Pharmacoepidemology and pharmacoeconomics

Prescription Event Monitoring:

PEM is a non-interventional, observational cohort form of pharmacovigilance.

It is the method of studying the safety of new medications used by the general practitioner.

PEM was developed by Professor Bill Inman at the Drug Safety and Research Unit (DSRU) at Southampton in 1981.

Evolution of PEM

Pre-marketing clinical trials are effective in studying the efficacy of medicine but are not able to define many aspects of drug safety because:

1. Small no of patients

2. Large no of patients receiving the drug for small durations

3. Doses and formulations of the drug may change during drug development

4. Exclusion of special population from the clinical trials

5. The contribution of spontaneous reporting system in detecting hazards such as oculomucocutaneous syndrome with practolol led Inman to establish the system of Prescription Event Monitoring (PEM) at DSRU.

6. In New Zealand, the medicines adverse reaction committee (MARC) is responsible for conducting such studies for academic purposes and the programme is known as Intensive medicine monitoring programme(IMMP).

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In UK, all the patients are registered with NHS-GP provides the primary care and act as a gateway to specialist and hospital care

File notes in general practice contains information about primary care, secondary and tertiary care (life long record)

GP issues prescription for medications he considers medically warranted

Patient takes the prescription to the pharmacist, who dispenses the medication and sends the prescription to the PPD (which is a part of NHS-BSA), for reimbursement

PPD provides DSRU with electronic copies of all the prescriptions issued throughout UK, for the drugs being monitored

Products that are selected for study by PEM

1. New drugs, expected to be used widely

2. Established products, used for new indication/ new population

Collection of exposure data begins soon after the new product is launched

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These arrangements operate for a length of time necessary for the DSRU to collect first 50,000 prescriptions, that identify 20,000-30,000 patients given the new drug being monitored

For each patient in the study, DSRU prepares a computerized longitudinal record in the date order of drug use

After 3-12 months from the date of first prescription for each patient, the DSRU sends the prescriber a green form questionnaire

This is done on an individual patient basis

Doctor receives maximum of 4 green forms in a month

Green form for PEM study on Celecoxib

Request information on:

Age

Sex

Indication for Rx

Dose

Start date

Stop date

Concurrent diseases

Concomitant therapy

All events that have occurred since Rx

Cause of death

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Each green form is reviewed by a medical/ scientific officer monitoring the study, to identify possible serious ADRs or events requiring action

Events are coded and entered in database using a hierarchical dictionary arranged by system-organ class with specific lower terms grouped under broader higher terms

PEM process:

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Advantages

1. PEM is non-interventional

2. The method is national in scale and thus provides real world data

3. Exposure data is derived from dispensed prescriptions

4. Method can detect adverse reactions or syndromes that none of the reporting doctors suspected to be due to the drug

5. Method allows close contact between the research staff and reporting doctors

6. ADR reporting is more complete by this method

7. Method is found to be successful in regularly producing data in 10,000 or more patients given newly marketed drugs

8. Method identifies patient with ADRs who can be studied further

9. Allows comparison of safety profile of drugs belonging to the same therapeutic group

10. Evaluate signals generated by other systems or databases

Disadvantages:1.Not all green forms are returned

2.PEM depends upon reporting by doctors. Underreporting is possible

3.PEM is currently restricted to general practice

4.Its not known whether the patient took the dispensed medication

5.Detection of rare ADRs is not always possible

Pharmacoepidemology and pharmacoeconomics

Applications of PEM

1. Searching for signal

2. Assessment of important AE

3. Medically important events

4. Reason for stopping the drug

5. Analysis of events during the study while on drugs

6. Ranking of ID and reason for withdrawal

7. Automated signal generation

8. Long latency adverse reactions

9. Comparison with external data

10. Outcomes of pregnancy

11. Studies to examine hypothesis generated by other methods

12. Studies of background effects and diseases

Example for PEM

A study was carried out to assess the sedation properties of 4 anti-histaminic in the market loratadine, cetrizine, fexofenadine and acrivastine

Objectives: To investigate the frequency with which sedation was reported in post marketing surveillance studies of four second generation antihistamines: loratadine, cetrizine, fexofenadine, and acrivastine

Design: Prescription event monitoring studies.

Setting: Prescriptions were obtained for each cohort in the immediate post marketing period.

Subjects: Event data were obtained for a total of 43,363 patients.

Main outcome measure: Reporting of sedation or drowsiness.

Results: The odds ratios for the incidence of sedation were 0.63 (95% confidence interval 0.36 to 1.11; P = 0.1) for fexofenadine; 2.79 (1.69 to 4.58; P < 0.0001) for acrivastine, and 3.53 (2.07 to 5.42; P < 0.0001) for cetrizine compared with loratadine. No increased risk of accident or injury was evident with any of the four drugs.

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Conclusions: Although the risk of sedation was low with all four drugs, fexofenadine and loratadine may be more appropriate for people working in safety critical jobs. This study not only showed the sedative effects of the anti-histaminic, and compared them, it also gave an idea about the incidence of other ADRs associated with the 4 drugs.

Incidence density of ADRs in first month of treatment with 4 anti-histamine

Incidence density of events related to sedation in first month of treatment with 4 anti-histaminics

Pharmacoepidemology and pharmacoeconomics

Record Linkage Systems:

Record linkage is the process of bringing together two or more records relating to the same individual (person), family or entity (e.g. event, object, geography, business etc).

It is the process of assembling the outcomes of drug exposure into a single database

Record linkage can be considered as part of the data cleaning process

Provides rapid access to records of thousands of patients and thus reduces the time required for exploring the relationship between drug exposure and outcomes

Diagrammatic representation of a linkage between two or more independent entries

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An ideal database would include records from inpatient, outpatient, emergency care, mental health care, laboratory and radiological tests, prescribed and over-the-counter medications as well as alternative therapies

All the parts should be easily linked by a unique patient identifier

It should be updated regularly

Need for record linkage

Objective of record linking

The objective of the linking process is to determine whether two or more records refer to the same person, object or event

Types of record linkage methods

Researchers and the community‘s demand for detailed statistical information

Reducing respondent burden and costs

Improving data quality and timeliness

In response to increasing business and health needs.

In reducing the complexity of data

International collaborative works

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Deterministic record linkage

A pair of records is said to be a link if the two records agree exactly on each element within a collection of identifiers called the match key.

For example, when comparing two records on last name, street name, year of birth, and street number, the pair of records is deemed to be a link only if the names agree on all characters, the years of birth are the same, and the street numbers are identical.

Probabilistic Record Linkage

Pairs of records are classified as links, possible links, or non-links.

Here, we consider the probability of a match in the given observed data.

In probability matching, a threshold of likelihood is set (which can be varied in different circumstances) above which a pair of records is accepted as a match, relating to the same person, and below which the match is rejected

General record linkage system

Types of Record Linkage

Strategies

Probabilistic

Deterministic

Pharmacoepidemology and pharmacoeconomics

Claims databases

Patient goes to pharmacy drug gets dispensed pharmacy bills the insurance carrier for cost of that medication

Should specify which drug was dispensed, amount dispensed, etc.

Patient goes to hospital/physician for medical care bills the insurance carrier for cost of the medical care

Should justify the bill with diagnosis

Common patient identification no link pharmacy and medical care claims

Medical record databases

Recent development with increased use of computerization in medical care

Computers are used to record medical information

Advantages

Provide large sample size, esp. for pharmacoepidemiological studies

Inexpensive

Data will be complete

Population based

Include information on outpatient drugs and diseases

Avoid recall and interviewer bias

Disadvantages

Uncertainty of diagnosis data

Pharmacoepidemology and pharmacoeconomics

May not contain information regarding smoking, alcohol, date of menopause, etc.

May not contain data of medications obtained without prescription or outside insurance carriers prescription plan

Instability of population due to job changes, changes in insurance plans, etc.

Include illnesses severe enough to come to medical attention

Applications

1. Data Quality

2. Bias

3. Coverage

4. Tracing Tool

5. Benchmarking/Calibration

6. Building New Data Sources (e.g., Registries)

7. Creation of patient-oriented, rather than event-oriented statistics

8. Reducing costs and respondent burden