Basic event unavailability.
\r\n\tThis book will intend to look at different migrant patterns, voluntary and involuntary migration, over the last three centuries. What influenced people to leave their home countries, family, and friends and settle somewhere else? The book may include histories of the 19th century, consider tragedies and movements activated by political events in the 20th century, and/or look at recent events of the 21st century. Push and pull factors are important points. While most of us may be influenced in a negative way by the current happenings in Eastern Europe, the Russian invasion and resulting tragedies also demonstrate some very positive human traits – the preparedness of Ukraine’s surrounding countries to help those in need and to provide a safe place for the present.
\r\n\tWhether one looks at voluntary or involuntary migration into any country, after a period of adjustment, migrants do play a positive role. The research found that migrants contribute to the economy (food, shelter, employment, tax) and enrich a country’s cultural norms. Prerequisites for successful settlements are that the host society adopts a tolerant approach and that the migrants recognize the law and the language of the host country. Nothing is ever easy or without controversy, but I am a migrant (German Australian), and life in Australia has been relatively harmonious. Issues that could be considered in the book are multicultural societies (do monocultural societies still exist?) and theories of acculturation versus integration (settlement processes).
\r\n\tTwo further issues are very important in relation to human migration. There is climate change, global warming, and the environment, which clearly affect people’s movement. Small island populations are very concerned about rising sea levels. 2021 has also seen floods costing human lives: Turkey (August 2021), Brazil (December 2021), Chile (January 2021), and South India (November 2021), to name but a few. In Australia (March 2022), farms and whole townships in New South Wales and Queensland have been flooded for the second time in five years, and plans to resettle these towns are considered. Official and social media provide ample coverage of the events, which leads me to the next issue. There is today’s very important role of the media, of the official and social media. We are constantly bombarded with images of human war tragedies and flood victims. People in industrialized, western countries must be the best-informed populace. How far do the images and up-to-date TV news influence us, make us change our behavior, and perhaps even consider us more generous than we have been?
\r\n\tClimate change and the media are relatively new to the human migration debate, but both issues play important parts, and some interesting discussions are appreciated.
\r\n\t
Nuclear power is a source of sustainable energy, making a significant contribution in the generation of electricity worldwide. Nuclear reactors provide clean energy and is ensured to be safe through the thorough study of nuclear power plants (NPPs) called the probabilistic safety assessment (PSA). PSA is used as an evaluation tool which recognizes the potential risks and accident scenarios resulting in an accident due to the liability of failure of certain components or systems as a whole [1, 2, 3]. In an attempt at the aversion of catastrophes, several safety systems are put in place where containment spray system is one among the various redundant safety features in pressurized water reactors (PWRs). The system is aimed at heat removal within the containment when appropriate along with the reduction of radionuclide concentration discharged into the atmosphere. This safety-related system is situated in the auxiliary building and containment of the reactor [4].
A nuclear reactor is provided to take strict safety measures against radioactive contamination of the environment using a diverse arrangement of multiple redundant safety systems [5]. The containment spray system is an engineered safety feature to preserve the integrity of the containment in case of over pressurization. It is designed to bring down the internal peak pressure of the containment by half in a span of 24 hours in case of a loss of coolant accident (LOCA), along with removal of fission products [6].
The CSS consists of two identical but independent trains where each train consists of a containment spray pump (CSP), a containment spray heat exchanger (CSHX), a containment spray mini-flow heat exchanger, associated pipes and valves, and containment spray headers located in the upper dome of the containment. A simplified diagram of the CSS is shown in Figure 1. The two trains are redundant systems which has the capability of providing 100 percent flow individually during accident conditions and also provides reliability in case one of the trains stop working and makes them testable. In case of maintenance, one of the trains can be shut down and worked on while the other is free to operate.
Containment spray system in APR-1400.
The CSS in APR-1400 is designed as such that when the internal pressure of the containment surpasses the design limit, a containment spray actuation signal (CSAS) is sent to the pumps to start operation [7]. Once the actuation signal is received, the in-containment refueling water storage tank (IRWST) is used as the suction source for the containment spray system. The CSS pumps discharge water from the IRWST through the set of heat exchangers before sending it to the overhead spray. Once the containment pressure is detected to reach a certain value, the valves open automatically to allow the flow of spray water to the nozzles. The water travels to the spray headers and are divided into small droplets to fall throughout the containment.
Two isolating valves in the system are located in the pipe between the tank and the spray nozzles. The valves are in a closed position under normal operation to isolate the CSS from the rest of the plant. Additional valves are present to ensure the isolation of the overhead spray. The spray nozzles are arranged in concentric circles, at several angles, to ensure adequate coverage of the containment volume resulting in continuous cooling of the reactor system. The droplets from the spray headers cools the atmosphere and the remnants fall into the holdup volume tank (HVT), which is transported back to the IRWST. This certain design of the CSS is maintained in the APR-1400 plant design in countries like Korea, USA, and UAE [8, 9].
The containment spray pumps are centrifugal pumps responsible for transporting water from the IRWST to the spray nozzles at the top of the dome. The pumps discharge into the heat exchangers to cool the water. The CSS pumps are identical to the shutdown cooling pumps (SCP) and thus are interchangeable. In case the CSS pumps are inoperative, the SCPs can be used to carry out the operation.
The containment spray heat exchangers are used to remove heat from the containment spray water in the event of an accident. The heat exchangers have a U-tube design with a tube and shell side. The hot water is passed through the tube transferring heat to the cold water in the shell side.
The containment spray headers are located at the top of the containment dome in concentric circles which ensures adequate coverage of the containment building volume and homogeneous distribution of the spray water. The isolation valves in the spray headers control the flow of water into the nozzles and open on the receipt of a CSAS. This reduces the chances of accidental spraying when it is not intended to. The design of the spray nozzles is required to be as such to minimize clogging. They are required to avoid any internal moving parts or restrictions which could possibly interfere with the passage of the flow or restrict it [10].
RiskSpectrum Analysis Tools (RSAT) is a software that enables study of PSA utilizing fault trees. It conducts several analyses such as MCS analysis, uncertainty analysis, importance/sensitivity analysis, and time-dependent analysis. A fault tree was modeled in the RiskSpectrum software according to the fault tree presented in the WASH-1400 report. The report is a 1975 reactor safety study report conducted to assess the accident risks in U.S. commercial nuclear power plants. The unavailability’s of the events involved in the fault tree are given in Table 1 [11].
Event | Unavailability (q) | |
---|---|---|
Subsystem A | Subsystem B | |
CXVA004X | CXVB004X | 1.00E-03 |
CST100AC | CST100BC | ε |
CMV100AC | CMV100BC | 1.00E-04 |
CXVA002X | CXVB002X | 1.00E-02 |
CFLA01AP | CFLB01BP | 1.10E-04 |
CCL1A01G | CCL1B01G | 3.00E-04 |
CM0A01AF | CM0B01BF | ε |
CST1A01F | CST1B01F | 1.00E-03 |
CPMA01AA | CPMB01BA | 1.00E-03 |
CPMA01AF | CPMB01BF | 1.50E-05 |
CCVA001C | CCVB001C | 1.00E-04 |
CNZA001P | CNZB001P | 1.30E-04 |
GCL01 | GCL02 | 4.60E-03 |
JD00 | JC00 | 4.10E-05 |
JK00 | JJ00 | 1.10E-06 |
CTK0001R | ε | |
CTK0001L | ε | |
CVT0001P | 4.40E-07 | |
JH00 | 4.10E-05 | |
JG00 | 4.10E-05 |
Basic event unavailability.
Note: The symbol “ε” denotes that the unavailability is assumed to be negligible.
The fault trees constructed for containment spray system are shown in Figures 2–4.
Containment spray system fault tree (Part 1 of 3).
Containment spray system fault tree (Part 2 of 3).
Containment spray system fault tree (Part 3 of 3).
The MCS analysis conducted on the containment spray system showed that the top event probability (Insufficient fluid flow to CSIS nozzles in containment) is 3.47E-04 with 171 minimal cut sets. The first 10 minimal cut sets against the probability are represented in Figure 5. It shows that the minimal cut set with the highest probability of 1.00E-04 is a combination of operator error leaving the manual valve open in both subsystems A and B.
Top 10 minimal cut sets in a CSS system.
Importance analysis was also conducted on the fault trees which showed the risk decrease factor, risk increase factor, and the sensitivity for each event in Figures 6–8, respectively. Risk decrease factor is the reduction in risk if the feature was assumed to be optimized or made perfectly reliable whereas, risk increase factor is the increase in risk if the feature was assumed to be absent or to fail. The graphs below show that manual valves left open in subsystem A due to operator error has the highest RDF, RIF, and sensitivity in the CSS system, followed by the identical event in subsystem B.
Basic event RDF.
Basic event RIF.
Basic event sensitivity.
Containment spray systems are a crucial part of nuclear power plants as a safety feature. The system is aimed at heat removal within the containment. The CSS consists of two identical, independent trains and is designed as such that when the internal pressure of the containment surpasses the design limit, a containment spray actuation signal starts operation of the CSS. Water from the storage tank is transported to the overhead sprays at the top of the dome from where water is sprayed to ensure adequate coverage of the containment volume resulting in continuous cooling of the reactor system. Analysis of the constructed fault tree in RiskSpectrum from the WASH-1400 report gave in return the top event probability and the list of minimal cut sets. It also showed that the highest RDF, RIF, and sensitivity belonged to the event of operator error where the manual valve is left open in train A.
The authors are thankful to university of Sharjah for providing research facilities.
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 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.
Main contributors of Pharmacoepidemiology.
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, 6, 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].
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].
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].
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.
Study type/design | Data source(s) | Year | Statistical methods | Variable(s) of interest | Reference |
---|---|---|---|---|---|
Cross-sectional | Survey questionnaire data | 2018 | Descriptive (percentages, frequencies, charts, median, ratios) | Drug availability, Drug pricing, Affordability | [34] |
Controlled trial | Articles | 2017 | - | Vaccination times, Dosage amounts | [35] |
Cross-sectional | Prospective population census, passive surveillance, serological studies and healthcare utilisation surveys | 2017 | Descriptive (charts, percentages) | Pathogen transmission, exposure and susceptibility | [36] |
Randomisation | Basic survey | 2019 | Descriptive (percentages) | Drug abuse, Prevalence | [37] |
Cohort | Anonymised patient record database | 2013–2016 | Descriptive (percentages), inferential-negative binomial regression (confidence intervals) | Incidence and mortality ratios | [38] |
Randomized Clinical Trial | Clinical data | 2012 | Descriptive (proportions), inferential (chi-square test, Kruskal-Wallis test, confidence intervals, incidence rate ratio, p-values, risk ratios) | Antiretroviral (ARV) usage, initiation | [39] |
Key Informant Interviews (KII) and Focus Groups (FGs) | Recorded and transcribed qualitative data | 2019 | Thematic analysis | Vaccination trials | [40] |
Matched case–control study | Case–control study data | 1993 | Descriptive (tables, frequencies, percentages) and inferential (conditional logistic regression, relative risks, odds ratio, likelihood ratios, and confidence intervals) | BCG vaccine, efficacy, leprosy | [41] |
Cross-sectional | Drug prescription data from hospital electronic database | 2020 | Descriptive (frequencies and percentages for categorical variables) and (means, medians, standard deviations (SD), and interquartile ranges (IQR) for continuous variables). Mean and SD were used for normal distribution and median and IQR were used for skewed distribution. | Drug utilization | [42] |
Retrospective | Pharmacokinetic data of children > = 2 years and adults | 2018 | Both descriptive and inferential models (mean absolute error from non-linear statistical models) | Drug dosing and clearance | [43] |
A review of computer aided research studies and usage of statistical models in Pharmacoepidemiology.
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].
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].
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].
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, 72, 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 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].
Database name | Host(s) | Design | Data | Location | Target population | Data coverage | Reference(s) |
---|---|---|---|---|---|---|---|
Electronic Patient Registration System | Queen Elizabeth Central Hospital | Multiple | Multiple | Malawi | Various | Vital signs data, treatment, demographic data, diagnostic information | [38] |
IADB.nl | Multiple | Multiple | Netherlands | Over 500,000 people | Live and stillbirth pregnancy identification, medicine use data, prescriptions from 54 community pharmacies | [52] | |
DEFF Research Database | Ministry of science, Technology and innovation; Ministry of Culture; Ministry of Education | Multiple | Multiple | Denmark | Countrywide | Dispensed drugs, with potential for linkage to outcomes | [75] |
Odense University Pharmacoepidemiological Database (OPED) | University of Southern Denmark | Multiple | Multiple | County of Funen in Denmark | Countrywide | Reimbursed prescriptions | [76] |
Disease Analyser Patient Database | Multiple | Multiple | Germany | German, UK, French, and Austrian population | Diagnoses, prescriptions, risk factors (such as smoking and obesity), and laboratory values for approximately 10 million patients | [77] | |
German Longitudinal Prescription Database (LRx) | Multiple | Multiple | Germany | Countrywide | Diseases, drug utilisation, treatment costs, 60% of prescriptions reimbursed by statutory health insurance funds in Germany | [78] | |
Database on Veterinary Clinical Research in Homeopathy. | Multiple | Multiple | Germany | Many | 200 entries of randomised clinical trials, non-randomised clinical trials, observational studies, drug proving, case reports and case series | [79] | |
UK General Practice Research Database (GPRD). | UK Department of Health | Longitudinal | Case reports | UK | 5 million patients; Countrywide | Collated information from over 500 general physicians’ practices | [80, 81] |
Clinical Research Database | Memorial Sloan-Kettering Cancer Center (MSKCC) | Multiple | Multiple | Patients on IRB approved studies who have passed through bone marrow transplant (BMT) | Diseases, pathology, infusion, treatment, among others. | [82] | |
Cancer Research DataBase (CRDB) | Cancer informatics project | Multiple | Mediation and data warehousing | Small molecule | Small molecule data, computational docking results, functional assays, and protein structure data | [83] | |
Danish Database for Biological Therapies in Rheumatology (DANBIO) | DANBIO | Multiple | Multiple | Denmark | Patients taking Biological treatments | Patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and axial spondyloarthritis (Ax SpA), who are followed longitudinally | [84, 85] |
The FoodCast Research Image Database (FRIDa) | Multiple | Multiple | Sweden | Wide range of foodstuff and related materials | 877 images from eight different categories: natural-food, natural-non-food items. Artificial food-related objects | [86] | |
Pharmacy Dispensing Database | Multiple | Multiple | Netherlands, Denmark, Norway, Wales, France and Tuscany-Italy | Countrywide | Medicine use data | [87] | |
Danish National Patient Registry, Norway Medical Birth Registry | Multiple | Multiple | Norway and Denmark | Countrywide | Pregnancy loss identification | [87] | |
Influenza Research Database (IRD) | Bioinformatics Resource Center | Multiple | Multiple | US | All species of influenza virus sequence data | Influenza virus data, analytical and visualisation tools for influenza virus, personal workbenches for storing data | [88, 89] |
Beth Israel Deaconess Medical Centre | Washington heart Centre, Beth Israel hospital, Boston | Multiple | Multiple | USA | Countrywide | Patient problems, medication, lab results | [90] |
USDA’s National Nutrient Database for Standard Reference, the Dietary Supplement Ingredient Database, the Food and Nutrient Database for Dietary Studies, and the USDA’s Food Patterns Equivalents Database | US Department of Agriculture (USDA) | Multiple | Multiple | USA | Foodstuffs | Food and nutrients | [91] |
Camden and Islington NHS Foundation Trust (C&I) Research Database | South London and Maussley NHS Foundation Trust (SLaM) | Multiple | Multiple | UK | Countrywide | 108,168 mental health patients; 23,538 were receiving active care | [92] |
Population and Housing Census (PHC), Health and Welfare Survey (HWS), Socio-Economic Survey (SES), Reproductive Health Survey (RHS), National Disability Survey (NDS), Multiple Indicator Cluster Survey (MICS) | National Statistics Office (NSO) | Interviews, face to face, self-enumeration, internet | Cross sectional | Thailand | Various | General population, health insurance, illness, health services, payment, equity, injury, co-morbidity, income, expenditure, debt, household distribution, family planning, maternal and child health. AIDS, Cancer, infertility, sex education, adolescent health | [93] |
Cancer Registry | National Cancer Institute (NCI) | Longitudinal | Case reports | Thailand | All patients | Cancerous diseases, medicines | [93] |
Thai Vigibase | Health Product Vigilance Centre (HPVC) | Case reports | Thailand | All patients | Adverse events | [93] | |
Adverse Events Database | Pharmaceutical and Medical Devices Agency (PMDA) | Case reports | Japan | Countrywide | Adverse events | [93] | |
National Community Pharmacy Group | Multiple | Multiple | South Africa | Countrywide | Drug utilisation | [94] | |
South African Medicine Claims Data | Pharmaceutical Benefit Management Company (PBM) | Multiple | Multiple | South Africa | Countrywide | Medicines claims | [95] |
Strategic Typhoid Alliance Across Africa (STRATAA) | Malawi, Nepal, and Bangladesh | Countrywide | Demographic data, typhoid disease data | [96] | |||
VigiBase | Uppsala Monitoring Centre (UMC) | Multiple | Multiple | Sweden | Worldwide | Adverse drug events | [97] |
District Health Information System 2 (DHIS-2) | Kenya Medical Research Institute (KEMRI), Kamuzu Central Hospital | Multiple | Multiple | Kenya, Malawi, Uganda, Zambia [98] | Various | General health records and drug supply | [99, 100] |
Mitishamba Database of Natural Products | University of Nairobi | Anti-Malaria drugs | Natural products | Kenya | Sub-Saharan Africa | Medicinal plants | [101] |
International Databases to Evaluate AIDS (IeDEA-EA) | KEMRI, Mbarara Univ. & Tanzania | HIV-AIDS care | Drugs and Personal Protective Equipment (PPEs) | Kenya, Tanzania, Uganda | East African population | HIV care treatment | [102] |
Computer databases currently used in pharmacoepidemiology research.
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].
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].
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.
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.
The authors declare no conflict of interest.
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.
Adverse Drug Reactions Acquired Immunodeficiency Syndrome Antiretroviral drugs Asian Pharmacoepidemiology Network BCG-Bacille Calmette-Guerin Bone Marrow Transplant Canadian Drug Safety and Effectiveness Network Coronavirus Disease 2019 Danish Database for Biological Therapies in Rheumatology District Health Information System (version 2) Electronic Health Records Exploring and Understanding Adverse Drug Reactions European Network of Population-based Registries for the Surveillance of Congenital Anomalies Food and Drug Administration Focus Groups Discussion The FoodCast Research Image Database UK General Practice Research Database Health Product Vigilance Centre Health and Welfare Survey InterAction Database East African International Databases to Evaluate AIDS Innovative Medicines Initiatives Interquartile Range Influenza Research Database International Society for Pharmacoepidemiology Key Informant Interviews Multiple Indicator Cluster Survey Memorial Sloan-Kettering Cancer Centre National Cancer Institute National Disability Survey Nordic Pharmaco- Epidemiological Network National Statistical Office Odense University Pharmacoepidemiological Database Pharmaceutical Benefit Management Company Population and Housing Census Pharmaceutical and Medical Devices Agency Research on Outcomes of Therapeutics by an European ConsorTium Reproductive Health Survey Standard Deviations Socio-Economic Survey Statistical Package for Social Scientists Strategic Typhoid Alliance across Africa Task-force in Europe for Drug Development for the Young U.S. Department of Agriculture
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He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"2",type:"subseries",title:"Prosthodontics and Implant Dentistry",keywords:"Osseointegration, Hard tissue, Peri-implant soft tissue, Restorative materials, Prosthesis design, Prosthesis, Patient satisfaction, Rehabilitation",scope:"