Introduction
Universal health coverage (UHC) is a fundamental goal embedded within the Sustainable Development Goals (SDGs), reflecting the overarching aim of nations to establish sustainable and equitable healthcare systems. The global commitment to achieving UHC by 2030, as endorsed by countries worldwide through the SDGs, emphasizes the World Health Organization’s (WHO) definition of UHC, ensuring access to essential public health services, prevention, treatment, rehabilitation, and palliative care with the highest quality and safety standards, while mitigating financial burdens on individuals and communities [
1]. Within the broader context of the third SDG, an essential objective is to shield households from financial hardships and promote fairness in financial contributions, aiming to protect families from healthcare-related expenses. The healthcare system’s central mission includes minimizing vulnerabilities to catastrophic and impoverishing health expenditures, recognizing the pivotal role of financial equity [
2].
In many developing countries, including Iran, health financing predominantly relies on out-of-pocket payments (OOPs), wherein households allocate a portion of their income to healthcare expenses. However, the simplicity of this approach is accompanied by inefficiency and risk, particularly due to the lack of a risk pooling mechanism [
3]. Equity in healthcare systems is contingent on the alignment of service utilization with population needs (equity in delivery) and proportional financial contributions to individuals’ ability to pay (equity in financing) [
4].
When patients are unable to access necessary treatments and services, it not only affects their ability to pay for essentials such as food and housing but also diminishes their overall quality of life. This outcome often results from an imbalanced OOPs mechanism [
5]. The 2019 Iran National Health Accounts report highlighted a concerning upward trajectory, with out-of-pocket (OOP) expenditures constituting 37% of total health spending in 2019, surpassing the set target of 30% outlined in consecutive five-year development plans [
6‐
8]. This reliance on OOP payments has contributed to widespread financial hardships globally, affecting over 930 million individuals seeking healthcare and leading to an estimated 100 million people falling into poverty annually [
9].
The substantial reliance on OOP payments in Iran, accounting for approximately 39.49% of current health expenditures, as reported by the World Health Organization’s Eastern Mediterranean Regional Office (EMRO), has resulted in millions of individuals foregoing necessary healthcare, leaving critical medical needs unmet [
10,
11]. The burden of CHE is a critical concern within the Iranian healthcare system, as evidenced by a series of systematic reviews and meta-analyses, Aryankhesal et al. (2018) conducted a comprehensive study, revealing that approximately 7.5% of households in Iran faced CHE [
12]. This finding was echoed in subsequent research by Rezaei et al. (2019), who reported an average prevalence of 7% of households experiencing CHE [
13]. Furthermore, Doshmangir et al.‘s meta-analysis in 2020 provided a nuanced perspective, indicating that the population-level rate of CHE in Iran was 4.7%, with a notable impact on 25.3% of households when assessed across various diseases [
14].
An investigation into catastrophic and impoverishing health expenditures, two pivotal indicators of financial protection within the context of the Sustainable Development Goals (SDGs), reveals that the Iranian healthcare system is progressing toward achieving UHC. As we strive towards achieving UHC by 2030, understanding and addressing the factors contributing to CHE become paramount. This study endeavors to contribute to this imperative by employing foresight science and cross-impact analysis to forecast future trends in catastrophic health expenditures and propose strategic interventions. Through an exploration of potential scenarios, the research aims to inform policymakers and healthcare stakeholders in developing a resilient and equitable healthcare framework for the future.
Semi-structured interviews
As part of the cross-impact analysis’s first step, semi-structured interviews were conducted with stakeholders to identify comprehensive factors influencing catastrophic and impoverishing healthcare expenditures in Iran. The focus was on confirming factors identified in the previous step.
Sampling and inclusion criteria
Purposive and snowball sampling identified stakeholders with an in-depth understanding and rich information about the study’s focus, including health policymakers, managers, and researchers from various disciplines.
Data collection
Ten in-depth interviews were conducted by MH between March and April 2022, with informed consent obtained from participants. An interview guide (Supplementary
6), developed through literature review and meetings, clarified the research’s main objective.
Data analysis
Thematic content analysis was employed, and emerging themes were identified and consolidated iteratively. The analysis utilized MAXQDA 2020 qualitative software to identify themes.
The pilot test methods
With the purpose of conducting the pilot testing of this semi structured interview, the researcher has put a lot of effort into ensuring the important things related to it including the participants, setting, research instrument, and three procedures of the interview session (1. pre-interview stage, 2. during the interview stage and 3. post-interview stage).
Trustworthiness
To enhance dependability, the first author primarily handled data collection, ensuring consistency in queries. Credibility was strengthened through collaborative data analysis by two authors. Participants verified interview texts and extracted codes to enhance transferability.
Step 2: Finalize, categorize, and prioritize key variables
In this step, the research team achieved consensus to create a definitive list of key variables, ensuring each variable’s clarity and understanding among all respondents. A self-administered questionnaire was employed for finalizing and categorizing these variables, using a seven-point Likert scale (1– strongly disagree to 7– strongly agree). The Likert scale gauged respondents’ agreement or disagreement with each driver related to catastrophic and impoverishing health expenditure. Additionally, the STEEP framework was adopted in the questionnaire to categorize key drivers into social, technological, environmental, economic, and political/legal dimensions [
16]. To enable nationwide expert participation, communication was established through email and online meetings. Participants were informed of the voluntary nature of their participation, and their anonymity and personal information confidentiality were assured. The informed consent form was read and signed by participants.
Subsequently, the Wilson matrix was employed to evaluate and prioritize the impact and uncertainty of each scenario driver on the future. This matrix ranks factors based on potential impact and probability, identifying critical uncertainties that form the basis of scenario construction [
23]. To pinpoint the most critical scenario elements, experts participated in a two-round Delphi survey, evaluating each factor (scenario driver) based on its potential impact on the objective and associated uncertainty. A questionnaire was distributed to twenty experts to gather their input, with two rounds ensuring comprehensive feedback and assessment. In the resulting matrix, factors with high priority are highlighted in light blue on the upper right side, those with medium priority are depicted in white, and factors with low priority are marked in green on the lower left side.
Step 3: Identification of key variables through cross-impact analysis (CIA)
In this phase, the interrelationships among existing variables are systematically described using the Cross-Impact Analysis (CIA) method, a popular tool in futures studies [
17]. CIA, considered a soft-systems tool, can be qualitative or quantitative. In this study, a qualitative approach based on structural analysis was employed [
24].
CIA Steps:
Variable definition:
Variables affecting the future exposure to Catastrophic Health Expenditure (CHE) were derived from the preceding steps.
Interactions analysis:
Variables entered an interaction matrix, and relationships were determined by experts.
Variables were weighted (0 to 3) based on the degree of influence.
This step involved the collection of variables, describing their relationships, and identifying key variables.
Chart analysis and visual representation:
The roles of variables were identified, and an influence–dependence value was assigned for interpretation.
A two-dimensional map with vertical and horizontal axes represented influence and dependence.
Chart zones:
Determinant/Influential Factors: Located in the northwest quarter, these factors are crucial inputs with a significant impact on the system.
Intermediate/Key Variables: Situated in the northeast quarter, these are both influential and dependent, divided into stake and target variables.
Dependent/Output Variables: Found in the southeast position, these are highly dependent on influential factors.
Autonomous/Excluded Variables: In the southwest quarter, these have little influence and dependence.
Clustered/Neuter Variables: Positioned in the border areas, these variables are likely to join other variables.
Selection of key variables:
Variables in determinant positions, with high influence and dependence, were identified as key and critical.
This structured approach using the CIA provides a comprehensive understanding of key variables influencing the future of catastrophic and impoverishing health expenditures. The systematic assessment aids in decision-making for robust future scenarios. (Supplementary
7).
Step 4: Scenario development through cross-impact balance (CIB) analysis
In the final stage, we employed the Cross-Impact Balance (CIB) analysis method for scenario development due to its qualitative orientation, aligning well with expert judgments and addressing data constraints [
25]. CIB offers a structured approach to eliciting expert knowledge about the strength and nature of relationships within a system, making it ideal for identifying qualitative scenarios.
Steps of the CIB process:
Expert panel assembly:
A panel with rich knowledge about key variables convenes.
Descriptor compilation:
A list of relevant system factors, known as descriptors, is compiled. Key factors are extracted from the MICMAC technique.
Qualitative alternatives definition:
Sets of qualitative alternatives (variants) defining possible states of the descriptors are determined.
Example:
a. X1 {xa, xb, xc}.
b. X2 {xx, xy, xz}.
c. X3 {xi, xj}.
Xn {x1 … xn}.
Impact Evaluation:
The expert panel determines the influence–dependence of key factors using cross-impact judgments on a qualitative scale.
Judgment scale:
+ 3: strongly promoting influence.
+ 2: promoting influence.
+ 1: weakly promoting influence.
0: no influence.
−1: weakly restricting influence.
−2: restricting influence.
−3: strongly restricting influence.
Cross-impact matrix is drawn using Scenario Wizard software.
Consistent scenario calculation:
Consistent configurations of the impact network (“consistent scenarios”) are calculated through the CIB algorithm.
Inconsistency coefficients (0–2) identify strong and weak scenarios.
Experts explain narrative expressions for each scenario.
In summary, our study design and method employed a structured and systematic approach to scenario development, relying on expert knowledge and qualitative judgment. The success of this approach hinges on the expertise of the panel, accuracy in cross-impact judgments, and the validity of the scenarios created.
Discussion
In developing countries such as Iran, the reliance on out-of-pocket (OOP) expenditures for healthcare financing has resulted in a concerning rise in households exposed to catastrophic and impoverishing health expenditures. This research aimed to evaluate the future impacts of various drivers, macro trends, and associated uncertainties shaping catastrophic and impoverishing health expenditures in Iran. Alternative scenarios for the year 2030 were presented through a qualitative methodology that employed scenario planning techniques, cross-impact analysis matrix tools, and the MicMac software to identify and analyze the drivers of uncertainty surrounding these health expenditures.
The Cross-Impact Analysis (CIA) technique, as a means of futures research, provided valuable insights by revealing the characteristic role and importance of variables in relation to each other in the system. This method allowed us to consider the potential impacts that future events may have on each other. Subsequently, the Scenario Wizard software, in collaboration with an expert panel, was utilized to pinpoint key uncertainties and articulate potential scenarios for the evolution of catastrophic and impoverishing health expenditures by 2030. The integration of innovative futures study methods, particularly scenario building, has become increasingly beneficial in contemporary healthcare planning and management, offering the flexibility needed to formulate strategic solutions for nationwide health economic issues.
The study further employed Cross-Impact Balance Analysis (CIB) as futures study tools, identifying the roles and significance of each variable within Iran’s health system. A systematic framework, based on CIA, established contextual relationships among the 29 variables affecting households exposed to catastrophic and impoverishing health expenditures in Iran. Fuzzy MICMAC analysis was then applied to evaluate the interactions among the identified variables, leading to the identification of 10 key variables crucial for developing scenarios using the cross-impact algorithm:
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Var36 Budget deficit of the health system.
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Var53 Economic sanctions against Iran.
-
Var25 Informal payments or under-the-counter payment.
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Var45 Conflict of interests of Iran’s health system decision-makers.
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Var21 Increasing consumption of expensive high-tech health care services.
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Var54 Inflation rate in the health sector.
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Var33 Induced demand (consumer or supplier).
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Var44 Lack of reliable and transparent electronic information systems.
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Var52 Lack of implementation of family physician and referral system in the whole country.
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Var41 The increase in the price of medicine and medical devices due to the increase in the exchange rate and the removal of the preferential currency subsidy.
According to cross-impact balance analysis, six scenarios with strong consistencies were identified, exploring the future of healthcare expenditure in Iran under different economic and policy environments. Scenario 1 emerged as the most plausible future, portraying a grim picture of healthcare in Iran characterized by reduced funding, severe economic sanctions, and widespread informal payments and corruption, resulting in escalating healthcare costs and financial hardships for households. In Scenario 1, the variable A3 (reducing the budget of the health system) exhibited the highest consistency value. The primary reason for the substantial out-of-pocket expenses and increasing catastrophic and impoverishing health expenditures was the constrained budget of the health sector compared to the national budget, leading to a general government budget deficit. Addressing this issue requires moving toward operational budgeting, cost-efficiency management (evidence-based decision-making), and creating a system for the timely receipt of revenues.
I3 (the Family Physician Program and referral system not implemented in Iran) was the second variable with high consistency in Scenario 1. Successful implementation of family physician policies depends on the political, economic, social, and cultural context in the country, emphasizing the need to consider these factors for effective policy execution.
G3 (the increase in consumer surplus due to induced demand) emerged as the third variable with high consistency in the first scenario. Policies to impede physician-induced demand, such as implementing the family physician plan at the national level, developing clinical guidelines for family physicians, and establishing a comprehensive health services system, are crucial to addressing this issue.
F3 (the inflation rate in the health sector increases at a rate twice higher than prevailing inflation rates) was the fourth variable with high consistency in Scenario 1. Effective control of the health inflation rate can be achieved through increasing insurance coverage, which could decrease the health inflation rate by controlling prices and compelling health service providers to stabilize prices.
H3 (lack of access to up-to-date and integrated information systems) emerged as the fifth variable with high consistency in Scenario 1. Establishing a comprehensive up-to-date information system is crucial for improving governance, surveillance, responsibility, resource mobilization, and financing functions in the health sector. This involves substituting traditional health service delivery structures with health information technologies, creating a national online data warehouse for health insurance enrollees, and strengthening the financing function.
J3 (eliminate preferential currency subsidies and sudden increase in medicines and medical device prices) was the sixth variable with high consistency in Scenario 1. The elimination of foreign currency subsidies for medicines would adversely affect accessibility, and careful consideration of political context and the addressing of exchange rate gaps are necessary.
B3 (sanctions have more severe consequences) emerged as the seventh variable with high consistency in Scenario 1. Intensifying sanctions led to an increased inflation rate in the health sector, decreased services by insurance companies, and a subsequent rise in out-of-pocket payments, negatively affecting access to healthcare.
C3 (informal payments are deeply ingrained in the healthcare sector) was the eighth variable with high consistency in Scenario 1. Mitigating the persistence of informal payments and high co-payments requires realistic tariff valuation, increased monitoring, timely payment to healthcare providers, deterrence laws, increased supervision and coordination between relevant organizations, ethics training, and performance-based payment.
D3 (severe conflict of interest) was the ninth variable with high consistency in Scenario 1. The complexity and conflict of interest in the health system require the adoption of conflict-of-interest policies and procedures, including annual conflict disclosures, to effectively manage this phenomenon.
E3 (increasing consumption of expensive high-tech health care services due to increased access to advanced medical technologies and an increase in willingness to pay) was the tenth variable with high consistency in Scenario 1. Health Technology Assessment is crucial in ensuring the maximum health benefit for the community and preventing the emergence of inefficient technologies.
The results underscore that Scenario 1, driven by a reduction in the health system budget, is a significant driver scenario in Iran’s health system, exacerbating households’ exposure to catastrophic and impoverishing health expenditures. It paints a challenging picture of the future of healthcare in Iran, with decreased funding, severe economic sanctions, and deeply ingrained informal payments and corruption leading to skyrocketing healthcare costs and financial hardships for households.
In this scenario, addressing the insufficient budget of the health sector, the successful implementation of family physician policies, policies to impede physician-induced demand, controlling the health inflation rate, establishing comprehensive up-to-date information systems, addressing exchange rate gaps, mitigating informal payments, managing conflicts of interest, and adopting health technology assessment are critical steps for improvement and strengthening of the health system. This study offers valuable insights into potential future scenarios of catastrophic and impoverishing health expenditures in Iran. By understanding and addressing the key variables, policymakers can make informed decisions to mitigate the impact of future challenges, ensuring a more resilient and equitable healthcare system for the nation.
While cross-impact analysis offers valuable insights, it is essential to acknowledge the inherent limitations associated with its application. The methodology relies on assumptions and expert opinions, introducing a degree of subjectivity into the analysis. The scenarios presented in this study are exploratory and based on the current understanding, and it is crucial to recognize that future developments may influence the actual trajectory of catastrophic and impoverishing health expenditures in Iran. The uncertainties inherent in projecting future events highlight the need for caution in interpreting the results and the importance of regularly revisiting and updating scenarios to reflect evolving realities.
Scenario analysis, encompassing methodologies such as MICMAC and Scenario Wizard analyses, is widely regarded as a more effective method compared to many contemporary approaches. However, it is imperative to highlight certain limitations associated with these techniques. A key consideration is the substantial reliance on the knowledge and expertise of the expert panel, and the presence of biases among panel members can significantly impact the results. To address this challenge, assembling a multidisciplinary team is essential, ensuring a diversity of perspectives and expertise that contributes to a more comprehensive and unbiased assessment.
The process of developing scenarios for future studies represents a simplified approach when contrasted with the intricate complexities and contradictions inherent in the real world. It is essential for readers to recognize that scenarios, by their nature, involve a level of abstraction from reality. Despite these challenges, the identification of critical factors, uncertainties, and potential scenarios equips decision-makers with the necessary context to navigate intricate interactions and emerging changes within the healthcare system. This facilitates the establishment of a foundation for prioritizing and implementing effective strategies in response to plausible future scenarios.
Despite the acknowledged limitations, the study’s robustness is grounded in its comprehensive analysis and identification of key variables influencing future health expenditures in Iran. The rigorous design of the methodology, including the use of cross-impact analysis tools such as CIA and CIB, adds to the strength of the study. While subjectivity is inherent in expert-driven methodologies, the large set of variables considered, statistical analyses employed, and scenario-building approach contribute to the overall reliability of the findings.
Conclusion
This study underscores the importance of developing flexible strategies and scenarios for improving the health system in developing countries, such as Iran, and highlights the need to address key variables affecting households exposed to catastrophic and impoverishing health expenditures. Based on the findings of this study, key components were identified, and by structural analysis of the relationships between them in MicMac software, the variables “budget deficit”, “economic sanctions”, “informal payments”, “inflation rate”, “conflict of interests”, “induced demand”, “increasing consumption of expensive high-tech healthcare services”, “lack of reliable and transparent electronic information systems”, “lack of implementation of family physician and referral system”, and “increase in the price of medicine and medical devices” as drivers. By addressing these variables, policymakers can take steps to improve access to healthcare and protect households from financial hardship.
The six main scenario spaces were mapped and narrated, extracted from possible and compatible alternative futures based on the results of Scenario wizard software, which is a reasoned and reliable basis for designing any strategy and policy in the future of healthcare expenditure in Iran. These scenarios provide a helpful tool for Iranian healthcare planners and policymakers to identify potential challenges and develop strategies to address them.
The study recommends several policy options for Iran’s health system according to the driver scenario. These include implementing operational budgeting, cost-efficiency management, and timely receipt of revenues, as well as reinforcing governance, regulation, financing, payment, and behavior dimensions. Other policies include increasing insurance coverage, investing more in the health system, using health information technologies, and adopting a conflict-of-interest policy. Iran should also use Health Technology Assessment and implement universal healthcare to ensure maximum health and prevent financial hardship.