ArticleBMC medical informatics and decision making2020
Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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.
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.
Who cites it
24 citing papers in PubMed.
- Midlife physical activity and late-life subjective cognitive complaints in women.The Gerontologist · 2026Article
- How representative are electronic health records? A record linkage study using individual-level census data.Social science & medicine (1982) · 2026Article
- Rising burden of enterococcal bacteremia in Victoria, Australia: population-based incidence and antimicrobial resistance trends from three decades of surveillance.Antimicrobial agents and chemotherapy · 2026Article
- Prevalence of Pincer Morphology in Early Adolescents From the General Population: A Population-Based Study.Arthritis care & research · 2025Article
- Radiological characteristics for guiding intra-arterial therapy in intermediate-stage hepatocellular carcinoma.BMC medicine · 2025Article
- Concordance between FVC and FEVBMJ open respiratory research · 2025Article
- Variation in PSA Levels by Age, Adiposity, Race and Ethnicity, and Genetic Risk: Implications for Prostate Cancer Screening.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Optimal spirometry thresholds for the prediction of chronic airflow obstruction: a multinational longitudinal study.ERJ open research · 2025Article
- Comparison of clinical outcomes between double arterial cannulation and single arterial cannulation in type A aortic dissection.Scientific reports · 2025Article
- Using Electronic Medical Records to Estimate the Prevalence of Pediatric Obesity in Mississippi.Journal of public health in the deep South · 2025Article
- Regular Family Meals Associated with Nutritional Status, Food Consumption, and Sedentary and Eating Behaviors of Brazilian Schoolchildren and Their Caregivers.Foods (Basel, Switzerland) · 2024Article
- Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study.JMIR medical informatics · 2024Article
- Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview.Journal of healthcare informatics research · 2024Article
- Mid-life adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and late-life subjective cognitive complaints in women.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Bias correction models for electronic health records data in the presence of non-random sampling.Biometrics · 2024Article
- Data Resource Profile: Results Analysis Base of Navarre (BARDENA).International journal of epidemiology · 2023Article
- Isolated small airways obstruction predicts future chronic airflow obstruction: a multinational longitudinal study.BMJ open respiratory research · 2023Article
- Association of ideal cardiovascular health with cardiovascular events and risk advancement periods in a Mediterranean population-based cohort.BMC medicine · 2022Article
- Beyond Confounding: Identifying Selection Bias in Observational Pulmonary and Critical Care Research.Annals of the American Thoracic Society · 2022Article
- Exploring a theory of change: Are increases in parental empowerment associated with healthier weight-related parenting practices?Social science & medicine (1982) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesHeight and weight data from electronic health records are increasingly being used to estimate the prevalence of childhood obesity. Here, we aim to assess the selection bias due to missing weight and height data from electronic health records in children older than five.
methodsCohort study of 10,811 children born in Navarra (Spain) between 2002 and 2003, who were still living in this region by December 2016. We examined the differences between measured and non-measured children older than 5 years considering weight-associated variables (sex, rural or urban residence, family income and weight status at 2-5 yrs). These variables were used to calculate stabilized weights for inverse-probability weighting and to conduct multiple imputation for the missing data. We calculated complete data prevalence and adjusted prevalence considering the missing data using inverse-probability weighting and multiple imputation for ages 6 to 14 and group ages 6 to 9 and 10 to 14.
resultsFor 6-9 years, complete data, inverse-probability weighting and multiple imputation obesity age-adjusted prevalence were 13.18% (95% CI: 12.54-13.85), 13.22% (95% CI: 12.57-13.89) and 13.02% (95% CI: 12.38-13.66) and for 10-14 years 8.61% (95% CI: 8.06-9.18), 8.62% (95% CI: 8.06-9.20) and 8.24% (95% CI: 7.70-8.78), respectively.
conclusionsAges at which well-child visits are scheduled and for the 6 to 9 and 10 to 14 age groups, weight status estimations are similar using complete data, multiple imputation and inverse-probability weighting. Readily available electronic health record data may be a tool to monitor the weight status in children.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.