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Prescription Event Monitoring & Record Linkage Systems

Prescription Event Monitoring & Record Linkage Systems

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Prescription Event Monitoring & Record Linkage Systems

  1. 1. Satish VeerlaPharm.D 1
  2. 2. EVOLUTION OF PEMPre-marketing clinical trials are effective instudying the efficacy of medicine but they havelimitations in defining the clinically necessary safetyof drugs. They are:-• Small number of patients.• The study products may received for short durations(only a single dose), which may not be able to detectrare ADR’s.• Pre-marketing developing programs are dynamic.• Special population are excluded.2
  3. 3. • The contribution of the spontaneous reporting system indetecting hazards such as the oculomucocutaneoussyndrome with practolol led Inman to establish thesystem of Prescription-Event Monitoring (PEM) at theDrug Safety Research Unit (DSRU) at Southampton in1981.• In New Zealand, the medicines adverse reactionscommittee (MARC) is responsible for conducting suchstudies for academic purposes and the programme isknown as the Intensive medicine monitoring programme(IMMP).3
  4. 4. WHAT IS PEM?• A non interventional observational cohorttechnique, which involves health professionalssubmitting data on all clinical events reported bya patient subsequent to the prescribing of a newdrug.• It is a method of studying the safety of newmedications that are used by general practitioners.• In PEM, the exposure data are national in scopethroughout the collection period and unaffectedby the kind of selection and exclusion criteria thatcharacterise clinical trials data. 4
  5. 5. 5
  6. 6. Here patients being prescribed monitoreddrugs, which include virtually all NewChemical Entities are studied. The criteria forstudy drug are:• NCE• New Pharmacological Principle• Predicted wide spread use• Suspected problems• Identified but unquantified risks6
  7. 7. • The Information on the 1st5000-18000prescriptions for that drug are then obtained.• Prescribers are contacted with a questionnaire todetermine subsequent events or clinicaloutcomes.• Experiences with the drugs can then be examinedand the incidence of various events can beestimated.• Comparisons are made between periods before &after drug use.e.g.: The occurrence of Jaundice with ErythromycinEstolate was found be such method of study 7
  8. 8. In one such study conducted by MARC, a Cohort of3926 patients taking perhexiline & 2837 takinglabetolol, 25% of all patients discontinued takingtheir drug under the study.ADRs were the reason for stopping in 20% & 43%,for each drug, respectively.• PEM provides clinically useful information becauseit establishes,• From these data, Incidence densities are calculatedfor all events reported during the treatment with themonitored drug.8
  9. 9. • Incidence density– IDt = No of events during treatment for period ‘t’ X1000No of patient-months of treatment for period ‘t’Numerator = No. of reports of each eventDenominator = No. of patients exposed to the drugA definite time frame = The period of treatment foreach patient• These Incidence Densities/Incidence rates areranked in order of frequency• These ranked lists indicate both the nature &relative frequency of the events reported when thesedrugs are used in general practice 9
  10. 10. • For an example, a study was carried out to assessthe sedation properties of 4 anti-histaminics in themarket loratadine, cetrizine, fexofenadine ancdacrivastatine:10
  11. 11. • Results: The odds ratios (adjusted for age and sex) for theincidence of sedation were 0.63 (95% confidence interval0.36 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 cetirizine compared with loratadine. Noincreased risk of accident or injury was evident with any ofthe four drugs.• Conclusions Although the risk of sedation was low with allfour drugs, fexofenadine and loratadine may be moreappropriate for people working in safety critical jobs.• This study not only showed the sedative effects of the anti-histaminics, and compared them, it also gave an idea aboutthe incidence of other ADRs associated with the 4 drugs.• In the UK, PEM studies for response rates for over 60 drugshave been carried out and documented. 11
  12. 12. ADVANTAGES• Calculation of incidence density• Carried out on a national scale• Comparison of ‘reasons for withdrawal’ and incidencedensity• Outcome of exposed pregnancies• Signal generation and exploration• Delayed reactions can be detected• Disease investigation 12
  13. 13. DISADVANTAGES• No method of measuring compliance• No method to determine the non-prescriptionmedication• Non-return of green forms• Does not extend to hospital monitoring• Data collection is an operational difficulty 13
  14. 14. 14RECORDLINKAGESYSTEM
  15. 15. HISTORY• The term record linkage was first used by the chief ofthe U.S. National Office of Vital Statistics, Dr. HalbertL. Dunn in a talk given in Canada in 1946.• Dr. Dunn advocated the use of a unique number (e.g.birth registration number).15
  16. 16. • Historically record linkage was assigned to clerks whowould search and review lists to bring together theappropriate pairs of records for comparison, seekadditional information when there were questionablematches, and finally make decisions regarding thelinkages based on established rules.16
  17. 17. HISTORY• Formal development of a theory of record linkagestarted with the pioneering work of Fellegi and Sunter(1969).• Several people have worked on extending ormodifying their procedure (Jaro 1989; Winkler 1994).17
  18. 18. NEED FOR RECORDLINKAGE18
  19. 19. What is Record Linkage?• Record linkage is the process of bringing together two ormore records relating to the same individual (person),family or entity (e.g. event, object, geography, businessetc).• To find syntactically distinct data entries that refer to thesame entity in two or more input files.• Part of the data cleaning process, which is a crucial firststep in the knowledge discovery process .19
  20. 20. Link/Event20
  21. 21. 21
  22. 22. DETERMINISTIC RECORDLINKAGE• A pair of records is said to be a link if the tworecords agree exactly on each element within acollection of identifiers called the match key.• ALL or NONE• For example, when comparing two records on lastname, street name, year of birth, and streetnumber, the pair of records is deemed to be a linkonly if the names agree on all characters, the yearsof birth are the same, and the street numbers areidentical. 22
  23. 23. PROBABILISTIC RECORDLINKAGE• Formalized by Fellegi and Sunter [1969].• Pairs of records are classified as links, possible links, ornon-links.• Here, we consider the probability of a match in the givenobserved data.• In probability matching, a threshold of likelihood is set(which can be varied in different circumstances) abovewhich a pair of records is accepted as a match, relating tothe same person, and below which the match is rejected.23
  24. 24. INFORMATION FLOW IN RLS24
  25. 25. STANDARDIZATION• In every data there exist many manual errors and non-matching abbreviations etc which may present themselvesas separate data without actually being so• First step• To clean and standardise the data• E.g. : For input data belonging to Mr. William MarcusSmith, entries could have been made by differentindividuals as :– Smith W. M.– William M. Smith– W.M. Smith– W.M. Smithe etc 25
  26. 26. BLOCKING• In order to reduce the search space (i.e. the numberof record pairs to be compared).• To group similar records together, called blocks orclusters.• The data sets are split into smaller blocks and onlyrecords within the same blocks are compared.• E.g. instead of making detailed comparisons of all90 billion pairs from two lists of 300,000 recordsrepresenting all businesses in a State of the U.S., itmay be sufficient to consider the set of 30 millionpairs that agree on U.S. Postal ZIP code.26
  27. 27. MATCHINGExact Matching Statistical Matching• Linkage of data for the sameunit (e.g., establishment)from different files.• Uses identifiers such asname, address, or tax unitnumber• Attempts to link files thatmay have few units incommon• Linkages are based onsimilar characteristics ratherthan unique identifyinginformation27
  28. 28. Requirements for defining a RLS The types of linkages required,Whether the linkages isperformed in batch and/orinteractive mode, The security provisions forconfidential data files, The speed of operation needed, The volume of records that canbe linked with the system, The initial cost of softwareincluding licensing andmaintenance costs, Whether the software isbundled with other softwarepackages, The simplicity and flexibilityin defining the rules used forlinkages, The accuracy and statisticaldefensibility of the product, The availability ofdocumentation and training,and The maintenance and supportof the software. 28
  29. 29. GENERAL RECORD LINKAGE SYSTEM29
  30. 30. USES• The system is used to improve data quality and coverage,for long term medical follow up of cohorts, for creatingpatient-oriented rather than event-oriented data, forbuilding new data sources, and for a range of otherstatistical purposes.• It helps create statistically relevant source of ‘new’information.• Answers research questions relating to genetics,occupational and environmental health and medicalresearch. 30
  31. 31. DRAWBACKS• Issues of privacy and confidentiality• Policies for conducting studies using suchsystems must be transparent31
  32. 32. APPLICATIONS• Duplication in data in minimized• Powerful tool for generating more value out ofexisting databases• Large projects regarding the census of anentire country can be planned• More detailed information can be obtained• Becomes easier to follow cohorts32
  33. 33. Thank you!33

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