Article Information

Priscilla P.M. Nyambayo^{1, *}, Michael S. Gold², Ushma C. Mehta³

¹Pharmacovigilance and Clinical Trials Division, Medicines Control Authority of Zimbabwe, Harare, Zimbabwe.

²University of Adelaide, Discipline of Paediatric, Women’s and Children’s Health Network, Adelaide, Australia.

³Centre for Infectious Disease Epidemiology and Research, School of Public Health, University of Cape Town, South Africa.

*Corresponding author: Priscilla P.M. Nyambayo. Pharmacovigilance and Clinical Trials Division, Medicines Control Authority of Zimbabwe, Harare, Zimbabwe.

Received: 25 May 2023; Accepted: 02 June 2023; Published: 19 June 2023

Citation: Priscilla P.M. Nyambayo, Michael S. Gold, Ushma C. Mehta. Data-Driven vigiPoint identification study of Zimbabwe ICSRs compared with RoW VigiBase data with and without USA reports. Fortune Journal of Health Sciences. 6 (2023): 237-245.

Abstract

Keywords

vigiPoint analysis method, vigiPoint score (shrinkage log-odds ratios) (SLORs), individual case safety reports (ICSRs)

Article Details

1. Introduction and Background

Since 1998 Zimbabwe has participated in the WHO international drug monitoring program which is co-ordinated by the Upsala Monitoring Centre (UMC), by uploading individual case safety reports (ICSRs) to the global database known as VigiBase that currently has over thirty-two million ICSRs [1-5]. The United States of America (USA) is the highest reporter with about 48% (15.3 million) reports [6]. Africa only contributed a total 0.9% ICSRs to VigiBase which excluded most SADC countries hence it was not possible to conduct regional comparative VigiPoint analysis with Zimbabwe ICSRs [2, 7]. Zimbabwe contributed 0.018% (5231) reports from January 1998 to July 2022. Adverse events (AEs)/ICSRs reporting patterns tend to vary between countries, reflecting differences in medicine and vaccine profiles, reporting method cultures, clinical practice, comorbid conditions, and pharmacogenetics [8]. Understanding these reports in the global context can be helpful for signal hypothesis generation and risk minimisation of patient vulnerabilities due to AEs. They can also provide an overview of the coverage of the national spontaneous reporting system, identifying opportunities for strengthening and reprogramming. VigiBase ICSRs data originates from various reporters and different countries with diverse quality information of the ICSRs known as VigiGrade completeness score, and limited causality assessment information that classifies the likelihood that a medicine and/or vaccine caused the AE [8, 9]. The reported AEs are coded with the Medical Dictionary for Regulatory Activities (MedDRA^®), medicines and vaccines are encoded with the WHO Drug Global dictionary for medicinal information facilitating interpretation and evaluation of safety signals [6, 10]. The Council for International Organizations of Medical Sciences (CIOMS) defined a signal as information that arises from one or multiple sources (including observations or experiments), which suggests a new potentially causal association or a new aspect of a known association between an intervention and an event or set of related events, either adverse or beneficial, that is judged to be of sufficient likelihood to justify verificatory action [11].

On a conceptual level, VigiBase has inbuilt signal detection statistical data mining analysis tools known as disproportionate analysis. These include the information component (IC), proportionate reporting ratio (PRR), and relative odds ratio (ROR). These tools are similar since they are all observed versus (vs) expected ratios. They mostly differ on how the scale is presented (logarithm for IC vs no logarithm for PRR and ROR) and what is included in the background (in IC reports the medicine of interest is included in the background, while excluded from the background for PRR). Thus, they always point in the same direction, so if the IC is elevated, then PRR and ROR are also elevated.

vigiPoint is also a disproportionality analysis, but not in the same way as the IC, PRR or ROR measures. The IC, PRR or ROR measures always compare the number of reports from a combination (e.g., paracetamol – rash) to the expected number of reports in the background (which usually includes the entire VigiBase database). In vigiPoint, the comparisons done are also disproportionality, however, the foreground is selected (e.g., all reports from Zimbabwe) and compared with the background (e.g., RoW VigiBase) and then vigiPoint analyses all structured fields in the reports to determine if any of those fields are disproportional to the selected background. It is important to note that none of these comparisons are exposure and event combinations; instead, vigiPoint® compares the fraction of reports with a certain feature in the foreground (e.g., reports with paracetamol, serious reports, reports with rash, reports with female patients, etc.) with the same features for the background. It is important to note that vigiPoint® aims to highlight features where the fraction deviates by at least ~40% between the foreground and the background. vigiPoint aims to answer the very general question: What are the main differences between the foreground dataset (e.g., reports from Zimbabwe) compared to the background dataset (e.g., reports from the rest of the world (RoW) with or without USA reports. VigiPoint analysis is useful to national pharmacovigilance centres since it examines ICSRs expectations, observations and highlights large differences [12]. VigiPoint analysis can compare performance of the national pharmacovigilance system ICSRs to ROW data, allowing for reflection on why differences are present, how the system has advantages and limitations in terms of signal hypothesis generation and opportunities for strengthening the national PV system to improve ability to detect signals [12, 13]. It can look at reporter profiles, overview of types of events reported and categories of medicines/vaccines and types of patients most likely to be included in the data [13].

Objective of the Study: The objective of this study was to explore vigiPoint differences in the medicines and vaccines ICSRs reporting patterns of Zimbabwe reports compared to the RoW with and without the USA reports. The differences included patients’ age range, ICSRs VigiGrade completeness score, types of AEs profiles, co-suspected drugs and their seriousness submitted to VigiBase from January 1998 to July 2022.

2. Methods and Materials

This study used vigiPoint, method of analysis of deduplicated Zimbabwe ICSRs (ADRs/SAEs /AEFIs) key features in VigiBase that are identified using odds ratios subjected to statistical shrinkage compared to RoW reports with and without USA Reports [12]. Such odds ratios were found by other studies to be advantageous over alternative measures of association such as mutual information, relative risk (including proportional reporting ratios (PRR) and information component (IC) values), and cosine similarity of working well for common as well as for rare covariates [8, 12, 14]. Figure 1 below adapted from Juhlin K. et al 2017 and Wakao R. et al 2019) shows the vigiPoint equation in relation to the Zimbabwe VigiBase data(subset of interest) and RoW(comparator, the data can be summarised in a standard 2 × 2 contingency Table 1 below, ) including each potential interest [8, 12].

Table 1: 2x2 Contingency table for Zimbabwe ICSRs subset of interest compared with rest of the World (RoW) ICSRs data.

The equation that vigiPoint uses is the following, where the 0.01*(a+b) is the shrinkage factor and the variables are:

Figure 1: The Zimbabwe VigiBase data and the RoW vigiPoint equation, adapted from Juhlin K. et al 2017 and Wakao R. et al 2019 [8, 12].

vigiPoint data-driven analysis method is an open-ended exploration in VigiBase/pharmacovigilance that compares a report subset to one or more reference subsets in terms of the relative frequency of a wide range of covariates, for example, patient sex, age, reported medicines, vaccines AEs, and reporting country [12, 14]. All non-categorical covariates based on the ICSR reports data in VigiBase are automatically divided (without selection bias) into relevant groups using the holistic inbuilt disproportionate analysis tools stated above such as IC before VigiPoint analysis such as patient age, suspected medicines/vaccines , type of AEs, seriousness, quality of reports (VigiGrade completeness), and reporter types [8, 12, 13]. VigiPoint tool then analyses each comparison [e.g., the relative frequency of reports from Zimbabwe, or that of reports with Bacille Calmette-Guérin (BCG) vaccine] is essentially univariate and independent of the other [12, 14]. The comparisons are done using shrinkage log-odds ratios with 99% credibility intervals and require that the full credibility intervals be at least above 0.5 or below 0 in the log2 scale for the feature to be highlighted, which is roughly equivalent to a 40% difference in the shrunk odds ratio [12, 14]. The distance in the log scale between zero (0) and the credibility interval is referred to as the vigiPoint® score [12, 14].

The vigiPoint shrinkage together with the high thresholds applied for the credibility intervals and the 0.5 threshold ensures that only robust, differences are identified [12, 14]. The shrinkage odds ratio of vigiPoint is considered an observed-to-expected ratio and is obtained as the Bayesian posterior mean of an intensity parameter μ for a Poisson Po(μ · E)-distributed observed number of reports (O), where the expected value E is bc/d(8). With a Gamma prior distribution with hyperparameter k for μ: G (k; k), the corresponding posterior distribution for μ is also Gamma (but with parameters O + k and E + k), and the shrunk odds ratio is computed as follows adapted from Wakao R. et al 2019 and Norén GN et al 2013 [8, 13]:

Credibility intervals that indicate a range of values of μ compatible with data can be calculated by the inverse of the Gamma cumulative distribution function [8]. The lower C% credibility interval limit of the shrunk log-odds ratio (or the upper C% confidence interval limit in the case of negative associations) is referred as the vigiPoint score, where C is the size of the credibility interval [8]. When this exceeds the pre-defined threshold T (or falls below − T for negative association), the corresponding covariate value or range is highlighted as a key feature [8]. For transparency, odds ratios presented as part of the results are unshrunk [7], In this study we used the standard implementation of vigiPoint with C = 99, k = 0.01 × n (where n is the size of the subset of interest; in the case of the Zimbabwe reports and T = 0.5 [8, 12].

We defined the study reporting pattern subsets of the Zimbabwe ICSRs of interest for which the permutation analysis of 99% credibility interval of the shrunk log-odds ratios above 0.5 or below −0.5 were flagged as key features. The shrinkage was set at the vigiPoint default corresponding to 0.018% of the size of the Zimbabwe data subset since at the time of the analysis the Zimbabwean reports made up 0.018% of the VigiBase database. This study conducted vigiPoint analysis of 5213 ICSRs from Zimbabwe versus RoW* with and RoW** without USA reports from 1998 to July 2022. The vigiPoint analysis was run on the VigiBase Zimbabwe unduplicated ICSRs dataset as the subset of interest and the discrepancy between the vigiPoint RoW with and without USA reports as the primary comparator and sensitivity analysis. The covariates of interest were patient age groups, AEs as MedDRA preferred terms (PTs), serious and non-serious AEs suspected medicines and vaccine as mainly WHO Drug active ingredients names, type of reporter, country of origin and quality of the ICSRs information as measured by the VigiGrade completeness score(9). High vigiGrade high completeness was score was defined as a score of 0.8 to 1.0 [9].

3. Results

Zimbabwe had higher reporting from nurses (75.3% vs 17.3%) compared with the RoW with and without USA reports, see Table 2 showing that the proportion of reports submitted by physicians is much lower in Zimbabwe compared to RoW and RoW excluding USA, 19.2% vs. 43.4%/32.9%. The proportion of reports submitted by pharmacists’ reports were less when compared to RoW and RoW without USA, 5.1% vs. 12.9%/10.3%. Consumer reports formed a negligible contribution to reports in Zimbabwe unlike RoW* with USA where such reports form a considerable proportion of total reports, 0.4% vs. 43.1%. In Table 3, most Zimbabwe reports were serious 71.6% vs 35.8%, and a larger fraction were fatal 5.3% vs 2.6%. Zimbabwe serious reports, most patients were recovered after hospitalisation and some AE might be due to suspected medicines or vaccines or underlying disease or other causes. However, the cause of death was not always evident since causality assessment is not part of the vigiPoint analysis due to the inherent limitation of the information contained in most VigiBase reports. In Table 3, most Zimbabwe ICSRs had higher quality known as VigiGrade completeness score 52.9% compared to RoW with and without USA 20.1% and 31.9% respectively. In Table 4 most Zimbabwe patient demographics age range reports were for young people aged 18 −44 years (43.1% vs. 30.7%), infants and young children 1−23 months (13.8% vs. 3.0%) and older children 2− 11 years (12.1% vs. 4.0%). Globally, reports for 45−65 years old patients were 21.1% vs 33% and 65-74 years 2.2% vs. 15%.

See Table 5 that shows the top 20-odd ADRs reported in Zimbabwe selected for comparison with RoW with and without USA reports showed that the most frequently reported MedDRA AE terms in Zimbabwe were rash (12.5% vs. 5.9%), peripheral neuropathy (4.9 %vs. 0.2%), gynaecomastia (3.5% vs. 0.1%), injection site abscess (2.3% vs. 0.1%) and anaemia (2.2% vs. 0.7%). The least reported reactions were pyrexia 3.3% vs. 6.5%, headache 2.3% vs. 7.2% and pruritus 2.3% vs. 5.8% but compared to RoW with the USA reports, headache was 2.3% vs. 6.1%, dyspnoea 0.5% vs. 3.1%, pain in extremity 0.5% vs. 1.9% and nausea 0.4% vs. 6.2%. Table 6 shows top 21 list of co-suspected medicines and vaccines although it was not always possible to single out a specific medicine or vaccine since 36.8% were given in 3-5 medicines or vaccines combinations.

Table 2: Key features of notifier (reporter) higher relative reporting rates for nurse reporters, and relatively lower notifiers physicians, pharmacists and consumers, for Zimbabwe reports compared with VigiBase® RoW with and without the USA, ICSRs.

RoW* =Rest of the World with the USA reports.

RoW ** =Rest of the World without the United States of America (USA) reports.

Table 3 : Zimbabwe ICSRs seriousness and VigiGrade completeness score characteristics compared to RoW* with and without the USA** ICSRs.

Table 4 : Zimbabwe patient age range characteristics compared to RoW* with and without the USA** ICSRs.

Table 5: Relative reporting rates for specific adverse events (AE) MedDRA preferred Terms for Zimbabwe compared to RoW*’s ICSRs with and without USA reports.

Table 6: Co-suspected medicines and vaccines commonly reported for most Zimbabwe reactions ICSRs compared to RoW with and without the USA reports.

4. Discussion

Most of the co-suspected medicines or vaccines with relatively high odd ratios and vigiPoint scores were in the youthful population since some studies observed that sub–Saharan Africa is home for 65 % to 85% of young people with HIV and TB globally(15, 16). Zimbabwe has a youthful demographic profile hence the higher fractional reporting for those younger than 44 years of age including infants and children. Zimbabwe had 0.14 physicians and 1.85 midwives/nurses per 1000 population below the Sustainable Development Goals (SDGs) index threshold of 4.45 midwives, nurses, and doctors per 1000 population, resulting in lower physicians reporting rates compared to RoW [17-19]. Also, Zimbabwe has higher reporting of serious reactions which could be accounted for several factors including comorbidities, limited primary health care services and probably immune reconstitution due to delayed antiretroviral therapy (ART) during the early years of limited availability of antiretrovirals (ARVs) [20, 21]. The Zimbabwe ICSRs contain a higher fraction of reports for co-suspected medicines such as stavudine or efavirenz that were phased out over the years. Some studies of efavirenz or nevirapine AEs in sub-Sharan Africa showed pharmacogenetic differences with 20% to 59% black ethnic populations being poor metabolizers of efavirenz or nevirapine due to the highly polymorphic cytochrome P450 2B6 (CYP2B gene with 516T allele) known to confer poor metabolism of both medicines [22-26]. Zimbabwe reporting patterns were maintained when USA data was excluded from the RoW comparisons showing the robust sensitivity of the vigiPoint and odds ratio analysis methodology. Further investigation is recommended of currently used ART cosuspected medicines such as dolutegravir related weight gain or tenofovir and kidney failure to ascertain the risk minimisation factors. There is a need to conduct in-depth risk-benefit mitigation measures of those medicines used currently for tuberculosis such as isoniazid known to cause liver toxicities and/or pellagra. Isoniazid is known to cause vitamin B3 deficiency, most likely due to its ability to interfere with niacin made cell-repair enzymes [27]. A study of Zimbabwe ICSRs that compared ADR profiles of patients on antiretrovirals (ART) versus patients on ART and anti-TBs in 2018 showed that co-administration of ART and antitubercular (anti-TB) medicines were associated with a higher frequency of medicine-induced liver toxicity, peripheral neuropathy, and that isoniazid preventative therapy was associated with a higher risk for psychosis and liver toxicity [4]. A descriptive study of the Zimbabwe vaccines ICSRs, including the causality assessment profiles, recommended more resources for post-mortem to ascertain cause of death after vaccination [1]. Also, a French study recommended the use of both qualitative causality assessment outcomes and quantitative signal detection methods for signal detection [28]. A scoping review study of VigiBase signal disproportionate analysis advised that signal detection should also be done with causality assessment data in addition to the IC analysis, PRR and ROR [29]. The VigiPoint data of top 21 cosuspected medicines included albendazole and praziquantel medicines for treatment of some tropical neglected diseases [30-32].There is, therefore, a need for further analysis of the Zimbabwe medicines ICSRs to include the causality assessment outcomes done by the National Pharmacovigilance Committee for further risk minimisation measures.

The advantages of using vigiPoint in this study is that it enabled describing patterns of reporting (i.e., identifying features or covariates for a specific set of reports as compared to other reports). It also employed the ROR with adaptive shrinkage hence was the same measure of association used for statistical signal detection but now with shrinkage. The advantage of the ROR over the other measures in this context was that it worked well for both rare and common covariates [12, 14]. vigiPoint uses adaptive shrinkage to protect against false-positive associations. The shrinkage applied was stronger as the aim was to detect patterns involving large numbers of reports. vigiPoint further used a 99% (instead of 95%) uncertainty interval for the ROR to only highlight covariates that deviate substantially between the reports that were compared [12].

5. Conclusion

The vigiPoint analysis study of the Zimbabwe’s reporting patterns compared to RoW with and without USA reports revealed key features such as differences in AEs profiles, demographic data depending on types of medicines and vaccines used over the years pharmacogenetics, comorbidities and clinical practice. This knowledge is essential in the global collaboration of risk minimisation including promotion of patient safety in Zimbabwe since it accounts for different medicines and vaccines used for various public health challenges encountered over the decades. Further studies of Zimbabwe medicines ICSRs pooled causality assessment outcomes are required for further risk minimisation measures in a resource-limited context including enhancement of consumer reporting using mHealth reporting tools.

Declarations

Ethics approval and consent to participate.

The study was approved by the Medical Research Council of Zimbabwe (MRCZ) ethical approval reference MRCZ/A/2268 and MRCZ ethical exemption (reference E/148) from consenting participants for passive medicines and vaccines ICSRs. Ethical approval was also obtained from the University of Cape Town (UCT) Human Research Ethics Committee (HREC 184/2020) for the vigiPoint study. The data was published in anonymised format.

Consent for Publication

The authors obtained consent for publication of the manuscript from the MCAZ National Pharmacovigilance Centre, the custodian of the Zimbabwe safety data since this is a work-related study.

Competing of interest

The authors declare no conflict of interest nor potential competing interest.

Funding:

The MCAZ funded the study in accordance with its mandate as the Zimbabwe National Pharmacovigilance Centre. However, additional funding was obtained from unrestricted technical research grants over the years from the WHO, Global Fund and UNICEF that assisted with enhancing pharmacovigilance programmes training health care workers (HCWs) countrywide in pharmacovigilance of medicines and vaccines since 2008 to 2022 that resulted in increased ICSRs reporting from 2008 to 2022.

Author contributions:

PPMN designed the study and wrote the main manuscript as the lead author. MSG and UCM supervised the study and reviewed the manuscript. PPMN analysed data and performed analysis including tables and figures with assistance from two data scientists at the Uppsala Monitoring Centre (UMC). All authors read and approved the final manuscript.

Acknowledgements:

The authors acknowledge all the ICSRs reporters in Zimbabwe HCWs, clinics, hospitals, from both private and public health programmes MoHCC-ART and TB programmes, Zimbabwe Expanded Program on Immunisation (ZEPI-MoHCC), Medicines Control Authority of Zimbabwe management and national pharmacovigilance centre staff, the national Pharmacovigilance and Clinical Trials advisory Committee. Special thanks to Mr Nils Erlanson and Dr Henric Taavola, Uppsala Monitoring Centre, Sweden Data scientists who assisted in conducting the vigiPoint analysis of VigiBase database in accordance with the study objectives, method, materials, and results.

Limitations

1. The study reviewed global country spontaneous ICSRS data only submitted to VigiBase yet some authors over the years claim that not all ICSRs are submitted to the national PV centres due to limited capacity for data management, under reporting or country policy in respect to VigiBase hence the reports may be relatively a small proportion of the total country safety data (2, 33).
2. Each ICSR in VigiBase might contain more than one AE hence the number of MedDRA system organ classification (SOCs) may be more than the number of ICSRs. Double counting of combination fixed dose preparations for antiretrovirals, antitubercular, antimalarials and combination vaccines may lead to some products being over-represented in the count of products implicated in ICSRs.

Some data ICSRs might have low VigiGrade completeness score and might exclude causality assessment information.

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