The impact of two consecutive prescription charges on
adherence to chronic medications in the Irish General
Medical Services population
Sarah-Jo Sinnott, University College Cork, Ireland
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Public
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1 400 000 000
1 200 000 000
1 000 000 000
800 000 000
€
600 000 000
400 000 000
200 000 000
0
2000
2001
2002
2004
2005
2006
2007
2008
2009
2010
2011
2012
Expenditure for medicines and devices and the GMS scheme from the years 2000-2012. Data obtained from
PCRS Financial and Statistical Analyses accessed from PCRS.ie
Banking Collapse and
Global Recession
EU-IMF Financial Bailout
2008
2010
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Introduction of 50c
prescription charge
2013
Charge increased Charge increased
to €2.50
to €1.50
What Risk?
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Whose Risk?
Patients
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Policy makers
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2008
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2010
2013
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Β0(intercept)
Β1(time)
Β2(policy)
Β3(time after policy)
Adherence
100
90
80
70
60
50
Adherence
40
30
20
10
0
1
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2
3
4
5
6
7
8
9
10
11
12
Long Term
Illness (LTI)
Results
GMS n = 39,314 GMS n = 33,394
LTI n = 3,831
LTI n = 4,217
GMS n = 7,149
LTI n = 4,076
GMS n = 80,264
Results
Approximate mean age
Approximate female
Baseline medication use
Oral hypoglycaemics
Insulin
Anti-hypertensives
Anti-hyperlipidaemics
Aspirin
GMS
62 yrs
51%
LTI
56 yrs
32%
Higher use
Higher Use
Higher Use
Higher Use
Higher Use
Changes in adherence in 2010 relative to
changes in adherence in 2013
4,00%
2,00%
0,00%
2009
-2,00%
% Change in
Adherence
Anti-hypertensives
2010
2011
2012
2013
2014
Anti-hyperlipidaemics
2010
-4,00%
2010
2010
-6,00%
2010
2013
-8,00%
2013
-10,00%
2013
2013
-12,00%
-14,00%
-16,00%
-18,00%
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50c
€1.50
Oral hypoglycaemics
Proton Pump Inhibitors
Changes in adherence in 2010 relative to
changes in adherence in 2013
4,00%
2,00%
0,00%
2009
-2,00%
% Change in
Adherence
Anti-hypertensives
2010
2011
2012
2013
2014
Anti-hyperlipidaemics
2010
-4,00%
2010
2010
-6,00%
2010
2013
-8,00%
2013
-10,00%
2013
2013
-12,00%
-14,00%
-16,00%
-18,00%
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50c
€1.50
Oral hypoglycaemics
Proton Pump Inhibitors
Long Term vs
Short Term
>
Thank you
s.sinnott@ucc.ie
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The Role of Clinical Pharmacists in avoiding
Patient's Health Risks
Dr. LAILA J. BADRAN (Ph.D.)
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*AUTHOR FULL NAME & AFFILIATION:
Dr. LAILA J. BADRAN (Ph.D.Pharmacy UK)
Former Advisor to the Minister of health for Drug Affairs
(Worked in the Ministry of Health for twenty seven years).
Now: Independent Expert: in Strategies, Policies, Regulations,
Legislations, Management, Administration QC&QA in Pharmacy.
*AUTHOR ADDRESS: Air Mail: Dr. LAILA J. BADRAN (Ph.D. Pharmacy)
P.O. Box: 921 941 Area Post Code: 11192-Amman/JORDAN
Email: dr_ljb@go.com.jo Telph: 009626 552 7548
Fax:009626 553 69 21-Mobile: 00962795552602
Factors that have major influences on
Clinical Pharmacist's Practices which reflect
on Patient's Health in our Jordanian
Governmental Hospitals:
Q-1Is Clinical Pharmacy Practice actually existing in
the 29 Governmental Hospitals in Jordan?
A-1Although there were long term training programs &
Scholarships since 2002 but the 29 Governmental
Hospitals are Lacking the required number of
Qualified Clinical Pharmacists:
A-1*In July 2004 there were only 4 Clinical
Pharmacists in 2 Governmental Hospitals.
*Until February 2014 the total number became
only 22 Clinical Pharmacists distributed into 10
Governmental Hospitals.
Q-2
Are the Qualified Clinical Pharmacists actually
allowed to practice their role?
Q-3-
Are there well defined clinical pharmacy
departments & Divisions in the Organizational
Structures of our Governmental Hospitals?
A-3Although since “July 2004-February 2006”: the Reformed
Organizational Structural Charts included Departments of
Clinical Pharmacy with the relevant Specialized Divisions
according to the available medical departments in each
Hospital: were presented to the MOH for adoption &
implementation.
A-2The Majority of Hospitals are not yet adopting the
reformed Organizational Charts, which resulted in
rejection of Clinical Pharmacy Practices, from the
medical team and opposition from some
Pharmacists.
Q-4Is there an applicable well established Job-
Description for Clinical Pharmacists?
A-4-
There is well defined Job-Description approved by
the Minister of Health (MOH) Since January 2005.
Q-5Are the Laws and Guidelines indicating the Role of
Clinical Pharmacists for protecting the patients
from any unexpected health risks during Clinical
Trials?
A-5The Laws and Guidelines for Clinical Trials have
some gaps concerning the safety of volunteers
specially when testing new drugs.
Results and Conclusions:
Phase I: Establishment of Clinical Pharmacy
Departments and Divisions:
There is an Urgent need to employ 99 new
Clinical Pharmacists to be Distributed (Based on
certain Criteria) Into the 29 Governmental
Hospitals (According to my Presented Study
since July 2004)
Phase II: Development of the Clinical Pharmacy
Departments and Divisions:
* The Second phase (after Three Years), need to
employ 120 new Qualified Clinical Pharmacists to
be Distributed Into the 29 Governmental
Hospitals
(According to my Presented Study 2004-2006)
Parallel with Phase I & II :
The Clinical Performance should be improved as a
result of upgrading the Laws, Regulations and
Guidelines that Govern the Profession
Parallel with Phase I & II:
The Clinical Performance should be improved if
the basic principles of the Job-Description are
amended by detailed instructions regarding
Clinical Pharmacist’s responsibilities.
Parallel With Phase I & II
The need to Upgrade the Existing rules and
regulations governing the clinical trials to ensure
the Patient’s safety.
*All Hospitals should adopt the new Reformed
Organizational Structure for Clinical Pharmacy
Departments and divisions (my presented Study
specially designed for each individual Hospital July 2004-February 2006).
*The Urgent need for steady and frequent
training for upgrading the scientific level of
Clinical Pharmacists by following the Highest
International Standards and Scientific tools
(Hakeem).
I do express my sincere thanks for the Organizers of this
Conference for giving me the opportunity for this participation at a
glance… knowing that I have had prepared detailed studies for
each topic that I have mentioned in this Presentation…
Hoping that next conference I could be invited as a speaker to give
a full detailed presentation.
Thank you very much
Greetings from Jordan
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A Systematic Approach to
personalised risk management
a.k.a Wake up and smell the coffee
Dr Brian Edwards, Principal Consultant NDA Regulatory Science Ltd and Vice
President , Pharmacovigilance and Drug Safety, ACRES
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DON’T GET ME WRONG.........
I’m a great fan of ’personalised medicine’
Please do not assume that there’s an effective safety system in
place acting primarily in the interests of patients to protect them
OF COURSE, I ACKNOWLEDGE THE FOLLOWING
There are many good and well meaning people working in
pharmaceutical sector many of whom work in very difficult
circumstances
Progress has been made.........we NEED TO CHANGE COURSE
and EMPHASIS
SYSTEM IS DYSFUNCTIONAL: BOTH ORWELLIAN and KAFKAESQUE
Meaningless
Words
Engaging
Openness
Clarity
Oversight
Terror of deviation
Conflict of interest
LACK OF SHARED MENTAL MODEL ACROSS THE
SYSTEM (OR EVEN WITHIN ONE ORGANISATION)
WE ARE NOT ALIGNED WITH NEEDS OF PATIENTS
No common understanding what Safety means and
what is a ‘safe product’
‘WILFUL IGNORANCE’ ABOUT SYSTEMATIC SAFETY – THE
HUMAN FACTOR
No systems training with rudimentary root cause analyses
Blame culture
Organisational greed
Poor organisational learning and abysmal error management
Inspections ‘miss ‘safety issues
We say we acting in the interests of
patients but what does that mean?
Requirements
Process
Performance
Metrics
Measurement
Monitoring
What are the
‘Requirements’
Incentives and
Expectations?
DRIVE
Safety=
Human
Performance
“Human factors encompasses all those factors that can
influence people and their behaviour”
 environmental
 organisational
 job factors
 individual characteristics
Clinical Human Factors Group
http://www.chfg.org/
Human error is both universal and inevitable
Errors are not intrinsically bad
We must learn from them !
Human factors: at the level of the individual, team and
the system
DECISION-MAKING
SITUATIONAL AWARENESS
LEADERSHIP
COMMUNICATION
ERROR MANAGEMENT
PERSONALITY AND BEHAVIOUR
Human factors are the main cause of systematic safety
failures and form the basis for quality control
Advice for patients and their families:
1) Get Informed
Where and who do I speak to for reliable information?
Beware of misinformation
Can I understand the patient information leaflet?
Advice for patients and their families:
2) Be Vigilant
Complacency and assumptions are enemies of safety
Look out for each other: involve the family
Advice for patients and their families:
3) Speak up and ask questions
Use ‘CUSS’
• I’m Concerned
• I’m Uncomfortable
• I’m Scared
• STOP
Challenge authority
Advice for patients and their families:
4) Use ‘Healthy Scepticism’: trust but verify
Work in a team and look after each other
based on just culture principles
Don’t make hierarchical assumptions: the
most experienced person often makes the
worst mistakes
Advice for patients and their families:
5) Share information and feedback
Join patient groups
Tell your story
Go online and spread the word
Trinity College
Dublin: Patient
Safety Group
for Ireland
planned
Pharmaceutical human factors group has started
for UK and Ireland
Why do need a Systems approach?
Most errors reflect predictable human failings in the context of
poorly designed systems.
Blame culture is toxic to Safety so we must move to a Just
Culture.
Analyse and implement system components
as part of system fixes (CAPA).
This is quality management as required by law !!!
We are late to act,
very late
This is no longer an option:
reality will not go away
It’s time to break out of the Matrix !
Thank you.
Please contact brian.edwards@ndareg.com
if you want to know how and want to act
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RISK AND RACE/ETHNICITY: Effect of body dimensions
on blood pressure and glucose metabolism in some
Nigerian groups.
Okoro, EO, MB; BS, Department of Medicine, University of Ilorin, Nigeria
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Hypertension (HBP) /
type 2 Diabetes (DM2)
becoming epidemic;
increasingly afflicting
younger people.
Mother ( 53 years) & daughter ( 17 years old ) with DM2 .
Expanding body
dimensions blamed for
changing disease
behaviour;
Body size/shape contributed only
≤ 12% of BP variations.
Explicitly, unit expansion of waistline
(WC) elevate SBP (0.35mmHg) & BMI
(1.1mmHg).
Similarly, BMI/WC accounted for
1% of blood sugar {RBS} variations.
Unsurprisingly, prevalence was
0.3% vs 2.3 % - 2.5 % elsewhere.
30
25
20
THREE
COMMUNITIES
STUDIED
15
OTHER
SIMILAR
NIGERIAN
CITIES
10
5
0
COMPARATIVE DIABETES
PREVALENCE
WHITES
Rising BMI/WC increases
BLACKS
IN
BP/RBS, but extent differs.
DIASPORA
R
I
S
K
NIGERIANS &
0OTHER
CONTINENTAL
AFRICANS
BMI/WC
Source : Okoro, EO et al 2014
• This could signal heterogeneity in disease mechanism.
• Consequently, weight control may be less effective especially;
If community standard of beauty
favours big size (PBF >5).
Source : Okoro EO et al 2008, 2011, 2014
Traditionally (e.g. Efik ) voluptuous
females are celebrated.
Even in Contemporary Nigeria
generous endowment are
sometimes paraded with intense
pride
Even in Contemporary Nigeria
And maidens undertake fattening process for attractiveness/marriage ability .
Surprisingly, BP/RBS less where
lifestyle physically intense/meals
traditional.
Source : Hamidu LJ, Okoro, EO, Ali MA 2000, Okoro EO et al 2014
But increasingly meaty diets can accelerate HBP/DM2 onset.
Nevertheless, identifying genetic
susceptibility in childhood; followed
by
Source : Okoro EO et al 2002, Ologe FE, Okoro EO, Oyejola BA 2005
healthy lifestyles internalization can avert HBP/DM2 across diverse
range of body types; despite intense media pressure,
to conform to other people’s notion of beauty.
Thank you.
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vigiPoint:
A Framework to Streamline Data Exploration
Kristina Juhlin, Uppsala Monitoring Centre
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Pinpointing key features of case series
Pinpointing key features of case series
Pinpointing key features of case series
Medication
errors
Key features?
Medication
errors
Pinpointing key features of case series
United States
Skin disorders
2-11 years
Medication errors
Consumers
Male patients
Medication
errors
Pinpointing key features of case series
United States
Skin disorders
2-11 years
Medication errors
Consumers
Male patients
Medication
errors
Pinpointing key features of case series
United States
Skin disorders
2-11 years
Medication errors
Consumers
Male patients
Medication
errors
vigiPoint concept
Cases
Reference
Comparing to one or more references
using shrinkage odds ratios
vigiPoint concept
Cases
Reference
Comparing to one or more references
using shrinkage odds ratios
vigiPoint concept
Cases
Reference
Comparing to one or more references
using shrinkage odds ratios
vigiPoint concept
Ref 1
Cases Ref 2
Ref 1
Ref 2
Cases
Ref 3
Comparing to two or more references
vigiPoint concept
Ref 1
Cases Ref 2
Ref 1
Ref 2
Cases
Ref 3
Comparing to two or more references
vigiPoint concept
United
States
89%
49%
2-11 years
10%
6%
Consumers
53%
17%
Med. errors
Skin
disorders
33%
22%
Respiratory
drugs
17%
12%
Male
52%
39%
vigiPoint concept
United
States
89%
49%
2-11 years
10%
6%
Consumers
53%
17%
Med. errors
Skin
disorders
33%
22%
Respiratory
drugs
17%
12%
Male
52%
39%
vigiPoint concept
United
States
89%
49%
2-11 years
10%
6%
Consumers
53%
17%
Med. errors
Skin
disorders
33%
22%
Respiratory
drugs
17%
12%
Male
52%
39%
Reproducible
Through adherence to pre-defined analysis
protocols.
Rapid
Through shortened times for design of
analysis and execution.
Comprehensive Through exhaustive exploration of all
possible patterns, within the defined scope.
Familiar
Through repeated presentation of results out
of the standard analytics framework.
Reproducible
Through adherence to pre-defined analysis
protocols.
Rapid
Through shortened times for design of
analysis and execution.
Comprehensive Through exhaustive exploration of all
possible patterns, within the defined scope.
Familiar
Through repeated presentation of results out
of the standard analytics framework.
Reproducible
Through adherence to pre-defined analysis
protocols.
Rapid
Through shortened times for design of
analysis and execution.
Comprehensive Through exhaustive exploration of all
possible patterns, within the defined scope.
Familiar
Through repeated presentation of results out
of the standard analytics framework.
Thank you.
Rapid fire talk
An Algorithm for Safety Monitoring of New
Medical Products Using National Health
Insurance Claims Database
Jong-Mi Seong, PhD,
Korea Institute of Drug Safety and Risk Management,
Seoul, Republic of Korea
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Conflict of Interest
 This study was conducted as an internal project of
Korea Institute of Drug Safety and Risk
Management.
 The authors have no conflict of interest to disclose.
Background
 Large health insurance claims database can serve as an
important resource for post-marketing safety surveillance.
 Health insurance claims databases have been used
extensively to evaluate previously identified signals in formal,
protocol-driven, epidemiological studies targeted at specific
safety questions.
Reference. Strom BL. Pharmacoepidemiology. 4th ed
Background

Health insurance claims databases can also be used to detect signals.

The main advantage of these data sources is that there is a well defined
patient denominator, thereby permitting the calculation of incidence
rates and comparisons of incidence rates between different drugs or
different patient subgroups.

However, the use of large healthcare claims data for signal detection is
still in its infancy.
Reference. Report of CIOMS Working Group VIII. Strom BL. Practical aspects of
signal detection in pharmacovigilance.
Objectives
 To develop an algorithm for identifying serious
adverse event (SAE) signals of newly marketed
medical products using the national health
insurance claims database.
Methods
1) Data resource
 Data resource for signal detection was the Health Insurance Review &
Assessment Service (HIRA) database.
• All Koreans are covered by national health insurance system and
healthcare providers are required to submit claims on medical services
to the HIRA for review of medical costs since 2000.
• The HIRA has an electronic data interchange (EDI) claims submission
system and the proportion of EDI claims accounts for about 99.7% of the
total claims.
• Accordingly, the HIRA database contains all medical information for
approximately 50 million Koreans.
Methods
1) Data resource
 The database contains longitudinal patient data that includes
patient demographics, diagnoses, and prescription drugs.
Patients’ characteristics
Diagnoses
Prescriptions
De-identified patient No.
Diagnoses
Drug brand name
Age
: ICD-10 code
Drug generic name
Gender
Date of diagnosis
Prescription date
Outpatient/Inpatient
Dose
Duration
Route of administration
Methods
2) Selection of SAEs: Targeted Safety Monitoring
 To develop a data-mining tool, designed to monitor pre-defined
SAE of newly marketed medical products routinely,
 all events that are judged as important in pharmacovigilance
from the US FDA’s Sentinel Initiative, the Observational Medical
Outcomes Partnership, and EU-ADR (Exploring and
Understanding Adverse Drug Reactions) were reviewed to
create a list of high-priority events.
Methods
2) Selection of SAEs: Targeted Safety Monitoring
 US FDA’s Sentinel Initiative
Cerebrovascular accident/transient ischemic attack, heart failure, atrial fibrillation, ventricular
arrhythmias, venous thromboembolism, depression, suicide or suicide attempts,
seizures/convulsions/epilepsy, pancreatitis, lymphoma, infection related to blood products/tissue
grafts/organ transplants, transfusion associated sepsis or septicemia, transfusion related ABO
incompatibility reactions, erythema multiforme/SJS/TEN, anaphylaxis, hypersensitivity reactions
other than anaphylaxis, pulmonary fibrosis and interstitial lung disease, acute respiratory failure,
orthopedic implant removal and revision, severe liver injury, acute myocardial infarction (AMI)
 OMOP
Angioedema, aplastic anemia, acute liver injury, bleeding, hip fracture, hospitalization, AMI,
mortality after MI, acute renal failure (ARF), GI ulcer hospitalization
Methods
2) Selection of SAEs: Targeted Safety Monitoring
 EU-ADR
Bullous eruptions (Stevens Johnson Syndrome, Lyell’s Syndrome), ARF, anaphylactic shock, AMI,
rhabdomyolysis, aplastic anemia/pancytopenia, neutropenia, cardiac valve fibrosis, acute liver
injury, extrapyramidal disorders, QT prolongation, suicidal behavior/attempt, confusional state,
thrombocytopenia, upper gastrointestinal bleeding, convulsions, peripheral neuropathy, maculopapular erythematous eruptions, venous thrombosis, mood changes: depression and mania,
amnesias, hemolytic anemia, acute pancreatitis
Methods
2) Selection of SAEs: Targeted Safety Monitoring
 All events were reviewed to determined if each event has
relatively higher diagnostic accuracy in the database and were
coded using ICD-10 by KIDS staff.
 Two clinical experts majored in pharmacoepidemiology
evaluated a priority of the events for monitoring and relevance
of case definition using ICD-10 codes.
Methods
3) Monitoring Scenarios
 For the targeted safety monitoring, pairs of newly marketed
medical products and selected SAEs can be generated.
 Automated implementation of design and analytic technique
selection will enable rapid and simultaneous monitoring of
many pre-specified pairs.
Methods
3) Monitoring Scenarios
 However, when evaluating pre-specified outcomes, targeted safety monitoring can
resemble ordinary epidemiologic studies, enabling the use of various design and
analytic techniques to minimize false-positive and false-negative alerts due to bias.
 The Mini-Sentinel Taxonomy Work Group distilled the list down to 64 scenarios
defined by combinations of characteristics that influence monitoring design choice.
 We reviewed the Report of the Mini-Sentinel Taxonomy Project Work Group to
choose the most appropriate analytic methods for targeted safety monitoring
Methods
3) Monitoring Scenarios
 We categories product-SAE pairs (i.e. monitoring scenarios) into
two designs, self-controlled designs and cohort-type designs,
based on the characteristics of exposures, SAE, and the
relations between them.
 We then defined and mapped a preferred analytic approach to
each scenario type
Results
 The SAE for monitoring were
No
.
Adverse events
ICD-10 codes
1
acute myocardial infarction
I21
2
atrial fibrillation
I48
3
ischemic stroke
I63
4
Heart failure
I50
5
upper gastrointestinal bleeding
K22.8, K25.0, K25.2, K25.4, K25.6, K26.0, K26.2,
K26.4, K26.6, K27.0, K27.2, K27.4, K27.6, K28.0,
K28.2, K28.4, K28.6, K29.0, K92.0, K92.1, K92.2
6
upper gastrointestinal ulcer
K22.1, K25, K26, K27, K28
7
acute liver failure
K71.0, K71.1, K71.2, K71.6, K71.9, K72.0, K72.9,
K76.2, K76.3
8
acute renal failure
N17
9
acute pancreatitis
K85
10
aplastic anemia
D60, D61
11
erythema multiforme
L51
12
anaphylactic shock
T78.2, T88.6
13
hip fracture
S72.0, S72.1, S72.2
Results
 Criteria for the design choices for each monitoring scenarios
were
• the characteristics of exposures: whether the exposure of
interest is transient or sustained
• the characteristics of SAE: SAEs may have an abrupt onset
(e.g. stroke, AMI) or they may be insidious in nature (e.g.
diabetes, HF).
• the strength of within- and between-person confounding
Results
 When the assumptions of self-controlled designs are fulfilled
(i.e. transient exposure, lack of within-person, time-varying
confounding, and abrupt SAE), sequence symmetry analysis is a
tool for detecting safety signals.
 As scenarios diverged from those in which these assumptions
were tenable, incident user concurrent control cohort design is
selected and applied survival analysis using Cox proportional
hazard model.
Results
A signal is considered to be present
• when the lower limit of the 95% confidence interval
of sequence ratio or relative ratio is one or more in
the self-controlled and cohort-type design,
respectively.
Results
Algorithm for
identifying SAE
signals of new
products using
the national
health
insurance
claims database
Conclusions
 This algorithm using population-based health care databases
make it possible to monitor SAEs of new medical products
rapidly in real-world patients and increase efficiency of
generating and refining safety signals.
Thank you.
Rapid fire talk
Relationship between structural
alerts in NSAIDs and hepatotoxicity
Naomi Jessurun, Pharm D
Netherlands’ Pharmacovigilance Centre
Rapid fire talk
Content:
•Idiosyncratic drug reactions
•Metabolic activation
•Influence of body burden
•Structural alerts in NSAIDs
•Objective of the research
•Study design
•Outcomes
•Conclusion / discussion
Idiosyncratic drug reactions
• Blood dyscrasias
• Cutaneous reactions
• HEPATOTOXICITY
Idiosyncratic drug reactions
hepatotoxicity
Allergic:
fever, rash, eosinophilia, a relatively short
latency, autoantibodies and the rapid
recurrence on re-exposure
Non-allergic:
The consistent absence of the features
above. Long latency period.
Metabolic bioactivation
Bioactivation of chemical substructures to
reactive metabolites, followed by binding to
macromolecules.
Not the parent drug!
Influence of body burden
There are no known examples of drugs
that cause IADRs when the clinical dose is 20
mg/day or less.
The improved safety of low-dose drugs may
arise from a reduction in total body burden to
reactive metabolite exposure.
Toxicophores in NSAIDs
Bromobenzene ring
(alkyl)aniline moiety
As in bromfenac, diclofenac and
lumiracoxib
Carboxylic acid moiety
Carboxylic acid moiety
Ibuprofen and naproxen: steric hindrance
is introduced
Objective of the research
The aim of this research was to assess
whether the number of structural alerts in
one molecule relates to reported cases of
hepatotoxicity in the WHO database.
Study design
The reported
hepatotoxicity of 5
NSAIDs was studied:
Hepatotoxicity was
defined as adverse
drug reactions coded in
the PT:
•
•
•
•
•
• Hepatitis
• Hepatic necrosis
• Hepatic function
abnormal
• Hepatic failure
Bromfenac
Lumiracoxib
Diclofenac
Ibuprofen
Naproxen
Study design
These adverse drug reactions were
compared with haemorrhage, an adverse
drug reaction not related to the forming
of reactive metabolites
20
Crude RORs
18
16
14
12
Hepatic failure
10
Hepatic function abnormal
Hepatic necrosis
8
Hepatitis
Haemorrhage
6
4
2
0
Naproxen, SA = Ibuprofen, SA = Diclofenac, SA
0
0
=2
Lumiracoxib,
SA = 2
Bromfenac, SA
=3
Results
Discussion /conclusion
• The results of this study are SUPPORTIVE
• BODY BURDEN of the drug and/or its metabolite(s), may
play a role
However:
• Information on the precise role of RMs is lacking;
• The impact of the withdrawal of bromfenac and
lumiracoxib from the market is unknown;
• Other mechanisms are not considered
Thank you.
Rapid fire talk
Indian Geriatrics at Risk of
Medical Related Adverse Consequences
Dr. G. Parthasarathi,
Dean, Faculty of Pharmacy,
JSS University, Mysore, India
Rapid fire talk
Background
• Geriatrics is an emerging clinical specialty in India.
• In recent decades, the life expectancy of humans has increased
due to social, economical and health care improvement.
• The proportion of the world’s geriatric population doubled in
the last century and will increase 2 to 3 fold during the first
century of this millennium. By 2050, the worldwide elderly
population is expected to reach 1.4 billion.
• India is expected to have 20% of Geriatric population. Hence,
represents significant proportion of the global elderly
population.
Background
• Ageing has significant effect on the responses to pharmacological
interventions.
• There is inadequate evidence and knowledge about responses of
geriatric patients to medications.
• Adverse Drug Events are predictable and most likely to happen in the
elderly.
• Polypharmacy has been reported to increase the risks for
inappropriate prescribing, ADEs, and morbidity and mortality in
elderly.
• Medication Related Problems are a major public health problem in the
elderly.
Who’s at Risk:
High Level Polypharmacy
• Multiple (≥3) diagnoses
(OR = 1.55; 95% CI, 1.16–2.08;P = 0.003)
• Angina pectoris
(OR = 2.58; 95% CI, 1.50–4.37; P < 0.001)
• Length of stay ≥10 days
(10–<15 days, OR = 3.14; 95% CI, 2.09–4.71; P < 0.001; and ≥15 days,
OR = 5.74; 95% CI, 2.43–13.51; P < 0.001)
The American Journal of Geriatric Pharmacotherapy. 2010;8:271-80.
Who’s at Risk:
High Level Polypharmacy
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•
•
•
Data from 814 hospitalized patients
Prevalence of polypharmacy: 45.0% (366/814)
Prelalence of and high-level polypharmacy 45.5% (370/814)
Factors Assessed: Age, sex, number of diagnoses, hospital length of stay , and
disease conditions
The American Journal of Geriatric Pharmacotherapy. 2010;8:271-80.
Who’s at Risk:
Medication-Related Problems
• Age (>/= 80 years) is an influential predictor of
Medication Related Problems
(OR 2.1; 95%CI 1.1–4.2; p = 0.03)
J Pharm Pract Res. 2010;40:279-83.
Who’s at Risk:
Medication-Related Problems
• Data from 411 hospitalized patients
• Prevalence of Medication Related Problems: 56% (230/411)
• Factors Assessed: Age, gender, number of diseases, number of drugs used and
length of hospital stay
J Pharm Pract Res. 2010;40:279-83.
Who’s at Risk:
Potentially Inappropriate Medication Use
• Increased number of concurrent medications’ use (≥9)
(OR: 1.9; 95% CI, 1.34-2.69; P<0.001)
J Postgrad Med 2010;56(3):186-91.
Who’s at Risk:
Potentially Inappropriate Medication Use
• Data from 814 hospitalized patients
• Prevalence of Potentially Inappropriate Medication Use: 23.5% (191/814)
• Factors Assessed: Age, gender, number of diseases, number of concurrent
medications at admission and during hospital stay, and length of hospital stay
J Postgrad Med 2010;56(3):186-91.
Who’s at Risk:
Adverse Drug Reactions
• Female gender
(OR: 1.52, 95% CI:1.04-2.22, P=0.03)
J Postgrad Med 2011;57:189-95.
Who’s at Risk:
Adverse Drug Reactions
• Data from 920 hospitalized patients
• Prevalence of Adverse Drug Reactions: 32.2% (296/920)
• Factors Assessed: Age, gender, number of diseases, number of drugs prescribed,
number of doses used, length of hospital stay, and history of medication allergy
J Postgrad Med 2011;57:189-95.
Who’s at Risk:
Adverse Drug Events
• High level polypharmacy
(OR:1.43, CI:1.04-1.98, P=0.028)
• Five or more days of stay in medicine wards
(5-9 Days: OR:2.39, CI:1.57-3.64, P<0.001)
(> 10 days: OR:2.72, CI: 1.59-4.65, P<0.001)
• Urinary tract infection
(OR: 1.91 , CI: 1.18-1.81, P=0.009)
Who’s at Risk:
Adverse Drug Events
ADVERSE DRUG EVENTS IN ELDERLY PATIENTS
IN MEDICAL WARDS OF TERTIARY CARE
TEACHING HOSPITALS
by
Mr. ANANDA HARUGERI, M. Pharm.
Ph. D Registration Number: RGUHS/Ph.D/P07/2007-08
A Dissertation submitted to the
Rajiv Gandhi University of Health Sciences, Karnataka, Bangalore
In Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY IN PHARMACEUTICAL SCIENCES
JUNE 2011
•
•
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Data from 1045 hospitalized patients
Prevalence of Adverse Drug Events (ADE): 29.8% (312/1045)
Prevalence of Hospital Admission due to ADE: 5.1% (53/1045)
Factors Assessed: age, gender, number of diseases, number of drugs prescribed at
admission and during hospital stay, number of doses used, polypharmacy, length
of hospital stay and certain diseases
Who’s at Risk:
Drug Induced Hypoglycemia
• Age
{OR 1.62 (1.16-2.27) p=0.005}
• Number of medications
[6-9 {OR 2.17 (1.19-3.95) p=0.010] and
≥10 {OR 2.75 (1.49-5.06) p<0.001}]
• Number of co-morbidities (≥5)
{OR 2.90 (1.87-4.51) p<0.000}
• Low body mass index
{OR 2.47 (1.27-4.82) p=0.010}
28th International Conference on Pharmacoepidemiology and Therapeutic Management.
August 23-26, 2012, Barcelona, Spain
Who’s at Risk:
Drug Induced Hypoglycemia
Drug Induced Hypoglycemia in Hospitalized Patients: Prevalence,
Incidence Rate and Risk Factors
Parthasarathi G, Patel R, Harugeri A, Ramesh M, Narahari MG1
Department of Pharmacy Practice, JSS College of Pharmacy, JSS University, Mysore
1Department of Internal Medicine, JSS Medical College and Hospital, JSS University, Mysore
• Data from 900 hospitalized patients
• Prevalence of Drug Induced Hypoglycemia: 20.7% (187/900)
• Factors Assessed: Age, gender, body mass index, number of disease conditions,
number of drugs prescribed, length of hospital stay, smoking, alcohol use, and
duration of diabetes.
28th International Conference on Pharmacoepidemiology and Therapeutic Management.
August 23-26, 2012, Barcelona, Spain
Who’s at Risk:
Potentially Inappropriate
Medications Vs. Other Medications
• Medications not listed in BC resulted in more number of ADRs than
medications listed in Beers Criteria
[χ2=98.4, P<0.001] [OR: 13.51, CI: 7.19-25; P<0.001]
J Postgrad Med 2010;56(3):186-91.
• Medications other than those listed in Beers Criteria (OR: 5.75, CI:
3.03–11.11; P < 0.001) or Screening Tool of Older Persons’ potentially
inappropriate Prescriptions (STOPP) (OR: 4.89, CI: 2.07–11.54; P < 0.001)
were more likely to be associated with Adverse Drug Reactions
Geriatrics & Gerontology International. 2012;12:506–514.
Who’s at Risk:
Potentially Inappropriate Medications Vs. Other Medications
J Postgrad Med 2010;56(3):186-91.
Geriatrics & Gerontology International. 2012;12:506–514.
Discussion
• More than one third of hospitalized elderly patients experience
medication related adverse consequences
• Interventions to rationalize medication usage in hospitalized Indian
elderly should focus on increasing the adherence to indications
• Risk of potentially inappropriate medication related adverse
consequences may be low due to low prevalence
• Interventions targeted only at potentially inappropriate medications
medications may do little to change the risk of adverse drug reactions
Discussion
• Multiple diseases, number of medications used during hospital stay,
polypharmacy, duration of hospital stay identified as risk factors were
consistent with literature reports
• Further large cohort studies are required to confirm our findings
• Develop and implement the strategies to prevent/minimize adverse
medication related consequences
Discussion
Intervention Strategies
• Promote appropriate drug use
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•
Focus on drugs commonly implicated
Drug monitoring supported by information technology
Pharmacist participation in ward rounds
Linking pharmacy and laboratory data
Use of simple and practical method to identify patients who are at risk
Thank you.
Rapid fire talk
Investigating nevirapine-associated
Stevens-Johnson Syndrome among HIVinfected pregnant women:
The Medunsa National Pharmacovigilance
Centre, 2007 – 2012.
Nomathemba Dube
Medunsa National Pharmacovigilance Centre
University of Limpopo, South Africa
Rapid fire talk
Nevirapine discontinuation circular:
06 April 2012
South African National Department
of Health recommendations
Stevens-Johnson Syndrome
• Stevens-Johnson Syndrome (SJS)
– Acute life threatening
– Rare (Incidence: ~ 2 per million population per year)
– Often caused by drugs
– Antituberculosis
– Anti-inflammatory
– Antiretroviral medication
SJS clinical presentation
Within 6 weeks of ingesting causative drug
-Cough
-Fever
-Sore throat
-Facial swelling
- Skin rash leading to skin ulcers and skin sloughing
HIV infection and SJS
• HIV-infected patients have a higher predisposition to
SJS
- decreased anti-oxidant levels owing to infection
- greater likelihood of using the drugs at higher dosages
than the general population
HOWEVER
- Reports of an association between SJS and pregnancy:
previously unknown
Study Aim
To determine if pregnancy is a risk factor for SJS among
HIV-infected women taking NVP-containing regimens
Map of Sentinel Sites
Data collection methods
• Matched case-control study (5:1 matching)
• Study population
- HIV positive women, ≥15 years, receiving
nevirapine-containing ARV medicines
• Data sources
- Medunsa National Pharmacovigilance Centre
database
- Pharmacovigilance Adverse Drug Reaction forms
Cases and Controls
• Cases
- Women
- Developed signs and symptoms within 3 – 46 days
of nevirapine-containing regimen initiation
- Diagnosed as SJS by physician
• Controls
- Women
- Taking a nevirapine-containing regimen
- Not classified as a case
Algorithm to implicate nevirapine as the
cause of SJS
1. Alternative causes (e.g. concomitant medication) were
excluded
2. Interval between the drug introduction and the onset
of a reaction was examined (for drug-induced SJS, 3 46 days)
3. Any improvement after drug withdrawal was noted
4. Any reaction if the drug was re-administered was
noted
Variables for matching controls to cases
• Controls were matched for
- age (within 5 years of the case)
- CD4 count (<250 cells/ml or ≥250 cells/ml)
Data analysis
• STATA version 12
• Conditional logistic regression analysis
• Odds ratios
• Chi-square
• 95% confidence intervals
• A p-value <0.05 was considered statistically significant
Demographic characteristics of patients, PV
study (2007 – 2012)
N=278
Characteristics
Median Age in years (Range)
Average CD4 count (cells/ml)
(Range)
Median duration on
treatment (days)
(Range)
Cases
(n=6)
29
(25 – 43)
Controls
(n=30)
29
(25 – 41)
237
(5 – 490)
234
(13 – 640)
27
191
(18 – 31)
(1 – 365)
Pregnancy characteristics of cases and
controls (2007 – 2012)
Characteristics
Pregnant
Yes
No
Total
Cases
n (%)
Controls
n (%)
5 (83)
1 (17)
6 (100)
7 (23)
23 (77)
30 (100)
Conditional logistic regression
Odds Ratio (95% CI)
14.28 (1.54 – 131.82)
Chi-square
7.56
P-value
0.006
Discussion
• Findings show that the chance of developing SJS
increases with pregnancy
• Toxicity of nevirapine during pregnancy has been
contradictory
• South African guidelines
- Pregnant HIV-infected women: fast tracked on
ART
- Initiated on nevirapine containing regimens
- Switch from efavirenz to nevirapine when
pregnant
Discussion
• Practice of prescribing nevirapine to pregnant women
may explain why they are at higher risk of developing
SJS
Limitations
• Small sample size
• No controls available with a closer match for duration
of NVP treatment to that of cases
Conclusion
• Increase in SJS has been shown amongst pregnant HIV
positive women taking nevirapine containing regimens
Recommendations
• Healthcare Workers
- offer informed consent to patient and recommend
effective appropriate contraception
- all other patients on nevirapine-containing
regimens closely monitored and nevirapine
discontinued immediately if side-effects are
experienced
• More studies should be conducted on nevirapine
safety
A special thank you to:
Medunsa National Pharmacovigilance Centre
(MNPC), Medunsa Campus, University of Limpopo
South African Field Epidemiology and Laboratory
Training Programme (SAFELTP), NICD-NHLS
School of Health Systems and Public Health
(SHSPH), University of Pretoria
Thank you.
Rapid fire talk