Elevated Core Temperature in Florida Fernery Workers: Results of a Pilot Study
Abstract
With expected increases in extreme weather, there may be a greater risk of injury from extreme heat in outdoor worker populations. To plan for future adaptation measures, studies are needed that can characterize workers’ physiologic responses to heat in outdoor settings such as agriculture. The objective of this study was to characterize occupational heat exposure, key vulnerability factors (e.g., gender, energy expenditure), and physiologic heat stress response in a sample of fernery workers. Forty-three fernery workers over 86 workdays were examined regarding heat-related illness (HRI) during the summer months of 2012 and 2013. The key outcome measure was whether a participant’s body core temperature (Tc) reached or exceeded 38.0°C (100.4ºF; Tc38). Participants’ Tc exceeded 38.0°C on 49 (57%) of the workdays, with 30 of 40 participants reaching or exceeding Tc38 on at least one workday. Adjusting for sex, there was a 12% increase in the odds of Tc38 for every 100 kilocalories of energy expended (OR: 1.12; 95% Confidence Interval [CI]: [1.03, 1.21]). Adjusting for energy expenditure, females had 5 times greater odds of Tc38 compared with males (OR: 5.38; 95% CI: [1.03, 18.30]). These findings provide evidence of elevated Tc in Florida fernery workers, indicating an increased risk of occupational HRI, and the need for policy and interventions to address this health risk.
Introduction
The Intergovernmental Panel on Climate Change (IPCC) is a group of expert scientists who provide regular assessments of the available evidence surrounding climate change. In 2014, the IPCC published a report entitled “Climate Change 2014–Impacts, Adaptation and Vulnerability” in response to the United Nations’ request for evidence to support decisions surrounding climate change adaptation and mitigation (Romero-Lankao et al., 2014). This report emphasized the importance of planning future climate change adaptation measures to help alleviate the potential human health risks in the face of a changing climate. The summers of 2013 through 2016 were the warmest on record, with each year’s temperatures surpassing prior records (National Oceanic and Atmospheric Association, National Centers for Environmental Information, 2016). In addition, heat waves in North America and Europe are predicted to increase in severity, duration, and frequency during the next century (Hansen, Sato, & Ruedy, 2012). With more extreme weather expected, there may be a greater risk of injury from extreme heat, especially for at-risk groups, like outdoor workers. To plan for future adaptation measures, studies are needed that can characterize workers’ physiologic responses to heat in these unique settings.
Agricultural workers are especially vulnerable to these rising temperatures and often work in remote settings. Workers employed in the agriculture industry experience heat-related death at a rate nearly 20 times that of all civilian workers in the United States (Centers for Disease Control and Prevention, 2008). Agricultural workers often perform physically demanding work in hot outdoor environments with little control over their heat exposure, which can put them at increased risk for heat-related illness (HRI). Under these conditions, workers are not only exposed to heat from the environment, but also internally generated metabolic heat from physical activity (Arbury et al., 2014; Hansen & Donohoe, 2003).
Workers with HRI can experience a range of symptoms starting with mild symptoms like heat cramps. As HRI progresses, workers can develop heat exhaustion which is accompanied by moderate HRI symptoms such as heavy sweating, headache, dizziness, or nausea and vomiting (Becker & Stewart, 2011). Severe HRI symptoms, including confusion and fainting, can be caused by central nervous system dysfunction (Becker & Stewart, 2011). Worsening confusion and disorientation may go unnoticed by a worker with heat exhaustion, contributing to further HRI severity. Severe HRI (i.e., heat stroke) can result in multi-organ failure and even death if not treated immediately through rapid cooling and emergency care (Casa, McDermott, et al., 2007). With the insidious nature of the progression from moderate to severe HRI, and the absence of federal regulations that mandate supervisor training in the early recognition and emergency treatment of HRI, it is important to document the level of HRI risk in the agricultural worker population and strategize future interventions. Only two states, California (Heat Illness Prevention, 2005) and Washington (Outdoor Heat Exposure, 2008), have mandated HRI prevention training for employers. The National Institute for Occupational Health and Safety (NIOSH; 2016) released the “Criteria for a Recommended Standard: Occupational Exposure to Heat and Hot Environments” that documents 38.0°C (100.4°F) as the recommended core body temperature limit. Heat exhaustion, the precursor to heat stroke, is usually accompanied by a core body temperature at or above this level (NIOSH, 2016).
Consistent and comprehensive approaches for characterizing workplace-based heat-related illness are needed to protect vulnerable agricultural worker populations. Over the last decade, the majority of published studies that examined HRI in agricultural workers were limited to survey data that included self-reported heat-related illness symptoms without additional biomonitoring data (Arcury et al., 2015; Bethel & Harger, 2014; Fleischer et al., 2013; Mutic et al., 2018; Spector, Krenz, & Blank, 2015). More recent work has identified biomonitoring protocols for examining heat-related illness and the body’s response to heat stress that include core body temperature monitoring in hot environments and actigraphy to account for metabolic heat generated by physical activity (Mac et al., 2017; Mitchell et al., 2017). Biomonitoring approaches can provide more insight into what agricultural workers are experiencing during the workday beyond self-reported survey data. While there is documentation of core body temperature findings in California agricultural workers (Mitchell et al., 2017), there is a paucity of evidence in the literature of elevated core temperature in agricultural workers in the Southeast United States. Provision of data from multiple regions and work settings across the United States allows for a unified approach for documenting heat illness risk nationwide. In addition, data from diverse agricultural regions provide a service to communities by producing information that can guide heat-risk mitigation planning by communities at the local level.
With the goal of characterizing HRI in a population of Florida agricultural workers, the Farmworker Association of Florida (FWAF) partnered with Emory University to implement a pilot study in Pierson, Florida, the Fern Capital of the World. Florida’s subtropical climate with consistently warm summers and periods of extreme heat events pose a heat hazard that differs from the agricultural communities in the Pacific Northwest, California, and North Carolina (Florida Department of Health, Division of Community Health Promotion, Public Health Research Unit, 2015). Fernery workers harvest ornamental plants, such as leatherleaf fern, in humid environments with reduced airflow. Fernery operations consist of large structures made of metal posts covered by black plastic mesh or occasionally under large-canopy shade trees (Flocks et al., 2013). This work environment was chosen since it is unique to Florida, and exemplifies hot and humid working conditions with reduced airflow, which is very different than agricultural environments in the Western United States.
The aims of this study were to (a) assess the feasibility of conducting sophisticated field-based biomonitoring of heat strain, (b) characterize occupational heat exposure and risk factors, and (c) characterize the physiologic heat stress response. The results of one of the aims, the feasibility study, have been reported previously which includes a detailed discussion of methods (Mac et al., 2017). In this article, we reported on the latter two aims. The study design was guided by components of the Farmworker Vulnerability to Heat Hazards Framework (Mac & McCauley, 2017), including the hazard (environmental heat stress), workplace exposure (duration of work and work intensity), sensitivity factors (age, years working in agriculture, body composition, and sex), and heat stress response (body core temperature reaching 38°C [100.4ºF] or above). Results from this study set the foundation for future work across the state of Florida that will guide how scientists, policy makers, and health care providers take action to support adaptation to occupational heat.
Method
Participant Recruitment
The data for this analysis were collected from a pilot study conducted during the summers of 2012 and 2013 by Emory University (Mac et al., 2017) and the FWAF. The purpose of this pilot study was to assess the feasibility of worksite-based physiologic bimonitoring of the body’s response to working in the heat and to gather preliminary data in a group of fernery workers. The town of Pierson, Florida, is a major producer of ornamental ferns and provided an ideal setting for this initial work. Fernery workers are exposed to heat and humidity at work and the fernery workers could easily access the FWAF office. Examining workers from the same type of crop operation helped to decrease variability among participants based on work type for this limited sample size. To generate preliminary data, participants wore biomonitoring equipment to work for a 3-day study period after a baseline visit.
Agricultural workers residing in Pierson, Florida, were recruited to participate in the study by community health workers, promotoras, through community ties and FWAF membership networks in and around Pierson, Florida. The FWAF is a grassroots organization comprised of agricultural workers and has offices across the state of Florida. Precise numbers of workers employed in the fernery industry are difficult to ascertain, but it is estimated that more than 10,000 fernery workers reside in Central Florida (Flocks et al., 2013). Our targeted sample size was 40 workers, with 20 workers during each summer of 2012 and 2013. Participants were prescreened for eligibility via phone or in person and then were enrolled in the study during the consenting process at the baseline visit. A US$30 grocery gift card was provided to offset the cost of travel to the FWAF office, for each day of their participation (4 days) at baseline, followed by three workdays for a total of US$120. Participants eligible for the study were (a) able to speak Spanish or English; (b) aged 18 to 54 years; (c) working in a fernery for at least the prior month; (d) without a history of diagnosed type 2 diabetes mellitus, hypertension, or any disorders of the digestive tract; (e) not pregnant; and (f) without a pacemaker. All study procedures were approved by the Emory University Institutional Review Board.
Data Collection and Analysis
Forty-three participants attended an evening baseline study visit followed by three consecutive workdays of biomonitoring. Biomonitoring equipment worn by the participants included an ActiGraph™ GTX3+ (ActiGraph, LLC, Pensacola, Florida) to capture workday energy expenditure, the CorTemp® Data Recorder (HQInc., Palmetto, Florida) to record CorTemp® pill sensor readings at 30-second intervals. The placement of the ActiGraph™ was changed from the wrist in 2012 to the waist in 2013. Actigraphy was collected for the first group of participants (n = 4) in 2012 using an actigraphy device geared toward sleep rather than energy expenditure and was therefore excluded from this analysis. The Actigraph™ GTX3+ monitors were initialized, calibrated, and downloaded using ActiLife6 software (ActiGraph, LLC, Pensacola, Florida). The ActiLife6 software includes multiple energy expenditure prediction equations to convert raw activity counts to energy expenditure in kilocalories. The Freedson VM3 Combination equation was selected for use which yields energy expenditure estimation using raw activity counts, body mass, and vector magnitude (McMinn, Acharya, Rowe, Gray, & Allan, 2013; Sasaki, John, & Freedson, 2011).
All study visits took place at the FWAF Office. At the baseline study visit, participants read the consent form in English or Spanish and, after consenting, answered sociodemographic questions including age and number of years they had worked in a fernery. This was followed by taking their biologic measurements, including weight (kgs) divided by height (m) squared to yield body mass index (BMI). At the conclusion of the baseline visit, participants were asked to swallow a CorTemp® (HQInc., Palmetto, Florida) temperature thermistor pill to record body core temperature readings, via intestinal temperature, throughout the following workday. For three consecutive days, participants came to pre-workday visits for assistance in correctly donning the biomonitoring equipment. At the end of the workday, participants returned to the FWAF office where they doffed this equipment.
Key outcome variable
Core body temperature is regarded as the “true temperature” of the human body. Rectal temperature is accepted as the most accurate measure; however, measurement of intestinal temperature is a valid alternative that is less invasive and practical for monitoring outside of traditional laboratory settings (Casa, Becker, et al., 2007). The CoreTemp® system with the ingestible temperature thermistor pill and the data recorder that is worn secured to a neoprene belt is concealable and allows for continuous monitoring in a field setting which would not be possible with tympanic or rectal temperature. This temperature methodology has been used safely in heat studies in a variety of populations including the military (Andrews, Deehl, Rogers, & Pruziner, 2016), athletes (Nassif et al., 2014), and patients enrolled in clinical trials (Chondronikola et al., 2016).
The physiological limit for core body temperature during the workday as set by the American Conference of Governmental Industrial Hygienists (ACGIH) is 38.0°C (100.4ºF) for unacclimatized workers (American Conference of Governmental and Industrial Hygienists, 2014). Workers who are acclimatized to heat, who have been medically selected, and are under supervision are set at 38.5°C (101.3ºF). For this analysis, 38.0°C was selected due to the pilot nature of this study. Acclimatization is thought to occur within 14 days in the majority of individuals (NIOSH, 2016).
The key outcome variable for this analysis was whether a participant’s core body temperature (Tc) reached or exceeded 38.0°C during a workday (Tc38), a bivariate outcome of “Yes” or “No.” This was derived from the duration of time a participant’s core body temperature reached or exceeded 38.0°C during the workday. If the time was at least 1 minute or greater, then the outcome variable was coded as a “Yes” for exceeding the ACGIH recommended physiologic limit. A time bout began when two Tc readings within 1 minute of each other reached or exceed 38.0°C, and a time bout ended at the next incidence of two Tc readings, within 1 minute of each other, falling below 38.0°C. Core temperature readings were collected every 30 seconds by the ingestible temperature pill and transmitted to the data recorder. Less than 10% of the readings were collected at 20- or 60-second intervals due to equipment malfunction. These data were still included in the sample because the time bout criteria were still applicable. Core body temperature readings were truncated by 30 minutes on each end of the workday to account for travel time from the FWAF office to the worksite. Only days with complete core temperature data were included in the analysis; if the sensor pill was excreted during the workday, the incomplete data were not utilized.
Covariates
Demographic variables were collected through the survey administered at the baseline study visit including years worked in agriculture, age, and sex. To capture environmental heat exposure, daytime wet-bulb globe temperatures (WBGTs) were estimated from meteorological data utilizing hourly averages within the workday of dry-bulb temperature and psychrometric wet-bulb temperature (Bernard & Barrow, 2013), from the Florida Automated Weather Network (FAWN) (Florida Automated Weather Network, 2016). Meteorological data were truncated to include from 4 a.m. to 6 p.m. to encompass the daytime working hours of the participants. Core temperature monitoring time described above was utilized to represent workday duration. Energy expenditure per hour was calculated by dividing workday energy expenditure, generated from the ActiLife6 software, by the workday duration.
Body mass index was calculated from height and weight (kg/m2) and then characterized into 3 categories (normal = 18.5-24.9, overweight = 25.0-29.9, obese = 30 and greater) (Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults, 1998). Body surface area was calculated using the DuBois formula (DuBois & DuBois, 1915).
Statistical Analysis
Data collected were entered into IBM SPSS (Version 24) and Microsft Excel (2007) and were exported to SAS ® (Version 9.4) for cleaning, coding, and statistical analysis. Descriptive statistics were examined using means, percentages, and data plots. To identify key risk factors for Tc38 (Yes/No), we performed logistic regression analysis utilizing a generalized estimating equations (GEE) approach to account for repeated measures on the same person. More specifically, GEE was employed to account for the statistical dependence of the same participants having temperature measurements from multiple workdays (Hanley, Negassa, & Forrester, 2003). The primary exposures we examined in this study were average daytime WBGT, energy expenditure, and workday duration. Based upon the literature, we selected risk factors a priori that we hypothesized would be associated with an elevated core temperature including years worked in a fernery (Jackson & Rosenberg, 2010), age (Becker & Stewart, 2011), sex (McLellan, 1998), as well as body fat percentage and BMI (Wallace et al., 2006).
We first calculated unadjusted odds ratios (ORs) and 95% Confidence Intervals (95% CI) to examine the relationship of Tc38 (Yes/No) with each of the covariates (i.e., exposures and risk factors). Multivariate models were constructed where each covariate was examined for its relationship with the key outcome variable (Tc38 [Yes/No]) after the addition of other exposure or risk factor variables found to have a statistically significant unadjusted OR. Due to the small sample, we limited the maximum model size to two covariates.
Results
The enrolment rate for the 69 invited agricultural workers was 62% (n = 43). The 43 participants who enrolled in the study were on average 36 years of age, 70% were female, and they averaged 13 years of fernery work (Table 1). Approximately three fourths of all of the participants were classified as overweight or obese.
| Characteristic | n | |
|---|---|---|
| Age (years) | 43 | 36 years ± 8 (range = 35) |
| Gender | ||
| Female | 30 | 70% |
| Male | 13 | 30% |
| Number of years working in agriculture | 42 | 13 ± 4 (range = 20) |
| BMI (kg/m2) | 37 | 28.3 ± 4.8 (range = 18.5) |
| Body weight category (BMI kg/m2) | 37 | |
| Underweight (<18.5 kg/m2) | 0 (0.0%) | |
| Normal weight (18.5-25 kg/m2) | 9 (24%) | |
| Overweight (25-30 kg/m2) | 15 (41%) | |
| Obese (≥30 kg/m2) | 13 (35%) | |
| Body mass (kg) | 43 | 353.8 |
| Body surface area (m2) | 37 | 3.4 |
Note. BMI = body mass index.
Workplace data are displayed in Table 2. The mean daytime WBGT for the study days was 27.2°C (81.0ºF), with little variability (SD = 0.8, range = 3.0). Workday durations for this sample ranged from 2.2 to 11.6 hours with a mean workday duration of 6 hours. Daily workday energy expenditure averaged 1,714 kcal (SD = 691).
| Variable | Workdays (n) | Minimum | Maximum | M (SD) |
|---|---|---|---|---|
| Average workday daytime WBGT (°C) | 86 | 25.6 | 28.6 | 27.2 (0.8) |
| Workday duration (hours) | 86 | 2.2 | 11.6 | 6 (1.9) |
| Energy expenditure (kcal/day) | 75 | 141 | 3,520 | |
| Workday 1 | 31 | 692 | 3,521 | 1,714 (691) |
| Workday 2 | 11 | 283 | 2,786 | 1,470 (680) |
| Workday 3 | 33 | 141 | 3,343 | 1,299 (799) |
Note. WGBT = wet-bulb globe temperature.
Of the 129 potential days of data, 43 were unusable primarily due to sensor pill excretion during the workday, which resulted in the loss of data from three participants. The remaining 40 participants from the combined summers of 2012 and 2013 yielded 86 workdays for temperature and physical activity analysis (Table 3). Participants’ body core temperature exceeded 38.0°C (100.4ºF) on 49 (57%) of the workdays examined. Thirty out of the 40 participants with at least one day of core temperature data reached Tc38 or above on at least one workday. The mean duration of time that a participant had a core temperature of 38.0°C (100.4ºF) or greater was 79 minutes (SD = 73, range = 255). The longest duration of time for meeting or exceeding the threshold was 285 minutes, while others remained at or above the threshold for less than an hour.
| Workdays examined | Tc ≥ 38.0°C | Duration of Tc ≥ 38.0°C if exceeded 38.0°C (minutes) | |
|---|---|---|---|
| Participants on the specified workday (%) | M ± SD (range) | ||
| Overall | 86 | 79 ± 73 (281) | |
| Workday 1 | 35 | 17 (49%) | 57 ± 75 (281) |
| Workday 2 | 14 | 13 (93%) | 118 ± 72 (229) |
| Workday 3 | 37 | 19 (51%) | 72 ± 65 (208) |
Due to variation in intestinal mobility, participants passed the core temperature pill at different rates. Participants could only have one temperature pill at a time to maintain accurate readings and new pills were only given the evening before the next study day. Therefore, by Workday 2 of the study, the majority of participants had passed the temperature pill and were awaiting administration of a new pill at the postworkday visit of Day 2. On Workday 2, 13 out of the 14 participants had body core temperatures that reached 38.0°C or above at some point during the workday. When demographics were examined, these 13 participants appeared to have similar characteristics to the whole sample. These participants were 86% female and 14% male, had an average age of 36 years (SD = 9), an average of 13 years (SD = 9) working in a fernery, and an average BMI of 29.2 (SD = 4.1). Therefore, we did not attribute the high proportion of participants reaching 38.0°C or above on Day 2 to be of significance, but rather due to the core temperature pill protocol.
When examined as a predictor, average daytime WBGT was not found to be a significant predictor of elevated core body temperature (OR: 1.15; 95% CI: [0.64, 2.12]; Table 4). When examined individually, female sex (OR: 2.82; 95% CI: [0.90, 8.85]), participant age (OR: 0.97; 95% CI: [0.90, 1.04]), workday duration (hours) (OR: 1.07; 95% CI: [0.81, 1.41]), or years working in a fernery (OR: 1.02; 95% CI: [0.91, 1.15]) were not statistically significant predictors of elevated core body temperature.
| Covariates | Bivarate models | Multivariate models (2 predictors) | |
|---|---|---|---|
| Adjusted for workday EEa | Adjusted with sex | ||
| OR [95% CI] | OR [95% CI] | OR [95% CI] | |
| Demographics | |||
| Female (ref = male) | 2.82 [0.90, 8.85] | 5.38 [1.58,18.30]** | — |
| Age | 0.97 [0.90, 1.04] | 0.997 [0.92, 1.084] | 0.97 [0.90, 1.04] |
| Years working in ferneries | 1.02 [0.91, 1.15] | 1.06 [0.94, 1.21] | 1.02 [0.91, 1.15] |
| BMI (kg/m2; ref = normal) | 1.003 [0.91, 1.11] | 1.01 [0.90, 1.14] | 0.98 [0.89, 1.09] |
| Overweight | 1.19 [0.30, 4.76] | 0.67 [0.14, 3.29] | 1.39 [0.37, 5.24] |
| Obese | 1.33 [0.31, 5.69] | 1.21 [0.20, 7.26] | 1.23 [0.31, 4.91] |
| Body mass (kg) | 1.00 [0.99,1.01] | 1.00 [0.99, 1.01] | 1.00 [0.99, 1.01] |
| Body surface area (m2) | 0.68 [0.16, 2.87] | 0.37 [0.09, 1.41] | 1.16 [0.22,6.20 ] |
| Working conditions | |||
| Average WBGT (°C) | 1.15 [0.64, 2.12] | 0.93 [0.46, 1.87] | 1.44 [0.76, 2.73] |
| Workday duration (hours) | 1.07 [0.81, 1.41] | 0.93 [0.68, 1.26] | 1.14 [0.87, 1.51] |
| Workday EEa (100 kcal) | 1.08 [1.005,1.15]* | — | 1.12 [1.03, 1.21]** |
Note. OR = Odds ratio; CI = confidence interval; BMI = body mass index; WBGT = wet-bulb globe temperature.
a
Workday energy expenditure (EE).
*
p < .5. **p < .01.
Body mass index when examined as a continuous variable was found to be nonsignificant (OR: 1.00; 95% CI: [0.91, 1.11]); however, overweight BMI (OR: 1.19; 95% CI: [0.30, 4.76]) and obese (OR: 1.33; 95% CI: [0.31, 5.69]). BMI relative to normal BMI remained insignificant with regard to predicting elevated core body temperature. None of the study participants were classified as underweight.
Total workday energy expenditure (kcal) was the only predictor of the bivariate models found to be a significant predictor of a participant reaching the body core temperature threshold of 38.0°C (OR: 1.08; 95% CI: [1.01, 1.15]). All the predictors that were nonsignificant in the bivariate models remained insignificant in multivariate models with two predictors, except for female sex, which became significant when adjusting for energy expenditure (OR: 5.38; 95% CI: [1.58, 18.30]). A plot of energy expenditure with the duration of time a participant’s body core temperature reached or exceeded 38.0°C (100.4ºF), compared by gender (Figure 1), supports the significant findings of the two-predictor model of sex with energy expenditure.
The significant multivariate model with two covariates, workday energy expenditure, and sex, suggested that with every 100 kilocalories of energy expenditure, a participant could be expected to have a 12% increased odds of their body core temperature reaching 38.0°C, and if that participant was female, a substantially increased odds of having a core temperature that exceeded 38.0°C as compared with the men in this sample.
Discussion
The identification of factors impacting the vulnerability of agricultural workers to environmental heat stress is an important component in the path to the development of interventions to attenuate HRI in this population. In this sample, participant body core temperature reached 38°C or above on 57% of the workdays examined, suggesting that this occupational health risk may be surprisingly common among fernery workers. In addition notable, female fernery workers appear to be at much higher risk of exceeding the safe temperature threshold than males, as are those working more intensely.
Heat strain which drives core body temperature rise arises from two sources: metabolic heat and environmental heat. Metabolic heat is generated from basic metabolic processes coupled with physical exertion in which the muscles warm (Taylor, Kondo, & Kenney, 2008), while environmental heat is generated by outside heat sources. The results from this analysis are in line with this expectation. The association between workday energy expenditure and a worker’s core body temperature reaching 38.0°C underscores the importance of further examination of work–rest cycles when facing occupational heat exposure. The Occupational Safety and Health Administration’s Heat Illness Prevention campaign for “Water. Rest. Shade.” provides basic guidance for how to protect workers from the heat (U.S. Department of Labor, Occupational Safety and Health Administration, n.d.). Unfortunately, there is no mandatory guidance for protecting workers from heat in the vast majority of states. Only California (Heat Illness Prevention, 2005) and Washington (Outdoor Heat Exposure, 2008) have mandates that require specific HRI prevention training for workers and employers including actions when heat risk is high and a plan for emergency procedures. Larger studies to further investigate risk factors for HRI and identify the workers who are most at risk for heat illness are needed. In addition, studies to pilot interventions for heat illness prevention including specific work–rest cycles in agricultural environments and cooling devices would add to the current state of the science. These studies will help to identify the most effective approaches for employers to modify work environments and practices to protect agricultural workers from the heat.
Gender differences in heat stress response between men and women identified in this pilot work can be attributed to a variety of factors. Increased levels of body fat could be a potential component of why the female workers in this sample had substantially higher odds of reaching a Tc of 38.0°C or above, according to a classic study by McLellan (1998). However, the model in the current pilot study was not able to show BMI, the primary measure for body composition in this study, as a significant predictor. Conversely, a large study of military recruits found BMI to predict heat-illness risk in male but not females and described aerobic fitness as a better predictor (Wallace et al., 2006). Some issues with BMI include poor sensitivity and specificity for detecting obesity, resulting in misclassification which may have introduced additional error when examining BMI as a predictor of Tc reaching 38.0°C or above (Rothman, 2008). In addition, there may have been insufficient variability in BMI in this sample for detecting obesity as a significant predictor.
The incorporation of more detailed measures of body composition (i.e., body fat percentage via skinfold measurement and body type morphology) utilized in other studies (Yokota, Bathalon, & Berglund, 2008; Yokota, Berglund, & Bathalon, 2012) may yield different results. Levels of respiratory fitness were not characterized in this study. If examined, respiratory fitness may have added additional justification for the gender differences in whether or not a participant’s Tc reached or exceeded 38.0°C. Classic studies examining military recruits have shown that respiratory fitness may impact an individual’s response to heat stress and levels of aerobic fitness (Havenith & van Middendorp, 1990). In addition, hormone changes during different phases of the ovulatory cycle were not examined (Kuwahara, Inoue, Abe, Sato, & Kondo, 2005). Dehydration assessment measures were only available for 2013 and therefore, were not included in this analysis. The level of dehydration has been shown in lab-based, heat physiology literature (Cheung & McLellan, 1998) to influence core body temperature. The degree of dehydration before and after the workday may further explain any gender differences found in future studies.
Strengths
Strengths of this pilot study included the innovative field-based approach to capture responses to heat stress in an agricultural worker population, adding to the literature regarding physiologic responses to heat stress in other populations. This study utilized sophisticated continuous monitoring of body core temperature and continuous actigraphy in a real-world work setting. In addition, the repeated measures design added increased validity beyond a cross-sectional design and this study informs the development of future studies.
Limitations
The current study was a pilot study of a convenience sample of limited size and the population for this study was comprised only one group of agricultural workers: fernery workers, resulting in limited generalizability. Changes in the ActiGraph™ placement between the two seasons added additional error. In addition, the wide range of energy expenditure readings, with some readings being unexplainably low, creates further error in the models.
We were unable to account for the impact of dehydration, a factor in body core temperature changes during exertion, nor were we able to examine the potential impact of respiratory fitness. Environmental heat stress data were collected from a local weather network rather than at the worksite, precluding the incorporation of between worksite differences in the model. Although the duration of time that these workers remained at or above the physiological limit varied widely, this data were collected during and off-peak time of season and so we are unable to assess the reality of peak season times. Fernery workers are predominately women, which resulted in an unbalanced sample of workers with regard to sex. The sex differences found in the results of this analysis require further examination in future studies.
Future Research
A larger study with a broader sampling of agricultural workers across multiple sites and crop environments would provide increased findings and support the development of a model to not only examine the predictors for body core temperature 38.0°C or above. In addition, future studies need to examine the risk factors for experiencing longer durations of a body core temperature that reached the threshold of 38°C.
Examining a higher body core temperature threshold of 38.5°C (101.3ºF) which is the physiological limit for acclimatized and medically selected workers (American Conference of Governmental and Industrial Hygienists, 2014) would be a helpful expansion of this analysis. The workers in this sample reported working in agriculture for over a decade on average and had to have been working in a fernery for the last month to be eligible for participation in this study. These findings indicate that these workers are likely acclimatized even though they have not been medically examined or cleared by their employer for their specific work tasks. Conversely, it is not known if these workers go through periods of time where acclimatization to the heat may be lost.
Further investigation into the time required to reach the Tc limits as well as the impact of the pattern of energy expenditure, including the timing and duration of rest breaks, can inform future interventions in this and other agricultural worker populations. Future studies should include dehydration measurements, a more comprehensive approach to body composition measurement with skinfold measurements, and a balanced sample with respect to sex.
Implications for Occupational Health Nursing Practice
Occupational health nurses are uniquely suited to perform research to address health concerns including heat illness that can characterize the current state as a threat to worker heath. Worksite-based biomonitoring to examine the key vulnerability risk factors that increase an agricultural worker’s vulnerability to occupational heat exposure is a timely and crucial endeavor for occupational health nurses, which can lead to the creation and testing of practical interventions that are sustainable and effective to prevent illness, disability, and death related to working in the heat.
The results of this analysis indicate that a large proportion of fernery workers examined in this sample are reaching or exceeding the recommended limits of body core temperature demonstrating the need for further research in this and other agricultural worker populations. It was the workers with the highest energy expenditure that were the most at risk in this sample. This finding calls occupational nurses to advocate for vulnerable worker populations. For example, many agricultural workers are paid by the piece rather than by the hour, pushing workers skip rest and hydration breaks to meet productivity quotas for their pay (Flocks et al., 2013). In addition to future research efforts to identify effective interventions, advocating for regular breaks, post-heat rehabilitation protocols, and hourly pay could be an effective approach to decreasing HRI in agricultural workers.
In this pilot study, female agricultural workers were found to have higher odds of exceeding the recommended core body temperature limits in this pilot study. While measures to prevent heat illness in all workers will benefit female workers, access to clean and convenient facilities for water and toilets is of particular importance. If female workers do not feel safe and comfortable with toileting facilities, they may drink less water during the day to avoid the need to urinate. It has been documented that female agricultural workers consistently believe that heat exposure can affect their health, during and after their childbearing years and during pregnancy (Flocks et al., 2013). Therefore, the development of heat illness prevention training specifically for pregnant women and studies are needed to assess the specific needs of pregnant workers and those of childbearing age in preventing heat illness. These training should include information about how to differentiate between symptoms of heat illness and the symptoms of other conditions like influenza, pesticide poisoning, or morning sickness.
Inquiry in future, larger studies need to include the duration of time spent above these threshold limits and time required to reach these limits. A more expansive examination of the factors placing individual workers at an increased risk for HRI could further elucidate the results of this analysis and inform occupational health nursing actions for HRI prevention. In addition, this pilot study could be replicated in other occupational populations exposed to heat. HRI and occupational heat hazards will continue to offer opportunities for occupational health nurses to impact health through developing interventions to address preventable morbidity and mortality from working in the heat.
Applying Research to PracticeThis study provides evidence of core body temperatures in agricultural workers that are above the limits recommended by the National Institute for Occupational Safety and Health, and identifies risk factors for heat illness in this population. Occupational health nurses (OHNs) are key to the design and implementation of heat-adaptive interventions. Interventions may include hourly pay rates instead of pay by productivity, plans that allow for workers to acclimate to the heat at the beginning of a season or a new position, the assurance of regular rest breaks in the shade, wearable cooling devices, and the provision of cool water and sports drinks with electrolytes. It is the responsibility of OHNs to the advocate for policies to protect workers from the heat while providing training for workers and supervisors on how to recognize heat illness and specific actions to take to minimize heat-related morbidity and mortality in the workplace.
Conflict of Interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute for Nursing Research under Grant 1F31NR014611-01 and under Grant T32NR012715 (PI: S. Dunbar) for trainee V. Mac; the National Institute for Occupational Health under Grant 2T42OH008438-10 (PI: T. Bernard) via a pilot award for V. Mac; and The American Association of Occupational Health Nurses Foundation Liberty Mutual Scholarship
ORCID iD
Valerie Vi Thien Mac https://orcid.org/0000-0002-8836-4540
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Biographies
Valerie Vi Thien Mac, FNP-C, PhD, is an assistant professor at Nell Hodgson Woodruff School of Nursing. Dr. Mac is an expert in occupational heat illness and her research focuses on biomonitoring and the health effects of heat for vulnerable and occupational populations.
Jose Antonio Tovar-Aguilar, PhD, is the interim executive director of the Farmworker Association of Florida and a medical anthropologist specializing in the impacts of the environment on health and disease among farmworker communities. Dr. Tovar-Aguilar has extensive experience in community based research and programs with Latino and Creole farmworker communities in Florida and the Caribbean.
Lisa Elon, MS, MPH, is senior associate faculty in the Biostatistics and Bioinformatics Department at the Rollins School of Public Health of Emory University. Ms. Elon has more than two decades of experience in the management and analysis of large datasets for biostatistical applications.
Vicki Hertzberg, PhD, is a professor and the director of the Center for Data Science in the Nell Hodgson Woodruff School of Nursing. Dr. Hertzberg’s research focuses on developing and applying statistical methods for the analysis of network data.
Eugenia Economos is the pesticide safety and environmental health project coordinator for the Farmworker Association of Florida. She is a longtime advocate and activist for environmental, indigenous, immigrant and social justice issues.
Linda A. McCauley, RN, PhD, is dean and professor at Nell Hodgson Woodruff School of Nursing. Dr. McCauley’s research focuses on occupational and environmental studies of working populations and children.
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Article first published online: July 17, 2019
Issue published: September 2019
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PubMed: 31315538
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