Dataset Information

Objectively measured pediatric obesity prevalence using the OneFlorida Clinical Research Consortium.

ABSTRACT: We characterized the prevalence of obesity among Florida children 2-19years old using electronic health records (EHRs). The obesity prevalence for 331,641 children was 16.9%. Obesity prevalence at 6-11years (19.5%) and 12-19years (18.9%) were approximately double the prevalence of obesity among children 2-5years (9.9%). The highest prevalence of severe obesity occurred in rural Florida (21.7%) and non-Hispanic children with multiple races had the highest obesity prevalence (21.1%) across all racial/ethnic groups. Our results highlight EHR as a low-cost alternative to estimate the prevalence of obesity and severe obesity in Florida children, both overall and within subpopulations.

SUBMITTER: Lemas DJ

PROVIDER: S-EPMC7861018 | biostudies-literature | 2019 Jan - Feb

REPOSITORIES: biostudies-literature

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Objectively measured pediatric obesity prevalence using the OneFlorida Clinical Research Consortium.

Lemas Dominick J DJ Cardel Michelle I MI Filipp Stephanie L SL Hall Jaclyn J Essner Rebecca Z RZ Smith Steven R SR Nadglowski Joseph J Donahoo W Troy WT Cooper-DeHoff Rhonda M RM Nelson David R DR Hogan William R WR Shenkman Elizabeth A EA Gurka Matthew J MJ Janicke David M DM

Obesity research & clinical practice 20181101 1

We characterized the prevalence of obesity among Florida children 2-19years old using electronic health records (EHRs). The obesity prevalence for 331,641 children was 16.9%. Obesity prevalence at 6-11years (19.5%) and 12-19years (18.9%) were approximately double the prevalence of obesity among children 2-5years (9.9%). The highest prevalence of severe obesity occurred in rural Florida (21.7%) and non-Hispanic children with multiple races had the highest obesity prevalence (21.1%) across all rac ...[more]

PMID: 30391132

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Project description:ObjectivesAlthough previous studies have reported lower mortality and morbidity in people with higher daily step counts, the association between frailty and objectively measured step counts has not been evaluated well. We investigated the association between step counts and prevalence of frailty in community-dwelling older adults.DesignA cross-sectional study.SettingThe Kyoto-Kameoka study in Japan.ParticipantsWe used data of 3,616 Japanese older adults, aged 65 years or older, with valid daily step count data, obtained by an accelerometer-based pedometer.MeasurementsThe step count during 4 or more days was objectively obtained by a validated triaxial accelerometer. Participants were classified by quartiles (Qs) based on their step counts. Frailty was defined using the Fried phenotype (FP) model and the Kihon Checklist (KCL). We evaluated the association between prevalence of frailty and step counts using multivariate logistic regression and the restricted cubic spline model.ResultsMean step counts across low-to-high Qs of distribution were 1,759, 2,988, 4,377, and 7,200 steps/day, respectively. The prevalence of frailty, as defined by the FP model and KCL, was 11.3% and 26.8%, respectively. After adjusting for confounders, there was a negative association between the odds ratio (OR) and prevalence of frailty, as defined by the FP model among people with higher step counts (Q1: reference; Q2: OR = 0.73; 95% confidence interval (CI) = 0.56-0.96; Q3: OR = 0.56; 95% CI = 0.42-0.76; and Q4: OR = 0.41; 95% CI = 0.30-0.57; P for trend <.001). The mean step count of the population was 4,081. The OR of frailty for a 1,000-steps/day increment was 0.74 (95% CI = 0.58-0.91) and 0.85 (95% CI = 0.72-0.97) below 4,000 steps and above 4,000 steps, respectively. In the spline model, this relationship was similar between the FP model and KCL.ConclusionThese findings suggest that slightly increasing the current step count, as by 1,000 steps/day (about 10 minutes of activity), may potentially prevent frailty. J Am Geriatr Soc 68:2310-2318, 2020.

Project description:BackgroundAccurate data on hypertension is essential to inform decision-making. Hypertension prevalence may be underestimated by population-based surveys due to misclassification of health status by participants. Therefore, adjustment for misclassification bias is required when relying on self-reports. This study aims to quantify misclassification bias in self-reported hypertension prevalence and prevalence ratios in the Portuguese component of the European Health Interview Survey (INS2014), and illustrate application of multiple imputation (MIME) for bias correction using measured high blood pressure data from the first Portuguese health examination survey (INSEF).MethodsWe assumed that objectively measured hypertension status was missing for INS2014 participants (n = 13,937) and imputed it using INSEF (n = 4910) as auxiliary data. Self-reported, objectively measured and MIME-corrected hypertension prevalence and prevalence ratios (PR) by sex, age group and education were estimated. Bias in self-reported and MIME-corrected estimates were computed using objectively measured INSEF data as a gold-standard.ResultsSelf-reported INS2014 data underestimated hypertension prevalence in all population subgroups, with misclassification bias ranging from 5.2 to 18.6 percentage points (pp). After MIME-correction, prevalence estimates increased and became closer to objectively measured ones, with bias reduction to 0 pp - 5.7 pp. Compared to objectively measured INSEF, self-reported INS2014 data considerably underestimated prevalence ratio by sex (PR = 0.8, 95CI = [0.7, 0.9] vs. PR = 1.2, 95CI = [1.1, 1.4]). MIME successfully corrected direction of association with sex in bivariate (PR = 1.1, 95CI = [1.0, 1.3]) and multivariate analyses (PR = 1.2, 95CI = [1.0, 1.3]). Misclassification bias in hypertension prevalence ratios by education and age group were less pronounced and did not require correction in multivariate analyses.ConclusionsOur results highlight the importance of misclassification bias analysis in self-reported hypertension. Multiple imputation is a feasible approach to adjust for misclassification bias in prevalence estimates and exposure-outcomes associations in survey data.

Dataset Information

Objectively measured pediatric obesity prevalence using the OneFlorida Clinical Research Consortium.

Publications

Objectively measured pediatric obesity prevalence using the OneFlorida Clinical Research Consortium.

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