Transcript R. P. Deolankar

R. P. Deolankar
Welcome to the series of lectures on
Disease Informatics
• Disappointment by research bodies to solve the real
disease problem is because of not having perception
of disease complexities
• Diseases have been defined in a simple manner leaving
several targets for combating disease unattended
• IT applications simplify complexities and could provide
better definition of diseases
• Informatics professionals need to be facilitated for
development of software for disease study using standard
guidelines
“Clean bowled” is not the
complete cricket
• Clean bowled: Current – one cause one effect -- disease
definition permits dismissal of the batsman (say viral disease)
only by the ball bowled by the bowler hitting the wicket (say
virus; the component cause of the disease that is
considered as the complete cause)
• Out: The treatments given by family physician based on his
clinical diagnosis also permit the dismissal of batsman if the
ball hit by the batsman is caught, by lbw, run out, stumped etc
(described as targeting super-component in later slides)
• Logic: To win the game team must be balanced. Team of only
bowlers, only batsman, only wicket keepers, only captains or
only umpires is a joke
Prerequisite for this lecture
Lecture no. 25371 DIG for Disease Informatics Group. Part I
Lecture no. 25381 DIG for Disease Informatics Group. Part II
Lecture no. 28921 Disease Informatics: Host factors simplified
Lecture no. 30331 Disease Informatics: Phytates driving from
the back-end to Influenza, Encephalitis, Hepatitis, Anemia at the
front-end.
• Lecture no. 31981 Disease Informatics: ICD-11 at the
doorstep
• Lecture no. 34011 Disease Informatics: Terms and Jargon to
begin with
• Lecture no. 34141: Disease Informatics: Brush up the terms
describing techniques and resources
•
•
•
•
Importance of “Family Physician”
Draws a mental picture of Disease Causal Chain (DiCC)
of a patient by:
• Recording clinical history, performing clinical check up
and treating individual
• Predicting disease that could occur in future and planning
prevention of further disease or complication
• Keeping confidential the diagnostic information of a
particular patient
Background begins
Public Health professional
 Focuses on community health protection and
improvement
 Has to define disease broadly and openly
 Disease definitions should fit to population and
environment rather than individual
 Subject matter of Epidemiology is covered under Public
Health
Epidemiology and public
health
• Epidemiologist is an investigator
• Investigates Disease Causal Chains of patients drawn by
family physicians (clinicians) to arrive at accord and
discord of the disease continuum
• Studies associations and establishes relationship of risk
factors of diseases
• On the basis, tries to find out component causes and
sufficient causes of the diseases
Genuine epidemiologist interacts with
Family physicians (clinicians)
• Genuine Epidemiologist keeps rapport with Clinicians to
share inferences drawn from cases and try to understand
chain of events of diseases
• Epidemiologist share risk factor information and
elaborates which factors drive the disease from backend
to frontend
• This interaction permits drawing of hypothetical cause
and effect diagrams to be verified by performing
experiments
Disease informatics
• Disease Informatics is the application of Information
Science in defining the diseases with least error,
identifying most of the targets to combat a cluster of
diseases (Disease Causal Chain) and designing a holistic
solution (Health strategy) to the problem.
• Researchers, Health workers, Clinicians, Epidemiologists
and Public Health personnel benefit from and contribute
to the Disease Informatics
The first logical step of disease investigation;
know the remarkable events
• Standard terminology can be used by Setting aside the
combination terms (anatomical + physiopathological)
from MeSH database of NCBI to provide the database for
events occurring in the Disease Causal Chains
• Identify most of the targets to combat a cluster of
diseases in a command area
• Achieve this by horizontally studying the clinical histories
of sample cases or by identifying the clinically remarkable
events in a cross section of the community
The second step; sequence
the events
• It needs to be known the sequence in which events occur
• It is also of the interest to know which factors drive the
disease processes from the backend events to the
frontend events
• Make cause and effect diagrams (fish bone)
• The causal factor components could be pooled in a pie
diagram to explain various sufficient causes of the
diseases
What is a case? (In simple words)
 Case is a person, case represents some characteristics
useful in the investigation
 Normal case = Normal person
 Disease case = A person showing features of a disease
 Non-case = A person not showing features of a disease
Case for a Public health
study
 Study subjects in a Public Health Study are cases
 Might comprises at-least two types of cases; Normal
cases + Disease cases
 The normal case is likely to be defined by the investigator
 The disease case (deviation from normal) goes naturally
Inclusion and exclusion
criteria
 Which cases are to be included in the study?
There are criteria that must be met by the cases for
inclusion in the study
 Which case is to be excluded?
The cases that would be considered as non-cases by a
certain criteria
Case definition
 Case definition of a disease is a description of diagnostic
criteria of the disease
Sometimes required in public health study
 Disease in an individual case is:
Disease defined by the case definition + “something
more”
Disease continuum
• How to infer the disease if certain persons show
immunodeficiency syndrome like AIDS without being HIV+ve?
• Generally speaking, Continuum = “case definition” +
something more. Continuum is a whole; covers cases
sometimes not conforming to the standard case definition.
• Let us call this “set of something more” as a supercomponent X (that covers component causes of the disease
not covered in the case definition)
• Components within super-component-X might vary from an
individual to individual
Solution to the disease through
Super-component X
• It is thought that chicken soup has no antiviral factor but
has natural healing powers for the common cold!!! Does
it modulate super-component-X
• Several nutraceuticals and functional foods work in this
manner and are broad based treatments
• Disease definition for a disease of an Individual ≠ Disease
definition prepared for public health purpose
Ayurveda, Siddha, Unani, Yoga and
super-component-X
• The Ancient Indian Medicine provides pre-seasonal
treatments (shodhan) to uproot seasonal infections and
diseases rather than performing pruning operation on
several diseases at the front end during season
• Unlike antiviral Oseltamivir, age-old medicines (like
Tribhuvan Kirti) tackling non-viral component causes
provide relief in several patients having Flu and Cold
• Daily routine (Dinacharya) and seasonal lifestyle
(Ritucharya) recommended in old days to prevent diseases
could be redefined to suit modern life
T1 ≠ T2 and also CD1 ≠ CD2
 Patient (P1) reports relief from a viral disease (D1;
laboratory diagnosis provided by the super-specialist
virologist) through a treatment (T1) given by family
physician of P1 on the basis of the clinical diagnosis
(CD1);
 Patient (P2) exhibiting the disease D1 given T1 by the
associate physician of the virologist fails to respond
 P2 then also finds relief by another treatment (T2) given
by his family physician on the basis of his clinical diagnosis
(CD2); why?
Treatment of an individual depends on
more elaborate definition of the disease
• Treatment of viral encephalitis using hormones in a case
when hormone deficiency is not part of the case
definition
• Treatment of viral diarrhea using enkephalinase inhibitor
in a case when enkephalin deficiency is not part of the
case definition
• Treatment of constipation using prebiotics in a case when
dysbiosis is not part of the case definition
Grades of disease definition
 Usually case definition describes what is normal,
suspected, strongly suspected, probable and laboratory
confirmed case
 This description vary from study to study by having or
not having certain component causes and also description
of the severity of the disease
Effect modification
 The odds ratio between cigarette smoking and lung
cancer may be smaller among individuals who consume
large quantities of beta carotene in their food when
compared to the analogous odds ratio among persons
who consume little or no beta carotene in their food; this
modification could be in an additive manner
Multiplicative interaction
• Poverty (represented by under-nutrition, unsafe water
and sanitation, and use of solid fuels are more common
among poor rural households in developing countries)
might interact with infectious etiology in a multiplicative
manner; represents statistical interaction
• Mortality attributed to the rotavirus gastroenteritis is
largely seen in developing countries rather than
developed countries
• Under-nutrition is the single leading global cause of health
loss
Complex of risk factors
 Zinc deficiency affects mortality from diarrhea directly
 It also affects mortality by reducing growth
 It may also be correlated with underweight, other
micronutrient deficiencies, and unsafe water and
sanitation
 This might be a combination of effect modification
(additive effects) as well as statistical interaction
(multiplicative)
By and large effect modification and statistical
interaction are used synonymously
What is burden?
• Burden is load or taxing on individual or family or society
•
•
•
•
or Nation or the globe
It could be cultural, chemical, pathological, economic,
social or socioeconomic
Disease burden is judged against disease events
Disease burden could be due to factors driving from
backend to frontend event of a Disease Causal Chain
Disease investigator needs to understand variety of
burdens
The subject begins
Utility of estimating Disease
Burden
 Important input to health decision-making and planning
processes
 Provides a framework for integrating, validating, analyzing
and disseminating information needed to assess the
comparative importance of diseases, injuries and risk
factors in causing premature death, loss of health and
disability in different populations
Body burden
 Body burden is the load of foreign chemicals in the body
 Most of the chemicals could be toxic
 Some of these chemicals could alter the functions of
genes
 Some of these could disrupt endocrine system
Burden of disease = Measurement of load or
taxing due to disease in a population
Prof. Christopher J. L. Murray
Prof. Alan D Lopez
Dr. Colin D. Mathers
Prof. Dean T. Jamison
Dr. Majid Ezzati
Burden of disease
 Unit of measurement for Burden of disease = DALY
DALY means Disability-Adjusted Live Year
 Unit of measurement of benefits from intervention =
QALY
QALY means Quality-Adjusted Life years
Disease
 Health is compromised due to diseases
 Diseases lead to death and / or disability
 Quality and quantity of life is reduced due to disease
 Mortality + Disability is proportional to the quantity of
disease
Disability
Disability
= Shortfall in an ideal health status
= an ideal health status – actual health status
DALY
DALY
= Life lost due to premature mortality
+ Years of life lost due to time lived in states of less than full
health
One DALY = One lost year of "healthy" life
DALYs across the population
 Sum of these DALYs across the population = Burden of
disease
 Top health = Least burden of disease
 Least burden of disease → living to an advanced age, free
of disease and disability
YLL
Years of Life Lost due to premature mortality in the
population
YLL = N * L
Where:
YLL= Years of Life Lost due to premature mortality in the
population
N = number of deaths
L = standard life expectancy at age of death in years
Severity of the disease
Severity of the disease is to be scaled
Scale for weight factor for severity of disease is
From 0 to 1
 0 implies perfect health
 1 implies death
Disease definition
Disease is usually defined by
case definition that depends on
 Causation
 The disability weight
 The population incidence and
 Prevalence
YLD
The Years Lost due to Disability (YLD)
YLD = I * DW * L
where:
I = number of incident cases
DW = disability weight
L = average duration of the case until remission or death
(years)
Disease mechanism
 YLD is estimated by cause
 For a particular disease there are several disease
mechanisms
 Each mechanism is composed of several component
causes
 There could be several case definitions for a particular
disease
DALY = YLL + YLD
 DALY’s are calculated for disease or health condition
 It is sum of
Years of Life Lost (YLL) due to premature mortality in the
population and
The Years Lost due to Disability (YLD) for incident cases
of the health condition
 DALY = YLL + YLD
Disease event
 Usually it is described by signs and symptoms
 Pathophysiological changes at certain anatomical location
 Root causes are generally categorized as genetic,
environmental or etiological
Clean hands save lives
• You may not find Panacea but you could probably find
intervention to prevent several diseases
• Hand-washing has been shown to cut the number of child
deaths from diarrhea (the second leading cause of child
deaths) by almost half and from pneumonia (the leading
cause of child deaths) by one-quarter
• Reference: Global Hand-washing Day: 15 October,
Planner’s Guide
• Conclusion: Backend measures uproot the disease while
front end measures prune the disease
Interventions against
diarrheal disease
•
•
•
•
•
Cost in proportion to hygiene promotion
Cholera immunizations 1655
Rotavirus immunizations 1627
Measles immunization 804
Oral rehydration therapy 450
Hygiene promotion
(including hand washing) 1
Reference: Global Handwashing Day, 15 October,
Planner’s Guide
Economic development
Hygiene promotion α economic development
Lower infant mortality rates α higher economic
growth
WHO estimates that a 10 year increase in average life
expectancy at birth translates into a rise of 0.3 – 0.4% in
economic growth per year
Sanitation and hygiene, and
diarrhea vaccine
• Good sanitation and hygiene prevents invasion of several
“disease causing microorganisms”
• Monovalent vaccine causes selective invasion to prevent
multiplication of a single serotype / species of
microorganism in a responsive patient. The commercial
life of vaccine may last till the microorganism mutates to
gain resistance to vaccine
• Hence, backend measures uproot the disease while front
end measures prune the disease
Transmission of the disease (!)
 Whatever is transmitted is not the disease but the
component cause of the disease in a predisposed patient
 The transmission results in specific immunity in resistant
individual and subclinical or clinical infection in a
susceptible case
Susceptibility
 The susceptibility to the disease is due to the host and/or
environmental factors
 Non-response to vaccine could also be due to the host
and/or environmental factors
 The policy of research for so called communicable
diseases should be primarily based on creation of healthy
“environment” or healthy “host X environment”
interaction
Vaccine policy
 Hence “vaccine” in general is not a substitute to creating
healthy “environment” or healthy “host X environment”
interaction; it is complimentary
 Poor vaccine policy could be the result of lack of
application of disease informatics
Inductors of probiosis provide
“environmental vaccines”
• Could reduce hostility of environment (microecology)
• The gut, vaginal or body environment could be altered by
induction of probiosis
• Evading the factors that cause dysbiosis could be an
inexpensive method in bringing down disease burden
• Should it be part of primary research policy for research?
Oral Polio Vaccine goes oral
to environmental
• Prepared using a live, attenuated virus, used during pulse
polio campaign
• Excreted vaccine virus is expected to spread through
water
• Vaccinee potentially precludes transmission of the wild
poliovirus to other hosts
• Could an arboviral vaccine virus be disseminated through
mosquitoes for prevention of arboviral diseases?
Thank you