MainClimate change is a critical global public health issue, with impacts extending beyond environmental changes, affecting human health, behavior and societal systems in complex ways1,2,3,4. Notably, rising global temperatures and increasing extreme weather events have been linked to various adverse health outcomes, including mental health disorders3,4,5,6,7. Epidemiological studies consistently highlight the negative effects of climate change on mental health, showing associations with post-traumatic stress disorder8,9, anxiety8,9,10, depression10, sleep disorders11 and suicide3,4,5,6.Among the mental health-related challenges, suicide remains one of the leading causes of death globally, claiming an estimated 720,000 lives annually12. Climate change has emerged as an important contributor to the existing suicide burden13. High temperatures can exacerbate poor mental health outcomes through insomnia, increased stress levels and psychological instability, all of which contribute to suicide risk3,4,5,14,15,16. Numerous studies have identified a positive association between ambient temperature and suicide, suggesting that higher temperatures increase suicide risk17,18,19,20. The largest-scale multi-city multi-country study demonstrated that higher ambient temperatures generally increased suicide risks with regional variations in the degree and shape of the association20. Nonlinear inverse J-shaped relationships have been reported in East Asian countries such as Japan, the Republic of Korea and Taiwan, where suicide risk tends to decrease at extremely high temperatures after reaching a peak. By contrast, nearly linear patterns appeared in Western countries such as Canada, Spain, Switzerland, the UK and the USA, where suicide risk continued to increase with higher temperatures. Meanwhile, in the Philippines, Brazil, Vietnam and South Africa, the nonlinear curves were unclear or showed large uncertainty20.Projections of climate-sensitive health responses are essential for anticipating future public health challenges owing to climate change and informing policy. Several studies have predicted the impacts of climate change on human health including morbidity and mortality across diverse countries21,22,23,24,25,26,27,28,29. Projections of temperature-related excess mortality under various climate change scenarios reveal regional disparities, with warmer and low-resource regions expected to bear a greater burden26. Under high-emission scenarios such as representative concentration pathway 8.5 (RCP8.5), which assumes continued high greenhouse gas emissions, warmer regions, including Central and South America, South Europe and particularly Southeast Asia, exhibit sharp increases in heat-related excess mortality (that is, mortality attributable to temperatures above the minimum mortality temperature), which outweigh reductions in cold-related excess mortality (that is, mortality attributable to temperatures below the minimum mortality temperature)26,27. Consequently, the net change in temperature-related excess mortality is projected to rise substantially in these regions. By contrast, temperate regions such as North Europe, East Asia and Australia, where warming is less intense, show smaller net increases in temperature-related excess mortality, primarily owing to pronounced declines in cold-related mortality26,27. These findings underscore the importance of policies to mitigate global warming and the disproportionate health risks faced by populations living in warmer and, in some cases, poorer regions.Despite the importance of projection studies, few have assessed the impact of projected climate change on suicide burden30,31,32. One study using the high-emission scenario RCP8.5 estimated that a 1 °C monthly average temperature increase would cause a 0.7% rise in suicide rates in US counties and 2.1% rise in Mexican municipalities, potentially resulting in 9,000–40,000 additional suicides up to the year 205030. A more recent study in Japan projected temperature-related suicide mortality to rise under all climate scenarios31. In the 2090s, the projected increases compared with the 2010s were estimated under different shared socioeconomic pathway (SSP) scenarios, 0.6% for SSP1-2.6, 1.3% for SSP2-4.5 and 2.4% for SSP5-8.5, with greater increases under more extreme scenarios. In China, a nationwide case–control study identified a clear positive association between ambient temperature and suicide mortality, estimating that around 15% of suicide deaths were linked to non-optimum temperatures. The study further projected that excess suicide mortality would continue to rise throughout this century under high-emission scenarios, while stabilizing under moderate- and low-emission scenarios32. However, because most studies are country specific, a global comparative analysis is needed to elucidate the future global suicide burden under climate change.This study aimed to (1) determine the extent of the temperature–suicide association based on observed data, (2) combine these associations with future temperatures under a changing climate and (3) predict future suicide mortality burden associated with temperatures across 751 locations in 26 countries by the 2050s (2050–2059). This work seeks to identify regional variations in the impacts of climate change on suicide risk and inform the development of effective suicide prevention and public health strategies.ResultsTable 1 summarizes the descriptive statistics of suicide and temperature for each of the 26 countries (based on data from 751 locations) as well as the suicide rate obtained from WHO Global Health Estimates33. The Republic of Korea, Japan, South Africa and Estonia reported high suicide rates of >20/100,000 population, whereas countries such as the Philippines, Mexico, Brazil and Paraguay recorded 5 °C) between observed and modeled temperatures after adjustment underwent a secondary bias correction by aligning their future mean temperatures with the national mean. Country-specific root mean square errors, presented in Supplementary Table 3, indicate close alignment between the calibrated modeled and observed temperatures. Table 2 presents the summary statistics of the modeled temperature estimates during the 2010s and projected increase for the 2050s by region and SSP scenario.Modeled daily suicide mortality were calculated by averaging the observed suicide data for each calendar day and repeating the daily averages in all years during the projection period (2010–2059), assuming no changes in population structure or suicide rate. Hence, the population and suicide rates from the observed data were used as the baseline.Statistical analysisTo project temperature-related suicide burden, we applied a statistical modeling framework26,30,31,62,64. First, location-specific temperature–suicide associations were estimated based on the observed data using a two-stage approach. Second, the estimated associations were extrapolated to cover the range of the temperature distribution through 2059, and future increases in suicide mortality attributable to temperatures were calculated under three key assumptions: no adaptation, no change in population structure and no change in suicide rate. A detailed explanation is provided in Supplementary Section 2.Estimation of location-specific temperature–suicide relationshipThe temperature–suicide association for each location was estimated using a two-stage approach. First, a conditional Poisson regression model65 was fitted for each location according to a time-stratified case-crossover design66. The stratum was defined by the three-way interaction of calendar year, month and day of the week, controlling for time-varying confounders such as seasonality and long-term temporal trends. To account for the nonlinear and delayed relationship between temperature and suicide, a distributed lag nonlinear model67 was used with a bidimensional cross-basis spline function and up to 3-day lag. The choice of lag was selected based on previous epidemiological studies31,32, which indicate that suicide risk is primarily influenced by short-term temperature exposure—typically within 2–3 days—rather than by prolonged lag effects. A natural cubic spline was used for the exposure–response association with a single internal knot at the 50th percentile of the location-specific temperature distribution. For the lag–response association, a discrete parameterization was used to define the intervals for lags 0 and 1–3. These specifications were determined through a model selection procedure (Supplementary Section 2.1.1 and Supplementary Table 4) and sensitivity analyses20,31,32 (Supplementary Fig. 11). Once the model was fit, the cross-basis coefficients were reduced to cumulate the exposure–response associations over the lags, and the reduced coefficients, which represent the cumulative temperature–suicide association, were used as inputs hereafter.Second, we applied a multivariate mixed-effects meta-regression to pool the location-specific overall cumulative associations68. Random effects were included to account for intercept variability at both the country and city levels, assuming a hierarchical structure where cities are nested within countries. Meta-predictors were included as fixed effects, such as region indicator, country-level GDP and location-specific variables including average temperature and daily mean temperature range during the observation period. These specifications were determined through a model selection procedure (Supplementary Section 2.1.2 and Supplementary Table 5). From the model, we derived the best linear unbiased predictions of the overall cumulative associations for each location, expressed as RR curves. This two-stage model improves location-specific estimates by leveraging information from locations with similar characteristics, particularly for low observed daily suicide counts68.Projection of region-specific impact of climate change on suicideThe burden of suicide mortality associated with temperatures (that is, temperatures lower or higher than the median of observed temperatures) was projected to compare the impact of climate change on suicide across regions26,31,32,63,65. The projection assumed (1) no behavioral or physiological adaptation to temperature change, (2) no change in population structure and (3) no change in baseline suicide rates. These assumptions are consistent with standard approaches in climate-health impact projection studies and allow for isolation of the temperature-attributable component of future suicide burden (Supplementary Section 2.2.1). The projection was based on the location-specific temperature–suicide associations, reference temperatures, modeled temperature and modeled suicide data.Next, a reference temperature needed to be chosen to calculate suicide mortality associated with temperatures. The reference temperature served as the baseline for suicide risk across all temperatures, with ANs or AFs calculated relative to this baseline. To ensure reasonable future projections, the reference temperature was selected within a range of temperatures expected to exist in the future, enabling consistent and meaningful comparisons of suicide risk across different time periods. In this study, the 50th percentile (median) of the observed temperature distribution at each location was selected as the reference, as it is likely to remain within the projected temperature range in the 2050s even under the most extreme scenario (SSP5-8.5). For example, in Fortaleza, Brazil, the lowest projected temperature in the 2050s under SSP5-8.5 is 27.6 °C, which coincides with its observed median temperature (27.6 °C), indicating that future temperatures are expected to begin at levels that historically correspond to the median temperature (Supplementary Table 6 and Supplementary Fig. 6).We centered the RR curves at the reference temperature (as in Fig. 1), and overall cumulative risks associated with temperature based on observed data were calculated relative to this reference and extrapolated through 2059. The RRs were calculated for future days with temperatures above/below the reference. For each combination of SSPs and GCMs, based on these RRs, daily ANs were calculated for each day from 2010 to 2059 at each location for all combinations of GCMs and SSP scenarios. The location-specific ANs were then aggregated by region, SSP and decade, and averaged across GCMs. The region-specific decadal ANs were divided by the total number of suicide deaths in each region to obtain decadal AF of suicide mortality associated with temperatures26,31,32,62. Finally, the difference in AF for each decade (2020s–2050s) relative to the 2010s (as presented in Fig. 2) was calculated to assess changes in future suicide burden associated with climate warming.To put it more concretely, we examined the temporal changes in AF (%) of suicide mortality associated with temperatures above and below the reference. Because the reference temperature corresponds to the median of observed temperature distribution, the total AF can be decomposed into contributions from temperatures below and above this reference. For this decomposition, daily ANs were separately aggregated for days with temperatures above and below the reference at each location and summed by region, SSP and decade. These aggregated values were then divided by the total number of suicide deaths in each region to obtain AFs for warm and cold temperatures, respectively. Accordingly, regional changes in total AF were decomposed into contributions from warm (above-reference) and cold (below-reference) temperatures (Supplementary Fig. 7). While the relative contributions varied by region and location, AFs associated with both warm and cold temperatures increased over time and more intense warming, especially SSP5-8.5. The increase in AF associated with warm temperatures reflects a rise in excess suicide mortality at higher-than-reference temperatures. By contrast, the rise in AF for cold temperatures results from a diminishing protective effect of lower-than-reference temperatures, reducing the contribution of these lower temperatures that would otherwise offset excess suicide mortality. The spatial distribution of these decomposed results for each location is shown in Supplementary Fig. 8 for warm temperatures (above the reference temperature) and Supplementary Fig. 9 for cold temperatures (below the reference temperature).We quantified the uncertainty in future temperature-related suicide mortality using Monte Carlo simulations at the meta-regression-model level64 (Supplementary Section 2.2.2). Specifically, n = 1,000 sets of coefficients were sampled from a multivariate normal distribution defined by the pooled coefficients and their variance–covariance matrix obtained from the second-stage meta-regression model. For each simulation, daily ANs were recalculated and aggregated to derive the empirical distribution of regional AFs. The 2.5th and 97.5th percentiles of this distribution were used as the 95% eCIs. These eCIs accounted for the correlation among location-specific predictions and uncertainty associated with the spatialization of random effects in the meta-regression model. Uncertainty from different climate projections was additionally incorporated by combining temperature estimates from five GCMs under three SSP scenarios, ensuring that the final impact estimates reflect uncertainty across both exposure–response estimation and climate projections. This approach captures the uncertainty in multiple stages of the analysis and reduces the risk of underestimation, particularly in analyses involving small-area data and low heterogeneity64.Using the minimum suicide temperature as reference temperatureWe compared our results with those obtained using the reference temperature defined as the temperature corresponding to the lowest suicide risk—the minimum suicide temperature (MinST) in the exposure–response curve (Supplementary Fig. 10). To avoid bias from extreme outliers, the MinST was identified within the 1st to 99th percentile range of observed daily temperatures, which in practice corresponded to the 1st temperature percentile in most locations (top panel in Supplementary Fig. 10). The overall shapes of the temperature–suicide association curves were similar to those obtained using the median temperature as the reference (top panel in Supplementary Fig. 10 and Fig. 1); however, using the MinST as the reference resulted in wider CI for RR estimates at higher temperatures (top panel in Supplementary Fig. 10). Using the MinST as a reference, temporal changes in total AF across future decades remained largely consistent with those obtained using the median-temperature reference (bottom panel in Supplementary Fig. 10 and Fig. 2). Although the absolute values of total AF varied slightly depending on the choice of reference temperature, AF estimates based on the MinST reference exhibited wider uncertainty across regions. Overall, the minimal differences observed between the two reference settings support the robustness of the results and the appropriateness of using the median temperature as the reference in the main analyses.Sensitivity analysisTo assess the robustness of our results, a sensitivity analysis was conducted, referring to previous projection studies20,31,32. Owing to data accessibility limitations, the sensitivity analysis was not performed remotely; instead, it was conducted using data from 426 locations included in this study. Since temperature can influence suicide risk over short-term periods and previous epidemiological studies31,32 have adopted a 3-day lag, we compared only 3-day and 6-day lag structures. Temporal variations in total AF were similar between the two settings, indicating robustness to lag selection (Supplementary Fig. 11).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.Data availabilityObserved daily suicide mortality and temperature data used in this study were obtained through the Multi-Country Multi-City (MCC) Collaborative Research Network (https://mccstudy.lshtm.ac.uk/) under data-sharing agreements with national and regional data providers and cannot be made publicly available owing to privacy considerations related to suicide mortality records. Access to the MCC dataset may be granted for scientific purposes via a formal application to the MCC network. Detailed information on data collection by country is provided in Supplementary Information. Modeled daily mean temperature data are publicly available from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database (https://www.isimip.org/) for the five General Circulation Models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0 and UKESM1-0-LL) under three SSP scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) used in this study.Code availabilityAll statistical analyses were performed in R (version 4.3.3; R Core Team, 2024). The following R packages were used: dlnm (version 2.4.7), gnm (version 1.1.5) and tsModel (version 0.6.1), with the base R splines package, for the first-stage distributed lag nonlinear models; mixmeta (version 1.2.0) and mvmeta (version 1.0.3) for the second-stage multivariate mixed-effects meta-regression; MASS (version 7.3.60.0.1) for Monte Carlo simulation; tidyr (version 1.3.1) and dplyr (version 1.1.4) for data processing. Figures were produced using ggplot2 (version 3.5.0), cowplot (version 1.1.3), patchwork (version 1.2.0), reshape2 (version 1.4.4) and gridExtra (version 2.3). World map boundaries were obtained via giscoR (version 0.6.1), sf (version 1.0.20), rmapshaper (version 0.5.0) and classInt (version 0.4.10). The R code scripts used for the main analyses, including the two-stage modeling framework, projection of attributable fractions and uncertainty quantification, are publicly available via GitHub at https://github.com/hye0-n0/temperature-suicide-projection. Note that the deposited code uses simulated placeholder data for illustrative purposes only, as the original observed data cannot be shared publicly owing to data-sharing agreements (‘Data availability’). Consequently, the outputs generated by the deposited code will differ from the results reported in this study. Additional analysis code is available from the corresponding authors upon reasonable request.ReferencesWatts, N. et al. 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An extended mixed-effects framework for meta-analysis. Stat. Med. 38, 5429–5444 (2019).Article PubMed Google Scholar Download referencesFundingH.R. and Y.C. were supported by grants from the National Research Foundation (NRF) of Korea funded by the Korea government (MSIT) (numbers RS-2022-NR068758 and RS-2026-25470287) and by the NRF Basic Science Research Program funded by the Ministry of Education (numbers 2019R1A6A1A10073887 and RS-2025-25397599). Y.K. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (number JP24K10701). V.H. was supported by a ‘Ramón y Cajal’ fellowship of the Spanish Ministry of Science and Innovation (RYC2022-036948-I), and the Wellcome project BREATHE (308914/Z/23/Z). A.G. is supported by the Wellcome project BREATHE (308914/Z/23/Z). J.K. and A.U. were supported by the Czech Science Foundation (number 25-17587S). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.Author informationAuthor notesThese authors contributed equally: Yoonhee Kim, Yeonseung Chung.Authors and AffiliationsGraduate School of Data Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaHyeyeong RoDepartment of Global Environmental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, JapanYoonhee KimDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, JapanMasahiro Hashizume & Paul Lester Carlos ChuaSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, JapanMasahiro Hashizume, Lina Madaniyazi & Xerxes SeposoSchool of the Environment, Yale University, New Haven, CT, USAMichelle L. BellCenter for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, JapanYasushi HondaEnvironment and Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene and Tropical Medicine, London, UKAntonio Gasparrini & Pierre MasselotDepartment of Statistics, Computer Science and Applications “G. Parenti, ” University of Florence, Florence, ItalyFrancesco SeraClimate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, AustraliaYuming Guo, Shanshan Li & Wenzhong HuangDepartment of Pathology, School of Medicine, University of São Paulo, São Paulo, BrazilMicheline De Sousa Zanotti Stagliorio CoelhoINSPER, São Paulo, BrazilPaulo Hilario Nascimento SaldivaSchool of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, CanadaEric LavigneEnvironmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, CanadaEric LavigneDepartment of Public Health, Universidad de los Andes, Santiago, ChilePatricia Matus CorreaCentro Latino Americano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, PeruNicolas Valdes-OrtegaDepartment of Environmental Health, School of Public Health, Fudan University, Shanghai, ChinaHaidong KanBiological Mission of Galicia (MBG) - Spanish Council for Scientific Research (CSIC), Pontevedra, SpainDominic RoyeCIBERESP, Madrid, SpainDominic Roye & Carmen ÍñiguezInstitute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech RepublicJan Kyselý & Aleš UrbanFaculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech RepublicJan Kyselý & Aleš UrbanInstitute of Family Medicine and Public Health, University of Tartu, Tartu, EstoniaHans Orru & Ene IndermitteCenter for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, FinlandJouni J. K. Jaakkola & Nillo RytiMedical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, FinlandJouni J. K. Jaakkola & Nillo RytiInstitute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Neuherberg, GermanyAlexandra Schneider, Veronika Huber & Maximilian SchwarzEstación Biológica de Doñana (EBD) - Spanish Council for Scientific Research (CSIC), Seville, SpainVeronika HuberDepartment of Epidemiology, Lazio Regional Health Service - ASL ROMA 1, Rome, ItalyPaola Michelozzi & Francesca de’DonatoDepartment of Environmental Health, National Institute of Public Health, Cuernavaca, MexicoMagali Hurtado Diaz & Eunice Elizabeth Félix ArellanoDepartment of Hygiene, Graduate School of Medicine, Hokkaido University, Sapporo, JapanXerxes SeposoFaculty of Geography, Babes-Bolay University, Cluj-Napoca, RomaniaIulian Horia HolobacaDepartment of Environmental Health. Rollins School of Public Health, Emory University, Atlanta, GA, USANoah ScovronickDepartment of Earth Sciences, University of Torino, Turin, ItalyFella AcquaottaGraduate School of Public Health, Seoul National University, Seoul, Republic of KoreaHo KimSchool of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Yangsan, Republic of KoreaWhanhee LeeInstitute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, SpainAurelio TobiasDepartment of Statistics and Computational Research, Universitat de València, València, SpainCarmen ÍñiguezInstitute of Social and Preventive Medicine, University of Bern, Bern, SwitzerlandAna Maria Vicedo-CabreraOeschger Center for Climate Change Research, University of Bern, Bern, SwitzerlandAna Maria Vicedo-CabreraSwiss Tropical and Public Health Institute, Allschwill, SwitzerlandMartina S. RagettliUniversity of Basel, Basel, SwitzerlandMartina S. RagettliEnvironmental and Occupational Medicine, National Taiwan University (NTU) College of Medicine and NTU Hospital, Taipei, TaiwanYue Leon GuoNational Institute of Environmental Health Science, National Health Research Institutes, Zhunan, TaiwanYue Leon GuoGraduate Institute of Environmental and Occupational Health Sciences, NTU College of Public Health, Taipei, TaiwanYue Leon GuoResearch Center for Environmental Changes, Academia Sinica, Taipei, TaiwanShih-Chun PanDepartment of Public Health Environments and Society, London School of Hygiene and Tropical Medicine, London, UKBen ArmstrongDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USAAntonella Zanobetti & Joel SchwartzDepartment of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamTran Ngoc Dang & Van Dung DoDepartment of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaYeonseung ChungAuthorsHyeyeong RoView author publicationsSearch author on:PubMed Google ScholarYoonhee KimView author publicationsSearch author on:PubMed Google ScholarMasahiro HashizumeView author publicationsSearch author on:PubMed Google ScholarLina MadaniyaziView author publicationsSearch author on:PubMed Google ScholarMichelle L. BellView author publicationsSearch author on:PubMed Google ScholarYasushi HondaView author publicationsSearch author on:PubMed Google ScholarAntonio GasparriniView author publicationsSearch author on:PubMed Google ScholarPierre MasselotView author publicationsSearch author on:PubMed Google ScholarFrancesco SeraView author publicationsSearch author on:PubMed Google ScholarYuming GuoView author publicationsSearch author on:PubMed Google ScholarShanshan LiView author publicationsSearch author on:PubMed Google ScholarWenzhong HuangView author publicationsSearch author on:PubMed Google ScholarMicheline De Sousa Zanotti Stagliorio CoelhoView author publicationsSearch author on:PubMed Google ScholarPaulo Hilario Nascimento SaldivaView author publicationsSearch author on:PubMed Google ScholarEric LavigneView author publicationsSearch author on:PubMed Google ScholarPatricia Matus CorreaView author publicationsSearch author on:PubMed Google ScholarNicolas Valdes-OrtegaView author publicationsSearch author on:PubMed Google ScholarHaidong KanView author publicationsSearch author on:PubMed Google ScholarDominic RoyeView author publicationsSearch author on:PubMed Google ScholarJan KyselýView author publicationsSearch author on:PubMed Google ScholarAleš UrbanView author publicationsSearch author on:PubMed Google ScholarHans OrruView author publicationsSearch author on:PubMed Google ScholarEne IndermitteView author publicationsSearch author on:PubMed Google ScholarJouni J. K. JaakkolaView author publicationsSearch author on:PubMed Google ScholarNillo RytiView author publicationsSearch author on:PubMed Google ScholarAlexandra SchneiderView author publicationsSearch author on:PubMed Google ScholarVeronika HuberView author publicationsSearch author on:PubMed Google ScholarPaola MichelozziView author publicationsSearch author on:PubMed Google ScholarFrancesca de’DonatoView author publicationsSearch author on:PubMed Google ScholarMagali Hurtado DiazView author publicationsSearch author on:PubMed Google ScholarEunice Elizabeth Félix ArellanoView author publicationsSearch author on:PubMed Google ScholarXerxes SeposoView author publicationsSearch author on:PubMed Google ScholarPaul Lester Carlos ChuaView author publicationsSearch author on:PubMed Google ScholarIulian Horia HolobacaView author publicationsSearch author on:PubMed Google ScholarNoah ScovronickView author publicationsSearch author on:PubMed Google ScholarFella AcquaottaView author publicationsSearch author on:PubMed Google ScholarHo KimView author publicationsSearch author on:PubMed Google ScholarWhanhee LeeView author publicationsSearch author on:PubMed Google ScholarAurelio TobiasView author publicationsSearch author on:PubMed Google ScholarCarmen ÍñiguezView author publicationsSearch author on:PubMed Google ScholarAna Maria Vicedo-CabreraView author publicationsSearch author on:PubMed Google ScholarMartina S. RagettliView author publicationsSearch author on:PubMed Google ScholarYue Leon GuoView author publicationsSearch author on:PubMed Google ScholarShih-Chun PanView author publicationsSearch author on:PubMed Google ScholarBen ArmstrongView author publicationsSearch author on:PubMed Google ScholarAntonella ZanobettiView author publicationsSearch author on:PubMed Google ScholarJoel SchwartzView author publicationsSearch author on:PubMed Google ScholarTran Ngoc DangView author publicationsSearch author on:PubMed Google ScholarVan Dung DoView author publicationsSearch author on:PubMed Google ScholarMaximilian SchwarzView author publicationsSearch author on:PubMed Google ScholarYeonseung ChungView author publicationsSearch author on:PubMed Google ScholarContributionsH.R., Y.C., M.H. and Y.K. conceived the study. H.R., L.M. and Y.K. wrote the computer code and conducted the analysis. H.R. produced and extracted the data necessary for the analysis. H.R. and Y.C. led the writing, with regular inputs from Y.K. and M.H. All authors (H.R., Y.C., Y.K., M.H., L.M., M.L.B., Y.H., A.G., P. Masselot, F.S., Y.G., S.L., W.H., M.D.S.Z.S.C., P.H.N.S., E.L., P.M.C., N.V.-O., H. Kan, D.R., J.K., A.U., H.O., E.I., J.J.K.J., N.R., A.S., V.H., P. Michelozzi, F.D., M.H.D., E.E.F.A., X.S., P.L.C.C., I.H.H., N.S., F.A., H. Kim, W.L., A.T., C.Í., A.M.V.-C., M.S.R., Y.L.G., S.-C.P., B.A., A.Z., J.S., T.N.D., D.V.D. and M.S.) contributed to the paper and to the interpretation of the results. All authors approved the final version of the paper.Corresponding authorsCorrespondence to Yoonhee Kim or Yeonseung Chung.Ethics declarationsCompeting interestsThe authors declare no competing interests.Peer reviewPeer review informationNature Mental Health thanks Brian O’Shea, Rainer Papsdorf, Michael Tong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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