Browsing by Author "Vitzthum, Virginia J."

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Anemia Project Data Set
Vitzthum, Virginia J.
This data set comprises measurements of hemoglobin concentration and other variables from rural highland Bolivian women. The data are stored in two formats: a single EXCEL file with two worksheets (one for each phase of data collection) and two csv files (one for each phase of data collection; data are identical to those in the corresponding Excel file worksheets). A Codebook (pdf format) describes the variables in detail. Analyses of these data have been published: Bedwell RM, Spielvogel H, Bellido D, Vitzthum VJ. 2017. "Factors influencing the use of biomedical health care by rural Bolivian anemic women: structural barriers, reproductive status, gender roles, and concepts of anemia." PLOS ONE (in press).
Modelling the Distribution of Anthropometrics: Gaussian Distributions, LMS Distributions, and Probability Plots
(Human Biology Association, 2024-05-20) Thornburg, Jonathan; Vitzthum, Virginia J.
In anthropologists' studies of growth and development, particularly in non-industrialized populations, sample sizes are often small and span a range of ages. These attributes can hamper analyses and hypothesis testing, and make comparisons to other populations difficult. $z$-scores are a commonly used statistic to mitigate these challenges. A $z$-score is the value of an individual's anthropometric ($y$) expressed in units of the standard deviation (SD) of the anthropometric for a suitable sex- and age-specific reference sample. That is, $z = (y - \mu)\big/\sigma$ where $\mu$ and $\sigma$ are the mean and SD of the reference sample, respectively. Depending on the research question, commonly used reference samples (e.g., WHO, CDC) are not necessarily suitable for all populations. Therefore, there are increasing efforts to construct population-specific growth references. If a growth reference provides the mean and SD for each age/sex bin, it's easy to compute the $z$-score corresponding to any individual's measurement by assuming a Gaussian (normal) distribution. $z$-scores may be computed by either using the mean/SD for the individual's sex/age bin, or (for improved accuracy) interpolating tabulated means/SDs to the individual's age. However, if the anthropometric has an asymmetric (skewed) distribution (as do weight, BMI, and many skinfolds), this approach results in systematically biased $z$-scores. Cole's LMS distribution can accurately represent the distributions of these and many other anthropometrics, avoiding this bias. But sometimes only percentiles are provided for each age/sex bin. We describe how to: (a) determine the mean/SD of the Gaussian distribution, or the coefficients of the LMS distribution, that best fit the published percentiles; (b) visually assess the quality of such a fit; and (c) extrapolate the distribution beyond the range of the published percentiles and visually assess the quality of such an extrapolation. We describe doing this fitting with common open-source software (Gnuplot, R, or SciPy (Python)), or with Microsoft Excel\texttrademark. The fitted coefficients can then be used to compute $z$-scores. We also describe how to extrapolate parameters (and thus compute $z$-scores) for an individual who is outside the tabulated age range, and we present a graphical assessment of any given extrapolation's quality.
Quantifying Selection Bias in Cross-Sectional Studies of Ovarian Hormones
(Wiley, 2009-03) Thornburg, Jonathan; Vitzthum, Virginia J.
Most studies of ovarian hormones in adult women collect data from a cross-sectional sample of participants meeting various selection criteria including not having been pregnant or breastfeeding for several months. Although this approach is intended to eliminate the effects of these factors on hormonal variation, it introduces a selection bias of unknown magnitude: in a non-contracepting population, those women with the highest fecundity are more likely to be either pregnant or lactating, and so not included in a study sample. Thus a cross-sectional sample disproportionately represents women with the lowest fecundity (and potentially the lowest hormone levels). Here we present a preliminary evaluation of the magnitude of this selection bias, focusing on progesterone ($P$) levels near the luteal peak. We use data from Project REPA, a longitudinal study of reproductive functioning in rural Bolivians, recruited without regard to reproductive status (Vitzthum, Spielvogel, and Thornburg \textit{Proceedings of the U.S. National Academy of Sciences/} 101, 1443 (2004)). Drawing from 542~non-conception cycles in 144~women, we construct simulated cross-sectional samples meeting various inclusion criteria and compare their anovulation rates and progesterone levels.
Quantitative Estimates of the Relative Contributions of Secular Trend and Community Type to Variation in Adolescent Growth in the Andean Highlands
(Human Biology Association, 2022-03) Vitzthum, Virginia J.; Thornburg, Jonathan
In high altitude populations, children's growth is affected by several factors including poor nutrition, genetics, and hypoxia. In the Andean altiplano, subsistence agropastoralism in rural areas is associated with heavy physical labor and seasonal food shortage, stressors less likely to be experienced in urbanized communities. Such persistent rural poverty drove increasing rural out-migration to urban and peri-urban communities throughout the 1980s and 1990s. During these periods and since there have been efforts by governmental and other entities to improve living conditions, economic options, and children's nutrition and overall health. However, it is rarely the case that such programs are implemented either continuously or evenly across communities. Over time, various reports suggest that child growth is improving in the Andes. But site-specific longitudinal studies are rare, making it difficult to disentangle the relative benefits of different community types (rural versus urbanized) from the impacts of regional secular (i.e., time dependent) trends in children's growth. In this analysis we investigate the relative contributions of rural-urban community differences versus wider regional improvements that have occurred over time in living conditions and economic opportunities, to Andean children's growth (height-for-age). We use height (often called the "biological standard of living") to assess children's growth because this anthropometric is notably sensitive to socioeconomic conditions We focus on adolescents (ages 11-14 years), who have been less studied than adults or younger children.
Supplementary Information (assay details and performance) for article "Links among inflammation, sexual activity and ovulation: Evolutionary trade-offs and clinical implications"
Lorenz, Tierney K.; Worthman, Carol M.; Vitzthum, Virginia J.