i” bi _ A i = i PRESCRIPTION | RECORD EVENT LINKAGE MONITORING | SYSTEMS (PEM)EVOLUTION OF PEM OPre-marketing clinical trials are effective in studying the efficacy of medicine but they have limitations in defining the clinically necessary safety of 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 detect rare ADR’s. * Pre-marketing developing programs are dynamic. * Special population are excluded.The contribution of the spontaneous reporting system in detecting hazards such as the oculomucocutaneous syndrome with practolol led Inman to establish the system of Prescription-Event Monitoring (PEM) at the Drug Safety Research Unit (DSRU) at Southampton in 1981. In New Zealand, the medicines adverse reactions committee (MARC) is responsible for conducting such studies for academic purposes and the programme is known as the Intensive medicine monitoring programme (IMMP).WHAT IS PEM? A non interventional observational cohort technique, which involves health professionals submitting data on all clinical events reported by a patient subsequent to the prescribing of a new drug. It is a method of studying the safety of new medications that are used by general practitioners. In PEM, the exposure data are national in scope throughout the collection period and unaffected by the kind of selection and exclusion criteria that characterise clinical trials data.The Process of Prescription Event Monitoring Biectronic copies of prescristions for study drug sent to DSRU This is the EXPOSURE DATA. ‘Signal Generation, Hypothesis Testing, = qm ——— Pee al Fotiow-up Studies OSRU sends Green Font questionnaire Prescriptions ‘Green Forrt to GP requesting routinely sert- questionnaire sent detaits of patient off to PPA back to OS RU, Benes This is the: OUTCOME DATA General Fheraaey Practitioner Prescrigtion for new ateg given to i patient oy GP Patient tates the prescription = to a. pharmacy for dispensing Patient r Process of PEMin the UKQhHere patients being prescribed monitored drugs, which include virtually all New Chemical Entities are studied. The criteria for study drug are: NCE New Pharmacological Principle Predicted wide spread use Suspected problems Identified but unquantified risksThe Information on the 1* 5000-18000 prescriptions for that drug are then obtained. Prescribers are contacted with a questionnaire to determine subsequent events or clinical outcomes. Experiences with the drugs can then be examined and the incidence of various events can be estimated. Comparisons are made between periods before & after drug use. e.g.: The occurrence of Jaundice with Erythromycin Estolate was found be such method of studyIn one such study conducted by MARC, a Cohort of 3926 patients taking perhexiline & 2837 taking labetolol, 25% of all patients discontinued taking their drug under the study. ADRs were the reason for stopping in 20% & 43%, for each drug, respectively. * PEM provides clinically useful information because it establishes, * From these data, Incidence densities are calculated for all events reported during the treatment with the monitored drug.* Incidence density —ID,= No of events during treatment for period ‘t’ X1000 No of patient-months of treatment for period ‘t’ Numerator = No. of reports of each event Denominator = No. of patients exposed to the drug A definite time frame = The period of treatment for each patient These Incidence Densities/Incidence rates are ranked in order of frequency These ranked lists indicate both the nature & relative frequency of the events reported when these drugs are used in general practice* For an example, a study was carried out to assess the sedation properties of 4 anti-histaminics in the market loratadine, cetrizine, fexofenadine ancd acrivastatine: Lovtadine Cefrcane Petvasine Ferolenadne ‘Loratadine Cetinaine Acivasine Fexofenadine Incidence de nsitp =—=——— = = i z — aaon = = = — ——— E, E SS E & > S &. é gs & £ SF Ss ‘de ff ee - # é ff of # oe ff ff fo é ¢ 8 Fig 2 Incidence density of events related to sedation in the frst Eveoimonth of treatment for four antihistaminesResults: The odds ratios (adjusted for age and sex) 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 cetirizine compared with loratadine. No increased risk of accident or injury was evident with any of the four drugs. 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- histaminics, and compared them, it also gave an idea about the incidence of other ADRs associated with the 4 drugs. In the UK, PEM studies for response rates for over 60 drugs have been carried out and documented.ADVANTAGES Calculation of incidence density Carried out on a national scale Comparison of ‘reasons for withdrawal’ and incidence density Outcome of exposed pregnancies Signal generation and exploration Delayed reactions can be detected Disease investigationDISADVANTAGES No method of measuring compliance No method to determine the non-prescription medication Non-return of green forms Does not extend to hospital monitoring Data collection is an operational difficultyHISTORY * The term record linkage was first used by the chief of the U.S. National Office of Vital Statistics, Dr. Halbert L. Dunn in a talk given in Canada in 1946. * Dr. Dunn advocated the use of a unique number (e.g. birth registration number).* Historically record linkage was assigned to clerks who would search and review lists to bring together the appropriate pairs of records for comparison, seek additional information when there were questionable matches, and finally make decisions regarding the linkages based on established rules.HISTORY * Formal development of a theory of record linkage started with the pioneering work of Fellegi and Sunter (1969). * Several people have worked on extending or modifying their procedure (Jaro 1989; Winkler 1994).NEED FOR RECORD LINKAGE In response to increasing business and health needs.What is Record Linkage? 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). To find syntactically distinct data entries that refer to the same entity in two or more input files. Part of the data cleaning process, which is a crucial first step in the knowledge discovery process .ProbabilisticDETERMINISTIC RECORD LINKAGE * A pair of records is said to be a /ink if the two records agree exactly on each element within a collection of identifiers called the match key. ALL or NONE 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 Formalized by Fellegi and Sunter [1969]. 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.INFORMATION FLOW IN RLS Blocking/ |__. ‘ Comparison Searching pct Velen Records | — Record, Decision Standardisation Pairs | Model \ Matching a ee ed il Figure 1: Information flow diagram of a record linkage svstem.STANDARDIZATION In every data there exist many manual errors and non- matching abbreviations etc which may present themselves as separate data without actually being so First step To clean and standardise the data E.g. : For input data belonging to Mr. William Marcus Smith, entries could have been made by different individuals as : — Smith W. M. — William M. Smith —W.M. Smith —W.M. Smithe etcBLOCKING In order to reduce the search space (i.e. the number of record pairs to be compared). To group similar records together, called blocks or clusters. The data sets are split into smaller blocks and only records within the same blocks are compared. E.g. instead of making detailed comparisons of all 90 billion pairs from two lists of 300,000 records representing all businesses in a State of the U.S., it may be sufficient to consider the set of 30 million pairs that agree on U.S. Postal ZIP code.MATCHING Exact Matching Statistical Matching * Linkage of data for the same * Attempts to link files that unit (e.g., establishment) may have few units in from different files. common * Uses identifiers such as + Linkages are based on name, address, or tax unit similar characteristics rather number than unique identifying informationRequirements for defining a RLS » The types of linkages required, * Whether the software is Whether the linkages is bundled with other software performed in batch and/or packages, interactive mode, > The simplicity and flexibility » The security provisions for in defining the rules used for confidential data files, linkages, > The speed of operation needed, * The accuracy and statistical > The volume of records that can _‘defensibility of the product, be linked with the system, > The availability of > The initial cost of software documentation and training, including licensing and and maintenance costs, » The maintenance and support of the software.GENERAL RECORD LINKAGE SYSTEM eS inl | standardisation Blocking / Indexing Cleaning and 7 Dares’ D poe standardisation | y Weightvector | Field classification . comparison ‘Non- Possible Clerical ce matches matches | |USES * The system is used to improve data quality and coverage, for long term medical follow up of cohorts, for creating patient-oriented rather than event-oriented data, for building new data sources, and for a range of other statistical purposes. * It helps create statistically relevant source of ‘new’ information. * Answers research questions relating to genetics, occupational and environmental health and medical research.DRAWBACKS * Issues of privacy and confidentiality * Policies for conducting studies using such systems must be transparentAPPLICATIONS Duplication in data in minimized Powerful tool for generating more value out of existing databases Large projects regarding the census of an entire country can be planned More detailed information can be obtained Becomes easier to follow cohorts