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

ABSTRACT: PURPOSE:Heat illnesses are important and potentially fatal conditions among physically active individuals. We determined predictors of heat illness among enlistees in a large military population experiencing common physical activity patterns. METHODS:We estimated the adjusted odds of mild (MHI) and severe (SHI) heat illness associated with demographic, health-related, and geographic factors among active-duty, US Army soldiers enlisting between January 2011 and December 2014 (N = 238,168) using discrete-time multivariable logistic regression analyses. RESULTS:We observed 2612 incident cases of MHI and 732 incident cases of SHI during 427,922 person-years of follow-up, with a mean and median of 21.6 and 21 months per subject. During the first 6 duty months, 71.3% of the MHIs and 60.2% of the SHIs occurred, peaking at month 2. The odds of MHI quadrupled among those with prior SHI (odds ratio [OR], 4.02; 95% confidence interval [CI], 2.67-6.03). Body mass index (BMI) extremes increased the odds substantially (OR at BMI ?30 kg·m: for MHI, 1.41 (CI, 1.19-1.67); for SHI, 1.94 (CI, 1.47-2.56); OR at BMI <18.5 kg·m: for MHI, 1.50 (CI, 1.01-2.21); for SHI, 2.26 (CI, 1.16-4.39)). Tobacco use was associated with a 55% increase (CI, 1.37-1.77) in MHI odds. The odds of MHI increased if taking nonsteroidal anti-inflammatory drugs, opioids, or methylphenidate stimulants. Lower age and lower entry aptitude scores were associated with progressively increased MHI odds. CONCLUSIONS:Most heat illnesses occurred at the outset of service, indicating the need for focused prevention methods at the initiation of military duty. Prior heat illness, BMI extremes, medications, and tobacco use represent potentially actionable risk factors to address by education, policy, and/or clinician intervention.

SUBMITTER: Nelson DA

PROVIDER: S-EPMC6045456 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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

Nelson D Alan DA Deuster Patricia A PA O'Connor Francis G FG Kurina Lianne M LM

Medicine and science in sports and exercise 20180801 8

<h4>Purpose</h4>Heat illnesses are important and potentially fatal conditions among physically active individuals. We determined predictors of heat illness among enlistees in a large military population experiencing common physical activity patterns.<h4>Methods</h4>We estimated the adjusted odds of mild (MHI) and severe (SHI) heat illness associated with demographic, health-related, and geographic factors among active-duty, US Army soldiers enlisting between January 2011 and December 2014 (N = 2 ...[more]

PMID: 29613996

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Project description:BackgroundOxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records.MethodsIn study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure).ResultsIn study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61).ConclusionsIt is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

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

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

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