Timing and Predictors of Mild and Severe Heat Illness among New Military Enlistees : Medicine & Science in Sports & Exercise

Journal Logo


Timing and Predictors of Mild and Severe Heat Illness among New Military Enlistees


Author Information
Medicine & Science in Sports & Exercise 50(8):p 1603-1612, August 2018. | DOI: 10.1249/MSS.0000000000001623


Heat illnesses comprise situation- and person-dependent physiologic responses that may occur when an individual is under thermal stress. In terms of severity and pathophysiology, heat illness diagnoses constitute a range of clinical syndromes, from relatively milder illnesses such as heat cramps to much more serious classic and exertional heat stroke (1,2). Rates of heat illness in the United States are mainly documented in the emergency setting and vary widely across the nation. The 2014 state-level rate of heat-related emergency department cases per 100,000 persons in the continental United States ranged from 4.6 to 32.6 (3). However, reported rates of heat illness may be underestimated (4,5).

Heat illness risk factors include being elderly or suffering from chronic illness, which can increase the risk of classic heat stroke (6,7), or being an active youth, placing one at risk for exertional heat stroke (7,8). Having a high body mass index (BMI) (9,10) or taking certain medications may also increase heat illness risk. Relevant medications include nonsteroidal anti-inflammatory drugs (NSAID) (11), of which one formulation was recently shown to increase heat stroke risk in an animal model (12), as well as stimulants and alpha-blockers (13). Prior heat illness is also believed to be a risk factor for subsequent, potentially more severe events (14).

Military personnel are a good population for studying incident heat illnesses. Exertional heat illnesses, in particular, represent a substantial occupational hazard during rigorous military service (15). Before entering service, potential soldiers are screened for health problems including serious prior heat illness, which is a disqualifying condition (16). After service entry, new soldiers progressively engage in similar types and levels of strenuous activity in known locations with diverse and sometimes extreme climates, which leads to elevated risk of exertional events.

Observations beginning at the outset of military service could therefore enable the capture of truly incident heat illness events, among which exertional etiologies are presumed to predominate, under relatively standardized conditions. Furthermore, large data reserves are maintained on soldiers, including information on multiple heat illness risk factors. Improved knowledge about the timing and predictors of heat illness in this population could translate to enhanced prevention efforts, reduced morbidity, and lives saved.

Therefore, the aims of this study were to retrospectively observe a population of new military enlistees in longitudinal data to 1) determine the temporal patterns of incident heat illness events during initial exposure to the strenuous activity associated with the military life; 2) assess the association of various demographic, health-related, and environmental exposures with mild (MHI) and severe (SHI) heat injuries; and 3) examine the contribution of geographic location to heat illness risk.



We conducted a retrospective cohort study by using the Stanford Military Data Repository. The analysis leveraged its capture of digitally recorded health encounters, dispensed prescription medications, demographics, and physical locations of all active-duty US Army enlisted soldiers (i.e., persons not initially commissioned as officers) serving between 2011 and 2014. We restricted the study population to individuals who began duty and served for any duration during this time span (N = 238,168). Subjects were observed in a person-month–based panel data structure for incident heat illness by using a time-to-event approach. Observation began at service entry and continued until censoring due to an outcome event, military discharge, or the end of the available data in December 2014, whichever occurred first.

Data sources were used to capture a sufficient range of outpatient and inpatient care to identify both the heat illness events and the potential predictors of interest. To produce the longitudinal analytic file for this study, medical record extracts were merged at the person-month level with comprehensive administrative and personnel information from official sources; identifiers were removed before research activity. These sources, which are maintained by the Department of Defense, are described in Table 1. This research was approved by the institutional review board at the Stanford University School of Medicine. The project received secondary review and approval by the Human Research Protections Office of the Defense Health Agency. Informed consent was deemed not applicable to this population-based, effectively deidentified data set on subjects from across the total US Army.

Descriptions of the data sets used.

Data Organization

The dependent and independent variables used in the regression models are listed below with brief descriptions.

Dependent variables

The primary outcomes of interest were MHI and SHI, which were established as discrete end points with dedicated analyses. Of the relevant syndromes, we noted that heat stroke has generally received the most attention in the research literature. Most heat stroke cases in this active, generally young study population were presumed to represent exertional rather than classic heat stroke. However, to identify heat illness events, we chiefly used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes that identify problems including heat stroke, but do not distinguish between classic and exertional forms. Although we did not have access to detailed chart content on the history of each case to assist us in discriminating such cases, a review of our clinical data, together with our temporary and permanent duty restriction (eProfile) data, indicated that, because of clinician and/or diagnosis coder choices in the population studied, heat illness diagnoses other than heat stroke were sometimes associated with serious sequelae. Associated outcomes included hospitalization and enduring occupational limitations assigned by clinicians.

We therefore classified MHI and SHI events by severity after examining the total health and administrative trajectories available in the SMDR data, as described hereinafter. For MHI and SHI, an individual with either outcome was provided a “1” value for a binary variable in the person-month of the incident event observed in outpatient or inpatient care. Cases constituted incident SHI when the subject was first diagnosed with heat stroke per ICD-9-CM code “992.0” or when diagnosed with another heat illness type documented with a “992” diagnosis code prefix (such as heat exhaustion or heat syncope), if there was evidence of severe effects related to the event. These effects included occupational limitations exceeding 60 d in length that were specifically assigned due to the incident heat illness occurrence and/or associated clinical sequelae. The selected sequelae were a hospital admission, renal disorders, blood dyscrasias, electrolyte disorders, and/or rhabdomyolysis. See supplementary Appendix 1 for additional information on the derivation and usage of these data (see Document, Supplemental Digital Content 1, Usage and derivation of MHI and SHI outcome events, https://links.lww.com/MSS/B247).

Incident cases of MHI were dichotomously defined by the first observed non–heat stroke heat illness diagnosis (i.e., any diagnosis with a “992” prefix other than “992.0,” which represents heat stroke) that occurred without associated lengthy work limitations, hospital admission, or any of the clinical comorbidities described previously in our SHI definition. See the online Appendix 1 for additional information (see Document, Supplemental Digital Content 1, Usage and derivation of MHI and SHI outcome events, https://links.lww.com/MSS/B247).

Independent variables

Demographic factors

Data from official personnel records provided control variables to address sex, age, race, and marital status for each subject.

Army Physical Fitness Test scores

Run times have been found to predict heat illness among military recruits in past studies (24,25). The US Army administers biannual physical fitness tests to its soldiers that include timed push-ups, sit-ups, and a 2-mile run (23). We used the total Army Physical Fitness Test (APFT) score, which includes scores assigned for run times, as a general indicator of strength and fitness including cardiovascular capacity. The approach was also taken because APFT scores rather than raw performance data are preweighted for sex and age (23). APFT data were arranged using a categorical variable indicating the score from the last fitness test in each subject’s longitudinal data.

Entry aptitude test scores

A higher cognitive ability has been associated with better general health outcomes in adolescents (26). We hypothesized that heat illness risk in this population of mostly young recruits may be inversely related to individual cognitive capacity reflected by aptitude testing scores, perhaps reflecting an increased capacity to understand and therefore engage in behavioral thermoregulation. To address cognitive capacity as a potential heat illness risk factor, we used data from the Armed Forces Qualifying Test (AFQT) portion of the Armed Services Vocational Aptitude Battery (18). The test is administered to all prospective, enlisted US military service applicants. The AFQT consists of language and mathematics tests and constitutes the main indicator of qualification for service and particular occupations. We categorized the AFQT score based on the frequency distribution of scores in the study population. AFQT scores were absent for those who transitioned from enlisted status to officers after entry and for an additional 14 enlisted subjects, which necessitated a dedicated category to cover these subjects.

Months of military service and early service

We included service time as a continuous variable accounting for months of duty to address the effect of exposure time and support statistical objectives to be discussed. Because of the event distributions to be reported, a further dichotomous covariate was included to address whether the subject was or was not in the first 4 months of service.


The availability of prescribed selected medications was identified using official pharmacy data (Table 1) that included quantities and usage directions for medications identified by American Hospital Formulary System (27) therapeutic classes. Potential use was based solely on the medical provider’s intent to treat the patient as reflected in prescription instructions; actual patient compliance was unknown to the study. Each binary medication variable was assigned a “1” value in a month in which the subject was previously prescribed medication enabling at least 30 d of potential use, such that the potential usage period concluded either in the month with the “1” value or at the end of the prior month.

Medication variables with a “1” value therefore always identified agents dispensed in a month previous to the month of a potentially associated outcome. This approach was taken to preserve causal inference in this person-month–based data set, wherein actual patient use of medication was unobservable. Otherwise, it would be more likely that an agent dispensed in the same month as an outcome could have first been taken after the outcome. The number of MHI outcomes among medication recipients permitted the assessment of associations with recently dispensed NSAIDs, opioids, alpha-blockers, amphetamines, and methylphenidates. Insufficient outcomes among those who received opioids, alpha-blockers, and methylphenidates for these variables were available to be included in the SHI model.


Height and weight readings drawn from health encounter and physical examination data, as well as from required biannual readings (22), were used to compute BMI. BMI was a running value constituting the last reading taken as of each observed person-month. We categorized these values by using the standard definitions of underweight, normal, overweight, and obese BMI ranges (28), which are quantified in Tables 2 and 3. We also provided a distinct category for those without BMI data.

Raw study population numbers (percentages) of newly enlisted US Army soldiers (N = 238,168) within selected subgroups with results of chi-square tests comparing factor distributions for those who were and were not diagnosed with MHI and SHI from 2011 to 2014.
OR, statistical significance indicatorsa and 95% CI for independent variablesb included in discrete-time logistic regression models used to assess the adjusted odds of MHI and SHI among newly enlisted US Army soldiers (N = 238,138) from 2011 to 2014.
Tobacco use

Given its potential effect on a wide range of health outcomes, tobacco use was identified from self-report at health care encounters and annual health screenings. We used a running, binary indicator to identify any prior statement of tobacco use as of each person-month.

Prior heat illness

In the analysis of each end point, MHI and SHI, the prior presence of the other type of heat illness was included as a dichotomous independent variable in any person-month before which the other outcome was observed. For example, hypothetically consider a subject who experienced two heat illnesses in the study’s observed time: an MHI event as we defined it, and 6 months before that event, an SHI. In the analysis of incident MHI events, the SHI event would constitute a “1” value for prior heat illness in each person-month after the SHI occurrence. In the SHI analysis, the variable for prior heat illness would carry a “0” value in the months preceding the SHI because the SHI was the first observed heat illness. This approach was taken to preserve and account for realistic health trajectories in which any possible sequence of heat illness events might occur and allowed us to quantify the contribution of prior heat illnesses to the adjusted odds of later incident occurrences of outcomes of the other severity level. See the supplementary Appendix 1 online for additional information on usage and derivation of data addressing heat illness events (see Document, Supplemental Digital Content 1, Usage and derivation of MHI and SHI outcome events, https://links.lww.com/MSS/B247).

Location of service

Soldiers spend their initial 10 wk of service at a Basic Combat Training (BCT) site. BCT is immediately followed by job-specific Advanced Individual Training at either the same or a different physical site (29), and subsequent service at initial duty assignments worldwide. We therefore included a covariate to estimate the effect of the prevailing climate during each observed person-month as subjects moved through different locations in their early careers. Each subject’s monthly location was classified using official service records and evidence of physical locations determined by facilities at which health care was received.

Sites were organized into groups by using a primarily latitude-based approach (see Table, Supplemental Digital Content 2, North latitudes and selected additional information for United States Army locations grouped for regression analyses, https://links.lww.com/MSS/B248). The categorical covariate accounted for US and non-US sites across the globe, including Europe and Korea. Figure 1 graphically displays the US sites and includes notation of BCT and/or Advanced Individual Training locations. Because of its unique location, climate, and relatively large Army population (30), Hawaii was provided a dedicated category. Recognizing the potential importance of factors such as altitude and proximity to bodies of water, in addition to latitude, the locations in each group, especially those at the geographic margins of potential groups, were extensively tested for consistency in the regression analysis to arrive at the final location combinations.

US military service locations.
Season of the year

A categorical covariate designated the season in which each person-month was observed. The seasons were defined as winter (December–February), spring (March–May), summer (June–August), and fall (September–November).

Duty restrictions

Opioids and NSAIDs may be prescribed to treat injuries that require medical duty restrictions. The presence of such conditions and/or the associated restrictions could affect activity levels and otherwise relate to the risk of heat illness. The models included covariates for temporary and permanent activity restrictions to control for this potential effect.

Statistical Analysis

Statistical power computations confirmed power exceeding 80% for associations of interest. Overall rates of MHI and SHI and rates per observed month of service were computed. Initial descriptive analyses included cross-tabulations and chi-square tests for differences in the frequency distributions of the chosen predictors among those with and without outcomes.

After preliminary analyses, we computed the adjusted odds ratios (OR) for MHI and SHI associated with the selected covariates using dedicated, multivariable discretetime logistic regression models for each outcome. In combination with the censoring plan and person-period data structure, the use of the service time covariate in logistic regression facilitated a similar outcome risk assessment as that produced by Cox proportional hazards models (31). Our methods using logistic regression provided OR values that are mathematically equivalent to hazard ratios in terms of providing a conditional heat illness probability value associated with observations as of a given time point.

All analyses were conducted using Stata 14 software (StataCorp, College Station, TX).


The 238,168 subjects were observed for a total 427,922 person-years of service. Mean observed time per subject was 21.6 months (median, 21; SD, 13.7; range, 1–48). We observed a total of 2612 incident MHI and 732 incident SHI events, which affected 1.1% and 0.3% of the study population, respectively. Of the MHI events observed, 1862 (71.3%) occurred during the first 6 duty months, as did 441 (60.2%) of the SHI events.

Figure 2 displays the timing of MHI and SHI events during the observed service time of up to 48 months in terms of events per 100,000 subjects. Rates of each event peaked in month 2 of service and tapered substantially thereafter. Simple descriptive analyses indicated that new soldiers who experienced heat illness differed in terms of multiple demographic and health behavior–related factors compared with those who had not experienced heat illness (Table 2).

Incident MHI and SHI events per 100,000 newly enlisted US Army soldiers in each service month.

Table 3 displays the results of the discrete-time, multivariable logistic regression models used to estimate adjusted associations between the selected factors and the incident appearance of each heat illness type. Several demographic factors were associated with heat illness events. The adjusted odds of either heat illness type were higher among women compared with men. The sole statistically significant effect associated with race was a 72% increase in the adjusted odds of SHI associated with black race when compared with Caucasians (95% confidence interval [CI], 1.46–2.03; P < 0.001). The odds of MHI decreased with increasing age, but no differences in the odds of SHI among differing age categories were noted. Subjects who had never been married were at 29% higher odds of SHI (CI, 1.05–1.59; P = 0.016) and formerly married subjects were at 52% higher odds of MHI (CI, 1.08–2.14; P = 0.017) than married subjects.

No statistically significant findings were noted for military pay grades. The variable was retained in the model because of the substantial effect sizes nonetheless revealed. Among the other military-specific factors, we noted several findings. Lower AFQT scores conferred progressively higher MHI odds. Those with missing AFQT data were at much higher adjusted odds of SHI (OR, 6.62; CI, 1.36–32.2; P = 0.020). No statistically significant findings were noted for the physical fitness score ranges we explored, but those with missing APFT data were at higher odds of SHI (OR, 1.66; CI, 1.17–2.36).

Among clinically focused variables, a prior SHI was associated with a fourfold increase in the odds of MHI (CI, 2.67–6.03; P < 0.001). A prior MHI conferred a 77% increase in the odds of later SHI, with marginal significance at the 95% confidence level (CI, 1.00–3.13; P = 0.052). The highest and lowest categories of BMI demonstrated relatively strong adjusted associations with each heat illness type. BMI ≥ 30 kg·m−2 was associated with a near-doubling of the odds of SHI (OR, 1.94; CI, 1.47–2.56; P < 0.001), and BMI < 18.5 kg·m−2 was associated with an even slightly higher OR (OR, 2.26; CI, 1.16–4.39; P = 0.016) when either was compared with those with normal BMI. Effect size estimates for these BMI strata were each lower, though still significant, in the MHI analysis (OR for BMI ≥ 30 kg·m−2, 1.41 (CI, 1.19–1.67; P < 0.001); OR for BMI < 18.5 kg·m−2, 1.50 (CI, 1.01–2.21; P = 0.042)). The absence of BMI data was associated with substantially reduced odds of either heat injury severity type.

Tobacco use was associated with a 55% increase in the adjusted odds of an MHI (CI, 1.37–1.77; P < 0.001), but the association was not statistically significant for SHI. The strongest medication effect with statistical significance was seen for methylphenidate stimulants, associated with more than five times the odds of MHI among its users (OR, 5.68; CI, 1.41–22.9; P = 0.015). NSAID use was not associated with SHI, although these agents were associated with an increase in the odds of MHI (OR, 1.31; CI, 1.05–1.64). Opioids nearly doubled the adjusted odds of MHI (CI, 1.08–3.41; P = 0.027). The use of alpha-blocker agents conferred a sixfold increase in the odds of MHI, though with marginal significance and a wide CI that arose from low usage rates in this population of new soldiers (CI, 0.84–44.1; P = 0.074). The presence of duty restrictions (not reported in Table 3) was not associated with statistically significant changes in heat illness odds.

Consistent with the unadjusted information in Figure 2, the adjusted odds of both heat illness types increased during the first 4 months of duty, in which soldiers typically engaged in basic and advanced training. Duty in the summer was associated with the greatest odds increase for either illness type, but spring and fall were also associated with higher odds when compared with winter. Interestingly, service time spent in location group 3 was associated with substantially higher effect estimates for both MHI and SHI than was time spent in either Hawaii or the more southern group 4.


Several critical findings in this work have implications for military and athletic training policy. The first such finding is the very early peak incidence of both heat illness types in the first few months of military service, indicating that efforts to reduce risk should be optimized at the earliest possible time in an Army soldier’s career. The second finding is that prior heat illness predicts later events. This observation suggests that although US Army prevention policies warn of potential risk among those with prior heat illnesses (14), these individuals might be afforded even greater attention. In particular, return-to-activity policies after heat illness should be assessed for proper implementation, and potentially modified if heat illness rates continue unchanged in a given setting. A third finding with policy implications is that rates of both MHI and SHI were lower in Hawaii and consistently warm or hot areas than in some higher-latitude locations with cooler climates. This variability may provide an opportunity to review training or other policies that could systematically differ across locations, perhaps as a product of prevailing climates.

Another possible explanation for the risk differences observed across location groups is increased acclimatization among subjects in the lowest-latitude areas and Hawaii. The issue of acclimatization to heat and activity deserves focused attention because it is an established approach leveraged to reduce heat injury risk among athletes (32) and soldiers (14,33). Exposing soldiers to graduated strenuous activity may be the most actionable approach at any geographic location because it may increase heat tolerance more than passive exposure to heat itself (34). Also, service members may originate from areas and/or during seasons where actual heat acclimatization may be challenging or impossible. Therefore, engaging in progressively increasing exercise intensities over a period of time before or immediately upon beginning military service may be helpful in reducing the early-service heat illness rate, particularly among soldiers who originate from cooler climates.

A related finding was that although we did not find statistically significant effects for quantified levels of physical fitness, missing APFT data were associated with increased odds of SHI. APFT data are more likely to be missing among soldiers with medical limitations. As a result, this finding could represent a deconditioning effect, in that soldiers might suffer from SHIs at increased rates upon return to activity after medical problems, further emphasizing the importance of acclimatization for the resumption of physically demanding duties. As discussed in the description of independent variables, we used total APFT scores versus the run times examined in prior research (24,25). It is worthwhile to note that, in addition to our focus on the general fitness indicator, sensitivity analyses were conducted to test whether run times demonstrated a different relationship with heat illness when compared with total APFT score. Results were unchanged.

Recent US military health surveillance reported higher unadjusted rates of heat stroke among men than among women, but reported higher unadjusted rates of other heat illnesses among women than among men (35). We found that women had higher adjusted odds for both MHI and SHI, which we did not define by simply dividing events into heat stroke versus other diagnoses. Our results may also differ from other analyses because ours were derived from regression analyses that controlled for a range of demographic and clinical factors, and our study only included Army soldiers.

Our finding is generally consistent with other research suggesting that women are less tolerant to heat during exercise than men (36). An additional explanation for sex differences in heat illness risk could be lower average aerobic capacity among women, as lower aerobic capacity has been reported to be a risk factor for heat illness (37). In our study, however, we did not observe any statistically significant effects for APFT scores; thus, this possibility may merit further study.

Our findings of increased MHI risk at lower ages in this young study population suggest the potential for multiple causal pathways. A tendency for younger soldiers to engage in more frequent and/or strenuous activity may explain increased risk among younger soldiers when controlling for service time and military rank. However, the entire population studied is generally expected to engage in relatively similar types and levels of physical training and other forms of regular occupational exertion. The age effect therefore deserves more study to determine the mechanisms for the association seen in this population. Mechanisms might include improved behavioral thermoregulation with age, and decreased resilience among young adults today that is revealed when exposed to exertion and heat. We additionally observed differing adjusted risk profiles for MHI and SHI associated with marital status, a finding of uncertain etiology and utility.

Prior heat illness, observed extremes of BMI, tobacco use, opioids, NSAIDs, and stimulants were also found to be risk factors for heat illness. These findings constitute further actionable evidence for those responsible for the safety and health of active individuals, and may assist targeted education on heat illness prevention. In particular, being underweight may represent an overlooked factor when assessing heat illness risk. Otherwise, the very low odds of either heat illness type in the absence of BMI data suggest a resilience effect, in that BMI data were mostly captured at health encounters. Soldiers who rarely need health care may be the least likely to suffer from problems including heat illness.

Because of the potential association of opioids and NSAIDs with duty restrictions stemming from injuries, the findings of increased risk for those on these medications may in fact be due to deconditioning followed by a rapid return to activity. To guard against this phenomenon, clinicians may consider writing duty restrictions to specifically include a phased return to activity in such cases. However, the opioid finding deserves further research that we are pursuing.

Our findings on medication effects also suggest that policies on the use of certain agents may need to be adjusted for active populations. NSAIDs may deserve particular attention in light of risk factor concerns in prior research (11) and emerging evidence from an animal model indicating a risk increase associated with one such agent, indomethacin (13). Other actions by the military might include broader, more standardized applications of the military’s practice of using markers on uniforms to identify the level of recent hydration and any history of prior heat illness (38), and reconsidering assignment locations and selection or retention of individuals with prior heat illness or who chronic use of high-risk agents.

A prior study examining tobacco use and heat injury in an active military population did not find a statistically significant association between these factors (39) but used a relatively small study population with more limited tobacco use. In our data, we noted that although heat illness rates were lower among those with tobacco use in a cross-sectional analysis, the converse association was observed when using longitudinal data in the multivariable analysis. Given our prior finding of an association between tobacco use and exertional rhabdomyolysis among active-duty soldiers (40), we suggest that there should be greater attention to tobacco cessation efforts in active populations. Additional efforts could focus on the availability of tobacco products or even consideration of the eligibility of tobacco users for service, recognizing practical difficulties that the most stringent measures may face.

The finding for increased MHI odds associated with low aptitude scores supported our theory that lower cognitive ability may increase the probability of heat illness. This association may exist due to the cognitive skills required to consistently engage in the individual behaviors involved in heat illness prevention, including hydration, activity modulation, and early symptom recognition. Coaches and leaders should consider the audience when instructing their charges on heat illness precautions because persons with differing cognitive capacities may process and use information very differently. The high effect size for absent aptitude data deserves further study because the main reason for its absence was the transition to officer status.

This study had a number of strengths and limitations. Strengths included the incorporation of relatively standard data on a very large population with a substantial degree of clinical and geographic detail. However, the somewhat low number of SHI events presented some challenges to variable construction and regression modeling. Otherwise, the military represents an excellent population to study because its members experience a relatively similar type and level of exertion and activity, often in warm climates. We do acknowledge that the unique culture and environment of the military population studied may limit the ability to generalize findings to certain other specific populations. We would suggest that this population’s composition of young adults engaging in relatively consistent strenuous activity, while often wearing protective and other equipment such as helmets, may be comparable to college and professional athletes and those in certain other occupations, such as firefighters.

An additional potential limitation is the ICD-9 coding that was largely used to define outcomes; diagnosis coding can be subject to clinician bias and adjustment by administrative staff. A related concern is the potential for differing associations to be present for exertional and nonexertional heat illness. We suspect that most cases in this very active population were exertional in nature because of the rigor of military training. However, ICD-9 diagnosis codes used to define outcomes do not specify whether or not an event is exertional in nature. Given the nature of the available data and the very large study population, chart reviews or other means needed to differentiate exertional and nonexertional cases were not possible.

We further recognize that once engaged in occupation-specific activities during regular duty at their post–initial training assignments, exposures among soldiers of differing military occupations may have been substantial with respect to exertion and heat exposure. The potential effect of exposure time for persons in a given occupation might therefore differ from those in another in ways that would require focused study. However, given the common exposures that soldiers experience during BCT and the fact that the highest heat illness rate by far occurred during that time (see Fig. 2), we suspect that minimal bias was introduced into our analysis by this phenomenon. Finally, we acknowledge the inability to confirm medication compliance, which may deviate substantially from clinical directions.

In conclusion, this longitudinal, retrospective cohort study identified multiple, potential intervention options for clinicians, leaders, or policy makers seeking to mitigate heat illness risk in a range of venues. The findings provide strong evidence that modifiable and actionable factors do exist and provide potential opportunities to reduce heat illness rates. Most heat illnesses occurred at the outset of service, indicating the need for focused prevention methods in the earliest phases of military duty/training. Prior heat illness, BMI extremes, medication, and tobacco use represent potentially actionable risk factors to address by education, policy, and/or clinician intervention.

The National Heart, Lung and Blood Institute funded this project in collaboration with the Uniformed Services University of the Health Sciences (MEM-91-3139). All data used in the study were provided under cooperative agreements with the US Army Medical Command.

The results of the study have been presented clearly, honestly, and without fabrication, falsification, or inappropriate data or data manipulation. The authors have no conflicts of interest to disclose. The views expressed in this article are those of the authors and do not reflect the views, endorsement, or official policies of the US Government, Department of Defense, Defense Health Agency, Department of the Army, the Uniformed Services University of Health Sciences, the US Army Medical Department, or the American College of Sports Medicine.


1. Centers for Disease Control Web site [Internet]. Heat StressHeat Related Illness; Types of Heat-Related Illnesses. Atlanta (GA): CDC; 2016 [cited 22 Jan 2018]. Available from: https://www.cdc.gov/niosh/topics/heatstress/heatrelillness.html.
2. Becker JA, Stewart LK. Heat-related illness. Am Fam Physician. 2011;83(11):1325–30.
3. Centers for Disease Control Web site: National Environmental Public Health Tracking Network [Internet]. Atlanta (GA): CDC. [cited 16 March 2018]. Available from: https://ephtracking.cdc.gov/DataExplorer/index.html?c=24&i=89&m=-1.
4. Xiang J, Bi P, Pisaniello D, Hansen A. Health impacts of workplace heat exposure: an epidemiological review. Ind Health. 2014;52(2):91–101.
5. Leigh JP, Du J, McCurdy SA. An estimate of the U.S. government’s undercount of nonfatal occupational injuries and illnesses in agriculture. Ann Epidemiol. 2014;24:254–9.
6. Kenney WL, Munce TA. Invited review: aging and human temperature regulation. J Appl Physiol (1985). 2003;95:2598–603.
7. Glazer JL. Management of heatstroke and heat exhaustion. Am Fam Physician. 2005;71(11):2133–40.
8. Howe AS, Boden BP. Heat-related illness in athletes. Am J Sports Med. 2007;35(8):1384–95.
9. Bedno SA, Urban N, Boivin MR, Cowan DN. Fitness, obesity and risk of heat illness among Army trainees. Occup Med (Lond). 2014;64(6):461–7.
10. Kerr ZY, Casa DJ, Marshall SW, Comstock RD. Epidemiology of exertional heat illness among US high school athletes. Am J Prev Med. 2013;44(1):8–14.
11. Ellett LMK, Pratt NL, LeBlanc VT, Westaway K, Roughead EE. Increased risk of hospital admission for dehydration or heat-related illness after initiation of medicines: a sequence summary analysis. J Clin Pharm Ther. 2016;14.
12. Audet GN, Dineen SM, Stewart DA, et al. Pretreatment with indomethacin results in increased heat stroke severity during recovery in a rodent model of heat stroke. J Appl Physiol (1985). 2017;123(3):544–57.
13. Lipman GS, Eifling KP, Ellis MA, Gaudio FG, Otten EM, Grissom CK. Wilderness Medical Society practice guidelines for the prevention and treatment of heat-related illness. Wilderness Environ Med. 2013;24:351–61.
14. US Army. Training and Doctrine (TRADOC) Regulation 350-29: Prevention of Heat and Cold Casualties. [Internet] Washington (DC): US Army; 2012 [cited 16 March 2018]. Available from: http://www.tradoc.army.mil/tpubs/regs/tr350-29.pdf.
15. Epstein Y, Druyan A, Heled Y. Heat injury prevention—a military perspective. J Strength Cond Res. 2012;26:S82–6.
16. US Army. Army Regulation 40–501: Standards of Medical Fitness (Rapid Action Revision). [Internet] Washington (DC): US Army; 2011 [cited 16 March 2018]. Available from: https://armypubs.army.mil/Search/ePubsSearch/ePubsSearchForm.aspx.
17. Department of Defense (DoD) Web site. DMDC (Defense Manpower Data Center) [Internet]. Washington (DC): DoD; 2018 [cited 22 Jan 2018]. Available from: https://www.dmdc.osd.mil/appj/dwp/index.jsp.
    18. US Army. Understanding the ASVAB Test. [Internet] Washington (DC): US Army; 2017 [cited 16 March 2018]. Available from: https://m.goarmy.com/learn/understanding-the-asvab.m.html.
    19. Military Health System Web site. Military Health System Data Repository. [Internet]. Washington (DC): Department of Defense; 2018 [cited 22 Jan 2018]. Available from: https://health.mil/Military-Health-Topics/Technology/Clinical-Support/Military-Health-System-Data-Repository.
      20. US Army. Periodic Health Assessment. [Internet] US Army; August 11, 2017 [cited 16 March 2018]. Available from: https://www.hrc.army.mil/STAFF/Periodic%20Health%20Assessment.
        21. Smith RM. eProfile: new commander’s tool assists in assessing, tracking unit’s medical readiness. Fort Hood Sentinel. March 24, 2011 [cited 16 March 2018]. Available from: http://www.forthoodsentinel.com/news/e-profile-new-commander-s-tool-assists-in-assessing-tracking/article_f3132e00-c75b-5929-b084-d7774354c388.html.
          22. US Army. Army Regulation 600-9: The Army Body Composition Program. [Internet] Washington (DC): US Army; 2013 [cited 16 March 2018]. Available from: https://armypubs.army.mil/Search/ePubsSearch/ePubsSearchForm.aspx.
          23. US Army. Field Manual 7-22: Army Physical Readiness Training. [Internet] Washington (DC): US Army; October 2012 [cited 16 March 2018]. Available from: https://armypubs.army.mil/Search/ePubsSearch/ePubsSearchForm.aspx.
          24. Gardner JW, Kark JA, Karnei K. Risk factors predicting exertional heat illness in male Marine Corps recruits. Med Sci Sports Exerc. 1996;28(8):939–44.
          25. Wallace RF, Kriebel D, Punnett L, et al. Risk factors for recruit exertional heat illness by gender and training period. Aviat Space Environ Med. 2006;77(4):415–21.
          26. Der G, Batty GD, Deary IJ. The association between IQ in adolescence and a range of health outcomes at 40 in the 1979 US National Longitudinal Study of Youth. Intelligence. 2009;37(6):573–80.
          27. American Society of Health-System Pharmacists. AHFS Pharmacologic-Therapeutic Classification. [Internet] Bethesda (MD): American Society of Health-System Pharmacists; [cited 16 March 2018]. Available from: http://www.ahfsdruginformation.com/ahfs-pharmacologic-therapeutic-classification/.
          28. Centers for Disease Control. About Adult BMI. [Internet] Atlanta (GA): Centers for Disease Control; 2017 [cited 16 March 2018]. Available from: https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html.
          29. US Army. Basic Combat Training. [Internet] Washington (DC): US Army; 2015 [cited 16 March 2018]. Available from: http://www.goarmy.com/soldier-life/becoming-a-soldier/basic-combat-training.html.
          30. US Army. About US Army Garrison-Hawaii. [Internet]. Schofield Barracks (HI): US Army; 2018 [cited 22 January 2018]. Available from: https://www.garrison.hawaii.army.mil/about.htm?tab=1.
          31. Gibbons RD, Meltzer D, Pope A, et al. Waiting for organ transplantation: results of an analysis by an Institute of Medicine Committee. Biostatistics. 2003;4(2):207–22.
          32. National Athletic Trainers Association. Fact Sheet: “Preseason Heat Acclimatization Guidelines for Secondary School Athletics” Consensus Statement. [Internet] Dallas (TX): National Athletic Trainers Association; 2016 [cited 16 March 2018]. Available from: http://www.nata.org/sites/default/files/HeatFactSheet.pdf.
          33. US Army Training and Doctrine Command Surgeon. Individual Guidance for Heat Acclimatization. [Internet] Washington (DC): US Army; 2016 [cited 12 Sept 2017]. Available from: http://www.tradoc.army.mil/surgeon/Pdf/HeatAcclimatizationGuide1.pdf.
          34. Cheung SS, McLellan TM. Heat acclimation, aerobic fitness, and hydration effects on tolerance during uncompensable heat stress. J Appl Physiol (1985). 1998;84(5):1731–9.
          35. Armed Forces Health Surveillance Bureau. Update: heat illness, active component, U.S. Armed Forces, 2016. MSMR. 2017;24(3):9–13.
          36. Kazman JB, Purvis DL, Heled Y, et al. Women and exertional heat illness: identification of gender specific risk factors. The United States Army Medical Department Journal, April–June 2015. [Internet] Washington (DC): US Army; 2015 [cited 10 Oct 2017]. Available from: http://www.cs.amedd.army.mil/FileDownloadpublic.aspx?docid=67ad7eca-1f7a-4ba3-92b9-aabc77cd80b9#page=50.
          37. Lisman P, Kazman JB, O’Connor FG, Heled Y, Deuster PA. Heat tolerance testing: association between heat intolerance and anthropometric and fitness measurements. Mil Med. 2014;179(11):1139–346.
          38. McIntyre AR. BCT Soldiers Keep their Cool during Southern Summer Heat. [Internet] Washington (DC): US Army; 2012 [cited 16 March 2018]. Available from: https://www.army.mil/article/80817/BCT_Soldiers_keep_their_cool_during_Southern_summer_heat.
          39. Bedno SA, Li Y, Han W, et al. Exertional heat illness among overweight U.S. Army recruits in basic training. Aviat Space Environ Med. 2010;81:107–11.
          40. Nelson DA, Deuster PA, Carter R 3rd, Kurina LM. Sickle Cell Trait, Rhabdomyolysis, and Mortality among U.S. Army Soldiers. N Engl J Med. 2016;375(5):435–42.


          Supplemental Digital Content

          Copyright © 2018 by the American College of Sports Medicine