What Factors Make EU Regulators Want to Communicate Drug Safety Issues Related to SGLT2 Inhibitors? An Online Survey Study

2.1 Study Design and Participants

We conducted an online cross-sectional survey study among medicines regulators of the EU regulatory network [18]. For recruitment, we first presented the study to the members of various committees and working parties at EMA, namely the PRAC, the Committee for Medicinal Products for Human Use (CHMP), and the Scientific Advice Working Party (SAWP). Committee members subsequently received an email including an information letter, a unique survey link, and a request to provide up to five further email addresses of clinical and pharmacovigilance assessors from their national regulatory agencies, whom we then also invited to participate in the study. There was no financial compensation for participation, and regulators who did not complete the survey received a maximum of three reminder emails in a 6-week period. To ensure that the included participants were only clinical and pharmacovigilance assessors, we used the response to the survey question “Do you have experience as an assessor of human medicinal products?” as a check. The survey was open from 19 April 2021 to 21 June 2021; responses were collected anonymously. The information letter provided to the participants, the survey, and the Checklist for Reporting Results of Internet E-Surveys (CHERRIES checklist) [19] are available in the Electronic Supplementary Material (ESM) 1.

2.2 Survey

The survey consisted of three parts: questions regarding demographic and professional characteristics, a rating-based conjoint analysis experiment containing various safety issues, and questions regarding regulators’ attitudes (ESM 1). The questions were asked in English, and the online format of the survey was created using the Research Electronic Data Capture 10.0.23 web application (REDCap—http://www.projectredcap.org) [20, 21]. The survey was piloted for ease of use, functionality, and content by 18 individuals, who were researchers at the University Medical Center, Groningen, the Netherlands, and medicines regulators at the Dutch Medicines Evaluation Board. These participants were excluded from participating in the study, and minor adaptations to the survey were made according to their feedback.

2.2.1 Demographic and Professional Characteristics

The survey assessed the following information regarding demographic and professional characteristics: age (continuous), gender (woman or man), country (listed as per the EMA website, grouped for the analyses in Northern, Southern, Eastern, and Western Europe as per United Nations division [22, 23]), experience in pharmacovigilance (yes or no), and experience in endocrinology (yes or no). Given the focus of the study on post-marketing safety issues of a medicine for T2DM, experience in pharmacovigilance and endocrinology were considered relevant for the study.

2.2.2 Rating-Based Conjoint Analysis Experiment

The rating-based conjoint analysis experiment was introduced by providing some basic information about a medicine for the treatment of T2DM. The medicine was presented as a hypothetical drug, without mentioning a specific medicine or class of medicines. However, the provided information regarding the medicine was based on real information regarding SGLT2 inhibitors and included a short summary of selected favourable and unfavourable effects. To obtain an indication of the responders’ benefit-risk evaluation of the drug, they were asked the question “How would you rate the benefit-risk balance of this drug?” (using a visual analogue scale (VAS) from 0 to 100).

Next, we presented various scenarios of safety issues described in terms of four characteristics, termed attributes. Each attribute had two or three alternatives, termed levels. The first attribute was the ADR, which could have three levels, namely DKA, amputation, or bone fracture. These ADRs have been associated with SGLT2 inhibitors and were described according to the definitions available in the EMA assessment reports of this drug class [24,25,26]. We selected these ADRs because of the previously reported discrepancies in safety advisories among regulatory agencies worldwide [5]. The other three attributes were hypothetical for each scenario and had two levels each (Table 1): (1) source of information (i.e., spontaneous reports/epidemiological studies or clinical trials), (2) level of causality (possible or probable), and (3) frequency of the ADR (two times higher or three times higher than the risk with the standard of care, which was specified for each ADR). These attributes were selected because of their possible relevance at the time of assessing a safety issue, based on input from pharmacovigilance experts and information from regulatory guidelines [24,25,26,27,28,29].

Table 1 Attributes and attribute levels used in the rating-based conjoint experiment

To obtain the minimum number of scenarios necessary to estimate all main effects and all possible interaction effects between the ADRs and the other attributes, we generated an orthogonal fractional factorial design for each ADR. This process resulted in a total of 12 scenarios, four per ADR, with differences in the level of at least one of the attributes. We created three blocks of scenarios based on the ADRs, and the order of the scenarios within each block was randomised. The order in which the blocks were presented in the survey was also randomised, and all participants were asked to assess the 12 scenarios.

For each scenario, the participants were asked three questions. The first question assessed their concern for the safety issue: “With this additional hypothetical information available, how concerned are you about this safety issue?” (VAS from 0 to 100). The next questions addressed their opinion on the need to communicate about the safety issue: “In your opinion, should the summary of product characteristics (SmPC) of the drug be updated?” (yes or no) and “In your opinion, should a direct healthcare professional communication (DHPC) be sent out?” (yes or no).

2.2.3 Regulators’ Attitudes

For the regulators’ attitudes, we assessed the influence of their beliefs about medicines and general risk perception, which were measured using the Beliefs about Medicines Questionnaire (BMQ) and the Domain-Specific Risk-Taking (DOSPERT) Scale, respectively. For the BMQ, we included the subscales of benefits (e.g., “Medicines help many people to live better lives”), harm (e.g., “People who take medicines should stop their treatment for a while every now and again”), and overuse (e.g., “Doctors use too many medicines”), each of which contains four items. Each item is scored on a 5-point Likert scale; therefore, the score of each subscale can range from 4 (strongly disagree) to 20 (strongly agree) [30]. The Cronbach alpha values for each subscale were 0.55, 0.50 and 0.66, respectively. For the DOSPERT scale, we included the domains of ethical (e.g., “Taking some questionable deductions on your income tax return”), financial (e.g., “Betting a day’s income at the horse races”), health and safety (e.g., “Drinking heavily at a social function”), recreational (e.g., “Going camping in the wilderness”), and social (e.g., “Admitting that your tastes are different from those of a friend”), each of which contain six items. The items are scored on a 7-point Likert scale, and the scores can range from 6 (not at all risky) to 42 (extremely risky) per domain [31]. The Cronbach alpha values for each domain, following the above order, were 0.63, 0.81, 0.66, 0.72, and 0.70, respectively.

2.2.4 Outcome Variables and Determinants

For the first study aim, the outcomes were the regulators’ opinions regarding the need to update the SmPC and to send a DHPC, and the determinant was the level of concern. For the second study aim, the outcome was the concern regarding the safety issues, and the determinants were the attributes of the safety issue, the demographic and professional characteristics, and the regulators’ attitudes (Fig. 1).

Fig. 1figure 1

Overview of the study aims, outcomes, and determinants. 1Aim A, to explore to what extent regulators’ opinions regarding the need to communicate through updating the SmPC or sending a DHPC is influenced by regulators’ concern about the safety issue. 2Aim B, to assess whether regulators’ concerns are influenced by certain characteristics of the safety issue, demographic and professional characteristics of the regulators, and regulators’ attitudes. SmPC summary of product characteristics, DHPC direct healthcare professional communication

2.3 Data Analyses

We used descriptive statistics for the analysis of the demographic and professional characteristics, the regulators’ attitudes, the benefit-risk evaluation, and the level of concern per scenario, as well as for the calculation of the proportion of participants who considered it necessary to communicate the risk per scenario. Only those participants who completed at least one question regarding their level of concern towards the safety issue, their opinion on the need to update the SmPC, or the need to send a DHPC were included in the study.

To determine the influence of the level of concern on the need to update the SmPC or to send a DHPC, we fitted generalised linear mixed-effects models (GLMMs) with a binomial distribution and a logit link function. In these models, the level of concern was included as a fixed effect and by-subject random intercepts and slopes for the level of concern were included as random effects. Results regarding the effect of the level of concern on the need to communicate are presented as the odds ratios of updating the SmPC or sending a DHPC for a 10 percentage-point increase in the level of concern and graphically by plotting the estimated population-level probabilities of updating the SmPC or sending a DHPC against the level of concern.

To assess the effects of the attributes of the safety issue, demographic and professional characteristics, and regulators’ attitudes on the level of concern, we fitted multiple linear mixed effects models. We began by fitting a crude model in which the attributes of the safety issue were included as the only fixed effects and by-subject random intercepts and slopes for the attributes of the safety issue were included as the random effects. Subsequently, we fitted separate follow-up models in which, while maintaining the fixed and random effects of the crude model, we added the other determinants (i.e., demographic and professional characteristics and regulators’ attitudes) one by one as fixed effects. We tested for the determinants’ main effect as well as all possible two-way interactions between each determinant and the attributes of the safety issue. We performed backward elimination to stepwise remove all non-significant interaction terms. Results are presented as estimated marginal means (also known as least-squares means), which reflect the predicted outcome for each level of a factor averaged over all possible combinations of the levels of the other factors in the model. They were unstratified for the crude model and stratified by demographic and professional characteristics, and by regulators’ attitudes for the follow-up models. The groups of categorical variables were pre-defined by definition (e.g., women vs. men), and groups of the continuous variables were created using the score of the variable at the 25th, 50th and 75th percentiles. Further details regarding the estimated marginal means as well as the regression coefficients of each model are presented in ESM 2.

Because the sampling scheme per country could have resulted in data clusters per country, we generated a multi-level model with observations grouped by individuals nested in country. These results showed no indication of clusters (ESM 2); therefore, no adjustments for the sampling scheme were made in the statistical analysis.

The analyses were performed in R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria; URL https://www.R-project.org/) with the packages lme4, emmeans and lmerTest [32,33,34]. Statistical significance was indicated by p-values less than 0.05. Figures were generated in R and Microsoft Excel® 2010 (Microsoft Corp., Redmond, WA, USA).

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