Designing a Model for Non-communicable Diseases Management During Pandemics in Iran

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Objectives: The aim of this study is to examine the components influencing the management of noncommunicable disease services during pandemics and to propose a model for Iran. Methods: This applied study, which is descriptive–analytical and exploratory in nature, was conducted in 2024. The study population consisted of managers at different levels of the health system and experts involved in providing chronic disease services. Data were collected using a researcher-made questionnaire whose variables were derived from comparative studies of selected countries. The validity of the questionnaire was assessed through expert judgment, and its reliability was determined using the lambda coefficient in the exploratory section and sensitivity analysis in the confirmatory section. To obtain reliable results, the final questionnaire was distributed among 370 participants in the exploratory phase and 600 participants in the confirmatory phase. Stratified sampling was used, and data analysis was performed using factor-based tests, leading to the extraction of the final model. Results: According to the final model, six main factors and nineteen subcomponents influencing the management of health services for chronic diseases during pandemics were identified. These included leadership and governance, financing, human resources, medicines, vaccines, products and required technologies, health information systems, and service delivery. Among these factors, financing had the greatest impact coefficient (1.025), while health information systems had the lowest impact coefficient (0.705) on the management of noncommunicable disease services during pandemics in Iran. Conclusions: Based on the resulting model, various factors influence the management of health services for chronic diseases during pandemics. Success in crisis management requires strong leadership, effective coordination, investment in digital infrastructure, strengthening workforce resilience, and developing comprehensive policies for the concurrent management of crises and chronic diseases. Moreover, special attention to equity in access to services—particularly for vulnerable groups—and reducing structural inequalities are essential requirements for improving the performance of health systems in the face of future crises. Noncommunicable Diseases Pandemics Health Services Administration COVID-19 Iran Figures Figure 1 1. Background Over recent decades, advances in improving health systems and public health, along with the emergence of modern and advanced technologies, have brought major achievements in increasing life expectancy within populations ( 1 ). This rise in life expectancy and the aging of population pyramids, combined with various factors such as unhealthy lifestyles, have led to an unprecedented increase in chronic non-communicable diseases worldwide. Chronic diseases affect individuals over long periods—often for the remainder of their lives—and result from the interaction of various genetic, physiological, environmental, and behavioral factors ( 2 ). Non-communicable diseases are not only recognized as a major public health challenge but also impose extensive socioeconomic consequences ( 3 ). The World Economic Forum, in a comprehensive study, has predicted that chronic diseases will lead to a loss of 47 trillion dollars in global economic development by 2030, representing approximately 5 percent of the total global GDP ( 4 ). While health systems were still struggling with fundamental challenges in managing non-communicable diseases, in December 2019, global media reported the emergence of a new type of coronavirus known as COVID-19—with high transmissibility and infectiousness—from Wuhan, China. Due to its rapid spread, within only a few weeks, the disease had reached countries worldwide, prompting the United Nations to officially declare it a global pandemic on March 11, 2020 ( 5 ). The spread of pandemics can affect the management of non-communicable diseases in various ways. On one hand, the implementation of nationwide lockdowns and social distancing programs can create major challenges regarding behavioral risk factors associated with chronic diseases, such as unhealthy diets and insufficient physical activity ( 6 ). Moreover, health system experiences during pandemics have shown that the stressful conditions of such crises, combined with fragile livelihood situations for a significant portion of the population, can heighten vulnerability among individuals with non-communicable diseases ( 7 ). In addition to the general impacts of epidemics on chronic diseases, a highly essential dimension is the direct effect of epidemics on health systems themselves, which significantly disrupts the provision of routine and vital health care services for chronic diseases. It is evident that during pandemics, a substantial share of health care resources, facilities, and capacities is allocated to treating patients affected by the epidemic ( 8 ). In addition to the direct impacts that pandemics have on the provision of essential care for chronic diseases, such pandemics can also significantly affect care-seeking behaviors among individuals with chronic conditions ( 9 ). 2. Objectives The issue of maintaining essential care related to chronic disease management is particularly important in developing countries such as Iran, as the health system infrastructure in these countries already faces numerous fundamental problems and challenges even prior to the emergence of such epidemics. Consequently, the onset of an epidemic imposes an extremely heavy burden on these health systems ( 10 ). This is especially critical given that approximately 85 percent of individuals with non-communicable diseases live in low- and middle-income countries ( 11 ). Furthermore, various studies addressing the management of chronic diseases during different disasters and crises have largely focused on challenges related to non-chronic conditions. Examples include the provision of health and medical care for non-communicable diseases during wars, conflicts, and natural disasters such as floods and earthquakes ( 12 – 14 ). However, comprehensive models concerning chronic disease management during pandemics have not been developed. This has created a significant gap within the care system and related research domains, as the emergence and global spread of COVID-19 clearly demonstrates that health care systems must not overlook infectious diseases and the substantial challenges they pose. Therefore, the present study aims to design a comprehensive model for management of non-communicable disease services during pandemics within Iran’s health system. 3. Methods This exploratory, applied, descriptive–analytical study was conducted in 2024. In the first phase, the factors influencing the design of a model for managing non-communicable chronic diseases were identified through a review-based approach. Subsequently, a comparative study was conducted to assess selected countries based on specific criteria, including leadership and governance, health system financing, the health workforce, essential products, medicines and required technologies, health information systems, and service delivery. These indicators were chosen due to their importance and influence on managing non-communicable chronic diseases during pandemics, in alignment with the World Health Organization’s model for disease management in crisis situations. The criteria for selecting countries included relevance to the research focus, diversity in health system structures, variation in socioeconomic contexts, data availability, political and organizational context, and evidence of innovative practices. The experiences of selected countries—Germany, Canada, the United States, the United Kingdom, Australia, Singapore, and Turkey—were examined to identify strengths and weaknesses in managing non-communicable chronic diseases during the COVID-19 pandemic. Based on the comparative study conducted, 115 items were extracted. To assess the validity of the questionnaire, 10 experts in chronic disease health services management were consulted, and face validity was confirmed by the specialists. The validity of the survey was evaluated using the Lawshe’s Content Validity Ratio Table (CVR) and the Waltz and Bausell method (CVI). According to the content validity analysis, out of the original 115 items, 50 were removed due to CVR values falling below the threshold of 0.62 (for 10 experts, according to the Lawshe’s table). A total of 65 items remained for exploratory factor analysis. Moreover, the content validity index for all remaining items exceeded 0.78, and the mean content validity indices for relevance (0.9302), clarity (0.9308), and simplicity (0.9310) were all above 0.90. Since in factor analysis studies sample size is determined based on latent variables, in this study the sample size was estimated as 370 participants for the exploratory analysis and 600 participants for the confirmatory analysis, accounting for a 10% attrition rate in each phase. The study population consisted of faculty members, staff, and managers involved in the care of patients with chronic non-communicable diseases in treatment and prevention sectors, who had at least ten years of work experience in chronic disease care and held a minimum of a master’s degree. The questionnaire was distributed online across the country. The questionnaire used in this study was developed by the research team specifically for this study to assess non-communicable disease (NCD) management during public health crises. The English version of the questionnaire is provided as Supplementary File 1. It consisted of 65 questions categorized into six main components and 17 subcomponents. Participants were asked to rate the importance and impact of each item using a 5-point Likert scale ranging from “strongly disagree” to “strongly agree.” To assess the reliability of the questionnaire in the exploratory phase, Guttman’s test was used. According to Table 1 , Guttman’s test classified respondents into five groups based on the homogeneity of their responses. The smallest lambda corresponded to the fourth group, with a lambda coefficient of 0.629; the overall reliability of the questionnaire was 0.82, indicating acceptable reliability. For the confirmatory phase, reliability was assessed using sensitivity analysis for Cronbach’s alpha. Based on the sensitivity analysis, the item justice1 (“During the public health crisis (the COVID-19 pandemic), all patients with non-communicable chronic diseases should receive health care services without discrimination.”) was removed from the model, and confirmatory factor analysis proceeded with 64 questions across 19 components. Before conducting exploratory and confirmatory factor analyses, Bartlett’s test and the KMO measure were used to assess the appropriateness of the data and sample adequacy. Varimax rotation was applied for axis rotation. In the final stage of the study, using exploratory factor analysis (EFA) and second-order confirmatory factor analysis (CFA), the designed model for managing non-communicable chronic diseases in Iran was evaluated, and its validity and reliability were confirmed through statistical methods. Data analysis was performed using SPSS and AMOS software. This study obtained ethical approval from the Research Ethics Committee of Islamic Azad University, Science and Research Branch (IR.IAU.TMU.REC.1401.043). 4. Results In this study, 356 participants in the exploratory phase and 578 participants in the confirmatory phase completed the questionnaire in full. Prior to conducting exploratory and confirmatory factor analyses, Bartlett’s test and the KMO measure were applied to assess the feasibility of analysis and the adequacy of the sample size. The results of the KMO sampling adequacy test for the exploratory factor analysis indicated that, given the KMO value was above 0.7, the sample size was appropriate. Additionally, since the significance value (sig) was less than 0.5, the correlations were not spherical, and exploratory factor analysis could be performed (KMO index: 0.769; chi-square: 13,974.983; sig < 0.001). Similarly, the results of the KMO test for confirmatory factor analysis showed that the sample size was adequate due to the KMO value being above 0.7. Moreover, because the significance value (sig) was less than 0.5, the correlations were not spherical, and exploratory factor analysis could be conducted (KMO index: 0.782; chi-square: 21,716.811; sig < 0.001). From the perspective of managing chronic disease health services during pandemics, the mean score was 191.001. The health workforce dimension received the highest score (39.216 ± 10.035), while the leadership and governance dimension had the lowest score (25.659 ± 6.735). The mean scores for the dimensions of service delivery, health financing, medicines, equipment and vaccines, and health information systems were 35.45 (± 7.68), 30.35 (± 7.70), 30.29 (± 7.94), and 30.05 (± 9.995), respectively (Table 1 ). For all items, the highest factor loading exceeded 0.5, and the highest loading was at least 0.3 greater than the other factor loadings for the same item. Therefore, all items remained classified within the model comprising 19 factors. In the comparative study, 17 components were initially identified, but exploratory factor analysis extracted two additional factors. The factors and their corresponding items are summarized in the table below, and based on their textual and semantic content, the 19 extracted components were named accordingly(Table 2 ). The factors influencing the management of chronic disease health services during pandemics were determined through second-order confirmatory factor analysis. The standardized coefficients were obtained as follows: Leadership and Governance (0.980), Health Financing System (1.025), Health Workforce (0.947), Medicines, Equipment, and Required Medical Technologies (0.949), Health Information System (0.705), and Service Delivery (0.720) (Fig. 1 ). The fit indices of the chronic disease management model during the COVID-19 pandemic were evaluated to determine its adequacy. The results indicated that, based on all obtained indices, the model demonstrated a good fit. The software did not suggest any applicable modifications to improve model fit (Table 3 ). The final model for managing chronic diseases during pandemics in Iran was presented with six main dimensions and 19 subdimensions . 5. Discussion The aim of this study was to identify the dimensions and components of non-communicable disease (NCD) management during pandemics. The results indicated that the health financing dimension had the strongest impact coefficient on the management of chronic non-communicable disease health services during pandemics. This finding highlights that efficiency, equity, and sustainability within the health financing system are key pillars of health system resilience during crises. In situations where resources are limited and treatment costs rise, financing mechanisms play a vital role in ensuring the continuity of care for chronic patients. Takian et al. (2020) emphasized the importance of universal health coverage to strengthen the prevention and control of NCDs during COVID-19 ( 15 ), but they provided limited in-depth analysis regarding the economic impacts on health systems and the national budgets required. Our study addresses this gap by emphasizing the critical role of sustainable and flexible financing in enhancing health system resilience. Any disruption or instability in funding flows can severely weaken the structural resilience of health systems. Noh et al. (2022) examined the effects of chronic underfunding of health systems on NCD patients in North Korea, yet they did not provide an in-depth analysis of efficient and effective financing systems ( 16 ). The present study, through a comparative approach, aimed to provide a more comprehensive analysis of resource allocation prioritization and the efficiency of financing systems across different countries. The findings of this study align with the research of Ezzati et al. (2023), who emphasized “insufficient resources” as a key challenge ( 17 ), as well as the work of Elhalawi and Sutantari (2022), which highlighted the necessity of “optimal use of resources” ( 18 ). Countries with centralized leadership exhibit greater resilience compared to those with decentralized structures. Ezzati et al. (2023) highlighted challenges such as “decentralized management systems,” “weak leadership,” and “lack of a comprehensive national plan,” concluding that managers should employ community-based and evidence-informed management approaches to build resilience ( 17 ), which aligns with the findings of our study. Monaco et al. (2021) identified the absence of NCD management guidelines as a barrier ( 19 ), underscoring the global importance of governance and organizational infrastructure. This dimension is further supported by Nasiri et al. (2021) and Pikari et al. (2017), who emphasized establishing clear national objectives and fostering intersectoral collaboration ( 20 , 21 ). These findings indicate that the lack of effective leadership and a defined governance framework remains a critical and persistent challenge in responding to health crises. Countries that implemented comprehensive mental health programs, financial incentives, and structural reforms in a coordinated manner demonstrated better performance in enhancing resilience and the well-being of health workforce personnel. Within the human resources dimension, the highest impact coefficient was associated with workforce welfare and support, consistent with the findings of Elhalawi and Sutantari (2022), who emphasized keeping the health workforce “healthy and ready” ( 18 ). These results indicate that not only the quantity but also the quality, training, and support of the health workforce are vital factors in a system’s capacity to respond to sudden shocks, such as COVID-19, and to ensure the continuous management of chronic diseases. In the context of health system resilience, Goya et al. (2023) examined the resilience of Iran’s health system against the COVID-19 pandemic, emphasizing the strengthening of preparedness and response capacities. However, their study was limited to Iran and did not address challenges faced by other countries or provide an international comparison ( 22 ). The study by Egg et al. (2021) on medication management for non-communicable diseases during COVID-19 indicated that disruptions in the supply chain of medicines and equipment were a global concern ( 23 ), which aligns with the findings of the present research. Akrami et al. (2022) reported suspensions in screening services due to disruptions in service delivery and access to equipment ( 24 ). Similarly, Ghatli et al. (2021) highlighted patient self-management challenges caused by “reduced care services,” noting that key challenges for chronic patients during COVID-19 included diminished healthcare services, limited routine follow-ups due to physical restrictions, economic hardships, lifestyle changes, and slow adaptation to new circumstances. In such situations, health teams must utilize available resources to provide optimal care and reduce patient suffering ( 25 ). All these studies emphasize disruptions in service delivery, which in our statistical model were also identified as an influential factor. A distinctive feature of the present study is its focus on equity in service delivery. The coefficient of 0.720 underscores the importance of this domain, highlighting that services must not only be maintained but also delivered with attention to vulnerable populations. This finding reinforces the recommendations of Akrami et al. (2022) regarding strategies that specifically address the needs of at-risk groups ( 24 ). The present study, with its emphasis on structural variables such as financing, governance, and medical equipment, places less focus on patient behavioral factors. This contrasts with studies that concentrated on the behavioral dimension. For instance, Ghatli et al. (2021) directly focused on “patient self-management challenges” ( 25 ), and Aba Abraham et al. (2023) highlighted “care-seeking behaviors” ( 26 ). 6. Conclusion The findings of this study indicate that key elements for effective management of non-communicable chronic diseases (NCDs) in crisis situations include: establishing a coherent national strategy and intersectoral collaboration mechanisms; ensuring resilient leadership and governance structures; providing sustainable financing and effective resource prioritization; equitable distribution, capacity building, and welfare support for the health workforce; ensuring access to essential medicines and technologies while maintaining quality and safety; integrating COVID-19–related data into health information systems; leveraging digital health solutions and ensuring data security; and ultimately delivering health services in a timely, equitable, and non-discriminatory manner. Continuity of care for NCD patients requires fundamental revision of health system infrastructures, not just temporary clinical protocols. The success or failure in maintaining follow-up, screening, and management services for these patients is directly linked to the institutional strength of the health system—specifically, the ability of governance and financing structures to secure resources and sustain service delivery under maximum disruption. This implies that the primary priority should be the reconstruction of financial and managerial systems to ensure that essential NCD services remain a priority during pandemic shocks. Declarations Acknowledgements The authors extend their sincere gratitude to all interview participants for their valuable time, insights, and contributions to this study. Author contributions TZ wrote the initial draft of the manuscript, with assistance from RL, NL, HK, and TZ. designed the study, with input from RL, and LN. HK and RL. conducted the statistical analyses, under the supervision of NL. All authors contributed revisions to the paper and approved the final version. TZ and RL are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Funding this research did not receive funding. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This article is derived from a doctoral thesis in health and medical services management, approved by the Islamic Azad University, Science and Research Branch, Tehran (Ethical Code: IR.IAU.SRB.REC.1401.043). The study adheres to the ethical standards outlined in the Declaration of Helsinki. Prior to data collection, informed consent was obtained from all participants and formally documented using a mandatory Yes/No question. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Hol WG, Verlinde CL. Non-communicable diseases. Insulin. 2006;106:107. Kretchy IA, Asiedu-Danso M, Kretchy JP. Medication management and adherence during the COVID-19 pandemic: perspectives and experiences from low-and middle-income countries. Res Social Administrative Pharm. 2021;17(1):2023–6. Engelgau Michael M, Mahal Ajay. The economic impact of non-communicable diseases on households in India. Globalization health. 2012;8(1):1–10. Bloom David E, Cafiero Elizabeth J-L, Eva A-G, Shafika BL, Reddy F, Sana, et al. The global economic burden of noncommunicable diseases. Program on the Global Demography of Aging; 2012. Chiara GM, Malvika V, De Vries EGE, Ravindran K, Rosa G. Peters Solange. 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Med J Islamic Repub Iran. 2022;36:167. Ghotbi Tahere S, Javad KE, Allah. Ghelichi-Ghojogh Mousa. Self-management of patients with chronic diseases during COVID19: a narrative review. J Prev Med Hyg. 2021;62(4):E814. - E21. Ghotbi T, Salami J, Kalteh EA, Ghelichi-Ghojogh M. Self-management of patients with chronic diseases during COVID-19: a narrative review. J Prev Med Hyg. 2021;62(4):E814–21. Tables Table 1. Mean Scores of the Dimensions of Chronic Disease Management During Pandemics Dimensions Mean Score Standard Deviation Leadership and Governance 25.659 6.735 Health Financing System 30.353 7.708 Health Workforce 39.216 10.035 Medicines, Equipment, and Vaccines 30.29 7.946 Health Information System 30.057 9.995 Health Service Delivery 35.451 7.684 Table 2. Extracted Factors and Their Designated Names Factor Items Number of Items Component Name Main Dimension 1 q01–q03 3 Existence of National Strategies Leadership and Governance 2 q04–q06 3 Intersectoral Collaboration 3 q07–q09 3 Adaptability 4 q10–q13 4 Availability of Required Financial Resources Health Financing System 5 q14–q16 3 Resource Prioritization 6 q17–q19 3 Efficiency and Effectiveness 7 q20–q23 4 Workforce Density and Distribution Health Workforce 8 q24–q26 3 Competence and Capability 9 q27–q29 3 Resilience 10 q30–q32 3 Welfare and Support 11 q33–q35 3 Availability Medicines, Equipment, and Medical Technologies 12 q36–q39 4 Quality and Safety 13 q40–q42 3 Legal Supervision 14 q43–q45 3 Data Integration Health Information System 15 q46–q49 4 Timeliness of Data 16 q50–q52 3 Telehealth 17 q53–q55 3 Data Privacy and Security 18 q56–q59 4 Access to Services Health Service Delivery 19 q60–q65 6 Equity in Access Table 3. Model Fit Indices Fit Index Acceptable Threshold Obtained Value CMIN/DF < 3 1.020 RMSEA 0.5 0.810 GFI > 0.8 0.914 AGFI > 0.8 0.900 NFI > 0.9 0.916 TLI > 0.9 0.998 CFI > 0.9 0.998 RFI > 0.9 0.905 IFI > 0.9 0.998 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Editor invited by journal 11 Dec, 2025 Submission checks completed at journal 10 Dec, 2025 First submitted to journal 10 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8241448","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570873505,"identity":"ef2157fb-0c1f-4048-9648-933e9ef7a045","order_by":0,"name":"Zahra Taghvaeikeshtkar","email":"","orcid":"","institution":"SR.C.,Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Taghvaeikeshtkar","suffix":""},{"id":570873506,"identity":"2a430d03-fc1c-4a13-b915-b59c143b7bf4","order_by":1,"name":"Leila Riahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACPiA+wNggIccGFeAhqIUNqsWYNC0MjA0MiQ1EO4yNgffhgZ87LNL72M8YMPyoYZAxJ6SZjYHd4GDvGYncNp4cA8aeYww8MgcIamFjOMDbBtTCkGPAwNvAwCNB2GFsDAf/tkmks/G/MWD8S6yWw0BbEtgkcgyYibOFGahFtk3CsE3iWcFhmWMShLXws7cxf3zbVicv35+88eGbGht7gloYmJHYBxgYCGsYBaNgFIyCUUAEAACxsy4SNZiRgQAAAABJRU5ErkJggg==","orcid":"","institution":"SR.C.,Islamic Azad University","correspondingAuthor":true,"prefix":"","firstName":"Leila","middleName":"","lastName":"Riahi","suffix":""},{"id":570873507,"identity":"de8febdc-9694-4ac8-b513-df33a935d397","order_by":2,"name":"Leila Nazarimanesh","email":"","orcid":"","institution":"SR.C.,Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Leila","middleName":"","lastName":"Nazarimanesh","suffix":""},{"id":570873508,"identity":"60d2ecb7-7dee-418f-8a9a-79fc99ebca01","order_by":3,"name":"Kamran 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1","display":"","copyAsset":false,"role":"figure","size":189845,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Model with Standardized Coefficients Estimation\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8241448/v1/1b6f71b462cb55daf7dca3c4.jpeg"},{"id":100406599,"identity":"36459443-0d54-4f4c-a9e9-d7b0948a9920","added_by":"auto","created_at":"2026-01-16 13:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":751723,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8241448/v1/529aadc6-2323-40ed-8022-3c1021135e52.pdf"},{"id":100012752,"identity":"bbeb4f22-0860-4096-b0e5-fb18b9fdf7ee","added_by":"auto","created_at":"2026-01-12 06:16:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":265567,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8241448/v1/4192c2a5fc3e3dcbefa1b271.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDesigning a Model for Non-communicable Diseases Management During Pandemics in Iran\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eOver recent decades, advances in improving health systems and public health, along with the emergence of modern and advanced technologies, have brought major achievements in increasing life expectancy within populations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This rise in life expectancy and the aging of population pyramids, combined with various factors such as unhealthy lifestyles, have led to an unprecedented increase in chronic non-communicable diseases worldwide. Chronic diseases affect individuals over long periods\u0026mdash;often for the remainder of their lives\u0026mdash;and result from the interaction of various genetic, physiological, environmental, and behavioral factors (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNon-communicable diseases are not only recognized as a major public health challenge but also impose extensive socioeconomic consequences (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The World Economic Forum, in a comprehensive study, has predicted that chronic diseases will lead to a loss of 47 trillion dollars in global economic development by 2030, representing approximately 5 percent of the total global GDP (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile health systems were still struggling with fundamental challenges in managing non-communicable diseases, in December 2019, global media reported the emergence of a new type of coronavirus known as COVID-19\u0026mdash;with high transmissibility and infectiousness\u0026mdash;from Wuhan, China. Due to its rapid spread, within only a few weeks, the disease had reached countries worldwide, prompting the United Nations to officially declare it a global pandemic on March 11, 2020 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe spread of pandemics can affect the management of non-communicable diseases in various ways. On one hand, the implementation of nationwide lockdowns and social distancing programs can create major challenges regarding behavioral risk factors associated with chronic diseases, such as unhealthy diets and insufficient physical activity (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Moreover, health system experiences during pandemics have shown that the stressful conditions of such crises, combined with fragile livelihood situations for a significant portion of the population, can heighten vulnerability among individuals with non-communicable diseases (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the general impacts of epidemics on chronic diseases, a highly essential dimension is the direct effect of epidemics on health systems themselves, which significantly disrupts the provision of routine and vital health care services for chronic diseases. It is evident that during pandemics, a substantial share of health care resources, facilities, and capacities is allocated to treating patients affected by the epidemic (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the direct impacts that pandemics have on the provision of essential care for chronic diseases, such pandemics can also significantly affect care-seeking behaviors among individuals with chronic conditions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Objectives","content":"\u003cp\u003eThe issue of maintaining essential care related to chronic disease management is particularly important in developing countries such as Iran, as the health system infrastructure in these countries already faces numerous fundamental problems and challenges even prior to the emergence of such epidemics. Consequently, the onset of an epidemic imposes an extremely heavy burden on these health systems (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This is especially critical given that approximately 85 percent of individuals with non-communicable diseases live in low- and middle-income countries (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, various studies addressing the management of chronic diseases during different disasters and crises have largely focused on challenges related to non-chronic conditions. Examples include the provision of health and medical care for non-communicable diseases during wars, conflicts, and natural disasters such as floods and earthquakes (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, comprehensive models concerning chronic disease management during pandemics have not been developed. This has created a significant gap within the care system and related research domains, as the emergence and global spread of COVID-19 clearly demonstrates that health care systems must not overlook infectious diseases and the substantial challenges they pose.\u003c/p\u003e \u003cp\u003eTherefore, the present study aims to design a comprehensive model for management of non-communicable disease services during pandemics within Iran\u0026rsquo;s health system.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThis exploratory, applied, descriptive\u0026ndash;analytical study was conducted in 2024. In the first phase, the factors influencing the design of a model for managing non-communicable chronic diseases were identified through a review-based approach. Subsequently, a comparative study was conducted to assess selected countries based on specific criteria, including leadership and governance, health system financing, the health workforce, essential products, medicines and required technologies, health information systems, and service delivery. These indicators were chosen due to their importance and influence on managing non-communicable chronic diseases during pandemics, in alignment with the World Health Organization\u0026rsquo;s model for disease management in crisis situations.\u003c/p\u003e \u003cp\u003eThe criteria for selecting countries included relevance to the research focus, diversity in health system structures, variation in socioeconomic contexts, data availability, political and organizational context, and evidence of innovative practices. The experiences of selected countries\u0026mdash;Germany, Canada, the United States, the United Kingdom, Australia, Singapore, and Turkey\u0026mdash;were examined to identify strengths and weaknesses in managing non-communicable chronic diseases during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eBased on the comparative study conducted, 115 items were extracted. To assess the validity of the questionnaire, 10 experts in chronic disease health services management were consulted, and face validity was confirmed by the specialists. The validity of the survey was evaluated using the Lawshe\u0026rsquo;s Content Validity Ratio Table (CVR) and the Waltz and Bausell method (CVI). According to the content validity analysis, out of the original 115 items, 50 were removed due to CVR values falling below the threshold of 0.62 (for 10 experts, according to the Lawshe\u0026rsquo;s table). A total of 65 items remained for exploratory factor analysis. Moreover, the content validity index for all remaining items exceeded 0.78, and the mean content validity indices for relevance (0.9302), clarity (0.9308), and simplicity (0.9310) were all above 0.90.\u003c/p\u003e \u003cp\u003eSince in factor analysis studies sample size is determined based on latent variables, in this study the sample size was estimated as 370 participants for the exploratory analysis and 600 participants for the confirmatory analysis, accounting for a 10% attrition rate in each phase. The study population consisted of faculty members, staff, and managers involved in the care of patients with chronic non-communicable diseases in treatment and prevention sectors, who had at least ten years of work experience in chronic disease care and held a minimum of a master\u0026rsquo;s degree. The questionnaire was distributed online across the country. The questionnaire used in this study was developed by the research team specifically for this study to assess non-communicable disease (NCD) management during public health crises. The English version of the questionnaire is provided as Supplementary File 1. It consisted of 65 questions categorized into six main components and 17 subcomponents. Participants were asked to rate the importance and impact of each item using a 5-point Likert scale ranging from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree.\u0026rdquo;\u003c/p\u003e \u003cp\u003eTo assess the reliability of the questionnaire in the exploratory phase, Guttman\u0026rsquo;s test was used. According to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Guttman\u0026rsquo;s test classified respondents into five groups based on the homogeneity of their responses. The smallest lambda corresponded to the fourth group, with a lambda coefficient of 0.629; the overall reliability of the questionnaire was 0.82, indicating acceptable reliability. For the confirmatory phase, reliability was assessed using sensitivity analysis for Cronbach\u0026rsquo;s alpha. Based on the sensitivity analysis, the item justice1 (\u0026ldquo;During the public health crisis (the COVID-19 pandemic), all patients with non-communicable chronic diseases should receive health care services without discrimination.\u0026rdquo;) was removed from the model, and confirmatory factor analysis proceeded with 64 questions across 19 components.\u003c/p\u003e \u003cp\u003eBefore conducting exploratory and confirmatory factor analyses, Bartlett\u0026rsquo;s test and the KMO measure were used to assess the appropriateness of the data and sample adequacy. Varimax rotation was applied for axis rotation. In the final stage of the study, using exploratory factor analysis (EFA) and second-order confirmatory factor analysis (CFA), the designed model for managing non-communicable chronic diseases in Iran was evaluated, and its validity and reliability were confirmed through statistical methods. Data analysis was performed using SPSS and AMOS software. This study obtained ethical approval from the Research Ethics Committee of Islamic Azad University, Science and Research Branch (IR.IAU.TMU.REC.1401.043).\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eIn this study, 356 participants in the exploratory phase and 578 participants in the confirmatory phase completed the questionnaire in full. Prior to conducting exploratory and confirmatory factor analyses, Bartlett\u0026rsquo;s test and the KMO measure were applied to assess the feasibility of analysis and the adequacy of the sample size.\u003c/p\u003e \u003cp\u003eThe results of the KMO sampling adequacy test for the exploratory factor analysis indicated that, given the KMO value was above 0.7, the sample size was appropriate. Additionally, since the significance value (sig) was less than 0.5, the correlations were not spherical, and exploratory factor analysis could be performed (KMO index: 0.769; chi-square: 13,974.983; sig\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eSimilarly, the results of the KMO test for confirmatory factor analysis showed that the sample size was adequate due to the KMO value being above 0.7. Moreover, because the significance value (sig) was less than 0.5, the correlations were not spherical, and exploratory factor analysis could be conducted (KMO index: 0.782; chi-square: 21,716.811; sig\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFrom the perspective of managing chronic disease health services during pandemics, the mean score was 191.001. The health workforce dimension received the highest score (39.216\u0026thinsp;\u0026plusmn;\u0026thinsp;10.035), while the leadership and governance dimension had the lowest score (25.659\u0026thinsp;\u0026plusmn;\u0026thinsp;6.735). The mean scores for the dimensions of service delivery, health financing, medicines, equipment and vaccines, and health information systems were 35.45 (\u0026plusmn;\u0026thinsp;7.68), 30.35 (\u0026plusmn;\u0026thinsp;7.70), 30.29 (\u0026plusmn;\u0026thinsp;7.94), and 30.05 (\u0026plusmn;\u0026thinsp;9.995), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor all items, the highest factor loading exceeded 0.5, and the highest loading was at least 0.3 greater than the other factor loadings for the same item. Therefore, all items remained classified within the model comprising 19 factors. In the comparative study, 17 components were initially identified, but exploratory factor analysis extracted two additional factors. The factors and their corresponding items are summarized in the table below, and based on their textual and semantic content, the 19 extracted components were named accordingly(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe factors influencing the management of chronic disease health services during pandemics were determined through second-order confirmatory factor analysis. The standardized coefficients were obtained as follows: Leadership and Governance (0.980), Health Financing System (1.025), Health Workforce (0.947), Medicines, Equipment, and Required Medical Technologies (0.949), Health Information System (0.705), and Service Delivery (0.720) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fit indices of the chronic disease management model during the COVID-19 pandemic were evaluated to determine its adequacy. The results indicated that, based on all obtained indices, the model demonstrated a good fit. The software did not suggest any applicable modifications to improve model fit (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe final model for managing chronic diseases during pandemics in Iran was presented with six main dimensions and 19 subdimensions .\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe aim of this study was to identify the dimensions and components of non-communicable disease (NCD) management during pandemics. The results indicated that the health financing dimension had the strongest impact coefficient on the management of chronic non-communicable disease health services during pandemics. This finding highlights that efficiency, equity, and sustainability within the health financing system are key pillars of health system resilience during crises. In situations where resources are limited and treatment costs rise, financing mechanisms play a vital role in ensuring the continuity of care for chronic patients.\u003c/p\u003e \u003cp\u003eTakian et al. (2020) emphasized the importance of universal health coverage to strengthen the prevention and control of NCDs during COVID-19 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), but they provided limited in-depth analysis regarding the economic impacts on health systems and the national budgets required. Our study addresses this gap by emphasizing the critical role of sustainable and flexible financing in enhancing health system resilience. Any disruption or instability in funding flows can severely weaken the structural resilience of health systems.\u003c/p\u003e \u003cp\u003eNoh et al. (2022) examined the effects of chronic underfunding of health systems on NCD patients in North Korea, yet they did not provide an in-depth analysis of efficient and effective financing systems (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The present study, through a comparative approach, aimed to provide a more comprehensive analysis of resource allocation prioritization and the efficiency of financing systems across different countries.\u003c/p\u003e \u003cp\u003eThe findings of this study align with the research of Ezzati et al. (2023), who emphasized \u0026ldquo;insufficient resources\u0026rdquo; as a key challenge (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), as well as the work of Elhalawi and Sutantari (2022), which highlighted the necessity of \u0026ldquo;optimal use of resources\u0026rdquo; (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCountries with centralized leadership exhibit greater resilience compared to those with decentralized structures. Ezzati et al. (2023) highlighted challenges such as \u0026ldquo;decentralized management systems,\u0026rdquo; \u0026ldquo;weak leadership,\u0026rdquo; and \u0026ldquo;lack of a comprehensive national plan,\u0026rdquo; concluding that managers should employ community-based and evidence-informed management approaches to build resilience (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), which aligns with the findings of our study. Monaco et al. (2021) identified the absence of NCD management guidelines as a barrier (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), underscoring the global importance of governance and organizational infrastructure. This dimension is further supported by Nasiri et al. (2021) and Pikari et al. (2017), who emphasized establishing clear national objectives and fostering intersectoral collaboration (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These findings indicate that the lack of effective leadership and a defined governance framework remains a critical and persistent challenge in responding to health crises.\u003c/p\u003e \u003cp\u003eCountries that implemented comprehensive mental health programs, financial incentives, and structural reforms in a coordinated manner demonstrated better performance in enhancing resilience and the well-being of health workforce personnel. Within the human resources dimension, the highest impact coefficient was associated with workforce welfare and support, consistent with the findings of Elhalawi and Sutantari (2022), who emphasized keeping the health workforce \u0026ldquo;healthy and ready\u0026rdquo; (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These results indicate that not only the quantity but also the quality, training, and support of the health workforce are vital factors in a system\u0026rsquo;s capacity to respond to sudden shocks, such as COVID-19, and to ensure the continuous management of chronic diseases.\u003c/p\u003e \u003cp\u003eIn the context of health system resilience, Goya et al. (2023) examined the resilience of Iran\u0026rsquo;s health system against the COVID-19 pandemic, emphasizing the strengthening of preparedness and response capacities. However, their study was limited to Iran and did not address challenges faced by other countries or provide an international comparison (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study by Egg et al. (2021) on medication management for non-communicable diseases during COVID-19 indicated that disruptions in the supply chain of medicines and equipment were a global concern (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), which aligns with the findings of the present research. Akrami et al. (2022) reported suspensions in screening services due to disruptions in service delivery and access to equipment (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Similarly, Ghatli et al. (2021) highlighted patient self-management challenges caused by \u0026ldquo;reduced care services,\u0026rdquo; noting that key challenges for chronic patients during COVID-19 included diminished healthcare services, limited routine follow-ups due to physical restrictions, economic hardships, lifestyle changes, and slow adaptation to new circumstances. In such situations, health teams must utilize available resources to provide optimal care and reduce patient suffering (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll these studies emphasize disruptions in service delivery, which in our statistical model were also identified as an influential factor. A distinctive feature of the present study is its focus on equity in service delivery. The coefficient of 0.720 underscores the importance of this domain, highlighting that services must not only be maintained but also delivered with attention to vulnerable populations. This finding reinforces the recommendations of Akrami et al. (2022) regarding strategies that specifically address the needs of at-risk groups (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study, with its emphasis on structural variables such as financing, governance, and medical equipment, places less focus on patient behavioral factors. This contrasts with studies that concentrated on the behavioral dimension. For instance, Ghatli et al. (2021) directly focused on \u0026ldquo;patient self-management challenges\u0026rdquo; (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and Aba Abraham et al. (2023) highlighted \u0026ldquo;care-seeking behaviors\u0026rdquo; (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe findings of this study indicate that key elements for effective management of non-communicable chronic diseases (NCDs) in crisis situations include: establishing a coherent national strategy and intersectoral collaboration mechanisms; ensuring resilient leadership and governance structures; providing sustainable financing and effective resource prioritization; equitable distribution, capacity building, and welfare support for the health workforce; ensuring access to essential medicines and technologies while maintaining quality and safety; integrating COVID-19–related data into health information systems; leveraging digital health solutions and ensuring data security; and ultimately delivering health services in a timely, equitable, and non-discriminatory manner.\u003c/p\u003e\n\u003cp\u003eContinuity of care for NCD patients requires fundamental revision of health system infrastructures, not just temporary clinical protocols. The success or failure in maintaining follow-up, screening, and management services for these patients is directly linked to the institutional strength of the health system—specifically, the ability of governance and financing structures to secure resources and sustain service delivery under maximum disruption. This implies that the primary priority should be the reconstruction of financial and managerial systems to ensure that essential NCD services remain a priority during pandemic shocks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors extend their sincere gratitude to all interview participants for their valuable time, insights, and contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTZ wrote the initial draft of the manuscript, with assistance from RL, NL, HK, and TZ. designed the study, with input from RL, and LN. HK and RL. conducted the statistical analyses, under the supervision of NL. All authors contributed revisions to the paper and approved the final version. TZ and RL are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethis research did not receive funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is derived from a doctoral thesis in health and medical services management, approved by the Islamic Azad University, Science and Research Branch, Tehran (Ethical Code: IR.IAU.SRB.REC.1401.043). The study adheres to the ethical standards outlined in the Declaration of Helsinki. Prior to data collection, informed consent was obtained from all participants and formally documented using a mandatory Yes/No question.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHol WG, Verlinde CL. Non-communicable diseases. Insulin. 2006;106:107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKretchy IA, Asiedu-Danso M, Kretchy JP. Medication management and adherence during the COVID-19 pandemic: perspectives and experiences from low-and middle-income countries. Res Social Administrative Pharm. 2021;17(1):2023\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngelgau Michael M, Mahal Ajay. The economic impact of non-communicable diseases on households in India. Globalization health. 2012;8(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloom David E, Cafiero Elizabeth J-L, Eva A-G, Shafika BL, Reddy F, Sana, et al. The global economic burden of noncommunicable diseases. Program on the Global Demography of Aging; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiara GM, Malvika V, De Vries EGE, Ravindran K, Rosa G. Peters Solange. The ESMO Call to Action on COVID-19 vaccinations and patients with cancer: Vaccinate. Monitor. Educate Annals Oncol. 2021;32(5):579\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKluge Hans Henri P, Wickramasinghe Kremlin, Rippin Holly L, Mendes Romeu, Peters David H, Kontsevaya, Anna, et al. Prevention and control of non-communicable diseases in the COVID-19 response. Lancet. 2020;395(10238):1678\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMariotti Agnese. The effects of chronic stress on health: new insights into the molecular mechanisms of brain\u0026ndash;body communication. Future Sci OA. 2015;1(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelena L-Q, Nima A, Ying TY, Leung Gabriel M, Oshitani Hitoshi F, Keiji, et al. Are high-performing health systems resilient against the COVID-19 epidemic? Lancet. 2020;395(10227):848\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshaque SF, Hubert A. Seidu Abdul-Aziz, Bain Luchuo Engelbert. Health knowledge and care seeking behaviour in resource-limited settings amidst the COVID-19 pandemic: A qualitative study in Ghana. PLoS ONE. 2021;16(5):e0250940.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed Syed AK, Shifat A, Motunrayo A, Kehkashan B, Pauline C, Yen-Fu CN, Nayeem, et al. Impact of the societal response to COVID-19 on access to healthcare for non-COVID-19 health issues in slum communities of Bangladesh, Kenya, Nigeria and Pakistan: results of pre-COVID and COVID-19 lockdown stakeholder engagements. BMJ Global Health. 2020;5(8):e003042.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization World Health. Global status report on noncommunicable diseases 2014. World Health Organization; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThu WTS, Ilisapeci K, Amerita R, Wendy S, Mark DA, Paula V, et al. Baseline status of policy and legislation actions to address non communicable diseases crisis in the Pacific. BMC Public Health. 2020;20:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdam C, Worth Heather. Samoan measles crisis is diverting resources from non-communicable diseases. BMJ. 2020;368.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussain Ashraf M, Lafta Riyadh K. Burden of non-communicable diseases in Iraq after the 2003 war. Saudi Med J. 2019;40(1):72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakian A, Bakhtiari A, Ostovar A. Universal health coverage for strengthening prevention and control of noncommunicable diseases in COVID-19 era. Med J Islamic Repub Iran. 2020;34:153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoh JW, Kim KB, Jang HE, Heo MH, Kim YJ, Cha J. Non-Communicable Diseases and Transitioning Health System in the Democratic People's Republic of Korea during COVID-19 Lockdown. Healthc (Basel). 2022;10(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEzzati F, Mosadeghrad AM, Jaafaripooyan E. Resiliency of the Iranian healthcare facilities against the Covid-19 pandemic: challenges and solutions. BMC Health Serv Res. 2023;23(1):207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhalawi Z, Sutantri S. Chronic Management during Pandemic COVID-19 from the Perspective Primary Health Care Practitioners. JOSING: Journal of Nursing and Health; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonaco A, Casteig Blanco A, Cobain MR, Costa E, Guldemond NA, Hancock C. The role of collaborative, multistakeholder partnerships in reshaping the health management of patients with noncommunicable diseases during and after the COVID-19 pandemic. Aging Clin Exp Res. 2021;33:2899\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeykari N, Hashemi H, Dinarvand R, Haji-Aghajani M, Malekzadeh R, Sadrolsadat A. National action plan for non-communicable diseases prevention and control in Iran; a response to emerging epidemic. J Diabetes Metab Disord. 2017;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasiri T, Yazdani S, Shams L, Takian A. Stewardship of noncommunicable diseases in Iran: a qualitative study. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGouya MM, Seif-Farahi K, Hemmati P. An overview of Iran's actions in response to the COVID-19 pandemic and in building health system resilience. Front Public Health. 2023;11:1073259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026uuml;gh T, van Boven JFM, Wettermark B, Menditto E, Pinnock H, Tsiligianni I. A Cross-Sectional Survey on Medication Management Practices for Noncommunicable Diseases in Europe During the Second Wave of the COVID-19 Pandemic. Front Pharmacol. 2021;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkrami F, Riazi-Isfahani S, Mahdavi Hazaveh A, Ghanbari Motlagh A, Najmi M, Afkar M. Primary Health Care Model for Non-Communicable Diseases Management during COVID-19 Pandemic in the Islamic Republic of Iran. Med J Islamic Repub Iran. 2022;36:167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhotbi Tahere S, Javad KE, Allah. Ghelichi-Ghojogh Mousa. Self-management of patients with chronic diseases during COVID19: a narrative review. J Prev Med Hyg. 2021;62(4):E814. - E21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhotbi T, Salami J, Kalteh EA, Ghelichi-Ghojogh M. Self-management of patients with chronic diseases during COVID-19: a narrative review. J Prev Med Hyg. 2021;62(4):E814\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Mean Scores of the Dimensions of Chronic Disease Management During Pandemics\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimensions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeadership and Governance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth Financing System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth Workforce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedicines, Equipment, and Vaccines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth Information System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth Service Delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2. Extracted Factors and Their Designated Names\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"732\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain Dimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq01\u0026ndash;q03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExistence of National Strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLeadership and Governance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq04\u0026ndash;q06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntersectoral Collaboration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq07\u0026ndash;q09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdaptability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq10\u0026ndash;q13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAvailability of Required Financial Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eHealth Financing System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq14\u0026ndash;q16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResource Prioritization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq17\u0026ndash;q19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEfficiency and Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq20\u0026ndash;q23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorkforce Density and Distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHealth Workforce\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq24\u0026ndash;q26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompetence and Capability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq27\u0026ndash;q29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq30\u0026ndash;q32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWelfare and Support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq33\u0026ndash;q35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAvailability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMedicines, Equipment, and Medical Technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq36\u0026ndash;q39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuality and Safety\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq40\u0026ndash;q42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLegal Supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq43\u0026ndash;q45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData Integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHealth Information System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq46\u0026ndash;q49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTimeliness of Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq50\u0026ndash;q52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTelehealth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq53\u0026ndash;q55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData Privacy and Security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq56\u0026ndash;q59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAccess to Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHealth Service Delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eq60\u0026ndash;q65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEquity in Access\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Model Fit Indices\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFit Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcceptable Threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eObtained Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCMIN/DF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Noncommunicable Diseases, Pandemics, Health Services Administration, COVID-19, Iran","lastPublishedDoi":"10.21203/rs.3.rs-8241448/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8241448/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spread of pandemics can affect the management of chronic diseases from various perspectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this study is to examine the components influencing the management of noncommunicable disease services during pandemics and to propose a model for Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis applied study, which is descriptive–analytical and exploratory in nature, was conducted in 2024. The study population consisted of managers at different levels of the health system and experts involved in providing chronic disease services. Data were collected using a researcher-made questionnaire whose variables were derived from comparative studies of selected countries. The validity of the questionnaire was assessed through expert judgment, and its reliability was determined using the lambda coefficient in the exploratory section and sensitivity analysis in the confirmatory section. To obtain reliable results, the final questionnaire was distributed among 370 participants in the exploratory phase and 600 participants in the confirmatory phase. Stratified sampling was used, and data analysis was performed using factor-based tests, leading to the extraction of the final model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the final model, six main factors and nineteen subcomponents influencing the management of health services for chronic diseases during pandemics were identified. These included leadership and governance, financing, human resources, medicines, vaccines, products and required technologies, health information systems, and service delivery. Among these factors, financing had the greatest impact coefficient (1.025), while health information systems had the lowest impact coefficient (0.705) on the management of noncommunicable disease services during pandemics in Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the resulting model, various factors influence the management of health services for chronic diseases during pandemics. Success in crisis management requires strong leadership, effective coordination, investment in digital infrastructure, strengthening workforce resilience, and developing comprehensive policies for the concurrent management of crises and chronic diseases. Moreover, special attention to equity in access to services—particularly for vulnerable\u003c/p\u003e\n\u003cp\u003egroups—and reducing structural inequalities are essential requirements for improving the performance of health systems in the face of future crises.\u003c/p\u003e","manuscriptTitle":"Designing a Model for Non-communicable Diseases Management During Pandemics in Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:16:21","doi":"10.21203/rs.3.rs-8241448/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"42710559150535585682407243033533613232","date":"2026-01-23T10:01:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28093771049926240151854531929210788532","date":"2026-01-07T13:13:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T11:31:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T09:16:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-12T01:36:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T06:06:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-12-10T05:59:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54dd358a-4468-4370-bbc6-47da8e1ea566","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T06:16:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:16:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8241448","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8241448","identity":"rs-8241448","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}