What Affects the Quality of Pharmacovigilance? Insights From Qualitative Comparative Analysis

ABSTRACT

Pharmacovigilance plays a significant role in guaranteeing the safety of medications for patients. Over the last three decades, China has significantly advanced its pharmacovigilance practices, yet the factors that drive the quality of pharmacovigilance remain unclear. This study aimed to investigate how multiple factors interactively influence the quality of pharmacovigilance and identify pathways for achieving high‐quality pharmacovigilance practices. A unique sample of pharmacovigilance‐specific inspection reports from 13 representative companies in China was adopted in analysis. Given the qualitative nature of the inspection reports, we utilized crisp‐set qualitative comparative analysis (csQCA) with five factors structure based on the technology–organization–environment (TOE) theoretical framework. The csQCA enabled us to elucidate the interactions among the antecedents of pharmacovigilance quality through quantitative univariate necessity analysis and configuration analysis. Three pathways contributing to high‐quality pharmacovigilance were identified, and “Dedicated and Qualified Person for Pharmacovigilance (DQPPV)” was shown to be involved in all three pathways. Upon examining the manner in which multiple variables influence the quality of pharmacovigilance, it becomes evident that the DQPPV represents a factor that warrants further investigation. The results of the configuration allow companies to implement targeted measures to enhance the functionality of the pharmacovigilance system and to improve the quality of the system. Further research could explore the influence of additional factors on pharmacovigilance efforts, which could then contribute to marketing authorization holders' (MAHs') pharmacovigilance efforts.

1. Introduction

Pharmacovigilance, as defined by the World Health Organization (WHO), involves the science and activities related to the detection, assessment, understanding, and prevention of adverse reactions and other drug‐related problems [1]. Since the thalidomide tragedy in 1961, which highlighted the need for robust drug safety monitoring, pharmacovigilance has substantially evolved. This evolution led to the establishment of the WHO Pilot Research Project for International Drug Monitoring in 1968 [2]. Currently, the WHO Program for International Drug Monitoring (PIDM) includes 23 associate and 157 full‐member countries [3], reflecting its global reach and influence. Additionally, several international pharmacovigilance agencies, such as the Council for International Organizations of Medical Sciences, have been established to promote the development of pharmacovigilance at an international level [4].

As one of the most populous countries in the world, China prioritizes the safety of pharmaceuticals. Substantial progress has been made in pharmacovigilance over the past three decades. The National Adverse Drug Reaction (ADR) Monitoring Center was established in 1989, and China became a member of the WHO PIDM in 1998 [3]. Since then, over 20 laws and regulations concerning pharmacovigilance have been enacted. The revised Medicinal Product Administration Law of the People's Republic of China, implemented in 2019, established the legal status of pharmacovigilance [5]. Furthermore, the National Medical Products Administration (NMPA) promulgated the Good Pharmacovigilance Practice in 2021, which marks a significant milestone in China's pharmacovigilance history [6]. The marketing authorization holder (MAH) system is also referred to in laws and regulations that require MAHs to assume responsibility for conducting pharmacovigilance work throughout the entire life cycle of the drug. One of these elements is the spontaneous reporting of adverse reactions to medicines. In 2023, the majority of China's reports were from professionals (90.1%), with 3.5% from MAHs [7], while reports from MAHs accounted for only 1.4% in 2017 [8].

Compared with international pharmacovigilance systems, the NMPA serves as China's central regulatory authority for pharmacovigilance, with implementation delegated to its subordinate agencies and provincial adverse drug reaction/event (ADR/ADE) monitoring centers. However, interdepartmental coordination remains insufficient, and postmarketing surveillance heavily relies on voluntary reporting, resulting in limited proactive monitoring capabilities [9]. By contrast, the US Food and Drug Administration (FDA) manages a centralized system with an advanced active surveillance mechanism. The FDA Adverse Event Reporting System and the Sentinel Initiative demonstrate real‐time monitoring capacity by integrating automated signal detection algorithms [10] with large‐scale healthcare databases to facilitate the rapid identification of emerging risks and timely regulatory interventions [11]. Similarly, the European Medicines Agency (EMA) ensures data interoperability across EU member states through the EudraVigilance system, while the Pharmacovigilance Risk Assessment Committee offers centralized supervision for risk signal evaluation. Japan's pharmacovigilance system is institutionally embedded into a legally integrated, two‐tiered system led by the Ministry of Health, Labor, and Welfare and operationally executed by the Pharmaceuticals and Medical Devices Agency (PMDA). As the central regulatory hub, the PMDA oversees ADR monitoring through the Japanese Adverse Drug Event Report database. To enhance transparency and social accountability, Japan has enhanced public supervision by opening ADR query functionalities to the public through the Drug Safety Information Network [12].

China's pharmacovigilance legal framework is primarily founded on the Medical Product Administration Law of the People's Republic of China and Good Pharmacovigilance Practice (GVP), having achieved significant progress in recent years. However, the lack of a dedicated pharmacovigilance law, along with a relatively low legal hierarchy and provisions that lack operational clarity, remains a key challenge. By contrast, the US FDA operates within a comprehensive legal framework, including the Federal Food, Drug, and Cosmetic Act (FD&C Act) and the Prescription Drug User Fee Act (PDUFA), both of which clearly define responsibilities across the entire drug lifecycle. Japan's Pharmaceuticals and Medical Devices (PMD) Act and its enforce regulations, complemented by GVP and Good Post‐marketing Study Practice (GPSP), establish a comprehensive legal framework that effectively fulfills the practical requirements of pharmacovigilance implementation [13]. Similarly, the EMA operates under a legally binding harmonized framework established by Directive 2010/84/EU and Regulation (EU) No. 1235/2010, creating a unified pharmacovigilance system enforced by national regulatory authorities. Key regulatory instruments include the Guideline on Good Pharmacovigilance Practices (GVP), a set of modular guidelines that standardize risk assessment and mitigation protocols, such as risk management plans (RMPs) for high‐risk drugs and periodic safety update report submissions.

Despite these advancements, several challenges persist in China's pharmacovigilance system. Incomplete technical guidelines lead to varied levels of pharmacovigilance practice among MAHs. Additionally, there is a significant shortage of qualified personnel and inadequate capacity within MAHs [14]. For instance, research conducted in Jiangsu Province [15] and Guangzhou City [16] has revealed weaknesses in risk management practices and system implementation among local MAHs. A recent study in Hainan Province further highlighted the general lack of attention to pharmacovigilance and insufficient implementation capacity among MAHs [17].

Given these challenges, there is a critical need to investigate the factors influencing the quality of pharmacovigilance (QPV) efforts. From the perspective of configuration, this study focused on 13 drug manufacturers inspected by the Adverse Drug Reaction Monitoring Center, employing the crisp‐set qualitative comparative analysis (csQCA) methodology to determine the requisite criteria for achieving high‐quality pharmacovigilance. Another objective is to explore the pathways through which these crucial elements influence the QPV and the mechanisms involved.

This research represents a pioneering effort to apply qualitative comparative analysis (QCA) to pharmacovigilance, expanding its applicability and providing practical guidance for MAHs in China. Additionally, the findings will serve as a reference for relevant departments to establish detailed and relevant technical specifications and operational guidelines, ultimately enhancing the quality of pharmacovigilance.

The key terms used in the csQCA are summarized in Table 1 [18].

TABLE 1.

Key terms in csQCA.

Key term	Definition
Necessity	If a condition is constant when the result is present, then it is required.
Truth table	Combinations of all conditions.
Consistency	The degree to which the results are interpreted by conditions.
Raw coverage	Proportion of cases that can be explained by combination of conditions.
Unique coverage	The number of cases that can only be explained by this combination of conditions.
Solution coverage	The extent to which all combinations of conditions cover the cases.
Complex solution	All possible combinations of conditions are presented based on the traditional logical operations.
Intermediate solution	Figure out sufficient conditions for a specific result by logically combining conditions.
Parsimonious solution	By logically combining conditions to figure out the conditions needed to attain a specific result.

2. Methods

2.1. Theoretical Framework

The framework of technology–organization–environment (TOE) theory, proposed by scholars Tomatzky and Fleischer in 1990, investigates the factors that influence technological innovation implementation within enterprises [19]. This theory is valuable for understanding the underlying causes and influences of complex business phenomena. The research question “What combinations of factors can improve the quality of corporate pharmacovigilance?” presents a complex causality problem. The QCA method, which focuses on “multiple causes and one outcome,” helps explain the complexity of multiple concurrent causalities. Thus, this study examined the antecedents and complex pathways affecting corporate pharmacovigilance via the QCA method within the TOE theory framework.

2.2. Methods

The data used in the study were collected from reports of the Adverse Drug Reaction Monitoring Center's specialized pharmacovigilance inspection of 13 representative MAHs in China. Using this method instead of the conventional method of questionnaires mitigates the influence of the subjectivity of the respondents on the results of the study. These regions were selected for their rapid industrial development and elevated economic status, which correlates with a heightened emphasis on pharmacovigilance compared with other cities, thereby providing additional representative data.

Various conventional analytical methods are still used in most pharmacovigilance studies. Conventional analytical methods mainly concentrate on analyzing how individual variables affect the outcome. However, in this study, a novel analytical technique called csQCA was implemented. Unlike traditional methods, csQCA focuses on how different variables affect results and allows us to determine the set of antecedent conditions (independent variables) that result in a concrete outcome (dependent variable). QCA is based on asymmetric relationships, and it overcomes the drawbacks of complementarity and linearity found in conventional multiple regression analysis [20]. Furthermore, QCA is a hybrid research method that combines quantitative analysis with a case study orientation. Two common procedures for conducting a QCA are crisp and fuzzy sets. In our exploration of the variables affecting the QPV efforts, a crisp set was chosen because it makes the resulting paths easy to comprehend and permits highly interpretable results [21].

Another advantage of QCA is that not only a unique combination but other different associations of variables may generate identical outcomes [22]. These different combinations of variables, known as recipes, form the basis of the outcomes of the method. Notably, we can analyze the effect of the presence and absence of these antecedent conditions on the emergence of the outcome via QCA [23]. Moreover, QCA is particularly effective with medium‐sized or small datasets, providing decisive results where traditional methods might struggle [24].

The Quality Objectives of Pharmacovigilance (QOPV) state that organizations must formulate quality objectives for their pharmacovigilance activities [6]. These objectives should be set not only to ensure that staff are able to carry out their duties and participate effectively in pharmacovigilance activities, but also to guarantee that pharmacovigilance activities are in line with regulatory requirements and that the pharmacovigilance system operates in a compliant manner.

The Scientific Methods for Detecting Risk Signals(SMDRS) refer to the ability to identify new drug safety risks in a timely manner or assess whether known risks have changed through the aggregation and analysis of adverse reaction information from various sources. This process, followed by risk assessment and management, has the ultimate goal of achieving a state in which the benefits of drug use outweigh the hazards. Within the EU pharmacovigilance system, signal detection plays a significant role in promoting public health by optimizing the safety and efficacy of medicines and informing their use [25]. Therefore, the implementation of a scientific and proper signal detection method and frequency enhances the efficient operation of the pharmacovigilance system.

The Dedicated and Qualified Person for Pharmacovigilance (DQPPV) indicates that MAHs should designate a person in charge of pharmacovigilance in accordance with regulatory requirements to guarantee the successful operation of the pharmacovigilance system and the accomplishment of quality objectives [6]. Nevertheless, at present, the heads of pharmacovigilance in the majority of companies are part‐time heads of other departments and lack the requisite time and level of professionalism to fulfill the role of a full‐time head of pharmacovigilance. Therefore, a dedicated and qualified person for pharmacovigilance is critical for guaranteeing high‐quality pharmacovigilance.

The Spontaneous Reporting Pathway (SRP) involves setting up a self‐reporting channel for adverse drug reactions on the MAHs' website, primarily for patients to report ADRs. This channel allows MAHs to collect data on adverse drug reactions from multiple sources and enables the detection and identification of risk signals and the implementation of risk management strategies to guarantee the safety of patients' medication. Although ADRs reported by patients themselves may lack standardization, they provide direct information on the prevalence of ADRs and their effect on quality of life [26]. Patient reports represent a valuable addition to the spontaneous reporting system and facilitate the monitoring of ADRs associated with OTCs [26].

Pharmacovigilance training (PVT) is mandated by good pharmacovigilance practice, which requires regular training and assessment of its effectiveness. Most employees of the organizations stated that they needed increased professional competence in a variety of areas, including pharmacovigilance laws and regulatory obligations [27]. All staff members involved in pharmacovigilance must receive PVT to enhance their professional competence, carry out pharmacovigilance tasks effectively, and ensure the efficient operation of the pharmacovigilance system.

The QPV is assessed based on the number of deficiencies identified in pharmacovigilance‐specific inspection reports. According to the guiding principles for pharmacovigilance inspections, deficiencies are divided into three categories: serious, major, and general. Therefore, the Delphi method was employed to determine the relative importance of different deficiencies and assign points according to the severity of the deficiencies. Ultimately, we scored the inspection reports based on the number of deficiencies mentioned in the reports to evaluate the quality of pharmacovigilance.

2.3. Analysis

Among the various QCA methods, csQCA was used for the data analysis in this study. The fundamental principle of csQCA is to establish clearly defined outcome variables for the analysis of social phenomena, such as the occurrence of a specific event and the use of that information to incorporate dichotomous conditional variables (0 = absent and 1 = present) to create a truth table [28]. First, we analyzed the relevance of each univariate variable in the truth table. Second, we conducted configuration analyses to explore combinations of independent variables. Consistency and coverage were used for parameter control, and ultimately, the combinations with superior explanatory power were analyzed. This approach enabled us to gain insights into the key factors and their interactions, providing a comprehensive understanding of the antecedents of pharmacovigilance quality.

3. Results and Discussion

3.1. Variable Selection and Assignment

The variables considered in this study included “Quality Objectives of Pharmacovigilance (QOPV),” “Scientific Methods for Detecting Risk Signals (SMDRS),” “Pharmacovigilance Training (PVT),” “Spontaneous Reporting Pathway (SRP),” and “Dedicated and Qualified Person for Pharmacovigilance (DQPPV).” These five variables were used as conditional variables, and the “Quality of Pharmacovigilance (QPV)” served as the outcome variable. The assignment criteria were followed to assign a value to each factor, and the outcomes of the variable assignments are listed in Table 2.

TABLE 2.

Variable assignment table.

Variable type	Variable	Secondary variables	Measurement
Conditional variables	Technological dimension	Scientific methods for detecting risk signals (SMDRS)	If company has scientific methods for detecting risk signals, the value of 1 is given, otherwise 0.
	Organizational dimension	Dedicated and qualified person for pharmacovigilance (DQPPV)	If company has a dedicated and qualified person for pharmacovigilance, not a concurrent job, the value of 1 is given, otherwise 0.
		Quality objectives of pharmacovigilance (QOPV)	If company has clear quality objectives of pharmacovigilance, the value of 1 is given, otherwise 0.
		Pharmacovigilance training (PVT)	If company has regular pharmacovigilance training, the value of 1 is given, otherwise 0.
	Environmental dimension	Spontaneous reporting pathway (SRP)	If spontaneous reporting of adverse reactions is permitted on a company's website, the value of 1 is given, otherwise 0.
Outcome variable		Quality of pharmacovigilance (QPV)	If high‐quality outcome is achieved, the value of 1 is given, otherwise 0.

Considering the scalability between the number of cases and antecedent conditions, a medium‐sized sample (10–40 cases) requires the number of condition variables to range from 4 to 7 [29]. On the basis of a review of previous studies, condition variables were chosen for this study in accordance with the theoretical paradigm TOE to formulate the impact of various factors on the attainment of high‐quality pharmacovigilance.

3.2. Univariate Necessity Analysis

Univariate necessity analysis is the initial stage of QCA aimed to characterize how strongly the variable X interprets the outcome Y. In QCA operations, univariate necessity analysis is mainly determined by the consistency index, which measures the degree of correlation between the condition variable and the outcome variable. The consistency index quantifies the extent to which the condition variable explains the outcome variable. A condition variable is necessary only if the consistency index is greater than 0.9 [30]. In addition, coverage indicates the strength of the condition's explanation of the outcome [29]. The results of the analysis of univariate necessity are shown in Table 3.

TABLE 3.

Univariate necessity analysis.

Variable	Consistency	Coverage
QOPV	0.800000	0.333333
SMDRS	0.200000	0.500000
PVT	0.200000	0.333333
SRP	0.400000	0.250000
DQPPV	1.000000	0.555556

3.3. Establish the Truth Table

On the basis of the previous criteria for allocating condition and outcome variables, the binary table of raw data that had undergone dichotomous distribution was input into fsQCA 4.1 software [31] to perform the operation and construct the truth table, which is indicated in Table 4. Here, 0 represents the absence of the outcome and 1 represents the presence of an outcome or condition.

TABLE 4.

Truth table.

QOPV	SMDRS	PVT	SRP	DQPPV	QPV	Number
0	0	0	0	1	1	1
1	0	1	0	1	1	1
1	1	0	1	1	1	1
1	0	0	0	1	0	2
1	0	0	1	1	0	3
1	0	0	1	0	0	2
1	0	0	0	0	0	1
1	1	1	1	0	0	1
1	0	1	1	1	0	1

3.4. Path Configuration and Analysis

Boolean algebra was used to derive the complex, intermediate, and parsimonious solutions, utilizing a threshold of Raw to 0.8 and PRI value to 0.7. Current mainstream studies on QCA techniques indicate that most scholars agree that intermediate solutions are optimal for analysis because of their moderate complexity, reasonable foundation, and inability to remove crucial criteria [32]. Therefore, this work provided a thorough investigation of intermediate solutions with parsimonious solutions. The results of the combination of conditions involved in intermediate solutions were translated and are presented in Table 5. The criteria for an intermediate solution can be categorized into two distinct groups: core conditions and edge conditions. Core conditions are those that exist in intermediate and parsimonious solutions, whereas edge conditions are those that exist only in intermediate solutions [33]. The black circles indicate the presence of a variable, and the white circles represent its absence; no circle means that the variable is not essential for that particular configuration. The larger circles represent the core conditions, and the smaller circles indicate the edge conditions. Three pathways were obtained from the analysis of the configuration of the outcome “high‐quality pharmacovigilance efforts,” with the consistency of the combinations being greater than 0.8 and the overall coverage being 0.6, which reached a high level.

TABLE 5.

Configuration of paths.

	Path 1	Path 2	Path 3
QOPV		●	●
SMDRS	○	○
PVT	○
SRP	○
DQPPV	●	●	●
Consistency	1
Raw coverage	0.2	0.2	0.2
Unique coverage	0.2	0.2	0.2
Solution coverage	0.6
Solution consistency	1

Note: The black circles indicate the presence of a variable, and the white circles represent its absence. No circle means that the variable is not essential for that particular configuration. The larger circles represent the core conditions, while the smaller circles indicate the edge conditions.

Path 1 indicates that even if there is only a dedicated and qualified person for pharmacovigilance and there is no clear quality objective of pharmacovigilance, MAH can achieve high‐quality pharmacovigilance. The dedicated leader of pharmacovigilance is distinct from other managers who also oversee pharmacovigilance as part of their role. First, in terms of time and effort, a dedicated director will undoubtedly allocate a remarkable proportion of their time and attention to work related to pharmacovigilance rather than drug development or marketing. Additionally, a dedicated director will not only be experienced in the work of pharmacovigilance, but also be familiar with laws and regulations about pharmacovigilance, thereby meeting the professional requirements of the GVP. These characteristics will enable directors to organize and coordinate pharmacovigilance resources reasonably and ensure the effective operation of the pharmacovigilance system and the high quality of the work to be completed.

Path 2 shows that PVT is the core condition and quality objective of pharmacovigilance, and a dedicated leader of pharmacovigilance comprises edge conditions. The increased demand for training arises from developments in the regulation of medicines and the consequent need to meet the corresponding legal obligations, including the requirement for MAHs to employ qualified people for pharmacovigilance work. Companies focus on conducting targeted PVT to improve the performance of their employees. Concurrently, with the guidance of a qualified professional in charge of pharmacovigilance, trained employees possess defined scientific and clear objectives that yield high‐quality work. Despite the absence of scientific methods for detecting risk signals, the company's pharmacovigilance system will be enhanced and efficient under the expertise of the committed leader and trained personnel.

Path 3 shows that scientific methods for detecting risk signals and spontaneous reporting pathways are core conditions for high‐quality work; moreover, dedicated leaders and quality objectives are edge conditions that are essential for a company to perform high‐quality pharmacovigilance. However, drug adverse reports were collected only from medical institutions. MAH can promptly uncover signals of drug risk and implement quick measures by using scientific methods for detecting risk signals and setting spontaneous reporting pathways for the public.

4. Discussion

Pharmacovigilance in China has rapidly developed in recent years [26]. The GVP, which was promulgated in 2021, marks the beginning of a new phase in the development of pharmacovigilance in China, establishing high standards for the country's pharmacovigilance system and practitioners. Therefore, the significance of the factors influencing the QPV work, which is currently receiving considerable attention in China, is unequivocal. Our results identify key factors contributing to effective pharmacovigilance practices and the pathways through which they are implemented.

This study utilized QCA, examining numerous cases to assess the pharmacovigilance efforts of MAHs throughout the country, whereas Hamza [34] used Matland's ambiguity–conflict model to qualitatively investigate the application of pharmacovigilance regulations in many regions. Abimbola O. Opadeyi [35] employed the WHO pharmacovigilance indicators to assess the state of pharmacovigilance at the country or regional level. Our study utilized these indicators to refine the research focus to firms.

Using a management‐related methodology, csQCA, we explored factors influencing the quality of the pharmacovigilance work performed by MAHs. We collected and analyzed 13 representative reports from special pharmacovigilance inspections. Through the TOE theoretical framework, QCA was used to explore factors related to pharmacovigilance. Following exploration, the data from the reports were further processed to construct a framework for the factors influencing the quality of pharmacovigilance. Subsequently, csQCA was employed to quantify the data and investigate the pathways of conditional factor combinations with explanatory power. Our findings indicated that the QPV is influenced by several factors that create different combinations of conditions, each yielding various outcomes.

In this study, we found that a dedicated and qualified person for pharmacovigilance was an important facilitator of the improvement of the quality of pharmacovigilance. Univariate necessity analysis revealed that the DQPPV is a necessary condition for achieving high‐quality outcomes (consistency = 1). The presence of the condition variable DQPPV is necessary for high‐quality pharmacovigilance. Previous research also indicated that the QPPV ensures the quality and optimal benefit/risk profile of medicines throughout their life cycle [36], a fundamental task of pharmacovigilance. Additionally, the consistency of the QOPV is 0.8, which suggests that although it is not a necessary condition, it still has a significant effect on the quality of pharmacovigilance.

Among the three path configurations, the DQPPV is the most significant driver of high‐quality pharmacovigilance, given that it is involved in all paths. Conversely, we discovered that the impact of PVT was the weakest in univariate necessity and multiple variable combination analyses. Thus, PVT plays a relatively minor role in enhancing the quality of pharmacovigilance. This contradicts the findings of previous studies showing that pharmacovigilance‐trained professionals will perform well in practice and demonstrate enhanced knowledge of the subject matter. This could be attributed to the fact that pharmacovigilance is relatively new in China. MAHs do not give it sufficient attention, and the given PVT focuses solely on form and lacks continuity. Moreover, there is an absence of evaluation of the effectiveness of training or the targeted improvement of PVT.

This study's contribution lies in being the first to apply the csQCA method to the study of quality improvement in the pharmacovigilance field, offering a reference for related research. In addition, csQCA was used to analyze the antecedents that affect the quality of pharmacovigilance. Three potential paths for enhancing the QPV work are presented for consideration, addressing the gap in the literature on quality improvement in MAHs' pharmacovigilance.

However, this study had several limitations. First, the application of csQCA inherently restricted the analysis to dichotomized variables, which may oversimplify complex pharmacovigilance quality metrics and continuous variables such as adverse event reporting timeliness and signal validation rates. To address this limitation, future research should adopt fsQCA, which allows for membership score calibration, thereby enabling a nuanced quantification of causal conditions.

Second, while the TOE framework provides an initial structural perspective, it may not fully capture the dynamic interdependencies between pharmacovigilance inputs and outcomes. Subsequent studies could instead employ the WHO‐recommended structure–process–outcome framework to systematically evaluate quality determinants, ensuring alignment with global benchmarking standards. Specifically, the structural dimension encompasses three core variables: organizational structure, personnel capacity, and resource allocation efficiency. The process dimension evaluates factors such as the standardization of management systems, the timeliness and completeness of ADR/ADE reporting and monitoring procedures, and the rigor of risk management implementation. The outcome dimension assesses indicators including ADR/ADE reporting compliance rates, actionable feedback from drug regulatory authorities, and the effectiveness of systematic analysis and evaluation cycles. Each of these variables is examined through tailored questionnaire items designed to capture detailed operational insights and performance metrics.

Furthermore, the limited sample size constrained generalizability because of geographic and economic heterogeneity. To mitigate this, our ongoing work focuses on a single province, utilizing stratified sampling and questionnaire‐based data collection to expand the sample while controlling for regional variability. This approach balances methodological rigor with enhanced representation, addressing scale limitations and framework applicability gaps identified in this study.

5. Conclusion

The QCA method was employed in this study to investigate the factors influencing the QPV and the pathways through which these factors operate. The study determined that having a “Dedicated and Qualified Person for Pharmacovigilance” is a significant factor in improving the quality of pharmacovigilance. However, enhancing the QPV requires the combined action of multiple factors rather than a single factor.

The theoretical significance of this article lies in its introduction of the TOE theoretical framework and QCA method to the field of corporate pharmacovigilance quality research. It extends their application to pharmacovigilance. This study constructed a configuration model of the five antecedent factors that affect the QPV. This study facilitates research on how MAHs can improve the QPV in terms of antecedent factors and pathway outcomes.

The practical significance of this article lies in that the following actions will involve disseminating these data to the pertinent authorities and MAHs to assist in the creation of papers that specifically support pharmacovigilance and provide guidance on how MAHs can enhance the quality of their pharmacovigilance efforts.

This study had some limitations, including a nonrepresentative sample selection and a nonscientific approach to selecting antecedent variables. With the growing focus on health and the rising use of medications, drug safety is becoming increasingly important. Therefore, a well‐established pharmacovigilance system is essential for ensuring high‐quality pharmacovigilance.

Author Contributions

Yuanyuan Zhang: conceptualization, methodology. Yadong Wang: data curation, writing – original draft preparation. Yue Chen: investigation. Xingjuan Xu: software, validation. Ting Ying: supervision. Runan Xia: resources. Xuefeng Xie: writing – reviewing and editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data supporting the findings of this study are available from the provincial drug regulatory authorities upon reasonable request. Due to restrictions related to data usage licenses, these data are not publicly available. However, they can be obtained from the authors upon reasonable request and with permission from the provincial drug regulatory authorities.