A modified physiological strain index for workplace-based assessment of heat strain experienced by agricultural workers.

BACKGROUND: As global temperatures rise, increasing numbers of individuals will work in hot environments. Interventions to protect their health are critical, as are reliable methods to measure the physiological strain experienced from heat exposure. The physiological strain index (PSI) is a measure of heat strain that relies on heart rate and core temperature but is challenging to calculate in a real-world occupational setting. METHODS: We modified the PSI for use in field settings where resting temperature and heart rate are not available and used the modified physiological strain index (mPSI) to describe risk factors for high heat strain (mPSI ≥ 7) experienced by agricultural workers in Florida during the summers of 2015 through 2017. mPSI was calculated for 221 workers, yielding 465 days of data. RESULTS: A higher heat index (ß = 0.185; 95% CI: 0.064, 0.307) and higher levels of physical activity at work (0.033; 95% CI: 0.017, 0.050) were associated with a higher maximum mPSI. More years worked in US agriculture (-0.041; 95% CI: -0.061, -0.020) were protective against a higher maximum mPSI. Out of 23 workdays that a participant experienced a maximum mPSI ≥ 7, 22 were also classified as strained by at least one other measure of high heat strain (core temperature [Tc] >38.5°C, sustained heart rate >(180 - age), and mean heart rate > 115 bpm). CONCLUSIONS: This study provides critical information on risk factors for elevated heat strain for agricultural workers and suggests a practical approach for using PSI in field-based settings.

Analyzing the predictors of health care utilization in the agricultural worker population using decision tree analysis: Does language matter?

OBJECTIVES: To analyze the predictors of health care utilization among respondents to the National Agricultural Worker Survey. Specifically, we hypothesized that English proficiency would predict utilization of health care services within the last 2 years. METHODS: Using the 2015-2016 National Agricultural Worker Survey, we performed a secondary data analysis to analyze the predictors of health care utilization within the last 2 years in the United States' agricultural worker population. Data were cleaned and analyzed using decision tree analysis, which produced a classification tree model that was trained on 90% of the data and validated on 10%. RESULTS: Exposure to English was not a predictor of health care utilization in our classification tree. The first major partition that predicted utilization was insurance status. Additional partitions were on age, gender, hypertension diagnosis, and public aid. CONCLUSIONS: By partitioning on insurance status and use of public aid, the decision tree provided evidence that systemic factors are key determinants of health care utilization in the agricultural worker community. This highlights the importance of agencies that connect agricultural workers with resources that provide insurance and improve access to health care. This is especially important given that agricultural workers are one of the highest risk groups for occupational injury or death in the United States.