A novel social-network-analysis-based approach for analyzing complex network of actors involved in accessibility of anti-cancer medications in Iran

Abstract

Background

The access to anti-cancer medications is influenced by policies formed via the convergence of various stakeholders. The aim of this study is to identify and analyse the stakeholders involved in formulating and implementing policies related to the accessibility of anti-cancer medications in Iran and their interactions that are relevant to the outcomes of these policies for the first time.

Methods

To achieve the objectives, a novel multistage social network analysis (SNA)-based approach that includes three phases is proposed. First, the actors were identified by a team consisting of multidisciplinary knowledgeable experts through 15 comprehensive interviews. Then, the influence relationships of these actors were comprehensively analysed through in-depth interviews with nine key informants involved in pharmaceutical policies through a structured questionnaire. Finally, a novel network of actors was determined accordingly, and a SNA-based approach proposed to reveal the intrinsic roles and various aspects of the importance of the network’s actors.

Results

The study identified a total of 45 actors, which were then classified into 4 categories on the basis of their public or private nature and their foreign or domestic origin. This established network helped in creating a comprehensive view of the main actors, and can help policymakers to solve the problems related to access to anti-cancer medications more effectively and prevent the creation of these problems in the future. In this way, the network identified specific actors that can benefit from increased attention and dialogue. The computational results revealed that the Iran Food and Drug Administration (IFDA), Pharmaceutical Importer Companies (PharIc) and Pharmaceutical Manufacturing Companies (PharMC) were highly important actors in terms of their connectivity to other actors. Additionally, law enforcement agencies (LEA) have shown limited effectiveness within this network.

Conclusions

This study highlights the importance of complex relationships among various actors and proposes a novel SNA-based approach to analyse them. Regarding the main steps of the proposed approach and the findings, it is imperative for pharmaceutical policy plans to involve a diverse group of experts from the beginning, prioritizing the preferences of stakeholders, and providing a patient-centred approach to prevent the worsening of resource shortages.

Background

Guaranteeing reliable accessibility and reasonable cost of medications is a complex issue, particularly for countries facing developmental obstacles or operating under the constraints of sanctions [1, 2]. The importance of this dilemma becomes particularly significant when considering costly illnesses, such as cancer [3]. Iran has a significant incidence rate of cancer in the Middle East [4]. Recent advancements in pharmaceutical research and novel therapeutic approaches have brought about a sense of hope for individuals with cancer, as they have been shown to improve both quality and duration of life [5]. However, challenges in accessing medications can lead to the discontinuation of therapy, thereby worsening mortality rates [6]. This kind of challenge makes it vital to address how to access anti-cancer medications.

The accessibility of patients to anti-cancer medications is influenced by various factors, including high costs and low affordability [7]. It has been hypothesized that in developed countries, the scarcity of these drugs can be attributed to a lack of profitable production incentives [8]. In contrast, countries with moderate or low economic profiles face challenges related to insufficient infrastructure, weakened regulatory supervision, limited domestic production capabilities and systemic deficiencies in pharmaceutical inventory and distribution. These factors collectively result in unpredictable patterns of access to cancer drugs [9]. The complexity of the accessibility paradigm is compounded by the requirements for validation to distribute subsidized medicine shipments, which can often result in significant delays in the commencement of therapy [10]. The obstacles described above create a sense of urgency regarding the accessibility of anti-cancer medication, a matter of great concern for healthcare systems and patients alike [11]. This issue entangles healthcare systems in an intricate network of sociopolitical factors, often subject to strong public scrutiny and emotional intensity [11, 12].

Shukar et al. (2022) suggested that in addition to constructive interactions among stakeholders, it is imperative to consistently improve its policies, which involve all parties, and provide customized financial support at various levels to enhance access to anti-cancer medications in Pakistan [9]. Ocran Mattila et al. (2021) emphasized the importance of implementing a comprehensive policy and program framework involving various stakeholders to provide access to cancer treatments [2]. Furthermore, Leppin et al. (2018) argued that the utilization of social network analysis (SNA) has the potential to be a valuable instrument for evaluating the impact and scope of trustee bodies, hence facilitating the ongoing improvement of implementation efforts [13].

In Iran, ensuring the reliable and uninterrupted accessibility of anti-cancer medications remains a pressing issue. The current scenario faces numerous obstacles, including high cost, black or informal markets, counterfeit pharmaceuticals and limited accessibility [14]. This research aims to identify and analyse the stakeholders and their respective roles in formulating and implementing policies effectively. Moreover, by examining their influence on pharmaceutical policy related to access to anti-cancer medicines in Iran, this study aims to provide valuable perspectives that could facilitate informed decision-making and practical strategies, ultimately improving the accessibility of cancer medication therapies in the area.

Theoretical framework

To provide valuable insights into the dynamics of the system and the complex network of actors affecting overall outcomes of policies related to the accessibility of anti-cancer medications in Iran, this study is built on three established theories, namely, health policy process, system thinking in healthcare and stakeholder theory in health policy implementation. In what follows, a brief explanation of these three theories and how they integrate into SNA is provided.

Health policy process

To achieve coherent implementation of health policies, previous research has stressed that key actors or stakeholders are the main part of the policy-making process [15]. The policies in healthcare sector are organized through the complex interaction of three components, including actors, context and process. Walt and Gibson’s famous health policy triangle as an approach of thinking about all the diverse factors that may affect policy was introduced in 1994 [15]. The increasing use of this framework is effective to recognize the influence of policy actors in dynamic policy processes, and consequently to support dialogue among actors [16].

Systems thinking in healthcare

In the past 15 years, there have been increasing recommendations to employ systems thinking in health systems due to their complex nature [17, 18]. Systems thinking is an approach that views systems with a holistic lens, emphasizing the interconnectedness of various components within the system [19].

Recently, there has been a significant rise in support for techniques rooted in systems thinking and complex science within the healthcare sector [20]. SNA has been widely utilized in various health sector studies as a systems thinking approach [21–25]. It has been employed to establish collaborative frameworks [26, 27] and to align with healthcare system policies and research initiatives [20, 28]. Despite its proven effectiveness in the healthcare domain, there is a notable dearth of research on the application of SNA in the pharmaceutical sector [29]. An analysis emphasized the practical aspects of systems thinking, complex science technique and their ability to collect adaptable and customized evidence that might enhance the rigour of evidence-based policy formulations [20]. However, it is crucial for these techniques to uphold transparency for policy stakeholders and cultivate a strong sense of stakeholder ownership to foster consensus and prompt shifts in stakeholder perspectives [20].

Stakeholder theory in health policy implementation

Stakeholder theory underscores the importance of involving stakeholders in the policy-making process to ensure diverse perspectives are considered and solutions align with all of their needs. This inclusive approach can lead to the creation of more enduring and comprehensive health policies. Moreover, collaboration among parties to establish robust networks can facilitate overcoming challenges and enhance the efficacy of health policy implementation [30].

In other words, crafting and executing effective health policies necessitate the presence of a well-coordinated network comprising diverse stakeholders [28]. These networks possess inherent characteristics that provide significant insights into individual nodes and highlight the connections between different network segments. These networks exhibit distinct inherent characteristics offering valuable insights into individual nodes and elucidate connections between different segments. A detailed analysis of these networks can yield a deeper comprehension of the intricate policy development structure and the interplay among stakeholders, emphasizing the significant contributions of specific entities [22]. As a sophisticated computational approach, SNA elucidates the complex network of interactions, connections and mutually beneficial relationships involving various stakeholders [31]. Moreover, SNA sheds light on certain areas within the network that require increased involvement, ensuring an enriched healthcare policy framework and promoting collaboration among stakeholders [22].

Integration of the theories into SNA

On the one hand, health policy process theory informs our understanding of how centrality measures may reveal power dynamics in policy-making processes. On the other hand, systems thinking helps interpret network-wide patterns revealed through SNA, such as clusters of actors or isolated nodes. In addition, stakeholder theory guides the categorization of actors and interpretation of their positions within the network structure.

The main contributions

The main contributions of this study are as follows:

• The main empirical contributions of this study are addressing the issues related to actors in the field of access to anti-cancer medications and forming several expert groups to systematically create and analyse the actors’ network by extracting influence relationships for the first time. It enables policy-makers to manage anti-cancer drug shortages by facilitating correct attention and dialogue between actors and has the potential to create preventive drug shortage programs.

• As the main methodological contribution, this is the first time that social network analysis techniques have been used to systematically analyse the network of the effective actors who improve access to anti-cancer medications in Iran.

• In addition, this is the first study that implements SNA to determine the position and importance of each actor in improving accessibility to anti-cancer medications in Iran.

• The innovative proposed method is flexible and designed in a way that makes it possible to simply analyse any other stakeholder network to develop policies in pharmaceuticals, public health and other related or unrelated areas.

Methods

To determine and examine the various factors that impact access to anti-cancer medications in Iran, a multi-stage study including three consecutive phases was conducted. First, the key actors were identified. Then, their interactions were analysed by an expert team, and the actors’ network was established. Finally, the social network analysis (SNA) method was utilized to analyse the actor network at the node level. These stages are described in detail below.

Identification and analysis of stakeholders

During the preliminary phase, the research question posed was, “Who are the key actors that contribute to or hinder the accessibility of anti-cancer medications within the Iranian context?” (See Supplementary File 1, Part I). In this stage, participants were selected by purposive and snowball sampling techniques. Experts with a high level of expertise in the field of pharmaceutical policies were interviewed and encouraged to discover further pertinent organizations. Each interview had a mean duration of 15 min, and the series of interviews concluded after 15 iterations, or specifically, when there was no further introduction of a new entity in two consecutive interviews. Before beginning, the participants were given a consent form and informed about the research aims. The sample of participants consisted of individuals involved in healthcare policy-making, media, hospital administration, insurance companies and the pharmaceutical industry. L.Z. conducted the interviews. Therefore, a thorough compilation of all relevant entities and stakeholders was collected.

In the second stage, in-depth interviews were conducted with key informants involved in pharmaceutical policies. Table 1 provides a detailed compilation of the respondents in question.

Table 1.

Participant summary

Phases of study	Specialized field	Number of participants
Phase 1	Health and pharmaceutical policy-maker	10
	Active in pharmaceutical industry, anti-cancer medicine	5
Phase 2	Health and pharmaceutical policy-maker	3
	Having relevant previous policy and management experience	2
	Active in pharmaceutical industry, anti-cancer medicine	2
	Faculty member in field of health and pharmaceutical policy	2

A structured questionnaire in the form of a matrix was developed for the second phase. This matrix assessed stakeholders according to their perceived impact and influence, as outlined in the Supplementary File, Part II. The questionnaire detected the existence of interrelationships and measured their amount or extent. In instances when the participant recognized a relationship, a nine-point scale was provided to measure the extent of the relationship. During this stage, the discourses were jointly facilitated by L.Z. and A.S., lasting for 3–4 h, with occasional segmentation into numerous sessions. The involvement of nine different experts is presented in Table 1. After data collection, a mean score was calculated for each pairwise actor’s interrelationships on the basis of the replies provided by the participants.

Implementation of social network analysis (SNA)

The quantitative empirical analysis of network data is one of the main aspects of social network investigations for analysing the behaviour of stakeholders, for which SNA metrics play a crucial role [32]. A wide range of SNA metrics has been proposed in the related literature, although density and centrality measures are the most commonly used [33, 34]. Centrality measures provide an essential tool for understanding stakeholder networks as well as other connected data structures such as graphs.

The centrality of an actor is a measure of its prominence or structural importance within the network of stakeholders, as reflected in its relationships with other actors [34]. In diverse substantive settings at the node level, a higher centrality value of an actor could highlight a specific aspect of its prominence or importance in the network, such as popularity, activity, power, influence, control, visibility, dependency and prestige [34]. Identifying these kinds of actors provides decision-makers with a valuable tool to enhance engagement and connectivity among actors, promote collaborative efforts, accelerate information sharing throughout the network, stimulate network expansion and innovation and prevent potential network breakages, among other essential considerations.

There are numerous centrality metrics for capturing various aspects of importance or prominence; therefore, it is vital to grasp their concepts and definitions. The following section provides an overview of the well-known centrality metrics used in this study.

• (A)Degree (DC), weighted degree (WDC), weighted in-degree (WIDC) and weighted out-degree centrality (WODC)

Degree centrality is the simplest centrality measure that counts the number of direct connections held by an actor. When analysing a directed network, the DC bifurcates into in-degree centrality (IDC) and out-degree centrality (ODC) [35]. The IDC and ODC count the number of incoming and outgoing connections of an actor, respectively. The IDC can be redefined as the sum of weights of all edges incident on the actor when considering the weights of connections in a weighted network, also known as the weighted in-degree centrality (WIDC). Similarly, weighted out-degree centrality (WODC) augments ODC by aggregating the edge weight dispatching from the actor. The weighted degree centrality (WDC) is the sum of the WIDC and WODC. In our study, the WODC and WIDC reflect the total direct influence that is exerted and absorbed by the actor, respectively.

Actors demonstrating high values of DC and/or WDC in the network:

• They are popular and well regarded inside the network [36].

• Hold the potential to disseminate most of the available information.

• These are conventionally dynamic contributors.

• Commonly operate as hubs or connectors.

• Might take vantage positions to harness resources.

• They are connected to many actors at the heart of the network.

• May possess alternative connections, strengthening independence from the remaining actors.

• They are commonly discerned as mediators and deal-makers.

• Epitomize heightened involvement and sociability [37].

• Their own considerable prestige in directed networks [38].

• (B)Closeness (CC) and harmonic closeness centralities (HCC)

CC measures reachability, establishing an actor’s proximity to every other actor in the network, regarding the average distance from everyone else [35]. HCC is a general-purpose variant of CC invented to handle the problem of infinite distances of unreachable actors within unconnected and/or directed networks [39, 40]. Given the inclusive nature of HCC, it is useful wherever CC finds relevance. Actors with high CC or HCC values:

• Exhibit shortened path lengths, ensuring prompt network access [41].

• They are skilled at efficient [42] and autonomous [37] information transfer.

• Seem to be relatively less dependent upon peripheral actors as well as other actors for communication or information procurement [36, 37, 43].

• Occupy critical positions to monitor the flow of information and activity.

• Typically, a panoramic picture of network dynamics is provided.

• Serve as efficacious broadcasters, capitalizing on optimal positioning to influence the whole network more quickly [35].

• They function as reservoirs of second-hand information and actions.

• Only a few intermediaries are required for contacting others; therefore, they are centrally involved [44] and tend to appear in the middle of the network [32].

• (C)Betweenness centrality (BC)

BC is quantified by counting the frequency with which an actor resides in the shortest path between two others in the network. It sheds light on those actors endowed with the capability to bridge disparate pairs of actors or regions in the network. A high BC value implies that the actor typically.

• Occupies a critical and powerful role in allowing information to pass from one part of the network to the other [41], often known as a bridge [35, 37, 45], broker or gatekeeper [32].

• Stands are potential bottlenecks whose disruption could jeopardize the integrity of information flow and cohesiveness of the network [32].

• Wields considerable command over information and action dissemination [36, 37, 42], and as a result, remains abreast of multifaceted social interactions [32].

• Considerable influence is exerted over what happens – and does not – within the network.

• It is perceived as a network leader [32].

• (D)Eigenvector (EC) and PageRank centralities (PRC)

Like degree centrality, eigenvector centrality assesses an actor’s influence regarding its connections within the network. Nevertheless, EC surpasses DC by incorporating the good connectivity of the actor’s neighbours, their neighbours and so forth across the entire network [45]. It measures how well a given actor is connected to other well-connected actors on a macro or global scale rather than on a micro or local scale.

PageRank centrality is a well-known variation of EC, computing the importance of an actor by considering not only the quantity, but also the quality, of its connections, as well as the connections of its connections, and so forth [42]. The main difference between the PRC and EC lies in the PRC’s incorporation of connection weights and directions to identify influential, important or significant actors. A high EC and/or PRC signifies that the actor:

• Holds a strategic advantage, positioning them as one of the network leaders on a macro scale.

• A substantial wide-reaching and long-term influence, directly or indirectly, is exerted over the entire network [36, 41], even if their local influence may not be the strongest. Indeed, the power of your associates contributes to your sphere of influence.

• It is linked to well-connected [43] and popular actors [36] and plays a central role in the network’s flow of ideas and information.

• (E)Eccentricity centrality (ECC)

Eccentricity centrality quantifies the distance between a specific actor and the farthest actor from it. Accordingly, a high ECC indicates that the farthest actor in the network is significantly distant, while a low ECC indicates that the farthest actor is relatively nearby. Moreover, a higher ECC value indicates greater actor independence, or equivalently less dependency, on others within the network.

• (F)Hub (HC) and authority centralities (AC)

Hub centrality and authority centrality represent two generalizations of EC, offering a more detailed analysis of actors. HC quantifies an actor’s centrality regarding its ability to establish outward connections with other actors, while AC measures its centrality in receiving inward connections, especially from hubs. As a general principle, AC assesses the quality of the actor, while HC gauges the quality of the actor’s connections.

In this study, geometric mean scores were employed on the basis of the perspectives of the participants and the association of each organization/institution with all others. The data were visualized and analysed using version 0.10 of the Gephi software.

Rigour strategies to enhance the trustworthiness of the study

Several well-known strategies are applied to enhance the credibility, authenticity, confirmability, transferability and dependability of the research findings in the literature [46]. In the current study, the following strategies were considered: (a) choosing the participants with the maximum multiplicity (i.e. transferability); (b) dipping the authors in the study for the long-term and member-checking by relevant experts (i.e. credibility); (c) rechecking the ultimate findings by participants (i.e. confirmability); and finally (d) including authors with different scientific circumstances in the data analysis process (i.e. dependability).

Results

Stakeholder identification and analysis

After the first stage, 45 stakeholders were identified, as reported in Table 2.

Table 2.

List of the identified main actors of improving access to anti-cancer medications in Iran and their category

No	Abbreviations	Full name	Category
1	Gov	Government	Public–domestic
2	Parl	Parliament	Public–domestic
3	MoIMT	Ministry of Industry, Mine and Trade	Public–domestic
4	DotMoHME	Ministry of Health and Medical Education, Deputy of treatment in the MoHME	Public–domestic
5	DodMoHME	Ministry of Health and Medical Education, Deputy of development in the MoHME	Public–domestic
6	DoeMoHME	Ministry of Health and Medical Education, Deputy of education in the MoHME	Public–domestic
7	DorMoHME	Ministry of Health and Medical Education, Deputy of research in the MoHME	Public–domestic
8	DohMoHME	Ministry of Health and Medical Education, Deputy of health in the MoHME	Public–domestic
9	SCI	Supreme Council of Insurance	Public–domestic
10	BI	Basic insurance	Public–domestic
11	SI	Supplementary insurance	Private–domestic
12	SubMoEFA	Ministry of Economic and Finance Affairs-subsidy	Public–domestic
13	TaxMoEFA	Ministry of Economic and Finance Affairs-tax	Public–domestic
14	CusMoEFA	Ministry of Economic and Finance Affairs-customs	Public–domestic
15	UOMS	Universities of medical sciences	Public–domestic
16	SPA	Scientific–professional associations	Public–domestic
17	Phys	Physicians	Private–domestic
18	Pharm	Pharmacies	Private–domestic
19	PharmDC	Pharmaceutical distributions companies	Private–domestic
20	Pat	Patients	Private–domestic
21	Pats’F	Patients’ family	Private–domestic
22	PharS	Pharmaceutical syndicates	Private–domestic
23	PharIC	Pharmaceutical Importers companies	Public–domestic
24	PharMC	Pharmaceutical manufacturing companies	Public–domestic
25	BM	Black market	Private–domestic
26	ChC	Chemotherapy centres	Public&private–domestic
27	Hos	Hospitals	Public&private–domestic
28	SM	Social media	Private–domestic
29	ICB	Iran Central Bank	Public–domestic
30	FpharC	Foreign pharmaceutical companies	Foreign
31	OFC	Other foreign companies	Foreign
32	GDS	Global drug shortages	Foreign
33	HMcParl	Health and medical commission of the parliament	Public–domestic
34	CC	Competition council	Public–domestic
35	IPoHS	Influential people outside of health sector	Private–domestic
36	RC	Red Crescent	Public-domestic
37	LEA	Law enforcement agencies	Public–domestic
38	VPoSTaKBE	Vice President of Science, Technology and Knowelage-based Economy	Public–domestic
39	VCF	Venture capital funds	Private–domestic
40	BBS	Banks and banking system	Public&private–domestic
41	IFDA	Iran Food and Drug Administration	Public–domestic
42	NGOs	NGOs	Private-domestic
43	CPPO	Consumers and Producers Protection Organization	Public–domestic
44	ICCIMA	Iran Chamber of Commerce	Public–domestic
45	AoMS	Academy of Medical Sciences	Public–domestic

Stakeholders were classified into four categories on the basis of their public or private nature and their foreign or domestic origin, as presented in Table 2. The identified stakeholders played various roles in different steps of facilitating access to anti-cancer medications. For example, the IFDA was involved in policy development, implementation and evaluation. At the same time, medical science universities participated in policy implementation and evaluation, and the MoHME contributed to policy development. A schematic representation of the network of those stakeholders who facilitate access to anti-cancer medicines in Iran is illustrated in Fig. 1. It should be mentioned that distinct colours have been used to clarify the stakeholders’ categories.

Fig. 1.

Obtained network of stakeholders, coloured on the basis of their category

Social network analysis (SNA)

The above-mentioned centrality measures are computed for all stakeholders and reported in Table 3. Furthermore, for the sake of analysis, the five best- and the five worst-scoring actors for the above-mentioned SNA metrics are presented in Tables 4 and 5, respectively.

Table 3.

Value of SNA metrics computed for the actor’s network

Actor	SNA metrics
	WIDC	WODC	WDC	CC	HCC	BC	ECC	AC	HC	EC	PRC
Gov	10.7	18.1	28.7	0.83	0.91	34.9	3	0.17	0.18	0.78	0.03
Parl	10.0	16.2	26.1	0.85	0.92	27.5	3	0.16	0.18	0.78	0.03
MoIMT	10.5	7.2	17.6	0.64	0.72	12.0	3	0.17	0.10	0.72	0.02
DotMoHME	17.8	11.2	29.0	0.71	0.81	34.3	3	0.20	0.14	1.00	0.04
DodMoHME	10.1	4.1	14.2	0.54	0.59	2.9	3	0.15	0.05	0.82	0.03
DoeMoHME	9.3	5.8	15.1	0.61	0.70	10.0	3	0.16	0.09	0.79	0.03
DorMoHME	7.3	2.6	10.0	0.54	0.59	3.7	3	0.13	0.05	0.68	0.03
DohMoHME	5.6	2.0	7.6	0.53	0.59	2.7	4	0.13	0.05	0.68	0.02
SCI	11.3	7.9	19.2	0.68	0.78	9.7	3	0.16	0.14	0.79	0.03
BI	13.4	11.2	24.6	0.73	0.83	11.9	3	0.18	0.16	0.82	0.02
SI	10.8	5.7	16.5	0.60	0.69	2.8	3	0.17	0.10	0.81	0.02
SubMoEFA	10.0	9.4	19.4	0.70	0.79	9.9	3	0.17	0.14	0.77	0.02
TaxMoEFA	6.5	9.1	15.6	0.67	0.76	3.8	3	0.13	0.13	0.56	0.02
CusMoEFA	8.8	10.7	19.5	0.72	0.81	9.3	3	0.15	0.15	0.66	0.02
UOMS	16.2	12.1	28.3	0.75	0.84	30.6	3	0.18	0.16	0.92	0.03
SPA	10.9	9.2	20.1	0.73	0.83	13.1	3	0.16	0.16	0.73	0.02
Phys	14.3	13.5	27.7	0.79	0.88	22.1	3	0.17	0.18	0.80	0.03
Pharm	15.0	11.1	26.1	0.72	0.82	10.0	3	0.17	0.16	0.80	0.02
PharmDC	13.3	8.1	21.4	0.67	0.77	7.8	3	0.18	0.13	0.77	0.02
Pat	18.5	14.2	32.7	0.76	0.86	18.1	3	0.19	0.17	0.91	0.03
Pats’F	14.0	11.3	25.3	0.73	0.83	14.9	3	0.17	0.16	0.81	0.03
PharS	13.6	12.2	25.8	0.86	0.93	27.3	3	0.19	0.19	0.84	0.03
PharIC	18.3	15.0	33.3	0.88	0.93	48.6	2	0.19	0.19	0.88	0.03
PharMC	16.7	16.3	33.0	0.94	0.97	51.0	2	0.19	0.20	0.85	0.03
BM	10.0	9.8	19.8	0.75	0.83	20.6	2	0.15	0.15	0.66	0.02
ChC	11.2	6.1	17.4	0.63	0.72	4.0	3	0.16	0.11	0.74	0.02
Hos	16.1	13.4	29.5	0.73	0.83	25.1	3	0.19	0.16	0.92	0.03
SM	7.8	11.1	18.9	0.83	0.91	26.7	3	0.15	0.18	0.68	0.02
ICB	7.2	18.2	25.5	0.83	0.90	49.8	2	0.12	0.17	0.49	0.02
FpharC	2.0	13.7	15.7	0.83	0.90	48.3	2	0.05	0.18	0.16	0.01
OFC	1.2	7.7	8.9	0.73	0.82	3.5	2	0.02	0.14	0.02	0.00
GDS	1.6	13.1	14.7	0.79	0.86	0.5	2	0.01	0.16	0.01	0.00
HMcParl	9.9	16.2	26.0	0.90	0.95	44.2	3	0.16	0.20	0.77	0.03
CC	6.2	5.1	11.3	0.66	0.75	3.8	3	0.11	0.12	0.50	0.02
IPoHS	7.4	12.4	19.8	0.86	0.93	13.7	3	0.13	0.20	0.61	0.02
RC	9.4	5.0	14.4	0.62	0.71	3.9	3	0.16	0.11	0.68	0.02
LEA	1.6	7.6	9.2	0.76	0.85	0.9	3	0.04	0.16	0.17	0.01
VPoSTaKBE	6.8	5.0	11.8	0.62	0.70	13.0	3	0.12	0.09	0.54	0.02
VCF	4.5	1.0	5.6	0.51	0.55	0.5	3	0.09	0.03	0.38	0.01
BBS	7.0	14.6	21.6	0.85	0.92	17.4	3	0.11	0.19	0.47	0.02
IFDA	19.3	19.2	38.4	0.96	0.98	89.8	2	0.20	0.20	1.00	0.04
NGOs	7.5	7.4	14.9	0.70	0.79	7.1	3	0.13	0.14	0.67	0.02
CPPO	6.4	6.5	12.9	0.69	0.78	3.8	3	0.11	0.13	0.45	0.01
ICCIMA	8.3	7.8	16.0	0.76	0.84	12.4	2	0.12	0.15	0.47	0.02
AoMS	3.0	2.2	5.2	0.55	0.61	1.1	3	0.07	0.06	0.39	0.02

Table 4.

First five best-scoring actors for the mentioned SNA metrics in the network

Rank	SNA metrics
	WIDC	WODC	WDC	CC	HCC	BC	ECC	AC	HC	EC	PRC
1	IFDA	IFDA	IFDA	IFDA	IFDA	IFDA	IFDA	IFDA	IFDA	Dot MoHME	Dot MoHME
2	Pat	ICB	PharIC	PharMC	PharMC	PharMC	PharMC	Dot MoHME	PharMC	IFDA	IFDA
3	PharIC	Gov	PharMC	HMcParl	HMcParl	ICB	ICB	Pat	HMcParl	Hos	UOMS
4	Dot MoHME	PharMC	Pat	PharIC	PharIC	PharIC	PharIC	Hos	IPoHS	UOMS	Hos
5	PharMC	HMcParl	Hos	IPoHS	IPoHS	FpharC	FpharC	PharIC	PharIC	Pat	Pat

Table 5.

First five worst-scoring actors for the mentioned SNA metrics in the network

Rank	SNA metrics
	WIDC	WODC	WDC	CC	HCC	BC	ECC	AC	HC	EC	PRC
1	OFC	VCF	AoMS	VCF	VCF	VCF	Doh MoHME	GDS	VCF	GDS	GDS
2	LEA	Doh MoHME	VCF	Doh MoHME	Doh MoHME	GDS	VCF	OFC	Dor MoHME	OFC	OFC
3	GDS	AoMS	Doh MoHME	Dod MoHME	Dod MoHME	LEA	LEA	LEA	Doh MoHME	FpharC	FpharC
4	FpharC	Dor MoHME	OFC	Dor MoHME	Dor MoHME	AoMS	AoMS	FpharC	Dod MoHME	LEA	LEA
5	AoMS	Dod MoHME	LEA	AoMS	AoMS	Doh MoHME	Dod MoHME	AoMS	AoMS	VCF	VCF

Moreover, regarding four hand-picked centrality measures, namely, WDC, HCC, BC and PRC, network maps of the stakeholders are illustrated in Fig. 2, sections a, b, c and d, respectively. Notably, the size of each stakeholder in the respective parts of this figure is directly proportional to the stakeholders’ values in the corresponding centrality measure.

Fig. 2.

Network maps of the stakeholders involved in promoting or relegating access to anti-cancer medications in Iran regarding the four hand-picked centrality measures: a WDC, b HCC, c BC, and d PRC

To enhance the understanding and tracking of the narrative, the results of centrality measures are reported and discussed separately through the segmentation previously provided in Sect. Implementation of Social Network Analysis (SNA).

• (A)WDC, WIDC and WODC

As presented in Tables 3, 4, 5 and Fig. 2, the IFDA, PharIc and PharMC emerged as the top-ranking stakeholders among those identified for access to other anti-cancer medications in Iran in terms of the WDC. Conversely, some stakeholders, such as the AoMS, VCF and DohMoHME, demonstrated the weakest performance in this metric. Notably, the ranks are different when considering the WIDC and WODC sub-measures, except for the highest rank (held by the IFDA). Given the directed and weighted nature of the edges within the stakeholder network, this outcome was expected in advance.

• (B)CC and HCC

The IFDA, PharMC and HMCParl were the stakeholders with the highest CC values, while the VCF, DohMoHME and DodMoHME ranked among the lowest. This finding implied that the former group benefits from a strategically structural position within the network, facilitating easier and quicker access to other stakeholders. In contrast, the latter group lacks such an advantage. Note that since the network under study is both weighted and connected, the ranking results in terms of HCC were equivalent to those of CC.

• (C)BC

A review of the BC results indicated that the IFDA, PharMC and ICB held the highest BC values, positioning them as strategic brokers capable of connecting or disconnecting groups of stakeholders. This strategic structural position grants them a unique competitive advantage so that they can easily cause the failure or greater solidarity of the network. In contrast, VCF, GDS and LEA exhibited the lowest ability to wield this type of power.

• (D)EC and PRC

Regarding the EC and PRC values calculated for the stakeholders, the DotMoHME and IFDA emerged as the network leaders exerting the most influence over the others. This signifies that they were not only well connected to the others to share their volition, but also that their connections were created with the other well-connected stakeholders. Unlike the aforementioned actors, the GDS, OFC and FpharC displayed limited abilities to establish good connections to well-connected actors.

• (E)ECC

Regarding the ECC scores computed for the stakeholders, the IFDA and PharMC reflected the least ECC values, implying they had less distance and consequently less independency from their farthest actors. In contrast, DohMoHME and VCF were the most independent actors from their farthest neighbours, indicating that they had not appropriate relationship with the furthermost actors in the network.

• (F)HC and AC

Finally, Tables 3, 4 and 5 revealed that IFDA, DotMoHME and Pat had the highest AC values, while GDS, OFC and LEA had the lowest AC values. Furthermore, IFDA, PharMC and HMcParl secured the top ranks in terms of the HC measure. In contrast, VCF, DorMoHME and DohMoHME were at the lower end of the rankings.

Discussion

This research aimed to ascertain the roles of key stakeholders and their network in influencing the outcomes of the anti-cancer medication access policy in Iran. The social network analysis (SNA) model of this study reveals specific network segments that warrant heightened focus and dialogue. This emphasis is vital for facilitating sound policy formulation and/or execution while fostering efficient collaboration among diverse stakeholders.

Considering that the Food and Drug Administration (IFDA) is the core market regulator in Iran, it is logical to have the first rank in more criteria, representing more prominence and importance of the actor in the network. However, since the Deputy of Treatment of the Ministry of Health and Medical Education (DotMoHME) determines treatment protocols and guidelines, this department has a significant role in the EC and PRC metrics, as it shows a positional advantage as a network leader on the macro scale, has a long-term influence either directly or indirectly over the whole network and most importantly, it is placed in the centre of the flow of ideas and information in the network.

There are previous reports that the DotMoHME, with recommendations of guidelines containing medications with low production and no or limited import, has caused market disruption and problems related to a lack of access to anti-cancer medications.

The crucial role of the manufacturers of anti-cancer medicine (PharMC) in this network, along with their superior rank in CC, HCC, BC, ECC and HC metrics, reflect quick access to other actors, results in transmitting information in the network more efficiently and also showing relatively less dependency on other actors, especially peripheral ones, in terms of information ownership. From this result, it can be concluded that domestic companies produce a significant part of the market share of anti-cancer medications at prices far lower than those of imported companies. In terms of improved accessibility and physical access, affordability is an important issue.

Patients are the first group affected by the processes of policy-making, decision-making and the implementation of laws related to access to anti-cancer medications. Although the patients with higher AC scores were in the third rank, they had higher WIDC scores in the second. Unfortunately, currently in Iran, patients, despite their potential power, do not have an effective role in policy-making and decision-making in this field, and the most influential patients belong to the IFDA, the Central Bank (ICB), the government and the pharmaceutical industry. If the patient-centred approach prevailed in the pharmaceutical policy-making of Iran and the patient was considered the main stakeholder, the patients would be involved in the decision-making. Nevertheless, the role of patients and their disease associations is seen only occasionally and when protest rallies are carried out. Therefore, patients as authoritative actors are less effective than regulatory organizations and the pharmaceutical industry in promoting access to anti-cancer medications.

It is worth mentioning that in the current situation, the producing and importing companies (PharMC and PharIC) are under the control of the IFDA and do not have an independent role nor fluent communication. Therefore, although they have a high rank in major metrics such as WDC, CC, HCC, BC and ECC, they have yet to be ranked in other metrics such as AC. Nevertheless, since the production of anti-cancer medications in Iran is mainly dependent on imported active pharmaceutical ingredients (APIs), the central bank has the power and an effective role in the import and production of anti-cancer medications because it can influence the import of finished products and APIs or the price of imported pharmaceutical products. Of course, in this particular case, the role of the ICB is more direct and greater for PharIC than for PharMC. The results show that the ICB acts independently in making decisions related to this field and is not even dependent on the main organization that regulates the market, namely, the IFDA. The role of the banking system is also significant in terms of betweenness centrality.

Although the Health and Medical Commission of the Parliament (HMcParl) has a good rank in CC, HCC and HC metrics, until now, HMcParl has not utilized this good position. Indeed, HMcParl can play the role of demanding and challenging various network actors, but it currently has a limited effect compared with others. Perhaps the reason is that HMcParl has not played an active role or used his potential. However, on the basis of the results, if this actor plays their role properly, it has the potential to improve access and be in a strategic role-playing position.

The study also showed that influential people outside of the health sector (IPoHS) were among the most effective actors in the network. Further, IPoHS is effective in improving access to anti-cancer medications such as HMcParl. This result emphasizes the role of communication and the networking of communication in the field of access because, as mentioned in the previous sections, the role of political communication can be more important than even that of the main stakeholder.

The hospitals (Hos) and universities of medical sciences (UOMS) have high ranks in EC and PRC metrics, which is in line with the fact that following the laws of Iran at the provincial level, the president of the UOMS is the deputy minister of MoHME and the first level of accountability. Most UOMSs and HOSs play the role of the executive body and interact with each other. Hos reports directly to UOMS and is under their direct supervision. However, these two actors are not responsible and do not play an effective role in improving access to anti-cancer medications. Unfortunately, it is sometimes seen that UOMS and HOS only inform patients that the medicine is unavailable, and do not look for it. However, it does not play an effective role in following up and eliminating these shortages. The participants in the present study believed that UOMS and Hos align with the existing centralization approach and directive policies.

Foreign pharmaceutical companies (FpharC) were ranked fifth in BC and ECC, representing critical and powerful positions and independence, respectively. The participants in the current study believed that because the Iranian pharmaceutical market is relatively close to foreign companies, there is no interaction or special communication between the Iranian pharmaceutical market in general and anti-cancer medications in particular. However, if the laws are amended, it will be possible for FpharC to improve access to anti-cancer medications. In other words, FpharC does its work and makes policies on the basis of the global market and international laws. They can interact with our policy-making and regulatory organizations only if they have a market share in Iran. There is similar experience in the market of antidiabetic medications.

In summary, the findings of this research demonstrated the significance of financial barriers and communication (including lobbying) as influential factors in advancing access to anti-cancer medications.

In Iran, the bottleneck of access to medicine is foreign currency because a major part of APIs and imported medicines can be supplied with foreign currency, which is the special condition of Iran, which has special regional and political–economic conditions.

Figure 3 shows the position of each actor in different SNA metrics.

Fig. 3.

SNA metrics for all stakeholders

It should be noted that previous studies have used the SNA method to identify policy development and implementation in the United States, the Netherlands, and many other countries [13, 20]. In the next section, the policy implications in Iran will be stated.

Policy implications in Iran

The successful implementation of policies aimed at improving access to anti-cancer medication necessitates a systematic approach that integrates policy reforms, resource optimization and robust evaluation frameworks. The transformation of the Iranian healthcare system demands meticulous consideration of multiple stakeholders and a systematic monitoring of outcomes.

Policy recommendations

Developing effective policies requires comprehensive stakeholder engagement and evidence-based decision-making. Collaboration between the Iranian parliament and authoritative organizations is essential for formulating policies that address both immediate needs and long-term sustainability.

Resource allocation

Efficient resource distribution remains crucial for improving anti-cancer medication accessibility in Iran. Regulatory organizations play a pivotal role in coordinating resource allocation and ensuring alignment with national health priorities.

Monitoring and evaluation frameworks

The establishment of comprehensive monitoring systems ensures continuous improvement and accountability. Coordinating the implementation of these frameworks necessitates collaboration among various stakeholders, including the IFDA, healthcare providers, insurance organizations and other key actors. Regular assessments of implementation progress aid in identifying areas requiring adjustments and ensuring ongoing system improvement. Consequently, regular stakeholder analyses ensures that implementation strategies remain aligned with evolving needs and capabilities of the healthcare system. The implementation strategy underscores the importance of developing local data infrastructure to support evidence-based decision-making. This involves implementing standardized data collection protocols, enhancing data sharing mechanisms and bolstering research capacity to generate reliable local evidence.

Study limitations and strengths

Our anti-cancer medicine application case was chosen for pragmatic and theoretical reasons. Improving access to anti-cancer medicine requires different actors with high complexity, especially in Iran, where there are many periodic shortages. Conversely, this uniqueness renders it a highly pertinent case study in the domains of policy development and implementation, serving to enlighten the contributions of various stakeholders.

This study adopted a cross-sectional approach, signifying that the network data do not depict the current partnership dynamics.

Only nine in-depth interviews were administered to form the network. The collection of network data is known for its inherent challenges. The study’s low response rate can limit the validity of the described SNA measures. Nevertheless, we have diligently ensured the presentation of quantitative network findings corroborated by qualitative interviews and participant observation data.

Although our data probably offer a more precise portrayal of the network’s structure, we must exercise caution since the presence of relationships and their strength, compared with the real world, may be overestimated. We scrutinized the actors through pairwise comparisons and highlighted the weakest relationships.

Conclusions

This study demonstrates that the structural configuration and strength of relationships among the stakeholders engaged in shaping the anti-cancer medication policy in Iran effectively synergize to mitigate the exacerbation of scarcity and access challenges.

This study is a very relevant case study in policy-developed and implemented fields to determine the role of different stakeholders. This shows that improving access to anti-cancer medicine requires different actors with high complexity. Therefore, drug policies should involve a multidisciplinary team to support stakeholders’ preferences and be patient-centred at the beginning of any action to prevent more scarcity and lack of access.

In the current SNA, the major actors are both within and outside of health sector. The current sanctions against Iran have made the flow of cash for importation of raw and finished material difficult. Accordingly, the role of the financial sector especially ICB is increasing. This mandates a more active role of the health sector in prioritization and timely importation of both raw and finished materials to avoid chaos in provision. Another important finding is the low power of the patients and health providers, despite it being essential that they have a voice in this network. This would be a strong step towards higher patient satisfaction and more sound, effective,and evidence-based treatments. Absence of the insurance companies in the ranking is another important finding. This finding, along with the findings of other research, indicates that the insurance companies in Iran have not yet become active in policy-making, despite their major role in providing domestic cash flow. These findings need to be considered in designing good governance to improve accessibility to anti-cancer medications in Iran.

Abbreviations

DC

Degree

WDC

Weighted degree

WIDC

Weighted in-degree

WODC

Weighted out-degree centralities

CC

Closeness

HCC

Harmonic closeness centralities

BC

Betweenness centrality

EC

Eigenvector

PRC

PageRank centralities

ECC

Eccentricity centrality

HC

Hub

AC

Authority centralities

SNA

Social network analysis

Gov

Government

Parl

Parliament

MoIMT

Ministry of Industry, Mine and Trade

DotMoHME

Ministry of Health and Medical Education, Deputy of treatment in the MoHME

DodMoHME

Ministry of Health and Medical Education, Deputy of development in the MoHME

DoeMoHME

Ministry of Health and Medical Education, Deputy of education in the MoHME

DorMoHME

Ministry of Health and Medical Education, Deputy of research in the MoHME

DohMoHME

Ministry of Health and Medical Education, Deputy of health in the MoHME

SCI

Supreme Council of Insurance

BI

Basic insurance

SI

Supplementary insurance

SubMoEFA

Ministry of Economic and Finance Affairs-subsidy

TaxMoEFA

Ministry of Economic and Finance Affairs-tax

CusMoEFA

Ministry of Economic and Finance Affairs-customs

UOMS

Universities of medical sciences

SPA

Scientific-professional associations

Phys

Physicians

Pharm

Pharmacies

PharmDC

Pharmaceutical distributions companies

Pat

Patients

Pats’F

Patients’ family

PharS

Pharmaceutical syndicates

PharIC

Pharmaceutical importers companies

PharMC

Pharmaceutical manufacturing companies

BM

Black market

ChC

Chemotherapy centres

Hos

Hospitals

SM

Social media

ICB

Iran Central Bank

FpharC

Foreign pharmaceutical companies

OFC

Other foreign companies

GDS

Global drug shortages

HmcParl

Health and medical commission of the parliament

CC

Competition council

IpoHS

Influential people outside of health sector

RC

Red Crescent

LEA

Law enforcement agencies

VPoSTaKBE

Vice President of Science, Technology and Knowledge-based Economy

VCF

Venture capital funds

BBS

Banks and banking system

IFDA

Iran Food and Drug Administration

CPPO

Consumers and Producers Protection Organization

ICCIMA

Iran Chamber of Commerce

AoMS

Academy of Medical Sciences

Author contributions

K.B.L. and L.Z. participated in the conception and design of the study. Both L.Z. and A.S. contributed to the acquired data. E.A. and L.Z. performed the methodology, data curation, formal analysis and preparing the figures and interpretation of data. E.A. performed the multi-stage social network analysis in related software. L.Z. and E.A. wrote the original draft. E.A., L.Z. and K.B.L. reviewed and edited the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

Funding

This work was supported by grants (ID: 996384) from the NIMAD. The funder had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

Availability of data and materials

The datasets used and/or analysed during the current study is available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Written informed consent was obtained from all the participants. The protocol for this study was approved by the National Institute of Medical Sciences Research Development of Iran (NIMAD ID: 996384). A comprehensive description about the study was introduced to the participants; they were allowed to reject answering or withdrawing at any time thereafter. The participants were assured that their answers would be kept confidential and that their names would not be disclosed during the study and in the final report.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.