Universitätsverlag Potsdam
Refine
Has Fulltext
- no (46) (remove)
Year of publication
Document Type
- Article (18)
- Monograph/Edited Volume (18)
- Part of Periodical (7)
- Conference Proceeding (1)
- Course Material (1)
- Moving Images (1)
Is part of the Bibliography
- yes (46)
Keywords
- stunting (4)
- Summer Schools (3)
- malnutrition (3)
- Just so stories (2)
- SEPE (2)
- body height (2)
- body mass index (2)
- education (2)
- maturation (2)
- migration (2)
Institute
- Institut für Biochemie und Biologie (25)
- Extern (15)
- Department Grundschulpädagogik (11)
- Sozialwissenschaften (2)
- Department Musik und Kunst (1)
- Department Psychologie (1)
- Institut für Anglistik und Amerikanistik (1)
- Institut für Informatik und Computational Science (1)
- Institut für Jüdische Studien und Religionswissenschaft (1)
- Institut für Umweltwissenschaften und Geographie (1)
No evidence of growth impairment after forced migration in Polish school children after World War II
(2023)
Background: Migration is omnipresent. It can come hand in hand with emotional stress which is known to influence the growth of children.
Objective: The aim of this study was to analyse whether type of migration (forced or voluntary) and the geographic direction had influenced the growth of Polish children after World War II.
Sample and Methods: A sub dataset of 2,208 individuals between the ages of 2-20, created from data of the 2nd Polish Anthropological Survey carried out in 1966–1969, including anthropometrical data and social and demographic information based on questionnaire, was used to analyse migration effects.
Results: No association could be found between the direction of migration and the height of the children. The confidence intervals of the means of all classified migration categories overlap significantly and the effect size of the influence of migration category on height is ds=.140, which is too low to see any effects, even if there were one.
Conclusion: Neither forced nor voluntary migration in Poland after World War II led to a change in height in children of migrating families.
The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.
Background: Assessing short-term growth in humans is still fraught with difficulties. Especially when looking for small variations and increments, such as mini growth spurts, high precision instruments or frequent measurements are necessary. Daily measurements however require a lot of effort, both for anthropologists and for the subjects. Therefore, new sophisticated approaches are needed that reduce fluctuations and reveal underlying patterns.
Objectives: Changepoints are abrupt variations in the properties of time series data. In the context of growth, such variations could be variation in mean height. By adjusting the variance and using different growth models, we assessed the ability of changepoint analysis to analyse short-term growth and detect mini growth spurts.
Sample and Methods: We performed Bayesian changepoint analysis on simulated growth data using the bcp package in R. Simulated growth patterns included stasis, linear growth, catch-up growth, and mini growth spurts. Specificity and a normalised variant of the Matthews correlation coefficient (MCC) were used to assess the algorithm’s performance. Welch’s t-test was used to compare differences of the mean.
Results: First results show that changepoint analysis can detect mini growth spurts. However, the ability to detect mini growth spurts is highly dependent on measurement error. Data preparation, such as ranking and rotating time series data, showed negligible improvements. Missing data was an issue and may affect the prediction quality of the classification metrics.
Conclusion: Changepoint analysis is a promising tool to analyse short-term growth. However, further optimisation and analysis of real growth data is needed to make broader generalisations.
Human growth data analysis and statistics – the 5th Gülpe International Student Summer School
(2023)
The Summer School in Gülpe (Ecological Station of the University of Potsdam) offers an exceptional learning opportunity for students to apply their knowledge and skills to real-world problems. With the guidance of experienced human biologists, statisticians, and programmers, students have the unique chance to analyze their own data and gain valuable insights. This interdisciplinary setting not only bridges different research areas but also leads to highly valuable outputs. The progress of students within just a few days is truly remarkable, especially when they are motivated and receive immediate feedback on their questions, problems, and results. The Summer School covers a wide range of topics, with this year’s focus mainly on two areas: understanding the impact of socioeconomic and physiological factors on human development and mastering statistical techniques for analyzing data such as changepoint analysis and the St. Nicolas House Analysis (SNHA) to visualize interacting variables. The latter technique, born out of the Summer School’s emphasis on gaining comprehensive data insights and understanding major relationships, has proven to be a valuable tool for researchers in the field. The articles in this special issue demonstrate that the Summer School in Gülpe stands as a testament to the power of practical learning and collaboration. Students who attend not only gain hands-on experience but also benefit from the expertise of professionals and the opportunity to engage with peers from diverse disciplines.
Twenty-four scientists met for the annual Auxological conference held at Krobielowice castle, Poland, to discuss the diverse influences of the environment and of social behavior on growth following last year’s focus on growth and public health concerns (Hermanussen et al., 2022b). Growth and final body size exhibit marked plastic responses to ecological conditions. Among the shortest are the pygmoid people of Rampasasa, Flores, Indonesia, who still live under most secluded insular conditions. Genetics and nutrition are usually considered responsible for the poor growth in many parts of this world, but evidence is accumulating on the prominent impact of social embedding on child growth. Secular trends not only in the growth of height, but also in body proportions, accompany the secular changes in the social, economic and political conditions, with major influences on the emotional and educational circumstances under which the children grow up (Bogin, 2021). Aspects of developmental tempo and aspects of sports were discussed, and the impact of migration by the example of women from Bangladesh who grew up in the UK. Child growth was considered in particular from the point of view of strategic adjustments of individual size within the network of its social group. Theoretical considerations on network characteristics were presented and related to the evolutionary conservation of growth regulating hypothalamic neuropeptides that have been shown to link behavior and physical growth in the vertebrate species. New statistical approaches were presented for the evaluation of short term growth measurements that permit monitoring child growth at intervals of a few days and weeks.
Nutrition, size, and tempo
(2023)
Nutrition is a prerequisite, but not a regulator of growth. Growth is defined as increase in size over time. The understanding of growth includes an understanding of the binary concept of physical time and individual tempo. Excess food causes tempo acceleration. Food restriction delays tempo. Tempo reflects the pace of life. It is a dynamic physical response to a broad spectrum of social, economic, political, and emotional (SEPE) factors and can affect life expectancy. Variations in tempo create distortions of the z-score patterns of height and weight. Illness or intermediate food shortage lead to intermediate halts in development and create short dips in the z-score patterns. Children who develop throughout life at delayed pace usually run at lower z-scores for height and weight, and show a characteristic adolescent trough; children who develop throughout life at faster than average pace usually run at higher z-scores and show a characteristic adolescent peak in their z-score patterns. During adolescence, almost half of the height variance is due to tempo variation. There is not one tempo for the whole body. Different organ systems grow and mature at different pace.
What does stunting tell us?
(2023)
Stunting is commonly linked with undernutrition. Yet, already after World War I, German pediatricians questioned this link and stated that no association exists between nutrition and height. Recent analyses within different populations of Low- and middle-income countries with high rates of stunted children failed to support the assumption that stunted children have a low BMI and skinfold sickness as signs of severe caloric deficiency. So, stunting is not a synonym of malnutrition. Parental education level has a positive influence on body height in stunted populations, e.g., in India and in Indonesia. Socially disadvantaged children tend to be shorter and lighter than children from affluent families.
Humans are social mammals; they regulate growth similar to other social mammals. Also in humans, body height is strongly associated with the position within the social hierarchy, reflecting the personal and group-specific social, economic, political, and emotional environment. These non-nutritional impact factors on growth are summarized by the concept of SEPE (Social-Economic-Political-Emotional) factors. SEPE reflects on prestige, dominance-subordination, social identity, and ego motivation of individuals and social groups.
Background: In the animal kingdom body size is often linked to dominance and subsequently the standing in social hierarchy. Similarly, human growth has been associated and linked to socioeconomic factors, including one’s social status. This has already been proposed in the early 1900s where data on young German school girls from different social strata have been compared.
Objectives: This paper aims to summarize and analyze these results and make them accessible for non-German speakers. The full English translation of the historic work of Dikanski (Dikanski, 1914) is available as a supplement. Further, this work aims to compare the historical data with modern references, to test three hypotheses: (1) higher social class is positively associated with body height and weight, (2) affluent people from the used historical data match modern references in weight and height and (3) weight distributions are skewed in both modern and historical populations.
Methods: Comparison of historical data from 1914 with WHO and 1980s German data. The data sets, for both body weight and height for 6.0- and 7.0-year-old girls, were fitted onto centile curves and quantile correlation coefficients were calculated.
Results: In historical data social status is positively associated with body height and weight while both are also normally distributed, which marks a significant difference to modern references.
Conclusion: Social status is positively associated with height, signaling social dominance, making children of affluent classes taller. Children from the historical data do not reach the average height of modern children, even under the best environmental conditions. The children of the upper social class were not skewed in weight distribution, although they had the means to become as obese as modern children.
Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction.
Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark.
Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch’s t-test was used. Network model estimation based on real-world data was done by stepwise selection.
Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark.
Results: Results after t-tests and determining confidence intervals (CI95%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package.
Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction.
Background: Clinicians often refer anthropometric measures of a child to so-called “growth standards” and “growth references. Over 140 countries have meanwhile adopted WHO growth standards.
Objectives: The present study was conducted to thoroughly examine the idea of growth standards as a common yardstick for all populations. Weight depends on height. We became interested in whether also weight-for-height depends on height. First, we studied the age-group effect on weight-for-height. Thereafter, we tested the applicability of weight-for-height references in short and in historic populations.
Sample and Methods: We analyzed body height and body weight and weight-for-height of 3795 healthy boys and 3726 healthy girls aged 2 to 5 years measured in East-Germany between 1986 and 1990.
We chose contemporary height and weight charts from Germany, the UK, and the WHO growth chart and compared these with three geographically commensurable growth charts from the end of the 19th century.
Results: We analyzed body height and body weight and weight-for-height of 3795 healthy boys and 3726 healthy girls aged 2 to 5 years measured in East-Germany between 1986 and 1990.
We chose contemporary height and weight charts from Germany, the UK, and the WHO growth chart and compared these with three geographically commensurable growth charts of the end of the 19th century.
Conclusion: Weight-for-height depends on age and sex and apart from the nutritional state, reflects body proportion and body built particularly during infancy and early childhood. Populations with a relatively short average height are prone to high values of weight-for-height for arithmetic reasons independent of the nutritional state.