Disrupted Care, Enduring Lessons: Health Systems Under Crisis in the Philippines, Zimbabwe, South Korea, and Israel A Mixed-Methods Comparative Review of Service Disruptions and Policy Resilience

Disrupted Care, Enduring Lessons: Health Systems Under Crisis in the Philippines, Zimbabwe, South Korea, and Israel A Mixed-Methods Comparative Review of Service Disruptions and Policy Resilience | 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 Systematic Review Disrupted Care, Enduring Lessons: Health Systems Under Crisis in the Philippines, Zimbabwe, South Korea, and Israel A Mixed-Methods Comparative Review of Service Disruptions and Policy Resilience Dr. Fernan Torreno, Famiela Torreno, LPT, MAEd This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7769361/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: Health systems remain acutely vulnerable to sudden shocks from natural disasters, epidemics, and armed conflict. Comparative cross-country evidence bridging low- and high-income contexts is limited. This study synthesizes quantitative and qualitative data from the Philippines (Typhoon Haiyan), Zimbabwe (cholera epidemics), South Korea (MERS outbreak), and Israel (armed conflicts, 2014–2023) to examine health service disruptions and resilience strategies. Methods: We conducted a mixed-methods systematic review of 40 peer-reviewed studies. Quantitative outcomes included service utilization (hospital admissions, outpatient visits, maternal and child health, infectious disease trends) and mortality. Qualitative and policy-focused studies were integrated via thematic analysis. Data sources included hospital records, national surveillance, insurance claims, syndromic surveillance (SPEED), and facility-level surveys. Results: Across settings, crises precipitated sharp, immediate declines in service utilization. In the Philippines, hospital admissions decreased significantly post-Haiyan, with obstetric care reduced (OR 0.4, 95% CI 0.3–0.6) and infectious/respiratory consultations increased【1–2】. Syndromic surveillance confirmed spikes in communicable disease visits and reduced non-communicable consultations【2】. Zimbabwe’s 2008–09 cholera epidemic caused 98,585 cases and 4,287 deaths (CFR 4.3%)【3】, with subsequent outbreaks sustaining high CFRs【4】. South Korea’s 2015 MERS epidemic reduced outpatient visits by ~ 17.2%【5】 and triggered healthcare avoidance in 34.5% of the population【6】. In Israel, ED visits declined by 13% during the 2014 Gaza conflict, while admissions rose 10% and 30-day mortality increased (OR 1.42, 95% CI 1.18–1.70)【7】. Evidence from Syria【8】, Yemen【9】, Ukraine【10】, and Ebola-affected West Africa【11】 revealed parallel disruptions in maternal, infectious, and chronic disease services. Qualitative studies consistently highlighted infrastructure damage, staff attrition, supply chain breakdowns, and disproportionate effects on vulnerable populations【12–13】. Conclusions: Despite contextual differences, common patterns emerge: abrupt disruption, delayed recovery, and disproportionate burdens on maternal, child, and chronic disease services. Policy lessons include syndromic surveillance (Philippines)【2】, adaptive telehealth (Israel)【7】, and resilient PHC networks (Zimbabwe, Yemen)【3,9】. Cross-crisis learning can inform global frameworks to strengthen health system resilience. Nursing City Management and Urban Policy Health system resilience service disruption disasters epidemics armed conflict Philippines Zimbabwe South Korea Israel Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Health systems are increasingly confronted by compound shocks—ranging from natural disasters and epidemics to armed conflicts—that test their capacity to deliver essential services and maintain continuity of care. Such crises often expose structural weaknesses and exacerbate inequities, producing measurable declines in health service utilization, delayed recovery, and avoidable excess mortality (1–3). In recent years, global health discourse has shifted from short-term emergency response toward the concept of health system resilience, defined as the ability to absorb, adapt, and transform in the face of acute shocks while sustaining core functions (4,5). The Philippines, classified as one of the world’s most disaster-prone countries, provides a critical case for studying health system performance under extreme climatic events. Following Typhoon Haiyan in 2013, hospitals in Eastern Visayas reported a sharp drop in obstetric admissions (OR 0.4, 95% CI 0.3–0.6), alongside surges in infectious and respiratory cases, while syndromic surveillance documented spikes in acute respiratory infection and diarrheal diseases (6,7). In Zimbabwe, the collapse of water and sanitation infrastructure during the 2008–09 cholera epidemic resulted in nearly 100,000 cases and more than 4,000 deaths (8). Recurring outbreaks—including the 2018 Chegutu cluster—illustrate how fragile systems perpetuate recurrent vulnerabilities despite global attention (9). The experience of South Korea during the 2015 Middle East Respiratory Syndrome (MERS) outbreak highlights challenges faced even by high-income settings. National insurance claims revealed a 17% decline in outpatient visits in the first two months, driven by widespread public avoidance of hospitals (10,11). Similarly, Israel, long exposed to armed conflict, provides insight into service disruptions in high-resource but high-risk environments. During the 2014 Gaza conflict, emergency department visits fell by 13%, yet hospital admissions paradoxically rose by 10%, with increased 30-day mortality (OR 1.42, 95% CI 1.18–1.70) (12). More recently, the 2023 Israel–Hamas war further strained specialty services, primary care, and national EMS systems (13). Comparative evidence from other crises—including the Syrian conflict, the war in Ukraine, health system collapse in Yemen, the Ebola epidemic in West Africa, and the Great East Japan Earthquake—reinforces these findings, underscoring recurring patterns of disrupted maternal care, chronic disease management, and communicable disease control (14–19). Despite diverse contexts, a common thread emerges: crises precipitate abrupt service disruption, disproportionately affect vulnerable populations, and expose gaps in governance, workforce capacity, and supply chains. Yet comparative analyses across low- and high-income settings remain scarce. Objectives: This study addresses this gap by conducting a mixed-methods comparative review of 40 peer-reviewed studies from four focal countries—Philippines, Zimbabwe, South Korea, and Israel—supplemented by evidence from other conflict and disaster settings. We aimed to: 1. Quantify service disruptions during crises (admissions, outpatient visits, maternal and child health, infectious and chronic diseases). 2. Identify recurrent structural and operational challenges. 3. Derive policy lessons for building resilient health systems that are adaptable to both conflict and disaster contexts. Methods Study Design We conducted a mixed-methods systematic review and evidence synthesis, integrating quantitative outcomes with qualitative and policy-oriented analyses. The review followed PRISMA 2020 guidelines for systematic reviews and meta-analyses (1) and adhered to the methodological standards of the Joanna Briggs Institute (JBI) for mixed-methods reviews (2). Eligibility Criteria We included peer-reviewed studies published between 2000 and 2025 that reported on: 1. Quantitative outcomes of health service disruption (e.g., hospital admissions, outpatient visits, maternal and child health utilization, infectious disease surveillance, mortality). 2. Qualitative studies or mixed-methods research describing challenges to health system functioning, resilience strategies, or policy adaptations. 3. Studies focusing on the Philippines (Typhoon Haiyan, 2013), Zimbabwe (cholera outbreaks, 2008–2024), South Korea (MERS, 2015), and Israel (conflict episodes, 2014–2023) were prioritized, with comparator evidence drawn from Syria, Yemen, Ukraine, West Africa (Ebola), and Japan (GEJE). Grey literature and non-peer-reviewed reports were excluded to maintain rigor. Information Sources and Search Strategy We systematically searched PubMed/MEDLINE, Scopus, Web of Science, and WHO Global Index Medicus up to September 2025. The search combined MeSH terms and keywords relating to “health system disruption”, “resilience”, “crisis”, “conflict”, “disaster”, and the four target countries. Example search string (PubMed): (Philippines OR Zimbabwe OR "South Korea" OR Israel) AND ("health services" OR "hospital admissions" OR "service disruption") AND (disaster OR epidemic OR conflict OR crisis) Reference lists of included studies were hand-searched to capture additional articles (3). Study Selection Titles and abstracts were screened independently by two reviewers. Full texts of potentially eligible studies were assessed against inclusion criteria. Discrepancies were resolved by consensus, with arbitration by a third reviewer when necessary. The selection process is summarized in Figure 1 (PRISMA flow diagram). Data Extraction A standardized matrix was used to extract data on: study ID, country, crisis type, period, unit of analysis, outcome measured, effect size (with 95% CI where reported), data source, and risk of bias. This framework is summarized in Table 1 (Characteristics of Included Studies). Table 1. Characteristics of Included Studies (n = 40) Study ID Country Crisis Type Period Unit of Analysis Data Source Outcomes Measured Risk of Bias 1 Philippines Typhoon Haiyan 2013–2014 Hospital admissions Hospital records Obstetric, infectious admissions Low 2 Philippines Typhoon Haiyan 2013–2014 Post-disaster surveillance SPEED Communicable vs non-communicable consultations Low 3 Philippines Typhoon Haiyan 2013–2014 Surveillance/continuity SPEED Infrastructure, continuity of care Moderate 4 Philippines Typhoon Haiyan 2013–2014 Surgical caseload Foreign medical team data Emergency surgeries, delays Moderate 5 Zimbabwe Cholera epidemic 2008–2009 National surveillance MOH/WHO Case counts, CFR (4.3%) Low 6 Zimbabwe Cholera epidemic 2008–2019 National surveillance WHO reports Cholera trends Moderate 7 Zimbabwe Cholera outbreak 2018 District surveillance MMWR report Case clusters Low 8 Zimbabwe Cholera outbreak 2023–2024 National surveillance WHO Nationwide cholera CFR Moderate 9 South Korea MERS epidemic 2015 Emergency care Hospital data ER utilization, mortality Low 10 South Korea MERS epidemic 2015 Health care utilization Hospital/insurance data Utilization trends Low 11 South Korea MERS epidemic 2015 Hospital avoidance Survey/economic data Avoidance behavior Low 12 South Korea MERS epidemic 2015 Hospital avoidance Surveys Avoidance rates (34.5%) Low 13 South Korea MERS epidemic 2015 Epidemiology Epidemiologic reports Hospital-to-hospital spread Low 14 Israel Gaza conflict 2014 ED admissions Hospital data ED visits, mortality Low 15 Israel Conflict 2021–2023 Outpatient clinics Hospital data Clinic disruptions Moderate 16 Israel Conflict 2023 Hospital surge National registry Mass casualty response Low 17 Israel Conflict 2023 Emergency operations Near-front hospitals Operational performance Moderate 18 Israel Conflict 2023 EMS response National EMS/MDA EMS activity patterns Low 19 Syria Armed conflict 2011–2020 Health facilities Surveillance Attacks on facilities, access High 20 Syria Armed conflict 2011–2020 Hospitals Local registries Cascading impacts on health High 21 Ukraine Armed conflict 2022–2023 Hospitals National data Hospital functioning Moderate 22 Ukraine Armed conflict 2022–2023 Hospital services Health records Service disruptions Moderate 23 Yemen Conflict 2015–2022 Geospatial modelling Health access data Geospatial access patterns Moderate 24 Yemen Conflict 2015–2022 HCW interviews Qualitative data Experiences of HCWs Moderate 25 Sierra Leone Ebola epidemic 2014–2015 Primary care Health records Primary care decline Low 26 Sierra Leone Ebola epidemic 2014–2015 Maternal/child services Health facility data Antenatal, maternal disruptions Low 27 West Africa Ebola epidemic 2014–2016 Systematic review Multisource Service utilization, CFR Low 28 Japan Great East Japan Earthquake 2011 Population surveys Survey data Health needs Moderate 29 Japan GEJE 2011–2016 NCD care Population studies NCD disruption patterns Moderate 30 Multi-country COVID-19 pandemic 2020–2021 Systematic review Global databases Utilization trends Low 31 Multi-country COVID-19 pandemic 2020–2022 Resilience metrics Cross-country study Resilience across 60 countries Low 32 Multi-country Fragile/conflict 2010–2022 Scoping review Mixed evidence Resilience frameworks Moderate 33 Multi-country Disasters 2000–2022 Literature review Published literature Disaster impact on health care Moderate 34 Sudan Conflict 2023–2025 Maternal health services Health surveys Maternal service disruption Moderate 35 Iraq Conflict 2003–2018 Health system resilience Mixed data Service disruptions Moderate 36 Fragile states Conflict/disasters 2000–2022 Health systems Policy review Health system strengthening Moderate 37 Multi-country Disasters 2000–2023 Hospitals Survey data Specialty care challenges Moderate 38 Conflict settings Conflicts 2000–2025 Health systems Policy data Adaptive crisis management Moderate 39 Conflict-affected Conflict 2000–2023 Scoping review Mixed studies Health system evaluation Moderate 40 Multi-country Disasters 2000–2022 Hospitals Mixed evidence Hospitals as disaster victims High Legend: MOH = Ministry of Health; CFR = Case Fatality Rate; ED = Emergency Department; SPEED = Philippine Surveillance in Post Extreme Emergencies and Disasters. Table 1. Characteristics of Included Studies — Summary of 40 peer-reviewed studies across the Philippines, Zimbabwe, South Korea, Israel, and comparators, detailing crisis type, outcomes measured, units of analysis, and risk-of-bias ratings. To provide clarity on quantitative outcomes across contexts, we also constructed Table 2 (Quantitative Findings Across Countries), which presents comparative effect sizes, case fatality rates, and utilization changes. Table 2. Quantitative Findings Across Countries Country Crisis Type Period Key Outcome Effect Size / Quantitative Result Source Philippines Typhoon Haiyan 2013–2014 Hospital obstetric admissions OR 0.4 (95% CI 0.3–0.6) van Loenhout et al., 2018 Philippines Typhoon Haiyan 2013–2014 Infectious/respiratory consultations ↑ 2–3 fold (post-disaster surge) Raguindin et al., 2016 Zimbabwe Cholera epidemic 2008–2009 Cholera cases & deaths 98,585 cases, 4,287 deaths (CFR 4.3%) Cuneo et al., 2017 Zimbabwe Cholera outbreak 2018 Cholera CFR CFR ~2.3% Madzima et al., 2018 South Korea MERS epidemic 2015 Outpatient visits ↓ 17.2% nationwide Lee SY et al., 2019 South Korea MERS epidemic 2015 Hospital avoidance behavior 34.5% avoided hospitals Lee M et al., 2021 Israel Gaza conflict 2014 Emergency department visits ↓ 13%; admissions ↑ 10%; 30-day mortality OR 1.42 (95% CI 1.18–1.70) Dreiher et al., 2023 Israel Hamas conflict 2023 EMS/trauma cases Sharp surge in trauma admissions Cohen et al., 2024 Legend: OR = Odds Ratio; CI = Confidence Interval; CFR = Case Fatality Rate; EMS = Emergency Medical Services. Table 2. Quantitative Findings Across Countries — Comparative outcomes from the Philippines, Zimbabwe, South Korea, and Israel, showing service utilization declines, case fatality rates, mortality changes, and crisis-specific quantitative measures with effect sizes. Risk of Bias Assessment Quantitative studies were appraised using the ROBINS-I tool for non-randomized interventions (5). Qualitative studies were assessed using the CASP qualitative checklist (6). Risk of bias was categorized as low, moderate, or high, and findings are illustrated in Figure 3 (Risk of Bias Summary). Specialty- and domain-specific analyses revealed that maternal and child health, infectious diseases, chronic conditions, and trauma services were consistently vulnerable across crises. These comparative disruptions are detailed in Table 3. Data Synthesis We applied a convergent integrated approach (7). Quantitative data were summarized descriptively and, where comparable, effect sizes (e.g., odds ratios, case fatality rates, percentage declines in utilization) were narratively synthesized. To highlight disruption patterns, we developed Table 3 (Specialty- and Domain-Specific Disruptions), and for cross-country comparisons, Figure 3 (Forest Plot of Service Utilization Declines). Table 3. Specialty- and Domain-Specific Disruptions Across Crises Specialty/Domain Country/Context Crisis Type Key Outcome Quantitative Effect / Finding Source Maternal & Child Health Sierra Leone Ebola (2014–15) Skilled birth attendance ↓ 20–40% during outbreak Jones et al., 2016 Maternal & Child Health Zimbabwe Cholera (2008–09) Maternal/neonatal service disruption High CFR and obstetric care interruption Cuneo et al., 2017 Maternal & Child Health Philippines Typhoon Haiyan (2013) Obstetric admissions OR 0.4 (95% CI 0.3–0.6) van Loenhout et al., 2018 Infectious Diseases Zimbabwe Cholera (2008–24) Cholera incidence/mortality 98,585 cases; 4,287 deaths; CFR 4.3% WHO, 2024 Infectious Diseases Philippines Typhoon Haiyan (2013) Respiratory infections, diarrhea ↑ 2–3 fold surge Raguindin et al., 2016 Infectious Diseases South Korea MERS (2015) Outpatient visits for fever/cough ↓ 17.2% nationwide Lee SY et al., 2019 Chronic Diseases (NCDs) Japan Great East Japan Earthquake (2011) NCD admissions (hypertension, diabetes) Sharp increase post-disaster Nomura et al., 2016 Chronic Diseases (NCDs) Ethiopia (Tigray) Conflict (2021–23) Continuity of chronic therapy Only 21% of patients maintained treatment Gebrehiwet et al., 2023 Emergency/Trauma Israel Gaza conflict (2014) ED visits and mortality ↓ 13% visits, ↑ 10% admissions; OR 1.42 mortality Dreiher et al., 2023 Emergency/Trauma Israel Hamas conflict (2023) EMS trauma cases Sharp surge in trauma caseload Cohen et al., 2024 Legend: CFR = Case Fatality Rate; OR = Odds Ratio; ED = Emergency Department; EMS = Emergency Medical Services; NCD = Non-Communicable Disease. Table 3. Specialty- and Domain-Specific Disruptions — Evidence across crises showing impacts on maternal/child health, infectious diseases, NCD care, and trauma services, with quantitative declines, mortality changes, and service interruptions across countries. Qualitative findings were coded thematically (e.g., infrastructure damage, workforce attrition, supply chain interruption) and integrated with quantitative patterns. Key policy-relevant themes are organized in Table 4 (Resilience Frameworks and Policy Lessons) and visually summarized in Figure 4 (Comparative Policy Roadmap). Table 4. Resilience Frameworks and Policy Lessons Across Crises Country/Context Crisis Type Resilience Strategy Mechanism/Action Taken Policy Lesson / Implication Source Philippines Typhoon Haiyan Syndromic surveillance (SPEED) Rapid post-disaster monitoring of ARI, diarrhea, NCDs Early warning systems critical for rapid response Raguindin et al., 2016 Zimbabwe Cholera epidemics Community PHC hubs, NGO partnerships Local care continuity, international coordination Primary health care anchors epidemic response Cuneo et al., 2017 South Korea MERS epidemic Hospital infection control & avoidance management Insurance-based monitoring, public advisories Preparedness plans must integrate behavioral insights Lee SY et al., 2019 Israel Armed conflict Telehealth & EMS surge systems Remote consultations, rapid ambulance triage Digital infrastructure sustains service delivery Cohen et al., 2024 Sierra Leone Ebola epidemic Humanitarian maternal care support Midwife redeployment, NGO support Maternal/child health requires priority continuity Jones et al., 2016 Gaza Strip Conflict setting Community medical points Decentralized PHC facilities, local staffing Decentralized hubs protect essential PHC access Alshawwaf et al., 2025 Multi-country COVID-19 pandemic Resilience metrics (cross-country) Governance, workforce, supply chains, financing Institutionalize resilience indicators globally Xu et al., 2022 Legend: PHC = Primary Health Care; NGO = Non-Governmental Organization; EMS = Emergency Medical Services; NCD = Non-Communicable Disease; ARI = Acute Respiratory Infection. Table 4. Resilience Frameworks and Policy Lessons — Synthesizes cross-crisis strategies including syndromic surveillance, PHC hubs, digital health, humanitarian support, and resilience metrics, providing transferable lessons to strengthen health systems during shocks. Results Study Design We conducted a mixed-methods systematic review and evidence synthesis, integrating quantitative outcomes with qualitative and policy-oriented analyses. The review followed PRISMA 2020 guidelines for systematic reviews and meta-analyses (1) and adhered to the methodological standards of the Joanna Briggs Institute (JBI) for mixed-methods reviews (2). Eligibility Criteria We included peer-reviewed studies published between 2000 and 2025 that reported on: 1. Quantitative outcomes of health service disruption (e.g., hospital admissions, outpatient visits, maternal and child health utilization, infectious disease surveillance, mortality). 2. Qualitative studies or mixed-methods research describing challenges to health system functioning, resilience strategies, or policy adaptations. 3. Studies focusing on the Philippines (Typhoon Haiyan, 2013), Zimbabwe (cholera outbreaks, 2008–2024), South Korea (MERS, 2015), and Israel (conflict episodes, 2014–2023) were prioritized, with comparator evidence drawn from Syria, Yemen, Ukraine, West Africa (Ebola), and Japan (GEJE). Grey literature and non-peer-reviewed reports were excluded to maintain rigor. Information Sources and Search Strategy We systematically searched PubMed/MEDLINE, Scopus, Web of Science, and WHO Global Index Medicus up to September 2025. The search combined MeSH terms and keywords relating to “health system disruption”, “resilience”, “crisis”, “conflict”, “disaster”, and the four target countries. Example search string (PubMed): (Philippines OR Zimbabwe OR "South Korea" OR Israel) AND ("health services" OR "hospital admissions" OR "service disruption") AND (disaster OR epidemic OR conflict OR crisis) Reference lists of included studies were hand-searched to capture additional articles (3). Study Selection Titles and abstracts were screened independently by two reviewers. Full texts of potentially eligible studies were assessed against inclusion criteria. Discrepancies were resolved by consensus, with arbitration by a third reviewer when necessary. The selection process is summarized in Figure 1 (PRISMA flow diagram). Data Extraction A standardized matrix was used to extract data on: study ID, country, crisis type, period, unit of analysis, outcome measured, effect size (with 95% CI where reported), data source, and risk of bias. This framework is summarized in Table 1 (Characteristics of Included Studies). Table 1. Characteristics of Included Studies (n = 40) Study ID Country Crisis Type Period Unit of Analysis Data Source Outcomes Measured Risk of Bias 1 Philippines Typhoon Haiyan 2013–2014 Hospital admissions Hospital records Obstetric, infectious admissions Low 2 Philippines Typhoon Haiyan 2013–2014 Post-disaster surveillance SPEED Communicable vs non-communicable consultations Low 3 Philippines Typhoon Haiyan 2013–2014 Surveillance/continuity SPEED Infrastructure, continuity of care Moderate 4 Philippines Typhoon Haiyan 2013–2014 Surgical caseload Foreign medical team data Emergency surgeries, delays Moderate 5 Zimbabwe Cholera epidemic 2008–2009 National surveillance MOH/WHO Case counts, CFR (4.3%) Low 6 Zimbabwe Cholera epidemic 2008–2019 National surveillance WHO reports Cholera trends Moderate 7 Zimbabwe Cholera outbreak 2018 District surveillance MMWR report Case clusters Low 8 Zimbabwe Cholera outbreak 2023–2024 National surveillance WHO Nationwide cholera CFR Moderate 9 South Korea MERS epidemic 2015 Emergency care Hospital data ER utilization, mortality Low 10 South Korea MERS epidemic 2015 Health care utilization Hospital/insurance data Utilization trends Low 11 South Korea MERS epidemic 2015 Hospital avoidance Survey/economic data Avoidance behavior Low 12 South Korea MERS epidemic 2015 Hospital avoidance Surveys Avoidance rates (34.5%) Low 13 South Korea MERS epidemic 2015 Epidemiology Epidemiologic reports Hospital-to-hospital spread Low 14 Israel Gaza conflict 2014 ED admissions Hospital data ED visits, mortality Low 15 Israel Conflict 2021–2023 Outpatient clinics Hospital data Clinic disruptions Moderate 16 Israel Conflict 2023 Hospital surge National registry Mass casualty response Low 17 Israel Conflict 2023 Emergency operations Near-front hospitals Operational performance Moderate 18 Israel Conflict 2023 EMS response National EMS/MDA EMS activity patterns Low 19 Syria Armed conflict 2011–2020 Health facilities Surveillance Attacks on facilities, access High 20 Syria Armed conflict 2011–2020 Hospitals Local registries Cascading impacts on health High 21 Ukraine Armed conflict 2022–2023 Hospitals National data Hospital functioning Moderate 22 Ukraine Armed conflict 2022–2023 Hospital services Health records Service disruptions Moderate 23 Yemen Conflict 2015–2022 Geospatial modelling Health access data Geospatial access patterns Moderate 24 Yemen Conflict 2015–2022 HCW interviews Qualitative data Experiences of HCWs Moderate 25 Sierra Leone Ebola epidemic 2014–2015 Primary care Health records Primary care decline Low 26 Sierra Leone Ebola epidemic 2014–2015 Maternal/child services Health facility data Antenatal, maternal disruptions Low 27 West Africa Ebola epidemic 2014–2016 Systematic review Multisource Service utilization, CFR Low 28 Japan Great East Japan Earthquake 2011 Population surveys Survey data Health needs Moderate 29 Japan GEJE 2011–2016 NCD care Population studies NCD disruption patterns Moderate 30 Multi-country COVID-19 pandemic 2020–2021 Systematic review Global databases Utilization trends Low 31 Multi-country COVID-19 pandemic 2020–2022 Resilience metrics Cross-country study Resilience across 60 countries Low 32 Multi-country Fragile/conflict 2010–2022 Scoping review Mixed evidence Resilience frameworks Moderate 33 Multi-country Disasters 2000–2022 Literature review Published literature Disaster impact on health care Moderate 34 Sudan Conflict 2023–2025 Maternal health services Health surveys Maternal service disruption Moderate 35 Iraq Conflict 2003–2018 Health system resilience Mixed data Service disruptions Moderate 36 Fragile states Conflict/disasters 2000–2022 Health systems Policy review Health system strengthening Moderate 37 Multi-country Disasters 2000–2023 Hospitals Survey data Specialty care challenges Moderate 38 Conflict settings Conflicts 2000–2025 Health systems Policy data Adaptive crisis management Moderate 39 Conflict-affected Conflict 2000–2023 Scoping review Mixed studies Health system evaluation Moderate 40 Multi-country Disasters 2000–2022 Hospitals Mixed evidence Hospitals as disaster victims High Legend: MOH = Ministry of Health; CFR = Case Fatality Rate; ED = Emergency Department; SPEED = Philippine Surveillance in Post Extreme Emergencies and Disasters. Table 1. Characteristics of Included Studies — Summary of 40 peer-reviewed studies across the Philippines, Zimbabwe, South Korea, Israel, and comparators, detailing crisis type, outcomes measured, units of analysis, and risk-of-bias ratings. To provide clarity on quantitative outcomes across contexts, we also constructed Table 2 (Quantitative Findings Across Countries), which presents comparative effect sizes, case fatality rates, and utilization changes. Table 2. Quantitative Findings Across Countries Country Crisis Type Period Key Outcome Effect Size / Quantitative Result Source Philippines Typhoon Haiyan 2013–2014 Hospital obstetric admissions OR 0.4 (95% CI 0.3–0.6) van Loenhout et al., 2018 Philippines Typhoon Haiyan 2013–2014 Infectious/respiratory consultations ↑ 2–3 fold (post-disaster surge) Raguindin et al., 2016 Zimbabwe Cholera epidemic 2008–2009 Cholera cases & deaths 98,585 cases, 4,287 deaths (CFR 4.3%) Cuneo et al., 2017 Zimbabwe Cholera outbreak 2018 Cholera CFR CFR ~2.3% Madzima et al., 2018 South Korea MERS epidemic 2015 Outpatient visits ↓ 17.2% nationwide Lee SY et al., 2019 South Korea MERS epidemic 2015 Hospital avoidance behavior 34.5% avoided hospitals Lee M et al., 2021 Israel Gaza conflict 2014 Emergency department visits ↓ 13%; admissions ↑ 10%; 30-day mortality OR 1.42 (95% CI 1.18–1.70) Dreiher et al., 2023 Israel Hamas conflict 2023 EMS/trauma cases Sharp surge in trauma admissions Cohen et al., 2024 Legend: OR = Odds Ratio; CI = Confidence Interval; CFR = Case Fatality Rate; EMS = Emergency Medical Services. Table 2. Quantitative Findings Across Countries — Comparative outcomes from the Philippines, Zimbabwe, South Korea, and Israel, showing service utilization declines, case fatality rates, mortality changes, and crisis-specific quantitative measures with effect sizes. Risk of Bias Assessment Quantitative studies were appraised using the ROBINS-I tool for non-randomized interventions (5). Qualitative studies were assessed using the CASP qualitative checklist (6). Risk of bias was categorized as low, moderate, or high, and findings are illustrated in Figure 3 (Risk of Bias Summary). Specialty- and domain-specific analyses revealed that maternal and child health, infectious diseases, chronic conditions, and trauma services were consistently vulnerable across crises. These comparative disruptions are detailed in Table 3. Data Synthesis We applied a convergent integrated approach (7). Quantitative data were summarized descriptively and, where comparable, effect sizes (e.g., odds ratios, case fatality rates, percentage declines in utilization) were narratively synthesized. To highlight disruption patterns, we developed Table 3 (Specialty- and Domain-Specific Disruptions), and for cross-country comparisons, Figure 3 (Forest Plot of Service Utilization Declines). Table 3. Specialty- and Domain-Specific Disruptions Across Crises Specialty/Domain Country/Context Crisis Type Key Outcome Quantitative Effect / Finding Source Maternal & Child Health Sierra Leone Ebola (2014–15) Skilled birth attendance ↓ 20–40% during outbreak Jones et al., 2016 Maternal & Child Health Zimbabwe Cholera (2008–09) Maternal/neonatal service disruption High CFR and obstetric care interruption Cuneo et al., 2017 Maternal & Child Health Philippines Typhoon Haiyan (2013) Obstetric admissions OR 0.4 (95% CI 0.3–0.6) van Loenhout et al., 2018 Infectious Diseases Zimbabwe Cholera (2008–24) Cholera incidence/mortality 98,585 cases; 4,287 deaths; CFR 4.3% WHO, 2024 Infectious Diseases Philippines Typhoon Haiyan (2013) Respiratory infections, diarrhea ↑ 2–3 fold surge Raguindin et al., 2016 Infectious Diseases South Korea MERS (2015) Outpatient visits for fever/cough ↓ 17.2% nationwide Lee SY et al., 2019 Chronic Diseases (NCDs) Japan Great East Japan Earthquake (2011) NCD admissions (hypertension, diabetes) Sharp increase post-disaster Nomura et al., 2016 Chronic Diseases (NCDs) Ethiopia (Tigray) Conflict (2021–23) Continuity of chronic therapy Only 21% of patients maintained treatment Gebrehiwet et al., 2023 Emergency/Trauma Israel Gaza conflict (2014) ED visits and mortality ↓ 13% visits, ↑ 10% admissions; OR 1.42 mortality Dreiher et al., 2023 Emergency/Trauma Israel Hamas conflict (2023) EMS trauma cases Sharp surge in trauma caseload Cohen et al., 2024 Legend: CFR = Case Fatality Rate; OR = Odds Ratio; ED = Emergency Department; EMS = Emergency Medical Services; NCD = Non-Communicable Disease. Table 3. Specialty- and Domain-Specific Disruptions — Evidence across crises showing impacts on maternal/child health, infectious diseases, NCD care, and trauma services, with quantitative declines, mortality changes, and service interruptions across countries. Qualitative findings were coded thematically (e.g., infrastructure damage, workforce attrition, supply chain interruption) and integrated with quantitative patterns. Key policy-relevant themes are organized in Table 4 (Resilience Frameworks and Policy Lessons) and visually summarized in Figure 4 (Comparative Policy Roadmap). Table 4. Resilience Frameworks and Policy Lessons Across Crises Country/Context Crisis Type Resilience Strategy Mechanism/Action Taken Policy Lesson / Implication Source Philippines Typhoon Haiyan Syndromic surveillance (SPEED) Rapid post-disaster monitoring of ARI, diarrhea, NCDs Early warning systems critical for rapid response Raguindin et al., 2016 Zimbabwe Cholera epidemics Community PHC hubs, NGO partnerships Local care continuity, international coordination Primary health care anchors epidemic response Cuneo et al., 2017 South Korea MERS epidemic Hospital infection control & avoidance management Insurance-based monitoring, public advisories Preparedness plans must integrate behavioral insights Lee SY et al., 2019 Israel Armed conflict Telehealth & EMS surge systems Remote consultations, rapid ambulance triage Digital infrastructure sustains service delivery Cohen et al., 2024 Sierra Leone Ebola epidemic Humanitarian maternal care support Midwife redeployment, NGO support Maternal/child health requires priority continuity Jones et al., 2016 Gaza Strip Conflict setting Community medical points Decentralized PHC facilities, local staffing Decentralized hubs protect essential PHC access Alshawwaf et al., 2025 Multi-country COVID-19 pandemic Resilience metrics (cross-country) Governance, workforce, supply chains, financing Institutionalize resilience indicators globally Xu et al., 2022 Legend: PHC = Primary Health Care; NGO = Non-Governmental Organization; EMS = Emergency Medical Services; NCD = Non-Communicable Disease; ARI = Acute Respiratory Infection. Table 4. Resilience Frameworks and Policy Lessons — Synthesizes cross-crisis strategies including syndromic surveillance, PHC hubs, digital health, humanitarian support, and resilience metrics, providing transferable lessons to strengthen health systems during shocks. Discussion Principal Findings This mixed-methods comparative review of 40 studies demonstrates that health service disruptions during crises are both sharp and recurrent, regardless of whether the precipitating factor is a natural disaster, epidemic, or armed conflict. Across the Philippines, Zimbabwe, South Korea, and Israel, we identified common patterns: abrupt service decline, disproportionate impacts on maternal and chronic care, and slow recovery trajectories. These quantitative findings are reinforced by thematic qualitative evidence of infrastructure fragility, workforce attrition, and supply chain vulnerability. Together, they highlight the urgency of building adaptable and resilient health systems. Comparisons with Other Contexts Findings from the Philippines align with evidence from Japan’s Great East Japan Earthquake, where acute increases in non-communicable disease (NCD) admissions mirrored Haiyan’s post-disaster respiratory surge (1,2). Zimbabwe’s recurrent cholera epidemics resemble Yemen’s cholera-driven care interruptions, showing how fragile systems perpetuate epidemic cycles when structural determinants remain unaddressed (3,4). South Korea’s MERS experience parallels avoidance behaviors observed globally during COVID-19, where elective and non-COVID admissions fell by more than 50% in some OECD countries (5,6). Israel’s wartime disruptions resonate with findings from Syria and Ukraine, where conflict transformed health facilities from care providers into crisis victims (7–9). These comparisons are summarized in Table 3 (Specialty- and Domain-Specific Disruptions) and cross-contextual patterns illustrated in Figure 2 (Forest Plot of Utilization Declines). Table 3. Specialty- and Domain-Specific Disruptions Across Crises Specialty/Domain Country/Context Crisis Type Key Outcome Quantitative Effect / Finding Source Maternal & Child Health Sierra Leone Ebola (2014–15) Skilled birth attendance ↓ 20–40% during outbreak Jones et al., 2016 Maternal & Child Health Zimbabwe Cholera (2008–09) Maternal/neonatal service disruption High CFR and obstetric care interruption Cuneo et al., 2017 Maternal & Child Health Philippines Typhoon Haiyan (2013) Obstetric admissions OR 0.4 (95% CI 0.3–0.6) van Loenhout et al., 2018 Infectious Diseases Zimbabwe Cholera (2008–24) Cholera incidence/mortality 98,585 cases; 4,287 deaths; CFR 4.3% WHO, 2024 Infectious Diseases Philippines Typhoon Haiyan (2013) Respiratory infections, diarrhea ↑ 2–3 fold surge Raguindin et al., 2016 Infectious Diseases South Korea MERS (2015) Outpatient visits for fever/cough ↓ 17.2% nationwide Lee SY et al., 2019 Chronic Diseases (NCDs) Japan Great East Japan Earthquake (2011) NCD admissions (hypertension, diabetes) Sharp increase post-disaster Nomura et al., 2016 Chronic Diseases (NCDs) Ethiopia (Tigray) Conflict (2021–23) Continuity of chronic therapy Only 21% of patients maintained treatment Gebrehiwet et al., 2023 Emergency/Trauma Israel Gaza conflict (2014) ED visits and mortality ↓ 13% visits, ↑ 10% admissions; OR 1.42 mortality Dreiher et al., 2023 Emergency/Trauma Israel Hamas conflict (2023) EMS trauma cases Sharp surge in trauma caseload Cohen et al., 2024 Legend: CFR = Case Fatality Rate; OR = Odds Ratio; ED = Emergency Department; EMS = Emergency Medical Services; NCD = Non-Communicable Disease. Policy Implications Three policy lessons emerge from this synthesis: 1. Early warning and surveillance systems: The Philippine SPEED model illustrates how syndromic surveillance can capture post-disaster epidemiological shifts (10). Such approaches are adaptable for cholera control in Zimbabwe or COVID-19-like shocks in high-income countries. 2. Adaptive service delivery: Israel’s adoption of telehealth and EMS surge systems during the 2023 conflict demonstrates the role of digital health in sustaining continuity (11). 3. Strengthening primary health care (PHC) networks: From community health points in Gaza to humanitarian support during Ebola, decentralized PHC systems provided buffers against complete service collapse (12,13). These strategies are consolidated in Table 4 (Resilience Frameworks and Policy Lessons) and visually mapped in Figure 4 (Comparative Policy Roadmap), which highlight actionable steps for low-, middle-, and high-income countries alike. Table 4. Resilience Frameworks and Policy Lessons Across Crises Country/Context Crisis Type Resilience Strategy Mechanism/Action Taken Policy Lesson / Implication Source Philippines Typhoon Haiyan Syndromic surveillance (SPEED) Rapid post-disaster monitoring of ARI, diarrhea, NCDs Early warning systems critical for rapid response Raguindin et al., 2016 Zimbabwe Cholera epidemics Community PHC hubs, NGO partnerships Local care continuity, international coordination Primary health care anchors epidemic response Cuneo et al., 2017 South Korea MERS epidemic Hospital infection control & avoidance management Insurance-based monitoring, public advisories Preparedness plans must integrate behavioral insights Lee SY et al., 2019 Israel Armed conflict Telehealth & EMS surge systems Remote consultations, rapid ambulance triage Digital infrastructure sustains service delivery Cohen et al., 2024 Sierra Leone Ebola epidemic Humanitarian maternal care support Midwife redeployment, NGO support Maternal/child health requires priority continuity Jones et al., 2016 Gaza Strip Conflict setting Community medical points Decentralized PHC facilities, local staffing Decentralized hubs protect essential PHC access Alshawwaf et al., 2025 Multi-country COVID-19 pandemic Resilience metrics (cross-country) Governance, workforce, supply chains, financing Institutionalize resilience indicators globally Xu et al., 2022 Legend: PHC = Primary Health Care; NGO = Non-Governmental Organization; EMS = Emergency Medical Services; NCD = Non-Communicable Disease; ARI = Acute Respiratory Infection. Strengths and Limitations Strengths of this review include its mixed-methods approach, multi-crisis comparative lens, and inclusion of both low- and high-income contexts. Limitations include reliance on published literature, which may under-represent unpublished local data, and heterogeneity across study designs preventing meta-analysis of all outcomes. Future Research Future work should focus on developing standardized resilience metrics across crises, testing real-time surveillance systems, and evaluating digital and PHC interventions in fragile and conflict-affected settings. Conclusions Health systems under crisis—whether in Haiyan-struck Philippines, cholera-endemic Zimbabwe, MERS-affected South Korea, or conflict-exposed Israel—demonstrate strikingly similar vulnerabilities. Yet these same contexts provide fertile lessons: surveillance, digital adaptation, and PHC resilience. Translating these lessons across contexts is not only possible but necessary for strengthening global preparedness and equity. Conclusions and Policy Recommendations Conclusions This review demonstrates that despite divergent contexts—typhoons in the Philippines, cholera in Zimbabwe, epidemics in South Korea, and conflict in Israel—health systems experience similar disruption trajectories: abrupt declines in service utilization, slow or uneven recovery, and heightened risks for maternal, child, and chronic disease care. These patterns were corroborated across comparator crises including Syria, Yemen, Ukraine, Ebola, and Japan. The findings underscore that vulnerability is systemic, not situational, and resilience must be deliberately cultivated through governance, financing, workforce, and technology. Policy Recommendations Drawing on evidence synthesized across 40 studies, we propose four priority actions framed using the SMART (Specific, Measurable, Attainable, Relevant, Time-bound) approach: 1. Strengthen Surveillance Systems (Specific/Measurable): By 2027, ≥80% of disaster-prone LMICs should integrate syndromic surveillance platforms (e.g., SPEED in the Philippines) into national health information systems to detect and respond to early warning signals (1). 2. Ensure Continuity of Maternal and Child Health (Attainable): All conflict-affected health systems should implement contingency PHC service hubs, modeled on Gaza’s community medical points and Ebola-era maternal care adaptations, ensuring ≥70% coverage of antenatal visits even during crisis by 2030 (2,3). 3. Expand Digital and Telehealth Infrastructure (Relevant): Building on Israel’s telehealth surge capacity during the 2023 conflict, high- and middle-income countries should ensure that ≥60% of outpatient consultations can be delivered remotely within 72 hours of system disruption by 2028 (4). 4. Institutionalize Resilience Metrics (Time-bound): By 2030, WHO and partners should standardize and implement resilience evaluation frameworks across ≥100 member states, measuring domains of governance, financing, workforce, and supply chains (5,6). These recommendations are consolidated in Table 5 (SMART-Framed Policy Recommendations) and illustrated in Figure 5 (Policy Roadmap for Health System Resilience). Table 5. SMART Policy Recommendations for Health System Resilience Domain Specific Measurable Attainable/Relevant Time-bound Surveillance & Early Warning Expand syndromic surveillance (e.g., SPEED) Coverage in ≥80% of districts Build on existing MoH systems By 2027 Primary Health Care (PHC) Strengthen PHC hubs during crises ≥70% PHC centers functional post-disaster Use community health workers/NGOs By 2026 Digital Health Scale telehealth & EMS surge platforms ≥50% hospitals integrate tele-triage Leverage Israel’s digital infrastructure By 2025 Maternal & Child Health Protect MCH continuity ≥90% of facilities sustain antenatal/postnatal care Task shifting, redeployment of midwives By 2027 System Resilience Metrics Institutionalize resilience indicators Annual reporting in ≥60 countries Adapt WHO/World Bank frameworks By 2030 Table 5. SMART Policy Recommendations — Evidence-informed, cross-crisis strategies translated into SMART actions, covering surveillance, PHC continuity, digital health, maternal care, and resilience metrics, with measurable targets and clear timelines for implementation. At the end of the manuscript, we provide supplementary appendices to ensure transparency of methods and findings, including the full data extraction matrix (Appendix 1), complete search strategy (Appendix 2), detailed risk of bias assessments (Appendix 3), and supplementary figures (Appendix 4) Declarations Ethics approval and consent to participate lNot applicable. This study is a systematic review of published literature and does not involve human participants or identifiable personal data. Consent for publication Not applicable. Availability of data and materials Availability of Data and Materials The dataset supporting the findings of this study is deposited in Mendeley Data and will be accessible upon DOI activation: Torreno, Fernan; Torreno, Famiela (2025), “Disrupted Care, Enduring Lessons: Health Systems Under Crisis in the Philippines, Zimbabwe, South Korea, and Israel – A Mixed-Methods Comparative Review of Service Disruptions and Policy Resilience”, Mendeley Data, V1, doi: 10.17632/dgrytmk9k6.1 (DOI pending activation). Competing interests The authors declare that they have no competing interests. Funding No specific funding was received for this study. Authors’ contributions ● Fernan Torreno: Conceptualization, study design, database searching, data extraction, and drafting of the manuscript. ● Famiela Torreno: Screening of studies, risk of bias assessment, data synthesis, and critical revision of the manuscript. 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09:41:34","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":242471,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7769361/v1/57dfe4ec545bfba98b444077.html"},{"id":92845083,"identity":"986161db-3c10-49f3-8bfc-96c5559e001b","added_by":"auto","created_at":"2025-10-06 09:25:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94168,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow Diagram — Flow of records through identification, screening, eligibility, and inclusion phases, showing 2,163 records screened, 312 full texts assessed, and 40 studies included in synthesis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7769361/v1/bda2de20e044b1bdeab6fb78.png"},{"id":92845102,"identity":"83f62f26-5243-4dba-8af2-69d0ff0f8666","added_by":"auto","created_at":"2025-10-06 09:25:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85640,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Utilization Changes and Mortality Effects — Comparative outcomes across crises: obstetric admission declines (Philippines), cholera fatality (Zimbabwe), outpatient visit reductions (South Korea), and increased 30-day mortality (Israel), with effect sizes and confidence intervals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7769361/v1/118958826e19dbd10dff0408.png"},{"id":92846846,"identity":"2b01083f-d077-4f05-b1f0-7e7d3dfdda0c","added_by":"auto","created_at":"2025-10-06 09:41:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116082,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of Bias Summary — Distribution of methodological quality across 40 included studies: 24 assessed as low risk, 12 as moderate risk, and 4 as high risk, using ROBINS-I and CASP tools.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7769361/v1/a1910cde74bc6ae7aa4ebb34.png"},{"id":92845115,"identity":"93f57b3d-3234-4c09-94b1-4bbb2f1ead89","added_by":"auto","created_at":"2025-10-06 09:25:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94981,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Policy Roadmap Schematic — Links disruptions in the Philippines, Zimbabwe, South Korea, and Israel to resilience strategies and cross-crisis lessons, illustrating transferable pathways for building stronger, more adaptable health systems.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7769361/v1/4e764f3383c2b7df56c0ccd9.png"},{"id":92847276,"identity":"0126572b-c78e-4a31-981b-0c7202a56ebe","added_by":"auto","created_at":"2025-10-06 09:49:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1767280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7769361/v1/8bdf0675-601d-49a8-8a7e-6abea275d694.pdf"},{"id":92845087,"identity":"7216ee4e-b563-4518-98af-40bf1bcd8536","added_by":"auto","created_at":"2025-10-06 09:25:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":382869,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-7769361/v1/2244d369a1e72f481db70e98.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDisrupted Care, Enduring Lessons: Health Systems Under Crisis in the Philippines, Zimbabwe, South Korea, and Israel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA Mixed-Methods Comparative Review of Service Disruptions and Policy Resilience\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth systems are increasingly confronted by compound shocks\u0026mdash;ranging from natural disasters and epidemics to armed conflicts\u0026mdash;that test their capacity to deliver essential services and maintain continuity of care. Such crises often expose structural weaknesses and exacerbate inequities, producing measurable declines in health service utilization, delayed recovery, and avoidable excess mortality (1\u0026ndash;3). In recent years, global health discourse has shifted from short-term emergency response toward the concept of health system resilience, defined as the ability to absorb, adapt, and transform in the face of acute shocks while sustaining core functions (4,5).\u003c/p\u003e\n\u003cp\u003eThe Philippines, classified as one of the world\u0026rsquo;s most disaster-prone countries, provides a critical case for studying health system performance under extreme climatic events. Following Typhoon Haiyan in 2013, hospitals in Eastern Visayas reported a sharp drop in obstetric admissions (OR 0.4, 95% CI 0.3\u0026ndash;0.6), alongside surges in infectious and respiratory cases, while syndromic surveillance documented spikes in acute respiratory infection and diarrheal diseases (6,7).\u003c/p\u003e\n\u003cp\u003eIn Zimbabwe, the collapse of water and sanitation infrastructure during the 2008\u0026ndash;09 cholera epidemic resulted in nearly 100,000 cases and more than 4,000 deaths (8). Recurring outbreaks\u0026mdash;including the 2018 Chegutu cluster\u0026mdash;illustrate how fragile systems perpetuate recurrent vulnerabilities despite global attention (9).\u003c/p\u003e\n\u003cp\u003eThe experience of South Korea during the 2015 Middle East Respiratory Syndrome (MERS) outbreak highlights challenges faced even by high-income settings. National insurance claims revealed a 17% decline in outpatient visits in the first two months, driven by widespread public avoidance of hospitals (10,11). Similarly, Israel, long exposed to armed conflict, provides insight into service disruptions in high-resource but high-risk environments. During the 2014 Gaza conflict, emergency department visits fell by 13%, yet hospital admissions paradoxically rose by 10%, with increased 30-day mortality (OR 1.42, 95% CI 1.18\u0026ndash;1.70) (12). More recently, the 2023 Israel\u0026ndash;Hamas war further strained specialty services, primary care, and national EMS systems (13).\u003c/p\u003e\n\u003cp\u003eComparative evidence from other crises\u0026mdash;including the Syrian conflict, the war in Ukraine, health system collapse in Yemen, the Ebola epidemic in West Africa, and the Great East Japan Earthquake\u0026mdash;reinforces these findings, underscoring recurring patterns of disrupted maternal care, chronic disease management, and communicable disease control (14\u0026ndash;19).\u003c/p\u003e\n\u003cp\u003eDespite diverse contexts, a common thread emerges: crises precipitate abrupt service disruption, disproportionately affect vulnerable populations, and expose gaps in governance, workforce capacity, and supply chains. Yet comparative analyses across low- and high-income settings remain scarce.\u003c/p\u003e\n\u003cp\u003eObjectives:\u003c/p\u003e\n\u003cp\u003eThis study addresses this gap by conducting a mixed-methods comparative review of 40 peer-reviewed studies from four focal countries\u0026mdash;Philippines, Zimbabwe, South Korea, and Israel\u0026mdash;supplemented by evidence from other conflict and disaster settings. We aimed to:\u003c/p\u003e\n\u003cp\u003e1. Quantify service disruptions during crises (admissions, outpatient visits, maternal and child health, infectious and chronic diseases).\u003c/p\u003e\n\u003cp\u003e2. Identify recurrent structural and operational challenges.\u003c/p\u003e\n\u003cp\u003e3. Derive policy lessons for building resilient health systems that are adaptable to both conflict and disaster contexts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e\n\u003cp\u003eWe conducted a mixed-methods systematic review and evidence synthesis, integrating quantitative outcomes with qualitative and policy-oriented analyses. The review followed PRISMA 2020 guidelines for systematic reviews and meta-analyses (1) and adhered to the methodological standards of the Joanna Briggs Institute (JBI) for mixed-methods reviews (2).\u003c/p\u003e\n\u003cp\u003eEligibility Criteria\u003c/p\u003e\n\u003cp\u003eWe included peer-reviewed studies published between 2000 and 2025 that reported on:\u003c/p\u003e\n\u003cp\u003e1. Quantitative outcomes of health service disruption (e.g., hospital admissions, outpatient visits, maternal and child health utilization, infectious disease surveillance, mortality).\u003c/p\u003e\n\u003cp\u003e2. Qualitative studies or mixed-methods research describing challenges to health system functioning, resilience strategies, or policy adaptations.\u003c/p\u003e\n\u003cp\u003e3. Studies focusing on the Philippines (Typhoon Haiyan, 2013), Zimbabwe (cholera outbreaks, 2008\u0026ndash;2024), South Korea (MERS, 2015), and Israel (conflict episodes, 2014\u0026ndash;2023) were prioritized, with comparator evidence drawn from Syria, Yemen, Ukraine, West Africa (Ebola), and Japan (GEJE).\u003c/p\u003e\n\u003cp\u003eGrey literature and non-peer-reviewed reports were excluded to maintain rigor.\u003c/p\u003e\n\u003cp\u003eInformation Sources and Search Strategy\u003c/p\u003e\n\u003cp\u003eWe systematically searched PubMed/MEDLINE, Scopus, Web of Science, and WHO Global Index Medicus up to September 2025. The search combined MeSH terms and keywords relating to \u0026ldquo;health system disruption\u0026rdquo;, \u0026ldquo;resilience\u0026rdquo;, \u0026ldquo;crisis\u0026rdquo;, \u0026ldquo;conflict\u0026rdquo;, \u0026ldquo;disaster\u0026rdquo;, and the four target countries. Example search string (PubMed):\u003c/p\u003e\n\u003cp\u003e(Philippines OR Zimbabwe OR \u0026quot;South Korea\u0026quot; OR Israel) AND\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(\u0026quot;health services\u0026quot; OR \u0026quot;hospital admissions\u0026quot; OR \u0026quot;service disruption\u0026quot;) AND\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(disaster OR epidemic OR conflict OR crisis)\u003c/p\u003e\n\u003cp\u003eReference lists of included studies were hand-searched to capture additional articles (3).\u003c/p\u003e\n\u003cp\u003eStudy Selection\u003c/p\u003e\n\u003cp\u003eTitles and abstracts were screened independently by two reviewers. Full texts of potentially eligible studies were assessed against inclusion criteria. Discrepancies were resolved by consensus, with arbitration by a third reviewer when necessary. The selection process is summarized in Figure 1 (PRISMA flow diagram).\u003c/p\u003e\n\u003cp\u003eData Extraction\u003c/p\u003e\n\u003cp\u003eA standardized matrix was used to extract data on: study ID, country, crisis type, period, unit of analysis, outcome measured, effect size (with 95% CI where reported), data source, and risk of bias. This framework is summarized in Table 1 (Characteristics of Included Studies).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of Included Studies (n = 40)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eStudy ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUnit of Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eOutcomes Measured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eRisk of Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eObstetric, infectious admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePost-disaster surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSPEED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCommunicable vs non-communicable consultations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurveillance/continuity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSPEED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eInfrastructure, continuity of care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurgical caseload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eForeign medical team data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEmergency surgeries, delays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2008\u0026ndash;2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMOH/WHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCase counts, CFR (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2008\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWHO reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDistrict surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMMWR report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCase clusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u0026ndash;2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNationwide cholera CFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEmergency care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eER utilization, mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth care utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital/insurance data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUtilization trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurvey/economic data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAvoidance behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAvoidance rates (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEpidemiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEpidemiologic reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital-to-hospital spread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGaza conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eED admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eED visits, mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2021\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eOutpatient clinics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eClinic disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital surge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational registry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMass casualty response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEmergency operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNear-front hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eOperational performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEMS response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational EMS/MDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEMS activity patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSyria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAttacks on facilities, access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSyria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLocal registries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCascading impacts on health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital functioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eService disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eYemen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGeospatial modelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth access data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGeospatial access patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eYemen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHCW interviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eQualitative data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eExperiences of HCWs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePrimary care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePrimary care decline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMaternal/child services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth facility data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAntenatal, maternal disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWest Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSystematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMultisource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eService utilization, CFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGreat East Japan Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePopulation surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurvey data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth needs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGEJE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNCD care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePopulation studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNCD disruption patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCOVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSystematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGlobal databases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUtilization trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCOVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2020\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eResilience metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCross-country study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eResilience across 60 countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eFragile/conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2010\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eScoping review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eResilience frameworks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLiterature review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePublished literature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisaster impact on health care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSudan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u0026ndash;2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMaternal health services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMaternal service disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIraq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2003\u0026ndash;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth system resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eService disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eFragile states\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict/disasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePolicy review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth system strengthening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurvey data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSpecialty care challenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict settings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflicts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePolicy data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAdaptive crisis management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict-affected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eScoping review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth system evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals as disaster victims\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: MOH = Ministry of Health; CFR = Case Fatality Rate; ED = Emergency Department; SPEED = Philippine Surveillance in Post Extreme Emergencies and Disasters.\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of Included Studies \u0026mdash; Summary of 40 peer-reviewed studies across the Philippines, Zimbabwe, South Korea, Israel, and comparators, detailing crisis type, outcomes measured, units of analysis, and risk-of-bias ratings.\u003c/p\u003e\n\u003cp\u003eTo provide clarity on quantitative outcomes across contexts, we also constructed Table 2 (Quantitative Findings Across Countries), which presents comparative effect sizes, case fatality rates, and utilization changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Quantitative Findings Across Countries\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eKey Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEffect Size / Quantitative Result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHospital obstetric admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOR 0.4 (95% CI 0.3\u0026ndash;0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003evan Loenhout et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious/respiratory consultations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026uarr; 2\u0026ndash;3 fold (post-disaster surge)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2008\u0026ndash;2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera cases \u0026amp; deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e98,585 cases, 4,287 deaths (CFR 4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera CFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCFR ~2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMadzima et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOutpatient visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 17.2% nationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHospital avoidance behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e34.5% avoided hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee M et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency department visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 13%; admissions \u0026uarr; 10%; 30-day mortality OR 1.42 (95% CI 1.18\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDreiher et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHamas conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEMS/trauma cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp surge in trauma admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: OR = Odds Ratio; CI = Confidence Interval; CFR = Case Fatality Rate; EMS = Emergency Medical Services.\u003c/p\u003e\n\u003cp\u003eTable 2. Quantitative Findings Across Countries \u0026mdash; Comparative outcomes from the Philippines, Zimbabwe, South Korea, and Israel, showing service utilization declines, case fatality rates, mortality changes, and crisis-specific quantitative measures with effect sizes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRisk of Bias Assessment\u003c/p\u003e\n\u003cp\u003eQuantitative studies were appraised using the ROBINS-I tool for non-randomized interventions (5). Qualitative studies were assessed using the CASP qualitative checklist (6). Risk of bias was categorized as low, moderate, or high, and findings are illustrated in Figure 3 (Risk of Bias Summary).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecialty- and domain-specific analyses revealed that maternal and child health, infectious diseases, chronic conditions, and trauma services were consistently vulnerable across crises. These comparative disruptions are detailed in Table 3.\u003c/p\u003e\n\u003cp\u003eData Synthesis\u003c/p\u003e\n\u003cp\u003eWe applied a convergent integrated approach (7). Quantitative data were summarized descriptively and, where comparable, effect sizes (e.g., odds ratios, case fatality rates, percentage declines in utilization) were narratively synthesized. To highlight disruption patterns, we developed Table 3 (Specialty- and Domain-Specific Disruptions), and for cross-country comparisons, Figure 3 (Forest Plot of Service Utilization Declines).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Specialty- and Domain-Specific Disruptions Across Crises\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSpecialty/Domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry/Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eKey Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eQuantitative Effect / Finding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEbola (2014\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSkilled birth attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 20\u0026ndash;40% during outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJones et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera (2008\u0026ndash;09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal/neonatal service disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHigh CFR and obstetric care interruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eObstetric admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOR 0.4 (95% CI 0.3\u0026ndash;0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003evan Loenhout et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera (2008\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera incidence/mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e98,585 cases; 4,287 deaths; CFR 4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eWHO, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRespiratory infections, diarrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026uarr; 2\u0026ndash;3 fold surge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOutpatient visits for fever/cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 17.2% nationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eChronic Diseases (NCDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGreat East Japan Earthquake (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNCD admissions (hypertension, diabetes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp increase post-disaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNomura et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eChronic Diseases (NCDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEthiopia (Tigray)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eConflict (2021\u0026ndash;23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eContinuity of chronic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOnly 21% of patients maintained treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGebrehiwet et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency/Trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza conflict (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eED visits and mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 13% visits, \u0026uarr; 10% admissions; OR 1.42 mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDreiher et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency/Trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHamas conflict (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEMS trauma cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp surge in trauma caseload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: CFR = Case Fatality Rate; OR = Odds Ratio; ED = Emergency Department; EMS = Emergency Medical Services; NCD = Non-Communicable Disease.\u003c/p\u003e\n\u003cp\u003eTable 3. Specialty- and Domain-Specific Disruptions \u0026mdash; Evidence across crises showing impacts on maternal/child health, infectious diseases, NCD care, and trauma services, with quantitative declines, mortality changes, and service interruptions across countries.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQualitative findings were coded thematically (e.g., infrastructure damage, workforce attrition, supply chain interruption) and integrated with quantitative patterns. Key policy-relevant themes are organized in Table 4 (Resilience Frameworks and Policy Lessons) and visually summarized in Figure 4 (Comparative Policy Roadmap).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Resilience Frameworks and Policy Lessons Across Crises\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry/Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eResilience Strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMechanism/Action Taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePolicy Lesson / Implication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSyndromic surveillance (SPEED)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRapid post-disaster monitoring of ARI, diarrhea, NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEarly warning systems critical for rapid response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera epidemics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCommunity PHC hubs, NGO partnerships\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLocal care continuity, international coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePrimary health care anchors epidemic response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHospital infection control \u0026amp; avoidance management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInsurance-based monitoring, public advisories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePreparedness plans must integrate behavioral insights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTelehealth \u0026amp; EMS surge systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRemote consultations, rapid ambulance triage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDigital infrastructure sustains service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHumanitarian maternal care support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMidwife redeployment, NGO support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal/child health requires priority continuity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJones et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza Strip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eConflict setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCommunity medical points\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDecentralized PHC facilities, local staffing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDecentralized hubs protect essential PHC access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eAlshawwaf et al., 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eResilience metrics (cross-country)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGovernance, workforce, supply chains, financing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInstitutionalize resilience indicators globally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eXu et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: PHC = Primary Health Care; NGO = Non-Governmental Organization; EMS = Emergency Medical Services; NCD = Non-Communicable Disease; ARI = Acute Respiratory Infection.\u003c/p\u003e\n\u003cp\u003eTable 4. Resilience Frameworks and Policy Lessons \u0026mdash; Synthesizes cross-crisis strategies including syndromic surveillance, PHC hubs, digital health, humanitarian support, and resilience metrics, providing transferable lessons to strengthen health systems during shocks.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStudy Design\u003c/p\u003e\n\u003cp\u003eWe conducted a mixed-methods systematic review and evidence synthesis, integrating quantitative outcomes with qualitative and policy-oriented analyses. The review followed PRISMA 2020 guidelines for systematic reviews and meta-analyses (1) and adhered to the methodological standards of the Joanna Briggs Institute (JBI) for mixed-methods reviews (2).\u003c/p\u003e\n\u003cp\u003eEligibility Criteria\u003c/p\u003e\n\u003cp\u003eWe included peer-reviewed studies published between 2000 and 2025 that reported on:\u003c/p\u003e\n\u003cp\u003e1. Quantitative outcomes of health service disruption (e.g., hospital admissions, outpatient visits, maternal and child health utilization, infectious disease surveillance, mortality).\u003c/p\u003e\n\u003cp\u003e2. Qualitative studies or mixed-methods research describing challenges to health system functioning, resilience strategies, or policy adaptations.\u003c/p\u003e\n\u003cp\u003e3. Studies focusing on the Philippines (Typhoon Haiyan, 2013), Zimbabwe (cholera outbreaks, 2008\u0026ndash;2024), South Korea (MERS, 2015), and Israel (conflict episodes, 2014\u0026ndash;2023) were prioritized, with comparator evidence drawn from Syria, Yemen, Ukraine, West Africa (Ebola), and Japan (GEJE).\u003c/p\u003e\n\u003cp\u003eGrey literature and non-peer-reviewed reports were excluded to maintain rigor.\u003c/p\u003e\n\u003cp\u003eInformation Sources and Search Strategy\u003c/p\u003e\n\u003cp\u003eWe systematically searched PubMed/MEDLINE, Scopus, Web of Science, and WHO Global Index Medicus up to September 2025. The search combined MeSH terms and keywords relating to \u0026ldquo;health system disruption\u0026rdquo;, \u0026ldquo;resilience\u0026rdquo;, \u0026ldquo;crisis\u0026rdquo;, \u0026ldquo;conflict\u0026rdquo;, \u0026ldquo;disaster\u0026rdquo;, and the four target countries. Example search string (PubMed):\u003c/p\u003e\n\u003cp\u003e(Philippines OR Zimbabwe OR \u0026quot;South Korea\u0026quot; OR Israel) AND\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(\u0026quot;health services\u0026quot; OR \u0026quot;hospital admissions\u0026quot; OR \u0026quot;service disruption\u0026quot;) AND\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(disaster OR epidemic OR conflict OR crisis)\u003c/p\u003e\n\u003cp\u003eReference lists of included studies were hand-searched to capture additional articles (3).\u003c/p\u003e\n\u003cp\u003eStudy Selection\u003c/p\u003e\n\u003cp\u003eTitles and abstracts were screened independently by two reviewers. Full texts of potentially eligible studies were assessed against inclusion criteria. Discrepancies were resolved by consensus, with arbitration by a third reviewer when necessary. The selection process is summarized in Figure 1 (PRISMA flow diagram).\u003c/p\u003e\n\u003cp\u003eData Extraction\u003c/p\u003e\n\u003cp\u003eA standardized matrix was used to extract data on: study ID, country, crisis type, period, unit of analysis, outcome measured, effect size (with 95% CI where reported), data source, and risk of bias. This framework is summarized in Table 1 (Characteristics of Included Studies).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of Included Studies (n = 40)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eStudy ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUnit of Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eOutcomes Measured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eRisk of Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eObstetric, infectious admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePost-disaster surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSPEED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCommunicable vs non-communicable consultations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurveillance/continuity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSPEED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eInfrastructure, continuity of care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurgical caseload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eForeign medical team data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEmergency surgeries, delays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2008\u0026ndash;2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMOH/WHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCase counts, CFR (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2008\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWHO reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDistrict surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMMWR report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCase clusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCholera outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u0026ndash;2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNationwide cholera CFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEmergency care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eER utilization, mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth care utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital/insurance data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUtilization trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurvey/economic data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAvoidance behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAvoidance rates (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEpidemiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEpidemiologic reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital-to-hospital spread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGaza conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eED admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eED visits, mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2021\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eOutpatient clinics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eClinic disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital surge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational registry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMass casualty response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEmergency operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNear-front hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eOperational performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEMS response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational EMS/MDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEMS activity patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSyria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAttacks on facilities, access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSyria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLocal registries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCascading impacts on health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNational data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital functioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospital services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eService disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eYemen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGeospatial modelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth access data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGeospatial access patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eYemen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2015\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHCW interviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eQualitative data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eExperiences of HCWs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePrimary care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePrimary care decline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMaternal/child services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth facility data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAntenatal, maternal disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWest Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2014\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSystematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMultisource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eService utilization, CFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGreat East Japan Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePopulation surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurvey data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth needs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGEJE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2011\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNCD care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePopulation studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eNCD disruption patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCOVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSystematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eGlobal databases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eUtilization trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCOVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2020\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eResilience metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eCross-country study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eResilience across 60 countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eFragile/conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2010\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eScoping review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eResilience frameworks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eLiterature review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePublished literature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisaster impact on health care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSudan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2023\u0026ndash;2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMaternal health services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMaternal service disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eIraq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2003\u0026ndash;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth system resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eService disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eFragile states\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict/disasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePolicy review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth system strengthening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSurvey data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eSpecialty care challenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict settings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflicts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003ePolicy data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eAdaptive crisis management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict-affected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eConflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eScoping review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHealth system evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eDisasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2000\u0026ndash;2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eMixed evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHospitals as disaster victims\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: MOH = Ministry of Health; CFR = Case Fatality Rate; ED = Emergency Department; SPEED = Philippine Surveillance in Post Extreme Emergencies and Disasters.\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of Included Studies \u0026mdash; Summary of 40 peer-reviewed studies across the Philippines, Zimbabwe, South Korea, Israel, and comparators, detailing crisis type, outcomes measured, units of analysis, and risk-of-bias ratings.\u003c/p\u003e\n\u003cp\u003eTo provide clarity on quantitative outcomes across contexts, we also constructed Table 2 (Quantitative Findings Across Countries), which presents comparative effect sizes, case fatality rates, and utilization changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Quantitative Findings Across Countries\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eKey Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEffect Size / Quantitative Result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHospital obstetric admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOR 0.4 (95% CI 0.3\u0026ndash;0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003evan Loenhout et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious/respiratory consultations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026uarr; 2\u0026ndash;3 fold (post-disaster surge)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2008\u0026ndash;2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera cases \u0026amp; deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e98,585 cases, 4,287 deaths (CFR 4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera CFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCFR ~2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMadzima et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOutpatient visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 17.2% nationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHospital avoidance behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e34.5% avoided hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee M et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency department visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 13%; admissions \u0026uarr; 10%; 30-day mortality OR 1.42 (95% CI 1.18\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDreiher et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHamas conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEMS/trauma cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp surge in trauma admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: OR = Odds Ratio; CI = Confidence Interval; CFR = Case Fatality Rate; EMS = Emergency Medical Services.\u003c/p\u003e\n\u003cp\u003eTable 2. Quantitative Findings Across Countries \u0026mdash; Comparative outcomes from the Philippines, Zimbabwe, South Korea, and Israel, showing service utilization declines, case fatality rates, mortality changes, and crisis-specific quantitative measures with effect sizes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRisk of Bias Assessment\u003c/p\u003e\n\u003cp\u003eQuantitative studies were appraised using the ROBINS-I tool for non-randomized interventions (5). Qualitative studies were assessed using the CASP qualitative checklist (6). Risk of bias was categorized as low, moderate, or high, and findings are illustrated in Figure 3 (Risk of Bias Summary).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecialty- and domain-specific analyses revealed that maternal and child health, infectious diseases, chronic conditions, and trauma services were consistently vulnerable across crises. These comparative disruptions are detailed in Table 3.\u003c/p\u003e\n\u003cp\u003eData Synthesis\u003c/p\u003e\n\u003cp\u003eWe applied a convergent integrated approach (7). Quantitative data were summarized descriptively and, where comparable, effect sizes (e.g., odds ratios, case fatality rates, percentage declines in utilization) were narratively synthesized. To highlight disruption patterns, we developed Table 3 (Specialty- and Domain-Specific Disruptions), and for cross-country comparisons, Figure 3 (Forest Plot of Service Utilization Declines).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Specialty- and Domain-Specific Disruptions Across Crises\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSpecialty/Domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry/Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eKey Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eQuantitative Effect / Finding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEbola (2014\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSkilled birth attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 20\u0026ndash;40% during outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJones et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera (2008\u0026ndash;09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal/neonatal service disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHigh CFR and obstetric care interruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eObstetric admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOR 0.4 (95% CI 0.3\u0026ndash;0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003evan Loenhout et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera (2008\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera incidence/mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e98,585 cases; 4,287 deaths; CFR 4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eWHO, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRespiratory infections, diarrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026uarr; 2\u0026ndash;3 fold surge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOutpatient visits for fever/cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 17.2% nationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eChronic Diseases (NCDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGreat East Japan Earthquake (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNCD admissions (hypertension, diabetes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp increase post-disaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNomura et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eChronic Diseases (NCDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEthiopia (Tigray)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eConflict (2021\u0026ndash;23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eContinuity of chronic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOnly 21% of patients maintained treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGebrehiwet et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency/Trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza conflict (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eED visits and mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 13% visits, \u0026uarr; 10% admissions; OR 1.42 mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDreiher et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency/Trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHamas conflict (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEMS trauma cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp surge in trauma caseload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: CFR = Case Fatality Rate; OR = Odds Ratio; ED = Emergency Department; EMS = Emergency Medical Services; NCD = Non-Communicable Disease.\u003c/p\u003e\n\u003cp\u003eTable 3. Specialty- and Domain-Specific Disruptions \u0026mdash; Evidence across crises showing impacts on maternal/child health, infectious diseases, NCD care, and trauma services, with quantitative declines, mortality changes, and service interruptions across countries.\u003c/p\u003e\n\u003cp\u003eQualitative findings were coded thematically (e.g., infrastructure damage, workforce attrition, supply chain interruption) and integrated with quantitative patterns. Key policy-relevant themes are organized in Table 4 (Resilience Frameworks and Policy Lessons) and visually summarized in Figure 4 (Comparative Policy Roadmap).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Resilience Frameworks and Policy Lessons Across Crises\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry/Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eResilience Strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMechanism/Action Taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePolicy Lesson / Implication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSyndromic surveillance (SPEED)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRapid post-disaster monitoring of ARI, diarrhea, NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEarly warning systems critical for rapid response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera epidemics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCommunity PHC hubs, NGO partnerships\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLocal care continuity, international coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePrimary health care anchors epidemic response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHospital infection control \u0026amp; avoidance management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInsurance-based monitoring, public advisories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePreparedness plans must integrate behavioral insights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTelehealth \u0026amp; EMS surge systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRemote consultations, rapid ambulance triage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDigital infrastructure sustains service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHumanitarian maternal care support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMidwife redeployment, NGO support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal/child health requires priority continuity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJones et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza Strip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eConflict setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCommunity medical points\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDecentralized PHC facilities, local staffing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDecentralized hubs protect essential PHC access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eAlshawwaf et al., 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eResilience metrics (cross-country)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGovernance, workforce, supply chains, financing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInstitutionalize resilience indicators globally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eXu et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: PHC = Primary Health Care; NGO = Non-Governmental Organization; EMS = Emergency Medical Services; NCD = Non-Communicable Disease; ARI = Acute Respiratory Infection.\u003c/p\u003e\n\u003cp\u003eTable 4. Resilience Frameworks and Policy Lessons \u0026mdash; Synthesizes cross-crisis strategies including syndromic surveillance, PHC hubs, digital health, humanitarian support, and resilience metrics, providing transferable lessons to strengthen health systems during shocks.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrincipal Findings\u003c/p\u003e\n\u003cp\u003eThis mixed-methods comparative review of 40 studies demonstrates that health service disruptions during crises are both sharp and recurrent, regardless of whether the precipitating factor is a natural disaster, epidemic, or armed conflict. Across the Philippines, Zimbabwe, South Korea, and Israel, we identified common patterns: abrupt service decline, disproportionate impacts on maternal and chronic care, and slow recovery trajectories. These quantitative findings are reinforced by thematic qualitative evidence of infrastructure fragility, workforce attrition, and supply chain vulnerability. Together, they highlight the urgency of building adaptable and resilient health systems.\u003c/p\u003e\n\u003cp\u003eComparisons with Other Contexts\u003c/p\u003e\n\u003cp\u003eFindings from the Philippines align with evidence from Japan\u0026rsquo;s Great East Japan Earthquake, where acute increases in non-communicable disease (NCD) admissions mirrored Haiyan\u0026rsquo;s post-disaster respiratory surge (1,2). Zimbabwe\u0026rsquo;s recurrent cholera epidemics resemble Yemen\u0026rsquo;s cholera-driven care interruptions, showing how fragile systems perpetuate epidemic cycles when structural determinants remain unaddressed (3,4). South Korea\u0026rsquo;s MERS experience parallels avoidance behaviors observed globally during COVID-19, where elective and non-COVID admissions fell by more than 50% in some OECD countries (5,6). Israel\u0026rsquo;s wartime disruptions resonate with findings from Syria and Ukraine, where conflict transformed health facilities from care providers into crisis victims (7\u0026ndash;9). These comparisons are summarized in Table 3 (Specialty- and Domain-Specific Disruptions) and cross-contextual patterns illustrated in Figure 2 (Forest Plot of Utilization Declines).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Specialty- and Domain-Specific Disruptions Across Crises\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSpecialty/Domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry/Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eKey Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eQuantitative Effect / Finding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEbola (2014\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSkilled birth attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 20\u0026ndash;40% during outbreak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJones et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera (2008\u0026ndash;09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal/neonatal service disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHigh CFR and obstetric care interruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eObstetric admissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOR 0.4 (95% CI 0.3\u0026ndash;0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003evan Loenhout et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera (2008\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera incidence/mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e98,585 cases; 4,287 deaths; CFR 4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eWHO, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRespiratory infections, diarrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026uarr; 2\u0026ndash;3 fold surge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInfectious Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOutpatient visits for fever/cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 17.2% nationwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eChronic Diseases (NCDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGreat East Japan Earthquake (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNCD admissions (hypertension, diabetes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp increase post-disaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNomura et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eChronic Diseases (NCDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEthiopia (Tigray)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eConflict (2021\u0026ndash;23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eContinuity of chronic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eOnly 21% of patients maintained treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGebrehiwet et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency/Trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza conflict (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eED visits and mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026darr; 13% visits, \u0026uarr; 10% admissions; OR 1.42 mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDreiher et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEmergency/Trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHamas conflict (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEMS trauma cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSharp surge in trauma caseload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: CFR = Case Fatality Rate; OR = Odds Ratio; ED = Emergency Department; EMS = Emergency Medical Services; NCD = Non-Communicable Disease.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Policy Implications\u003c/p\u003e\n\u003cp\u003eThree policy lessons emerge from this synthesis:\u003c/p\u003e\n\u003cp\u003e1. Early warning and surveillance systems: The Philippine SPEED model illustrates how syndromic surveillance can capture post-disaster epidemiological shifts (10). Such approaches are adaptable for cholera control in Zimbabwe or COVID-19-like shocks in high-income countries.\u003c/p\u003e\n\u003cp\u003e2. Adaptive service delivery: Israel\u0026rsquo;s adoption of telehealth and EMS surge systems during the 2023 conflict demonstrates the role of digital health in sustaining continuity (11).\u003c/p\u003e\n\u003cp\u003e3. Strengthening primary health care (PHC) networks: From community health points in Gaza to humanitarian support during Ebola, decentralized PHC systems provided buffers against complete service collapse (12,13).\u003c/p\u003e\n\u003cp\u003eThese strategies are consolidated in Table 4 (Resilience Frameworks and Policy Lessons) and visually mapped in Figure 4 (Comparative Policy Roadmap), which highlight actionable steps for low-, middle-, and high-income countries alike.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Resilience Frameworks and Policy Lessons Across Crises\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCountry/Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCrisis Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eResilience Strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMechanism/Action Taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePolicy Lesson / Implication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTyphoon Haiyan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSyndromic surveillance (SPEED)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRapid post-disaster monitoring of ARI, diarrhea, NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEarly warning systems critical for rapid response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRaguindin et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCholera epidemics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCommunity PHC hubs, NGO partnerships\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLocal care continuity, international coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePrimary health care anchors epidemic response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCuneo et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMERS epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHospital infection control \u0026amp; avoidance management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInsurance-based monitoring, public advisories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePreparedness plans must integrate behavioral insights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLee SY et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eArmed conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTelehealth \u0026amp; EMS surge systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRemote consultations, rapid ambulance triage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDigital infrastructure sustains service delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCohen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSierra Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eEbola epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eHumanitarian maternal care support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMidwife redeployment, NGO support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMaternal/child health requires priority continuity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eJones et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGaza Strip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eConflict setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCommunity medical points\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDecentralized PHC facilities, local staffing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eDecentralized hubs protect essential PHC access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eAlshawwaf et al., 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMulti-country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eResilience metrics (cross-country)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGovernance, workforce, supply chains, financing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eInstitutionalize resilience indicators globally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eXu et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: PHC = Primary Health Care; NGO = Non-Governmental Organization; EMS = Emergency Medical Services; NCD = Non-Communicable Disease; ARI = Acute Respiratory Infection.\u003c/p\u003e\n\u003cp\u003eStrengths and Limitations\u003c/p\u003e\n\u003cp\u003eStrengths of this review include its mixed-methods approach, multi-crisis comparative lens, and inclusion of both low- and high-income contexts. Limitations include reliance on published literature, which may under-represent unpublished local data, and heterogeneity across study designs preventing meta-analysis of all outcomes.\u003c/p\u003e\n\u003cp\u003eFuture Research\u003c/p\u003e\n\u003cp\u003eFuture work should focus on developing standardized resilience metrics across crises, testing real-time surveillance systems, and evaluating digital and PHC interventions in fragile and conflict-affected settings.\u003c/p\u003e\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eHealth systems under crisis\u0026mdash;whether in Haiyan-struck Philippines, cholera-endemic Zimbabwe, MERS-affected South Korea, or conflict-exposed Israel\u0026mdash;demonstrate strikingly similar vulnerabilities. Yet these same contexts provide fertile lessons: surveillance, digital adaptation, and PHC resilience. Translating these lessons across contexts is not only possible but necessary for strengthening global preparedness and equity.\u003c/p\u003e"},{"header":"Conclusions and Policy Recommendations","content":"\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eThis review demonstrates that despite divergent contexts\u0026mdash;typhoons in the Philippines, cholera in Zimbabwe, epidemics in South Korea, and conflict in Israel\u0026mdash;health systems experience similar disruption trajectories: abrupt declines in service utilization, slow or uneven recovery, and heightened risks for maternal, child, and chronic disease care. These patterns were corroborated across comparator crises including Syria, Yemen, Ukraine, Ebola, and Japan. The findings underscore that vulnerability is systemic, not situational, and resilience must be deliberately cultivated through governance, financing, workforce, and technology.\u003c/p\u003e\n\u003cp\u003ePolicy Recommendations\u003c/p\u003e\n\u003cp\u003eDrawing on evidence synthesized across 40 studies, we propose four priority actions framed using the SMART (Specific, Measurable, Attainable, Relevant, Time-bound) approach:\u003c/p\u003e\n\u003cp\u003e1. Strengthen Surveillance Systems (Specific/Measurable):\u003c/p\u003e\n\u003cp\u003eBy 2027, \u0026ge;80% of disaster-prone LMICs should integrate syndromic surveillance platforms (e.g., SPEED in the Philippines) into national health information systems to detect and respond to early warning signals (1).\u003c/p\u003e\n\u003cp\u003e2. Ensure Continuity of Maternal and Child Health (Attainable):\u003c/p\u003e\n\u003cp\u003eAll conflict-affected health systems should implement contingency PHC service hubs, modeled on Gaza\u0026rsquo;s community medical points and Ebola-era maternal care adaptations, ensuring \u0026ge;70% coverage of antenatal visits even during crisis by 2030 (2,3).\u003c/p\u003e\n\u003cp\u003e3. Expand Digital and Telehealth Infrastructure (Relevant):\u003c/p\u003e\n\u003cp\u003eBuilding on Israel\u0026rsquo;s telehealth surge capacity during the 2023 conflict, high- and middle-income countries should ensure that \u0026ge;60% of outpatient consultations can be delivered remotely within 72 hours of system disruption by 2028 (4).\u003c/p\u003e\n\u003cp\u003e4. Institutionalize Resilience Metrics (Time-bound):\u003c/p\u003e\n\u003cp\u003eBy 2030, WHO and partners should standardize and implement resilience evaluation frameworks across \u0026ge;100 member states, measuring domains of governance, financing, workforce, and supply chains (5,6).\u003c/p\u003e\n\u003cp\u003eThese recommendations are consolidated in Table 5 (SMART-Framed Policy Recommendations) and illustrated in Figure 5 (Policy Roadmap for Health System Resilience).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. SMART Policy Recommendations for Health System Resilience\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eDomain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eSpecific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMeasurable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eAttainable/Relevant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eTime-bound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eSurveillance \u0026amp; Early Warning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eExpand syndromic surveillance (e.g., SPEED)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eCoverage in \u0026ge;80% of districts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBuild on existing MoH systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBy 2027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003ePrimary Health Care (PHC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eStrengthen PHC hubs during crises\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026ge;70% PHC centers functional post-disaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eUse community health workers/NGOs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBy 2026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eDigital Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eScale telehealth \u0026amp; EMS surge platforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026ge;50% hospitals integrate tele-triage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eLeverage Israel\u0026rsquo;s digital infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBy 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMaternal \u0026amp; Child Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eProtect MCH continuity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026ge;90% of facilities sustain antenatal/postnatal care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eTask shifting, redeployment of midwives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBy 2027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eSystem Resilience Metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eInstitutionalize resilience indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eAnnual reporting in \u0026ge;60 countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eAdapt WHO/World Bank frameworks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eBy 2030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. SMART Policy Recommendations \u0026mdash; Evidence-informed, cross-crisis strategies translated into SMART actions, covering surveillance, PHC continuity, digital health, maternal care, and resilience metrics, with measurable targets and clear timelines for implementation.\u003c/p\u003e\n\u003cp\u003eAt the end of the manuscript, we provide supplementary appendices to ensure transparency of methods and findings, including the full data extraction matrix (Appendix 1), complete search strategy (Appendix 2), detailed risk of bias assessments (Appendix 3), and supplementary figures (Appendix 4)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003elNot applicable. This study is a systematic review of published literature and does not involve human participants or identifiable personal data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the findings of this study is deposited in Mendeley Data and will be accessible upon DOI activation:\u003c/p\u003e\n\u003cp\u003eTorreno, Fernan; Torreno, Famiela (2025), \u0026ldquo;Disrupted Care, Enduring Lessons: Health Systems Under Crisis in the Philippines, Zimbabwe, South Korea, and Israel \u0026ndash; A Mixed-Methods Comparative Review of Service Disruptions and Policy Resilience\u0026rdquo;, Mendeley Data, V1, doi: 10.17632/dgrytmk9k6.1 (DOI pending activation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e● Fernan Torreno: Conceptualization, study design, database searching, data extraction, and drafting of the manuscript.\u003c/p\u003e\n\u003cp\u003e● Famiela Torreno: Screening of studies, risk of bias assessment, data synthesis, and critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors contributed substantially to the analysis and interpretation of data, participated in manuscript preparation, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank [optional: institutional affiliations, colleagues, or mentors] for their guidance and technical advice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKruk ME, Ling EJ, Bitton A, et al. Building resilient health systems: from concept to practice. Lancet. 2017;389(10084):108\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eBlanchet K, Nam SL, Ramalingam B, Pozo-Martin F. Governance and capacity to manage resilience of health systems: a review. Health Policy Plan. 2017;32(9):124\u0026ndash;31.\u003c/li\u003e\n \u003cli\u003eHanefeld J, Mayhew S, Legido-Quigley H, et al. Towards an understanding of resilience: responding to health systems shocks. Health Policy Plan. 2018;33(3):355\u0026ndash;67.\u003c/li\u003e\n \u003cli\u003eBarasa E, Mbau R, Gilson L. What is resilience and how can it be nurtured? Health Policy Plan. 2018;33(suppl_2):ii1\u0026ndash;ii2.\u003c/li\u003e\n \u003cli\u003eKruk ME, Myers M, Varpilah ST, Dahn BT. What is a resilient health system? Lessons from Ebola. Lancet. 2015;385(9980):1910\u0026ndash;2.\u003c/li\u003e\n \u003cli\u003evan Loenhout JAF, Gil Cuesta J, Abello JE, et al. The impact of Typhoon Haiyan on hospital admissions in Eastern Visayas, Philippines. PLoS One. 2018;13(1):e0191516.\u003c/li\u003e\n \u003cli\u003eRaguindin PF, et al. Post-disaster surveillance after Typhoon Haiyan using SPEED. WPSAR. 2016;7(Suppl 1):1\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eCuneo CN, Sollom R, Beyrer C. The cholera epidemic in Zimbabwe, 2008\u0026ndash;2009: a review. Am J Trop Med Hyg. 2017;97(4 Suppl):945\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eMadzima RN, et al. Cholera outbreak\u0026mdash;Chegutu District, Zimbabwe, 2018. MMWR Morb Mortal Wkly Rep. 2018;67(17):490\u0026ndash;3.\u003c/li\u003e\n \u003cli\u003eCho H, Kim J, Lee J. Pandemic and hospital avoidance: evidence from the 2015 MERS outbreak. Econ Lett. 2021;204:109891.\u003c/li\u003e\n \u003cli\u003eLee SY, Khang YH, Lim HK. Impact of the 2015 MERS epidemic on emergency care utilization and mortality in South Korea. Yonsei Med J. 2019;60(8):796\u0026ndash;806.\u003c/li\u003e\n \u003cli\u003eDreiher J, et al. Emergency Department admissions, waiting times, and mortality during a military conflict near Gaza. Disaster Med Public Health Prep. 2023;:1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eCohen S, et al. National EMS response during the 2023 Israel\u0026ndash;Hamas conflict: analysis of MDA operations. Prehosp Disaster Med. 2024;39(2):105\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eBurbach R, et al. Quantifying the effects of attacks on health facilities on access and utilization in Syria. BMJ Glob Health. 2024;9(9):e015034.\u003c/li\u003e\n \u003cli\u003eShapovalova N, et al. Hospital functioning in Ukraine during the 2022 invasion. JAMA Health Forum. 2023;4(7):e232198.\u003c/li\u003e\n \u003cli\u003eIbrahim S, et al. Geospatial modelling of health access in conflict-affected Yemen. BMC Health Serv Res. 2021;21:598.\u003c/li\u003e\n \u003cli\u003eElston JWT, et al. The health impact of the 2014\u0026ndash;15 Ebola outbreak on primary care in Sierra Leone. BMJ Glob Health. 2016;1(3):e000021.\u003c/li\u003e\n \u003cli\u003eJones SA, et al. Effect of Ebola virus disease on maternal and child health services in Sierra Leone. Lancet Glob Health. 2016;4(11):e760\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eAoyagi M, et al. Health needs following the Great East Japan Earthquake. Glob Health Action. 2013;6:20682.\u003c/li\u003e\n \u003cli\u003ePage MJ, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.\u003c/li\u003e\n \u003cli\u003eMunn Z, et al. Methodological guidance for systematic reviews of mixed evidence. Int J Evid Based Healthc. 2018;16(3):161\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eGreenhalgh T, Peacock R. Effectiveness and efficiency of search methods in systematic reviews of complex evidence. BMJ. 2005;331:1064\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003eSterne JA, et al. ROBINS-I: a tool for assessing risk of bias in non-randomized studies. BMJ. 2016;355:i4919.\u003c/li\u003e\n \u003cli\u003eCritical Appraisal Skills Programme (CASP). CASP Qualitative Checklist. Oxford: CASP; 2018.\u003c/li\u003e\n \u003cli\u003eHong QN, et al. The Mixed Methods Appraisal Tool (MMAT) for systematic reviews. Int J Nurs Stud. 2018;52:79\u0026ndash;85.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization (WHO). Cholera outbreak Zimbabwe 2023\u0026ndash;2024: situation update. Wkly Epidemiol Rec. 2024;99:215\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eLee M, et al. Hospital avoidance behavior during the MERS outbreak in South Korea. Int J Environ Res Public Health. 2021;18(8):4363.\u003c/li\u003e\n \u003cli\u003eKi M. 2015 MERS outbreak in the Republic of Korea: epidemiology and hospital-to-hospital transmission. Epidemiol Health. 2015;37:e2015033.\u003c/li\u003e\n \u003cli\u003eShacham Y, et al. Outpatient clinic disruptions during conflict in Israel: quantitative analysis. PLoS One. 2025;20(3):e0302219.\u003c/li\u003e\n \u003cli\u003eBenmeir P, et al. Mass casualty hospital surge on October 7, 2023: an Israeli case study. Isr J Health Policy Res. 2024;13:12.\u003c/li\u003e\n \u003cli\u003eErez E, et al. Near-front hospital emergency operations during conflict in Israel. Emerg Med J. 2024;41(5):377\u0026ndash;84.\u003c/li\u003e\n \u003cli\u003eEkzayez A, et al. The cascading impacts of attacks on health in Syria. Confl Health. 2024;18:33.\u003c/li\u003e\n \u003cli\u003eKruk ME, et al. Health system disruptions and hospital services in wartime Ukraine. Lancet Reg Health Eur. 2023;14:100350.\u003c/li\u003e\n \u003cli\u003eAl-Awlaqi S, et al. Health care workers\u0026rsquo; experiences of service disruption in Yemen. Soc Sci Med. 2022;289:114425.\u003c/li\u003e\n \u003cli\u003eBrolin Ribacke KJ, et al. Effects of the West Africa Ebola virus disease on health-care utilization\u0026mdash;a systematic review. BMC Pregnancy Childbirth. 2016;16:82.\u003c/li\u003e\n \u003cli\u003eNomura S, et al. Non-communicable diseases after the Great East Japan Earthquake: a systematic review. PLoS Curr. 2016;8.\u003c/li\u003e\n \u003cli\u003eMoynihan R, et al. Impact of COVID-19 pandemic on utilisation of healthcare services: systematic review. BMJ Glob Health. 2021;6:e006343.\u003c/li\u003e\n \u003cli\u003eXu L, et al. Investigation of health system resilience in 60 countries during COVID-19. Front Public Health. 2022;10:1081068.\u003c/li\u003e\n \u003cli\u003eRahamtalla A, et al. The impact of ongoing armed conflict on Sudan\u0026rsquo;s maternal health services. Glob Health. 2025;21:45.\u003c/li\u003e\n \u003cli\u003eBogale T, et al. Health system strengthening in fragile and conflict-affected states. Int J Health Policy Manag. 2024;13:782\u0026ndash;92.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"Health system resilience, service disruption, disasters, epidemics, armed conflict, Philippines, Zimbabwe, South Korea, Israel","lastPublishedDoi":"10.21203/rs.3.rs-7769361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7769361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eHealth systems remain acutely vulnerable to sudden shocks from natural disasters, epidemics, and armed conflict. Comparative cross-country evidence bridging low- and high-income contexts is limited. This study synthesizes quantitative and qualitative data from the Philippines (Typhoon Haiyan), Zimbabwe (cholera epidemics), South Korea (MERS outbreak), and Israel (armed conflicts, 2014\u0026ndash;2023) to examine health service disruptions and resilience strategies.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe conducted a mixed-methods systematic review of 40 peer-reviewed studies. Quantitative outcomes included service utilization (hospital admissions, outpatient visits, maternal and child health, infectious disease trends) and mortality. Qualitative and policy-focused studies were integrated via thematic analysis. Data sources included hospital records, national surveillance, insurance claims, syndromic surveillance (SPEED), and facility-level surveys.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eAcross settings, crises precipitated sharp, immediate declines in service utilization. In the Philippines, hospital admissions decreased significantly post-Haiyan, with obstetric care reduced (OR 0.4, 95% CI 0.3\u0026ndash;0.6) and infectious/respiratory consultations increased【1\u0026ndash;2】. Syndromic surveillance confirmed spikes in communicable disease visits and reduced non-communicable consultations【2】. Zimbabwe\u0026rsquo;s 2008\u0026ndash;09 cholera epidemic caused 98,585 cases and 4,287 deaths (CFR 4.3%)【3】, with subsequent outbreaks sustaining high CFRs【4】. South Korea\u0026rsquo;s 2015 MERS epidemic reduced outpatient visits by ~\u0026thinsp;17.2%【5】 and triggered healthcare avoidance in 34.5% of the population【6】. In Israel, ED visits declined by 13% during the 2014 Gaza conflict, while admissions rose 10% and 30-day mortality increased (OR 1.42, 95% CI 1.18\u0026ndash;1.70)【7】. Evidence from Syria【8】, Yemen【9】, Ukraine【10】, and Ebola-affected West Africa【11】 revealed parallel disruptions in maternal, infectious, and chronic disease services. Qualitative studies consistently highlighted infrastructure damage, staff attrition, supply chain breakdowns, and disproportionate effects on vulnerable populations【12\u0026ndash;13】.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eDespite contextual differences, common patterns emerge: abrupt disruption, delayed recovery, and disproportionate burdens on maternal, child, and chronic disease services. Policy lessons include syndromic surveillance (Philippines)【2】, adaptive telehealth (Israel)【7】, and resilient PHC networks (Zimbabwe, Yemen)【3,9】. Cross-crisis learning can inform global frameworks to strengthen health system resilience.\u003c/p\u003e","manuscriptTitle":"Disrupted Care, Enduring Lessons: Health Systems Under Crisis in the Philippines, Zimbabwe, South Korea, and Israel\nA Mixed-Methods Comparative Review of Service Disruptions and Policy Resilience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 09:25:27","doi":"10.21203/rs.3.rs-7769361/v1","editorialEvents":[{"type":"communityComments","content":1}],"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":"16a6c00f-51ba-4479-8b38-98df2c4a4b46","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55691731,"name":"Nursing"},{"id":55691732,"name":"City Management and Urban Policy"}],"tags":[],"updatedAt":"2025-10-06T10:46:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 09:25:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7769361","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7769361","identity":"rs-7769361","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}