EPİDEMİYOLOJİDE NEDEN, NEDENSELLİK VE AÇIKLAYICI MODELLER

Öz

Neden ve neden-sonuç ilişkilerini anlamaya yarayan nedensellik kavramı tıbbın ve epidemiyolojinin olduğu kadar başta felsefe olmak üzere pek çok bilimin ilgi alanında olan canlı bir tartışma konusudur. İnsan sağlığına ve hastalıkların oluşumuna ilişkin nedensellik açıklamalarında biyolojik mekanizmalarının yetersiz kaldığı, bireyden bireye farklılıkların görülebilmesi nedeniyle konuyu bireysel düzeyde ele almanın yeterli olmadığı, etkilerin oluşmasında zaman faktörünün önemli bir bileşen olduğu, hatta pek çok değişkenin birbirileri ile olan ilişki ve etkileşimlerinin rol oynadığı bilinmektedir. Neden-sonuç ilişkisi konusundaki bilimsel paradigmalar özellikle sanayi devriminden sonra hızlı bir evrim geçirmiştir. Tıbbın gelişmesine önemli katkıda bulunmuş olan Henle-Koch postülatları, Hill kriterleri, epidemiyolojik üçgen, nedensellik ağı, pasta modeli gibi deterministik olan ve olmayan, olasılıklı nedensellik açıklamaları belirli dönemlerde önemli başarıların kazanılmasını sağlamış olsalar da bugün için yeni bir paradigmaya ihtiyaç olduğu anlaşılmaktadır. Günümüzün önemli halk sağlığı sorunlarının başında yer alan kronik hastalıkları açıklamada yetersiz kalan mevcut nedensellik yaklaşımlarının yerini döngüsel nedensellik, sistemler epidemiyolojisi ve karmaşıklık biliminin nedensellik yaklaşımlarının alacağı anlaşılmaktadır. Bu nedenle epidemiyoloji ile ilgilenen her profesyonelin yeni paradigma arayışlarda yerini bir an önce alması önemlidir.

Anahtar Kelimeler

• Epidemiyoloji
• nedensellik
• döngüsel nedensellik
• karmaşıklık bilimi

CAUSE, CAUSALITY AND EXPLANATORY MODELS IN EPIDEMIOLOGY

Öz

The concept of causality, which helps to understand causes and cause-effect relationships, is a live discussion topic in the interest area of many sciences, especially philosophy, as well as medicine and epidemiology. It is known that the biological mechanisms are insufficient to explain causality regarding human health and occurrence of many diseases. It is not easy to handle the issue at an individual level due to interindividual and intraindividual variations. Duration of the exposure to causal agents is another important component in the formation of effects besides the complex relationships and interactions of multiple variables. Scientific paradigms on the cause-effect relationship have undergone a rapid evolution especially after the industrial revolution. Although many deterministic, non-deterministic, and probabilistic explanations of causality, such as, Henle-Koch postulates, Hill’s criteria, epidemiological triangle, causality networks, pie model, algorithms, which have contributed significantly to the development of medicine, have provided significant success in certain periods, it is clear that a new paradigm is needed today. Current causality approaches are not sufficient to explain the cause-effect relationships fort he occurrence of chronic diseases, which are the most significant public health problems of today. Old paradigms seem to be replaced by new approaches, such as, circular causality, causality models in systems epidemiology, and complexity science. Therefore, it is important that every professional dealing with epidemiology takes his place in the search for a new paradigm as soon as possible.

Anahtar Kelimeler

• Epidemiology
• causality
• circular causality
• complexity science

Kaynakça

1. 1- Susser M. What is a cause and how do we know one? Am J Epidemiol. 1991;133(7):635-48.
2. 2- Porta M (ed.). A dictionary of epidemiology. Sixth edition, Oxford University Press, 2014:39-40.
3. 3- Rothman KJ, Greenland S. Basic Concepts. In: Ahrens W., Pigeot I. (eds) Handbook of Epidemiology. Springer, Berlin, Heidelberg 2014:77. Available from:https://doi.org/10.1007/978-3-540-26577-1_2
4. 4- Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.
5. 5- Rothman KJ (ed.). Causal inference. Chestnut Hill, MA:Epidemiology Resources, 1988.
6. 6- MacMahon B, Pugh TF. Epidemiology principles and methods. Boston:Little, Brown and Company, 1970.
7. 7- Evans AS. Causation and disease: The Henle-Koch postulates revisited. Yale J Biol Med. 1976;49:175-95.
8. 8- Hill AB. The environment and disease: Association or causation. Proc R Soc Med. 1965;58:295-300.

9. 9- Krieger N. Epidemiology and the web of causation:has anyone seen the spider? Soc Sci Med. 1994;39:887-903.
10. 10- Mausner JS, Bahn AK. Epidemiology—An Introductory Text. 2nd Edition, WB Saunders Company, Philadelphia, 1985.
11. 11- Rothman KJ, Greenland S. Causation and causal inference in epidemiology. Am J Public Health. 2005;95:144-50.
12. 12- Pearl J. Causal inference in the health sciences: A Conceptual Introduction. Health Services and Outcomes Research Methodology. 2001;2:189-220.
13. 13- Greenland S. Causal analysis in the Health Sciences. J Am Statist Assoc. 2000;95:286-9.
14. 14- Little RJ, Rubin DB. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Ann Rev Public Health. 2000;21:121-45.
15. 15- Zeilinger A. The message of the quantum. Nature. 2005;438,743 doi:10.1038/438743a
16. 16- Gillies D. Causality, Probability and Medicine. Routledge, New York, 2019.
17. 17- Popper KR. The Propensity Interpretation of the Calculus of Probability, and the Quantum Theory. Içinde: S. Körner (ed.) Observation and Interpretation, Proceedings of the Ninth Symposium of the Colston Research Society, University of Bristol. 1957: pp. 65-70, pp. 88-9.
18. 18- Popper KR. The Propensity Interpretation of Probability, British Journal for the Philosophy of Science. 1959;10:25-42.
19. 19- Popper KR. A World of Propensities. Bristol, UK: Thoemmes, 1990.
20. 20- Good IJ. A Theory of Causality. British Journal for the Philosophy of Science. 1959;9:307-10.
21. 21- Humphreys P. Why Propensities Cannot be Probabilities. The Philosophical Review. 1985;94:557-70.
22. 22- Pearl J. Causality. Models, Reasoning, and Inference. Cambridge University Press, 2000.
23. 23- Hesslow G. Discussion: Two Notes on the Probabilistic Approach to Causality. Philosophy of Science. 1976;43:290-2.
24. 24- Foraita R, Spallek J, Zeeb H. Directed Acyclic Graphs. İçinde: Ahrens W, Pigeot I. (eds) Handbook of Epidemiology. Springer, New York, 2014:1482-518. doi:10.1007/978-0-387-09834-065.
25. 25- Christofides N. Graph theory: an algorithmic approach. Academic Press. 1975:170-4.
26. 26- Thulasiraman K. Swamy MNS. “Acyclic Directed Graphs", Graphs: Theory and Algorithms. John Wiley and Son, 1992:118.
27. 27- Available from: https://homepages.math.uic.edu/~leon/cs-mcs401-s08/handouts/graphs-intro.pdf[cited 2021 Oct 10]
28. 28- Glen S. Acyclic Graph & Directed Acyclic Graph: Definition, Examples. From StatisticsHowTo.com: Elementary Statistics for the rest of us! [cited 2021 Oct 10] Available from: https://www.statisticshowto.com/directed-acyclic-graph/
29. 29- Pearl J. Causality: models, reasoning, and inference. Second edition, Cambridge University Press, 2009.
30. 30- Meta Science: Research as a Complex System. Chickens, eggs, and mutual causality. Available from: https://thecomplexself.wordpress.com/tag/systems/
31. 31- Butland B, Jebb S, Kopelman P, McPherson K, Thomas S, Mardell J, et al. Tackling Obesities: Future Choices – Project report. 2nd Edition, 2007. Available from: https://www.gov.uk/government/publications/reducing-obesity-future-choices