Evaluating Coincident Relationships Between Obesity Incidence and Normal Weight Incidence From Birth Through

William H Yeaton1, Megha K Shah2, Brian G Moss3

  • 11 Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.

Insights

Childhood obesity incidence and normal weight status incidence were substantial from infancy through kindergarten. Children with higher initial BMI were more likely to become obese and less likely to achieve normal weight.

Area of Science:

  • Pediatric health
  • Childhood obesity
  • Nutritional science

Background:

  • Childhood obesity is a significant public health concern.
  • Understanding weight status trends in early childhood is crucial for intervention.
  • Normal weight status is essential for healthy development.

Purpose of the Study:

  • To examine the concurrent relationship between obesity incidence and normal weight status incidence and prevalence.
  • To analyze weight status changes in children from 9 months to kindergarten.

Main Methods:

  • Utilized a multistage, probability sample from the Early Childhood Longitudinal Study-Birth cohort.
  • Included a representative sample of 9950 US preschool children followed from birth through kindergarten.
  • Employed direct anthropometric measures to report prevalence and incidence rates across four follow-up periods.

Main Results:

  • Obesity prevalence ranged from 13%-20%, significantly lower than normal weight status prevalence (66%-70%).
  • Lower socioeconomic status, Hispanic, and non-Hispanic Black children faced a greater risk of obesity.
  • Obesity incidence decreased by two-thirds (15.6%), while normal weight status incidence decreased by almost half (44.6%) from 9 months to kindergarten.

Conclusions:

  • Substantial rates of obesity and normal weight incidence were observed at 9 months, decreasing but remaining high through kindergarten.
  • Children with high initial BMI were highly likely to become obese and unlikely to achieve normal weight status by kindergarten.
Abstract

Related Concept Videos

Obesity01:24

Obesity

The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
1.5K
Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
12.6K
Drug Dosing: Obese Patients01:21

Drug Dosing: Obese Patients

In the United States, obesity is a prominent concern. It is linked to heightened mortality rates due to increased occurrences of conditions such as hypertension, atherosclerosis, coronary artery disease, and diabetes compared to nonobese individuals. A patient is classified as obese if their actual body weight surpasses the ideal or desirable body weight by 20%, based on Metropolitan Life Insurance Company data. Ideal body weights consider average weights and heights for males and females...
325
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
19.9K
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
2.2K
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.9K