Countries by Prevalence of overweight, weight for height, female (modeled estimate, % of children under 5)
Australia ranks first among 163 countries with 25% of female children under 5 classified as overweight by weight-for-height measure, more than 44 times higher than Myanmar at the bottom with 0.6%. The gap between the top and bottom of this ranking spans over 24 percentage points. This wide spread reflects sharply different nutritional conditions across regions and income levels.
Ranking 2024
Values shown in %.
Analysis
This metric measures the prevalence of overweight among female children under 5, defined as weight for height, as a modeled estimate expressed as a percentage of children in that age group. The unit is percent, and higher values indicate greater prevalence of overweight. The ranking covers 163 countries, all with data from within the last 7 years as of 2024, and 100% of data points are classified as official quality. The indicator code is SH.STA.OWGH.ME.FE.ZS from the World Bank World Development Indicators. Nine extreme outliers were identified using a 3-standard-deviation threshold, and the data range spans from 0.60% to 25.00%, a spread of over 4,000%.
Australia tops the ranking at 25%, a notable distance ahead of second-ranked Papua New Guinea at 17.3% and third-ranked Turks and Caicos Islands at 17.2%. Tunisia (rank 4, 16.4%), Albania (rank 5, 15.6%), and Ukraine (rank 6, 15%) round out the top six. The upper tier includes a geographically diverse mix: Latin American countries such as Paraguay (rank 7, 14.4%) and Argentina (rank 9, 13.4%) appear alongside Balkan states like Bosnia and Herzegovina (rank 14, 12.2%) and North Macedonia (rank 18, 11.5%). Several Middle Eastern and North African countries including Algeria (rank 13, 12.5%) and Egypt (rank 23, 10.5%) also rank in the upper quarter.
The middle of the ranking includes countries across a range of regions and income levels. Vietnam ranks 31st at 9.8%, while the United States places 39th at 9.0% and the Russian Federation 40th at 8.9%. Germany ranks considerably lower at 133rd with 3.1%, placing it below many lower-income countries. Japan ranks 153rd at just 1.8%. Several high-income countries therefore appear in the lower half of this ranking, while some lower-income nations in Africa and the Pacific rank higher. Notably, Singapore ranks 123rd at 3.5%, and South Korea ranks 80th at 5.1%. Nigeria ranks 157th at 1.5%, and Bangladesh ranks 157th at the same value.
Because these are modeled estimates rather than direct survey counts, values may not reflect real-time or granular national conditions. Modeled data can smooth over subnational variation, urban-rural differences, and year-to-year fluctuations. The average year-over-year change across countries is 3.8%, suggesting relative stability in the data but also limiting the detection of rapid shifts. The metric covers only children under 5 and only females, so it does not represent the broader child population or male counterparts. Countries with limited survey infrastructure may have estimates based on older underlying data that is then modeled forward, which can affect accuracy. Rankings should be read as approximations rather than precise national measurements.
Methodology
Data are drawn from the World Bank World Development Indicators, indicator code SH.STA.OWGH.ME.FE.ZS, for the reference year 2024. The metric measures the prevalence of overweight, weight for height, female, as a modeled estimate expressed as a percentage of children under 5. The source note definition is incorporated as provided. The ranking covers 163 countries, all of which meet the recency threshold with data from within the last 7 years; 100% of data points are classified as official quality, exceeding the 70% threshold. Nine extreme outliers were flagged using a 3-standard-deviation threshold. The data range runs from 0.60% (Myanmar) to 25.00% (Australia), a spread of approximately 4,067%. Average year-over-year change across the dataset is 3.8%, well within the 50% volatility threshold, indicating stable underlying data. As modeled estimates, values represent statistical approximations and may not capture rapid changes or subnational variation.