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ENCePP Guide on Methodological Standards in Pharmacoepidemiology


16.4. Methods for pharmacovigilance impact research

Note: Chapter 16.4. (formerly 15.4.) has not been updated for Revision 11 of the Guide, as contents remain up-to-date.


16.4.1. Introduction

16.4.2. Outcomes

16.4.3. Considerations on data sources

16.4.4. Study designs

16.4.5. Analytical methods

16.4.6. Measuring unintended effects of regulatory interventions



16.4.1. Introduction


Pharmacovigilance activities aim to protect patients and promote public health. This includes implementing risk minimisation measures that lead to changes in the knowledge and behaviour of individuals (e.g. patients, consumers, caregivers and healthcare professionals) and in healthcare practice. Impact research aims to generate evidence to evaluate the outcomes of these activities which may be intended or unintended. This approach has been adopted in the EMA Guideline on good pharmacovigilance practices (GVP) - Module XVI – Risk minimisation measures: selection of tools and effectiveness indicators (Rev 2), which is currently undergoing revision (see Guideline on good pharmacovigilance practices (GVP) - Module Risk Minimisation Measures for the draft of Rev. 3).


Pharmacovigilance activities are frequently examined for their impact on processes of healthcare delivery, such as healthcare outcomes or drug utilisation patterns following changes to the product information. In addition, measuring dissemination of risk minimisation is of importance as well as changes in knowledge, awareness and behaviour of healthcare professionals and patients.


These effects can be assessed separately, or combined in a framework, which is more challenging and therefore rarely done. An example of such a standardised framework includes evaluation of the effectiveness of risk minimisation measures through four domains: data, knowledge, behaviour and outcomes (Evaluating the effectiveness of risk minimisation measures: the application of a conceptual framework to Danish real-world dabigatran data; Pharmacoepidemiol Drug Saf. 2017;26(6):607-14). Further testing of this method is needed, however, to ascertain its usefulness in regulatory practice. 


Measuring the impact of pharmacovigilance activities may be challenging as these activities may target stakeholder groups at different levels of the healthcare system, co-exist with other unrelated events that can influence healthcare, and can use several tools applied simultaneously or sequentially to deliver information and influence behaviour (Measuring the impact of pharmacovigilance activities, challenging but important; Br J Clin Pharmacol. 2019;85(10):2235-7). In addition to the intended outcomes of pharmacovigilance activities, there may be unintended outcomes which are important to be measured as they could counteract the effectiveness of risk minimisation. Another challenging aspect is separating the outcomes of individual pharmacovigilance activities from simultaneous events such as media attention, reimbursement policies, publications in scientific journals, changes in clinical guidelines and practice, or secular trends in health outcomes.


This Chapter provides a detailed guidance on the methodological conduct of impact studies.


16.4.2. Outcomes


Outcomes to be studied in impact research are closely tied to the nature and objective of the pharmacovigilance activities. Because regulatory actions are mostly tailored to individual medicinal products, there is no standard outcome that could be measured for each activity and the concepts outlined in this chapter need to be applied on a case-by-case basis (Post-approval evaluation of effectiveness of risk minimisation: methods, challenges and interpretation; Drug Saf. 2014;37(1):33-42).


Outcome measures provide an overall indication of the level of risk reduction that has been achieved with a specific risk minimisation measure in place. This may also require measuring outcomes not linked to the specific medicinal product but representing potential unintended consequences of regulatory interventions e.g., change of non-target drug use in a population leading to less favourable health outcomes. Examples are provided in Table XVI.1 of the Guideline on good pharmacovigilance practices (GVP) - Module Risk Minimisation Measures.


Relevant outcomes may include: information dissemination and risk knowledge; changes in behaviour or clinical practice; drug utilisation patterns (e.g. prescribing or dispensing rates, use of treatment alternatives); and health outcomes (Measuring the impact of medicines regulatory interventions - Systematic review and methodological considerations; Br J Clin Pharmacol. 2018;84(3):419-33).


Dissemination of information and risk knowledge can be assessed in a quantitative, qualitative or mixed-methods manner. Quantitative assessment can involve measuring the proportion of healthcare professionals and patients aware of the risk minimisation measure as well as their level of comprehension (Effectiveness of Risk Minimization Measures to Prevent Pregnancy Exposure to Mycophenolate-Containing Medicines in Europe; Pharmaceut Med. 2019;33(5):395-406). Qualitative measures often focus on understanding of attitudes about the risk minimisation measure, impact of external factors on implementation and information update whilst mixed methods utilise both qualitative and quantitative approaches.


Assessment of behavioural changes is performed to measure if changes towards intended behaviour have been achieved, and to what extent. These measures align with those applied when measuring dissemination of information and risk knowledge. Quantitative assessment can include measuring the proportion of patients exposed to a medicinal product which is not in accordance with authorised use (off label use, contraindicated use, interactions). A qualitative assessment may allow an in-depth understanding of enablers and barriers in relation to awareness, attitudes towards use of the medicinal product and the causes why intended outcomes may not have been achieved.


Health outcomes should preferably be measured directly. They may include clinical outcomes such as all-cause mortality, congenital defects or other conditions that prompted the pharmacovigilance activity. Direct measurement of health outcomes is not always feasible or may not be necessary, for example when it can be replaced with indirect measures. Indirect surrogate measures may use data on hospitalisations, emergency department admissions or laboratory values e.g. blood pressure as a surrogate for cardiac risk, as outlined in Practical Approaches to Risk Minimisation for Medicinal Products: Report of CIOMS Working Group IX. An example of use of a surrogate measure is glycaemic outcomes (HbA1C change from baseline) in patients with diabetes mellitus using the Veterans Integrated Services Network database; the results confirmed a 45% discontinuation of thiazolidinedione use in this population and a worsening of glycaemic control following safety warning publicity in 2007, which may have driven the decline in usage of this class of medicines (Impact of thiazolidinedione safety warnings on medication use patterns and glycemic control among veterans with diabetes mellitus; J Diabetes Complications 2011;25(3):143-50).


Depending on the nature of the safety concern and the regulatory intervention, or when the assessment of patient-relevant health outcomes is unfeasible (e.g. inadequate number of exposed patients, rare adverse reaction), the dissemination of safety information, risk knowledge or behavioural changes may be alternative objectives of impact research (Guideline on good pharmacovigilance practices (GVP) - Module VIII – Post-authorisation safety studies (Rev 3).


16.4.3. Considerations on data sources


The impact of pharmacovigilance activities can be measured using both primary and secondary data collection, although the literature shows that the latter is more commonly used (Measuring the impact of medicines regulatory interventions - Systematic review and methodological considerations; Br J Clin Pharmacol. 2018;84(3):419-33). Chapter 7 of this Guide provides a general description of the main characteristics, advantages and disadvantages of various data sources. Chapter 7.1.2. provides guidance on primary data collection through surveys.


The impact of pharmacovigilance activities should be interpreted with a view to the limitations of the data sources used for the evaluation (A General Framework for Considering Selection Bias in EHR-Based Studies: What Data Are Observed and Why?; EGEMS. (Wash DC.) 2016;4(1):1203). Researchers should have a clear understanding of the limitations of the different data sources when planning their research and assess whether these limitations could impact the results in one direction or the other in such a way that their interpretation may be significantly influenced, for example due to bias or unmeasured confounders. As for all observational studies, the evaluation of the usefulness and limitation of a given data source for the study requires a very good understanding of the research question.


Primary data collection, via interviews or surveys, can usually never cover the complete target population. Therefore, a sampling approach is often required which can involve those that prescribe, dispense or use the medicinal product. Sampling should be performed in accordance with the Guideline on good pharmacovigilance practices (GVP) - Module XVI Addendum II, ensuring target population representativeness. The following elements should be considered to minimise bias and optimise generalisability: sampling procedures (including sample size), design and administration of the data collection instrument, analytical approaches and overall feasibility (including ethics).


Different databases are unlikely to capture all impact–relevant outcomes, even when they are linked to one another. Data of good quality may be available on hard outcomes such as death, hospital admission, emergency room visit or medical contacts but claims databases rarely capture primary care diagnoses, symptoms, conditions or other events that do not lead to a claim, such as suicidal ideation, abuse or misuse. An accurate definition of the outcomes also often requires the development of algorithms that need validation in the database that will be used for impact measurement.


Nurse-Led Medicines' Monitoring for Patients with Dementia in Care Homes: A Pragmatic Cohort Stepped Wedge Cluster Randomised Trial (PLoS One 2015;10(10):e0140203) reported that only about 50% of the less serious drug-related problems listed in the product information are recorded in patient notes. If generalisable to electronic data sources, this would indicate that incomplete recording of patient-reported outcomes of low severity may reduce the likelihood of identifying some outcomes related to a pharmacovigilance activity, for example a change in the frequency of occurrence of an adverse drug reaction (ADR). Combining different approaches such as integrating a patient survey would be necessary to overcome this situation.


Missing information on vulnerable populations, such as pregnant women, and missing mother-child or father-child links is a significant barrier to measuring the impact of paternal/maternal exposure or behaviour. For example, the impact of pregnancy prevention programmes could not be accurately assessed using European databases that had been used to report prescribing in pregnancy (The limitations of some European healthcare databases for monitoring the effectiveness of pregnancy prevention programmes as risk minimisation measures; Eur J Clin Pharmacol. 2018;74(4):513-20). This was largely due to inadequate data on planned abortions and exposure to oral contraceptives.


Depending on the initial purpose of the data source used for impact research, information on potential confounders may be missing, such as indication of drug use, co-morbidities, co-medication, smoking, diet, body mass index, family history of disease or recreational drug use. Missing information may impair a valid assessment of risk factors for changes in health care practice, but this limitation should be considered in light of the research question. In some settings, record linkage between different types of data sources including different information could provide comprehensive data on the frequency of ADRs and potential confounders (Health services research and data linkages: issues, methods, and directions for the future; Health Serv Res. 2010;45(5 Pt 2):1468-88; Selective Serotonin Reuptake Inhibitor (SSRI) Antidepressants in Pregnancy and Congenital Anomalies: Analysis of Linked Databases in Wales, Norway and Funen, Denmark; PLoS One 2016;11(12):e0165122; Linking electronic health records to better understand breast cancer patient pathways within and between two health systems; EGEMS. (Wash DC.) 2015;3(1):1127).


16.4.4. Study designs Single time point cross-sectional study


The cross-sectional study design as defined in Appendix of the Guideline on good pharmacovigilance practices (GVP) - Module VIII – Post-authorisation safety studies (Rev 3) collects data at a single point in time after implementation of a regulatory intervention. However, cross-sectional studies have limitations as a sole measure of the impact of interventions. Cross-sectional studies may include data collected through surveys and can be complemented with data from other studies, e.g. on patterns of drug use (Healthcare professional surveys to investigate the implementation of the isotretinoin Pregnancy Prevention Programme: a descriptive study; Expert Opin Drug Saf. 2013;12(1):29-38; Prescriptive contraceptive use among isotretinoin users in the Netherlands in comparison with non-users: a drug utilisation study; Pharmacoepidemiol Drug Saf. 2012;21(10):1060-6). Before-and-after study


A before-and-after study is defined as an evaluation (at one point in time) before and (one point in time) after the date of the intervention and/or its implementation. When uncontrolled, before-and-after studies need to be interpreted with caution as any baseline trends are ignored, potentially leading to the intervention effect being incorrectly estimated. Including a control (e.g., a population that did not receive the intervention or a drug not targeted by the risk minimisation measure) can strengthen this design by minimising potential confounding. However, identifying a suitable control group may be challenging or unfeasible as any regulatory action aimed at reducing risk is intended to be applied to the entire target population (see Post-approval evaluation of effectiveness of risk minimisation: methods, challenges and interpretation; Drug Saf. 2014;37(1):33-42 and Measuring the impact of medicines regulatory interventions - Systematic review and methodological considerations; Br J Clin Pharmacol. 2018;84(3):419-33). When a suitable control group is available, the difference-in-differences (DiD) method can be used. The DiD method is a controlled before-and-after design whereby comparisons are made between two similar groups under different conditions. The outcome can be measured either at a single pre-intervention and post-intervention time point, or by comparing pre- and post-intervention means, but it does not incorporate time. The DiD method then takes the difference for both groups (exposed and control) before and after the intervention, thereby controlling for varying factors in estimating the impact of the intervention (see The use of controls in interrupted time series studies of public health interventions; Int J Epidemiol 2018;47:2082–93 and Difference-in-Differences Method in Comparative Effectiveness Research: Utility with Unbalanced Groups; Appl Health Econ Health Policy. 2016; 14: 419–29). The DiD method relies upon the assumption that both groups are similar and trends are parallel, hence may be susceptible to residual confounding as a result of differences between the groups. Time series design


A time series is a sequence of data points (values) usually gathered at regularly spaced intervals over time. These data points can represent a value or a quantification of outcomes that are used for impact research. The underlying trend of a particular outcome is ‘interrupted’ by a regulatory intervention at a known point in time. Time series data can be analysed using various methods, including interrupted time series (ITS) and Joinpoint analysis. Cohort study


The cohort study design as defined in Appendix of the Guideline on good pharmacovigilance practices (GVP) - Module VIII – Post-authorisation safety studies (Rev 3) can be useful in impact research to establish the base population for the conduct of drug utilisation studies or to perform aetiological studies.


Cohort studies can be used to study exposure to the medicine targeted by regulatory interventions before and after its implementation, and indeed to perform drug utilisation studies in clinical populations targeted by these interventions. To model their impact on health outcomes, more complex study designs may be required, that are the subject of further research.


The following are examples of cohort studies being used for: Randomised controlled trial


The randomised controlled trial (RCT) as defined in Appendix of the Guideline on good pharmacovigilance practices (GVP) - Module VIII – Post-authorisation safety studies (Rev 3) can be useful in evaluating the effectiveness of different interventions but it is not always possible to randomise individual participants and few examples exist (Improved therapeutic monitoring with several interventions: a randomized trial; Arch Intern Med. 2006;166(17):1848-54). Designs including cluster randomised trials or step-wedge trials may be more feasible, in which randomisation is conducted at the level of organisation, when a phased roll-out is being considered (Research designs for studies evaluating the effectiveness of change and improvement strategies; Qual Saf Health Care 2003;12(1):47-52). RCTs could be considered more often to generate evidence on the impact of pharmacovigilance interventions by evaluating interventions that potentially enhance agreed safety information and normal methods of dissemination and communication channels.


16.4.5. Analytical methods


The analytical methods to be applied in impact research depend on the study design and approach to data collection. Various types of analyses have been used to assess the impact of a regulatory guidance, as described in: Measuring the impact of medicines regulatory interventions - Systematic review and methodological considerations (Br J Clin Pharmacol. 2018;84(3):419-33); Impact of regulatory guidances and drug regulation on risk minimization interventions in drug safety: a systematic review (Drug Saf. 2012;35(7):535-46); and A descriptive review of additional risk minimisation measures applied to EU centrally authorised medicines 2006-2015 (Expert Opin Drug Saf. 2017;16(8):877-84). Descriptive statistics


Descriptive measures are the basis of quantitative analyses in studies evaluating the impact of regulatory interventions. Whilst appropriate to describe the population to understand generalisability, simple descriptive approaches do not determine whether statistically significant changes have occurred (Measuring the impact of medicines regulatory interventions - Systematic review and methodological considerations; Br J Clin Pharmacol. 2018;84(3):419-33). When simple descriptive statistics are used, they are often insufficiently valid to determine statistical significance. Time series analysis


Interrupted time series (ITS) analysis


ITS analysis, sometimes referred to as interrupted segmented regression analysis, can provide statistical evidence about whether observed changes in a time series represent a real decrease or increase by accounting for secular trends. ITS has commonly been used to measure the impact of regulatory interventions and is among the more robust approaches to pharmacovigilance impact research (Measuring the impact of medicines regulatory interventions - Systematic review and methodological considerations; Br J Clin Pharmacol. 2018;84(3):419-33; Impact of EMA regulatory label changes on systemic diclofenac initiation, discontinuation, and switching to other pain medicines in Scotland, England, Denmark, and The Netherlands; Pharmacoepidemiol Drug Saf. 2020;29(3):296-305; The Effect of Safety Warnings on Antipsychotic Drug Prescribing in Elderly Persons with Dementia in the United Kingdom and Italy: A Population-Based Study; CNS Drugs 2016;30(11):1097-109).


ITS is well suited to study changes in outcomes that are expected to occur relatively quickly following an intervention, such as change in prescribing, and can consist of averages, proportions, counts or rates. ITS can be used to estimate a variety of outcomes including: the immediate change in outcome after the intervention; the change in trend in the outcome compared to before the intervention; and the effects at specific time periods following the intervention.


Common segmented regression models fit a least squares regression line to each time segment and assume a linear relationship between time and the outcome within each segment.


When the effects of interventions take time to manifest, this can be accounted for through the use of lag times in the analysis to avoid incorrect specification of the intervention effect. To model these effects, one can exclude from the analysis outcome values that occur during the lag or during the intervention period. Alternatively, with enough data points, the period may be modelled as a separate segment.


ITS regression requires that the time point of the intervention is known prior to the analysis and sufficient data points are collected before and after the intervention for adequate power. Studies with a small number of data points should be interpreted with caution as they may be underpowered.


An assumption of ITS segmented regression analysis is that time points are independent of each other. Autocorrelation is a measure of how correlated data collected closely together in time are with each other. If autocorrelation is present, it may violate the underlying model assumptions that observations are independent of each other and can lead to an over-estimation of the statistical significance of effects. Autocorrelation can be checked by examining autocorrelation and partial autocorrelation function plots and checking the Durbin-Watson statistic or performing the Breusch-Godfrey test (Testing for serial correlation in least squares regression. I; Biometrika. 1950;37(3-4):409-28; Testing for serial correlation in least squares regression. II; Biometrika. 1951;38(1-2):159-78). Factors such as autocorrelation, seasonality and non-stationarity should therefore be checked and may require more complicated modelling approaches if detected, e.g. autoregressive integrated moving average (ARIMA) models (Impact of FDA Black Box Warning on Psychotropic Drug Use in Noninstitutionalized Elderly Patients Diagnosed With Dementia: A Retrospective Study; J Pharm Pract. 2016;29(5):495-502; IMI Work Package 5: Benefit –Risk Integration and Visual Representation).


Long time periods may also be affected by historical changes in trend that can violate model assumptions. Therefore, data should always be visually inspected and reported.


Data point outliers that are explainable, such a sudden peak in drug dispensing in anticipation of a drug restriction policy can be controlled for using an indicator term. Outliers that result from random variation can be treated as regular data point.


Another caveat when conducting ITS analysis relates to possible outcome measure ceiling or floor effects. For example, when studying the impact of an intervention in improving the proportion of patients treated with a drug, the outcome has a natural ceiling of 100% and thus, depending of the initial level of measurement, minimal change in the outcome is observed.


Time-varying confounding, such as from concomitant interventions, may be addressed by use of a control outcome in the same population or a control population using the same outcome. An advantage on ITS analysis is the ease in stratifying results by different groups.


Joinpoint analysis


Accurately establishing the date of the intervention time period may be challenging (e.g. during a phased roll out of a regulatory intervention or when attempting to assess different parts of a regulatory intervention). In such instances, more complex modelling techniques and other approaches time series approaches could be considered.


Statistical analysis using joinpoint regression identifies the time point(s) where there is a marked change in trend (the ‘joinpoint’) in the time series data and estimates the regression function compared with previously identified joinpoints. Joinpoints can be identified by using permutation tests using Monte Carlo methods or Bayesian Information Criterion approaches (Permutation tests for joinpoint regression with applications to cancer rates; Stat Med. 2000;19(3):335-51). As the final number of joinpoints is established on the basis of a statistical criterion, their position is not fixed. Therefore, joinpoint regression does not require that the date of the regulatory intervention is pre-specified. It can be used to estimate the average percent change in an outcome, which is a summary measure of the trend over a pre-specified fixed interval. It can also be used to undertake single or pairwise comparisons. Other statistical techniques


Different types of regression models can be applied to the time series data once it has been properly organised depending upon the exact question being asked such as Poisson regression (Interrupted time series regression for the evaluation of public health interventions: a tutorial; Int J Epidemiol. 2017;46(1):348-55. Erratum in: Int J Epidemiol. 2020;49(4):1414). These methods are based on the assumption that error terms are normally distributed. When time series analysis measurements are based at extreme values (e.g. all are near 0% or near 100% or with low cell counts near 0) alternative approaches may be required (e.g. aggregate binomial regression models) and advice from an experienced statistician is recommended. Examples of impact research using time series analysis


Before-and-after after time series have been used to evaluate the effects of:

Examples of the use of Joinpoint regression analysis: Regression modelling


Multivariable regression allows controlling for potential confounding factors or to study factors associated with the impact or non-impact of regulatory interventions.  


An analysis with multivariate regression was used in Measuring the Effectiveness of Safety Warnings on the Risk of Stroke in Older Antipsychotic Users: A Nationwide Cohort Study in Two Large Electronic Medical Records Databases in the United Kingdom and Italy (Drug Saf. 2019;42(12):1471-85). The Medicines and Healthcare Regulatory Agency (MHRA) and the Italian Drug Agency (AIFA) both launched a safety warning on the risk of stroke and all-cause mortality with antipsychotics in older people with dementia. In the UK, the MHRA launched a warning in March 2004 for the use of risperidone and olanzapine which was expanded to all antipsychotics in March 2009. In Italy, AIFA restricted prescribing of antipsychotics in the elderly to specific prescribing centres in July 2005, which was followed by communication about these restrictions in May 2009. A retrospective new-user cohort study was undertaken to estimate incidence rates of stroke in elderly incident antipsychotic users. The authors showed a significant reduction of stroke after both safety warnings in the UK, while there was no impact of the warning on incidence rates of stroke in Italy. Metabolic screening in children receiving antipsychotic drug treatment (Arch Pediatr Adolesc Med. 2010;164(4):344-51) measured the impact of a class warning issued by the Food and Drug Administration (FDA) for all second-generation antipsychotics (SGAs) regarding the risk of hyperglycaemia and diabetes mellitus in 2003. This warning stated that glucose levels should be monitored in at-risk patients. A retrospective new-user cohort study was undertaken to estimate population-based rates of glucose and lipid testing in children after the availability of FDA warnings and to identify predictors of the likelihood of receiving glucose or lipid testing among SGAs-treated children after adjusting for covariates. Children without diabetes taking albuterol but no SGA drugs were used as controls. The authors showed that most included children starting treatment with SGAs did not receive recommended glucose and lipid screening.


More sophisticated methodologies, such as propensity-score matching (Chapter, instrumental variable analysis (Chapter and time-varying exposures and covariates (Chapter may be implemented in regression analyses if relevant.


Whichever design and method of analysis is used, consideration should be given to reporting both relative and absolute effects. Other types of analytical methods


Metrics such as “Population Impact Number of Eliminating a Risk factor over time t” (PIN-ER-t), and “Number of Events Prevented in a Population” (NEPP) have proven valuable in assessing the impact of removing a risk factor on public health, and may be useful in assessing impact of regulatory interventions. Illustrative examples for population impact analyses include Potential population impact of changes in heroin treatment and smoking prevalence rates: using Population Impact Measures (Eur J Public Health 2009;19(1):28-31) and Assessing the population impact of low rates of vitamin D supplementation on type 1 diabetes using a new statistical method (JRSM Open 2016;7(11):2054270416653522). Further, statistical analysis using impact metrics is possible where proxy measures are used to assess the impact that one event or resource has on another, as shown in Communicating risks at the population level: application of population impact numbers (BMJ. 2003;327(7424):1162-5); the benefit-risk case study report for rimonabant in IMI Work Package 5: Benefit –Risk Integration and Visual Representation; and in Population Impact Analysis: a framework for assessing the population impact of a risk or intervention (J Public Health (Oxf.) 2012;34(1):83-9).


Predictive modelling techniques may provide an insight into future impact of regulatory actions. Modelling the risk of adverse reactions leading to product withdrawal alongside drug utilisation data can assess the number of patients at risk of experiencing the adverse reactions per year, and provide an estimate of the number of patients per year which are protected from as a result of regulatory action (Population Impact Analysis: a framework for assessing the population impact of a risk or intervention; J Public Health (Oxf.) 2012;34(1):83-9; Assessing the population impact of low rates of vitamin D supplementation on type 1 diabetes using a new statistical method; JRSM Open 2016;7(11):2054270416653522).


Chronographs, typically used for rapid signal detection in observational longitudinal databases, have been used to visualise the impact of regulatory actions. Although this is a novel method that could potentially be applied to rapidly assess impact, the method lacks ways to control for confounding. In addition, further validation may be required to understand in which situations this works well or not (A Novel Approach to Visualize Risk Minimization Effectiveness: Peeping at the 2012 UK Proton Pump Inhibitor Label Change Using a Rapid Cycle Analysis Tool; Drug Saf. 2019;42(11):1365-76).


16.4.6. Measuring unintended effects of regulatory interventions


Pharmacovigilance activities can have unintended consequences, which could in some cases counteract the effectiveness of risk minimisation measures. To determine the net attributable impact of pharmacovigilance activities, besides the intended outcomes, other outcomes associated with potential unintended consequences may need to be measured and incorporated into the design of impact research (see Table XVI.1 of the Guideline on good pharmacovigilance practices (GVP) - Module Risk Minimisation Measures). Examples of such studies include the Effect of withdrawal of fusafungine from the market on prescribing of antibiotics and other alternative treatments in Germany: a pharmacovigilance impact study (Eur J Clin Pharmacol. 2019;75(7):979-84), which was associated with an increase in prescribing of other nasal or throat preparations but no increase in alternative antibiotic prescribing. Another example concerns the unintended increased use of conventional antipsychotics in two European countries after the introduction of EU risk minimisation measures for the risk of stroke and all-cause mortality with atypical antipsychotic drug use (The Effect of Safety Warnings on Antipsychotic Drug Prescribing in Elderly Persons with Dementia in the United Kingdom and Italy: A Population-Based Study; CNS Drugs 2016;30(11):1097-109). Further, prescribers may extrapolate warnings for one group of patients to other groups (spill-over effects), although they may not share the same risk factors. In 2003, the FDA warned of an association between SSRI prescription and suicidality in paediatric patients (<18 years of age). Subsequently, the number of prescriptions of SSRIs in newly diagnosed adult patients fell without compensation by alternative medicines or treatment (Spillover effects on treatment of adult depression in primary care after FDA advisory on risk of pediatric suicidality with SSRIs; Am J Psychiatry 2007;164(8):1198-205).


Socio-economic factors may also play an important role in implementing regulatory interventions at local level. It has been suggested that practices in affluent communities are more likely to implement regulatory interventions faster than over-stretched or under-resourced practices in more deprived communities and that permanent changes in daily practice in these communities may take longer (THE INTERNATIONAL MARCÉ SOCIETY FOR PERINATAL MENTAL HEALTH BIENNIAL SCIENTIFIC CONFERENCE; Arch Womens Ment Health 2015;18:269–408; Prescribing of antipsychotics in UK primary care: a cohort study; BMJ Open 2014;4(12):e006135).


Both health care service providers and users may circumvent or ‘work round’ restrictions. Where medicines are restricted or restrictions are perceived as inconvenient, patients may turn to buying medicines over the internet, self-medicating with over-the-counter medicines or using herbals or other complementary medicines. Healthcare professionals may subvert requirements for additional documentation by realigning diagnostic categories (Changes in rates of recorded depression in English primary care 2003-2013: Time trend analyses of effects of the economic recession, and the GP contract quality outcomes framework (QOF); J Affect Disord. 2015;180:68-78) or switch to medicines where patient monitoring is not mandated (Incorporating Comprehensive Management of Direct Oral Anticoagulants into Anticoagulation Clinics; Pharmacotherapy 2017;37(10):1284-97). The effects of progressive dextropropoxyphene withdrawal in the EU since 2007 on prescribing behaviour showed an increased use of same level analgesics but also an increased use of paracetamol as monotherapy. Aggregated dispensation data suggested that the choice of analgesics depended on physician speciality, healthcare setting, indication, patients’ comorbidities and age, underlining the complexity and international differences in pain management (Use of analgesics in France, following dextropropoxyphene withdrawal; BMC Health Serv Res. 2018;18(1):231).



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