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Keywords:

  • risk minimization intervention;
  • therapeutic risk management;
  • REMS;
  • effectiveness;
  • pharmacoepidemiology

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

Introduction

Since the introduction of therapeutic risk management regulatory guidance, an increase in the number of risk minimization interventions (RMIs) published in the literature has been observed. Methods used to evaluate their effectiveness remain, however, poorly examined.

Objective

This paper aimed to conduct a literature review on the methods of evaluation of effectiveness of RMIs and to identify methodological gaps.

Methods

The search was conducted using MEDLINE and Embase between 1 January 2000 and 31 December 2010, and updated on 1 April 2013. The following characteristics were extracted from each study: target population for the RMI, target population for the assessment of effectiveness, study design, data sources, and effectiveness outcome(s).

Results

A total of 188 unique RMIs were identified in the literature, of which effectiveness was evaluated in only 65 (34.6%) at the time of publication. The largest proportion of studies reviewed (n = 49, 75.4%) attempted to evaluate changes in behavior through prescribing or laboratory test practices. One quarter of studies evaluated the effect of RMIs on the occurrence of adverse events. Only a minority of studies used robust designs, such as randomized controlled trials (n = 6, 9.2%) or a quasi-experimental design with a parallel comparison group (n = 8, 12.3%).

Conclusion

Lack of robust methodological design used in published studies on RMI effectiveness evaluation is an important methodological gap in the evaluation of RMI effectiveness. © 2014 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.


BACKGROUND

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

In 2005 and 2006, respectively, the US Food and Drugs Administration (FDA)[1] and the European Medicines Agency[2] issued guidance documents on therapeutic risk management planning. The European Union recently implemented a new pharmacovigilance legislation.[3, 4] Risk minimization interventions (RMIs) are part of risk management plans and include all actions beyond product labeling, which aim at enhancing drug benefit–risk.[1] We conducted a systematic review of the literature and of agency websites[5] in order to describe the characteristics of the various RMIs that have been implemented. The most frequent RMIs consisted of education material targeted to healthcare professionals and/or patients. More restrictive RMIs, such as controlled distribution programs, accounted for only 6.6% of publications and 1.2% RMIs posted on agency websites.

Regulatory guidance states that the effectiveness of RMIs should be evaluated.[1] Assessment of effectiveness aims at determining the performance of the RMI itself and at identifying potential deficiencies that may warrant further interventions. Despite the available guidance documents, there are no specific recommendations on the methods of assessment of effectiveness of RMIs.[1, 6] The objectives of our review were as follows: (1) to characterize the study designs, target populations, data sources, and measures of success (effectiveness outcomes) that have been used in RMI evaluation studies published in the literature and (2) to identify methodological gaps in methods of effectiveness evaluation.

METHODS

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

Literature search strategy

A literature search was conducted using MEDLINE and Embase and followed the PRISMA Statement for Reporting Systematic Reviews and Meta-analyses of Studies.[7] Medical Subject Heading (MeSH) terms were used where possible. The term “drug toxicity” was searched along with “patient education,” “health care professional education,” “prescriber education,” “patient alert card,” “patient registry,” “medication guide,” “drug legislation,” “informed consent,” “restricted distribution,” “physician authorisation,” “drug monitoring,” “Dear Health Care Professional Letter,” “Dear Doctor Letter,” and “black box warning.” The references of all retained articles, including review articles, were scanned for potential relevant papers (“snowballing”). The initial search was completed in March 2011 and was updated on 1 April 2013, without changing the search strategy. All abstracts identified through the electronic search were reviewed by three independent reviewers, and any disagreements were resolved with a majority (two out of three assessments) decision.

Inclusion and exclusion criteria

To be included in the review, articles must have been published between 1 January 2000 and 31 March 2013 inclusively and involve drug products, use in humans, RMIs, or tools used to increase the reporting of adverse events (AEs). Natural healthcare products, devices, diagnostic chemicals, pregnancy registries without follow-up, medication errors, and products not used as therapy for illness were not retained.

Data extraction

Among those studies with an evaluation component, we systematically extracted the following: type of RMI, target population, AE(s) of special interest, region, and year of publication. Methodological components of assessment of effectiveness consisted of the following: target population for the evaluation, study design, data source, effectiveness outcome(s), and main findings.

Analysis

Components of evaluation were assessed using the theoretical framework adapted from Cabana et al.[8] and Hudon et al.[9] and depicted in Figure 1. On the basis of this framework, adherence to RMI recommendations and integration of such recommendations into medical practice are influenced by physician and patient characteristics, as well as by the healthcare setting. Factors influencing adherence to RMIs are divided into three temporal phases: (i) knowledge and awareness; (ii) attitude; and (iii) behavior. As shown in Figure 1, a large amount of information and disagreement with recommendations are both important deterrents of effectiveness. Integration of the RMI into the pathway of patient care may explain regional differences in RMI effectiveness. Finally, medical practice is also influenced by lack of healthcare resources and/or reimbursement constraints. The strength of the evidence of RMI effectiveness was assessed using the Shadish et al.[10] ranking of designs for causal inference, which ranges from robust experimental designs such as randomized controlled trials to weaker designs such as observational studies without a comparison group.

image

Figure 1. Theoretical framework to assess the effectiveness of risk minimization interventions. Adapted from Cabana et al. (8) and Hudon et al. (9)

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RESULTS

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

Literature search

A total of 4935 sources were identified from the MEDLINE and Embase searches. Of these, 154 met the inclusion and exclusion criteria, and another 34 sources were obtained from snowballing, yielding a total of 188 sources retained for the review. Of these, 65 (34.6%) reported an effectiveness evaluation (Figure 2). The distribution of types of RMIs with an evaluation of effectiveness is shown in Figure 3, and the characteristics of the evaluation studies are described in the Supporting Information. Education material, training, black-box warning, and medication management system appeared to be most frequently evaluated in the literature.

image

Figure 2. Results of the literature search on risk minimization interventions

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image

Figure 3. Number of studies with an evaluation component, according to type of risk minimization intervention. DHCP = Dear Health Care Professional letter; SW = safety warnings; EM = education materials; CM = communication materials; MMS = medication management system; IC = informed consent; RD = restricted distribution; R = registry; General = overall knowledge on risk minimization intervention tools

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Components of risk minimization

The vast majority of studies with an evaluation component focused on the behavioral construct of risk minimization (n = 49, 75.4%), measured through prescribing and laboratory testing practices. Other studies evaluated knowledge or attitudes (n = 9, 13.5%). Fewer studies addressed more than one component: knowledge and attitudes,[11, 12] knowledge and behavior,[13-15] and attitudes and behavior.[16-18] Only one study, consisting of a survey, addressed all three components of risk minimization (knowledge, attitudes, and behavior).[19] One quarter of studies (n = 16, 24.6%) evaluated the impact of the RMI on the occurrence of AEs.

Study designs used to assess the effectiveness of risk minimization interventions

Study designs used to assess each component of risk minimization (knowledge, attitudes, and behavior) are shown in Table 1. It should be noted that the effectiveness of a given RMI could be evaluated through more than one study. For example, the effectiveness of safety warnings on antidepressants issued by the FDA was evaluated in six studies published in the literature.[17, 20-24] Also, six studies evaluating label changes and Dear Health Care Professional (DHCP) letters on cisapride were found.[25-30]

Table 1. Distribution of study designs used to assess the various components of risk minimization interventions
  KnowledgeAttitudesBehaviorClinical outcome (adverse event)
Randomized controlled trials 0114
Interrupted time series analysisWith comparison0021
Without comparison00170
Pre–post interventionWith comparison0034
Without comparison12103
Cohort studies/registryWith comparison0011
Without comparison0052
Cross-sectional studies 1041
Surveys 4550
Qualitative assessment 3110
Total 994916

The great majority of studies that evaluated behavior used interrupted time series analyses or pre–post intervention designs without a comparison group, often using data from administrative claims databases. Given the availability of historical data (i.e., pre-intervention), most of the RMIs involving safety warnings or DHCP letters were evaluated using time trends in prescribing or laboratory test monitoring. We have identified only six studies that used a parallel comparison group. Such comparison groups were either randomized[31] or consisted of other geographical locations (states)[32] or medical centers where the RMI was not implemented,[33, 34] another drug not targeted by the intervention,[35] or young adults when children or adolescents were the target of the intervention.[36] In another study[27] in the absence of a reference population, all external factors, such as publicity and media coverage, which could also influence prescribing trends in addition to the DHCP letter, were qualitatively assessed. Restricted distribution programs, generally implemented at the time of product approval, tended to be evaluated through cohort studies.[35, 37, 38]

Knowledge and attitudes were most often evaluated using surveys or qualitative assessments,[17, 42, 75, 76] whereas a randomized trial[39] or pre–post intervention without a comparison group[12] was rarely used. As shown in Table 1, among the 16 studies that evaluated the impact of the RMI on the occurrence of AEs, robust designs such as RCTs[33, 34, 40, 41] or comparative designs (n = 6) were often used.

Effectiveness outcomes

Measures of effectiveness, or success, of RMIs included prescription or dispensing rate (n = 18, 27.6%), frequency of AEs (n = 16, 24.6%), including pregnancy rate during isotretinoin or thalidomide treatment, contraindicated co-prescription rate (n = 10, 15.4%), laboratory testing rate (n = 7, 10.8%), knowledge retention and attitude toward the RMI (n = 7, 10.8%), changes in medical practice (n = 6, 9.2%), rate of treatment initiation (n = 4, 6.2%), awareness of RMI (n = 4, 6.2%), rate of use of treatment alternatives (n = 3, 4.6%). Several studies evaluated the effectiveness of the RMI with more than one outcome.[14, 15, 17, 18, 36, 42-44]

Of the 41 RMIs consisting of education, 31 used prescription rate or contraindicated co-prescription rate as a measure of effectiveness. Using the information matrix included in the Supporting Information, we have ascertained the aim of the intervention and the measure of effectiveness, from which we have assessed concordance. In nearly one third (n = 18) of the studies, the effectiveness measure did not correspond to the aim of the intervention. Inconsistencies originated from the following: prescription rates were used as measures of effectiveness while the RMI aimed at preventing the occurrence of AEs (n = 10),[20-24, 45-49] or the RMI sought to enhance laboratory testing (n = 2).[50, 51] In other instances, occurrence of AEs was used as the effectiveness measure while the intervention aimed at avoiding concomitant use of contraindicated medications (n = 2),[52, 53] at increasing laboratory testing (n = 2),[54, 55] or at enhancing information disclosure by healthcare professionals (n = 1).[56]

For studies that involved surveys, the definition of RMI effectiveness was not explicit, consisting mainly of a change in practice (change in medication dosage or drug, referral of patients to a specialist, performance of baseline assessment before initiation of a medical treatment, etc.) (n = 5),[14, 15, 17, 18, 42] knowledge retention (n = 5),[11, 19, 42, 43, 52] awareness of RMIs (n = 5),[14, 15, 17, 18, 57] and increase of disclosure of information (n = 1).[58] In cases where effectiveness was defined as knowledge retention, there was no specification on what represented adequate knowledge retention or what information should the target population have retained.[19, 43, 52]

Main data sources used to ascertain effectiveness outcomes included claims databases (n = 34, 52.3%), questionnaires (n = 19, 29.2%), medical charts or electronic health records (n = 9, 13.8%), and clinical data or laboratory findings (n = 4, 6.2%). Some studies have evaluated the effect of an RMI on the initiation of new treatments (i.e., incident drug users),[20, 23, 46, 48, 59, 60] whereas others focused on the prescribing rate for all users (incident and prevalent).[13, 21, 24, 36, 41, 45, 49, 53, 61-65, 77] Some studies defined new use as the absence of a prescription record during the preceding 4 months[23, 48] or 12 months.[20, 46] Among studies that examined the effectiveness of an RMI on the rate of concomitant use of contraindicated medications, concomitant use was most frequently defined as two contraindicated products with active prescriptions that overlap on at least one day.[15, 26, 27, 29, 30, 58, 66]

Three surveys cited in the literature targeted different healthcare professionals, namely physicians, nurses, pharmacist, and social workers.[11, 12, 67] Two surveys targeted only pharmacists,[18, 19] and two surveys only specialists, pediatricians,[14] and neurologists.[15] The completion rate for the surveys ranged from 50%[19] to 100%[67]. All surveys were comprised of multiple-choice questions. One survey utilized a scale to rate the healthcare professionals' attitude toward RMI[19], and one collected doctors' comments.[14]

Results of assessment of risk minimization interventions

Among the studies reviewed, RMIs appeared to be successful or partly successful (n = 40, 61.5%) or 15 (23.1%) were not, and for nine studies (13.8%), it was not possible to draw a conclusion regarding their effectiveness because either the study was descriptive, had no comparison group, or had no threshold for effectiveness defined. After the implementation of the RMI, consisting mainly of warnings or DHCP letters, studies found a decrease in prescribing or co-prescribing rate of the study drug (n = 24),[16, 20, 21, 23-30, 40, 41, 44-46, 49, 51, 54, 62-64, 66, 68] a decrease in frequency of AEs (n = 10),[33, 34, 54, 55, 60, 61, 69-71, 78] a decrease in pregnancy rate while on thalidomide (n = 1),[38] an increase in laboratory testing rate (n = 6),[31, 35, 44, 50, 65, 72] or a change in medication dosage (n = 1).[65]

When successive communication interventions were implemented over time,[24, 26-31, 49, 51, 62, 63, 72] the first ones were often found unsuccessful (e.g., not reaching the expected decrease in prescription rate), whereas the latter interventions seemed more effective.[26-28, 30, 50] Wilkinson et al. showed that multiple safety alerts are required to yield a significant decrease in the co-prescription rate of cisapride and contraindicated drugs.[30]

DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

Interrupted time series analyses or pre–post intervention analyses without comparison groups were the most frequent designs used to assess the effectiveness of RMIs. These are associated with well-known limitations. First, in the absence of a parallel comparison group (i.e., a group of patients unexposed to the intervention), it is difficult to ascertain if the observed outcome is attributable to the intervention or to the presence of external factors. Second, they are not suitable for drugs that have newly been introduced on the market because of the absence of pre-intervention data. For these reasons, they have mainly been used to assess the effect of regulatory interventions on drugs that have been marketed for a while. Only a minority of studies used a reference group, and it has been suggested that, whenever it is not possible, reference data (e.g., literature review or historical data) could be used.[73]

Experimental or quasi-experimental designs, although more robust, are rarely feasible because most RMIs, such as DHCP letters or safety warnings, are implemented in the entire population. Designs used to evaluate the effect of RMIs on the occurrence of AEs tended to be more robust than those that addressed the other components of risk minimization (i.e., knowledge, attitudes, and behavior). However, such studies were a minority and involved RMIs implemented at the level of the region or institution rather than in entire populations. In order to circumvent this limitation, some authors have attempted to use drugs or subpopulations not targeted by the RMI as a comparison population. Although appealing, the comparability of such reference should be assessed. For other types of RMIs, the selection of observational or descriptive studies without comparison groups could be explained by the lack of clear guidance on the design. For example, FDA guidance anticipates that RMI evaluations “would involve the analysis of observational or descriptive data.” At the same time, it is recommended, whenever feasible, to include at least two evaluation methods for each critical RMI goal.[1]

Most of the evaluation studies assessed behavioral changes using prescription or dispensing data. Assessing behavioral changes in a population without previously having assessed the knowledge and the attitude toward the RMI can introduce unknown confounders. Consequently, it is not possible to perform a root cause analysis of suboptimal behaviors using solely prescription data. Only one of the published studies evaluated all three components of the effectiveness framework (i.e., knowledge, attitude, and behavior). Neither European nor US guidance clearly emphasizes the assessment of all three components. Within the European dual approach, that is, implementation assessment and level of risk control assessment, the knowledge and attitudes could be a part of implementation assessment.[6] The FDA guidance highlights the assessment of the effectiveness of the RMI through health outcomes, for example, complication rate, and recommends “assessments of comprehension, knowledge, attitudes, and/or desired safety behaviors about drug safety risks” if “a health outcome cannot be particularly accurately measured”. Recently, the Office of the Inspector General determined that most RMIs, implemented in the USA, were not meeting their goals because of deficiencies in patient and prescriber awareness of drug risks.[74]

Among the RMIs consisting of education on key safety messages, the most utilized effectiveness outcome consisted of prescription rate or contraindicated co-prescription rate. By measuring the contraindicated co-prescribing rate, one may estimate the number of patients who are potentially at risk of AE. If the aim of the intervention is to prevent at-risk patients from receiving the drug, then such an outcome is an appropriate measure of success. However, if the intervention aims at preventing the occurrence of AEs in the target population, then occurrence of AEs would be preferable over prescribing rates.

Main data sources used to ascertain effectiveness outcomes included claims databases, questionnaires, medical charts or electronic health records, and clinical data or laboratory findings. The use of claims databases to assess prescription rate is associated with information bias given that such databases record patterns of dispensing, dispensing frequency being influenced by prescribing practices, and patient acquisition behavior. Acquisition behavior is in turn a function of access and treatment compliance. In some studies, effectiveness has also been determined using the occurrence of AEs ascertained in claims databases. Although these databases are routinely used in observational studies, they are associated with well-known limitations that have been insufficiently addressed in the published studies on RMIs. Inaccuracy of diagnostic codes inherent to physician billing claims is likely to be associated with an overestimation or underestimation of the effectiveness of a given intervention.

In many studies, study outcome consisted of a continuous variable, for example, prevalence of drug use, and any change over time that reached statistical significance was an indicator of success. This is a limitation, especially for studies using administrative claims databases, where statistical significance is often reached owing to large sample sizes. More guidance on a threshold of behavior change that would indicate success of the intervention would be required.

CONCLUSION

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

To our knowledge, this review is the first of its kind to have been conducted. The main limitation is that only sources identified in the literature were included. A more exhaustive search of other resources, such as RMIs posted on drug agency websites or manufacturer websites, was considered. However, the amount of information disclosed on these latter resources is generally not sufficient to extract all the methodological elements required for the review.

This literature review highlights some of the methodological gaps in the assessment of the effectiveness of RMIs. Only 34.6% of the RMI publications found in the literature included an evaluation component. The effectiveness evaluation of recent RMIs may be in progress or planned, and for this reason, the results are not yet available in the published sources. In studies that included an evaluation component, the measure of effectiveness did not appear consistent with the aim or the target population of the RMI for one third of the studies. These observations reveal that some evaluation studies are based on an inappropriate theoretical framework.

Because of the heterogeneity of the target populations, drugs, and outcomes under study, it is difficult to rank the types of RMIs according to their effectiveness. Nevertheless, lack of robust designs is a major methodological gap and could be partly due to difficulties in finding appropriate reference population and/or absence of a clear guidance in regulatory documents. Especially for newly marketed drugs, the absence of an appropriate comparison group is a major challenge that should be addressed in future studies.

CONFLICT OF INTEREST

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

There are no conflicts of interest to declare for A. M. C., I. G., L. N., C. C. , and Y. M. S. F. is an employee of AstraZeneca Canada.

KEY POINTS

  • The study presents a literature review on the methods of evaluation of effectiveness of risk minimization interventions (RMI).
  • About one third of the RMI publications found in the literature included an evaluation component.
  • The majority of studies have evaluated prescription behavior and considerably less assessed the impact of the intervention on the occurrence of adverse events.
  • A major methodological gap is a lack of robust designs, owing to absence of comparison groups.

ACKNOWLEDGEMENTS

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information

This review was conducted through a research grant from AstraZeneca Canada.

Anne-Marie Cloutier is a recipient of a scholarship from the Canadian Institutes for Health Research (CIHR).

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information
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Supporting Information

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. CONFLICT OF INTEREST
  9. ETHICS STATEMENT
  10. ACKNOWLEDGEMENTS
  11. REFERENCES
  12. Supporting Information
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