Estudios transversales: Cross-sectional studies

Cross-sectional studies are epidemiological design which can be considered as descriptive or analytical designs depending on the general objective. This is a quickly and economical design and allows to calculate the prevalence of a condition. Also, the relationship of temporality between the exposition and the outcome is being measured simultaneously on a unique period, not being possible to identify a directionality in the temporality. When there is an analytic objective, the association measure used is the Prevalence Ratio (PR), specially when the prevalence of the outcome is more or equal to 10% or the Odds Ratio (OR) when that prevalence is lower. To quantify this association different regression models like Binomial log or Poisson log can be used, including generalized lineal models. If the association measure is OR, the most common used model is the multiple logistic regression.Los estudios observacionales transversales pueden ser de tipo descriptivos o también analíticos dependiendo del objetivo general. Este diseño es rápido, económico y permite el cálculo directo de la prevalencia de una condición. Además, la relación de temporalidad entre la exposición y el efecto son medidas de forma simultánea en un único período, no siendo posible identificar una direccionalidad en la temporalidad. Cuando estos estudios persiguen un objetivo general analítico, la medida de asociación es la Razón de Prevalencias (RP), especialmente cuando la prevalencia del efecto es mayor o igual a 10% o el Odds Ratio (OR) cuando la prevalencia es baja. Para cuantificar esta asociación pueden utilizarse diferentes modelos de regresión como el binomial log o Poisson log, incluyendo los modelos lineales generalizados. Cuando la medida de asociación a utilizar es el OR, el modelo más comúnmente empleado es la regresión logística múltiple.&nbsp

Obtaining adjusted prevalence ratios from logistic regression model in cross-sectional studies

In the last decades, it has been discussed the use of epidemiological prevalence ratio (PR) rather than odds ratio as a measure of association to be estimated in cross-sectional studies. The main difficulties in use of statistical models for the calculation of PR are convergence problems, availability of adequate tools and strong assumptions. The goal of this study is to illustrate how to estimate PR and its confidence interval directly from logistic regression estimates. We present three examples and compare the adjusted estimates of PR with the estimates obtained by use of log-binomial, robust Poisson regression and adjusted prevalence odds ratio (POR). The marginal and conditional prevalence ratios estimated from logistic regression showed the following advantages: no numerical instability; simple to implement in a statistical software; and assumes the adequate probability distribution for the outcome

Self-determination theory and diminished functioning: the role of interpersonal control and psychological need thwarting

Drawing from self-determination theory (Ryan & Deci, 2002), three studies explored the social-environmental conditions that satisfy versus thwart psychological needs and, in turn, impact psychological functioning and well/ill-being. In cross-sectional Studies 1 and 2, structural equation modeling analyses supported latent factor models in which need satisfaction was predicted by athletes’ perceptions of autonomy support and need thwarting was better predicted by perceived coach control. Athletes’ perceptions of need satisfaction predicted positive outcomes associated with sport participation (vitality and positive affect) whereas perceptions of need thwarting more consistently predicted maladaptive outcomes (disordered eating, burnout, depression, negative affect, and physical symptoms). In addition, athletes’ perceptions of psychological need thwarting were significantly associated with perturbed physiological arousal (elevated levels of secretory immunoglobulin A) prior to training. The final study involved the completion of a diary and supported the relations observed in the cross-sectional studies at a daily level. These findings have important implications for the operationalization and measurement of interpersonal styles and psychological needs

Sleep apnea in adults: How accurate is clinical prediction?

Questionnaires, physical examination, and clinical prediction rules estimate the pretest probability of obstructive sleep apnea hypopnea syndrome (OSAHS), but are not specific enough to make the diagnosis (strength of recommendation [SOR]: B, meta- analyses, prospective cross-sectional studies). The Epworth Sleepiness Scale is a reliable measure of daytime sleepiness (SOR: B, factor analysis). The Berlin Questionnaire, Mallampati score, and truncal obesity can be used to assess pretest probability of OSAHS (SOR: B, multivariate analyses, cross-sectional studies)

Quantifying Selection Bias in Cross-Sectional Studies of Ovarian Hormones

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.National Science Foundation, University of California, Binghamton University, Indiana Universit

What's the best test for HSV-2 after exposure?

Enzyme-linked immunosorbent assay (ELISA ) tests based on herpes simplex virus 2's (HSV -2) glycoprotein G have demonstrated high sensitivity and specificity in determining seropositivity for HSV-2 antibodies (strength of recommendation [SOR]: C, based on cross-sectional studies). ELISA tests not based on glycoprotein G are also highly sensitive, but they are less specific for HSV-2 and are prone to false-positive results because of cross-reactivity with HSV -1 antibodies (SOR: C, based on cross-sectional studies)

Integrating clinical data from cross-sectional and longitudinal studies

Clinical trials are typically conducted over a population in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large population but do not allow us to model the temporal nature of disease. Longitudinal studies on the other hand, are used to explore how these processes develop over time but can be expensive and time-consuming, and only cover a relatively small window within the disease process. This paper explores a technique for integrating cross-sectional and longitudinal studies to build models of disease progression

You Get What You Vote For: Electoral Determinants of Economic Freedom

While several cross-sectional studies (La Porta et. al. 2002, Norton 2002) examine institutional and cultural determinants of economic freedom, changes in economic freedom remain unexamined. I construct a measure of median voter preferences and find changes in voter preferences for economic freedom to be a significant determinant of changes in economic freedom in a panel of 25 OECD countries. The voter preference measure is robust to several alternative specifications, including the addition of institutional variables.Economic Freedom, Median Voter, Manifesto Data, Voter Preferences,