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model: tool to understand infectious disease (ID) workings

reasons for modeling

explore course of ID

mechanisms that influence ID spread

understand transmission and control

predict impact of intervention

SIR model: Susceptible --> Infected --> Recovered

assumes: not birth/death, lifelong immunity, no age, no seasons

variations exist i.e. SI, SIS, SIRS

infection peak: people infected equals people recovered

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R0: number of secondary infections per primary case

in fully susceptible population

R0 > 1 --> outbreak

R0 < 1 --> no outbreak

R0 = secondary cases ÷ primary cases

calculate secondary cases (cases caused by another infection)

calculate primary cases (cases who caused/could have caused other infections)

R0 = β x c x D

β = p(transmission) per contact

physical distancing, hand hygiene

c = number of contacts per time (unit)

working from home, stopping large events

D = duration of infectiousness per time (unit)

testing cases, quarantining contacts

no units: shows amount of people infected

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doubling time: time required for number of new cases to double

generation time: average time between two consecutive "generations" of infections

number of new infectious caused by a case

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minimum effect size an intervention must achieve to have a meaningful impact on the outcome

(1 - p) x R0 ≤ 1 OR

1 - (1 ÷ R0) ≤ p

If R0 = 2.5 , to what extent do the variables need to be reduced so the epidemic can no longer occur in a fully susceptible population?

1 - (1 ÷ R0) =

1 - (1 ÷ 2.5) =

0.6 = intervention must reduce variables by at least 60%

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proportion of individual in a population who need to be immune to an ID to prevent sustained transmission of the ID in the population

p represents the critical vaccination level

If vaccination is 100% efficacious, at least 60% of the population must be vaccinated

If 100% of the population is vaccinated, the vaccine must be at least 60% efficacious

herd immunity: large portion of community becomes immune to ID

protection of those non-immune

vaccination rate is higher than recommended - why?

not 100% efficacious

protection is temporary

non-vaccinated clustered

individual biology

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SIR model can be specified using R0

I = number of infected people

N = total population size

δ or recovery rate = 1 ÷ D

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Rt: number of secondary infections per primary case when not all population is susceptible due to

immunity post recovery

immunity from control measures

Rt < 1 --> effective control

during an epidemic, Rt declines over time

Rt < 1 --> pre-peak, expanding cases

Rt = 0 --> peak, every recovery = an infection

Rt > 1 --> post-peak, declining cases

R0: basic reproductive number

emergence of infections

epidemic prevention

Rt: effective reproductive number

insights into current state of epidemic

effect of control measures

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Challenges of infectious disease control

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Challenges of infectious disease control

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1. difficult/impossible to eliminate ID

1. asymptomatic transmission

2. high global mobility

3. non-human reservoirs

2. economic and societal damage

1. interventions

3. societal response

1. uptake of interventions

4. ethical dilemmas

1. costs of protecting

2. who to protect

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prioritizing relevant health intervention from importance

different diseases and injuries -->

but limited resources

incidence. prevalence, mortality --> not enough for prioritization

DALY: disability adjusted life year (since 1993)

allows measuring of relative importance

morbidity and mortality in one index

time (metric), healthy time (unit)

0 (full health) --> 1 (death)

summarizes info from population health into a single number

variations exist i.e. QALY, HALE

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DALY = YLL (life lost) + YLD (life lost to disability)

YLL = Σ (d x e)

d = sum of all fatal cases

e = remaining life expectancy (at age of death)

YLD = Σ(n x t x dw)

n = number of cases

t = duration of disease

dw = disability weight

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GBD to find what prevent long, healthy life

global, national, regional levels

GBD: measure of disability and disease from different causes

annual updates from 1990-2019

measures

death, incidence, prevalence

YLL, YLD, DALY

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classification of injuries

force of nature, war, legal intervention

unintentional

traffic

non-traffic

intentional

self harm

interpersonal violence

burden of injury shows how to assess proper measures

important cause for morbidity and mortality

patterns across cause, age, sex, region, time

most common for NL: falls (usually elderly)

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risk factor: attribute or exposure associated with disease occurrence

modifiable factors: diet, health behavior, smoking

non-modifiable factors: genetics

causation: needs epidemiological evidence, not just observation

how do we determine causes of a disease?

how important is a particular cause for a disease

what study designs are needed to understand this?

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requirements for causal inference

temporality - exposure before disease

plausibility - biological plausibility

consistency - replication

strength of association - large association is better

dose-response relationship - more exposure causes more disease

reversibility - removing risk factor reduces risk

elements for explanatory research question

what do we want to know from the causal relationship?

what is the exposure

what is the outcome of interest

during what time period?

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finds incidence

C1 (people without disease) --> C2 (did people develop disease?)

analytical study to identify potential causes

risk factor: risk is higher for those exposed

protective factor: risk is higher for those non-exposed

risk difference = I(exposed) - I(non exposed)

relative risk/risk ratio = I(exposed) ÷ I(non exposed)

hazard ratio: is your risk of dying within a time higher if you are exposed

pros

temporality

see dynamic pattern of exposure and disease outcome

cons

time consuming

expensive

needs large population

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finds prevalence (in exposed or unexposed)

prevalence = P(exposed) ÷ P(unexposed)

pros

quick

finds association

fixed and nonmodifiable factors

cons

temporality

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finds odds (ratio)

select cases and controls --> ask about past exposure

odds: chance of outcome occurring vs. outcome not occurring

odd of case being exposed or not exposed

odds of control being exposed or not exposed

odds ratio = O(exposed) ÷ O(nonexposed)

pros

quick

rare diseases

long induction period

cons

recall bias

selection bias

does not show incidence or prevalence

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finds correlation

data based on existing population level data

unit of analysis: groups

pros

easy

used in large populations

non-individual variables (air pollution, weather)

cons

ecological fallacy: inferences made about individuals from population data

confounding variables

weak consistency/replication

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how much can a risk factor be attributed to a disease?

attributable fraction (AF): proportion of rate of disease in those exposed (which is attributable to the exposure itself)

same formula as risk difference

AF = (incidence in exposed - incidence in nonexposed) ÷ incidence in exposed

population attributable fraction (PAF): proportion of rate of disease among the whole population (which is attributable to the exposure)

PAF = (incidence in total pop. - incidence in nonexposed) ÷ incidence in total pop.

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RR (relative risk) = (a ÷ (a + b)) ÷ (c ÷ (c + d))

cohort studies

cross sectional studies (prevalence ratio)

RD (risk difference) = (a ÷ (a + b)) - (c ÷ (c + d))

cohort studies

OR (odds ratio) = (a x d) ÷ (b x c)

cohort studies

cross sectional studies

case control studies

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AF = (incidence in exposed - incidence in nonexposed) ÷ incidence in exposed

patient level i.e. physician

PAF = (incidence in total pop. - incidence in nonexposed) ÷ incidence in total pop.

population level i.e. public and global health

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population strategy

risk factors with modest increased risk, common in population

more health benefits to focus on population instead of those at risk

individual strategy

if high risk can be easy identified, an individual based strategy can be useful

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prevention plan cycles: for the development, selection or adaptation of an intervention to prevent or control health problems

prevention: actions that prevent disease occurrence or limit the negative consequences of a disease

control: actions to prevent disease transmission using

1. mathematical definition

2. the epidemiological triad of an infectious disease

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strategy

1. health promotion (not disease-specific) i.e. healthy lifestyles, healthy education

2. health protection (not disease-specific) i.e. protection from risk, policies, intervention

3. disease prevention (disease-specific) i.e. suicide prevention day

population approach depends on population attributable fraction

high risk approach--> protect risk groups

general approach --> protect whole population

opportunistic approach --> reach out to individuals to find risks

collective approach --> reach out to a collected to find risks

stage of disease

primordial/precautionary: no disease or direct risk factor

target: system

modifiable risk factor

primary: normal, no disease

target: risk factor

modifiable risk factor

secondary/pre-clinical phase: biological onset --> first symptoms

target: disease

for this, a long preclinical phase to allow detection and treatment

tertiary/clinical phase: after symptoms

target: disease consequences

if disease cannot be cured anymore

healthcare

national government, municipalities covers:

universal healthcare (everyone)

selective healthcare (at risk groups i.e. inactive)

health insurance (basic, supplement) covers:

indicated (people with early symptoms i.e. pre-diabetes)

healthcare related (chronically ill i.e. diabetes)

NL: Public Health Act, Health Insurance Act are government based

population approach

focus on primary prevention

Environmental Act: promotion of healthy environments

i.e. nature, water, housing, infrastructure, pH of water

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1. assess burden

measure the health status of a population: what is the effect of a disease on society?

African American women (AAW) are more likely to die of breast cancer than caucasian women, but they have a lower morbidity.

2. identify causes

develop an aim: what are the causes and reasons of the disease?

AAW are less likely to participate in adequate screening and have more no shows. they also have underlying barriers: fear (of partner abandonment, outcome), belief (faith in god), inability (competing demands).

3. measure effectiveness (see next)

4. determine efficiency

prevention plans vs. change in health status

results achieved vs. resources expended

based on existing data vs. based on new data

check confounding variables

Are there existing programs to affect this aim? i.e. phone calls for reminder for screening, questions about important barriers.

5. implement intervention

use of intervention on a large scale

targets for health promotion

need personnel, equipment

6. monitor and feedback

monitor diagnosis levels, mortality, change in burden

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screening: strategy used in a population to identify an unrecognized disease in individuals without symptoms

pre-symptomatic, asymptomatic, unrecognizable symptomatic disease

aim: improve life expectancy/quality of life in population

early detection, diagnosis, treatment

screening has high specificity (limits harms on health)

population level

asymptomatic

clinical care has high sensitivity (detects a disease)

individual level

symptomatic

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Wilson and Jungner, 1968 --> updated in 2008

1. health problem (condition should be important health problem)

2. accepted treatment (there should be accepted treatment for the cases)

3. diagnosis (facilities for diagnosis and treatment should be available)

4. pre-clinical (there should be recognizable latent/early symptomatic stage)

5. testing (there should be suitable test or examination)

6. acceptability (test should be acceptable to the population)

7. history (natural history of disease should be adequately understood)

8. patients (there should be an agreed policy on whom to treat as patients)

9. costs (the costs should be economically balanced)

10. continuity (case-finding should be a continuing process)

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evidence for effectiveness

lead time bias: screening might give give the diagnosis earlier but does not change treatment and QOL/death

length time bias: disease with long latency has more chance of being screened

i.e. cancers that grow slow will be detected better

proof for effectiveness: randomized controlled trials

outcome needs to be disease specific mortality

weighing benefits vs harms

physical, psychological, social harm

cost effectiveness

scarce resources: we need efficient interventions for health maximization

costs

inviting individuals

screening hosts

maintenance of programs

long term care costs for positive cases (TP and FP)

use of QALY's or the cost of QALY (total screening cost ÷ QALYs gained)

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2 types

1. organized/population/mass screening

high investments (inviting, registration, quality control)

high quality (high coverage, less FP, use of guidelines)

2. opportunistic screening

case finding

screening test: diagnostic test applied in screening context

100% sensitivity and specificity --> perfect

useless if p(positive test) is same for cases and non-cases

requirements: safe, affordable, acceptable

sensitivity: how good is a test at identifying people with the disease correctly?

finds TP

ability of test to detect disease

independent of disease prevalence

specificity: how good is a test at identifying people without disease correctly?

finds TN

ability of test to distinguish non-disease people

more important for rare diseases

independent of disease prevalence

positive predictive value: probability a positive test result is correct

dependent on disease prevalence

negative predictive value: probability a negative test result is correct

dependent on disease prevalence

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informativeness of study

prior/posterior knowledge (consistency) + new knowledge (accuracy)

to find new level of knowledge

hypothesis free studies: low prior knowledge, low p-values

i.e. genome wide association studies, proteomics

equipose: probability a new treatment is more effective than control should be 50%

otherwise it is unethical

not everything can have a clinical trial

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accuracy or validity: correctness of the study

more important than precision

precision: reproducibility of study

consistency, reliability

external validity

generalizability (are these results applicable in other populations?)

internal validity

selection bias: sample is not representative of whole population

information bias: measurement error

non differential (random): error in exposure, outcome, confounder

differential (nonrandom): error in outcome determinant

differential (misclassification): recall bias, non-blinding

change in effect (dilution, overestimation) --> use blinding

confounding bias: shared cause between exposure and outcome

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selection bias --> use of clinical criteria and randomization

information bias --> blinding (single blind, double blind, triple blind)

confounding bias --> randomization, stratification, multivariable modeling