Analytic Approaches in Epidemiology: Strengths & Limitations Report

This report delves into analytic approaches in epidemiology, addressing the complexities of causation, the limitations of current methodologies, and the relevance of epidemiological studies. It begins by exploring the concept of causation, examining various definitions such as necessary, sufficient, and probabilistic factors, and highlighting the challenges in establishing causal relationships. The report then identifies limitations in epidemiological approaches, such as the failure to distinguish between ontology and epistemology, and the inadequacy of causal models. Finally, it evaluates the validity of Rothman's assertion regarding the decline of epidemiology, arguing that epidemiology remains a vital field in identifying risk factors, drawing causal inferences, and providing a scientific foundation for public health interventions. The report emphasizes the importance of epidemiology in community diagnosis, healthcare evaluation, and the development of preventive measures.

Running head: ANALYTIC APPROACHES IN EPIDEMIOLOGY
Analytic approaches in epidemiology- its strengths and limitations
Name of the Student
Name of the University
Author Note

1ANALYTIC APPROACHES IN EPIDEMIOLOGY
Question 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.
Question 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

2ANALYTIC APPROACHES IN EPIDEMIOLOGY
(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.
Question 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.

3ANALYTIC APPROACHES IN EPIDEMIOLOGY
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), 1776-1786.