Obesity in the Pediatric Population of the National (Nationwide) Inpatient Sample (NIS), USA
Bibliographic record
Abstract
BACKGROUND: The incidence of childhood obesity has received a lot of attention lately, especially in the United States. The increased prevalence of pediatric obesity and its association with comorbidities has piqued the attention of more scientists in the epidemic's patterns. Our research examined the National (Nationwide) Inpatient Sample (NIS) data set for hospitalized persons aged 18 years or younger with primary or secondary obesity between 2016 and 2019 to investigate the prevalence, risk factors, and related diseases. METHODS: We retrospectively examined individuals with primary or secondary obesity from 2016 to 2019 using the NIS database. To extract the weighted samples, we utilized the International Classification of Diseases (ICD)-10 diagnostic codes E66, E660, E6601, E6609, E662, E668, and E669. Individuals with drug-related obesity or obesity caused by a recognized pathologic disease unrelated to high-calorie intake were excluded. First, we queried the total population, then separated them by age category and picked our population of interest, i.e., those aged 18 and under. The NIS is a deidentified database available to the public. It collects data on around 8 million hospitalizations annually, accounting for roughly 20% of all admissions in the United States. Results: The findings show that between 2016 and 2019, prevalence rates of childhood obesity were still on the rise and plateaued in 2019. There were 28,484,087 study subjects in this weighted sample between 2016 and 2019. Of these, 13.9% (3,946,889) were diagnosed with obesity. The sample population for those 18 years of age or under was 62,669 (1.5%) children with obesity with a mean age of 14 (SD = 4). Also, there was a 64.2% female preponderance. The obtained yearly showed a steady and significant rise from 2016 to 2018 (24% vs. 26%), with a slight decline in 2019 (25%; p < 0.001). Even though the white population had the highest overall prevalence of childhood obesity (40.9%), the Hispanic and black people had a higher prevalence per population, with a 0.5% and 0.33% prevalence, respectively, compared to 0.14% in the white population (p < 0.0001). When geographical regions were considered, south had the highest rate (36.40%), followed by the west (24.71%) and the midwest (23.56%). The analysis also showed that people with lower median household income (0-25th percentile) had the highest rate of childhood obesity (38.17%) compared to higher-income earners (13.19%). CONCLUSION: In our finding, obesity in the pediatric population is still increasing, continuing on its previously recorded trajectory. Various recommendations from health policymakers have bolstered efforts to tackle this escalating pandemic. However, additional information on the compliance, use, and adherence to these policies by healthcare professionals and members of the public, as well as the consequence of utilization or compliance to these guidelines, is needed. Nevertheless, given the continuous growth of childhood obesity, despite the avalanche of these recommendations, the issue of compliance arises, or other essential risk factors might have been overlooked. Additional studies may be needed to unmask this looming phenomenon.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".