THE BIOLOGICAL STATUS OF CHILDREN LIVING IN DISADVANTAGED REGIONS OF HUNGARY

Testing the cross-applicability of juvenile sex estimation from diaphyseal dimensions

Sex estimation is a crucial component of the biological profile. Stull et al. (2017) have proposed a promising juvenile sex estimation method using long bone measurements taken from a South African sample, providing relatively high classification accuracies and made easy to use via the KidStats web-based app. In this study, we test the models developed by Stull et al. (2017) on an external historic population from Lisbon, Portugal, in order to determine whether the models can be reliably applied to archeological and forensic populations outside of the original population sample. The study sample consisted of 102 individuals (45 females and 57 males) aged under 13 years at death from the Lisbon identified skeletal collection. Measurements from these individuals were used to test the flexible discriminant analysis (FDA) models given by Stull et al. (2017). Allocation accuracies were calculated for boys and girls and children over and under 2 years separately and combined. Our findings show that the models developed by Stull et al. (2017) yield poor accuracy when applied to our external population and thus can potentially be misapplied on archeological skeletal remains or forensic remains of unknown origin. A number of statistical issues may explain why models fail to be transportable or even generalizable, namely multicollinearity, model overfitting and overly optimist bootstrapped cross-validation rates. It is also likely that population differences in size and sexual size dimorphism also affected the applicability of the models. We emphasize the importance of externally validating prediction models, particularly if they are intended to be applied across populations. Our study addresses Stull and co-worker's request for further validation of the method on populations outside of South Africa, as the models cannot be confidently applied in the field until it has been externally validated.

Substantial variation across geographic regions in the obesity prevalence among 6-8 years old Hungarian children (COSI Hungary 2016)

Background

There have been previous representative nutritional status surveys conducted in Hungary, but this is the first one that examines overweight and obesity prevalence according to the level of urbanization and in different geographic regions among 6–8-year-old children. We also assessed whether these variations were different by sex.

Methods

This survey was part of the fourth data collection round of World Health Organization (WHO) Childhood Obesity Surveillance Initiative which took place during the academic year 2016/2017. The representative sample was determined by two-stage cluster sampling. A total of 5332 children (48.4% boys; age 7.54 ± 0.64 years) were measured from all seven geographic regions including urban (at least 500 inhabitants per square kilometer; n = 1598), semi-urban (100 to 500 inhabitants per square kilometer; n = 1932) and rural (less than 100 inhabitants per square kilometer; n = 1802) areas.

Results

Using the WHO reference, prevalence of overweight and obesity within the whole sample were 14.2, and 12.7%, respectively. According to the International Obesity Task Force (IOTF) reference, rates were 12.6 and 8.6%. Northern Hungary and Southern Transdanubia were the regions with the highest obesity prevalence of 11.0 and 12.0%, while Central Hungary was the one with the lowest obesity rate (6.1%). The prevalence of overweight and obesity tended to be higher in rural areas (13.0 and 9.8%) than in urban areas (11.9 and 7.0%). Concerning differences in sex, girls had higher obesity risk in rural areas (OR = 2.0) but boys did not. Odds ratios were 2.0–3.4 in different regions for obesity compared to Central Hungary, but only among boys.

Conclusions

Overweight and obesity are emerging problems in Hungary. Remarkable differences were observed in the prevalence of obesity by geographic regions. These variations can only be partly explained by geographic characteristics.

Trial registration

Study protocol was approved by the Scientific and Research Ethics Committee of the Medical Research Council (61158–2/2016/EKU).

Anthropometric dimensions provide reliable estimates of abdominal adiposity: A validation study

Abdominal fat accumulation is a major risk factor for cardiometabolic morbidity and mortality. The purpose of the study is to assess the possibility of developing accurate estimation equations based on body measurements to determine total abdominal (TFA), subcutaneous (SFA) and visceral fat area (VFA). Hungarian volunteers (n=198) aged between 20 and 81 years were enrolled in the study, which was conducted between July and November 2014. All persons underwent anthropometric measurements and computer tomographic (CT) scanning. Sex-specific multiple linear regression analyses were conducted in a subgroup of 98 participants to generate estimation models, then Bland-Altman's analyses were applied in the cross-validation group to compare their predictive efficiency. The variables best predicting VFA were hip circumference, calf circumference and waist-to-hip ratio (WHR) for males (R²=0.713; SEE=5602.1mm²) and sagittal abdominal diameter (SAD), WHR, thigh circumference and triceps skinfold for females (R²=0.845; SEE=3835.6mm²). The SFA prediction equation included SAD, thigh circumference and abdominal skinfold for males (R²=0.848; SEE=4124.1mm²), body mass index and thigh circumference for females (R²=0.861; SEE=5049.7mm²). Prediction accuracy was the highest in the case of TFA: hip circumference and WHR for males (R²=0.910; SEE=5637.2mm²), SAD, thigh circumference and abdominal skinfold for females (R²=0.915; SEE=6197.5mm²) were used in the equations. The results suggested that deviations in the predictions were independent of the amount of adipose tissue. Estimation of abdominal fat depots based on anthropometric traits could provide a cheap, reliable method in epidemiologic research and public health screening to evaluate the risk of cardiometabolic events.