Individual differences in normal body temperature: longitudinal big data analysis of patient records

Abstract

ObjectiveTo estimate individual level body temperature and to correlate it with other measures of physiology and health.DesignObservational cohort study.SettingOutpatient clinics of a large academic hospital, 2009-14.Participants35 488 patients who neither received a diagnosis for infections nor were prescribed antibiotics, in whom temperature was expected to be within normal limits.Main outcome measuresBaseline temperatures at individual level, estimated using random effects regression and controlling for ambient conditions at the time of measurement, body site, and time factors. Baseline temperatures were correlated with demographics, medical comorbidities, vital signs, and subsequent one year mortality.ResultsIn a diverse cohort of 35 488 patients (mean age 52.9 years, 64% women, 41% non-white race) with 243 506 temperature measurements, mean temperature was 36.6°C (95% range 35.7-37.3°C, 99% range 35.3-37.7°C). Several demographic factors were linked to individual level temperature, with older people the coolest (–0.021°C for every decade, P<0.001) and African-American women the hottest (versus white men: 0.052°C, P<0.001). Several comorbidities were linked to lower temperature (eg, hypothyroidism: –0.013°C, P=0.01) or higher temperature (eg, cancer: 0.020, P<0.001), as were physiological measurements (eg, body mass index: 0.002 per m/kg2, P<0.001). Overall, measured factors collectively explained only 8.2% of individual temperature variation. Despite this, unexplained temperature variation was a significant predictor of subsequent mortality: controlling for all measured factors, an increase of 0.149°C (1 SD of individual temperature in the data) was linked to 8.4% higher one year mortality (P=0.014).ConclusionsIndividuals’ baseline temperatures showed meaningful variation that was not due solely to measurement error or environmental factors. Baseline temperatures correlated with demographics, comorbid conditions, and physiology, but these factors explained only a small part of individual temperature variation. Unexplained variation in baseline temperature, however, strongly predicted mortality.

A Proof Of Concept For A Syndromic Surveillance System Based On Routine Ambulance Records In The South-west Of England, For The Influenza Season 2016/17

“…The lowest oral measurement (37.2 • C) was found by Mackowiak et al (1992) [16] to be at 6am and the highest (37.7 • C) at 4pm. A cohort study conducted by Obermeyer et al (2017) [17] in 35,488 healthy patients confirmed the daily variation and found a normal range of 35.3-37.7 • C. This study also established a negligible discrepancy of -0.03 • C of tympanic versus oral temperature [17]. Therefore, temperatures between >37.7-42 • C, were considered as pyrexia where the upper cut-off accounts for hyperpyrexia [7].…”

Decreasing human body temperature in the United States since the Industrial Revolution

“…Some factors known to influence body temperature were not included in our final model due to missing data (ambient temperature and time of day) or complete lack of information (dew point) (4). Adjusting for ambient temperature, however, would likely have amplified the changes over time due to lack of heating and cooling in the earlier cohorts.…”

“…A compilation of 27 modern studies, however (3), reported mean temperature to be uniformly lower than Wunderlich's estimate. Recently, an analysis of more than 35,000 British patients with almost 250,000 temperature measurements, found mean oral temperature to be 36.6C, confirming this lower value (4). Remaining unanswered is whether the observed difference 35 between Wunderlich's and modern averages represents true change or bias from either the method of obtaining temperature (axillary by Wunderlich vs. oral today) or the quality of thermometers and their calibration (1).…”

Feel the heat: a short history of body temperature

“…11 In healthy young adults at least, 37°C is not the overall daily mean temperature, the mean temperature at any particular time of day, or the most commonly recorded temperature 8…”

“…But whereas Wunderlich lacked the techniques needed to process his data, Obermeyer and colleagues challenge the average reader’s understanding by choosing an analysis notable for a confusing mixture of complicated modelling techniques 11. Their analysis has as much to say about the promise and the perils of big data mining as it does about clinical thermometry or the legacy of Wunderlich.…”