Scientific Methods and Tools for Safety Surveillance

#### **Chapter 9**

## Computer-Aided Pharmacoepidemiology in Drug Use and Safety: Examining the Intersection between Data Science and Medicines Research

*Ibrahim Chikowe and Elias Peter Mwakilama*

### **Abstract**

Pharmacoepidemiology is a relatively new area of study that focuses on research aimed at producing data about drugs' usage and safety in well-defined populations. Its significant impact on patient safety has translated into improving health care systems worldwide, where it has been widely adopted. This field has developed to an extent that policy and guidelines makers have started using its evidence alongside that produced from randomised controlled clinical trials. Although this significant improvement has been partly attributed to the adoption of statistics and computer-aided models into the way pharmacoepidemiology studies are designed and conducted, certain gaps still exist. This chapter reports some of the significant developments made, along with the gaps observed so far, in the adoption of statistics and computing into pharmacoepidemiology research. The goal is to highlight efforts that have led to the new pharmacoepidemiology developments, while examining the intersection between data science and pharmacology through research narrative reviews of computer-aided pharmacology. The chapter shows the significant number of initiatives that have been applied/adopted to improve pharmacoepidemiology research. Nonetheless, further developments in integrating pharmacoepidemiology with computers and statistics are needed in order to enhance the research agenda.

**Keywords:** Database, data science, computer-aided, pharmacovigilance, safety, adverse drug reaction

#### **1. Introduction**

Pharmacoepidemiology is a research field that applies epidemiological concepts into clinical pharmacology. It is important in the provision of an evidence base for pharmacotherapy, due to the abundance of digital data that is mostly scanty [1, 2]. Pharmacoepidemiology studies aim to quantify patterns of drug use, as well as adverse drug events, and include prescribing, use appropriateness, adherence to treatment regimen and persistence patterns, along with factors that assist in predicting medication use. In addition, pharmacoepidemiology studies involve drug

#### **Figure 1.** *Main contributors of Pharmacoepidemiology.*

safety studies in large populations that focus on common and uncommon, as well as predictable and unpredictable, adverse drug reactions (ADRs) [3]. In this case, all the studies rely on meta-data sources, and include primary data, comprising national data sources and surveys or registries; and secondary data comprising administrative databases, claims databases, as well as primary care electronic health and medical records. **Figure 1** presents the general description of pharmacoepidemiology [4] being a multidisciplinary type of research field which intersects mathematical disciplines with pharmacology.

Recently, it has been established that clinical trial-oriented studies alone are mostly found to be insufficient to provide conclusive data about the drug's safety and occurrence of adverse effects in larger populations, especially the occurrence of idiosyncratic adverse events and other rare events. This is attributed to both the smaller populations and shorter time periods in which the medicines are tested. Additionally, the effectiveness of the medicines is not fully determined by the time the medicines are launched into the market. Post-marketing surveillance, with the help of either statistical or computing models on longitudinal data, becomes a critical tool for solving these challenges. Furthermore, it is important to highlight that adverse drug events and drug's efficacy can vary between clinical trial protocols and health care delivery systems [5–7]. Therefore, pharmacoepidemiology research data has found its way into many aspects of health care systems, such as policy making, drug utilisation and safety decision making, clinical trial design or validation, as well as guidance for the improvement of medical prescription by physicians. Additionally, it is also essential for research and project implementation, methodology development, vaccine and medical devices safety assessment, as well as for minimisation of medication errors and drug-induced toxicities [8].

#### **2. Challenges and opportunities linked to pharmacoepidemiology**

Pharmacoepidemiology research provides very important data for the benefit of patients' safety and care since the data generated is more informative and reliable when the study is well designed. Pharmacoepidemiology research offers many advantages, including the use of large patient samples and inclusion of

subpopulations that are under research in uncontrolled conditions [1]. It also describes and estimates the risks and other drug safety or efficacy phenomena in practice [9]. Pharmacoepidemiology approaches make the studies cheaper and faster, when compared to the randomised controlled trials initially performed prior to marketing or after marketing, thus enabling the researchers to assess generic medications, as well as medications after a long period of use. The methods used in pharmacoepidemiology research can also be adapted for their use in pharmacovigilance to assist in unearthing unknown side effects or ADRs, together with the discovery of new drug usages [10].

However, pharmacoepidemiology research also has its own drawbacks, such as contamination of the data with confounding factors and many sources of bias (information bias, selection bias), due to the non-randomised nature of treatment selection, being harder to draw conclusions [1, 11]. In addition, although inclusion of statistical models into pharmacoepidemiology has been already seen, little is known about integrating pharmacology with community behaviour models, such as social networks. Nonetheless, different scholars have suggested several ways of improving pharmacoepidemiology research, including the use of active comparison groups and within-individual designs, as well as propensity scoring [12]. Additionally, pharmacoepidemiology studies have also been improved by triangulation of multiple analytical and data collection approaches, aiming to enhance the confidence in inferred causal relationships [13]. The developments made in the use of databases, computer and statistical models, and big data have led to enormous improvements in the robustness of pharmacoepidemiology studies and the production of reliable data that is being considered as good evidence for inclusion in guidelines, alongside data generated from randomised controlled trials [14].

Having shown that pharmacoepidemiology research is now producing data that is important for health care guidelines and policy development, it is essential that researchers can collaborate with guideline writers to ensure that they frame their questions to get useful answers. On the other hand, pharmacoepidemiology researchers should design their studies in such a way that guideline writers are provided with concrete answers, thus reducing the uncertainty in the evidence base. Additionally, since pharmacoepidemiology depends on statistical and data sciences, there is a need for further development of techniques in these fields to improve the application of pharmacoepidemiology. It is also important to enhance public engagement and capacity building (data resources and researcher base) to take full advantage of future opportunities [1].

#### **3. Computational and statistical models in pharmacoepidemiology**

The advent and development of computers has led to the development of databases that have become essential in pharmacoepidemiology. Several Electronic Health Records (EHRs) systems have been developed to keep longitudinal digital records of patient health information that are generated after a series of visits in a hospital setting [15]. EHRs contain patient data related to diseases, medicines and laboratory results, if any, and enable the provision of patient centred treatment by the health care providers [16, 17]. When these databases are linked or nationalised, it prevents patients repeatedly describing their medical histories, in case of treatment transfers. In addition, such data can be accessed by policy makers or researchers [18]. The use of computerised databases has led to a significant reduction in adverse events and prescription errors [19, 20], shorter hospital stays and lower mortality [21], along with better patient tracking, information exchange, efficient handling of information, and real-time data provision [16, 22]. Large

pharmacoepidemiology data bases facilitate research, but they require well trained personnel to produce and handle big data [17, 23]. The use of electronic data has led to a significant reduction in the manual effort of data collection, easy incorporation of regional data into a study, minimal need for recalls, and removal of interviewer bias [24].

#### **3.1 Progress and limitations**

#### *3.1.1 Usage of computational and statistical models*

So far, a very close link between pharmacology and computational and statistical models has been established (**Figure 1**). In his work, Bentley [25] provides a well organised chapter describing the key statistical models used in the field of pharmacoepidemiology, both at descriptive and inferential analysis levels. Description uses measures of central tendency (e.g. mean), dispersion (e.g. variance), range (e.g. range, maximum and minimum), expressed in tables (e.g. cross-tabulations) and charts but inference may use regression models (e.g. linear, logistic, and Cox). These statistical techniques and descriptions aid in understanding data on usage and effects of drug administration at community level although it is also important to have a good knowledge of the potential errors involved in the design and analysis of pharmacoepidemiology studies [26].

Statistics play a major role in managing the quantifiable errors present in pharmacoepidemiology data analysis and interpretation [27]. Despite a growing interest in applying epidemiology statistical methods in pharmaceutical studies, a proper usage of the statistical techniques in research studies is often still lacking. For example, Suissa [26] states that pharmacoepidemiology observational research studies are hugely affected by information bias (when selecting variables of interest for the study), selection bias (during inclusion and exclusion of subjects), and confounding bias (due to imbalances in covariates). To circumvent these problems, both randomised controlled trials and cohort and case control studies, also used in epidemiological studies [28], have therefore been recommended by several researchers in pharmacoepidemiology [29].

Accordingly, in order to appraise the significance of epidemiological data and the design of studies on drug risk and safety, we reviewed a couple of research studies that have been conducted in developing countries, including in Malawi. We tried to focus on citing the key statistical and computational methods used in such research studies. To achieve this, we have used a similar approach to the one described by Sequi et al. [30] who presented a review of studies to underscore the processes of analysing and reporting data related to paediatric drug utilisation. Out of the 22 studies, the majority (91%) reported at least one descriptive measure, with the mean being the most common one (82%, 18/22), followed by the standard deviation (23%, 5/22). The chi-square test was observed in 12 studies, while graphical analysis was reported in 14 papers. However, only 16 papers reported the number of drug prescriptions and/or packages, while 10 reported the prevalence of the drug prescription. Consequently, the authors observed that only a few of the studies reviewed applied statistical methods and reported data in a satisfactory manner [27].

In a review paper which has set a position on current usage of statistical models in pharmacoepidemiology, Rosli and others [31] systematically reviewed published studies on drug utilisation in hospitalised neonates in Europe, the United States, India, Brazil, and Iran. The findings were not far from those reported by [30] such that a majority (70%) used descriptive statistics to analyse pharmacoepidemiology

data. Nonetheless, some quite remarkable variations were observed regarding to the study design and methodology, sources of data, and sampling process among the selected studies. Of the included studies, 45% were based on cross-sectional or retrospective designs, 40% were prospective, and the remainder (15%) were point prevalence surveys.

Likewise, a 2020 review of 84 drug utilisation studies among neonates by Al-Turkait et al. [32] has shown that median, ranges and mean are frequently reported statistical parameters used for describing pharmacoepidemiology data, and that the style of reporting is mostly descriptive. However, in general public health, Hayat et al. [33] found a variety of statistical methods that were identified in the 216 papers reviewed, whereby 81.9% used an observational study design. 93.1% substantive analysis, 95% used descriptive statistics (tabular or graphical) while statistical inference (t-test, Chi-square, correlation with confidence intervals and p-values) was used in 76%. Logistic regression models were frequently used (38.4%), followed by linear regression models (19.4%).

Sequi et al. [30] recommended that the methodology of drug utilisation studies needs to be improved and we have also observed that drug use in the community is affected by drug availability, pricing, and affordability [34]. Therefore, the logistical and socio-economic aspects of pharmacoepidemiology studies should not be ignored. These two observations were the two key benchmarks for scoring the papers we have found and reviewed. For each study, we extracted information on the study design/type, data sources, period, assessment of variables used and corresponding statistical estimates (incidence, prevalence, pharmacy sales, prescription data), and diagnostic assessment. **Table 1** provides the overall summary details of the included papers.

By analysing **Table 1**, we have noticed that the status of pharmacoepidemiology research in some developing countries, like Malawi, is still at an infancy stage, compared to other developing countries that have adopted advanced inferential analyses into their pharmacoepidemiology research. Our findings do not differ from those reported by Sequi et al. [30], which the majority of the papers focused on the use of descriptive statistics. In addition, few studies clearly demonstrated the use of social/human behaviour network models in pharmacoepidemiology research [44, 45]. The inclusion of social/human behaviour network models into pharmacoepidemiology research is fundamental in the understanding of community structure and behaviour, for instance before mass drug administration during an outbreak such as COVID-19 [46, 47].

#### *3.1.2 Big data in pharmacoepidemiology*

Big data is another translational and frontier scientific discipline at the interface of computer science and statistics [48]. This field has found its way into pharmacoepidemiology research by simplifying the data interpretation and trend analysis of the volumes of data produced from many sources in health records [49]. With big data, pharmacoepidemiology research experts and data scientists detect ADRs, and collaborate in signal detection, verification and validation of medication or vaccine safety signals, as well as in the expansion of analytic methodologies for analysing the large volumes of heterogeneous data [14]. For example, the Exploring and Understanding Adverse Drug Reactions (EU-ADR) European project has incorporated innovative research methods in their pharmacovigilance research through the use of a web platform, aiming to provide advanced medication data exploration and assessment features. This enables data scientists and pharmacoepidemiology experts to mine EHRs for drug-events of their interest [4, 50].



**Table 1.** *A review of computer aided research studies and usage of statistical models in Pharmacoepidemiology.*

#### *Computer-Aided Pharmacoepidemiology in Drug Use and Safety: Examining the Intersection… DOI: http://dx.doi.org/10.5772/intechopen.98730*

#### **3.2 Databases**

#### *3.2.1 Importance of databases*

Apart from the statistical innovations that have been incorporated into pharmacoepidemiology research, computer databases, networks and software are also playing a critical role in enhancing the field of pharmacoepidemiology, and notable developments have been reported in North America, Europe, and the Asia-Pacific region [51]. The rapid development of computer-aided technology has led to the improvement of electronic health records, which have further led to the advancement of many databases that may be used locally or internationally. Consequently, this has allowed for the possibility of conducting pharmacoepidemiology studies using multiple databases in one or more countries [5]. Several mechanisms have been developed to ensure maximum benefit from the multinational databases and collaborations, such as the creation of research networks [5].

The use of multinational databases enables researchers and policy makers to compare how medications and medical devices are utilised and prescribed, as well as to compare their safety profiles in different settings [51]. It also allows the identification of the underlying factors for the differences or similarities observed, which may include different patient selection, delivery systems and genetic differences [51]. Moreover, it relates drug effects (beneficial or adverse) with differences in ethnic groups (receptor and cytochrome polymorphism effect) and lifestyle (such as dietary habits), among others [52].

Furthermore, the use of multiple databases has overcome sample size problems for rare exposures, outcomes of medications, or rare diseases [5]. While it is challenging to get sufficient power when studying one area, data from multiple databases increase the sample size, thus providing the required statistical power. Additionally, the general use of meta-data may help to solve problems experienced by some countries or areas that do not have their own policies, medications, or medical devices [53]. Therefore, multiple databases provide reference points for such cases. Multiple databases also provide a platform for collaboration and communication amongst researchers in different and distant nations, which has led to the advancement of research in pharmacoepidemiology [5].

#### *3.2.2 Multi-database networks*

According to Sturkenboom and Schink [51], electronic healthcare databases have allowed analyses of drug and vaccine utilisation, including investigations of comparative effectiveness and safety. Consequently, both local and international databases have been developed worldwide for use in pharmacoepidemiology. In North America, administrative databases, such as the Health Services Databases in Saskatchewan [54] and the Ontario Health Insurance Plan [55] in Canada, have been set up to manage health care delivery costs, with the fundamental purpose of allowing fiscal tracking and accounting for the delivery of health care from a payer perspective. In the USA, databases managed by Government payers for claims data, for instance Medicaid and Medicare, data are also used in research [56].

Since some of the databases do not cover the entire population, some research networks have been set-up to facilitate multi-database studies that can cover the whole nation. These include the Canadian Drug Safety and Effectiveness Network (CDSEN), set-up in 2007 by the Canadian government, which connects multiple researchers across Canada with expertise in pharmacoepidemiology research [57, 58] as well as the USA Food and Drug Administration (FDA), whom established a

Sentinel Initiative in 2008 with the purpose of refining safety signals that would enable the development of a scalable and transparent organisational structure to study the safety of medical products [59], mainly through the organisation of multiple databases managed via one research governance structure [5, 60].

Similar initiatives have also been adopted in Europe. The EU-ADR [61] was initiated by the European Commission to develop a drug safety surveillance system reliant on connections amongst databases in European countries. This initiative benefits from reliable clinical data obtained from the electronic healthcare records of over 30 million of patients within all the participating countries, thus ensuring an efficient analysis of drug safety issues. Another initiative adopted along the same lines is the Pharmacoepidemiology Research on Outcomes of Therapeutics by an European ConsorTium (PROTECT), which involves 19 collaborative international working groups, networks and research projects in Europe [62]. Nordic countries have established the Nordic Pharmaco-Epidemiological Network (NorPEN), aiming to promote research collaboration and initiate cross-country population-based comparative research in pharmacoepidemiology, for further promotion of safer medication use [63].

The Asian Pharmacoepidemiology Network (AsPEN) was formed in 2008 by four countries, namely Korea, Japan, Australia, and Taiwan, and has currently expanded to Singapore, China, India, Hong Kong, and Thailand [64]. The AsPEN [65] was created to provide mechanisms for supporting pharmacoepidemiology research in Asia, as well as to facilitate the identification and validation of emerging safety issues among the Asian countries. The diversity of the countries provides multi-cultural and ethnic sources of safety data [63, 64]. Nevertheless, this is still an ongoing process, as some countries are still developing their own databases and infrastructures. Special attention should be given to the challenges of handling such multi-complex meta-data, and may involve collaboration of mathematicians, statisticians, epidemiologists and computer scientists (**Figure 1**).

Research networks specialised in certain subpopulations have also been initiated with the goal of studying populations under-represented in clinical trials, such as children, older people, and pregnant women. The most notable networks established for this purpose comprise the Task-force in Europe for Drug Development for the Young (TEDDY) [66]; the European network of population-based registries for the surveillance of congenital anomalies (EUROCAT) [67], for providing early warnings of new teratogenic exposures on congenital anomalies; the Innovative Medicines Initiatives (IMI) [68], for fostering collaboration between different stakeholders (the European Union and the European pharmaceutical industry) in order to address growing challenges in bringing new medicines to market and the rapidly evolving healthcare landscape; the VACCINE.GRID [69], a global network of leading public health organisations concerned with vaccine benefits and risk assessment; and the International Society for Pharmacoepidemiology (ISPE), an international professional organisation dedicated to the open exchange of scientific information for the benefit of people, drug safety in pregnancy, vaccine safety and/or biologics safety [70].

Last but not least, we have also noticed that computational infrastructures have been developed in places where data participants can transform their data locally, as well as execute standardised analytical programs and combine the results [45]. Data science has also been exploited in pharmacoepidemiology research, where it is used in the evaluation of various analytical methods in the context of a network of databases [45, 47]. Common data models that are capable of accommodating heterogeneous databases and executing large-scale statistical analyses [71–73], whose resources sometimes can be downloaded from a website [74], have also been developed. **Table 2** illustrates a few databases that are currently being used as well






**Table 2.**

*Computer databases currently used in pharmacoepidemiology research.*

*New Insights into the Future of Pharmacoepidemiology and Drug Safety*

as those comprising data that may be potentially used to improve pharmacoepidemiology research. Although this is not an exhaustive list, these databases may serve as a supplement to those already reported [51].

Although the majority of pharmacoepidemiology research is found in developed countries, most of these databases are open for re-use of data, thus providing an opportunity for enhanced pharmacoepidemiology research, for instance in Asia and Africa [103].

#### *3.2.3 Challenges with use of databases*

Databases have limitations that affect their use in pharmacoepidemiology. Bias is one of the challenges and may be categorised into confounding, selection bias and time-related bias [98]. Confounding is further sub classified into confounding by indication, unmeasured or residual confounding, time-dependent confounding, and health user or adherer effect. Selection bias is reported to be associated with database use, being in the subcategories of protopathic bias, losses to follow up, prevalent user bias, and missing data. Another type of bias widely reported is measurement bias, which comes in the form of miscalculation bias, miscalculation of exposure, as well as miscalculation of outcomes. Time-related bias is classified into immortal bias, immeasurable time bias, time-window bias and time-lag bias [98].

#### **4. Conclusions**

Through a cross-examination of the intersection between data science principles and pharmacoepidemiology, this chapter has demonstrated that pharmacoepidemiology has greatly evolved over the years, from being a mere research field to one that is playing a significant role in the enhancement of patient safety, as well as in the development of health care guidelines and policies. Our examination of the intersection between data science techniques and pharmacoepidemiology was limited to the policy and research narratives of computer-aided pharmacoepidemiology studies across the globe. The level of evidence generated from several studies indicates that the field is now as important as randomised clinical trials have been, which can be attributed to the adoption of statistical and computational principles and practices. However, it is important to highlight that, although there has been a significant number of initiatives reported to improve pharmacoepidemiology research, the identified gaps and challenges presented in this chapter show that this field still has some potential to grow, for instance by properly integrating the existing data science techniques with appropriate principles and practices. The inclusion of both logistical and social/human behaviour network models into pharmacoepidemiology is strongly recommended.

#### **Acknowledgements**

This publication was made possible with funding from the Agency for Scientific Research and Training (ASRT) in Malawi. Sincere thanks are due to Dr. David Scott for the technical, language editing and proofreading support on the manuscript.

#### **Author contributions**

IC conceived the study, performed the review of pharmacoepidemiology databases and participated in the manuscript writing process. EM reshaped the argument of the study, reviewed research papers on statistical and computing models, and participated in the manuscript writing process. All authors have read and approved the final manuscript.

## **Conflict of interest**

The authors declare no conflict of interest.

#### **Appendices and nomenclature**



### **Author details**

Ibrahim Chikowe1 \* and Elias Peter Mwakilama<sup>2</sup>

1 Pharmacy Department, College of Medicine, University of Malawi, Blantyre, Malawi

2 Mathematical Sciences Department, Chancellor College, University of Malawi, Zomba, Malawi

\*Address all correspondence to: chikoweib@yahoo.co.uk; ichikowe@medcol.mw

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 10**

## Basics and Essentials of Medical Devices Safety Surveillance

*Vivekanandan Kalaiselven, Shatrunajay Shukla, Nikita Mishra and Pawan Kumar*

#### **Abstract**

Medical devices are being used in healthcare facilities for diagnosis, monitoring, prevention and treatment of an array of diseases. To ensure user/ patient safety associated with the medical devices being used in healthcare industry, it is of utmost importance to closely monitor the adverse events associated with the medical devices through a robust, sustainable and scaled surveillance. Materiovigilance Programme of India (MvPI) provides a reliable system to report adverse events associated with medical devices. Under MvPI, various modalities to report adverse events associated with medical devices have been developed. These modalities include an editable medical device adverse event reporting form, a toll-free helpline number and a field safety corrective action form (FSCA). FSCA form is used to notify the regulatory authority and healthcare professionals on corrective actions or recall by the manufacturer. Due to the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, one-page editable form has been developed to boost the adverse event reporting of Personal Protective Equipments (PPEs). MvPI also coordinates with healthcare facilities and medical device industries across the country for reporting the medical device-related adverse events. The collected scientific data is utilized to develop regulatory policies and enhance measures to ensure the quality of medical devices. All the healthcare workers are, therefore, encouraged to report adverse events to MvPI. This chapter aims to describe the systems, procedures and modalities available for the reporting of Medical Device Adverse Events (MDAEs) in India, in order to intensify the nature of reporting and creating an environment that encourages the public to perform MDAE reporting.

**Keywords:** Adverse event, COVID-19, Materiovigilance Programme of India, Personal Protective Equipments, Causality assessment, Medical device

#### **1. Introduction**

Over the last years, medical devices have been playing a pivotal role in the diagnosis and management of a variety of diseases [1]. With the advancement in the technology and increased public demand for high quality medical care, the global medical device industry has surpassed USD 350 billion in annual revenue, and in India a growth rate of 20% has been seen in healthcare industry. These devices have also created substantial risks to the patients with high profile recalls [2]. Nearly 5,000 individual classes of medical devices, tens of thousands of medical device

suppliers, and millions of healthcare providers exist worldwide, which clearly depicts that device-related issues are likely to occur. The outcome of an adverse event related to medical devices can be serious and result in illness, injury or even death, which have led experts to call for the monitoring of the safe and effective use of medical devices after its regulatory approval [3].

Materiovigilance Programme of India (MvPI) was launched in 2015 and has implemented a robust system to ensure the safety of medical devices. The aim of this programme is to identify the adverse events associated with the use of medical devices and to eliminate the device-related risks through a systematic reporting system [4]. In India, the Medical Devices Rules (MDR) were notified on January 31st, 2017 and became effective from January 1st, 2018. As per the MDR, G.S.R. 78 (E), Chapter 4, Section 26 (ii) "the License Holder shall inform the State Licensing Authority (SLA) or Central Licensing Authority (CLA), as the case may be of the occurrence of any suspected unexpected serious adverse events and take necessary action thereon, including any recall within 15 days of such event coming to the notice of License Holder" [5] (**Table 1**). The MDR, in concurrence with MvPI, has significantly influenced the post marketing surveillance of medical devices among the healthcare professionals, by ensuring their quality and patient/user safety [5]. This chapter aims to describe the systems, procedures and modalities available for the reporting of Medical Device Adverse Events (MDAEs) in India, in order to intensify the nature of reporting and creating an environment that encourages the public to perform MDAE reporting.


#### **2. MvPI Focus Groups for MDAE reporting**

The following groups play a significant role in the smooth functioning of MvPI:

#### **2.1 Medical Device Adverse Event Monitoring Centres (MDMCs) and Adverse Drug Reactions Monitoring Centres (AMCs)**

Healthcare facilities, including district/government/private hospitals, and autonomous bodies are recognized as MDMCs and AMCs by the NCC-MvPI, IPC and NCC-PvPI, IPC respectively. The function of the MDMC is to raise awareness about the programme and reporting of MDAEs. The MvPI collects reports from the MDMCs, AMCs. Under the MvPI, 50 MDMCs have been identified so far in India to collect the report of the events associated with the use of medical devices [6]. The AMCs established under the Pharmacovigilance Programme of India (PvPI) are also participating in MDAE reporting. Around 311 centres have been identified in the country to report the adverse events resulting from the use of drugs/medical products [7].

#### **2.2 Medical device industries**

Medical device industries, including manufacturers, importers, distributors, etc., are approached and encouraged to report the MDAE, particularly with their own medical devices. As there may be chances of re-occurrences of the adverse events, medical device industries play a key role in medical device safety surveillance [8].

#### **2.3 Healthcare professionals**

Healthcare facilities include healthcare professionals, such as clinical specialists, biomedical engineers, nurses, pharmacists, hospital technology managers, and technicians, as well as patients. Healthcare professionals are in direct contact both with the patients and the medical devices used in the healthcare facilities, and are hence the key personnel in MDAE reporting [9].

#### **2.4 Accreditation bodies**

Accreditation bodies essentially identify the capability of the hospitals to deliver quality care. Indian healthcare institutions get accreditation from bodies such as the National Accreditation Board for Hospitals and Healthcare providers (NABH). The IPC has signed a Memorandum of Understanding (MoU) with the NABH, under the Quality Council of India (QCI), to ensure for total cooperation of the hospitals on the reporting of adverse events associated with medical devices in the hospital [10].

#### **3. Modalities for MDAE reporting**

The NCC for MvPI, IPC has developed the below-mentioned reporting tools to collect MDAEs. All the reporting tools are available on the IPC website. The healthcare professionals, MAHs and all the Personal Protective Equipments (PPEs) users are encouraged to report adverse events associated with medical devices [11].

#### **3.1 MDAE reporting form**

The MDAE reporting form primarily aims to collect the adverse events associated with the use of medical devices, *In-Vitro* Diagnostics (IVDs), and medical equipments. The healthcare professionals and others including, but not limited to, manufacturers, importers, distributors, and hospital managers are solicited to report the adverse events for known, unknown, serious, non-serious, frequent or rare adverse events. The MDAE reporting form assembles adverse event information associated with medical devices and consists of the following nine sections (**Figure 1**) [11].

#### *3.1.1 General information*

This section includes the date of report, *i.e*. the date in which the report was filled, and the type of report that specifies whether the event is Initial/Follow-up/Final/Trend. The initial report is the first event notification report which may include the minimal required information, for instance, device information, details of adverse event and reporter details. A report may be marked as a follow-up report when additional information is available from the previously reported event. The report may be submitted as a final report when all the information associated with the event is available and collected. If the reporter is observing a significant number of similar adverse events, the reporter may tick the trend option [12].

#### *3.1.2 Reporter details*

This section comprises the type of reporter, along with the details of the reporter, including name, address, contact number and e-mail address. A reporter may be a manufacturer, importer, distributor, healthcare professional, patient, or other. The information provided in this section is kept confidential and only utilised for further follow-up.

#### *3.1.3 Device category*

This section refers to the general information about the medical device used. The device category section in the MDAE reporting form consists of three subsections, namely medical device, medical equipment and IVDs. The medical equipment and IVDs subsections refer to specific categories of medical devices. Medical devices requiring calibration, maintenance, repair, user training and decommissioning – are usually managed by clinical engineers. Medical equipment is used for the specific purposes of diagnosis and treatment of disease or rehabilitation following disease or injury. It can be used either alone or in combination with any accessory, consumable or other piece of medical equipment. Medical equipment excludes implantable, disposable or single-use medical devices. The IVD medical devices includes a medical device, used either alone or in combination, intended by the manufacturer for the *in-vitro* examination of specimens derived from the human body solely or principally to provide information for diagnostic, monitoring or compatibility purposes. All the other devices not covered under the definitions of medical equipments and IVDs should be included into the medical device subsection.

#### *3.1.4 Device description*

This section describes the specific details of the suspected medical device: device name or the brand name used for marketing of the device, manufacturing or import firm name and address, the information of local distributer, the lot/batch number,

#### *Basics and Essentials of Medical Devices Safety Surveillance DOI: http://dx.doi.org/10.5772/intechopen.97248*


#### **Figure 1.**

*Pictorial representation of medical device adverse event (MDAE) reporting form.*

serial number, year of manufacturing. Furthermore, in case of medical equipment, the additional information also includes installation date, last calibration date and preventive maintenance date, and software version is also asked. Many countries use different nomenclature systems for naming the medical device. The most prominent codes known are the Global Medical Device Nomenclature (GMDN) and Universal Medical Device Nomenclature System (UMDNS). In the reporting form, the reporter has an option to add the nomenclature code of the device while reporting the event.

#### *3.1.5 Event description*

This section includes the most important dates associated with the adverse event, such as the date in which the event or any near miss incident occurred, etc. Furthermore, this section also comprises the information about device operator, device location and the detailed description of the event. The reporter may mark an event as serious in case it fulfils the seriousness criteria described in MDR 2017 [5]. Otherwise, the event may be marked as non-serious.

#### *3.1.6 Patient information*

This section contains the patient information, including its medical history and final patient outcome after the adverse event has occurred. Additionally, the patient hospital ID, age, gender is also comprised in this section.

#### *3.1.7 Healthcare facility information*

This section includes the details of the hospital in which the event took place, as well as the details of a contact person at the hospital, for the further follow-up communication related to the adverse event.

#### *3.1.8 Causality assessment*

This section aims to collect the information regarding the investigation process carried out by the clinical specialists from the healthcare facility, or by the concerned personnel from the manufacturing organization, to further drawing out the root cause of the problem and the immediate action taken to reverse the adverse effect, if possible. The root cause will ascertain the most likely reason for the occurrence of the adverse event.

#### *3.1.9 Manufacturer/Authorized representative investigation & action taken*

This section provides the information related to the investigation methods performed by the manufacturer/authorized representative and device history, which includes a review of similar events occurred from the same batch/lot, the analysis report of the event related to the medical device, and the corrective/ preventive action/recall taken to prevent the patient from being affected by the device, if any. The MDAE form is designed in such a manner that it collects the maximum information required, which may be helpful for the identification of the MDAE and for creating the database of the medical device-related errors, thus enabling to trace the trend of adverse events associated with medical devices. The MDAE form may also help the medical device stakeholders to provide appropriate information and enhance the quality of the information collected.

#### **3.2 PPE Reporting Form**

During the prevailing situation regarding the COVID-19 pandemic, the NCC-MvPI specially designed a one-page editable MDAE reporting form, which primarily aims to collect the adverse events associated with the use of PPEs used for medical purposes (**Figure 2**) [11]. The information required to be filled in the reporting form under the different categories is as follows [11]:


#### **Figure 2.**

*Pictorial representation of personal protective equipment (PPE) reporting form.*

#### *3.2.1 General information*

This section includes the exact date in which the event was reported to the NCC-MvPI, IPC, and the type of report that specifies whether the event is Initial/Follow-up. The initial report is the first notification about an adverse

event submitted to the NCC-MvPI, IPC, once the reporter became aware of it. The follow-up report comprises the additional information about the previous report.

#### *3.2.2 Reporter details*

This section comprises the details of the reporter, including name, address, contact number and e-mail address. The information provided in this section is kept confidential and only utilized for the follow-up.

#### *3.2.3 PPE type*

This section encompasses the type of PPE involved in the adverse event/reactiongloves, coverall, goggles, N-95 masks, shoe covers, face shields, body bags, triple layer medical mask, among others.

#### *3.2.4 PPE details*

This section describes the specific details of the PPE involved in the adverse event. These details include the brand name, manufacturer/importer/distributer name, batch number, model number, license number, unique certification code, test standard followed, manufacturing date and expiry date.

#### *3.2.5 Location of event*

This section refers to the location where the adverse event has occurred, and includes inpatient department, quarantine facilities, emergency department, etc.

#### *3.2.6 Type of event*

This section comprises the seriousness of the event. If the event involves the following outcomes: death/life threatening/disability or permanent damage/hospitalization/congenital anomaly, then it should be marked as serious. Otherwise, the event may be marked as non-serious.

#### *3.2.7 User details*

This section covers the details of the PPE user, including user initials, age, gender, etc.

#### *3.2.8 Event description*

This section includes the detailed description of an adverse event associated with PPEs.

#### *3.2.9 Hospital/quarantine facility details*

This section provides the details related to the hospital/quarantine facilities, including name, address and contact person. The PPE form is designed in such a manner that it collects all the required information, which may be used for the identification of PPE-related adverse events and for creating the database of such adverse events.

#### **3.3 Field safety corrective action (FSCA) notification form**

The FSCA [11] refers to any action taken to reduce a risk of death or serious deterioration in the state of health associated with the use of a medical device, including the: (i) device returned to the manufacturer, (ii) device design changes, (iii) device software upgrade, (iv) labelling changes, (v) changes in instructions for use or directions for use or technical manual, (vi) device destruction and (vii) device exchange. For more information, see [13].

#### **3.4 Legal obligation**

The submitted MDAE report does not have any legal implication concerning the reporters. The patients' identity will be held under strict confidence and protected to its full extent. As the reporting programme is voluntary in nature, healthcare providers are encouraged to report adverse events for a better understanding of the risk associated with the use of medical devices, and to safeguard the health of the Indian population [14].

#### **3.5 Essential data for effective reporting**

This section includes the following information: date of event, reporter contact information, device name, manufacturer/importer/distributor details, catalogue number., lot/batch number., serial number., model number., date of implantation/explantation (if applicable), seriousness of the event, event description, patient history, patient outcome, healthcare facility information, root cause and corrective/preventive action [13].

#### **3.6 Factors contributing to a serious adverse event**

The improper functioning of the medical devices, manufacturing defects, design and labelling issues, user and procedural errors are some examples of the major contributing factors that can lead to the occurrence of a serious adverse event if underestimated [15].

#### **4. Helpline facility for reporting adverse events**

The IPC has already launched a toll-free helpline facility, helpline Number- 1800 180 3024 (Monday to Friday- 9:00 am to 5:30 pm), for the reporting of adverse drug reactions by healthcare professionals and others [16]. Currently, this facility is also being extended to the report of any adverse event associated with the use of medical devices. Both the data management and the procedure adopted to receive the information from the healthcare professionals, patients and others are given in **Figure 3**.

#### **5. Enrolment process as MDMC under MvPI**

Healthcare facilities including district/government/private hospitals, and autonomous bodies are recognized as MDMCs and AMCs respectively by the NCC-MvPI, IPC and NCC-PvPI, IPC. The function of MDMC is to raise awareness about the programme and reporting of MDAEs. A 'Letter of Intent' is required to be submitted by the head of the Institution/hospital for participating in this nationwide programme to *New Insights into the Future of Pharmacoepidemiology and Drug Safety*

#### **Figure 3.**

*Flow diagram representing the report of adverse events related to medical devices through helpline.*

monitor MDAEs [17]. After the suitability examination by the competent authority, the proposed centre may be recognized as an MDMC under MvPI. These centres are expected to collate data on adverse events associated with medical devices and IVDs under the MvPI and report them to the NCC-MvPI, IPC. For the proper functioning of MvPI activities in their respective centres, a research associate will be appointed by the NCC-MvPI, IPC [18]. The research associate will be responsible for the collection of reports and conduction of training programs on materiovigilance, aiming to sensitize the healthcare professionals and the general public. The technical team at the MDMC performs the validation of the report by carrying out the causality assessment to identify any causal/temporal relationship between the event and the medical device. The workflow for determining the report responsible for the identification of adverse events significantly related to medical devices is shown at **Figure 4**.

*Basics and Essentials of Medical Devices Safety Surveillance DOI: http://dx.doi.org/10.5772/intechopen.97248*

**Figure 4.**

*Medical device-related adverse events identification flowchart used at medical device adverse event monitoring centres (MDMC).*

#### **6. Report Handling and Management**

Initially, the reports are collected and analysed at the NCC-MvPI, IPC, by applying the globally recognized scientific standards/parameters to ensure the quality of the reports. In the second level, these analysed cases are forwarded to the subject expert group panel for review, and technical interpretation is drawn considering

both the clinical, as well as technical aspects. In the third level, these reports are placed before the core technical committee for the conclusions and recommendations, and are further forwarded to the national regulatory authority for implementation of the necessary actions (**Figure 4**) [19].

#### **7. Data Generated**

The NCC has collected the reported adverse events and provided a comparison of the serious adverse events reported in the index period from January to December during the years of 2018, 2019 and 2020. In total, NCC has received 3187 adverse events, consisting of 1986 serious and 1201 non-serious events. Out of the serious adverse events reported, 23% were reported in 2018, 37% in 2019 and 40% in 2020. When comparing the reported data, an increase of 75% in serious adverse event reporting could be observed. Out of the adverse events reported, 73% of the reports were received from MAHs, 23% from MDMCs and 4% from AMCs [19]. This confirms and highlights the importance of the modalities developed, as they have significantly helped to improve the reporting of adverse events related to medical devices.

#### **8. Conclusion**

The tools developed for reporting may stimulate the communication between medical device users and the regulatory authorities to closely monitor medical device safety. In order to generate proper regulatory decisions and to ensure the quality and efficacy of the medical devices that are being sold and distributed in the Indian market, MvPI has shown to provide a robust and sustainable system for collecting and reporting adverse events associated with medical devices. This will highly encourage all the healthcare professionals, MAH and the public to efficiently report adverse events associated with medical devices.

#### **Acknowledgements**

The authors are grateful to Ministry of Health & Family Welfare, Government of India for the financial support provided to run the MvPI efficiently.

#### **Conflict of interest statement**

The authors declare no potential conflict of interest.

#### **Abbreviations**


*Basics and Essentials of Medical Devices Safety Surveillance DOI: http://dx.doi.org/10.5772/intechopen.97248*


### **Author details**

Vivekanandan Kalaiselven\*, Shatrunajay Shukla, Nikita Mishra and Pawan Kumar Materiovigilance Associate in Materiovigilance Programme of India, Indian Pharmacopoeia Commission, Ministry of Health and Family Welfare, Government of India, Ghaziabad, Uttar Pradesh, India

\*Address all correspondence to: vivekarts@rediffmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Edited by Maria Teresa Herdeiro, Fátima Roque, Adolfo Figueiras and Tânia Magalhães Silva*

In the last decade, pharmacoepidemiology has emerged as an important field to study the use/effects of drugs in large populations in real life, allowing for improved benefits and effectiveness of drugs as well as a decline in drug-related risks. The correct assessment, reporting, monitoring, and prevention of adverse events in drugs' development, as well as therapy and post-market surveillance, is essential to improve clinical therapies and health outcomes. This book provides a comprehensive and unique overview of the relevance, new insights, and recent findings of pharmacoepidemiology and drug safety in public health.

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New Insights into the Future of Pharmacoepidemiology and Drug Safety

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*Edited by Maria Teresa Herdeiro, Fátima Roque, Adolfo Figueiras and Tânia Magalhães Silva*