Understanding occupational heat exposure in the United States and proposing a quantifying stress index

Uneven heat burden in the sunshine state: Spatial patterns and socio-economic disparities of heat-related illness in Florida

Climate change has increased the frequency and severity of extreme heat events globally, adversely affecting socio-economic conditions and public health. However, extreme heat has disparate effects on different population groups and the socio-economic determinants of its health effects are not well understood. In this study, we analyzed the spatial patterns of heat-related illness (HRI) visit rates at the zip-code level in Florida and applied statistical methods to examine the relationships between HRIs and environmental and socio-economic variables. Hierarchical regression analysis was used to evaluate the socio-economic effects on HRI visit rates under the same heat conditions. This is a two-step approach: we first included heat indicators in the baseline model and then added the socio-economic variables to assess their unique contributions in predicting HRI visits. Our findings indicate that temperature can only explain a small fraction of the variance in HRI cases (R2 = 0.04, p < 0.01), while socio-economic variables show stronger associations (R2 = 0.42, p < 0.01 in urban areas and R2 = 0.20, p < 0.01 in rural areas). Notably, marginalized and disadvantaged populations (e.g., individuals in poverty, those employed in construction, and those with disabilities) are positively associated with HRIs (p < 0.01). These findings highlight the disproportionate impacts of heat-related health issues on disadvantaged groups, calling for climate justice policy interventions. Additionally, a comparative analysis between rural and urban areas revealed different determinants of HRIs. Our study enhances the understanding of the socio-economic determinants and disparities of HRIs in Florida, providing actionable insights for policymakers and health agencies to prioritize emergency services and heat resilience planning.

Reliability of Wearable Technology to Monitor Core Temperature Among Helicopter-Based EMS Crews

BACKGROUND

Excessive heat stress led to more than 400 deaths in the United States from 2011 to 2021. Common methods for heat injury prevention revolve around measurements of the environment and fail to account for the unique individual response to stressors.

METHODS

An observational approach was utilized with nine helicopter-based emergency medical services personnel during emergency flights to compare core temperature readings obtained from an ingestible temperature monitoring pill and the estimated core temperature reading of the Slate Safety Band V2 wearable device. Comparison of data was conducted within Microsoft Excel programming to determine the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean biased error (MBE), and Bland-Altman plot development.

FINDINGS

A significant bias (t = 17.58, p < .001) toward the Slate Safety device reading higher with an average difference of -0.48°C (-0.86°F) was found, meaning the average temperature reading is 0.48°C (-0.86°F) higher with the Slate Safety device. A significant correlation of .26 (p < .001) was noted between the ingestible pill and the wearable device with a 95% confidence interval of 0.23 to 0.29. Aggregate core temperature data demonstrated an MSE of 0.43, an RMSE of 0.65, an MAE of 0.54, and an MBE of -0.48.

CONCLUSIONS/APPLICATION TO PRACTICE

The ability to monitor the physiological parameters of a worker remotely adds safety tools relative to the risks of heat stress. The slightly higher reading associated with the Slate Safety wearable device provides an added safety margin to protect our workers.

Trending Occupational Fatalities and Injuries: An Assessment of Projected Climate Change Related Impacts in the United States since 1992

BACKGROUND

Some impacts of climate change that are expected to affect the American workforce are rising temperatures, greater prevalence of wildland fires, increase in Lyme disease, and exposure to insecticides. The purpose of this study was to assess how fatal and non-fatal occupational injuries due to environmental heat, forest/brush fires, Lyme disease, and exposure to insecticides have changed over time in the United States and if there were any significant relationships between national occupational injury/illness data and national temperature trends.

METHODS

Linear regression models assessed fatal and non-fatal injuries/illnesses since 1992 by both the frequency of incidents and the proportion of total incidents and the effects of national average temperatures.

RESULTS

There were significant increases in occupational fatalities and illnesses due to exposure to environmental heat and national average annual temperatures were predictive of heat exposure fatalities and illnesses.

CONCLUSION

Heat exposure is an occupational hazard that must be managed carefully in the coming years. Organizations will need to take more aggressive heat exposure control measures as temperatures continue to rise and remain hotter for longer periods during the year. While not currently showing increasing trends on a national scale, the prevalence of occupational incidents due to forest/brush fires, Lyme disease, and insecticides should be monitored as the United States experiences more of the projected impacts of climate change.

Mental health and air temperature: Attributable risk analysis for schizophrenia hospital admissions in arid urban climates

Health researchers have examined the physiological impacts of extreme air temperature on the human body. Yet, the mental health impacts of temperature have been understudied. Research has shown that the environment can create circumstances that exacerbate mental health issues. This may be particularly challenging for some of the fastest growing cities, located in hot, dry climates. Given the theoretical relationship between air temperature and mental health, we seek to measure the association between temperature and schizophrenia hospital admissions in an arid urban climate and quantify the associated public health burden. We collected 86,672 hospitalization records for schizophrenia from 2006 to 2014 in Maricopa County, Arizona, USA. Using a distributed lag non-linear model (DLNM), we tested for a statistical association between temperature and schizophrenia hospital admissions after controlling for year, season, weekends, and holidays. We calculated the cumulative attributable risk of nighttime temperature on schizophrenia for the entire dataset as well as among demographic subgroups. The relative risk of schizophrenia hospital admissions increased with both high and low temperatures. Statistical models using daily minimum temperature were more strongly associated with hospitalization than those using mean or maximum. Schizophrenia hospital admissions increased on days with minimum temperatures above 30 °C and below 3 °C, with some subgroups experiencing higher rates of hospitalization. The total fraction of schizophrenia hospital admissions attributable to non-optimal minimum temperature is 3.45 % (CI: -4.91-10.80 %) and high minimum temperature is 0.28 % (CI: -1.18-1.78 %). We found that non-whites and males appear to be at a slightly increased risk than the general population, although there did not appear to be a statistically significant difference. A conservative estimate of healthcare costs annually from non-optimal temperature attributed schizophrenia hospitalization is $1.95 million USD. Therefore, nighttime cooling strategies and efforts could increase the accessibility of shelters to reduce overnight exposure to extreme air temperature.