Economic Downturns as a Public Threat to Mental Health Outcomes: A Systematic Review and Meta-Analysis

Economic Downturns as a Public Threat to Mental Health Outcomes: A Systematic Review and Meta-Analysis | 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 Economic Downturns as a Public Threat to Mental Health Outcomes: A Systematic Review and Meta-Analysis Ioannis Adamopoulos, Aida Vafae Eslahi, Nektarios Karanikas, Niki Syrou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8444991/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: While individual studies indicate that economic crises pose a significant threat to population mental health, the aggregated magnitude and variation of this impact across different contexts remain unclear. This systematic review and meta-analysis synthesizes global evidence on the mental health consequences of economic downturns. Methods: A systematic search of PubMed, EMBASE, Web of Science, Scopus, PsycINFO, and EconLit, alongside and relevant government health department websites was conducted in accordance with PRISMA guidelines. Studies published between 01 January 2000 and 18 December 2025 that examined mental health outcomes in relation to economic crises were eligible. A random-effects meta-analysis using Comprehensive Meta-Analysis (CMA) software was conducted to estimate pooled prevalence, with subgroup analyses conducted according to mental health outcome, geographic region, and economic crisis phase. Study quality was assessed using the Newcastle–Ottawa Scale, and risk of bias was evaluated using the robvis tool. Publication bias was assessed using funnel plots and Egger’s regression test. Results: Thirty-nine studies from 14 countries met the inclusion criteria. The pooled prevalence of adverse mental health outcomes associated with economic crises was 6.4% (95% CI: 4.1–10.1%). Subgroup analyses showed the highest pooled prevalence for self-harm (18.9%), followed by somatoform disorders (17.6%) and distress (14.7%). Marked geographic variation was evident, with higher pooled prevalence estimates reported in several European countries. Mental health burden differed by crisis phase, with the highest prevalence observed during the post-crisis period (16.3%), followed by the pre-crisis phase (7.6%), Conclusions: Economic crises are associated with a substantial and heterogeneous burden of mental health problems, particularly self-harm. The magnitude of impact varies by outcome type, geographic context, and crisis phase, with evidence suggesting delayed and sustained effects beyond the acute crisis period. These findings highlight the need to integrate mental health protection into economic crisis preparedness and recovery policies, including safeguarding mental health services, strengthening social safety nets during and after economic downturns, and implementing a dual strategy of preventive community support for distress and acute clinical intervention for high-risk cohorts. The study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number (CRD420251272042). Health Economics & Outcomes Research Psychology Critical Care & Emergency Medicine Epidemiology Austerity economic recession public health mental disorders public health policy meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Economic crises, defined as periods of substantial financial instability characterized by declining gross domestic product (GDP), rising unemployment, collapsing credit systems, and widespread fiscal austerity [ 1 ]. The 19th century witnessed numerous financial contractions, including the Long Depression (1873–1896) following the collapse of European banks and declining commodity prices, which led to mass unemployment and impoverishment in affected regions [ 1 , 2 ]. The early 20th century saw the Great Depression (1929–1939), which profoundly altered global economic and social landscapes. Unemployment skyrocketed, banks failed, and widespread poverty contributed to malnutrition and the spread of infectious diseases in many countries [ 3 , 4 ]. Following World War II, the global economic order stabilized somewhat, but crises continued to emerge, often triggered by speculative asset bubbles, mismanaged fiscal policies, or exogenous shocks such as oil price spikes [ 5 , 6 ]. More recent examples illustrate the globalized nature of economic vulnerability. The Asian financial crisis of 1997–1998 caused severe economic contraction in several East Asian countries, resulting in job losses, decreased household income, and a documented rise in communicable diseases such as tuberculosis [ 7 – 9 ]. The bursting of the dot-com bubble in 2000 and subsequent lending excesses culminated in the 2007–2008 global financial crisis, widely regarded as the most severe economic shock since the Great Depression. This crisis resulted in widespread corporate bankruptcies, massive job losses, a collapse in private sector lending, surging public debt, and a sharp drop in global trade [ 7 , 10 – 16 ]. More recently, economic shocks driven by pandemics, migration, armed conflicts, and climate change have further strained public health systems, particularly in low- and middle-income countries [ 17 – 19 ]. Rising unemployment, currency devaluation, and inflation reduce household purchasing power and limit access to essential services, amplifying the negative health consequences of these crises. While the immediate focus during such downturns is often on economic indicators, the ripple effects extend far beyond financial markets are recurring phenomena with profound social and public health consequences. Individuals, households, and communities experience elevated stress, reduced access to essential goods and services, and heightened vulnerability to disease [ 20 , 21 ]. The relationship between macroeconomic instability and public health outcomes has been increasingly documented, with evidence showing that recessions and financial collapses can adversely affect mental health, exacerbate inequalities, and disrupt healthcare provision [ 22 , 23 ]. The World Health Organization (WHO) describes mental health as encompassing a wide range of activities that directly or indirectly relate to mental well-being, aligning with its overall definition of health as “a state of complete physical, mental, and social well-being, and not merely the absence of disease.” This approach is also in line with the perspective of the American Psychological Association (APA), which defines psychological disorders as patterns of symptoms or behaviors that impair an individual’s well-being and interfere with normal daily functioning [ 24 ]. Economic crises affect mental health through multiple direct and indirect pathways. One primary pathway is increasing psychosocial stress, driven by unemployment, income insecurity, and uncertainty about the future. Elevated stress levels are strongly associated with depression, anxiety, substance use, and suicidal behavior [ 25 – 28 ]. Evidence supports that populations experiencing high unemployment rates during recessions show increased prevalence of mental health disorders, and suicide rates often rise in parallel, particularly among working-age males and younger adults [ 29 , 30 ]. A second pathway is decreasing healthcare access and service provision. Fiscal austerity and declining public revenues often lead to cuts in health budgets, reduced availability of preventive and primary care, and increased out-of-pocket expenses for patients [ 20 , 31 ]. During the 2008 global financial crisis, several European countries implemented austerity policies that reduced funding for hospitals, mental health services, and public health programs, disproportionately affecting vulnerable populations [ 32 – 34 ]. In low-income countries, reductions in donor support and local health budgets can impair vaccination campaigns, infectious disease surveillance, and treatment programs, leading to increased morbidity and mortality [ 7 , 17 , 19 ]. The conditions above can exacerbate existing conditions as reduced access often leads to discontinuation or inadequacy of treatment for those with chronic conditions. Patients with several mental illnesses are at significantly higher risk of relapse when monitoring, medication management, and community support workers are unavailable [ 35 , 36 ]. For individuals with complex needs, the lack of community care can lead to an inability to cope with daily life, resulting in severe self-neglect, hygiene issues, and nutritional deficiencies. Also, delays in accessing early intervention for disorders like depression or anxiety can make these conditions more difficult to treat when help is finally secured. Navigating complex, underfunded systems is another potent stressor that can trigger or worsen anxiety [ 37 ]. Third, changes in lifestyle and dietary behaviors during economic crises can exacerbate health risks. Financial stress may drive individuals toward cheaper, nutrient-poor diets, increased tobacco or alcohol consumption, and reduced physical activity. Behavioral shifts, combined with weakened health services, can lead to higher prevalence of chronic diseases, obesity, and non-communicable disease-related mortality [ 7 , 27 , 38 , 39 ]. Several mental health problems are related to the conditions above. For example, a nutrient-poor, high-sugar diet can exacerbate depression symptoms by causing blood sugar fluctuations that affect mood and energy. The physiological effects of poor nutrition (e.g., magnesium or B-vitamin deficiencies) can lower the threshold for panic attacks. Increased and long-term alcohol and tobacco use alter brain chemistry, which can cause or worsen clinical depression. Obesity resulting from poor diet and inactivity can lead to social withdrawal and diminished self-worth. Poor nutrition (e.g., high caffeine/sugar), lack of physical activity, and financial stress are a primary triad for chronic insomnia [ 40 – 42 ]. Fourth, the socioeconomic gradient in health becomes particularly pronounced during economic downturns. Poorer populations are disproportionately affected due to limited resources, pre-existing health disparities, and lower individual resilience to unexpected adversities. Rural populations, marginalized urban communities, and informal workers often bear the burden of adverse health outcomes during crises, including increased malnutrition and injuries, elevated infectious disease exposure, and preventable mortality [ 43 – 45 ]. A mental health implication of these specific conditions is cumulative trauma. Individuals in lower socioeconomic positions experience chronic stress due to a lack of autonomy and social participation. This manifests as higher baseline levels of cortisol, leading to permanent changes in brain architecture and increased vulnerability to depressive disorders. Also, the failure of systems to provide protection during financial crises leads to a profound sense of abandonment by society, often manifesting as a form of post-traumatic stress disorder [ 25 , 46 – 48 ]. In children within these populations, malnutrition during critical windows of brain development can lead to permanent cognitive deficits. Moreover, deficiencies in micronutrients can contribute to the onset of depression and cognitive fatigue. In environments where disease exposure is high individuals can develop severe health-related anxiety and hyper-vigilance. While infectious diseases can have direct neuropsychiatric effects (e.g., "brain fog," lethargy, and depressive symptoms following viral or bacterial infections, untreated injuries in informal workers could result in chronic pain, opioid or alcohol dependency, and clinical depression. Despite growing interest, evidence on the mental health impacts of economic crises remains fragmented. Many studies focus on specific countries, population subgroups, or outcomes, and few integrate infectious and non-communicable diseases, mental health, and healthcare system disruptions in a single analysis. This review and meta-analysis aim to fill this gap by evaluating the impact of economic crises on mental health. By integrating data across multiple countries, income levels, and health outcomes, this review provides a holistic understanding of how economic instability shapes population mental health outcomes and resilience. Recognizing these linkages is crucial for designing interventions that protect mental health during financial hardship and reduce inequities exacerbated by economic crises. Methods Search strategy This systematic review and meta-analysis study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist (Figure 1 & Additional file 1) [49]. The study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number (CRD420251272042). A comprehensive search was conducted in electronic databases including PubMed, EMBASE, Web of Science, Scopus, Psychological Information Database (PsycINFO), and Economics Literature (EconLit) which index publications relevant to the scope of this research. Additional searches were conducted in WHO Global Health Observatory and IRIS repositories, the Organisation for Economic Co-operation and Development (OECD) iLibrary, and national government health department websites (e.g., ministries of health and national public health institutes) to identify relevant reports and grey literature not indexed in bibliographic databases. Reference lists of included articles, relevant reviews, and meta-analyses were also manually screened to ensure comprehensive coverage. Considering the changing population demographics and under the goal to capture evidence from more recent crises. All studies published between 1 January 2000 and 18 December 2025, available at the time of the final literature search, were considered eligible. The search strategy combined terms related to economic stressors and mental health outcomes using Boolean operators. The following search string was used in the title and abstract fields of the databases: (“austerity” OR “economic crisis” OR “fiscal crisis” OR “financial crisis” OR “economic recession” OR “economic depression” OR “economic insecurity” OR “banking crisis” OR “unemployment” OR “personnel downsizing” OR “job loss”) AND (“mental health” OR “mental disorder” OR “mental illness” OR “depression” OR “anxiety” OR “suicide” OR “psychological distress” OR “depressive disorder” OR “mood disorder” OR “psychiatric disorder” OR “psychological wellbeing” OR “emotional distress”). Study selection process Following the removal of duplicates, two reviewers independently screened all titles and abstracts against the predetermined eligibility criteria presented in Table 1. Studies that clearly did not meet the inclusion criteria were excluded at this stage. The full text of potentially relevant articles were then obtained and independently assessed by the same two reviewers for eligibility. Any disagreements between reviewers were resolved through discussion, and when consensus could not be reached, a third reviewer was consulted for final decision-making. Table 1. Inclusion and exclusion criteria for study selection. Inclusion criteria Exclusion criteria Primary empirical studies of any design (quantitative, qualitative, or mixed-methods) examining the association between an economic crisis and mental health outcomes and reporting original and primary data Non-primary study designs, such as case reports, case series, review articles (systematic, scoping, narrative), meta-analyses, letters, editorials, commentaries, conference abstracts without full text, and publications containing non-original or secondary data Studies that clearly define or describe exposure to an economic crisis event Studies with exposures not including an economic crisis event Studies reporting mental health outcomes measured using validated instruments Studies reporting mental health outcomes through non-validated instruments or solely qualitative analysis, or studies reporting only health behaviors (e.g., smoking, alcohol consumption) without mental health outcomes Studies reporting data to allow effect size estimation Studies reporting data that cannot be used for effect size estimation Studies focusing on either the general population or specific vulnerable social groups (e.g., unemployed individuals, migrants) Studies focusing on relatively narrow groups (e.g. a specific company) Studies published between 01 January 2000 and 18 December 2025 Studies published before 01 January 2000 and after 18 December 2025 Studies published in English Studies published in a language other than English Full-text available through the subscriptions of the host university Full-text unavailable through the subscriptions of the host university Full-text articles retrievable through institutional subscriptions or other reasonable means Studies for which the full text could not be retrieved after reasonable efforts. This includes lack of access through institutional subscriptions, non-response from corresponding authors, incomplete or erroneous bibliographic information, or corrupted, inaccessible, or unreadable full-text documents. Quality assessment An assessment of methodological rigor was conducted for all included studies via the Newcastle-Ottawa Scale (NOS) [50]. This tool facilitates rating across three key domains: Selection (maximum 5 points), Comparability (maximum 2 points), and Outcome (maximum 3 points). Studies scoring 7–9 on the NOS were classified as high quality and included. Studies scoring 4–6 were classified as moderate quality. Studies scoring < 4 or lacking sufficient methodological clarity were excluded due to high risk of bias or insufficient data [51]. Second, to critically evaluate the risk of bias in estimating the causal effect of economic crisis exposure on mental health outcomes, the Risk-of-bias VISualization (robvis) web-based tool was employed [52]. The NOS and robvis assessments were conducted independently by two reviewers. Any discrepancies were resolved through discussion or consultation with a third reviewer. Software and data analysis All statistical analyses were conducted using Comprehensive Meta-Analysis (CMA) software, Version 3.0 [53]. The primary aim was to calculate a pooled estimate of the event rate (proportion) and its corresponding 95% confidence interval (CI). Given the anticipated clinical and methodological heterogeneity across the included studies, a random-effects model was employed for all primary analyses. This model accounts for both within-study variance and between-study variance, providing a more conservative and generalizable pooled estimate than a fixed-effect model. Statistical heterogeneity across studies was examined using Cochrane’s Q test and the I ² statistic. I² values were categorized as indicating low (75%) heterogeneity [54]. A funnel plot was generated by plotting the standard error of each study's logit event rate against its effect size (logit event rate). In the absence of bias, the plot should resemble a symmetrical inverted funnel. To statistically evaluate funnel plot asymmetry, Egger's linear regression test was performed. This test assesses whether the intercept of the regression line, which predicts the standardized effect by its precision, significantly deviates from zero. A p -value of less than 0.05 was considered indicative of statistically significant publication bias. Results Study screening The systematic search across nine databases and registers identified 15,043 records. After removal of 13,147 duplicate records, 1,896 unique records were screened by title and abstract, of which 1,773 were excluded for not meeting the inclusion criteria. A total of 355 reports were sought for retrieval; however, 232 reports could not be retrieved due to unavailability of full texts, lack of access through databases or institutional subscriptions, incomplete or erroneous bibliographic information, corrupted or unreadable files, or failure to obtain reports after contacting corresponding authors. Consequently, 123 reports were assessed for full-text eligibility (Figure 1). Following full-text review, 84 reports were excluded due to non-original or secondary data (n = 12), inappropriate outcome measures (n = 21), overlapping datasets (n = 17), insufficient relevant data (n = 26), or publication in languages other than English (n = 8). Ultimately, 39 studies met all eligibility criteria and were included in the systematic review and meta-analysis. The study selection process is summarized in the PRISMA flow diagram (Figure 1). Quality assessment and risk of bias The quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Based on this assessment, 23 studies (59.0%) were rated as high quality (scores of 7–9), and 16 studies (41.0%) were rated as moderate quality (scores of 4–6). No studies were rated as low quality (scores <4). The detailed quality ratings are presented in Additional file 2. The risk of bias for the 39 included non-randomized studies was assessed using the robvis tool across five domains. The detailed assessment for each individual study provided in Figure 2 along with a visual summary of the judgments is presented in Figure 3. The overall assessment revealed a strong methodological foundation across the evidence base. The vast majority of studies (all 39) were judged to have a "Low risk" of bias across all five domains (D1–D5), as indicated by the uniform green checkmarks in Figure 2. Consequently, the overall risk of bias was judged as "Low" for all 39 included studies. This consistent "Low risk" rating across domains indicates that: 1) The selection of exposed and non-exposed cohorts (D1) was appropriately handled; 2) Deviations from intended exposures (D2) were not a significant source of bias; 3) Missing outcome data (D3) were minimal or adequately addressed; 4) The measurement of outcomes (D4) was robust and unlikely to differ between groups; 5) The selection of the reported result (D5) did not appear to be biased. The summary graph in Figure 3 quantitatively reflects this pattern, showing nearly 100% of studies rated as "Low risk" for each domain and for the overall judgment. This high and consistent rating strengthens confidence in the validity of the synthesized findings from these studies. Study characteristics The studies summarized in Table 2 encompass 39 publications from a diverse range of geographic regions, with a particular concentration in Southern European countries like Greece and Spain, which were severely impacted by the 2008 financial crisis and subsequent austerity measures. Methodologically, the research employs a variety of designs, including cross-sectional surveys, longitudinal panel studies, repeated cross-sectional analyses, and ecological time-trend analyses. The populations investigated span general adult populations, specific age groups such as older adults and adolescents, clinical patients, and vulnerable subgroups experiencing unemployment or financial strain. Sample sizes range from smaller clinical cohorts of around 1,600 individuals to large national surveys exceeding 30,000 participants. The primary mental health outcomes reported are depression, psychological distress, suicidality (including ideation, attempts, and mortality), anxiety, and self-harm. Collectively, the findings consistently indicate a significant association between economic recessions characterized by job loss, foreclosure, and austerity and a deterioration in population mental health, often exacerbating existing social and economic inequalities (Table 2). Table 2. Study characteristics. ID Study (Authors, Year) Study design Population Country Time period Economic Context Mental health Outcome(s) Key findings [55] Cagney et al. (2014) Longitudinal panel Older adults (≥57 years) USA 2005–2006, 2010–2011 Great Recession, foreclosure crisis Depressive illnesses Higher neighborhood foreclosure rates associated with increased odds of depression onset (ORs: 1.45–1.75 across foreclosure stages). [56] Kokkevi et al. (2014) Cross-sectional Adolescents (15–19 years) Greece 2011 Severe economic recession in Greece Suicide attempts; running away from home 11.3% reported suicide attempts; 11.6% reported running away. Shared psychosocial correlates included poor family relationships, school dissatisfaction, substance use, and emotional problems. No direct link to economic status in regression. [57] Åsgeirsdóttir et al. (2016) Population-based registry General population Iceland 2003–2012 Economic boom (pre-2008) and collapse (post-2008) Suicide attempts and self-harm Men showed peak during economic boom; decrease in new attendances post-crisis for both genders. Unemployment rise associated with reduced attendances for men. [58] Basta et al. (2018) Retrospective observational General population of Crete Greece 1999–2013 Economic crisis (post-2008) Suicide mortality rates Increase in middle-aged/elderly men; regional disparities linked to mental health services. [59] Blomqvist et al. (2014) Repeated cross-sectional Women aged 18–64 Sweden 2006, 2010 Economic recession, social insurance reforms Mental distress, Limiting longstanding illness Increased mental distress in all labour market groups; inequalities widened, explained by social/economic conditions. [60] Borges et al. (2010) Cross-sectional Adults aged 18+ 21 countries 2001–2007 Pre- and early crisis 12-month suicide ideation, plans, attempts Similar prevalence across developed/developing countries; risk indices predict attempts accurately [61] Bartoll et al. (2013) Cross-sectional Adults aged 16–64 Spain 2006–2007, 2011–2012 Economic crisis (post-2008) Poor mental health Mental health worsened in men, improved slightly in women; inequalities increased in men. [62] Bracone et al. (2024) Prospective cohort 1,647 adults from the Moli-sani cohort Italy 2005–2006 to 2017–2020 Great Recession (late 2000s) Depressive illnesses Economic hardship was associated with increased depression symptoms, decreased mental health perception, and poorer physical health over time. [63] Borrell et al. (2017) Ecological time-trend analysis Residents >25 years in Basque Country & Barcelona Spain 2001–2004, 2005–2008, 2009–2012 Economic recession Suicide mortality rate Inequalities in suicide mortality by education remained stable among men before and during the recession; no clear increase in inequalities was observed. [64] Corcoran et al. (2015) Interrupted time series analysis National suicide and self-harm registry data Ireland 1980–2012, 2004–2012 Economic recession & austerity Suicide and self-harm rates Male suicide increased by 57% and self-harm by 31% during the recession; men aged 25–64 were most affected. Female self-harm also increased significantly. [65] Gill et al. (2012) Cross-sectional surveys Primary care attendees Spain 2006 vs. 2010 Financial crisis Mood, anxiety, somatoform Significant increases in mood, anxiety, somatoform, and alcohol-related disorders among primary care attendees during the crisis, linked to unemployment and mortgage difficulties. [66] Economou et al. (2011) Repeated cross-sectional Representative sample of 2,256 adults Greece 2009 vs. 2011 Peak of the Greek sovereign debt crisis, austerity measures, high unemployment. Suicidal ideation; suicide attempts. A 36% increase in reported suicide attempts from 2009 to 2011. Individuals with high economic distress were significantly more likely to report suicide attempts (10% vs. 0.6%) and ideation (21.2% vs. 7.4%). [67] Drydakis (2014) Longitudinal panel study Working-age adults (18–65) in the labor force; person-observations Greece 2008–2013 Financial crisis Poor mental health. Unemployment had a significant negative effect on both SRH and mental health. The detrimental effects were significantly stronger during the high-unemployment crisis period (2010–13). Women were more negatively affected than men. [68] Dunlap et al. (2016) Cross-sectional, nationally representative 21,100 adults from the U.S. civilian non-institutionalized population USA 2008–2010 The "Great Recession"; high unemployment and mortgage foreclosure crisis. Serious Psychological Distress; Substance Use Disorders; Mental Health Service Utilization. Individual-level factors (unemployment, poverty) predicted. Macroeconomic conditions (high county unemployment & state mortgage delinquency rates) were significantly associated with lower mental health service use among those with SPD. Lack of insurance was also a key barrier. [69] Åslund et al. (2014) Cross-sectional 20,538 adults aged 18–85 Sweden 2008 General unemployment Psychosomatic symptoms, low psychological well-being (GHQ-12) Unemployment associated with worse mental health. Low social capital (esp. tangible support) had additive negative effects. No buffering effect found. [70] Economou et al. (2019) Cross-sectional 2,188 adults Greece 2013 Economic recession, austerity Major depression (SCID), suicidality Income and financial difficulties independently associated with depression. Income linked to suicidality in men only. Financial difficulties strongly linked to depression in both genders. [71] Tamayo-Fonseca et al. (2018) Repeated cross-sectional survey analysis Adults ≥16 years, Valencian Community residents Spain 2005, 2010 Economic crisis onset (2008–2010) in Spain Risk of poor mental health (GHQ-12 ≥3) Prevalence of poor mental health increased from 20.0% (2005) to 27.8% (2010). Unemployment and low income contributed significantly to the rise. [72] Forbes & Krueger (2019) Longitudinal survey U.S. adults, Midlife in the United States (MIDUS) sample United States 2003–2004 (pre-recession), 2012–2013 (post-recession) The Great Recession (2007–2009) Symptoms of depression, generalized anxiety, panic, problematic alcohol/substance use Recession impacts (financial, job-related, housing) were associated with higher odds of internalizing symptoms. Population-level mental health improved, but individual-level impacts were negative. [73] Elbogen et al. (2020) Longitudinal survey U.S. adults, NESARC sample United States 2001–2002 (Wave 1), 2004–2005 (Wave 2) Pre- and post-2001 economic downturn Suicide attempts, suicidal ideation Cumulative financial strain (debt, unemployment, homelessness, low income) predicted suicide attempts. Four financial risk factors increased suicide attempt probability 20-fold. [74] Astell-Burt & Feng (2013) Repeated cross-sectional survey Working-age adults (16–64 years) United Kingdom 2006–2010 (quarterly data) 2008 economic recession Self-reported poor health, depression, mental illness, cardiovascular/respiratory problems Poor health prevalence increased from 25.7% (2009) to 29.5% (2010). Increases were seen across all employment and occupational groups, not just the unemployed. [75] Koutra et al. (2020) Cross-sectional survey College students Greece During economic crisis (post-2008) Ongoing austerity, high unemployment Non-suicidal self-injury (NSSI); suicidal ideation/behaviors 27% NSSI, 38.6% suicidal ideation. Social capital not protective. Depression and stress were significant predictors of NSSI/SIB. [76] Katikireddi et al. (2012) Repeat cross-sectional analysis Working-age adults (25–64 years) England 1991–2010 2008 recession onset GHQ-12 caseness (poor mental health) Mental health deteriorated in men post-2008, not explained by employment status. Women showed no significant change. Inequalities increased over decade but not specifically due to recession. [77] Nour et al. (2016) Repeated cross-sectional Canadian working-age adults (15–64 years) Canada 2007–2013 2008 global financial crisis, stimulus, and austerity periods Poor self-reported mental health, anxiety disorders, mood disorders, heavy alcohol drinking, decreased fruit/vegetable consumption Austerity period associated with increased odds of poor mental health, anxiety/mood disorders, heavy drinking, and decreased healthy eating. Stimulus period linked to heavy drinking. [78] Miret et al. (2014) Cross-sectional household Non-institutionalized adults aged ≥18 years in Spain; compared with ESEMED (2001/2002) Spain 2011–2012 (compared to 2001–2002) Economic crisis and austerity measures Suicidal ideation, suicide planning, suicide attempts (lifetime and 12-month prevalence) No significant change in suicidality prevalence compared to pre-crisis period. Factors associated with suicidality varied by age: younger adults (unemployment, heavy drinking), middle-aged (loneliness), older adults (financial problems). [79] Modrek & Cullen (2013) Longitudinal cohort Employees of a U.S. aluminum manufacturing company USA 2006–2010 2007–2009 “Great Recession” Incident diagnoses of hypertension, diabetes, asthma/COPD, depression Workers in high-layoff plants had increased risk of hypertension (especially hourly workers) and diabetes (salaried workers). No significant association with depression or asthma/COPD. [80] Ostamo & Lönnqvist (2001) Longitudinal cohort, sample-based monitoring Residents of Helsinki aged 15+ treated for suicide attempts Finland 1989–1997 Severe economic recession, unemployment up to 18% Attempted suicide rates, methods, alcohol use Overall attempted suicide rates remained stable; male rates decreased significantly; female rates increased slightly; convergence of gender rates; poisoning as method increased [81] Paraschakis et al. (2018) Retrospective forensic Suicide cases in Piraeus area from forensic records Greece 2006–2010 vs. 2011–2015 Severe economic crisis, austerity measures Completed suicides, psychiatric medication, drug/alcohol use, suicide methods Slight decrease in suicides during crisis; higher psychiatric medication intake (especially males); no significant change in methods or substance use [82] Odone et al. (2017) Repeated cross-sectional Italian national population aged 25+ from ISTAT surveys Italy 2005 vs. 2013 Ongoing economic crisis, high unemployment Poor mental health (SF-12 MCS score), risk of depression/anxiety Poor mental health increased from 21.5% to 24.4%; highest rise in young males (24%); vulnerable groups at higher risk but not disproportionately affected by crisis [83] Pruchno et al. (2016) Longitudinal panel Older adults aged 50–74 USA 2006–2008 to 2011–2012 Great Recession (2008) Depressive symptoms (CES-D-10), incident/chronic/remitted depression Significant increase in depressive symptoms post-recession; incident depression linked to job loss, caregiving, illness; women, married, employed, higher-middle income most affected [84] Sareen et al. (2011) Prospective longitudinal 34,653 adults (≥20 years) USA 2001–2005 (2 waves, 3 years apart) Pre-recession period DSM-IV Axis I & II disorders, suicide attempts Lower household income associated with lifetime disorders & suicide attempts; income reduction linked to incident mood, anxiety, substance use disorders. [85] Rodrigues & Nunes (2017) Cross-sectional, ecological Working-age adults (15–64 years) hospitalized for major depression Portugal 2008 vs. 2013 Pre- vs. during economic crisis Hospitalization for major depression Hospitalization rates increased during crisis; higher in rural/low-density areas; influenced by bed availability. [86] Ruiz-Pérez et al. (2017) Cross-sectional, multilevel Adults ≥16 years from National Health Survey Spain 2006 vs. 2011–2012 Pre- vs. post-recession Self-reported poor mental health (GHQ-12) Lower health spending & higher temporary employment linked to worse mental health, especially in men. [87] Shi et al. (2011) Repeated cross-sectional Adults ≥16 years from monthly Australia 2002–2009 (monthly) Pre- & during GFC Anxiety, depression, stress, psychological distress, suicidal ideation No overall increase in mental health problems during GFC; anxiety increased in part-time workers, decreased in full-time workers. [88] Sicras-Mainar (2015) Retrospective, longitudinal, observational Patients diagnosed with Major Depressive Disorder in primary care Spain 2008–2009 (pre-crisis) vs. 2012–2013 (crisis) Period of severe economic crisis in Spain, with high unemployment and austerity. 1. MDD Prevalence 2. Antidepressant (AD) consumption & patterns 3. Treatment persistence MDD prevalence increased from 5.4% to 8.1% during the crisis. AD use rose by 35.2%, while drug expenditures fell 38.7%. Most patients (60.8%) discontinued or did not change initial AD treatment. [89] Vanderoost et al. (2013) Cross-sectional Patients aged 18–49 years visiting general practices Belgium (Flanders & Wallonia) Sept–Dec 2010 Period following the 2009 financial crisis, characterized by corporate reorganizations and dismissals. Suicidal thoughts in the past 12 months 11.7% had seriously considered suicide in the past year. Recent employment loss was a significant independent risk factor for suicidal thoughts (OR=8.8). Other factors: being single, poor social contacts, depressive complaints. [90] Thomas et al. (2007) Longitudinal panel General population aged >16 years from the British Household Panel Survey United Kingdom 1991–2000 Period not defined as a national "crisis," but study models the impact of individual employment transitions. Psychological distress (GHQ-12 score >3) Transitions to unemployment increased risk of distress (Men: OR 3.15; Women: OR 2.60). This effect was partially mediated by subjective financial deterioration. Gaining employment reduced distress only if it improved financial circumstances. [91] Economou et al. (2013) Cross-sectional Nationally representative adults aged 18–69 Greece 2009, 2011 (pre- and post-crisis) Severe economic crisis, high unemployment (16.6% in 2011), GDP decline Suicidal ideation and reported suicide attempts (past month) Significant increase in suicidal ideation (5.2% to 6.7%) and suicide attempts (1.1% to 1.5%) from 2009 to 2011. High-risk groups: men, married individuals, those with depression, financial strain, low interpersonal trust, previous suicide attempts. [92] Wang et al. (2010) Cross-sectional Working population aged 25–65 Canada (Alberta) Jan 2008 – Oct 2009 Global economic crisis, rising job insecurity 12-month prevalence of Major Depressive Disorder, dysthymia, anxiety disorders 12-month Major Depressive Disorder increased from 5.1% (pre-Sept 2008) to 7.6% (post-March 2009). Lifetime dysthymia also increased. Men and married/common-law individuals showed significant increases in MDD. No significant change in anxiety disorders. [93] Bonnie Lee et al. (2017) Interrupted time series analysis (nationwide, prospective, population-based) Adults aged 24–59 enrolled in Taiwan's National Health Insurance Taiwan January 2007 – December 2012 Global financial crisis (2008), economic recession, rising unemployment, GDP decline Hospitalizations due to depressive illnesses (bipolar disorder, depressive disorder, affective disorder, neurotic depression; ICD-9: 296, 311, 300.4) Low-income groups had ~10x higher adjusted hospitalization rates than high-income groups. Low-income men showed an 18.0% increase in hospitalization rates starting April 2008. Low-income women showed a 14.2% increase starting April 2008. High-income women showed a gradual 5.0% monthly increase starting April 2008. Middle-income men showed a temporary decrease in hospitalization rates. Overall, women had higher hospitalization rates than men across all income groups. Analysis results The meta-analysis of 39 studies yielded a pooled prevalence of mental health outcomes related to economic crises. Under the random-effects model, the overall pooled prevalence was 6.4% (95% CI: 4.1–10.1%) (Figure 4). Heterogeneity was high ( I ² = 99.99%, p = 0.00), indicating substantial variability across the included studies, which was expected given the diversity in populations, outcome measures, and economic contexts. Publication bias Visual inspection of the funnel plot (Figure 5) did not reveal substantial asymmetry. Egger’s regression test indicated a significant intercept (56.09; 95% CI: 0.66–111.53; p = 0.047), suggesting the presence of small-study effects and potential publication bias in the meta-analysis. Subgroup analysis based on mental health outcome There were nine types of mental health disorders recognized in the included studies, with pooled estimates ranging from 0.7 to 18.9%. Subgroup analysis of mental health outcomes revealed that the highest pooled prevalence was related to self-harm (18.9%, 95% CI: 17.4–20.4%), followed by somatoform disorder (17.6%, 95% CI: 17.0–18.3%) and the lowest was observed for suicide (0.7%, 95% CI: 0–15.3%) (Figure 6). The pooled estimated for other mental health disorders was as follows: Distress (14.7%, 95% CI: 17.1–28.0%); poor mental health (13.0%, 95% CI: 6.2–25.1%); depressive illnesses (5.8%, 95% CI: 3.3– 10.1%); suicide attempts (5.6%, 95% CI:1.9– 15.8%); generalized anxiety (5.2%, 95% CI:1.3– 18.3%); and suicidal thoughts (4.2%, 95% CI:3.3– 5.3%) (Figure 6). Subgroup analysis based on geographic region Based on the studies included in the analysis, mental health outcomes impacted by economic crisis were reported in 14 countries (Table 2 & Figure 7). The largest number of studies was conducted in Greece (8 studies), followed by Spain (7 studies) and USA (7 studies). The pooled prevalence on different countries ranged from 0 to 57.0%, with the following pooled estimates: 57.0% (95% CI: 55.7– 58.4%) in Finland, 23.9% (95% CI: 9.8– 47.7%) in Sweden, 20.7% (95% CI: 9.8– 38.6%) in Spain, 16.0% (95% CI: 7.0– 32.4%) in Italy, 13.1% (95% CI: 10.9–15.8%) in Belgium, 10.8% (95% CI: 2.4– 37.5%) in UK, 5.1% (95% CI: 2.9– 8.7%) in USA, 4.7% (95% CI: 1.1– 17.5%) in Greece, 3.6% (95% CI: 1.1– 11.3%) in Canada, 2.7% (95% CI: 2.5– 2.8%) in Australia, 1.4% (95% CI: 1.3–1.4%) in Iceland, 1.3% (95% CI: 1.3– 1.3%) in Taiwan, 0.2% (95% CI: 0.2– 0.2%) in Portugal; and 0.0% (95% CI: 0.0–0.0%) in Ireland (Figure 7). Subgroup analysis based on economic crisis phase There were three economic crisis phases recognized by our included studies: pre-crisis, during crisis and post crisis. Four studies reported more than one phases, with a pooled estimate of 2.8% (95% CI: 2.0– 3.7%) (Figure 8). The during-crisis phase was accounted in 16 studies, with a pooled estimate of 4.1% (95% CI: 1.6– 10%). Post crisis yielded the highest pooled estimate from 14 studies (16.3%; 95% CI: 10.0– 25.5%), followed by the pre-crisis phase covered in 12 studies (7.6%; 95% CI: 3.8– 14.5%) (Figure 8). Discussion Overall picture This systematic review and meta-analysis provides comprehensive quantitative evidence that economic downturns are associated with a substantial burden of adverse mental health outcomes across different populations. By synthesizing data from studies conducted in diverse economic and geographic contexts, this study extends existing literature by quantifying pooled prevalence estimates, identifying outcome-specific patterns, and demonstrating that the mental health impacts of economic crises vary significantly by disorder type, geographic region, and phase of the crisis. The high heterogeneity was expected given the diversity of study designs, populations, outcome definitions, and economic contexts represented. Rather than undermining the findings, this heterogeneity highlights the complex and context-dependent ways in which macroeconomic shocks translate into psychological harm. For instance, the outlier observed in Finland (57.0% prevalence) is attributable to the specific clinical nature of the sample, which focused on individuals treated for suicide attempts [79], whereas lower estimates in Greece (3.6%) or Portugal (0.2%) often reflected general population surveys. Furthermore, the use of varied diagnostic tools, ranging from self-reported screening scales like the GHQ-12 [69, 71, 76, 86, 90] to formal clinical diagnoses based on DSM-IV criteria [84], contributes significantly to the observed heterogeneity. Mental health outcomes A key finding of this study is the pronounced variation in prevalence across different mental health outcomes. Self-harm, somatoform disorders, and psychological distress exhibited the highest pooled prevalence estimates, whereas depression, anxiety, suicidal thoughts, suicide attempts, and completed suicide showed lower pooled estimates. This pattern suggests that economic crises may initially manifest as non-specific psychological distress and somatic symptoms, which are more prevalent and more sensitive to social stressors, before progressing to more severe or clinically defined psychiatric outcomes in a subset of individuals [25–27, 65]. The high prevalence of self-harm observed across studies underscores the severity of distress experienced during periods of economic instability. Self-harm may represent a maladaptive coping mechanism in response to acute psychosocial stressors such as job loss, debt, housing insecurity, and uncertainty about the future. The discrepancy between high self-harm prevalence (18.9%) and low suicide prevalence (0.7%) indicates that economic stress disproportionately triggers non-fatal maladaptive coping mechanisms. However, the 57% increase in male suicide in Ireland [64] during the recession suggests that even if the absolute prevalence remains low, the relative increase is critical for public health policy. Nevertheless, the relatively low pooled prevalence of suicide should not be interpreted as evidence of minimal risk; rather, it reflects the rarity of completed suicide at the population level and substantial methodological variation in its measurement. Importantly, even small absolute increases in suicide rates during economic crises can translate into significant public health consequences [20, 26, 27, 30, 94]. Depressive and anxiety disorders showed moderate pooled prevalence estimates, consistent with prior literature demonstrating increased incidence and symptom severity among individuals exposed to unemployment, income loss, and financial strain [95–98]. The lower pooled estimates for these disorders relative to distress and somatoform symptoms may reflect differences in diagnostic thresholds, under-diagnosis, or delayed onset following prolonged exposure to economic hardship. Regional differences Marked geographic variation was observed in pooled prevalence estimates across countries [25, 27, 29, 30, 76]. Higher estimates were reported in several European countries that experienced severe economic contraction and austerity measures following the 2008 global financial crisis. These findings suggest that the mental health consequences of economic downturns are not determined solely by the magnitude of economic shock, but are strongly shaped by social protection systems, labor market policies, and healthcare accessibility [20, 21, 23, 27, 99]. For instance, the lower prevalence observed in settings like Australia [87] and parts of Canada [77] may reflect the impact of fiscal stimulus packages and more robust initial social safety nets, which offered some buffer to the population against the immediate shocks observed in austerity-stricken Southern Europe. In general, countries with weaker social safety nets, higher unemployment persistence, and austerity-driven reductions in public spending may expose populations to prolonged stressors that exacerbate mental health risks. Conversely, settings with stronger welfare protections and sustained investment in health and social services may mitigate some of the psychological harm associated with economic crises. These observations align with the broader literature indicating that policy responses play a critical role in shaping health outcomes during periods of economic instability [20–22, 27, 99]. During recessions, governments adopt austerity measures, including cutting funding for mental‑health and social services. In countries facing strict fiscal consolidation, reductions in preventive and community-based mental health services have been associated with increases in suicide and psychiatric admissions [100]. Reduced access to counselling, medications, and community support, thus, leaves vulnerable individuals with far fewer resources just when they need them most. Finally, the broader economic and social context significantly shapes outcomes. Research indicates that in nations with weaker social safety nets and lower pre-crisis employment protections, the impact of financial downturns on suicide rates and mental disorders tends to be more severe [101]. Crisis phases The subgroup analysis by economic crisis phase revealed that the highest pooled prevalence of mental health outcomes occurred during the post-crisis period, followed by the pre-crisis phase, with lower prevalence observed during the acute crisis itself. This temporal pattern suggests the presence of delayed or cumulative effects of economic stress, whereby prolonged exposure to financial hardship, unemployment, and austerity gradually erodes mental well-being over time [22, 27, 30, 76]. Several mechanisms may explain this phenomenon. During the acute phase of a crisis, individuals and communities may exhibit short-term resilience or adaptive coping strategies. However, as economic recovery stalls, savings are depleted, social supports weaken, and access to healthcare diminishes, psychological distress may intensify. The post-crisis period often coincides with prolonged unemployment, debt accumulation, and sustained reductions in public services, which may collectively drive worsening mental health outcomes [27, 102–104]. Nonetheless, the relatively high prevalence observed in the pre-crisis phase suggests a 'pre-emptive' psychological impact. This suggests that market volatility and the anticipation of austerity generate significant distress prior to the official onset of recession. Furthermore, the Iceland study [57] demonstrates that economic booms can also be associated with peak mental health attendances, suggesting that macroeconomic instability, rather than just contraction, is the primary driver of psychological harm. Study implications The findings of this study support a multifactorial framework linking economic downturns to adverse mental health outcomes and carry clear implications for public health and social policy. Overall, the study underscores that economic crises are not only macroeconomic challenges but also public mental health emergencies. Strategic policy interventions that combine social protection, healthcare access, and targeted psychosocial support have the potential to reduce the mental health burden of economic downturns, protect vulnerable populations, and promote faster societal recovery. Psychosocial stress arising from job loss, income insecurity, and housing instability constitutes a primary pathway, increasing vulnerability to depression, anxiety, self-harm, and suicidal behavior. Behavioral pathways may further amplify risk, as financial strain is associated with increased substance use, poorer diet, reduced physical activity, and social isolation [27, 105–107]. Also, a gendered response to economic strain is evident; while several studies noted increased distress and suicide in males [61, 64], others highlighted that women were more affected by depressive symptoms and service utilisation barriers [67, 83, 93]. Evidence from occupational settings further demonstrates that adverse work-related conditions, including high workload, environmental hazards, and organizational stressors, are strongly associated with burnout and psychological distress among employed populations, which may be amplified during periods of economic instability [108]. System-level mechanisms are also critical. Economic crises often prompt austerity policies that reduce funding for mental health services precisely when demand increases. Disruptions in access to care, medication discontinuation, and reduced availability of community-based services may exacerbate existing conditions and hinder early intervention. Together, these pathways create a reinforcing cycle in which economic hardship undermines mental health, which in turn impairs social and economic recovery [102, 109–111]. The pronounced mental health burden associated with economic downturns suggests that proactive, coordinated policy responses are critical to mitigate psychological harm during periods of economic instability. Evidence from countries with stronger social safety nets indicates that policies aimed at income stabilization, unemployment support, and access to affordable healthcare can buffer the negative mental health effects of financial crises. Policy implications and strategies to mitigate mental health impacts of economic crises Specifically, considering our findings in conjunction with extant literature about measures that can prevent or mitigate the conditions leading to adverse health outcomes, policies should prioritize the following: 1) Strengthening social protection systems. Expansion of unemployment benefits, housing assistance, and income support programs can reduce acute psychosocial stressors and prevent the cascading effects of financial hardship on mental health. Policymakers should consider maintaining or enhancing these measures even after the acute phase of an economic crisis, as the post-crisis period may be associated with the highest prevalence of mental health outcomes [20, 112]. 2) Ensuring access to mental health services. Economic crises often coincide with increased demand for mental health care at a time when public resources may be constrained. Investments in community-based mental health services, telehealth, and early intervention programs can prevent progression from distress and somatic symptoms to more severe psychiatric disorders, self-harm, and suicide [113, 114]. 3) Targeted support for high-risk populations vulnerable groups, including those experiencing long-term unemployment, debt accumulation, or housing instability, may benefit from integrated social and mental health services. Programs that combine financial counseling, job retraining, and psychosocial support can address multiple pathways linking economic stress to mental illness [95, 115]. 4) Monitoring and surveillance. Routine collection of mental health indicators during economic downturns can inform timely policy responses and allow for the identification of emerging high-risk groups. Data-driven interventions can help governments allocate resources efficiently and evaluate the effectiveness of social and health policies [116]. 5) Mitigating long-term socioeconomic disparities. Policies that reduce structural inequalities, such as progressive taxation, universal healthcare, and equitable labor protections, may attenuate the cumulative psychological toll of economic crises and foster resilience at the population level [117–119]. Limitations While this study has several strengths, including a comprehensive search strategy, rigorous quality assessment, and the inclusion of diverse populations and economic contexts, the findings must be interpreted considering a few important limitations. First, the high heterogeneity reflects substantial variation across studies and limits the precision of pooled estimates. Second, most included studies were observational, precluding causal inference. Additionally, the evidence base is heavily weighted toward high-income countries, limiting generalizability to low- and middle-income settings where economic shocks may have even more severe consequences. Last, the significant Egger's test indicates a potential overestimation of prevalence due to small-study effects. This suggests that smaller studies with non-significant findings or lower prevalence rates may remain unpublished, potentially biasing the pooled estimate of 6.4% upwards. Conclusion This systematic review and meta-analysis demonstrates that economic crises are associated with a significant and heterogeneous burden of adverse mental health outcomes at the population level. Self-harm, psychosomatic symptoms, and psychological distress emerge as particularly prevalent outcomes. Importantly, the mental health impact of economic crises varies by geographic context and is moderated by the phase of the crisis, with evidence suggesting delayed and sustained effects extending beyond the acute economic shock and mental health risks beginning in the pre-crisis phase due to anticipatory stress. Our findings underscore that economic instability constitutes not only a financial challenge but a major public mental health threat. The pathways linking economic crises to psychological harm are multifaceted, involving psychosocial stress, behavioral responses, and systemic failures driven by austerity and reduced access to care. Besides, the threat is gender-specific, with males at higher risk of suicide mortality while females often exhibiting higher depressive symptom burdens. Consequently, protecting mental health must be recognized as a core component of economic crisis preparedness and recovery. Policy responses to future economic downturns should prioritize strengthening social safety nets, maintaining employment protections, and safeguarding access to mental health services during and after crises. There is need for both preventive mental health support to address high-prevalence distress/self-harm, and acute clinical intervention to address suicide risk, as a "one-size-fits-all" economic recovery plan might be insufficient. Austerity-driven reductions in health and social spending risk amplifying long-term mental health harm and undermining recovery. Last, we acknowledge that the identified burden, while substantial, may be subject to publication bias in smaller observational studies, reinforcing the need for more rigorous, large-scale surveillance. Hence, future research should focus on underrepresented regions, long-term mental health trajectories, and the evaluation of policy interventions that buffer populations against the psychological consequences of economic shocks. Ultimately, resilient economies require resilient populations, and economic recovery should be measured not only by financial indicators but by improvements in population mental well-being. Abbreviations WHO: World Health Organization CMA: Comprehensive Meta-Analysis CI: Confidence interval NOS: Newcastle-Ottawa Scale Robvis: Risk-of-bias VISualization OECD: Organisation for Economic Co-operation and Development PsycINFO: Psychological Information Database EconLit : Economics Literature PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses PROSPERO: Prospective Register of Systematic Reviews APA: American Psychological Association GDP: Gross domestic product Declarations Declarations Ethics approval and consent to participate This study is based solely on literature reviews and reports. It did not involve human participants or animals; therefore, ethical approval was not required. Consent for publication Not applicable. Competing interests The authors declare no conflicts of interest. The author I.A. serves as an Editor for the BMC Public Health Journal, and he was not involved in the editorial handling or peer-review process of this manuscript. Funding This work received no financial support from any organization. Authors’ contributions Conceptualization, I.A., A.V.E, and N.K.; methodology, I.A., and A.V.E.; software, I.A., and A.V.E.; validation, I.A., and A.V.E.; formal analysis, I.A., and A.V.E.; investigation, A.V.E., I.A., and N.K.; resources, I.A., and A.V.E.; data curation, I.A., A.V.E.; writing—original draft preparation, I.A., A.V.E., M.Y., N.S., P.T., N.K., and G.D. writing—review and editing, I.A., and A.V.E., and N.K.; visualization, A.V.E., N.K., and I.A.; supervision, I.A., and A.V.E.,; project administration, I.A. All the authors reviewed, edited, and approved the final manuscript. Acknowledgements Authors’ contributions Conceptualization, I.A., A.V.E, and N.K.; methodology, I.A., and A.V.E.; software, I.A., and A.V.E.; validation, I.A., and A.V.E.; formal analysis, I.A., and A.V.E.; investigation, A.V.E., I.A., and N.K.; resources, I.A., and A.V.E.; data curation, I.A., A.V.E.; writing—original draft preparation, I.A., A.V.E., M.Y., N.S., P.T., N.K., and G.D. writing—review and editing, I.A., and A.V.E., and N.K.; visualization, A.V.E., N.K., and I.A.; supervision, I.A., and A.V.E.,; project administration, I.A. All the authors reviewed, edited, and approved the final manuscript. Funding This work received no financial support from any organization. Data availability All data used in this study are available from the corresponding author upon request. Ethics approval and consent to participate This study is based solely on literature reviews and reports. It did not involve human participants or animals; therefore, ethical approval was not required. 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WHO European review of social determinants of health and the health divide. Lancet. 2012;380:1011–29. Pickett KE, Wilkinson RG. Inequality: an underacknowledged source of mental illness and distress. Br J Psychiatry. 2010;197:426–8. Lund C, Brooke-Sumner C, Baingana F, Baron EC, Breuer E, Chandra P, et al. Social determinants of mental disorders and the Sustainable Development Goals: a systematic review of reviews. The lancet psychiatry. 2018;5:357–69. Additional Declarations The authors declare no competing interests. Supplementary Files SuplemantaryAdditionalfile1.docx Table. Methodological quality assessment of included studies using the Newcastle-Ottawa Scale (NOS). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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12:58:03","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205753,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/357e5021bdf6a9212c992dc5.png"},{"id":99492882,"identity":"0eed2f45-a9db-4f14-bf5f-61ef483524df","added_by":"auto","created_at":"2026-01-05 05:34:01","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44428,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/ebd4ae694b9bbe359e4922d9.png"},{"id":99790146,"identity":"b8618efe-ae88-41ef-a305-da8e090f51dd","added_by":"auto","created_at":"2026-01-08 12:56:49","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":232188,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/4e95dcd806b27c1bc97f18a8.png"},{"id":99492880,"identity":"af39b1e8-41ee-4f6f-9ff0-ca70179076f7","added_by":"auto","created_at":"2026-01-05 05:34:01","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120357,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/abcb5cc0b311b65447bbae6d.png"},{"id":99492888,"identity":"caa44395-1b3e-40bf-bb27-edbc60dfc55b","added_by":"auto","created_at":"2026-01-05 05:34:01","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73791,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/33660357e62556d730638340.png"},{"id":99492885,"identity":"e60a9053-ce77-4c78-ae06-7baf3ff4fee7","added_by":"auto","created_at":"2026-01-05 05:34:01","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":262748,"visible":true,"origin":"","legend":"","description":"","filename":"rs84449910structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/c8f54aa74c66eaefcb4ff023.xml"},{"id":99492883,"identity":"7e7acd0c-0149-4c35-9bde-2ad904b7ac76","added_by":"auto","created_at":"2026-01-05 05:34:01","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":284027,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/3965dbba5c93560bf68d6f65.html"},{"id":99790391,"identity":"150723ba-5fc6-413e-bbe4-7756c23e8e70","added_by":"auto","created_at":"2026-01-08 12:58:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112006,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram of the study design process.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/6ccfa6451daa000d48552302.png"},{"id":99791325,"identity":"f428c8da-d83e-4532-b58a-95ad9802f1d3","added_by":"auto","created_at":"2026-01-08 12:59:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2742134,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of bias assessment of included studies using the Risk-of-bias VISualization (robvis) tool (traffic light plot). Green circles indicate low risk of bias, yellow circles indicate some concerns, and red circles indicate high risk of bias within each domain. (The overall judgment reflects the highest level of concern across domains, in line with robvis guidance. Studies were not excluded based solely on the overall risk-of-bias rating; rather, these assessments were used to inform interpretation of results and the certainty of the evidence).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/00ea6e3c75d963da9224855b.png"},{"id":99492864,"identity":"11eff1d1-524b-4491-acdc-f9facf59c1bf","added_by":"auto","created_at":"2026-01-05 05:34:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144444,"visible":true,"origin":"","legend":"\u003cp\u003eSummary plot of the overall risk of bias across studies. The bar graph illustrates the percentage of studies judged to have low risk, some concerns, or high risk of bias across all Risk-of-bias VISualization (robvis) domains and for the overall judgment.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/e93c61dd27a181d521f021ec.png"},{"id":99790236,"identity":"7550b3fa-33a0-44cf-9e2b-669cb6ef211f","added_by":"auto","created_at":"2026-01-08 12:57:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1290610,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the pooled prevalence of mental health outcomes under the random-effects model. The size of each square represents the study's weight in the meta-analysis, and the horizontal lines indicate the 95% confidence interval for each study. The diamond at the bottom represents the overall pooled estimate and its 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/23a3c3301c1814c949324a1b.jpeg"},{"id":99492866,"identity":"24f622d1-30af-4673-81fd-c9fd014a9522","added_by":"auto","created_at":"2026-01-05 05:34:00","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":155044,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot to assess potential publication bias. Each dot represents an individual study’s effect estimate plotted against its standard error. In the absence of bias, studies should be distributed symmetrically around the pooled effect size (vertical line).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/2f1272a6ad07aa11aee307a8.jpeg"},{"id":99790861,"identity":"caad0237-529b-4edd-94e1-11f00835cf3c","added_by":"auto","created_at":"2026-01-08 12:58:47","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1156855,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the pooled prevalence stratified by specific mental health outcome. Subgroup meta-analyses show the prevalence estimates for suicide, self-harm, distress, somatoform disorder, poor mental health, generalized anxiety, depression, suicidal thoughts, and suicide attempts.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/66f0031bdd38ae0452a8058b.jpeg"},{"id":99492869,"identity":"dc982f33-424f-4555-a78d-c304b1d423c8","added_by":"auto","created_at":"2026-01-05 05:34:00","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":563453,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing pooled prevalence estimates stratified by geographic region, with subgroup analyses conducted to compare prevalence across different world regions.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/644afc20c6715f0a8955db41.jpeg"},{"id":99790453,"identity":"084c4b2d-9299-4987-9fb0-1d986b216749","added_by":"auto","created_at":"2026-01-08 12:58:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":154700,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the pooled prevalence of mental health outcomes stratified by economic crisis phase. Subgroup meta-analysis compares prevalence estimates for studies conducted during the pre-crisis, during crisis, post-crisis, and pre-post/during crisis periods. Each box represents an individual study's event rate, with horizontal lines showing the 95% confidence interval. The diamonds represent the pooled prevalence estimate for each subgroup under the mixed-effects model.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/fd883ed94fe4b81bf0bb40e3.png"},{"id":99803069,"identity":"5e4f14d8-245b-4036-bdf3-a5429a40c1a0","added_by":"auto","created_at":"2026-01-08 14:09:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7748991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/1910d1a3-a030-4cd9-9aa1-0a2c6916088d.pdf"},{"id":99492861,"identity":"4e9ed822-5e51-474c-add2-78625f093757","added_by":"auto","created_at":"2026-01-05 05:34:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20347,"visible":true,"origin":"","legend":"\u003cp\u003eTable. Methodological quality assessment of included studies using the Newcastle-Ottawa Scale (NOS).\u003c/p\u003e","description":"","filename":"SuplemantaryAdditionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8444991/v1/a7744e17ba7874249d68783b.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEconomic Downturns as a Public Threat to Mental Health Outcomes: A Systematic Review and Meta-Analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eEconomic crises, defined as periods of substantial financial instability characterized by declining gross domestic product (GDP), rising unemployment, collapsing credit systems, and widespread fiscal austerity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The 19th century witnessed numerous financial contractions, including the Long Depression (1873\u0026ndash;1896) following the collapse of European banks and declining commodity prices, which led to mass unemployment and impoverishment in affected regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The early 20th century saw the Great Depression (1929\u0026ndash;1939), which profoundly altered global economic and social landscapes. Unemployment skyrocketed, banks failed, and widespread poverty contributed to malnutrition and the spread of infectious diseases in many countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Following World War II, the global economic order stabilized somewhat, but crises continued to emerge, often triggered by speculative asset bubbles, mismanaged fiscal policies, or exogenous shocks such as oil price spikes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore recent examples illustrate the globalized nature of economic vulnerability. The Asian financial crisis of 1997\u0026ndash;1998 caused severe economic contraction in several East Asian countries, resulting in job losses, decreased household income, and a documented rise in communicable diseases such as tuberculosis [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The bursting of the dot-com bubble in 2000 and subsequent lending excesses culminated in the 2007\u0026ndash;2008 global financial crisis, widely regarded as the most severe economic shock since the Great Depression. This crisis resulted in widespread corporate bankruptcies, massive job losses, a collapse in private sector lending, surging public debt, and a sharp drop in global trade [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore recently, economic shocks driven by pandemics, migration, armed conflicts, and climate change have further strained public health systems, particularly in low- and middle-income countries [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Rising unemployment, currency devaluation, and inflation reduce household purchasing power and limit access to essential services, amplifying the negative health consequences of these crises. While the immediate focus during such downturns is often on economic indicators, the ripple effects extend far beyond financial markets are recurring phenomena with profound social and public health consequences. Individuals, households, and communities experience elevated stress, reduced access to essential goods and services, and heightened vulnerability to disease [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The relationship between macroeconomic instability and public health outcomes has been increasingly documented, with evidence showing that recessions and financial collapses can adversely affect mental health, exacerbate inequalities, and disrupt healthcare provision [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) describes mental health as encompassing a wide range of activities that directly or indirectly relate to mental well-being, aligning with its overall definition of health as \u0026ldquo;a state of complete physical, mental, and social well-being, and not merely the absence of disease.\u0026rdquo; This approach is also in line with the perspective of the American Psychological Association (APA), which defines psychological disorders as patterns of symptoms or behaviors that impair an individual\u0026rsquo;s well-being and interfere with normal daily functioning [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Economic crises affect mental health through multiple direct and indirect pathways. One primary pathway is increasing psychosocial stress, driven by unemployment, income insecurity, and uncertainty about the future. Elevated stress levels are strongly associated with depression, anxiety, substance use, and suicidal behavior [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Evidence supports that populations experiencing high unemployment rates during recessions show increased prevalence of mental health disorders, and suicide rates often rise in parallel, particularly among working-age males and younger adults [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA second pathway is decreasing healthcare access and service provision. Fiscal austerity and declining public revenues often lead to cuts in health budgets, reduced availability of preventive and primary care, and increased out-of-pocket expenses for patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. During the 2008 global financial crisis, several European countries implemented austerity policies that reduced funding for hospitals, mental health services, and public health programs, disproportionately affecting vulnerable populations [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In low-income countries, reductions in donor support and local health budgets can impair vaccination campaigns, infectious disease surveillance, and treatment programs, leading to increased morbidity and mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The conditions above can exacerbate existing conditions as reduced access often leads to discontinuation or inadequacy of treatment for those with chronic conditions. Patients with several mental illnesses are at significantly higher risk of relapse when monitoring, medication management, and community support workers are unavailable [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor individuals with complex needs, the lack of community care can lead to an inability to cope with daily life, resulting in severe self-neglect, hygiene issues, and nutritional deficiencies. Also, delays in accessing early intervention for disorders like depression or anxiety can make these conditions more difficult to treat when help is finally secured. Navigating complex, underfunded systems is another potent stressor that can trigger or worsen anxiety [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, changes in lifestyle and dietary behaviors during economic crises can exacerbate health risks. Financial stress may drive individuals toward cheaper, nutrient-poor diets, increased tobacco or alcohol consumption, and reduced physical activity. Behavioral shifts, combined with weakened health services, can lead to higher prevalence of chronic diseases, obesity, and non-communicable disease-related mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Several mental health problems are related to the conditions above. For example, a nutrient-poor, high-sugar diet can exacerbate depression symptoms by causing blood sugar fluctuations that affect mood and energy. The physiological effects of poor nutrition (e.g., magnesium or B-vitamin deficiencies) can lower the threshold for panic attacks. Increased and long-term alcohol and tobacco use alter brain chemistry, which can cause or worsen clinical depression. Obesity resulting from poor diet and inactivity can lead to social withdrawal and diminished self-worth. Poor nutrition (e.g., high caffeine/sugar), lack of physical activity, and financial stress are a primary triad for chronic insomnia [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFourth, the socioeconomic gradient in health becomes particularly pronounced during economic downturns. Poorer populations are disproportionately affected due to limited resources, pre-existing health disparities, and lower individual resilience to unexpected adversities. Rural populations, marginalized urban communities, and informal workers often bear the burden of adverse health outcomes during crises, including increased malnutrition and injuries, elevated infectious disease exposure, and preventable mortality [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A mental health implication of these specific conditions is cumulative trauma. Individuals in lower socioeconomic positions experience chronic stress due to a lack of autonomy and social participation. This manifests as higher baseline levels of cortisol, leading to permanent changes in brain architecture and increased vulnerability to depressive disorders. Also, the failure of systems to provide protection during financial crises leads to a profound sense of abandonment by society, often manifesting as a form of post-traumatic stress disorder [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn children within these populations, malnutrition during critical windows of brain development can lead to permanent cognitive deficits. Moreover, deficiencies in micronutrients can contribute to the onset of depression and cognitive fatigue. In environments where disease exposure is high individuals can develop severe health-related anxiety and hyper-vigilance. While infectious diseases can have direct neuropsychiatric effects (e.g., \"brain fog,\" lethargy, and depressive symptoms following viral or bacterial infections, untreated injuries in informal workers could result in chronic pain, opioid or alcohol dependency, and clinical depression. Despite growing interest, evidence on the mental health impacts of economic crises remains fragmented. Many studies focus on specific countries, population subgroups, or outcomes, and few integrate infectious and non-communicable diseases, mental health, and healthcare system disruptions in a single analysis.\u003c/p\u003e \u003cp\u003eThis review and meta-analysis aim to fill this gap by evaluating the impact of economic crises on mental health. By integrating data across multiple countries, income levels, and health outcomes, this review provides a holistic understanding of how economic instability shapes population mental health outcomes and resilience. Recognizing these linkages is crucial for designing interventions that protect mental health during financial hardship and reduce inequities exacerbated by economic crises.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSearch strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review and meta-analysis study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist (Figure 1 \u0026amp; Additional file 1) [49]. The study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number (CRD420251272042). A comprehensive search was conducted in electronic databases including PubMed, EMBASE, Web of Science, Scopus, Psychological Information Database (PsycINFO), and Economics Literature (EconLit) which index publications relevant to the scope of this research. Additional searches were conducted in WHO Global Health Observatory and IRIS repositories, the Organisation for Economic Co-operation and Development (OECD) iLibrary, and national government health department websites (e.g., ministries of health and national public health institutes) to identify relevant reports and grey literature not indexed in bibliographic databases. Reference lists of included articles, relevant reviews, and meta-analyses were also manually screened to ensure comprehensive coverage. Considering the changing population demographics and under the goal to capture evidence from more recent crises.\u003c/p\u003e\n\u003cp\u003eAll studies published between 1 January 2000 and 18 December 2025, available at the time of the final literature search, were considered eligible. The search strategy combined terms related to economic stressors and mental health outcomes using Boolean operators. The following search string was used in the title and abstract fields of the databases:\u003c/p\u003e\n\u003cp\u003e(\u0026ldquo;austerity\u0026rdquo; OR \u0026ldquo;economic crisis\u0026rdquo; OR \u0026ldquo;fiscal crisis\u0026rdquo; OR \u0026ldquo;financial crisis\u0026rdquo; OR \u0026ldquo;economic recession\u0026rdquo; OR \u0026ldquo;economic depression\u0026rdquo; OR \u0026ldquo;economic insecurity\u0026rdquo; OR \u0026ldquo;banking crisis\u0026rdquo; OR \u0026ldquo;unemployment\u0026rdquo; OR \u0026ldquo;personnel downsizing\u0026rdquo; OR \u0026ldquo;job loss\u0026rdquo;) AND (\u0026ldquo;mental health\u0026rdquo; OR \u0026ldquo;mental disorder\u0026rdquo; OR \u0026ldquo;mental illness\u0026rdquo; OR \u0026ldquo;depression\u0026rdquo; OR \u0026ldquo;anxiety\u0026rdquo; OR \u0026ldquo;suicide\u0026rdquo; OR \u0026ldquo;psychological distress\u0026rdquo; OR \u0026ldquo;depressive disorder\u0026rdquo; OR \u0026ldquo;mood disorder\u0026rdquo; OR \u0026ldquo;psychiatric disorder\u0026rdquo; OR \u0026ldquo;psychological wellbeing\u0026rdquo; OR \u0026ldquo;emotional distress\u0026rdquo;).\u003c/p\u003e\n\u003ch3\u003eStudy selection process\u003c/h3\u003e\n\u003cp\u003eFollowing the removal of duplicates, two reviewers independently screened all titles and abstracts against the predetermined eligibility criteria presented in Table 1. Studies that clearly did not meet the inclusion criteria were excluded at this stage. The full text of potentially relevant articles were then obtained and independently assessed by the same two reviewers for eligibility. Any disagreements between reviewers were resolved through discussion, and when consensus could not be reached, a third reviewer was consulted for final decision-making.\u003c/p\u003e\n\u003cp\u003eTable 1.\u0026nbsp;Inclusion and exclusion criteria for study selection.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003ePrimary empirical studies of any design (quantitative, qualitative, or mixed-methods) examining the association between an economic crisis and mental health outcomes and reporting original and primary data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eNon-primary study designs, such as case reports, case series, review articles (systematic, scoping, narrative), meta-analyses, letters, editorials, commentaries, conference abstracts without full text, and publications containing non-original or secondary data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies that clearly define or describe exposure to an economic crisis event\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies with exposures not including an economic crisis event\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies reporting mental health outcomes measured using validated instruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies reporting mental health outcomes through non-validated instruments or solely qualitative analysis, or studies reporting only health behaviors (e.g., smoking, alcohol consumption) without mental health outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies reporting data to allow effect size estimation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies reporting data that cannot be used for effect size estimation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies focusing on either the general population or specific vulnerable social groups (e.g., unemployed individuals, migrants)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies focusing on relatively narrow groups (e.g. a specific company)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies published between 01 January 2000 and 18 December 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies published before 01 January 2000 and after 18 December 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies published in English\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies published in a language other than English\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eFull-text available through the subscriptions of the host university\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eFull-text unavailable through the subscriptions of the host university\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eFull-text articles retrievable through institutional subscriptions or other reasonable means\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eStudies for which the full text could not be retrieved after reasonable efforts. This includes lack of access through institutional subscriptions, non-response from corresponding authors, incomplete or erroneous bibliographic information, or corrupted, inaccessible, or unreadable full-text documents.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn assessment of methodological rigor was conducted for all included studies via the Newcastle-Ottawa Scale (NOS) [50]. This tool facilitates rating across three key domains: Selection (maximum 5 points), Comparability (maximum 2 points), and Outcome (maximum 3 points). \u0026nbsp;Studies scoring 7\u0026ndash;9 on the NOS were classified as high quality and included. Studies scoring 4\u0026ndash;6 were classified as moderate quality. Studies scoring \u0026lt; 4 or lacking sufficient methodological clarity were excluded due to high risk of bias or insufficient data [51]. Second, to critically evaluate the risk of bias in estimating the causal effect of economic crisis exposure on mental health outcomes, the Risk-of-bias VISualization (robvis) web-based tool was employed [52]. The NOS and robvis assessments were conducted independently by two reviewers. Any discrepancies were resolved through discussion or consultation with a third reviewer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware and data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using Comprehensive Meta-Analysis (CMA) software, Version 3.0 [53]. The primary aim was to calculate a pooled estimate of the event rate (proportion) and its corresponding 95% confidence interval (CI). Given the anticipated clinical and methodological heterogeneity across the included studies, a random-effects model was employed for all primary analyses. This model accounts for both within-study variance and between-study variance, providing a more conservative and generalizable pooled estimate than a fixed-effect model. Statistical heterogeneity across studies was examined using Cochrane\u0026rsquo;s Q test and the \u003cem\u003eI\u003c/em\u003e\u0026sup2; statistic. \u003cem\u003eI\u0026sup2;\u0026nbsp;\u003c/em\u003evalues were categorized as indicating low (\u0026lt;25%), moderate (25\u0026ndash;75%), and high (\u0026gt;75%) heterogeneity [54]. A funnel plot was generated by plotting the standard error of each study\u0026apos;s logit event rate against its effect size (logit event rate). In the absence of bias, the plot should resemble a symmetrical inverted funnel. To statistically evaluate funnel plot asymmetry, Egger\u0026apos;s linear regression test was performed. This test assesses whether the intercept of the regression line, which predicts the standardized effect by its precision, significantly deviates from zero. A \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 was considered indicative of statistically significant publication bias.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy screening\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe systematic search across nine databases and registers identified 15,043 records. After removal of 13,147 duplicate records, 1,896 unique records were screened by title and abstract, of which 1,773 were excluded for not meeting the inclusion criteria. A total of 355 reports were sought for retrieval; however, 232 reports could not be retrieved due to unavailability of full texts, lack of access through databases or institutional subscriptions, incomplete or erroneous bibliographic information, corrupted or unreadable files, or failure to obtain reports after contacting corresponding authors. Consequently, 123 reports were assessed for full-text eligibility (Figure 1).\u003c/p\u003e\n\u003cp\u003eFollowing full-text review, 84 reports were excluded due to non-original or secondary data (n = 12), inappropriate outcome measures (n = 21), overlapping datasets (n = 17), insufficient relevant data (n = 26), or publication in languages other than English (n = 8). Ultimately, 39 studies met all eligibility criteria and were included in the systematic review and meta-analysis. The study selection process is summarized in the PRISMA flow diagram (Figure 1).\u003c/p\u003e\n\u003ch3\u003eQuality assessment and risk of bias\u003c/h3\u003e\n\u003cp\u003eThe quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Based on this assessment, 23 studies (59.0%) were rated as high quality (scores of 7\u0026ndash;9), and 16 studies (41.0%) were rated as moderate quality (scores of 4\u0026ndash;6). No studies were rated as low quality (scores \u0026lt;4). The detailed quality ratings are presented in Additional file 2.\u003c/p\u003e\n\u003cp\u003eThe risk of bias for the 39 included non-randomized studies was assessed using the robvis tool across five domains. The detailed assessment for each individual study provided in Figure 2 along with a visual summary of the judgments is presented in Figure 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overall assessment revealed a strong methodological foundation across the evidence base. The vast majority of studies (all 39) were judged to have a \u0026quot;Low risk\u0026quot; of bias across all five domains (D1\u0026ndash;D5), as indicated by the uniform green checkmarks in Figure 2. Consequently, the overall risk of bias was judged as \u0026quot;Low\u0026quot; for all 39 included studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis consistent \u0026quot;Low risk\u0026quot; rating across domains indicates that: 1) The selection of exposed and non-exposed cohorts (D1) was appropriately handled; 2) Deviations from intended exposures (D2) were not a significant source of bias; 3) Missing outcome data (D3) were minimal or adequately addressed; 4) The measurement of outcomes (D4) was robust and unlikely to differ between groups; 5) The selection of the reported result (D5) did not appear to be biased.\u003c/p\u003e\n\u003cp\u003eThe summary graph in Figure 3 quantitatively reflects this pattern, showing nearly 100% of studies rated as \u0026quot;Low risk\u0026quot; for each domain and for the overall judgment. This high and consistent rating strengthens confidence in the validity of the synthesized findings from these studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies summarized in Table 2 encompass 39 publications from a diverse range of geographic regions, with a particular concentration in Southern European countries like Greece and Spain, which were severely impacted by the 2008 financial crisis and subsequent austerity measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethodologically, the research employs a variety of designs, including cross-sectional surveys, longitudinal panel studies, repeated cross-sectional analyses, and ecological time-trend analyses. The populations investigated span general adult populations, specific age groups such as older adults and adolescents, clinical patients, and vulnerable subgroups experiencing unemployment or financial strain. Sample sizes range from smaller clinical cohorts of around 1,600 individuals to large national surveys exceeding 30,000 participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary mental health outcomes reported are depression, psychological distress, suicidality (including ideation, attempts, and mortality), anxiety, and self-harm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollectively, the findings consistently indicate a significant association between economic recessions characterized by job loss, foreclosure, and austerity and a deterioration in population mental health, often exacerbating existing social and economic inequalities (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Study characteristics.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eStudy (Authors, Year)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eTime period\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eEconomic Context\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eMental health Outcome(s)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eKey findings\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[55]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCagney et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal panel\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOlder adults (\u0026ge;57 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2005\u0026ndash;2006, 2010\u0026ndash;2011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreat Recession, foreclosure crisis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDepressive illnesses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher neighborhood foreclosure rates associated with increased odds of depression onset (ORs: 1.45\u0026ndash;1.75 across foreclosure stages).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[56]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKokkevi et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdolescents (15\u0026ndash;19 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSevere economic recession in Greece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicide attempts; running away from home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.3% reported suicide attempts; 11.6% reported running away. Shared psychosocial correlates included poor family relationships, school dissatisfaction, substance use, and emotional problems. No direct link to economic status in regression.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[57]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026Aring;sgeirsd\u0026oacute;ttir et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePopulation-based registry\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral population\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIceland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2003\u0026ndash;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic boom (pre-2008) and collapse (post-2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicide attempts and self-harm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMen showed peak during economic boom; decrease in new attendances post-crisis for both genders. Unemployment rise associated with reduced attendances for men.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[58]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBasta et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetrospective observational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral population of Crete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1999\u0026ndash;2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic crisis (post-2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicide mortality rates\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncrease in middle-aged/elderly men; regional disparities linked to mental health services.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[59]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlomqvist et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeated cross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWomen aged 18\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweden\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006, 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic recession, social insurance reforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental distress, Limiting longstanding illness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncreased mental distress in all labour market groups; inequalities widened, explained by social/economic conditions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[60]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBorges et al. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdults aged 18+\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 countries\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2001\u0026ndash;2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre- and early crisis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12-month suicide ideation, plans, attempts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSimilar prevalence across developed/developing countries; risk indices predict attempts accurately\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[61]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBartoll et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdults aged 16\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006\u0026ndash;2007, 2011\u0026ndash;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic crisis (post-2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor mental health\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental health worsened in men, improved slightly in women; inequalities increased in men.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[62]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBracone et al. (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,647 adults from the Moli-sani cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2005\u0026ndash;2006 to 2017\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreat Recession (late 2000s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDepressive illnesses\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic hardship was associated with increased depression symptoms, decreased mental health perception, and poorer physical health over time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[63]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBorrell et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcological time-trend analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResidents \u0026gt;25 years in Basque Country \u0026amp; Barcelona\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2001\u0026ndash;2004, 2005\u0026ndash;2008, 2009\u0026ndash;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic recession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicide mortality rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInequalities in suicide mortality by education remained stable among men before and during the recession; no clear increase in inequalities was observed.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[64]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCorcoran et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterrupted time series analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational suicide and self-harm registry data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIreland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1980\u0026ndash;2012, 2004\u0026ndash;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic recession \u0026amp; austerity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicide and self-harm rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale suicide increased by 57% and self-harm by 31% during the recession; men aged 25\u0026ndash;64 were most affected. Female self-harm also increased significantly.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[65]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGill et al. (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary care attendees\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006 vs. 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinancial crisis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMood, anxiety, somatoform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificant increases in mood, anxiety, somatoform, and alcohol-related disorders among primary care attendees during the crisis, linked to unemployment and mortgage difficulties.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[66]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomou et al. (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeated cross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepresentative sample of 2,256 adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2009 vs. 2011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeak of the Greek sovereign debt crisis, austerity measures, high unemployment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicidal ideation; suicide attempts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA 36% increase in reported suicide attempts from 2009 to 2011. Individuals with high economic distress were significantly more likely to report suicide attempts (10% vs. 0.6%) and ideation (21.2% vs. 7.4%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[67]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDrydakis (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal panel study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorking-age adults (18\u0026ndash;65) in the labor force; person-observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008\u0026ndash;2013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinancial crisis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor mental health.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnemployment had a significant negative effect on both SRH and mental health. The detrimental effects were significantly stronger during the high-unemployment crisis period (2010\u0026ndash;13). Women were more negatively affected than men.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[68]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDunlap et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional, nationally representative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21,100 adults from the U.S. civilian non-institutionalized population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008\u0026ndash;2010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe \u0026quot;Great Recession\u0026quot;; high unemployment and mortgage foreclosure crisis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSerious Psychological Distress; Substance Use Disorders; Mental Health Service Utilization.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividual-level factors (unemployment, poverty) predicted. Macroeconomic conditions (high county unemployment \u0026amp; state mortgage delinquency rates) were significantly associated with\u0026nbsp;lower\u0026nbsp;mental health service use among those with SPD. Lack of insurance was also a key barrier.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[69]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026Aring;slund et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20,538 adults aged 18\u0026ndash;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral unemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePsychosomatic symptoms, low psychological well-being (GHQ-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnemployment associated with worse mental health. Low social capital (esp. tangible support) had additive negative effects. No buffering effect found.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[70]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomou et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,188 adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic recession, austerity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMajor depression (SCID), suicidality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncome and financial difficulties independently associated with depression. Income linked to suicidality in men only. Financial difficulties strongly linked to depression in both genders.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[71]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTamayo-Fonseca et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeated cross-sectional survey analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdults \u0026ge;16 years, Valencian Community residents\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2005, 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic crisis onset (2008\u0026ndash;2010) in Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk of poor mental health (GHQ-12 \u0026ge;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrevalence of poor mental health increased from 20.0% (2005) to 27.8% (2010). Unemployment and low income contributed significantly to the rise.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[72]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eForbes \u0026amp; Krueger (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU.S. adults, Midlife in the United States (MIDUS) sample\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2003\u0026ndash;2004 (pre-recession), 2012\u0026ndash;2013 (post-recession)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe Great Recession (2007\u0026ndash;2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSymptoms of depression, generalized anxiety, panic, problematic alcohol/substance use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRecession impacts (financial, job-related, housing) were associated with higher odds of internalizing symptoms. Population-level mental health improved, but individual-level impacts were negative.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[73]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElbogen et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU.S. adults, NESARC sample\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2001\u0026ndash;2002 (Wave 1), 2004\u0026ndash;2005 (Wave 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre- and post-2001 economic downturn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicide attempts, suicidal ideation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumulative financial strain (debt, unemployment, homelessness, low income) predicted suicide attempts. Four financial risk factors increased suicide attempt probability 20-fold.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[74]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAstell-Burt \u0026amp; Feng (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeated cross-sectional survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorking-age adults (16\u0026ndash;64 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006\u0026ndash;2010 (quarterly data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008 economic recession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-reported poor health, depression, mental illness, cardiovascular/respiratory problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor health prevalence increased from 25.7% (2009) to 29.5% (2010). Increases were seen across all employment and occupational groups, not just the unemployed.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[75]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKoutra et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCollege students\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDuring economic crisis (post-2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOngoing austerity, high unemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-suicidal self-injury (NSSI); suicidal ideation/behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27% NSSI, 38.6% suicidal ideation. Social capital not protective. Depression and stress were significant predictors of NSSI/SIB.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[76]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKatikireddi et al. (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeat cross-sectional analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorking-age adults (25\u0026ndash;64 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1991\u0026ndash;2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008 recession onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGHQ-12 caseness (poor mental health)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental health deteriorated in men post-2008, not explained by employment status. Women showed no significant change. Inequalities increased over decade but not specifically due to recession.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[77]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNour et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeated cross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCanadian working-age adults (15\u0026ndash;64 years)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2007\u0026ndash;2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008 global financial crisis, stimulus, and austerity periods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor self-reported mental health, anxiety disorders, mood disorders, heavy alcohol drinking, decreased fruit/vegetable consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAusterity period associated with increased odds of poor mental health, anxiety/mood disorders, heavy drinking, and decreased healthy eating. Stimulus period linked to heavy drinking.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[78]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiret et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional household\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-institutionalized adults aged \u0026ge;18 years in Spain; compared with ESEMED (2001/2002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2011\u0026ndash;2012 (compared to 2001\u0026ndash;2002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic crisis and austerity measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicidal ideation, suicide planning, suicide attempts (lifetime and 12-month prevalence)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant change in suicidality prevalence compared to pre-crisis period. Factors associated with suicidality varied by age: younger adults (unemployment, heavy drinking), middle-aged (loneliness), older adults (financial problems).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[79]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModrek \u0026amp; Cullen (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal cohort\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEmployees of a U.S. aluminum manufacturing company\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006\u0026ndash;2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2007\u0026ndash;2009 \u0026ldquo;Great Recession\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncident diagnoses of hypertension, diabetes, asthma/COPD, depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorkers in high-layoff plants had increased risk of hypertension (especially hourly workers) and diabetes (salaried workers). No significant association with depression or asthma/COPD.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[80]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOstamo \u0026amp; L\u0026ouml;nnqvist (2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal cohort, sample-based monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResidents of Helsinki aged 15+ treated for suicide attempts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1989\u0026ndash;1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSevere economic recession, unemployment up to 18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAttempted suicide rates, methods, alcohol use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall attempted suicide rates remained stable; male rates decreased significantly; female rates increased slightly; convergence of gender rates; poisoning as method increased\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[81]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParaschakis et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetrospective forensic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicide cases in Piraeus area from forensic records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006\u0026ndash;2010 vs. 2011\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSevere economic crisis, austerity measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompleted suicides, psychiatric medication, drug/alcohol use, suicide methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSlight decrease in suicides during crisis; higher psychiatric medication intake (especially males); no significant change in methods or substance use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[82]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOdone et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeated cross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItalian national population aged 25+ from ISTAT surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2005 vs. 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOngoing economic crisis, high unemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor mental health (SF-12 MCS score), risk of depression/anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor mental health increased from 21.5% to 24.4%; highest rise in young males (24%); vulnerable groups at higher risk but not disproportionately affected by crisis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[83]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePruchno et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal panel\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOlder adults aged 50\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006\u0026ndash;2008 to 2011\u0026ndash;2012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreat Recession (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDepressive symptoms (CES-D-10), incident/chronic/remitted depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificant increase in depressive symptoms post-recession; incident depression linked to job loss, caregiving, illness; women, married, employed, higher-middle income most affected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[84]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSareen et al. (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProspective longitudinal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34,653 adults (\u0026ge;20 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2001\u0026ndash;2005 (2 waves, 3 years apart)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-recession period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDSM-IV Axis I \u0026amp; II disorders, suicide attempts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower household income associated with lifetime disorders \u0026amp; suicide attempts; income reduction linked to incident mood, anxiety, substance use disorders.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[85]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRodrigues \u0026amp; Nunes (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional, ecological\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorking-age adults (15\u0026ndash;64 years) hospitalized for major depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008 vs. 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre- vs. during economic crisis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospitalization for major depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHospitalization rates increased during crisis; higher in rural/low-density areas; influenced by bed availability.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[86]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRuiz-P\u0026eacute;rez et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional, multilevel\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdults \u0026ge;16 years from National Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2006 vs. 2011\u0026ndash;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre- vs. post-recession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-reported poor mental health (GHQ-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower health spending \u0026amp; higher temporary employment linked to worse mental health, especially in men.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[87]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShi et al. (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepeated cross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdults \u0026ge;16 years from monthly\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2002\u0026ndash;2009 (monthly)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre- \u0026amp; during GFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnxiety, depression, stress, psychological distress, suicidal ideation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo overall increase in mental health problems during GFC; anxiety increased in part-time workers, decreased in full-time workers.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[88]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSicras-Mainar (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetrospective, longitudinal, observational\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePatients diagnosed with Major Depressive Disorder in primary care\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2008\u0026ndash;2009 (pre-crisis) vs. 2012\u0026ndash;2013 (crisis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeriod of severe economic crisis in Spain, with high unemployment and austerity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1. MDD Prevalence 2. Antidepressant (AD) consumption \u0026amp; patterns 3. Treatment persistence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMDD prevalence increased from 5.4% to 8.1% during the crisis. AD use rose by 35.2%, while drug expenditures fell 38.7%. Most patients (60.8%) discontinued or did not change initial AD treatment.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[89]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVanderoost et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePatients aged 18\u0026ndash;49 years visiting general practices\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBelgium (Flanders \u0026amp; Wallonia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSept\u0026ndash;Dec 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeriod following the 2009 financial crisis, characterized by corporate reorganizations and dismissals.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicidal thoughts in the past 12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.7% had seriously considered suicide in the past year. Recent employment loss was a significant independent risk factor for suicidal thoughts (OR=8.8). Other factors: being single, poor social contacts, depressive complaints.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[90]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThomas et al. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLongitudinal panel\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral population aged \u0026gt;16 years from the British Household Panel Survey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1991\u0026ndash;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeriod not defined as a national \u0026quot;crisis,\u0026quot; but study models the impact of individual employment transitions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePsychological distress (GHQ-12 score \u0026gt;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTransitions to unemployment increased risk of distress (Men: OR 3.15; Women: OR 2.60). This effect was partially mediated by subjective financial deterioration. Gaining employment reduced distress only if it improved financial circumstances.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[91]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomou et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNationally representative adults aged 18\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2009, 2011 (pre- and post-crisis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSevere economic crisis, high unemployment (16.6% in 2011), GDP decline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuicidal ideation and reported suicide attempts (past month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificant increase in suicidal ideation (5.2% to 6.7%) and suicide attempts (1.1% to 1.5%) from 2009 to 2011. High-risk groups: men, married individuals, those with depression, financial strain, low interpersonal trust, previous suicide attempts.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[92]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWang et al. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorking population aged 25\u0026ndash;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCanada (Alberta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJan 2008 \u0026ndash; Oct 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobal economic crisis, rising job insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12-month prevalence of Major Depressive Disorder, dysthymia, anxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12-month Major Depressive Disorder increased from 5.1% (pre-Sept 2008) to 7.6% (post-March 2009). Lifetime dysthymia also increased. Men and married/common-law individuals showed significant increases in MDD. No significant change in anxiety disorders.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e[93]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBonnie Lee et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterrupted time series analysis (nationwide, prospective, population-based)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdults aged 24\u0026ndash;59 enrolled in Taiwan\u0026apos;s National Health Insurance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJanuary 2007 \u0026ndash; December 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobal financial crisis (2008), economic recession, rising unemployment, GDP decline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospitalizations due to depressive illnesses (bipolar disorder, depressive disorder, affective disorder, neurotic depression; ICD-9: 296, 311, 300.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-income groups had ~10x higher adjusted hospitalization rates than high-income groups.\u003c/p\u003e\n \u003cp\u003eLow-income men showed an 18.0% increase in hospitalization rates starting April 2008. Low-income women showed a 14.2% increase starting April 2008.\u003c/p\u003e\n \u003cp\u003eHigh-income women showed a gradual 5.0% monthly increase starting April 2008.\u003c/p\u003e\n \u003cp\u003eMiddle-income men showed a temporary decrease in hospitalization rates.\u003c/p\u003e\n \u003cp\u003eOverall, women had higher hospitalization rates than men across all income groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe meta-analysis of 39 studies yielded a pooled prevalence of mental health outcomes related to economic crises. Under the random-effects model, the overall pooled prevalence was 6.4% (95% CI: 4.1\u0026ndash;10.1%) (Figure 4). Heterogeneity was high (\u003cem\u003eI\u003c/em\u003e\u0026sup2; = 99.99%, \u003cem\u003ep\u003c/em\u003e= 0.00), indicating substantial variability across the included studies, which was expected given the diversity in populations, outcome measures, and economic contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisual inspection of the funnel plot (Figure 5) did not reveal substantial asymmetry. Egger\u0026rsquo;s regression test indicated a significant intercept (56.09; 95% CI: 0.66\u0026ndash;111.53; \u003cem\u003ep\u003c/em\u003e = 0.047), suggesting the presence of small-study effects and potential publication bias in the meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis based on mental health outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were nine types of mental health disorders recognized in the included studies, with pooled estimates ranging from 0.7 to 18.9%.\u0026nbsp;Subgroup analysis of mental health outcomes revealed that the highest pooled prevalence was related to self-harm (18.9%, 95% CI: 17.4\u0026ndash;20.4%), followed by somatoform disorder (17.6%, 95% CI: 17.0\u0026ndash;18.3%) and the lowest was observed for suicide (0.7%, 95% CI: 0\u0026ndash;15.3%) (Figure 6). The pooled estimated for other mental health disorders was as follows: Distress (14.7%, 95% CI: 17.1\u0026ndash;28.0%); poor mental health (13.0%, 95% CI: 6.2\u0026ndash;25.1%); depressive illnesses (5.8%, 95% CI: 3.3\u0026ndash; 10.1%); suicide attempts (5.6%, 95% CI:1.9\u0026ndash; 15.8%); generalized anxiety (5.2%, 95% CI:1.3\u0026ndash; 18.3%); and suicidal thoughts (4.2%, 95% CI:3.3\u0026ndash; 5.3%) (Figure 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis based on geographic region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the studies included in the analysis, mental health outcomes impacted by economic crisis were reported in 14 countries (Table 2 \u0026amp; Figure 7). The largest number of studies was conducted in Greece (8 studies), followed by Spain (7 studies) and USA (7 studies). The pooled prevalence on different countries ranged from 0 to 57.0%, with the following pooled estimates: 57.0% (95% CI: 55.7\u0026ndash; 58.4%) in Finland, 23.9% (95% CI: 9.8\u0026ndash; 47.7%) in Sweden, 20.7% (95% CI: 9.8\u0026ndash; 38.6%) in Spain, 16.0% (95% CI: 7.0\u0026ndash; 32.4%) in Italy, 13.1% (95% CI: 10.9\u0026ndash;15.8%) in Belgium, 10.8% (95% CI: 2.4\u0026ndash; 37.5%) in UK, 5.1% (95% CI: 2.9\u0026ndash; 8.7%) in USA, 4.7% (95% CI: 1.1\u0026ndash; 17.5%) in Greece, 3.6% (95% CI: 1.1\u0026ndash; 11.3%) in Canada, 2.7% (95% CI: 2.5\u0026ndash; 2.8%) in Australia, 1.4% (95% CI: 1.3\u0026ndash;1.4%) in Iceland, 1.3% (95% CI: 1.3\u0026ndash; 1.3%) in Taiwan, 0.2% (95% CI: 0.2\u0026ndash; 0.2%) in Portugal; \u0026nbsp;and 0.0% (95% CI: 0.0\u0026ndash;0.0%) in Ireland (Figure 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis based on economic crisis phase\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were three economic crisis phases recognized by our included studies: pre-crisis, during crisis and post crisis. Four studies reported more than one phases, with a pooled estimate of 2.8% (95% CI: 2.0\u0026ndash; 3.7%) (Figure 8). The during-crisis phase was accounted in 16 studies, with a pooled estimate of 4.1% (95% CI: 1.6\u0026ndash; 10%). Post crisis yielded the highest pooled estimate from 14 studies (16.3%; 95% CI: 10.0\u0026ndash; 25.5%), followed by the pre-crisis phase covered in 12 studies (7.6%; 95% CI: 3.8\u0026ndash; 14.5%) (Figure 8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eOverall picture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review and meta-analysis provides comprehensive quantitative evidence that economic downturns are associated with a substantial burden of adverse mental health outcomes across different populations. By synthesizing data from studies conducted in diverse economic and geographic contexts, this study extends existing literature by quantifying pooled prevalence estimates, identifying outcome-specific patterns, and demonstrating that the mental health impacts of economic crises vary significantly by disorder type, geographic region, and phase of the crisis. The high heterogeneity was expected given the diversity of study designs, populations, outcome definitions, and economic contexts represented. Rather than undermining the findings, this heterogeneity highlights the complex and context-dependent ways in which macroeconomic shocks translate into psychological harm.\u0026nbsp;\u0026nbsp;For instance, the outlier observed in Finland (57.0% prevalence) is attributable to the specific clinical nature of the sample, which focused on individuals treated for suicide attempts [79], whereas lower estimates in Greece (3.6%) or Portugal (0.2%) often reflected general population surveys. Furthermore, the use of varied diagnostic tools, ranging from self-reported screening scales like the GHQ-12 [69, 71, 76, 86, 90] to formal clinical diagnoses based on DSM-IV criteria [84], contributes significantly to the observed heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMental health outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key finding of this study is the pronounced variation in prevalence across different mental health outcomes. Self-harm, somatoform disorders, and psychological distress exhibited the highest pooled prevalence estimates, whereas depression, anxiety, suicidal thoughts, suicide attempts, and completed suicide showed lower pooled estimates. This pattern suggests that economic crises may initially manifest as non-specific psychological distress and somatic symptoms, which are more prevalent and more sensitive to social stressors, before progressing to more severe or clinically defined psychiatric outcomes in a subset of individuals [25\u0026ndash;27, 65].\u003c/p\u003e\n\u003cp\u003eThe high prevalence of self-harm observed across studies underscores the severity of distress experienced during periods of economic instability. Self-harm may represent a maladaptive coping mechanism in response to acute psychosocial stressors such as job loss, debt, housing insecurity, and uncertainty about the future. The discrepancy between high self-harm prevalence (18.9%) and low suicide prevalence (0.7%) indicates that economic stress disproportionately triggers non-fatal maladaptive coping mechanisms. However, the 57% increase in male suicide in Ireland [64] during the recession suggests that even if the absolute prevalence remains low, the relative increase is critical for public health policy. Nevertheless, the relatively low pooled prevalence of suicide should not be interpreted as evidence of minimal risk; rather, it reflects the rarity of completed suicide at the population level and substantial methodological variation in its measurement. Importantly, even small absolute increases in suicide rates during economic crises can translate into significant public health consequences [20, 26, 27, 30, 94]. Depressive and anxiety disorders showed moderate pooled prevalence estimates, consistent with prior literature demonstrating increased incidence and symptom severity among individuals exposed to unemployment, income loss, and financial strain [95\u0026ndash;98]. The lower pooled estimates for these disorders relative to distress and somatoform symptoms may reflect differences in diagnostic thresholds, under-diagnosis, or delayed onset following prolonged exposure to economic hardship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarked geographic variation was observed in pooled prevalence estimates across countries [25, 27, 29, 30, 76]. Higher estimates were reported in several European countries that experienced severe economic contraction and austerity measures following the 2008 global financial crisis. These findings suggest that the mental health consequences of economic downturns are not determined solely by the magnitude of economic shock, but are strongly shaped by social protection systems, labor market policies, and healthcare accessibility [20, 21, 23, 27, 99]. For instance, the lower prevalence observed in settings like Australia [87] and parts of Canada [77] may reflect the impact of fiscal stimulus packages and more robust initial social safety nets, which offered some buffer to the population against the immediate shocks observed in austerity-stricken Southern Europe. In general, countries with weaker social safety nets, higher unemployment persistence, and austerity-driven reductions in public spending may expose populations to prolonged stressors that exacerbate mental health risks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, settings with stronger welfare protections and sustained investment in health and social services may mitigate some of the psychological harm associated with economic crises. These observations align with the broader literature indicating that policy responses play a critical role in shaping health outcomes during periods of economic instability [20\u0026ndash;22, 27, 99]. During recessions, governments adopt austerity measures, including cutting funding for mental‑health and social services. In countries facing strict fiscal consolidation, reductions in preventive and community-based mental health services have been associated with increases in suicide and psychiatric admissions [100]. Reduced access to counselling, medications, and community support, thus, leaves vulnerable individuals with far fewer resources just when they need them most. \u0026nbsp;Finally, the broader economic and social context significantly shapes outcomes. Research indicates that in nations with weaker social safety nets and lower pre-crisis employment protections, the impact of financial downturns on suicide rates and mental disorders tends to be more severe [101].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCrisis phases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis by economic crisis phase revealed that the highest pooled prevalence of mental health outcomes occurred during the post-crisis period, followed by the pre-crisis phase, with lower prevalence observed during the acute crisis itself. This temporal pattern suggests the presence of delayed or cumulative effects of economic stress, whereby prolonged exposure to financial hardship, unemployment, and austerity gradually erodes mental well-being over time [22, 27, 30, 76]. Several mechanisms may explain this phenomenon. \u0026nbsp;During the acute phase of a crisis, individuals and communities may exhibit short-term resilience or adaptive coping strategies. However, as economic recovery stalls, savings are depleted, social supports weaken, and access to healthcare diminishes, psychological distress may intensify. The post-crisis period often coincides with prolonged unemployment, debt accumulation, and sustained reductions in public services, which may collectively drive worsening mental health outcomes [27, 102\u0026ndash;104].\u003c/p\u003e\n\u003cp\u003eNonetheless, the relatively high prevalence observed in the pre-crisis phase suggests a \u0026apos;pre-emptive\u0026apos; psychological impact. This suggests that market volatility and the anticipation of austerity generate significant distress prior to the official onset of recession. Furthermore, the Iceland study\u0026nbsp;[57]\u0026nbsp;demonstrates that economic booms can also be associated with peak mental health attendances, suggesting that macroeconomic instability, rather than just contraction, is the primary driver of psychological harm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study support a multifactorial framework linking economic downturns to adverse mental health outcomes and carry clear implications for public health and social policy. Overall, the study underscores that economic crises are not only macroeconomic challenges but also public mental health emergencies. Strategic policy interventions that combine social protection, healthcare access, and targeted psychosocial support have the potential to reduce the mental health burden of economic downturns, protect vulnerable populations, and promote faster societal recovery.\u003c/p\u003e\n\u003cp\u003ePsychosocial stress arising from job loss, income insecurity, and housing instability constitutes a primary pathway, increasing vulnerability to depression, anxiety, self-harm, and suicidal behavior. Behavioral pathways may further amplify risk, as financial strain is associated with increased substance use, poorer diet, reduced physical activity, and social isolation [27, 105\u0026ndash;107]. Also, a gendered response to economic strain is evident; while several studies noted increased distress and suicide in males\u0026nbsp;[61, 64], others highlighted that women were more affected by depressive symptoms and service utilisation barriers\u0026nbsp;[67, 83, 93]. Evidence from occupational settings further demonstrates that adverse work-related conditions, including high workload, environmental hazards, and organizational stressors, are strongly associated with burnout and psychological distress among employed populations, which may be amplified during periods of economic instability\u0026nbsp;[108]. System-level mechanisms are also critical. Economic crises often prompt austerity policies that reduce funding for mental health services precisely when demand increases. Disruptions in access to care, medication discontinuation, and reduced availability of community-based services may exacerbate existing conditions and hinder early intervention. Together, these pathways create a reinforcing cycle in which economic hardship undermines mental health, which in turn impairs social and economic recovery\u0026nbsp;[102, 109\u0026ndash;111].\u003c/p\u003e\n\u003cp\u003eThe pronounced mental health burden associated with economic downturns suggests that proactive, coordinated policy responses are critical to mitigate psychological harm during periods of economic instability. Evidence from countries with stronger social safety nets indicates that policies aimed at income stabilization, unemployment support, and access to affordable healthcare can buffer the negative mental health effects of financial crises.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy implications and strategies to mitigate mental health impacts of economic crises\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecifically, considering our findings in conjunction with extant literature about measures that can prevent or mitigate the conditions leading to adverse health outcomes, policies should prioritize the following:\u003c/p\u003e\n\u003cp\u003e1) Strengthening social protection systems. Expansion of unemployment benefits, housing assistance, and income support programs can reduce acute psychosocial stressors and prevent the cascading effects of financial hardship on mental health. Policymakers should consider maintaining or enhancing these measures even after the acute phase of an economic crisis, as the post-crisis period may be associated with the highest prevalence of mental health outcomes [20, 112].\u003c/p\u003e\n\u003cp\u003e2) Ensuring access to mental health services. Economic crises often coincide with increased demand for mental health care at a time when public resources may be constrained. Investments in community-based mental health services, telehealth, and early intervention programs can prevent progression from distress and somatic symptoms to more severe psychiatric disorders, self-harm, and suicide [113, 114].\u003c/p\u003e\n\u003cp\u003e3) Targeted support for high-risk populations vulnerable groups, including those experiencing long-term unemployment, debt accumulation, or housing instability, may benefit from integrated social and mental health services. Programs that combine financial counseling, job retraining, and psychosocial support can address multiple pathways linking economic stress to mental illness [95, 115].\u003c/p\u003e\n\u003cp\u003e4) Monitoring and surveillance. Routine collection of mental health indicators during economic downturns can inform timely policy responses and allow for the identification of emerging high-risk groups. Data-driven interventions can help governments allocate resources efficiently and evaluate the effectiveness of social and health policies [116].\u003c/p\u003e\n\u003cp\u003e5) Mitigating long-term socioeconomic disparities. Policies that reduce structural inequalities, such as progressive taxation, universal healthcare, and equitable labor protections, may attenuate the cumulative psychological toll of economic crises and foster resilience at the population level [117\u0026ndash;119].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile this study has several strengths, including a comprehensive search strategy, rigorous quality assessment, and the inclusion of diverse populations and economic contexts, the findings must be interpreted considering a few important limitations. First, the high heterogeneity reflects substantial variation across studies and limits the precision of pooled estimates. Second, most included studies were observational, precluding causal inference. Additionally, the evidence base is heavily weighted toward high-income countries, limiting generalizability to low- and middle-income settings where economic shocks may have even more severe consequences. Last, the significant Egger\u0026apos;s test indicates a potential overestimation of prevalence due to small-study effects. This suggests that smaller studies with non-significant findings or lower prevalence rates may remain unpublished, potentially biasing the pooled estimate of 6.4% upwards.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review and meta-analysis demonstrates that economic crises are associated with a significant and heterogeneous burden of adverse mental health outcomes at the population level. Self-harm, psychosomatic symptoms, and psychological distress emerge as particularly prevalent outcomes. Importantly, the mental health impact of economic crises varies by geographic context and is moderated by the phase of the crisis, with evidence suggesting delayed and sustained effects extending beyond the acute economic shock and mental health risks beginning in the pre-crisis phase due to anticipatory stress. Our findings underscore that economic instability constitutes not only a financial challenge but a major public mental health threat. The pathways linking economic crises to psychological harm are multifaceted, involving psychosocial stress, behavioral responses, and systemic failures driven by austerity and reduced access to care. Besides, the threat is gender-specific, with males at higher risk of suicide mortality while females often exhibiting higher depressive symptom burdens. Consequently, protecting mental health must be recognized as a core component of economic crisis preparedness and recovery. Policy responses to future economic downturns should prioritize strengthening social safety nets, maintaining employment protections, and safeguarding access to mental health services during and after crises. There is need for both preventive mental health support to address high-prevalence distress/self-harm, and acute clinical intervention to address suicide risk, as a \"one-size-fits-all\" economic recovery plan might be insufficient. Austerity-driven reductions in health and social spending risk amplifying long-term mental health harm and undermining recovery. Last, we acknowledge that the identified burden, while substantial, may be subject to publication bias in smaller observational studies, reinforcing the need for more rigorous, large-scale surveillance. Hence, future research should focus on underrepresented regions, long-term mental health trajectories, and the evaluation of policy interventions that buffer populations against the psychological consequences of economic shocks. Ultimately, resilient economies require resilient populations, and economic recovery should be measured not only by financial indicators but by improvements in population mental well-being.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWHO: World Health Organization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCMA: Comprehensive Meta-Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNOS: Newcastle-Ottawa Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRobvis: Risk-of-bias VISualization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOECD: Organisation for Economic Co-operation and Development\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePsycINFO: Psychological Information Database\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEconLit : Economics Literature\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePROSPERO: Prospective Register of Systematic Reviews\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPA: American Psychological Association\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGDP: Gross domestic product\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study is based solely on literature reviews and reports. It did not involve human participants or animals; therefore, ethical approval was not required.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no conflicts of interest. The author I.A. serves as an Editor for the BMC Public Health Journal, and he was not involved in the editorial handling or peer-review process of this manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work received no financial support from any organization.\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eConceptualization, I.A., A.V.E, and N.K.; methodology, I.A., and A.V.E.; software, I.A., and A.V.E.; validation, I.A., and A.V.E.; formal analysis, I.A., and A.V.E.; investigation, A.V.E., I.A., and N.K.; resources, I.A., and A.V.E.; data curation, I.A., A.V.E.; writing\u0026mdash;original draft preparation, I.A., A.V.E., M.Y., N.S., P.T., N.K., and G.D. writing\u0026mdash;review and editing, I.A., and A.V.E., and N.K.; visualization, A.V.E., N.K., and I.A.; supervision, I.A., and A.V.E.,; project administration, I.A. All the authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, I.A., A.V.E, and N.K.; methodology, I.A., and A.V.E.; software, I.A., and A.V.E.; validation, I.A., and A.V.E.; formal analysis, I.A., and A.V.E.; investigation, A.V.E., I.A., and N.K.; resources, I.A., and A.V.E.; data curation, I.A., A.V.E.; writing\u0026mdash;original draft preparation, I.A., A.V.E., M.Y., N.S., P.T., N.K., and G.D. writing\u0026mdash;review and editing, I.A., and A.V.E., and N.K.; visualization, A.V.E., N.K., and I.A.; supervision, I.A., and A.V.E.,; project administration, I.A. All the authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received no financial support from any organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data used in this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based solely on literature reviews and reports. It did not involve human participants or animals; therefore, ethical approval was not required.\u0026nbsp;\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\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest. The author I.A. serves as an Editor for the BMC Public Health Journal, and he was not involved in the editorial handling or peer-review process of this manuscript.\u003c/p\u003eData availability\u003c/h2\u003e \u003cp\u003eAll data used in this study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorgan EV. The Great Depression, 1873-96. In: The Theory and Practice of Central Banking, 1797\u0026ndash;1913. 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J Health Psychol. 2007;12:750\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eGunnell D, Appleby L, Arensman E, Hawton K, John A, Kapur N, et al. Suicide risk and prevention during the COVID-19 pandemic. The Lancet Psychiatry. 2020;7:468\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eMarmot M, Allen J, Bell R, Bloomer E, Goldblatt P. WHO European review of social determinants of health and the health divide. Lancet. 2012;380:1011\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003ePickett KE, Wilkinson RG. Inequality: an underacknowledged source of mental illness and distress. Br J Psychiatry. 2010;197:426\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eLund C, Brooke-Sumner C, Baingana F, Baron EC, Breuer E, Chandra P, et al. Social determinants of mental disorders and the Sustainable Development Goals: a systematic review of reviews. The lancet psychiatry. 2018;5:357\u0026ndash;69.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hellenic Open University","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":"Austerity, economic recession, public health, mental disorders, public health policy, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-8444991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8444991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e While individual studies indicate that economic crises pose a significant threat to population mental health, the aggregated magnitude and variation of this impact across different contexts remain unclear. This systematic review and meta-analysis synthesizes global evidence on the mental health consequences of economic downturns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A systematic search of PubMed, EMBASE, Web of Science, Scopus, PsycINFO, and EconLit, alongside and relevant government health department websites was conducted in accordance with PRISMA guidelines. Studies published between 01 January 2000 and 18 December 2025 that examined mental health outcomes in relation to economic crises were eligible. A random-effects meta-analysis using Comprehensive Meta-Analysis (CMA) software was conducted to estimate pooled prevalence, with subgroup analyses conducted according to mental health outcome, geographic region, and economic crisis phase. Study quality was assessed using the Newcastle–Ottawa Scale, and risk of bias was evaluated using the robvis tool. Publication bias was assessed using funnel plots and Egger’s regression test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Thirty-nine studies from 14 countries met the inclusion criteria. The pooled prevalence of adverse mental health outcomes associated with economic crises was 6.4% (95% CI: 4.1–10.1%). Subgroup analyses showed the highest pooled prevalence for self-harm (18.9%), followed by somatoform disorders (17.6%) and distress (14.7%). Marked geographic variation was evident, with higher pooled prevalence estimates reported in several European countries. Mental health burden differed by crisis phase, with the highest prevalence observed during the post-crisis period (16.3%), followed by the pre-crisis phase (7.6%),\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eEconomic crises are associated with a substantial and heterogeneous burden of mental health problems, particularly self-harm. The magnitude of impact varies by outcome type, geographic context, and crisis phase, with evidence suggesting delayed and sustained effects beyond the acute crisis period. These findings highlight the need to integrate mental health protection into economic crisis preparedness and recovery policies, including safeguarding mental health services, strengthening social safety nets during and after economic downturns, and implementing a dual strategy of preventive community support for distress and acute clinical intervention for high-risk cohorts.\u003c/p\u003e\n\u003cp\u003eThe study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number (CRD420251272042).\u003c/p\u003e","manuscriptTitle":"Economic Downturns as a Public Threat to Mental Health Outcomes: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-05 05:33:56","doi":"10.21203/rs.3.rs-8444991/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53553fa2-9ca7-428c-a05d-6a869abb4066","owner":[],"postedDate":"January 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60188256,"name":"Health Economics \u0026 Outcomes Research"},{"id":60188257,"name":"Psychology"},{"id":60188258,"name":"Critical Care \u0026 Emergency Medicine"},{"id":60188259,"name":"Epidemiology"}],"tags":[],"updatedAt":"2026-01-05T05:33:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-05 05:33:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8444991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8444991","identity":"rs-8444991","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}