Evidence map›Paper›PMID 37399028›Full record

ArticleJAMA pediatrics2023

Body Mass Index and Associated Medical Expenditures in the US Among Privately Insured Individuals Aged 2 to 19 Years in 2018.

Ashutosh Kumar, Lyudmyla Kompaniyets, Brook Belay, Samantha L Pierce, Scott D Grosse, Alyson B Goodman

Erratum issuedAbstract read
In one paragraph

Article in JAMA pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ashutosh KumarDivision of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Lyudmyla KompaniyetsDivision of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Brook BelayDivision of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Samantha L PierceDivision of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Scott D GrosseOffice of the Director, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia.
Alyson B GoodmanDivision of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Nearly 40% of US youth aged 2 to 19 years do not have a body mass index (BMI) in the healthy weight category. However, there are no recent estimates for BMI-associated expenditures using clinical or claims data. Objective: To estimate medical expenditures among US youth across all BMI categories along with sex and age groups. Design, Setting, Participants: This cross-sectional study used IQVIA's ambulatory electronic medical records (AEMR) data set linked with IQVIA's PharMetrics Plus Claims database from January 2018 through December 2018. Analysis was performed from March 25, 2022, through June 20, 2022. It included a convenience sample of a geographically diverse patient population from AEMR and PharMetrics Plus. The study sample included privately insured individuals with a BMI measurement in 2018 and excluded patients with pregnancy-related visits. Exposure: BMI categories. Main Outcomes and Measures: Total medical expenditures were estimated using generalized linear model regression with γ distribution and log-link function. For out-of-pocket (OOP) expenditures, a 2-part model was used that included logistic regression to estimate the probability of positive expenditures followed by generalized linear model. Estimates were shown with and without accounting for sex, race and ethnicity, payer type, geographic region, age interacted with sex and BMI categories, and confounding conditions. Results: The sample included 205 876 individuals aged 2 to 19 years; 104 066 were male (50.5%) and the median age was 12 years. Compared with those with healthy weight, total and OOP expenditures were higher for all other BMI categories. Differences in total expenditures were highest for those with severe obesity ($909; 95% CI, $600-$1218) followed by underweight ($671; 95% CI, $286-$1055) compared with healthy weight. Differences in OOP expenditures were highest for those with severe obesity ($121; 95% CI, $86-$155) followed by underweight ($117; 95% CI, $78-$157) compared with healthy weight. Having underweight was associated with higher total expenditures at ages 2 to 5 years and 6 to 11 years by $679 (95% CI, $228-$1129) and $1166 (95% CI, $632-$1700), respectively; having severe obesity was associated with higher total expenditures at ages 2 to 5 years, 6 to 11 years, and 12 to 17 years by $1035 (95% CI, $208-$1863), $821 (95% CI, $414-$1227), and $1088 (95% CI, $594-$1582), respectively. Conclusions and Relevance: The study team found medical expenditures to be higher for all BMI categories when compared with those with healthy weight. These findings may indicate potential economic value of interventions or treatments aimed at reducing BMI-associated health risks.

Indexed as

Health ExpendituresObesity, MorbidAdolescentBody Mass IndexChildCross-Sectional StudiesFemaleHumansInsurance, HealthMaleObesityPregnancyThinness

Identifiers

PMID37399028
PMCPMC10318546

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.