The term “anthro,” especially within the context of data analysis and public health, often refers to anthropometry. Anthropometry involves the measurement of the human body’s size and proportions. These measurements are vital indicators of nutritional status, growth, and overall health, particularly in children. This article focuses on the “anthro” R package and its significance in analyzing anthropometric data.
The “anthro” R package is a powerful tool designed for calculating z-scores, prevalence estimates (along with their confidence intervals), and summary statistics related to anthropometric indicators. These calculations are based on methodologies recommended by the World Health Organization (WHO) and UNICEF, as detailed in their guide, Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old.
This package, available on CRAN (Comprehensive R Archive Network), provides standardized results for several key indicators:
- Length/height-for-age
- Weight-for-age
- Weight-for-length
- Weight-for-height
- Body mass index-for-age (BMI-for-age)
- Head circumference-for-age
- Arm circumference-for-age
- Triceps skinfold-for-age
- Subscapular skinfold-for-age
The availability of the “anthro” package on CRAN makes it readily accessible to researchers and practitioners working in public health, nutrition, and related fields.
STATA Macro for Anthropometric Data Analysis
In addition to the R package, UNICEF has developed a STATA macro for analyzing survey data related to anthropometry in children under five. This macro aligns with the WHO/UNICEF guidelines and the “anthro” R package. It offers analysis for length/height-for-age, weight-for-age, weight-for-length, and weight-for-height.
The STATA macro can be accessed through the UNICEF Data & Analytics Division or via their GitHub repository.
Other Macros and Methodological Alignment
While SAS and SPSS macros for anthropometric analysis exist, it’s crucial to note that these may not be entirely aligned with the latest methodologies for calculating prevalence estimates and confidence intervals. However, they can still be valuable for calculating z-scores and prevalence estimates (excluding confidence intervals). Updates to these macros are anticipated in the future.
Why is Anthropometric Data Important?
Anthropometric data provides insights into a population’s health and well-being. Monitoring these indicators helps identify potential health issues early, especially in vulnerable populations like young children. Analyzing this data with tools like the “anthro” R package allows for:
- Identifying Malnutrition: Anthropometry is a key tool for detecting undernutrition (wasting, stunting, underweight) and overnutrition (overweight, obesity).
- Assessing Growth and Development: Tracking growth patterns over time helps ensure children are developing appropriately.
- Evaluating Intervention Programs: Anthropometric data can be used to assess the impact of nutrition interventions and public health programs.
- Monitoring Population Health: Analyzing trends in anthropometric indicators provides valuable information about the overall health status of a population.
By using standardized tools and methodologies like the “anthro” package, researchers and practitioners can ensure data comparability and reliability across different studies and settings. This is essential for effective public health planning and intervention. Understanding “anthro” and the tools associated with it is therefore crucial for those working to improve child health and nutrition worldwide.
