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7/28/2019 08 Pharmacoepidemiology
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NG ICRI Pharmacoepidemiology
Pharmacoepidemiology
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Outline of presentation
Definitions
The importance of pharmacoepidemiology
Studies on drug use
Studies on drug effects
Signal generation
Risk quantification
Hypothesis testing
Problem solving
Applications of pharmacoepidemiology
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Definitions
Epidemiology-Study of distribution and
determinants of disease in populations
Clinical epidemiology:How to critically
evaluate medical literature and how to apply
principles of epidemiology to clinicalmedicine
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Pharmacoepidemiology: Definition - 1
Pharmacoepidemiology is the study of
Use of drugsEffect of drugs
in large numbers of subjects.
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Pharmacoepidemiology Definition2
Application of the principles of epidemiology
to drug use and drug effect in large numbers
patients
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Evolution of Pharmacoepidemiology as a science
1961Thalidomide disaster
Case reports of limb malformations in offspring of women.
Treated with thalidomide
Development of systems for Adverse Drug Reactions Monitoring.
Development of the science of Pharmacoepidemiology.
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The importance of pharmacoepidemiology- the
problem of ADRs
Account for 5% of all hospital admissions.
Occur in 10-20% of hospital inpatients.
Cause death in 0.1% of medical and 0.01% of surgical inpatients.
Adversely effect patients quality of life.
Cause patients to lose confidence in their doctors.
Increase costs of patients care.
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The importance of pharmacoepidemiology- the
inadequacies of clinical trials
For the investigation of certain drug events,
models are not possible e.g. pregnancy.
RCTs are often inadequate to answer questions
on safety.
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Aims of Pharmacoepidemiological studies
Signal generation
Risk quantification
Hypothesis testing
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Studies on drug use
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Drug utilization studies
WHO definition: The marketing, distribution, prescriptionand use of drugs in a society with the resultant medical,
social and economic consequences
Quantitative or Qualitative (DUR or drug utilization
reviews)
DURs focus on specific drugs or class of drugs
(4th generation cephalosporins, aminoglycosides)
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The concepts of DDD and PDD
The defined daily dose (DDD) is the estimated
average maintenance dose per day of a drug when
used for its major indication (e.g aspirin)
Expressed as DDDs/1000 population
In hospitals, DDDs/100 bed days, adjusted for
occupancy
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Prescribed daily dose -PDD
The prescribed daily dose or PDD is the average daily
dose of a drug that has actually been prescribed
Calculated from a representative sample of prescriptions
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The need for drug utilization studies
Indication of pattern of use of drugs
Early signals of irrational use of drugs
Allows comparisons between regions, countries
Interventions to improve drugs use
Continuous quality improvement
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Study of drug effects
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Aims of Pharmacoepidemiological studies?
Signal generation
Risk quantification
Hypothesis testing
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What is a signal?
Reported information on a possible causal relationshipbetween an adverse event and a drug, the relationship
being unknown or incompletely documented previously.
Usually more than one report is required to generate a
signal depending upon the seriousness of the event and
quality of the information.
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Rawlins and Thompson classification -1991
Type A Predictable (Augmented)
Dose-dependent
Related to the drugs
pharmacological properties.
Diarrhea with antibiotic
use.
Type B Bizarre, unpredictable.
Dose-independent(idiosyncratic).
Unrelated
Hypersensitivity withpenicillin.
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Methods of signal generation
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Spontaneous reporting - 1
A system by which practicing clinicians are encouraged to
report any and all adverse events with a drug
Reports complied at the National Centre
Reports from National Centers are then sent to the
Uppsala Monitoring Centre.
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Spontaneous reporting:- Advantages
Early warning signals
Relatively inexpensive
Do not interfere with clinical practice
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Spontaneous reporting: Disadvantages
Information inadequate, incomplete, notverifiable
Cause- effect difficult to establish
Voluntary, thus under reporting
Incidence and prevalence difficult to calculate
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Prescription Event monitoring - 1
Used for both signal generation and risk quantification
Used to study large cohorts of drugs
500018,000 prescriptions from prescribers studied
Information:
All adverse events
Death from any cause
Hospitalization Fetal abnormalities
Changes in laboratory values
Advantages: Calculation of incidence
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Prescription event monitoringwhen?
New chemical entity
Predicted widespread use
Identified but unquantified risks
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Post-marketing surveillance
Carried out by the pharmaceutical industry
A single cohort of 5000-10,000 patients studied
Follow up- months/years
Data submitted to regulatory authorities
d li k
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Record linkage Monitoring drug use and effect via database research
Databases provide accessible information on thousands of patients
Each patient in the database has a unique identifier
Examples of information- hospitalization, infections developed, death,birth defects, lab investigations, physician services
Databases can also be linked
Disadvantages: Accuracy of data, missing data, lack of data
Advantages: Speed
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Methods of
Risk Quantification
&
Hypothesis testing
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What is risk?
The probability of developing an outcome,regardless of severity.
Wh i h h i ?
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What is a hypothesis ?
A hypothesis is a supposition based on observation or reflection.
Cigarette smoking causes lung cancer.
Use of O.C pills causes hypertension and thromboembolic
phenomenon.
Epidemiological studies allow you to accept or reject a hypothesis.
Wh i k t k ti ?
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Why measure risk postmarketing?
Risk quantification often requires large sample sizes
Rule of 3
An AE of 1/3000 will require 9000 subjects to be
studied
Pre marketing studies usually pick up Type A adverse
events (dose dependent)
i k
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Risk versus exposure
Single group risk [ cases/ total exposure],
does not account for baseline risk.
Risk is always calculated against an another
group- unexposed or an experimental
exposure
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Classification of risk - CIOMS
Very common 1 in 10 exposures Common < 1 in 10 1 in 100 exposures Uncommon < 1 in 100 1 in 1000 exposures Rare < 1: 1000 1:10,000 exposures Very rare > 1 in 10,000 exposures
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Measurement of risk
Absolute risk (AR)
Absolute risk reduction (ARR)
Relative risk (RR)
Relative risk reduction (RRR)
Odds ratio (OR)
Number needed to treat (NNT)
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Problem - 1
In a randomized trial, investigators
compared mortality rates in patients with
bleeding oesophageal varices treated eitherby endoscopic ligation or endoscopic
sclerotherapy. After a mean follow up of 10
months, 18/64 pts treated with ligation died,29/65 pts treated with sclerotherapy died.
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(1) Absolute risk (AR)
Simplest measure of association
Absolute risk of dying in the ligation group is18/64 or 28%
Absolute risk of dying in the sclerotherapy groupis 29/65 or 45%
f i k
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Measurement of risk
Absolute risk (AR)
Absolute risk reduction (ARR)
Relative risk (RR)
Relative risk reduction (RRR)
Odds ratio (OR)
Number needed to treat (NNT)
(2) Ab l t i k d ti (ARR)
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(2) Absolute risk reduction (ARR)
Establishes a relation between the two absolute risks
Also called as Risk difference (RD)
Tells us what proportion of pts are spared the outcome if
they receive the experimental therapy (rather than
conventional therapy)
ARR = 0.445-0.281 = 0.165 = 16.5%
M f i k
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Measurement of risk
Absolute risk (AR)
Absolute risk reduction (ARR)
Relative risk (RR) Relative risk reduction (RRR)
Odds ratio (OR)
Number needed to treat (NNT)
(3) Th t f l ti i k
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(3) The concept of relative risk
RR or risk ratio
Defined as the ratio of the risk rate in the
exposed population versus unexposed orcontrol versus experimental population
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Contingency table/ 2x2 table
Anoutcome
Did not developan outcome
Total
Exposed to
drug/test
a b a + b
Not exposed to
drug/control
c d c + d
RR = a = P1a+b
c = P2
c+d
P1P2
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P1= 0.446
P2= 0.281
P1/P2 = 0. 63
Thus the risk of death in subjects is 2/3rds that
with ligation
Or
Subjects receiving sclerotherapy were 1.58 times
or one and a half times as likely to die as
compared to ligation treated subjects
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Measurement of risk
Absolute risk (AR)
Absolute risk reduction (ARR)
Relative risk (RR)
Relative risk reduction (RRR)
Odds ratio (OR)
Number needed to treat (NNT)
(4) Relative Risk Reduction RRR
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(4) Relative Risk Reduction- RRR
Another measure of assessing effectiveness of
treatment
What is the proportion of baseline risk that isremoved by the experimental therapy
ARR / Baseline risk
Relative risk reduction (RRR)
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Relative risk reduction (RRR)
ARR = 0.446 - 0.281
Baseline risk = 0.446 (sclerotherapy group)
RRR = 0.165/0.446 = 0.369
Or ligation decreases the risk of death by 37%
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Measurement of risk
Absolute risk (AR)
Absolute risk reduction (ARR)
Relative risk (RR)
Relative risk reduction (RRR)
Odds ratio (OR)
Number needed to treat (NNT)
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(5 ) Odds Ratio
Measures the strength of association
between an exposure and outcome
C l l i f dd i
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Calculation of odds ratio
Odds in the exposed/odds in the unexposed
Odds in the exposed: a/a+b = ab/a+b b
Odds in the unexposed:c/c+d = cd/c+d d
C ti t bl / 2 2 t bl
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Contingency table/ 2x2 table
An
outcome
Did not develop
an outcome
Total
Exposed todrug/test a b a + b
Not exposed to
drug/control
c d c + d
OR = ad/bc
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Calculation of odds ratioDeath No Death Total
Exposed to ligation 18
a
46
b
64
a+b
Exposed to
sclerotherapy
29
c
36
d
65
c+d
OR
ad/bc
Odds Ratio
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Odds Ratio
Odds of dying in the ligation group is 0.39
Odds of dying in the sclerotherapy group is 0.80
Odds ratio = 0.39/0.80 = 0.48
Inference: Pts treated with ligation have half as likely to die as
compared to sclerotherapy
Or, 0.8/0.39, pts treated with sclerotherapy as twice as likely to die as
compared to ligation treated subjects
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Measurement of risk
Absolute risk (AR)
Absolute risk reduction (ARR)
Relative risk (RR)
Relative risk reduction (RRR)
Odds ratio (OR)
Number needed to treat (NNT)
(6) Number needed to treat
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(6) Number needed to treat
Another way to express the impact of the new treatment
Allows comparison between treatments
NNT = 1/ARR
For every 17 pts treated with ligation, 1 pt will be saved
For 100 pts, 28 pts will die with ligation
For 100 pts, 44.6 pts will die with sclerotherapy
Case control and cohort studies
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Case control and cohort studies
Case control
Proceeds from effect to cause.
Starts with the disease.
Fewer subjects.
Quicker results.
Relative inexpensive.
Cohort
Proceeds from cause toeffect.
Starts with the risk factor.
Larger number of subjects.
Longer follow up.
Expensive.
Levels of Evidence - 1
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Levels of Evidence - 1
Level 1- RCTs, direct comparisons of drugs within
the same class, rather than with placebos, for effect
on important treatment outcomes
Level 2: 1) RCTs, direct comparisons of drugs
within the same class, but on validated surrogate
outcomes or 2) Comparisons of active agents with
placebos on clinically important outcomes or
validated surrogate outcomes
Levels of evidence 2
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Levels of evidence- 2
Level 3: Placebo controlled trials where outcomesare restricted to unvalidated surrogate markers
Level 4: Non randomized studies (case control,
cohort studies)
Applications of Pharmacoepidemiology
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Applications of Pharmacoepidemiology
Estimation/quantitation of risk
Patient counseling
Formulation of public health/policy decisions
Formulation of therapeutic guidelines and discovery of
new indications
Pharmacoeconomic decision making
E ti ti f i k f d
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Estimation of risks of drug use
The most common application
Use of case-control and cohort studies
E.g., Clozapine and agranulocytosis and
neutropenia (1:5000 versus 1:200)
F l ti f bli h lth li d i i
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Formulation of public-health policy decisions
Is the risk associated with the drugs too
high?
Does the labeling need to be changed?
Is there too much of inappropriate
prescribing?
P ti t li
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Patient counseling
Termination of pregnancy versus
continuation of pregnancy when the risk of
malformation is low/high
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Formulation of therapeutic decision making
Effectiveness and safety of drugs used in
groups of people not included in Phase III
trials (elderly, pediatrics)
E.g, Use of ciprofloxacin for typhoid fever
in children
Summary
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Summary
Application of principles of epidemiology to the study of the use andeffects of drugs in large numbers of patients
Deals with signal generation, risk quantification and hypothesis
generation
Applications in risk quantification, patient counseling, therapeutic,
regulatory and economic decision making