Dataset Information

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

ABSTRACT: OBJECTIVE:To estimate individual level body temperature and to correlate it with other measures of physiology and health. DESIGN:Observational cohort study. SETTING:Outpatient clinics of a large academic hospital, 2009-14. PARTICIPANTS:35?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 MEASURES:Baseline 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. RESULTS:In 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). CONCLUSIONS:Individuals' 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.

SUBMITTER: Obermeyer Z

PROVIDER: S-EPMC5727437 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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

Obermeyer Ziad Z Samra Jasmeet K JK Mullainathan Sendhil S

BMJ (Clinical research ed.) 20171213

<h4>Objective</h4>To estimate individual level body temperature and to correlate it with other measures of physiology and health.<h4>Design</h4>Observational cohort study.<h4>Setting</h4>Outpatient clinics of a large academic hospital, 2009-14.<h4>Participants</h4>35 488 patients who neither received a diagnosis for infections nor were prescribed antibiotics, in whom temperature was expected to be within normal limits.<h4>Main outcome measures</h4>Baseline temperatures at individual level, estim ...[more]

PMID: 29237616

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Project description:PurposeThere are discrepancies in mental health treatment outcomes between ethnic groups, which may differ between genders. NHS Talking Therapies for anxiety and depression provide evidence-based psychological therapies for common mental disorders. This study examines the intersection between ethnicity and gender as factors associated with psychological treatment outcomes. Aims were to explore by gender: (1) differences in psychological treatment outcomes for minoritized ethnic people compared to White-British people, (2) whether differences are observed when controlling for clinical and socio-demographic factors associated with outcomes, and (3) whether organization-level factors moderate differences in outcomes between ethnic groups.MethodsPatient data from eight NHS Talking Therapies for anxiety and depression services (n = 98,063) was used to explore associations between ethnicity and outcomes, using logistic regression. Stratified subsamples were used to separately explore factors associated with outcomes for males and females.ResultsIn adjusted analyses, Asian (OR = 0.82 [95% CI 0.78; 0.87], p < .001, 'Other' (OR = 0.79 [95%CI 0.72-0.87], p < .001) and White-other (0.93 [95%CI 0.89-0.97], p < .001) ethnic groups were less likely to reliably recover than White-British people. Asian (OR = 1.48 [95% CI 1.35-1.62], p < .001), Mixed (OR = 1.18 [95% CI 1.05-1.34], p = .008), 'Other' (OR = 1.60 [95% CI 1.38-1.84], p < .001) and White-other (OR = 1.18 [95% CI 1.09-1.28], p < .001) groups were more likely to experience a reliable deterioration in symptoms. Poorer outcomes for these groups were consistent across genders. There was some evidence of interactions between ethnic groups and organization-level factors impacting outcomes, but findings were limited.ConclusionsAcross genders, Asian, 'Other' and White-other groups experienced worse treatment outcomes across several measures in adjusted models. Reducing waiting times or offering more treatment sessions might lead to increased engagement and reduced drop-out for some patient groups.

Project description:BackgroundElectronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.ObjectiveThe objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.Research designA retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.SubjectsA total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.MeasuresPercentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.ResultsAttrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.ConclusionThis study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

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

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

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