final content Flashcards
what is an infectious disease
- cases are dependent on exposure to other cases
- starts with exposure to a pathogen, then infection occurs, then disease MAY occur
infection vs disease
infection: established pathogen within the host
disease: the effects of an infection (clinical signs and symptoms)
latent vs incubation period
latent period: when host becomes infected to when they become infectious to others
incubation period: from when host becomes infected to when symptoms/clinical signs of disease show
what period is long for asymptomatic hosts
incubation period
what is a differential equation
- a function measuring the change in one variable with respect to another
- a system of differential equations is a set where each equation may or may not be dependent on other variables
a 3-state system for stages of a disease model
state 1: suceptible
state 2: infected
state 3: recovered
what does disease transmission depend on
the number/proportion of susceptible individuals AND the number/proportion of infectious individuals
generation time vs serial interval
generation time = time between infections in 1st and 2nd case
serial interval = time between symptoms of the 1st and second case
how to calculate serial interval
serial interval = latent period + (infectious period/2)
reproductive number (R) vs basic reproductive number (R0)
R = the average number of secondary infections caused by an initial infectious individual
R0 = the average number of secondary infections caused by an initial infectious individual in a completely susceptible population
basic reproductive number value interpretations
R0 > 1: epidemic
R0 = 1: endemic
R0 < 1: fadeout
how to calculate R0 for a SIR model
R0 = c x p x d
c = rate of contact
p = probability of transmutation given contact
d = duration of infectiousness
how do you calculate the critical proportion to vaccinate (Pc)
Pc = 1- (1/R0)
- as R0 increases more people must become vaccinated
how do you calculate the effective reproductive number
Re = R) x proportion susceptible
steps in building and analyzing a disease model
- define population and pathogen of interest
- construct a system of equations
- code the model and conduct numerical analysis
- assess the effect that interventions have on model outcomes
what interventions can be used to control disease
- vaccination (full immunity)
- reduce contact
- reduce probability of infection given contact
- reduce period of infectiousness
- increase rate of recovery
why might we use mathematical models for disease control in epidemiology
- ethical concerns for experimental studies
- no disease present for observational studies
- expensive and timely
- to test “what if” scenarios
when would we use an experimental study instead of an observational study
- prophylactic/preventative studies
- therapeutic studies
- management strategies
types of experimental studies
- lab based experiment: researchers create a controllable environment
- Randomized control trial: researcher creates groups in a “real-world” setting, no control over environment
pros and cons of lab-based experiments
pros: provides best control of manipulative conditions, reduces confounding, can be repeated
cons: lacks external validity, is unrealistic
pros and cons of randomized control trials
pros: best form of blinding, answers specific questions
cons: issues with ethics and costs
strongest to weakest strength evidence based research
systematic reviews > meta-analyses > blinded RTCs > cohort studies > case-control studies > case series > single case report
how do we conduct RTCs
take study population, divide into 2 treatment groups, determine who is O+ and O- from both groups
10 key aspects of conducting RTCs
- clearly defined question
- is it ethical to intervene?
- what os the target population?
- what groups are needed?
- how are the groups created?
- is there evidence of a challenge?
- what are the outcomes?
- how big of a study would you need?
- how do we avoid bias in follow-up?
- statistical analysis
negative vs positive control group
negative: given nothing or a placebo
is it better than nothing?
positive: given the best possible current treatment
is it better than the old treatment?
why are clinical trials stronger than case series
clinical trials include a comparative group
what are cross-over studies
- each individual is their own control
- good for interventions that treat symptoms
- mostly used for studying chronic diseases
the number of subjects needed in a trial increases with…
- smaller treatment effect (difference between groups)
- larger variation among individuals
- more covarieties
- more groups
- more clusters
- longer duration
types of blinding
single blinded: either subject, observer or data analyst are blinded
double blinded: 2 of the 3 are blinded
triple blinded: all 3 are blinded
pros and cons of historical controls
pros: more practical/ethical, shorter duration
cons: recall bias
what is a systematic review
a systematic approach to synthesize scientific evidence for a specific topic
what is a meta-analyses
a statistical method used to combine the results of various studies in the systematic review - can’t do it without a systematic review first
benefits of systematic reviews
- transparency
- many studies considered simultaneously
- can do meta-analyses
limitations of systematic reviews
- garbage in, garbage out
- selection bias
- heterogeneity
- publication bias and inclusion of “grey literature”
how do we measure disease frequency
counts: number of people with disease
proportions: number with disease divided by population size
rates: an expression of event occurrence in a defined population in a specified period of time (“per unit something”)
what tool do we use to measure disease prevalence (existing cases)
proportions - ranges from 0-1
- only one measurement required
how do you calculate point prevalence
with disease at a single point in time / # in population that COULD have disease at that point in time
how do you calculate period prevalence
with disease during a specific time period / # in population that COULD have disease during that time period
what is censoring
when observation is stopped for some reason, could be
- end of study
- individual left cohort
- lost to follow-up
- death
why is prevalence NOT a measure of risk
it doesn’t take into account when the disease occurred - only tells us how many individuals are affected
2 observations/tests required to measure incidence
- establishes which individuals are disease free
- identifies which ones developed disease in the observation period
what are the 2 ways to measure incidence
- rate
- risk
- the numerator is the same for both
- denominator differs
what is risk (aka cumulative incidence)
- proportion of unaffected individuals who will develop the disease of interest over a specified time period
- PROPORTION, range 0-1
how do we calculate risk
of new cases during a time period / (initial # at risk - 1/2 withdrawals during that time period)
what is rate (aka incidence density)
- measures the average speed with which newly diagnosed cases of the disease develop
- the denominator is the sum of units of time each individuals was at risk and observed
2 ways to calculate rates
numerator is number of new cases for both
1. exact denominator: know exact details for each individual
2. approximate denominator: only have summary data
calculating rate with exact denominator
of new cases in population surf specified time period / net time individuals in population ate at risk during that time
calculating rate with the approximate denominator
of new cases in population during specified time period / ( 1/2 x (initial NAR - final NAR) x internal time component)
what is an internal time component
time period in the denominator or rate calculations (e.g. person time)
interpreting incidence rate
… cases per person-year (or whatever component of time you are using)
general relationship between prevalence and incidence
prevalence is proportional to incidence x duration
what is case fatality rate
proportion of individuals with a specific disease that die as a result of that during a given time period
of deaths among cases of x / # diagnosed cases of X
what is attack rate
used in outbreak investigations
exposed people who got ill / total # exposed people
what is an outbreak
when 2 or more cases meet the case definition with a common epidemiological link and onset of symptoms within the same time period
steps involved in an outbreak investigation
- confirm the outbreak
- develop outbreak case definition
- case identification and management
- epidemiological analysis
- reporting the outbreak
- investigating potential exposures
- implementing control measures
what measures of association can we use to determine the most likely cause of an outbreak
odds ratio - generated from case-control studies
risk ratio - generated Fromm cohort studies
if OR or RR is > 1 then exposure increased the odds or risk of illness
types of epidemic curves
point source: exposure from a single event, majority of cases in one incubation period
continuous source:ongoing exposure, not confined to one point in time
propagated source: spread of pathogen from one susceptible individual to multiple
intermittent source: exposure in spurts, is ongoing but intermittent
