Health Impacts of the ‘Iron Swords’ War on Evacuees Displaced from Their Homes in Israel

Health Impacts of the ‘Iron Swords’ War on Evacuees Displaced from Their Homes in Israel | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Health Impacts of the ‘Iron Swords’ War on Evacuees Displaced from Their Homes in Israel Carmil Azran, Cheli Melzer Cohen, Beatriz Hemo, Sara Kivity, Orpaz Hadad, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8467913/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Armed conflicts and large-scale population displacement pose major challenges to healthcare systems, even in high-income countries with universal health coverage. On October 7, 2023, the “Iron Swords” war led to the internal displacement of tens of thousands of civilians in Israel, creating a unique opportunity to examine continuity of preventive and chronic care during a prolonged national emergency. Objectives: To assess changes in preventive healthcare utilization, mental health medication use, and chronic disease management among populations evacuated from their homes during the Iron Swords war, and to compare trends between evacuees and non-evacuated district residents. Methods: This retrospective population-based study used de-identified electronic health record data from Maccabi Healthcare Services, Israel’s second-largest health maintenance organization. Adults aged ≥ 21 years with continuous enrollment prior to October 7, 2023, were included. Evacuees from northern and southern regions were compared with non-evacuated district residents across two post-war periods and corresponding pre-war periods. Outcomes included screening tests, incidence of chronic diseases, monitoring of chronic conditions, and dispensation of medications for mental health and sleep disorders. Results: Following the outbreak of war, evacuees experienced a marked decline in preventive screening, including diabetes, hypertension, and cancer screening, particularly during the first six months. Partial or full compensation occurred in some measures during later periods, but annual rates remained lower for breast and colorectal cancer screening. Use of antidepressant and anxiolytic medications increased substantially, especially among southern evacuees, with up to a 90% annual increase compared with pre-war levels. Modest worsening in glycemic and blood pressure control was observed in certain subgroups, while the incidence of major chronic diseases remained largely unchanged. Conclusions: Internal displacement during the ‘Iron Swords’ war was associated with substantial disruptions in preventive care and increased mental-health–related medication use among evacuees. Despite these challenges, Israel’s strong, integrated health system enabled rapid restoration of many services and supported continuity not only in chronic disease management but also in ongoing evaluation of preventive care patterns even during a national crisis. This capacity provides important insights for strengthening preparedness and response strategies and underscores the value of robust health information systems and coordinated primary care in managing large-scale civilian displacement. Health Policy displacement health policy preventive care mental health emergency preparedness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Major crises, including armed conflicts impose multifaceted challenges on public health systems, even in developed countries where continuity of care is expected to be maintained under crisis conditions. The recent Swords of Iron war, which began on October 7, 2023, led to the internal displacement of tens of thousands of civilians from northern and southern Israel. This large-scale evacuation offers a unique opportunity to examine how a developed healthcare system manages preventive and ongoing care during acute national emergencies. Forced displacement during armed conflicts disrupts healthcare accessibility, continuity of treatment, and preventive medicine. While extensive literature documents the mental and physical toll of displacement worldwide [1–4], less is known about how well-organized health systems in developed countries maintain continuity of care during such disruptions, particularly regarding chronic disease management, preventive medicine, medication adherence, and community-based support [5-7]. Empirical evidence from previous conflicts indicates that displaced populations face significant disruptions in healthcare access, continuity of treatment, and preventive services [6,10–12]. Studies have reported a deterioration in the management of non-communicable diseases such as diabetes and hypertension, partly due to medication interruptions and reduced follow-up with healthcare providers [13,14]. Beyond these medical aspects, displacement often leads to increased overall health risks and higher utilization of healthcare services, including emergency care, reflecting both acute stress responses and gaps in routine management [15-20]. In Israel, the National Health Insurance Law ensures universal health coverage, guaranteeing that every citizen is insured and entitled to continuous medical care through one of four national health funds (HMOs). This structure provides a unified and equitable foundation for both preventive and ongoing care, including during times of national crises. Following the outbreak of war, the healthcare system encountered unique challenges in supporting evacuees from two regions: residents of southern Israel who were directly exposed to extreme trauma and violence, while those from the north primarily faced prolonged threat and relocation due to ongoing security risks. Moreover, the duration and conditions of evacuation varied between the regions, southern evacuees were displaced rapidly and were usually settled as community, whereas in the north, the high-intensive combat initiated almost a year after, however, northern evacuees returned to their homes sooner. These differences provide a natural context for comparing health service utilization and preventive care patterns under varied stress and displacement conditions. Preliminary surveys conducted by Maccabi Healthcare Services (MHS), the second largest HMO in Israel, during the conflict indicated declines in self-perceived health and an increase in unmanaged chronic conditions and general distress, even among individuals who were not directly affected by hostilities [10]. Such findings underscore the broader systemic implications of prolonged emergencies on preventive health and care continuity in a developed healthcare environment. Given these considerations, this study aims to evaluate the management of physical and mental health, with an emphasis on preventive medicine, among populations evacuated during the Swords of Iron war. By identifying region-specific differences and health management challenges, the study seeks to inform future crisis preparedness, healthcare resource allocation, and policy development for developed countries facing large-scale internal displacement. Methods Study design and setting The study utilized de-identified data from the central computerized database of Maccabi Healthcare Services (MHS), the second largest state-mandated health provider in Israel, serving more than 2.8 million members. This population is considered representative of the broader Israeli population. The study included adults aged over 21 who were MHS members as of October 7, 2023, and had at least 12 months of continuous membership prior to that date. Five groups were defined for analysis in the northern and southern regions (separately): (a) Evacuees; (b) Other district residents (excluding individuals who were not evacuated or lived nearby to evacuated villages). Additionally, to evaluate overall trends, a fifth group was defined as the entire MHS population. Study periods In this study, two 6-month periods following the outbreak of the war were defined: November 1, 2023, to April 30, 2024, and May 1, 2024, to October 30, 2024. For comparison, two corresponding periods from the previous year were also analyzed: October 1, 2022, to March 30, 2023, and April 1, 2023, to September 30, 2023. The first three weeks following the outbreak were excluded to allow time for both community members and healthcare services to reorganize in their new locations. Additionally, the whole years before and after the outbreak were evaluated (Figure 1). Study variables definitions The dataset used in this study comprised individual-level demographic information and clinical data. Clinical data included anthropometric and laboratory assessments, procedural records identified via Current Procedural Terminology (CPT) codes, diagnostic information based on International Classification of Diseases, Ninth Revision (ICD-9) codes, and medication dispensation records classified according to Anatomical Therapeutic Chemical (ATC) codes. These data sources were integrated into automated registries developed by MHS to identify and monitor individuals with chronic and other conditions. Comorbidities were defined based on enrollment in the relevant MHS registries. Residential socioeconomic status (SES) was assessed using a 10-point scale, categorized into low (1–4), medium (5–7), and high (8–10) SES groups. This SES index was developed by Points Location Intelligence Ltd. and has been shown to correlate strongly with the SES classification provided by the Israeli Central Bureau of Statistics.[21] Baseline characteristics of the study population were assessed as of October 6, 2023, prior to the outbreak of the war. Endpoints definition The outcomes in this study were: (a) performance of screening tests among target population; (b) incidence of chronic diseases; (c) performance of check-up examinations among individuals with chronic disease; (d) dispensations of anti-depressants/ depression or insomnia. For each outcome, a target population was specified. All outcome definitions and target populations are summarized in Supplementary table S1. As the incidence rate of chronic diseases was low, only the annual periods were evaluated. Statistical analysis All study groups are described on October 6, 2023, prior to the outbreak of the war. Baseline variables were presented as mean (SD) for continuous variables with normal distribution or median [IQR] for continuous variables with skewed distribution. Categorical variables were presented as number of individuals and proportions. For each outcome, in the beginning of each study period, a target population was defined based on individual's age, sex and performance of examinations until then described in Supplementary table S1. In this study, we present the proportion of individuals who fulfill each outcome in each study group. We evaluated differences in trends between evacuees, and other residents who lived in that district between each follow-up. In dichotomic outcomes, as there were differences in the outcomes at baseline period between evacuees to other residents , we estimated differences in trends between two regions and follow-up period and its corresponding prior year by using logistic regression, adjusted for age, sex and socioeconomic status. For continuous variables, as no difference between groups was observed during both corresponding prior year periods, t-test was conducted between evacuees and other district residents (separately for northern and southern districts). P-value of <0.05 of difference in trends between groups was considered as statistically significant. Analyses were conducted using R version 4.4.1 or higher. Ethics The protocol was approved by MHS Institutional Review Board (IRB), and the study was conducted in accordance with the International Council for Harmonisation - Good Clinical Practice (ICH-GCP) and Declaration of Helsinki, Ethical Principles for Medical Research Involving Human Subjects. Due to the retrospective and anonymized nature of the data, the ethical committee approved a waiver of consent from the participants. Results As of October 7, 2023, a total of 1,761,354 individuals aged 21 and older were enrolled in MHS. Of these, 1,673,543 had been continuously enrolled for at least one year prior to the outbreak of war. Among them, 6,220 individuals resided in evacuated villages in the southern region, and 5,613 in evacuated villages in the northern region. Compared to other MHS members residing in the southern district, those evacuated were younger, with median age (interquartile range [IQR]) of 44 years [31,58] and 48 years [34,61] among evacuated and all other district members. Evacuated individuals from the northern district had a higher socioeconomic status compared to their regional counterparts, with 8.1% classified as low SES, compared to 21% among non-evacuated members from same district (Table 1). Overall, during the first six months following the outbreak of the war, there was generally a decline among evacuees compared to other district members in the rate of screening tests for diabetes, hypertension, breast cancer, colorectal cancer, and cervical cancer. However, in the subsequent six months, there was partial or full compensation in the performance of these screening tests (Figure 2). Among evacuees from the north, the rate of screening for diabetes prior to war was 37.3% and 39.7% in the two pre-war periods, respectively (compared to 38.4% and 37.9% in the control group). Following the outbreak of the war, the screening rate declined to 30.9% in the first post-war period but rose to 36.9% in the second period (compared to 36.7% and 38.0% in the control group; p-value<0.01; Figure 2A). When evaluating annual screening rates, evacuees from the south demonstrated full compensation, maintaining overall screening levels comparable to the pre-war period. In contrast, evacuees from the north experienced 11% reduction in annual screening rates, declining from 60.8% before the war to 54.3% afterwards (p value<0.001). Meanwhile, among other residents of the northern district who were not evacuated, a modest 3% decrease from 60.7% to 59.4% in diabetes screening rates was observed. Evacuees from the south demonstrated similar trends as the Evacuees from the north (Figure 3A) Similar trends were observed in screening for hypertension, however among southern evacuees the compensation was ever higher in the second post-war period, while among northern evacuees, the compensation was lower (Figure 2B, Figure 3B). In colorectal cancer screening, a decline was evident when examining annual rates following the outbreak of the war. Among northern evacuees, screening rates dropped significantly from 27.4% in the year prior to the war to 18.3% in the following year. In comparison, among other residents of the same district who were not evacuated, screening rates declined more moderately, for example, among north district residents, the performance of screening tests was slightly declined from 21.5% to 19.1% (Figure 3C). Among evacuees from both southern and northern regions, a significant decline in breast cancer screening performance was observed during the initial post-war outbreak period (Figure 2D). Screening rates decreased by 65.8% from 37.7% to 12.9% (p-value<0.001) among southern evacuees and from 26.0% to 14.9% among northern evacuees (p-value<0.01). In the subsequent post-outbreak period, a notable compensatory increase in screening activity was recorded. When analyzing annual screening rates, a significant reduction was again evident in both regions: from 44.1% to 28.9% in the south and from 42.0% to 22.6% in the north. This decline contrasts with the overall trend observed across all MHS, where annual screening performance increased from 29.3% to 34.1% (Figure 3D), highlighting the disproportionate impact on evacuee populations. Among evacuees from the south, prior to the war, the rate of cervical cancer screening was 12% in each period (compared to 12% and 13% in the control group). After the outbreak of the war, this rate dropped to 10% in the first period but increased to 18% in the second period (compared to 12% and 15% in the control group; Figure 2E). Alongside the decline in screening tests, particularly during the initial post-war outbreak period, a marked increase in the dispensation of anti-anxiety and depression for mental health conditions was observed among evacuees from the southern region. The proportion of individuals using these medications rose from 9% in the pre-war periods to 13% and 17% in the subsequent periods (Figure 2G). Notably, there was a 90% increase in the purchase of these medications in the year following the outbreak compared to the year prior from 11.5% to 21.0% (Figure 3G). This contrasts with only an 8% increase from 12.6% to 13.6% observed among other residents of the south district. In the northern region, the increase in the dispensation of anti-anxiety and depression medications was more moderate, with approximately a 5% rise among both evacuees and residents who remained in their homes. A similar, but more modest trend was observed in the purchase of medications for sleep disorders. Among evacuated southern residents, there was a 40% increase in the year following the war compared to the previous year (at least two purchases), while among other southern residents, the increase was only 9%. In the north, the increase was again more moderate- about 5% among evacuees and 7% among other residents (Figure 3H). Following the outbreak of war, there was a substantial decline in the evaluation and documentation of body mass index (BMI) across all MHS members. The annual rate of recorded BMI measurements dropped from 33.3% to 17.5%. This decline was even more pronounced among evacuees: among those from the southern region, the rate decreased from 39.3% to 19.8%, while among northern evacuees, it fell from 32.6% to 15.5% (Figure 3F). No significant changes were observed in the incidence rates of diabetes, atherosclerotic cardiovascular disease disease or cancer. A modest decline was noted in the incidence of hypertension, with annual rates decreasing from 24.2 to 13.4 per 10,000 members among southern evacuees, compared to smaller reduction from 16.7 to 14.0 per 10,000 members among the other southern (Figure 4). Among individuals with diabetes, the annual rates of HbA1c testing remained high stable across all MHS members. However, a slight but significant increase in HbA1c levels was observed among evacuees from south during the second post-war outbreak period, rising from a pre-war mean (SD) of 6.9% (1.3) before the war to 7.1% (1.6) (Figure 5E). Among individuals with hypertension, evacuated northern residents experienced a modest increase in both systolic and diastolic blood pressure during the initial post-war period. This was followed by a slight decrease in the second follow-up period (Figure 5F, G). Discussion This study provides novel insights into the short- and mid-term health consequences of prolonged internal displacement during the Swords of Iron war, with comprehensive real-world data from a large Israeli HMO. Consistent with prior research from other conflict settings, our findings demonstrate that forced evacuation is associated with disruptions in preventive healthcare, increased reliance on mental health medications, and potential deterioration in control of chronic conditions, despite stable incidence rates of major diseases (emphasized finding) [6,10,13,22,23]. The observed decline in screening activities, particularly for breast, colorectal, and diabetes-related tests, while partial recovery of screening rates was seen in the second post-war period—more evident in the south than in the north—annual data indicate incomplete compensation, especially for colorectal and breast cancer screening in both regions. These gaps raise concern for delayed diagnoses and poorer long-term outcomes However, compared with other conflict-affected regions, Israel’s developed, and highly organized healthcare system demonstrated notable resilience and adaptability. Despite facing unprecedented operational challenges, the HMOs which provide the mainly both primary and secondary care-maintained service continuity for most of their members. Additionally, health services were proactively relocated and expanded through mobile clinics and digital platforms, ensuring ongoing preventive and chronic care for displaced populations. The Israel organizational structure differs substantially from that of many other countries that have experienced war-related displacement, such as Syria, Ukraine, or Lebanon, where fragmented healthcare governance and uneven insurance coverage severely constrained the ability to sustain continuity of care [22–25]. Israel’s centralized health information systems and mandatory HMO membership enabled proactive population management and real-time identification of at-risk patients. Such capabilities likely mitigated the magnitude of preventable health deterioration observed during the conflict. Notably, the disproportionate mental health impact in the south, reflected in a 90% annual increase in anxiolytic and antidepressant use, aligns with literature showing that populations more directly exposed to combat or displacement-related uncertainty exhibit higher psychological morbidity [4,11,18,27]. The relatively smaller increase in the north may reflect differences in displacement patterns, baseline SES, or access to psychosocial services, which warrants further qualitative investigation [9,28]. There were differences observed between the northers and southern population in almost all parameters. Most significant decline was seen in the northern population, especially in the screening and utilization of healthcare services. This can be attributed to the fact that they stayed in the war zone longer until evacuated. Also, more of the healthcare attention was to the southern population due to the extreme events they encountered on October 7 th (which was prolonged due to their loss of family member, community member, direct encounter with traumatic events, and hostages from their community). This was also all evident in the sharp increase in use of mental health medications. In parallel, modest increases in HbA1c and blood pressure in certain subgroups suggest a disruption in routine chronic disease control and monitoring, while the marked decline in BMI documentation may be related to a lower prioritization of conducting the assessment, both from the clinician's and the patient's perspective. These findings echo prior work showing that even when acute medical services are available, preventive and follow-up care often deteriorates during prolonged emergencies [6,9,13,20,29]. Ensuring continuity of chronic disease management during crises remains a key challenge for health systems. Overall, our results underscore the dual burden of displacement: the direct disruption of healthcare access and the indirect effects mediated by psychological stress, lifestyle changes, and community disintegration. The findings have immediate policy implications for post-crisis recovery programs, emphasizing the need for targeted screening campaigns, mental health outreach, and proactive chronic disease follow-up among displaced populations [6,12,17,30] At the same time, strengthening similar infrastructures in other countries – particularly those with fragmented insurance systems – could enhance public health preparedness for future emergencies and improve outcomes among displaced populations. Limitations This study has several limitations. First, its retrospective observational design precludes establishing causality. Second, the analysis relied on EHR data, which may underrepresent health outcomes not captured within the HMO system (e.g., care received outside MHS). Third, mental health outcomes were inferred from medication dispensation, which does not capture non-pharmacologic interventions or undiagnosed conditions. Fourth, potential unmeasured confounders, such as individual trauma exposure or informal healthcare access, could influence results. Finally, the generalizability of findings may be limited to similar high-income countries with universal health coverage. Conclusions Prolonged internal displacement during the Swords of Iron war was associated with significant reduction in preventive healthcare, increased mental health medication use, and modest deterioration in chronic disease indicators, with differences between northern and southern evacuees. While some recovery occurred over time, substantial gaps remained after one year, underscoring the need for targeted recovery strategies. Strengthening healthcare continuity, prioritizing mental health support, use of telemedicine and medical technology, and implementing catch-up screening programs are essential to mitigate the long-term health impact of displacement during armed conflict. Similarly to the COVID-19 pandemic—during which Israel’s healthcare system was recognized globally as a model for population-wide vaccination and continuity of care—the current experience highlights that the ability to sustain coordinated, uninterrupted healthcare delivery is essential for resilience during crises, including environmental and wartime worldwide. Declarations Ethics approval: The study was approved by the Helsinki Committee of Maccabi Healthcare Services. Informed consent was waived due to the use of existing de-identified data. Competing interests: The authors declare that they have no competing interests. Orpaz Hadad contributed to this study while employed at Maccabi Healthcare Services. Her current employment in the pharmaceutical industry commenced after the completion of the study design and data analysis and had no influence on the study conduct, results, or interpretation. Funding: This study received no external funding. Availability of data and materials: The data are not publicly available due to privacy and confidentiality constraints but may be available from the corresponding author upon reasonable request and with appropriate approvals. Authors ’ contributions: Carmil Azran conceived the study. Avia Elmaliah and Orpaz Hadad designed the analytical approach and conducted the initial pilot. Cheli Melzer, Beatriz Hemo, and Naama Shamir-Stein contributed to the study design and performed the statistical analyses. Sara Kivity, together with all other authors, contributed to the interpretation of the results, drafting of the manuscript, and approved the final version. References Shvartsur R, Savitsky B. 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Abbreviations: ASCVD, Atherosclerotic cardiovascular disease; BMI, body mass index; MHS, Maccabi healthcare services. Characteristic All MHS N = 1,673,543 Southern evacuees N = 6,220 Southern residents N = 220,777 Northern evacuees N = 5,613 Northern residents N = 212,931 Age (years) 48 (34, 61) 44 (31, 58) 48 (34, 61) 48 (34, 59) 50 (35, 64) Females 877,380 (52%) 3,120 (50%) 115,951 (53%) 2,945 (52%) 113,170 (53%) Socioeconomic status Low 323,781 (19%) 1,569 (25%) 53,693 (24%) 457 (8.1%) 44,727 (21%) Medium 740,664 (44%) 3,862 (62%) 129,441 (59%) 3,521 (63%) 104,985 (49%) High 609,098 (36%) 789 (13%) 37,643 (17%) 1,635 (29%) 63,219 (30%) BMI (kg/m^2) 27.3 (24.0, 31.1) 28.1 (24.6, 32.4) 27.9 (24.6, 31.8) 27.6 (24.2, 31.5) 27.4 (24.2, 31.2) BMI- Missing 1,110,738 (66%) 3,755 (60%) 135,446 (61%) 3,758 (67%) 135,841 (64%) Diabetes 176,681 (11%) 703 (11%) 26,751 (12%) 677 (12%) 25,127 (12%) ASCVD 98,377 (5.9%) 383 (6.2%) 13,537 (6.1%) 371 (6.6%) 14,289 (6.7%) Heart failure 11,637 (0.7%) 46 (0.7%) 1,747 (0.8%) 34 (0.6%) 1,798 (0.8%) Atrial fibrillation 30,619 (1.8%) 104 (1.7%) 4,238 (1.9%) 110 (2.0%) 4,475 (2.1%) Hypertension 351,334 (21%) 1,465 (24%) 53,780 (24%) 1,296 (23%) 57,210 (27%) Cancer in the last 5 years 45,420 (2.7%) 136 (2.2%) 5,845 (2.6%) 159 (2.8%) 7,360 (3.5%) Currently smoker No 377,862 (23%) 1,680 (27%) 55,783 (25%) 1,028 (18%) 50,268 (24%) Yes 99,160 (5.9%) 724 (12%) 16,491 (7.5%) 495 (8.8%) 13,162 (6.2%) Missing 1,196,521 (71%) 3,816 (61%) 148,503 (67%) 4,090 (73%) 149,501 (70%) Additional Declarations The authors declare no competing interests. Supplementary Files SupplementarytableS1.odt List of study outcomes, target population and definition of the outcome. Abbreviations: CVD, cardiovascular disease; HPV, Human papillomavirus; PAP, Papanicolaou. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8467913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566497093,"identity":"592ef305-eff8-4c4d-8313-8ad9833879b1","order_by":0,"name":"Carmil 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06:28:19","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114435,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/a87be8046825d77666f51c60.html"},{"id":99581320,"identity":"7393ed4c-e486-4c1b-bae4-e4b58aa03499","added_by":"auto","created_at":"2026-01-06 06:28:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123723,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the study periods and the comparison method used. Each study period was compared to its corresponding baseline period one year earlier. Specifically, Period 1 was compared to Period -2, and Period 2 was compared to Period -1.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/021470506b8a5005a13a420e.png"},{"id":99581318,"identity":"b3646628-275a-45e7-9b85-333f14240744","added_by":"auto","created_at":"2026-01-06 06:28:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":289247,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of individuals who underwent screening examinations during two follow-up periods (0-6 months and 6-12 months) and corresponding baseline periods across study groups for the following conditions: (A) Diabetes; (B) Hypertension; (C) Colon cancer; (D) Breast cancer; (E) Cervical cancer; (F) Obesity. Additionally, proportion of individuals treated for: (G) Depression or anxiety; (H) Insomnia. The dashed vertical line indicates the war outbreak. Differences between evacuees and other district residents were assessed by comparing the follow-up period to the corresponding baseline period. Asterisks denote statistical significance of the observed difference: * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/99dbcde550d655b618f8f6e4.png"},{"id":99581328,"identity":"c4ceb0b4-c5e6-4c97-9a9b-287152081068","added_by":"auto","created_at":"2026-01-06 06:28:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":270283,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of individuals who underwent screening examinations during the annual follow-up period and corresponding baseline period across study groups for the following conditions: (A) Diabetes; (B) Hypertension; (C) Colon cancer; (D) Breast cancer; (E) Cervical cancer; (F) Obesity. Additionally, proportion of individuals treated for: (G) Depression or anxiety; (H) Insomnia. Differences between evacuees and other district residents were assessed by comparing the follow-up period to the corresponding baseline period. Asterisks denote statistical significance of the observed difference: * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/f903581d00784bac0e5a74c3.png"},{"id":99792948,"identity":"828a4288-556e-4b5a-9b85-ada58bc3b6ff","added_by":"auto","created_at":"2026-01-08 13:28:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":217683,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual incidence rates before and after the war outbreak across study groups for the following conditions: (A) Diabetes; (B) Hypertension; (C) Atherosclerosis cardiovascular disease; (D) Cancer. Differences between evacuees and other district residents were assessed by comparing the follow-up period to the corresponding baseline period. Asterisks denote statistical significance of the observed difference: * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/5ef5bfc1b674a96bf13a7130.png"},{"id":99792310,"identity":"6e94fba6-8786-42bd-9a3f-a5a287098cdd","added_by":"auto","created_at":"2026-01-08 13:17:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":383250,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of monitoring tests performed among individuals with diabetes or hypertension before and after war outbreak across study groups. For individuals with diabetes: proportion who underwent HbA1c testing every 6 months (A) and annually (C). Mean (SD) HbA1c values from the last evaluation withing 6-month time window (E) and annually (H). For individuals with hypertension: proportion with recorded blood pressure evaluation within 6 months (B) and annually (D). Mean (SD) values from the last systolic blood pressure evaluation within a 6-month time window (F) and annually (I). Mean (SD) values from the last diastolic blood pressure evaluation within a 6-month time window (G) and annually (J). The dashed vertical line indicates the war outbreak. Asterisks denote statistical significance of the observed difference: * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/49e9c99100f9d53002688e0e.png"},{"id":99804247,"identity":"911dbfba-d50d-4c65-9614-08dd2d168580","added_by":"auto","created_at":"2026-01-08 14:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1619042,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/6e1c94ef-8e7c-4031-bf26-1c418c004e8a.pdf"},{"id":99793348,"identity":"fdb6c073-0ddb-4170-af20-b60632d80e3d","added_by":"auto","created_at":"2026-01-08 13:31:27","extension":"odt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17827,"visible":true,"origin":"","legend":"\u003cp\u003eList of study outcomes, target population and definition of the outcome. Abbreviations: CVD, cardiovascular disease; HPV, Human papillomavirus; PAP, Papanicolaou.\u003c/p\u003e","description":"","filename":"SupplementarytableS1.odt","url":"https://assets-eu.researchsquare.com/files/rs-8467913/v1/a978ac14586fd082968cd69f.odt"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHealth Impacts of the ‘Iron Swords’ War on Evacuees Displaced from Their Homes in Israel\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor crises, including armed conflicts impose multifaceted challenges on public health systems, even in developed countries where continuity of care is expected to be maintained under crisis conditions. The recent Swords of Iron war, which began on October 7, 2023, led to the internal displacement of tens of thousands of civilians from northern and southern Israel. This large-scale evacuation offers a unique opportunity to examine how a developed healthcare system manages preventive and ongoing care during acute national emergencies.\u003c/p\u003e\n\u003cp\u003eForced displacement during armed conflicts disrupts healthcare accessibility, continuity of treatment, and preventive medicine. While extensive literature documents the mental and physical toll of displacement worldwide [1–4], less is known about how well-organized health systems in developed countries maintain continuity of care during such disruptions, particularly regarding chronic disease management, preventive medicine, medication adherence, and community-based support [5-7].\u003c/p\u003e\n\u003cp\u003eEmpirical evidence from previous conflicts indicates that displaced populations face significant disruptions in healthcare access, continuity of treatment, and preventive services [6,10–12]. Studies have reported a deterioration in the management of non-communicable diseases such as diabetes and hypertension, partly due to medication interruptions and reduced follow-up with healthcare providers [13,14]. Beyond these medical aspects, displacement often leads to increased overall health risks and higher utilization of healthcare services, including emergency care, reflecting both acute stress responses and gaps in routine management [15-20].\u003c/p\u003e\n\u003cp\u003eIn Israel, the National Health Insurance Law ensures universal health coverage, guaranteeing that every citizen is insured and entitled to continuous medical care through one of four national health funds (HMOs). This structure provides a unified and equitable foundation for both preventive and ongoing care, including during times of national crises. Following the outbreak of war, the healthcare system encountered unique challenges in supporting evacuees from two regions: residents of southern Israel who were directly exposed to extreme trauma and violence, while those from the north primarily faced prolonged threat and relocation due to ongoing security risks. Moreover, the duration and conditions of evacuation varied between the regions, southern evacuees were displaced rapidly and were usually settled as community, whereas in the north, the high-intensive combat initiated almost a year after, however, northern evacuees returned to their homes sooner. These differences provide a natural context for comparing health service utilization and preventive care patterns under varied stress and displacement conditions.\u003c/p\u003e\n\u003cp\u003ePreliminary surveys conducted by Maccabi Healthcare Services (MHS), the second largest HMO in Israel, during the conflict indicated declines in self-perceived health and an increase in unmanaged chronic conditions and general distress, even among individuals who were not directly affected by hostilities [10]. Such findings underscore the broader systemic implications of prolonged emergencies on preventive health and care continuity in a developed healthcare environment.\u003c/p\u003e\n\u003cp\u003eGiven these considerations, this study aims to evaluate the management of physical and mental health, with an emphasis on preventive medicine, among populations evacuated during the Swords of Iron war. By identifying region-specific differences and health management challenges, the study seeks to inform future crisis preparedness, healthcare resource allocation, and policy development for developed countries facing large-scale internal displacement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cu\u003eStudy design and setting\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilized de-identified data from the central computerized database of Maccabi Healthcare Services (MHS), the second largest state-mandated health provider in Israel, serving more than 2.8 million members. This population is considered representative of the broader Israeli population. The study included adults aged over 21 who were MHS members as of October 7, 2023, and had at least 12 months of continuous membership prior to that date. Five groups were defined for analysis in the northern and southern regions (separately): (a) Evacuees; (b) Other district residents (excluding individuals who were not evacuated or lived nearby to evacuated villages). Additionally, to evaluate overall trends, a fifth group was defined as the entire MHS population.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStudy periods\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, two 6-month periods following the outbreak of the war were defined: November 1, 2023, to April 30, 2024, and May 1, 2024, to October 30, 2024. For comparison, two corresponding periods from the previous year were also analyzed: October 1, 2022, to March 30, 2023, and April 1, 2023, to September 30, 2023. The first three weeks following the outbreak were excluded to allow time for both community members and healthcare services to reorganize in their new locations. Additionally, the whole years before and after the outbreak were evaluated (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStudy variables definitions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in this study comprised individual-level demographic information and clinical data. Clinical data included anthropometric and laboratory assessments, procedural records identified via Current Procedural Terminology (CPT) codes, diagnostic information based on International Classification of Diseases, Ninth Revision (ICD-9) codes, and medication dispensation records classified according to Anatomical Therapeutic Chemical (ATC) codes. These data sources were integrated into automated registries developed by MHS to identify and monitor individuals with chronic and other conditions. Comorbidities were defined based on enrollment in the relevant MHS registries. Residential socioeconomic status (SES) was assessed using a 10-point scale, categorized into low (1\u0026ndash;4), medium (5\u0026ndash;7), and high (8\u0026ndash;10) SES groups. This SES index was developed by Points Location Intelligence Ltd. and has been shown to correlate strongly with the SES classification provided by the Israeli Central Bureau of Statistics.[21]\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of the study population were assessed as of October 6, 2023, prior to the outbreak of the war.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEndpoints definition\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe outcomes in this study were: (a) performance of screening tests among target population; (b) incidence of chronic diseases; (c) performance of check-up examinations among individuals with chronic disease; (d) dispensations of anti-depressants/ depression or insomnia. For each outcome, a target population was specified. All outcome definitions and target populations are summarized in Supplementary table S1. As the incidence rate of chronic diseases was low, only the annual periods were evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStatistical analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll study groups are described on October 6, 2023, prior to the outbreak of the war. \u0026nbsp;Baseline variables were presented as mean (SD) for continuous variables with normal distribution or median [IQR] for continuous variables with skewed distribution. Categorical variables were presented as number of individuals and proportions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each outcome, in the beginning of each study period, a target population was defined based on individual\u0026apos;s age, sex and performance of examinations until then described in Supplementary table S1. \u0026nbsp;In this study, we present the proportion of individuals who fulfill each outcome in each study group. We evaluated differences in trends between evacuees, and other residents who lived in that district between each follow-up. In dichotomic outcomes, as there were differences in the outcomes at baseline period between evacuees to other residents , we estimated differences in trends between two regions and follow-up period and its corresponding prior year by using logistic regression, adjusted for age, sex and socioeconomic status. For continuous variables, as no difference between groups was observed during both corresponding prior year periods, t-test was conducted between evacuees and other district residents (separately for northern and southern districts).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP-value of \u0026lt;0.05 of difference in trends between groups was considered as statistically significant. Analyses were conducted using R version 4.4.1 or higher.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthics\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol was approved by MHS Institutional Review Board (IRB), and the study was conducted in accordance with the International Council for Harmonisation - Good Clinical Practice (ICH-GCP) and Declaration of Helsinki, Ethical Principles for Medical Research Involving Human Subjects. Due to the retrospective and anonymized nature of the data, the ethical committee approved a waiver of consent from the participants.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAs of October 7, 2023, a total of 1,761,354 individuals aged 21 and older were enrolled in MHS. Of these, 1,673,543 had been continuously enrolled for at least one year prior to the outbreak of war. Among them, 6,220 individuals resided in evacuated villages in the southern region, and 5,613 in evacuated villages in the northern region. Compared to other MHS members residing in the southern district, those evacuated were younger, with median age (interquartile range [IQR]) of 44 years [31,58] and 48 years [34,61] among evacuated and all other district members. Evacuated individuals from the northern district had a higher socioeconomic status compared to their regional counterparts, with 8.1% classified as low SES, compared to 21% among non-evacuated members from same district (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, during the first six months following the outbreak of the war, there was generally a decline among evacuees compared to other district members in the rate of screening tests for diabetes, hypertension, breast cancer, colorectal cancer, and cervical cancer. However, in the subsequent six months, there was partial or full compensation in the performance of these screening tests (Figure 2).\u003c/p\u003e\n\u003cp\u003eAmong evacuees from the north, the rate of screening for diabetes prior to war was 37.3% and 39.7% in the two pre-war periods, respectively (compared to 38.4% and 37.9% in the control group). Following the outbreak of the war, the screening rate declined to 30.9% in the first post-war period but rose to 36.9% in the second period (compared to 36.7% and 38.0% in the control group; p-value\u0026lt;0.01; Figure 2A). When evaluating annual screening rates, evacuees from the south demonstrated full compensation, maintaining overall screening levels comparable to the pre-war period. In contrast, evacuees from the north experienced 11% reduction in annual screening rates, declining from 60.8% before the war to 54.3% afterwards (p value\u0026lt;0.001). Meanwhile, among other residents of the northern district who were not evacuated, a modest 3% decrease from 60.7% to 59.4% in diabetes screening rates was observed. Evacuees from the south demonstrated similar trends as the Evacuees from the north (Figure 3A)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar trends were observed in screening for hypertension, however among southern evacuees the compensation was ever higher in the second post-war period, while among northern evacuees, the compensation was lower (Figure 2B, Figure 3B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn colorectal cancer screening, a decline was evident when examining annual rates following the outbreak of the war. Among northern evacuees, screening rates dropped significantly from 27.4% in the year prior to the war to 18.3% in the following year. In comparison, among other residents of the same district who were not evacuated, screening rates declined more moderately, for example, among north district residents, the performance of screening tests was slightly declined from 21.5% to 19.1% (Figure 3C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong evacuees from both southern and northern regions, a significant decline in breast cancer screening performance was observed during the initial post-war outbreak period (Figure 2D). Screening rates decreased by 65.8% from 37.7% to 12.9% (p-value\u0026lt;0.001) among southern evacuees and from 26.0% to 14.9% among northern evacuees (p-value\u0026lt;0.01). In the subsequent post-outbreak period, a notable compensatory increase in screening activity was recorded. When analyzing annual screening rates, a significant reduction was again evident in both regions: from 44.1% to 28.9% in the south and from 42.0% to 22.6% in the north. This decline contrasts with the overall trend observed across all MHS, where annual screening performance increased from 29.3% to 34.1% (Figure 3D), highlighting the disproportionate impact on evacuee populations.\u003c/p\u003e\n\u003cp\u003eAmong evacuees from the south, prior to the war, the rate of cervical cancer screening was 12% in each period (compared to 12% and 13% in the control group). After the outbreak of the war, this rate dropped to 10% in the first period but increased to 18% in the second period (compared to 12% and 15% in the control group; Figure 2E).\u003c/p\u003e\n\u003cp\u003eAlongside the decline in screening tests, particularly during the initial post-war outbreak period, a marked increase in the dispensation of anti-anxiety and depression for mental health conditions was observed among evacuees from the southern region. The proportion of individuals using these medications rose from 9% in the pre-war periods to 13% and 17% in the subsequent periods (Figure 2G). Notably, there was a 90% increase in the purchase of these medications in the year following the outbreak compared to the year prior from 11.5% to 21.0% (Figure 3G). This contrasts with only an 8% increase from 12.6% to 13.6% observed among other residents of the south district. In the northern region, the increase in the dispensation of anti-anxiety and depression medications was more moderate, with approximately a 5% rise among both evacuees and residents who remained in their homes.\u003c/p\u003e\n\u003cp\u003eA similar, but more modest trend was observed in the purchase of medications for sleep disorders. Among evacuated southern residents, there was a 40% increase in the year following the war compared to the previous year (at least two purchases), while among other southern residents, the increase was only 9%. In the north, the increase was again more moderate- about 5% among evacuees and 7% among other residents (Figure 3H).\u003c/p\u003e\n\u003cp\u003eFollowing the outbreak of war, there was a substantial decline in the evaluation and documentation of body mass index (BMI) across all MHS members. The annual rate of recorded BMI measurements dropped from 33.3% to 17.5%. This decline was even more pronounced among evacuees: among those from the southern region, the rate decreased from 39.3% to 19.8%, while among northern evacuees, it fell from 32.6% to 15.5% (Figure 3F).\u003c/p\u003e\n\u003cp\u003eNo significant changes were observed in the incidence rates of diabetes, atherosclerotic cardiovascular disease disease or cancer. A modest decline was noted in the incidence of hypertension, with annual rates decreasing from 24.2 to 13.4 per 10,000 members among southern evacuees, compared to smaller reduction from 16.7 to 14.0 per 10,000 members among the other southern (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong individuals with diabetes, the annual rates of HbA1c testing remained high stable across all MHS members. However, a slight but significant increase in HbA1c levels was observed among evacuees from south during the second post-war outbreak period, rising from a pre-war mean (SD) of 6.9% (1.3) before the war to 7.1% (1.6) (Figure 5E).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong individuals with hypertension, evacuated northern residents experienced a modest increase in both systolic and diastolic blood pressure during the initial post-war period. This was followed by a slight decrease in the second follow-up period (Figure 5F, G). \u0026nbsp; \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides novel insights into the short- and mid-term health consequences of prolonged internal displacement during the Swords of Iron war, with comprehensive real-world data from a large Israeli HMO. Consistent with prior research from other conflict settings, our findings demonstrate that forced evacuation is associated with disruptions in preventive healthcare, increased reliance on mental health medications, and potential deterioration in control of chronic conditions, despite stable incidence rates of major diseases (emphasized finding) [6,10,13,22,23].\u003c/p\u003e\n\u003cp\u003eThe observed decline in screening activities, particularly for breast, colorectal, and diabetes-related tests, while partial recovery of screening rates was seen in the second post-war period—more evident in the south than in the north—annual data indicate incomplete compensation, especially for colorectal and breast cancer screening in both regions. These gaps raise concern for delayed diagnoses and poorer long-term outcomes\u003cbr\u003e\u0026nbsp;However, compared with other conflict-affected regions, Israel’s developed, and highly organized healthcare system demonstrated notable resilience and adaptability. Despite facing unprecedented operational challenges, the HMOs which provide the mainly both primary and secondary care-maintained service continuity for most of their members.\u0026nbsp;\u003cbr\u003e\u0026nbsp;Additionally, health services were proactively relocated and expanded through mobile clinics and digital platforms, ensuring ongoing preventive and chronic care for displaced populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Israel organizational structure differs substantially from that of many other countries that have experienced war-related displacement, such as Syria, Ukraine, or Lebanon, where fragmented healthcare governance and uneven insurance coverage severely constrained the ability to sustain continuity of care [22–25].\u003cbr\u003e\u0026nbsp;Israel’s centralized health information systems and mandatory HMO membership enabled proactive population management and real-time identification of at-risk patients. Such capabilities likely mitigated the magnitude of preventable health deterioration observed during the conflict.\u003c/p\u003e\n\u003cp\u003eNotably, the disproportionate mental health impact in the south, reflected in a 90% annual increase in anxiolytic and antidepressant use, aligns with literature showing that populations more directly exposed to combat or displacement-related uncertainty exhibit higher psychological morbidity [4,11,18,27]. The relatively smaller increase in the north may reflect differences in displacement patterns, baseline SES, or access to psychosocial services, which warrants further qualitative investigation [9,28].\u003c/p\u003e\n\u003cp\u003eThere were differences observed between the northers and southern population in almost all parameters. Most significant decline was seen in the northern population, especially in the screening and utilization of healthcare services. This can be attributed to the fact that they stayed in the war zone longer until evacuated. Also, more of the healthcare attention was to the southern population due to the extreme events they encountered on October 7\u003csup\u003eth\u003c/sup\u003e (which was prolonged due to their loss of family member, community member, direct encounter with traumatic events, and hostages from their community). \u0026nbsp;This was also all evident in the sharp increase in use of mental health medications. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn parallel, modest increases in HbA1c and blood pressure in certain subgroups suggest a disruption in routine chronic disease control and monitoring, while the marked decline in BMI documentation may be related to a lower prioritization of conducting the assessment, both from the clinician's and the patient's perspective. These findings echo prior work showing that even when acute medical services are available, preventive and follow-up care often deteriorates during prolonged emergencies [6,9,13,20,29]. Ensuring continuity of chronic disease management during crises remains a key challenge for health systems.\u003c/p\u003e\n\u003cp\u003eOverall, our results underscore the dual burden of displacement: the direct disruption of healthcare access and the indirect effects mediated by psychological stress, lifestyle changes, and community disintegration. The findings have immediate policy implications for post-crisis recovery programs, emphasizing the need for targeted screening campaigns, mental health outreach, and proactive chronic disease follow-up among displaced populations [6,12,17,30]\u003cbr\u003e\u0026nbsp;At the same time, strengthening similar infrastructures in other countries\u0026nbsp;–\u0026nbsp;particularly those with fragmented insurance systems\u0026nbsp;– could enhance public health preparedness for future emergencies and improve outcomes among displaced populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, its retrospective observational design precludes establishing causality. Second, the analysis relied on EHR data, which may underrepresent health outcomes not captured within the HMO system (e.g., care received outside MHS). Third, mental health outcomes were inferred from medication dispensation, which does not capture non-pharmacologic interventions or undiagnosed conditions. Fourth, potential unmeasured confounders, such as individual trauma exposure or informal healthcare access, could influence results. Finally, the generalizability of findings may be limited to similar high-income countries with universal health coverage.\u003c/p\u003e"},{"header":"Conclusions ","content":"\u003cp\u003eProlonged internal displacement during the Swords of Iron war was associated with significant reduction in preventive healthcare, increased mental health medication use, and modest deterioration in chronic disease indicators, with differences between northern and southern evacuees. While some recovery occurred over time, substantial gaps remained after one year, underscoring the need for targeted recovery strategies. Strengthening healthcare continuity, prioritizing mental health support, use of telemedicine and medical technology, and implementing catch-up screening programs are essential to mitigate the long-term health impact of displacement during armed conflict.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eSimilarly to the COVID-19 pandemic—during which Israel’s healthcare system was recognized globally as a model for population-wide vaccination and continuity of care—the current experience highlights that the ability to sustain coordinated, uninterrupted healthcare delivery is essential for resilience during crises, including environmental and wartime worldwide.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Helsinki Committee of Maccabi Healthcare Services. Informed consent was waived due to the use of existing de-identified data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003cbr\u003e\u0026nbsp;Orpaz Hadad contributed to this study while employed at Maccabi Healthcare Services. Her current employment in the pharmaceutical industry commenced after the completion of the study design and data analysis and had no influence on the study conduct, results, or interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study received no external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The data are not publicly available due to privacy and confidentiality constraints but may be available from the corresponding author upon reasonable request and with appropriate approvals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e’\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtributions:\u003cbr\u003e\u003c/strong\u003eCarmil Azran conceived the study. Avia Elmaliah and Orpaz Hadad designed the analytical approach and conducted the initial pilot. Cheli Melzer, Beatriz Hemo, and Naama Shamir-Stein contributed to the study design and performed the statistical analyses. Sara Kivity, together with all other authors, contributed to the interpretation of the results, drafting of the manuscript, and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eShvartsur R, Savitsky B. Civilians under missile attack: post-traumatic stress disorder among the Jewish and Bedouin population of Southern Israel. Israel Journal of Health Policy Research 2024;13. https://doi.org/10.1186/s13584-024-00625-9.\u003c/li\u003e\n \u003cli\u003eAmsalem D, Haim‐Nachum S, Lazarov A, Levi‐Belz Y, Markowitz JC, Bergman M, et al. The effects of war-related experiences on mental health symptoms of individuals living in conflict zones: a longitudinal study. 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International Journal of Environmental Research and Public Health 2022;19:5661. https://doi.org/10.3390/ijerph19095661.\u003c/li\u003e\n \u003cli\u003eHananel R, Fishman R, Malovicki-Yaffe N. Urban diversity and epidemic resilience: The case of the COVID-19. Cities. 2022 Mar;122:103526. doi: 10.1016/j.cities.2021.103526.\u003c/li\u003e\n \u003cli\u003eKhader YS, Farah A, Shahin Y, Alfaqih MA, Burgan S, Al-Azzam S, et al. Impact of the Syrian conflict on health system performance in Jordan: a secondary analysis. \u003cem\u003eEast Mediterr Health J\u003c/em\u003e. 2020;26(8):912-919. doi:10.26719/emhj.20.071.\u003c/li\u003e\n \u003cli\u003eBlanchet K, Fouad FM, Pherali T. Syrian refugees in Lebanon: the search for universal health coverage. \u003cem\u003eConfl Health\u003c/em\u003e. 2016;10:12. doi:10.1186/s13031-016-0079-4.\u003c/li\u003e\n \u003cli\u003eDoocy S, Lyles E, Akhu-Zaheya L, Oweis A, Burnham G. Health service access and utilization among Syrian refugees in Jordan. \u003cem\u003eInt J Equity Health\u003c/em\u003e. 2016;15:108. doi:10.1186/s12939-016-0399-4.\u003c/li\u003e\n \u003cli\u003eRoberts B, Patel P, McKee M. Noncommunicable diseases and post-conflict countries. \u003cem\u003eBull World Health Organ\u003c/em\u003e. 2012;90(1):2\u0026ndash;2A. doi:10.2471/BLT.11.098863.\u003c/li\u003e\n \u003cli\u003eSwei A, Karah N, Osman D, Alhalabi Y, Alsamman M, Mohammad HA, et al. Breast cancer screening in displaced Syrian women: barriers and opportunities. \u003cem\u003eBMC Womens Health\u003c/em\u003e. 2022;22:318. doi:10.1186/s12905-022-01914-4.\u003c/li\u003e\n \u003cli\u003eSilove D, Ventevogel P, Rees S. The contemporary refugee crisis: an overview of mental health challenges. \u003cem\u003eWorld Psychiatry\u003c/em\u003e. 2017;16(2):130-139. doi:10.1002/wps.20438.\u003c/li\u003e\n \u003cli\u003eTurrini G, Purgato M, Ballette F, Nos\u0026egrave; M, Ostuzzi G, Barbui C. Common mental disorders in asylum seekers and refugees: umbrella review of prevalence and intervention studies. \u003cem\u003eInt J Ment Health Syst\u003c/em\u003e. 2017;11:51. doi:10.1186/s13033-017-0156-0.\u003c/li\u003e\n \u003cli\u003eWatson JT, Gayer M, Connolly MA. Epidemics after natural disasters. \u003cem\u003eEmerg Infect Dis\u003c/em\u003e. 2007;13(1):1\u0026ndash;5. doi:10.3201/eid1301.060779.\u003c/li\u003e\n \u003cli\u003eAbbara A, Blanchet K, Sahloul Z, Fouad FM, Maziak W, Coutts A. The health of internally displaced people in Syria: a call to action. \u003cem\u003eLancet\u003c/em\u003e. 2020;396(10247):458\u0026ndash;460. doi:10.1016/S0140-6736(20)31567-0.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1: Baseline characteristics of study groups included. Abbreviations: ASCVD, Atherosclerotic cardiovascular disease; BMI, body mass index; MHS, Maccabi healthcare services.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"931\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll MHS\u003c/strong\u003e \u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 1,673,543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouthern evacuees\u003c/strong\u003e \u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 6,220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouthern residents\u003c/strong\u003e \u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 220,777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorthern evacuees\u003c/strong\u003e \u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 5,613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorthern residents \u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;N = 212,931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e48 (34, 61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e44 (31, 58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e48 (34, 61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e48 (34, 59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e50 (35, 64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e877,380 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3,120 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e115,951 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2,945 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e113,170 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSocioeconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e323,781 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1,569 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e53,693 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e457 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e44,727 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e740,664 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3,862 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e129,441 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3,521 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e104,985 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e609,098 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e789 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e37,643 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1,635 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e63,219 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eBMI (kg/m^2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e27.3 (24.0, 31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e28.1 (24.6, 32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27.9 (24.6, 31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e27.6 (24.2, 31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e27.4 (24.2, 31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eBMI- Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1,110,738 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3,755 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e135,446 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3,758 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e135,841 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e176,681 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e703 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e26,751 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e677 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25,127 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eASCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e98,377 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e383 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13,537 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e371 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e14,289 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e11,637 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e46 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1,747 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e34 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1,798 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e30,619 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e104 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4,238 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e110 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4,475 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e351,334 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1,465 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e53,780 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1,296 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e57,210 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCancer in the last 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e45,420 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e136 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5,845 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e159 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7,360 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCurrently smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e377,862 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1,680 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e55,783 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1,028 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e50,268 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e99,160 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e724 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e16,491 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e495 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e13,162 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1,196,521 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3,816 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e148,503 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e4,090 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e149,501 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Maccabi Health Care Services","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"displacement, health policy, preventive care, mental health, emergency preparedness","lastPublishedDoi":"10.21203/rs.3.rs-8467913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8467913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eArmed conflicts and large-scale population displacement pose major challenges to healthcare systems, even in high-income countries with universal health coverage. On October 7, 2023, the \u0026ldquo;Iron Swords\u0026rdquo; war led to the internal displacement of tens of thousands of civilians in Israel, creating a unique opportunity to examine continuity of preventive and chronic care during a prolonged national emergency.\u003c/p\u003e\u003ch2\u003eObjectives:\u003c/h2\u003e \u003cp\u003eTo assess changes in preventive healthcare utilization, mental health medication use, and chronic disease management among populations evacuated from their homes during the Iron Swords war, and to compare trends between evacuees and non-evacuated district residents.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis retrospective population-based study used de-identified electronic health record data from Maccabi Healthcare Services, Israel\u0026rsquo;s second-largest health maintenance organization. Adults aged\u0026thinsp;\u0026ge;\u0026thinsp;21 years with continuous enrollment prior to October 7, 2023, were included. Evacuees from northern and southern regions were compared with non-evacuated district residents across two post-war periods and corresponding pre-war periods. Outcomes included screening tests, incidence of chronic diseases, monitoring of chronic conditions, and dispensation of medications for mental health and sleep disorders.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eFollowing the outbreak of war, evacuees experienced a marked decline in preventive screening, including diabetes, hypertension, and cancer screening, particularly during the first six months. Partial or full compensation occurred in some measures during later periods, but annual rates remained lower for breast and colorectal cancer screening. Use of antidepressant and anxiolytic medications increased substantially, especially among southern evacuees, with up to a 90% annual increase compared with pre-war levels. Modest worsening in glycemic and blood pressure control was observed in certain subgroups, while the incidence of major chronic diseases remained largely unchanged.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eInternal displacement during the \u0026lsquo;Iron Swords\u0026rsquo; war was associated with substantial disruptions in preventive care and increased mental-health\u0026ndash;related medication use among evacuees. Despite these challenges, Israel\u0026rsquo;s strong, integrated health system enabled rapid restoration of many services and supported continuity not only in chronic disease management but also in ongoing evaluation of preventive care patterns even during a national crisis. This capacity provides important insights for strengthening preparedness and response strategies and underscores the value of robust health information systems and coordinated primary care in managing large-scale civilian displacement.\u003c/p\u003e","manuscriptTitle":"Health Impacts of the ‘Iron Swords’ War on Evacuees Displaced from Their Homes in Israel","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 06:28:10","doi":"10.21203/rs.3.rs-8467913/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46199933-c1e0-453f-ac20-1b233290cb5b","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60298335,"name":"Health Policy"}],"tags":[],"updatedAt":"2026-01-06T06:28:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 06:28:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8467913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8467913","identity":"rs-8467913","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}