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Epidemiology research to examine the relationship between these biomarkers analyzed at the molecular level and disease was broadly named "molecular epidemiology". Specifically, "genetic epidemiology" has been used for epidemiology of germline genetic variation and disease. Genetic variation is typically determined using DNA from peripheral ...
Epidemiological (and other observational) studies typically highlight associations between exposures and outcomes, rather than causation. While some consider this a limitation of observational research, epidemiological models of causation (e.g. Bradford Hill criteria) [7] contend that an entire body of evidence is needed before determining if an association is truly causal. [8]
The science of epidemiology has had enormous growth, particularly with charity and government funding. Many researchers have been trained to conduct studies, requiring multiple skills ranging from liaising with clinical staff to the statistical analysis of complex data, such as using Bayesian methods.
Researchers have applied Hill’s criteria for causality in examining the evidence in several areas of epidemiology, including connections between exposures to molds and infant pulmonary hemorrhage, [14] ultraviolet B radiation, vitamin D and cancer, [15] [16] vitamin D and pregnancy and neonatal outcomes, [17] alcohol and cardiovascular ...
Clinical epidemiology is a subfield of epidemiology specifically focused on issues relevant to clinical medicine. The term was first introduced by virologist John R. Paul in his presidential address to the American Society for Clinical Investigation in 1938. [1] [2] It is sometimes referred to as "the basic science of clinical medicine". [3]
Safe and secure data is a crucial aspect of successful epidemiologic research. Exposure assessment and mapping. Typically always seen as an analytical weakness, the quality of exposure data, or reported accuracy of the spatial reach of epidemics, is especially important in spatial epidemiology.
Major research challenges in social epidemiology include tools to strengthen causal inference, [5] [6] methods to test theoretical frameworks such as Fundamental Cause Theory, [7] translation of evidence to systems and policy changes that will improve population health, [8] and mostly obscure causal mechanisms between exposures and outcomes. [9]
Sensitivity analysis studies the relation between the uncertainty in a model-based the inference [clarify] and the uncertainties in the model assumptions. [1] [2] Sensitivity analysis can play an important role in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study. [3]