Disease informatics for setting up Disease definition, drawing Disease Causal Chain / Web, marking Risk Events, Backend and Frontend Events, and Health Problem Solutions
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
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.
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
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
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.
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.
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.
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.)
possibility: If some drug induces hormonal insufficiency then avoidance of the
drug could be the optimal solution.
possibility: If it were mere hormonal deficiency then the soy-based or yam-based
nutraceuticals (NT) / iodised salt etc for prevention would provide optimum
Deolankar . Risk events and disease causal chains of Acute Infectious Paediatric
Diarrhoea. BMJ.COM, 3 May 2005 [FULL TEXT]
Deolankar. Epidemiology, Risk Events and Risk Factors of Japanese Encephalitis.
BMJ.COM, 7 Apr 2005 [FULL TEXT]
Competing interests: No competing interests