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Analytic Approaches In Epidemiology

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Analytic Approaches In Epidemiology Questions: 1.Discuss the strengths of the Epidemiologic approach in understanding notions of causation.  2.Discuss the limitations of the Epidemiologic approach in understanding notions of causation. 3.What, if anything, does the discipline of Epidemiology have to offer global society in the 21st century in understanding the causes of disease?   Answers: 1.Causations have been a part of epidemiology literature since decades. However, no single articulate definition is available to explain it. The elaborate theoretical concepts of causation that underlies the field of epidemiology often gets bypassed in favour of more quantitative terms like risk factors, rates and odds. Despite the numerous vague definitions available, epidemiology lays a profound interest in it because causation helps in identification of disease causes, which can be utilized to prevent severe health consequences (Parascandola & Weed, 2001). Thorough literature review associated causation with the terms:  necessary, production, probabilistic, sufficient and counterfacts. Causation may not necessarily follow any one of these factors. It can be a combination of a variety of components. The probabilistic and counterfactual definitions are not sufficient definitions. Cigarette smoking can be established as a cause of cancer only when the impact of other necessary components is assessed (Vandenbroucke, Broadbent & Pearce, 2016). Therefore, epidemiological research utilizes the notion of multifactorial disorder, which directly signifies that a certain disease can occur due to more than one cause or by a joint action of a plethora of component causes.   2.One major limitation arises when epidemiological approaches fail to distinguish between ontology and epistemology. While the former is about what a particular disease is, the latter elaborates on scientific knowledge to identify the etiology of a disease. Several epidemiologists have included interventions or observed frequencies while defining causation. However, the definition should not include actions taken to improve the disorder or measurement frequencies. Satisfactory differences between causal models and causation definition are also not met (Murtas, Dawid & Musio, 2017). a definition should always allow the possibility of an inherent chance in a natural processes. On the other hand, causal models decrease the influence of such chance, which may be related to the explanation of the model inversely. Causation fails to explain why some smoker develops lung cancer and other does not.  Moreover, they fail to explain the disappearance of infectious agent once the disease develops and all organisms exposed to the infectious agent may not acquire infection (Elwood, 2017). Moreover, the data gathered by such approaches are often ignored and they recognize only deterministic models as valid 3.Rothman’s assertion is not valid in present day context (Rothman, 2007). Epidemiology has not nearly gone, it is rather considered as the Cinderella of modern science. It helps in identifying risk factors for different diseases and draws inference on the causal associations by analyzing several studies. It provides useful information on identification of the hazard component. It helps in providing a scientific foundation related to the health condition. It also provides concise information on the demography of disease incidence, symptoms of the ailment, describes the natural history of the disease, identifies the etiology or associated risk factors (Mooney, Westreich & El-Sayed, 2015). It takes into account various quantitative tools for community diagnosis, provides valuable data needed to implement and evaluate healthcare services and suggests preventive and control measures and possible outcomes. Therefore, it is an important risk-assessment factor in 21st century.   References Elwood, M. (2017). Critical appraisal of epidemiological studies and clinical trials. Oxford University Press. Mooney, S. J., Westreich, D. J., & El-Sayed, A. M. (2015). Epidemiology in the era of big data. Epidemiology (Cambridge, Mass.), 26(3), 390. Murtas, R., Dawid, A. P., & Musio, M. (2017). New bounds for the Probability of Causation in Mediation Analysis. arXiv preprint arXiv:1706.04857. Parascandola, M., & Weed, D. L. (2001). Causation in epidemiology. Journal of Epidemiology & Community Health, 55(12), 905-912. Rothman, K. J. (2007). The rise and fall of epidemiology, 1950–2000 AD. International journal of epidemiology, 36(4), 708-710. Vandenbroucke, J. P., Broadbent, A., & Pearce, N. (2016). Causality and causal inference in epidemiology: the need for a pluralistic approach. International journal of epidemiology, 45(6), 1776-178

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