Molecular epidemiology of infectious diseases
1. PRINCIPLES OF MOLECULAR EPIDEMIOLOGY 27/09
WHAT IS MOLECULAR EPIDEMIOLOGY?
EPIDEMIOLOGY = the study of the relationships existing between diseases and factors
(environmental, behavioural, social…) prone to influence their frequency, distribution and evolution.
- Integration between molecular biology and traditional epidemiologic research.
- Characterised by richness and major challenge: molecular biologists and epidemiologists
often have a different vision of the reality, ‘myopia’ and ‘presbyopia’ respectively.
o Myopia → molecular biologist: look close at reality (DNA in a test tube)
o Presbyopia → epidemiologist: look from a higher distance at reality (broader view)
- Waiting for sufficient hybrids between these two, communication needs to be optimal
The epidemiology of an infectious disease is a dynamic feature. Travel and trade routes can cause
easy propagation of diseases over the world. Epidemiology is not static.
EPIDEMIOLOGY IS A BASIC SCIENCE OF PUBLIC HEALTH
It answers basic questions such as: what causes disease, how does disease spread, what prevents
disease and what works in controlling disease? Molecular epidemiology will use molecular tools to
answer these questions.
- Infection = all individuals in which a pathogen has installed. Will often remain asymptomatic
- Disease = in some cases of infection you will have disease. Not all infections lead to disease.
EPIDEMIOLOGY: WHAT FOR?
1. PROVIDE SCIENTIFIC BASIS TO PREVENT DISEASE & INJURY AND PROMOTE HEALTH.
A study which shows the rapid evolution of HIV in a single patient. This phylogenetic analysis is made on the sequence of the
HIV at different moments. It shows the virus is rapidly evolving.
2. DETERMINE RELATIVE IMPORTANCE TO ESTABLISH PRIORITIES FOR RESEARCH &
ACTION
Establishing priorities by risk mapping leishmaniasis cases in Iran. Different periods from 2009-2013 had places of higher risk
of leishmaniasis changing through time. A control program can target a specific region, knowing the parasite is moving.
3. IDENTIFY SECTIONS OF THE POPULATION AT GREATEST RISK TO TARGET
INTERVENTIONS.
Using molecular tools to identify the transmission chain. If a subject is infected with a certain variant, then you would like to
know where this patient got infected. You use molecular biology to identify the variants found in the environment. Also
typical in nosocomial infections to know what the source is. This allows for targeted intervention.
4. EVALUATE EFFECTIVENESS OF PROGRAMS IN IMPROVING THE HEALTH OF THE
POPULATION
Example: massive drug administration to accelerate elimination of drug resistant malaria.
, 5. STUDY NATURAL HISTORY OF DISEASE FROM PRECURSOR STATES THROUGH
CLINICAL COURSE
Study in a village in Nepal on the natural history of leishmania. They sampled humans with visceral leishmania and evaluated
where they were living. They also analysed animals living in proximity of the human subjects to analyse whether the animals
play a role as reservoir. They found many PCR positive animals, suggesting transmission between humans & animals.
6. CONDUCT SURVEILLANCE OF DISEASE AND INJURY OCCURRENCE IN POPULATIONS
One of the most important application of molecular epidemiology. This has also been demonstrated with the spread of COVID.
7. INVESTIGATE DISEASE OUTBREAKS
To determine the origin by using several tools to investigate the disease outbreak.
KEY EPIDEMIOLOGICAL CONCEPTS AND POSSIBLE BOTTLENECKS
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑤 𝑐𝑎𝑠𝑒𝑠 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑤 𝑐𝑎𝑠𝑒𝑠
Incidence rate (IR) = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑥 𝑡𝑖𝑚𝑒 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙 = 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛−𝑡𝑖𝑚𝑒
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔 ሺ𝑎𝑛𝑑 𝑛𝑒𝑤ሻ𝑐𝑎𝑠𝑒𝑠
Prevalence rate (PR) = 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 𝑟𝑖𝑠𝑘
<- no period of time in which it is measured
Graph: the red lines are subjects that are followed going through a certain infection.
- Vertical line: the prevalent cases = 10
o PR = 10 / size of population studied
o Expressed per 100.000 population
o Static concept
- Box: the incident cases = 7 (new cases arising)
o IR = 7 / size of population studied
o Expressed per year per 100.000 population
o Dynamic concept
Caution: often you only know the number of notified cases in a year. This can be underreported so
you have to be careful the incidence is always just estimated.
𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑡𝑖𝑣𝑖𝑒𝑠 ሺ𝑇𝑃ሻ 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠 ሺ𝑇𝑁ሻ
Sensitivity = Specificity =
𝐴𝑙𝑙 𝑐𝑎𝑠𝑒𝑠 𝐴𝑙𝑙 𝑛𝑜𝑛−𝑐𝑎𝑠𝑒𝑠
𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 ሺ𝑇𝑃ሻ 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠 ሺ𝑇𝑁ሻ
Positive predictive value = 𝐴𝑙𝑙 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠
Negative predictive value = 𝐴𝑙𝑙 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠
Sensitivity and specificity can be used to characterize the features of a diagnostic test. The predictive
value is the probability of those tested who are correctly classified.
Example: determine the sensitivity & specificity of a certain PCR for COVID-19. The cases are the
people who have the disease or infection, and the non-cases do not have the disease or infection.
, - Sensitivity = = 70%
- Specificity = 19..000 = 95%
- PPV = = 12.3%
- NPV = 19..060 = 99.7%
This table shows the relationship between the PPV and
prevalence. You see that at low prevalence of the disease the PPV
is 1.4%. At high prevalence the PPV is 93.3%. This concept shows
that a given test can have a rather good PPV when the prevalence
is high and when nearly everyone is sick, but once the prevalence
decreases, such as in a control program, the PPV will decrease.
Example: Chagas was highly prevalent some decades ago. Now there is a control program causing low infection
rates. This means the diagnostic tests are not good anymore in terms of PPV. Better tests need to be used now.
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 𝑥 𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒
Positive predictive value = ሺ𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 𝑥 𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒ሻ+ ሺሺ1−𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦ሻ 𝑥 ሺ1−𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒ሻሻ
When the prevalence decreases, the PPV is low. Then we can
increase the sensitivity or specificity. The higher the specificity, the
higher the PPV. However, if you increase the sensitivity this will
not have an effect on PPV. You thus need to use a test with higher
specificity if the prevalence is low!
The PPV of a particular test can also be improved by appropriate selection strategies:
1. Testing of "high risk" groups (subjects with clinical signs rather than normal subjects)
2. Use a higher cut-off with higher specificity or use a second test with a higher specificity
3. Use of multiple tests for interpretation of results.
THE IMPORTANCE OF A GOOD DESIGN OF YOUR STUDY
You can have different types of studies:
- Intervention trials (experimental): randomized controlled trial, intervention vs placebo
- Cohort studies (observational): used to study incidence, causes, prognosis. They measure
events in chronological order so they can be used to distinguish between cause and effect
- Case-control studies (observational): compare groups retrospectively. They seek to identify
possible predictors of outcome and are useful for studying rare diseases or outcomes
- Cross-sectional studies (observational): used to determine prevalence. They are relatively
quick and easy but do not permit distinction between cause and effect
SAMPLE SIZE
The sample size is often under-evaluated in molecular epidemiological studies, which can cause an
insufficient power to draw conclusions. Before the study start you need to design a sample size that
, will be needed. For example if we want to design a PCR that is positive when there is drug resistance,
and negative when there is not. If there is a marker present in 100% of the drug-resistant cases, we
would only need a sample size of 9 to get sufficient power to get validity on the marker. However, in
reality a marker of 100% is very rare. However, if the marker is present in 70% of the resistant cases,
but also present in 30% of the sensitive cases, you already need a sample size of 63 subjects.
BOTTLENECKS
CONFOUNDERS
Suppose there is a statistical relationship between ice-cream consumption and number of drowning
deaths for a given period. These two variables have a positive correlation with each other. An evaluator
might attempt to explain this correlation by inferring a causal relationship between the two variables
(either that ice-cream causes drowning, or that drowning causes ice-cream consumption). However, a
more likely explanation is that the relationship between ice-cream consumption and drowning is
spurious and that a third, confounding, variable (the season) influences both variables: during the
summer, warmer temperatures lead to increased ice-cream consumption as well as more people
swimming and thus more drowning deaths.
DESIGN YOUR STUDY WELL, AVOID CONFOUNDERS
We can control confounding by study design if we can make the exposed and unexposed groups similar
in respect to all disease determinants, through matching or randomized assignment of exposure…
CORRELATION VS. CAUSALITY
Correlation describes an association between variables: when one variable
changes, so does the other. It is a statistical indicator of the relationship
between variables. These variables change together: they covary. But this
covariation isn’t necessarily due to a direct or indirect causal link.
Causation means that changes in one variable brings about changes in the
other; there is a cause-and-effect relationship between variables. The two variables are correlated with
each other and there is also a causal link between them.
INFORMATION BIASES
