workplace risk and have not been evaluated in the context of prehospital medicine.…”

Section: Discussionmentioning

confidence: 99%

Climate Change Adaptation: Prehospital Data Facilitate the Detection of Acute Heat Illness in India

Ranadive

Desai

Sathish

et al. 2021

WJEM

View full text Add to dashboard Cite

Introduction Extreme heat is a significant cause of morbidity and mortality, and the incidence of acute heat illness (AHI) will likely increase secondary to anthropogenic climate change. Prompt diagnosis and treatment of AHI are critical; however, relevant diagnostic and surveillance tools have received little attention. In this exploratory cross-sectional and diagnostic accuracy study, we evaluated three tools for use in the prehospital setting: 1) case definitions; 2) portable loggers to measure on-scene heat exposure; and 3) prevalence data for potential AHI risk factors. Methods We enrolled 480 patients who presented to emergency medical services with chief complaints consistent with AHI in Ahmedabad, India, from April–June 2016 in a cross-sectional study. We evaluated AHI case definition test characteristics in reference to trained prehospital provider impressions, compared on-scene heat index measured by portable loggers to weather station measurements, and identified AHI behavioral and environmental risk factors using logistic regression. Results The case definition for heat exhaustion was 23.8% (12.1–39.5%) sensitive and 93.6% (90.9–95.7%) specific. The positive and negative predictive values were 33.5% (20.8–49.0%) and 90.1% (88.5–91.5%), respectively. Mean scene heat index was 6.7°C higher than the mean station heat index (P < 0.001), and station data systematically underestimated heat exposure, particularly for AHI cases. Heat exhaustion cases were associated with on-scene heat index ≥ 49°C (odds ratio [OR] 2.66 [1.13–6.25], P = 0.025) and a history of recent exertion (OR 3.66 [1.30–10.29], P = 0.014), while on-scene air conditioning was protective (OR 0.29 [0.10–0.85], P = 0.024). Conclusion Systematic collection of prehospital data including recent activity history and presence of air conditioning can facilitate early AHI detection, timely intervention, and surveillance. Scene temperature data can be reliably collected and improve heat exposure and AHI risk assessment. Such data may be important elements of surveillance, clinical practice, and climate change adaptation.

show abstract

Examining Agricultural Workplace Micro and Macroclimate Data Using Decision Tree Analysis to Determine Heat Illness Risk

Cited by 12 publications

References 22 publications

Characterization of heat index experienced by individuals residing in urban and rural settings

Characterization of heat index experienced by individuals residing in urban and rural settings

Using Twitter for Nursing Research: A Tweet Analysis on Heat Illness and Health

Climate Change Adaptation: Prehospital Data Facilitate the Detection of Acute Heat Illness in India

Contact Info

Product

Resources

About