[Identifying areas of epidemiological stratification in an onchocerciasis focus in Yanomami territory, Roraima,...

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Chagas disease, or American trypanosomiasis, is a vector-borne parasitic infection caused by Trypanosoma cruzi, a flagellated protozoan (kinetoplastid) of the family Trypanosomatidae. The disease is endemic in Latin America, although cases are increasingly reported worldwide due to human migration. Transmission most commonly occurs when feces of infected triatomine bugs contaminate bite wounds or mucosal surfaces; additional routes include congenital, transfusional, transplant-related, and oral...