Peer Reviewed Articles on Childhood Obesity in Florida
Obes Res Clin Pract. Author manuscript; bachelor in PMC 2021 Feb four.
Published in last edited course as:
PMCID: PMC7861018
NIHMSID: NIHMS1032017
Objectively Measured Pediatric Obesity Prevalence Using the OneFlorida Clinical Research Consortium
Dominick J. Lemas
1Department of Health Outcomes and Biomedical Informatics, Higher of Medicine, Academy of Florida, Gainesville, FL;
Michelle I. Cardel
aneDepartment of Health Outcomes and Biomedical Informatics, Higher of Medicine, University of Florida, Gainesville, FL;
Stephanie L. Filipp
oneDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL;
Jaclyn Hall
1Department of Wellness Outcomes and Biomedical Computer science, College of Medicine, University of Florida, Gainesville, FL;
Rebecca Z. Essner
2Florida Infirmary, Orlando, FL;
Steven R. Smith
iiFlorida Infirmary, Orlando, FL;
4Adventist Health System, Altamonte Springs, FL;
Joseph Nadglowski
5Obesity Action Coalition, Tampa, FL;
West. Troy Donahoo
aneDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, Academy of Florida, Gainesville, FL;
sixDivision of Endocrinology, Diabetes, and Metabolism, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL;
Rhonda M. Cooper-DeHoff
7Section of Pharmacotherapy & Translational Research, College of Pharmacy, University of Florida, Gainesville, FL;
David R. Nelson
8Clinical and Translational Scientific discipline Institute, University of Florida, Gainesville, FL;
William R. Hogan
1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL;
Elizabeth A. Shenkman
aneDepartment of Health Outcomes and Biomedical Information science, College of Medicine, University of Florida, Gainesville, FL;
Matthew J. Gurka
1Department of Health Outcomes and Biomedical Information science, College of Medicine, University of Florida, Gainesville, FL;
David Chiliad. Janicke
3Section of Clinical and Health Psychology, Higher of Public Health and Health Professions, University of Florida, Gainesville, FL;
Abstract
We characterized the prevalence of obesity among Florida children ii–nineteen years one-time using electronic wellness records (EHRs). The obesity prevalence for 331,641 children was 16.nine%. Obesity prevalence at 6–11 years (19.v%) and 12–xix years (eighteen.nine%) were approximately double the prevalence of obesity amid children ii–five years (ix.9%). The highest prevalence of astringent obesity occurred in rural Florida (21.vii%) and non-Hispanic children with multiple races had the highest obesity prevalence (21.one%) beyond all racial/ethnic groups. Our results highlight EHR as a low-cost culling to judge the prevalence of obesity and severe obesity in Florida children, both overall and inside subpopulations.
Keywords: severe obesity, electronic health records, childhood obesity, NHANES
1.1. INTRODUCTION
Pediatric overweight and obesity affect over 1 in 3 children in the US and represents a public health crunch [one]. The state of Florida is the 3rd near populated country in the Usa [two] and according to self-reported peak and weight information, ranks 41st for obesity amongst 2-to-4-year-olds (12.7%) and 37th for 15-to-19-yr-olds (10.9%) [3]. Notably, Florida is ranked 4th for combined overweight and obesity rates (36.six%) for 10–17 years quondam [three]. However information on the geographic and demographic distribution of obesity and severe obesity in Florida remains limited. Electronic health records (EHR's) have emerged every bit a large-scale information source with low error rates [4] that tin be leveraged to track population trends in obesity [5]. In this study, we characterize the prevalence of obesity and astringent obesity amid children 2–19 years of age according to self-identified race and ethnicity in urban and rural Florida children. Our report is among the first to leverage EHRs available through OneFlorida [6,7] as a low-cost culling to objectively evaluate the prevalence of obesity and severe obesity among Florida children from various geographic regions and racial/ethnic backgrounds.
1.2. METHODS
ane.2.1. The OneFlorida Query
Over 12 one thousand thousand unique patient records were available from OneFlorida as of early 2017, which included Medicaid claims records. Previous piece of work has demonstrated the OneFlorida Information Trust demographics are similar to estimates reported past the The states Census Bureau [viii,9]. In addition, participation in the OneFlorida Information Trust is voluntary, and is comprised of thousands of providers, clinics, practices, and multiple hospital systems throughout the state. Records from Medicaid members who visited OneFlorida health clinics that retain pinnacle/weight EHR data were included in our assay; however, Medicaid claims-merely records did not contain height and weight and were not included. After excluding Medicaid claims-only records, approximately 6.99 million EHR-based patient records from 2012–2016 remained for our analysis. Boosted inclusion criteria were a recorded sexual practice, race/ethnicity, birth date, a 5-digit nada code between '32003' and '34997' (Florida zip codes), and non-missing height/weight data for a minimum of ii separate medical encounters. If more than two encounters existed for a patient, the two most contempo encounters with summit/weight were used. Final requirements were that age must be between 2–xix years; individuals 20 years or older at either come across were excluded. Supplementary Figure one outlines selection of 331,641 individuals between 2–19 years that were included in the final analysis. This study was approved past OneFlorida's institutional review board at the Academy of Florida.
1.2.2. Obesity Status
The two most recent separate encounters with non-missing acme/weight were used to establish obesity status. Obesity status at a unmarried come across was determined using a diagnosis code of obesity or calculated BMI; BMI value ≥ 95th percentile for the kid'due south sex and age at the time of encounter. Astringent obesity was defined as a BMI ≥ 99th percentile. To have obesity in this study, patients must be classified as having obesity at both encounters. Information were analyzed by age and sex equally well every bit self-reported race and ethnicity. Age was calculated from the patient'due south birthdate on record at their first encounter. Null code is maintained in the OneFlorida database every bit the patient's about recently entered null code.
1.2.3. County-Level Analysis
County-level obesity prevalence were calculated and mapped to geographically characterize obesity prevalence. We aggregated OneFlorida patients from residential and post office zip codes to Zip Code Tabulation Areas (ZCTAs 2010), and secondarily to county equivalents, excluding Foreign Service overseas zilch codes. To account for the few ZCTAs impacted past county boundaries, population percentage weights (based on 2010 demography) were used to construct canton level equivalent counts. Rural-urban classifications were based on criteria from the National Middle for Health Statistics (NCHS) [10].
1.2.4. Statistical Analysis
We computed prevalence and 95% conviction intervals for detailed demographic breakdowns of obesity prevalence using SAS 9.4. Prevalence lonely were computed for each county, and data are displayed as choropleth maps; percentages are reported within ranges.
1.3. RESULTS
The OneFlorida pediatric obesity prevalence for 331,641 children (2–19 years) was 16.9% (Table 1). Obesity prevalence at 6–11 years (nineteen.v%) and 12–19 years (18.9%) were approximately double those of individuals ii–five years (nine.9%). Boys had a college prevalence of obesity compared to girls across all age groups. Inside most racial ethnic groups boys had higher prevalence of obesity, though the magnitude of sex disparity varied. Among patient groups with at to the lowest degree 1000 included records, non-Hispanic children with multiple self-reported races had the highest obesity prevalence (21.1%) and Non-Hispanic Asian children had the lowest (ix.9%) beyond all racial/ethnic groups. The highest prevalence of obesity observed in our analysis occurred in rural and small towns (21.vii%), which was 25% higher than observed in metropolitan areas (Effigy i). Further, rural-small town areas had a roughly 75% higher prevalence of severe obesity relative to metropolitan areas (Tabular array 2).
OneFlorida Pediatric Obesity Prevalence past County.
Geographic distribution of obesity prevalence at the canton-level in Florida children past quintile among pediatric patients with 2 dissever not-missing height and weight encounters recorded in the OneFlorida Data Trust between January 1, 2012 through Dec 31, 2016.
Table 1.
Demographic Breakdown of Obesity amongst Florida Children
| Children 2–nineteen years | Children 2–5 years | Children 6–eleven years | Children 12–xix years | ||||||
|---|---|---|---|---|---|---|---|---|---|
| North | Prevalence (95 CI) | N | Prevalence (95 CI) | Due north | Prevalence (95 CI) | Due north | Prevalence (95 CI) | ||
| Overall | 331,641 | 16.9 (16.8, 17.0) | 80,759 | 9.9 (nine.7, 10.i) | 110,047 | 19.5 (19.three, 19.8) | 140,835 | 18.nine (18.7, 19.1) | |
| Sexual activity | |||||||||
| Male person | 170,527 | 17.5 (17.3, 17.7) | 43,594 | 10.vii (10.iv, xi.0) | 57,648 | 20.4 (20.1, xx.seven) | 69,285 | nineteen.4 (19.one, 19.7) | |
| Female | 161,114 | 16.3 (16.1, 16.4) | 37,165 | 8.9 (8.6, 9.2) | 52,399 | 18.5 (18.2, 18.nine) | 71,550 | xviii.4 (18.ane, eighteen.7) | |
| Race-Ethnicity | |||||||||
| Non-Hispanic (NH) White | 101,677 | 14.8 (14.v, 15.0) | 19,199 | 7.8 (vii.4, 8.ii) | 29,486 | xv.4 (xiv.ix, 15.eight) | 52,992 | 17.0 (16.6, 17.3) | |
| NH Black | 58,815 | 18.3 (18.0, 18.6) | 13,824 | ix.1 (eight.vi, 9.vi) | 18,871 | 19.five (19.0, 20.1) | 26,120 | 22.iii (21.viii, 22.8) | |
| NH Asian | 3,139 | 9.9 (8.viii, 10.nine) | 717 | 7.nine (half-dozen.0, 9.ix) | 950 | eleven.5 (ix.4, thirteen.5) | 1,472 | ix.8 (8.three, eleven.three) | |
| NH American Indian/Alaskan | 315 | 15.half-dozen (11.vi, 19.6) | 71 | four.2 (0.0, eight.9) | 101 | 18.8 (11.ii, 26.4) | 143 | 18.9 (12.5, 25.iii) | |
| NH Hawaiian/Pacific Islander | 175 | twenty.0 (14.ane, 25.9) | 30 | 6.seven (0.0, 15.half-dozen) | 56 | 26.viii (fifteen.2, 38.4) | 89 | 20.ii (eleven.9, 28.6) | |
| Hispanic | 147,747 | xviii.1 (17.ix, 18.3) | 42,267 | 11.three (11.0, eleven.vi) | 54,268 | 22.0 (21.7, 22.four) | 51,212 | 19.five (19.i, 19.8) | |
| NH Multiple Race | 969 | 21.1 (18.5, 23.6) | 234 | 12.8 (viii.5, 17.1) | 353 | 21.v (17.two, 25.viii) | 382 | 25.vii (21.3, thirty.0) | |
| Other, Unknown, Refused | 18,804 | 15.ix (15.3, 16.4) | 4,417 | viii.2 (7.four, 9.0) | 5,962 | 18.3 (17.three, xix.3) | 8,425 | xviii.ii (17.3, 19.0) | |
| Race-Ethnicity & Sex | |||||||||
| Non-Hispanic (NH) White | M | 51,906 | xv.5 (fifteen.ii, xv.8) | ten,401 | 8.5 (eight.0, ix.1) | 15,643 | 15.eight (xv.2, sixteen.3) | 25,862 | 18.1 (17.seven, 18.six) |
| F | 49,771 | 14.0 (thirteen.7, 14.three) | viii,798 | vi.9 (vi.iv, seven.4) | 13,843 | 14.9 (xiv.iii, 15.5) | 27,130 | 15.8 (15.4, 16.3) | |
| NH Black | One thousand | 30,534 | 16.i (15.7, 16.5) | 7,557 | 9.1 (8.five, 9.8) | 9,969 | 17.half dozen (16.8, eighteen.3) | thirteen,008 | xviii.9 (xviii.two, xix.6) |
| F | 28,281 | 20.8 (20.3, 21.2) | six,267 | ix.0 (8.iii, 9.7) | 8,902 | 21.seven (xx.9, 22.6) | thirteen,112 | 25.seven (25.0, 26.five) | |
| NH Asian | Thou | one,582 | xiii.eight (12.1, 15.five) | 398 | 10.three (7.3, 13.3) | 493 | 15.viii (12.6, 19.0) | 691 | 14.v (eleven.8, 17.1) |
| F | 1,557 | 5.eight (four.vii, 7.0) | 319 | 5.0 (two.6, 7.iv) | 457 | 6.viii (4.5, 9.1) | 781 | 5.six (iv.0, vii.3) | |
| NH American Indian/Alaskan | Grand | 159 | thirteen.8 (8.5, 19.ii) | 39 | 5.1 (0.0, 12.1) | 55 | 20.0 (9.4, 30.6) | 65 | 13.8 (5.4, 22.2) |
| F | 156 | 17.3 (eleven.4, 23.two) | 32 | 3.1 (0.0, ix.2) | 46 | 17.4 (6.4, 28.three) | 78 | 23.one (13.7, 32.4) | |
| NH Hawaiian/Pacific Islander | Grand | 98 | 20.4 (12.4, 28.4) | twenty | ten.0 (0.0, 23.ane) | 33 | 21.two (7.iii, 35.2) | 45 | 24.iv (1.9, 37.0) |
| F | 77 | 19.5 (10.6, 28.3) | x | 0.0 (0.0, 0.0) | 23 | 34.8 (15.3, 54.2) | 44 | twenty.five (8.five, 32.4) | |
| Hispanic | K | 76,081 | 19.7 (19.four, xx.0) | 22,651 | 12.4 (12.0, 12.9) | 28,158 | 24.ii (23.seven, 24.7) | 25,272 | 21.2 (20.vii, 21.7) |
| F | 71,666 | 16.4 (xvi.1, 16.vi) | 19,616 | 10.0 (9.6, ten.iv) | 26,110 | xix.7 (xix.2, 20.2) | 25,940 | 17.8 (17.iii, xviii.3) | |
| NH Multiple Race | K | 493 | 20.i (sixteen.5, 23.6) | 126 | eleven.1 (5.6, 16.half-dozen) | 189 | 22.2 (sixteen.3, 28.1) | 178 | 24.2 (17.9, 30.4) |
| F | 476 | 22.one (xviii.3, 25.8) | 108 | 14.8 (viii.1, 21.five) | 164 | twenty.7 (14.5, 26.9) | 204 | 27.0 (twenty.9, 33.one) | |
| Other, Unknown, Refused | Yard | 9,674 | 16.5 (15.7, 17.2) | 2,402 | 8.9 (7.7, 10.0) | iii,108 | 19.iv (xviii.0, 20.8) | 4,164 | 18.seven (17.v, 19.9) |
| F | nine,130 | 15.2 (fourteen.v, 16.0) | 2,015 | 7.4 (6.3, 8.5) | ii,854 | 17.i (15.vii, xviii.v) | 4,261 | 17.7 (16.5, 18.8) | |
Table 2.
Childhood Obesity Prevalence by Urban and Rural Classifications
| Child | ||||
|---|---|---|---|---|
| Obesity | Severe Obesity | |||
| Metropolitan | 16.5 | 4.1 | ||
| Micropolitian (i.e., Small Metro) | 17.9 | 5.2 | ||
| Rural and Small Boondocks | 21.7 | 7.1 | ||
1.4. Give-and-take
Our study is the largest cantankerous-sectional investigation of pediatric obesity in Florida children (2–19 years), and among the kickoff to characterize the geographic distribution of both obesity and severe obesity, using state-wide EHRs. The prevalence of pediatric obesity in 331,641 Florida children (ii–19 years) included in the analysis was sixteen.9% and replicates estimates of national obesity prevalence (xvi.nine%) data bachelor through the National health and Nutrition Examination Survey (NHANES) amidst US children the same historic period during the same time catamenia (2012–2016) [i]. We as well institute the prevalence of obesity at 6–11 years (19.5%) and 12–nineteen years (18.9%) was approximately double those children 2–5 years (9.9%). The highest prevalence of obesity seems to occur in the northward central, as well as a small pocket in the central, regions of the state. This was corroborated by the analysis showing that rural-small-scale town areas had roughly a 75% college rate of severe obesity relative to metropolitan and micropolitan areas. Previous studies have reported high rates of childhood obesity in rural areas [xi]; however our study extends these observations by also reporting on astringent obesity. Severe obesity afflicts near 6% of all US children [12] where approximately 90% of individuals with severe obesity will grow up to be adults with at least form 2 obesity (BMI≥35 kg/m2) [thirteen]. Our results are highlighted by recent assay of NHANES data from 2015–2016 that reports a precipitous increase in severe obesity among children two–5 years (1). With the utilize of EHR data, a strength of this study is the big sample size that incorporates a full range of demographic information that allowed for subgroup analysis within the state of Florida, whereas state-based subgroup analysis is non possible with NHANES data [fourteen]. The main limitation of this study is that participants in our analysis must exist seen for routine clinical care and nosotros did not specifically evaluate those children excluded from the current analysis. Nevertheless, data analyzed in this written report (acme and weight) are traditionally nerveless as standard care at well-kid visits. Well-kid visits are recommended annually, and enquiry suggests that 92.7% of children accept had contact with a health intendance professional in the by yr [15]. Together, these data suggest the number of children excluded from our analysis, due to the multi-year timeframe for visit opportunity, is probable very modest and would not exist predicted to significantly bias our assay. Given that non-surgical handling for severe obesity is largely ineffective over the long-term [12], our analysis implicate EHRs from Florida health-systems as a depression-cost alternative to more traditional information collection methods for surveillance of obesity and severe obesity prevention, handling and health inequalities [6].
Supplementary Material
1
ane.5. ACKNOWLEDGMENTS
Research reported in this publication was supported in part past the OneFlorida Clinical Data Network, funded by the Patient-Centered Outcomes Inquiry Plant #CDRN-1501-26692, in part by the OneFlorida Cancer Control Alliance, funded past the Florida Section of Wellness'south James and Esther Male monarch Biomedical Research Program #4KB16, and in role by the Academy of Florida Clinical and Translational Science Establish, which is supported in part by the NIH National Center for Advancing Translational Sciences nether laurels number UL1TR001427. The content is solely the responsibility of the authors and does not necessarily correspond the official views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology, the OneFlorida Clinical Research Consortium, the Academy of Florida'south Clinical and Translational Science Institute, the Florida Department of Health, or the National Institutes of Health." All authors - designed research (project conception, evolution of overall research plan, and written report oversight); SLF, MJG, JH analyzed information or performed statistical analysis; DJL, MIC, JH, SRS, RZE, JN, TD, BS, DN, MJG, DMJ - interpreted data findings; DJL, MIC, DMJ wrote paper; DJL and DMJ had main responsibility for concluding content. All authors have read and approved the last manuscript and there is no conflict of interest.
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861018/
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