# Short stature-related factors and nomogram-based risk prediction in children aged 7-12: evidence from Chaozhou, China

## TL;DR

This study examines short stature in 7-12-year-old children in Chaozhou, China, identifying risk factors and developing a predictive model to help guide local interventions.

## Contribution

The study introduces a nomogram-based risk prediction model for short stature in children, validated with local data from Chaozhou.

## Key findings

- The overall prevalence of short stature was 3.7% among 7-12-year-old children in Chaozhou.
- A nomogram model based on identified risk factors showed good predictive accuracy with an area under the curve of 0.858.
- Paternal and maternal height, birth weight, and lifestyle factors were significant predictors of short stature.

## Abstract

Childhood height development is a crucial indicator of public health, with the prevalence of short stature serving as an important metric. This study aimed to investigate the height development status, prevalence of short stature, and associated risk factors among 7-12-year-old children in Chaozhou City, China, providing valuable reference data for local prevention and intervention strategies to address short stature.

A cross-sectional survey on the height of 7-12-year-old children was conducted in Chaozhou City, Guangdong Province, China. Standardized measurement tools were used to collect height data for epidemiological analysis. To explore risk factors for short stature, a questionnaire survey was administered to a random sample of the surveyed population. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with the risk of short stature and to construct a predictive model.

A total of 7,799 children participated in the height survey. Girls had significantly higher mean heights than boys at ages 8, 11, and 12 (all P < 0.001). The overall prevalence of short stature was 3.7%. Although girls had a higher prevalence than boys (4.0% vs. 3.4%), the difference was not statistically significant (P = 0.167). Multivariate logistic regression identified independent risk factors for short stature, including paternal height < 160 cm, maternal height <150 cm, birth weight < 2.5 kg, preterm birth, exercising < 3 times per week, sleep duration < 8 hours per day, and irregular diet. A preference for meat and dairy products was independently associated with a reduced risk of short stature. The nomogram model developed based on these factors demonstrated good predictive performance, with an area under the curve of 0.858 (95%CI 0.815-0.900).

The overall prevalence of short stature in 7-12-year-old children in Chaozhou was slightly higher than the national average. This study analyzed the risk factors for short stature in children, and the risk prediction model developed from these factors demonstrated good predictive accuracy for short stature prevalence. However, external validation in independent cohorts is necessary to confirm the robustness of the model.