Disease informatics for setting up Disease definition, drawing Disease Causal Chain / Web, marking Risk Events, Backend and Frontend Events, and Health Problem Solutions
30 May 2006
The Disease Informatics, as I would like to put forth, is the application of Information Science in defining the diseases with least error, identifying most of the targets to combat a cluster of diseases and designing a holistic solution to the problem.
Defining the diseases
The purposes of defining the diseases are to understand exactly what those are so that those are prevented or reversed. The basis of Disease Informatics is to operate on the fact that “most outcomes — whether disease or death — are caused by a chain or web consisting of many component causes”. Epidemiologists Rothman and Greenland having quoted by Bang et al (Journal of Perinatology (2005) 25, S35−S43), as the "one cause−one effect" understanding is a simplistic misbelief.
This is the baseline for this science. “Existence of chain or web consisting of many component causes” connotes lot of information and here comes the role of information scientists. Drs. Abhay and Rani Bang and their colleagues have successfully provided solutions to several health problems by performing on this fact. On the contrary, particularly in case of communicable diseases, the conventional approaches to have the definition of disease in 3 phases, i.e. suspected, probable and confirmed and arriving at a single cause have yet to generate feasible solutions for most of the real life health problem. The sole exercise is done to associate a pathogen with the disease and then declaring it as the cause. Considering simultaneously the non-communicable components of the disease could really change this picture and help in designing the health strategy. The same approach could be fruitfully used if role of multiple morbidities as pointed out by Drs. Bangs and their colleagues in the outcome is precisely recognized.
Quite a low incidence rate of a particular disease is result of the big denominator. The specific component that is considered as a causative agent to which population exposed is not enough to explain the total disease or most part of the disease. The disease definitions require intersection of some factors as denominator to make the definition complete and specific. Therefore the software should also help in generating array of intersections of risk factors.
Which intersection of risk factors could lead to the specific disease definition? This is the challenge in Spatial Epidemiology and for the Disease Informatics. Hence a team effort to define complex diseases thereby identifying all the targets to combat disease and design a holistic solution is absolutely necessary. The disease as it is understood today has shared + variable features. The universally shared features as against spatiality are generally considered for diseases definition. However, the most optimum solutions are spatiality dependent, shared by local people than universal.
Identifying the targets
The Disease Causal Chain (DiCC) could vary from patient to patient and diseases do occur as continuum. This implies that number of diseases by conventional approach is a small figure. While number of diseases by the DiCC approach would be a big number. The chain of events, DiCC, that could be handled by advanced techniques in information technology only.
The DiCC is made up of “events” and “risk factors that drive the disease process from backend event to the frontend event”. Each event has scope to branch out to give rise to frontend events. Similarly, event may happen as a result of more than one backend events happening simultaneously (multiple morbidities). Hence DiCC is web rather than tree. It would be easy to draw DiCC with the availability of databases of events and their connections.
The software should be derived to set aside the combination terms (anatomical + physiopathological) from MeSH database of NCBI. This will provide the database for events occurring in the DiCC. Some of these events could be classical risk factors for the disease.
Designing a holistic solution
Burden of several infectious diseases rely on certain backend events of DiCC. Frontend event measures are like pruning the branches of disease tree while backend event measures uproot the tree. The DiCC’s should be studied as a spatial epidemiological problem for all the diseases together present in the locality.
DiCC displays several targets and not just the one. Moreover, we know what occurs at the backend and what occurs at the frontend. Failure of herbals or new chemical entities to show antiviral activity in traditional or HTS assays does not nullify the traditionally established utility of principle under investigation in preventing viral disease therefore the ability of remedy to alter DiCC should be investigated. Not having done this, patients are deprived of several nutraceuticals and functional foods or lifestyle modalities capable of preventing or reversing the disease. They would be subjected to consuming drugs having tremendous side effects.
Probiotics reversing viral diarrhoea or hormones reversing viral encephalitis are examples of missing targets to combat complex viral diseases. Dysbiosis and endocrine anomaly are backend events of viral diarrhoea and encephalitis respectively. Interventions--simpler or complicated— to amend the DiCC so that the viral disease event is bypassed or does not occur even after exposure to the virus are the solutions to the disease.
1st possibility: If endocrine anomaly in the example being discussed were congenital then tackling the virus would prevent the encephalitis associated with the virus. However, several viruses are associated with encephalitis and several vaccines would require for imparting assured protection against encephalitis. (Else, gene therapy for anomaly rectification to the individual concerned, as a single intervention should be made feasible.)
2nd possibility: If some drug induces hormonal insufficiency then avoidance of the drug could be the optimal solution.
3rd possibility: If it were mere hormonal deficiency then the soy-based or yam-based nutraceuticals (NT) / iodised salt etc for prevention would provide optimum solution.
Rajendra P Deolankar . Risk events and disease causal chains of Acute Infectious Paediatric Diarrhoea. BMJ.COM, 3 May 2005 [FULL TEXT]
Rajendra P Deolankar. Epidemiology, Risk Events and Risk Factors of Japanese Encephalitis. BMJ.COM, 7 Apr 2005 [FULL TEXT]
Competing interests: None declared
Competing interests: None declared
National Institute of Virology, 20 A Dr. Ambedkar Road, Pune 411 001. INDIA
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