Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Machine learning to investigate policy-relevant social determinants of health and suicide rates in the United States

Nature Mental Health volume 3, pages 675–684 (2025)Cite this article

Subjects

Abstract

This study aimed to categorize county clusters of multidimensional social determinants of health (SDOH) using unsupervised machine learning and to analyze their association with county-level suicide rates, considering temporal, geographic and demographic variation. We analyzed aggregated SDOH data across 3,018 US counties for 2009, 2014 and 2019, which were linked to county-level suicide rates from the National Vital Statistics System. We identified three distinct SDOH clusters: ‘REMOTE’ (rural, elderly, marginalized environments, old housing, traditional systems, empty houses), ‘COPE’ (complex family dynamics, high consumption of health services, poverty, extreme heat) and ‘DIVERSE’ (dense, immigrant rich, environmentally challenged, economically unequal, racial/ethnic diversity, saturated health care, expensive housing). We used negative binomial regression after identifying clusters to estimate the associations between county-level SDOH clusters and suicide rates. Compared with other clusters, REMOTE was associated with higher overall suicide rates, particularly among men; COPE showed elevated suicide rates among whites; and DIVERSE exhibited increased rates among women and Black and Hispanic populations. The distribution of suicide rates across US states corresponded to the variations in SDOH cluster distribution within each state. These findings provide a foundation for designing more effective, data-driven suicide prevention strategies tailored to specific regional and demographic contexts.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: SDOH cluster distributions.
Fig. 2: SDOH variable distributions by SDOH clusters in 2009.
Fig. 3: Suicide rates by SDOH clusters in 2009, 2014 and 2019.
Fig. 4: Coefficients (95% CI) of negative binomial model between SDOH clusters and suicide rates.

Similar content being viewed by others

Data availability

All raw data in this study are publicly available and can be retrieved from the US National Center for Health Statistics, US Centers for Disease Control and Prevention Mortality Data (https://www.cdc.gov/nchs/nvss/deaths.htm) and US Agency for Healthcare Research and Quality Social Determinants of Health Database (https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html).

Code availability

Data analysis was performed using R version 4.4.2, leveraging several specialized packages to ensure robust and reproducible results. The ‘NbClust’ package (version 3.0.1) was used to determine the optimal number of clusters, while ‘cluster’ (version 2.1.6) facilitated clustering techniques and visualization. Core statistical computations and modeling were conducted using the ‘stats’ package (version 3.6.2). For regression modeling, including elastic net and regularization, the ‘glmnet’ package (version 4.1.8) was employed. In addition, geographically weighted regression was implemented using the ‘spgwr’ package (version 0.6-37). Code is available at github.com/X-PLORE-Lab/sdoh_clustering_suicide.

References

  1. Stone, D. M., Mack, K. A. & Qualters, J. Notes from the field: recent changes in suicide rates, by race and ethnicity and age group—United States, 2021. MMWR Morb. Mortal. Wkly Rep. 72, 160–162 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Provisional Suicide Deaths in the United States, 2022 (CDC, 2023); https://www.cdc.gov/media/releases/2023/s0810-US-Suicide-Deaths-2022.html

  3. Bridge, J. A. et al. Suicide trends among elementary school-aged children in the United States from 1993 to 2012. JAMA Pediatr. 169, 673–677 (2015).

    Article  PubMed  Google Scholar 

  4. Opara, I. et al. Suicide among Black children: an integrated model of the interpersonal–psychological theory of suicide and intersectionality theory for researchers and clinicians. J. Black Stud. 51, 611–631 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Martínez-Alés, G. et al. Age, period, and cohort effects on suicide death in the United States from 1999 to 2018: moderation by sex, race, and firearm involvement. Mol. Psychiatry 26, 3374–3382 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  6. AACAP AACAP Policy Statement on Increased Suicide Among Black Youth in the US (AACAP, accessed 12 October 2023); https://www.aacap.org/aacap/Policy_Statements/2022/AACAP_Policy_Statement_Increased_Suicide_Among_Black_Youth_US.aspx

  7. Xiao, Y. & Lu, W. Temporal trends and disparities in suicidal behaviors by sex and sexual identity among Asian American adolescents. JAMA Netw. Open 4, e214498 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Xiao, Y., Cerel, J. & Mann, J. J. Temporal trends in suicidal ideation and attempts among US adolescents by sex and race/ethnicity, 1991–2019. JAMA Netw. Open 4, e2113513 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lindsey, M. A., Sheftall, A. H., Xiao, Y. & Joe, S. Trends of suicidal behaviors among high school students in the United States: 1991–2017. Pediatrics 144, e20191187 (2019).

    Article  PubMed  Google Scholar 

  10. Disparities in Suicide | Suicide Prevention (CDC, accessed 19 January 2024); https://www.cdc.gov/suicide/facts/disparities-in-suicide.html

  11. About Multiple Cause of Death, 2018–2023 Single Race (CDC, accessed 1 January 1 2024); http://wonder.cdc.gov/mcd-icd10-expanded.html

  12. Social Determinants of Health (CDC, accessed 12 October 2023); https://www.cdc.gov/about/sdoh/index.html

  13. Social Determinants of Health (NCHHSTP and CDC, accessed 19 January 2024); https://www.cdc.gov/nchhstp/socialdeterminants/index.html

  14. VanderWeele, T. & Robinson, W. On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology 25, 473–484 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Shrider, E. A., Kollar, M., Chen, F. & Semega, J. Income and Poverty in the United States: 2020 (US Government Publishing Office, 2021).

  16. Cyr, M. E., Etchin, A. G., Guthrie, B. J. & Benneyan, J. C. Access to specialty healthcare in urban versus rural US populations: a systematic literature review. BMC Health Serv. Res. 19, 974 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Robertson, R. A., Standley, C. J., Gunn, J. F. III & Opara, I. Structural indicators of suicide: an exploration of state-level risk factors among Black and White people in the United States. J. Public Ment. Health 21, 23–34 (2022).

  18. Smith, N. D. L. & Kawachi, I. State-level social capital and suicide mortality in the 50 US states. Soc. Sci. Med. 120, 269–277 (2014).

    Article  PubMed  Google Scholar 

  19. Hoffmann, J. A., Farrell, C. A., Monuteaux, M. C., Fleegler, E. W. & Lee, L. K. Association of pediatric suicide with county-level poverty in the United States, 2007–2016. JAMA Pediatr. 174, 287–294 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Knipe, D., Padmanathan, P., Newton-Howes, G., Chan, L. F. & Kapur, N. Suicide and self-harm. Lancet 399, 1903–1916 (2022).

    Article  PubMed  Google Scholar 

  21. Luby, J. & Kertz, S. Increasing suicide rates in early adolescent girls in the United States and the equalization of sex disparity in suicide: the need to investigate the role of social media. JAMA Netw. Open 2, e193916 (2019).

    Article  PubMed  Google Scholar 

  22. Mustanski, B. & Espelage, D. L. Why are we not closing the gap in suicide disparities for sexual minority youth? Pediatrics 145, e20194002 (2020).

    Article  PubMed  Google Scholar 

  23. Tsai, A. C., Lucas, M. & Kawachi, I. Association between social integration and suicide among women in the United States. JAMA Psychiatry 72, 987–993 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  24. de Leo, D. Ageism and suicide prevention. Lancet Psychiatry 5, 192–193 (2018).

    Article  PubMed  Google Scholar 

  25. Van Orden, K. A. & Conwell, Y. Issues in research on aging and suicide. Aging Ment. Health 20, 240–251 (2016).

    Article  PubMed  Google Scholar 

  26. Tsai, A. C., Lucas, M., Sania, A., Kim, D. & Kawachi, I. Social integration and suicide mortality among men: 24-year cohort study of US health professionals. Ann. Intern. Med. 161, 85–95 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Perry, S. W. et al. Achieving health equity in US suicides: a narrative review and commentary. BMC Public Health 22, 1360 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Xiao, Y. et al. Patterns of social determinants of health and child mental health, cognition, and physical health. JAMA Pediatr. https://doi.org/10.1001/jamapediatrics.2023.4218 (2023).

  29. Skinner, A., Osgood, N. D., Occhipinti, J. A., Song, Y. J. C. & Hickie, I. B. Unemployment and underemployment are causes of suicide. Sci. Adv. 9, eadg3758 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Graves, J. M., Abshire, D. A., Mackelprang, J. L., Amiri, S. & Beck, A. Association of rurality with availability of youth mental health facilities with suicide prevention services in the US. JAMA Netw. Open 3, e2021471 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bower, M. et al. The impact of the built environment on loneliness: a systematic review and narrative synthesis. Health Place 79, 102962 (2023).

    Article  PubMed  Google Scholar 

  32. Kind, A. J. & Buckingham, W. R. Making neighborhood-disadvantage metrics accessible—The Neighborhood Atlas. N. Engl. J. Med. 378, 2456–2458 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L. & Lewis, B. A social vulnerability index for disaster management. J. Homel. Secur. Emerg. Manag. 8, 3 (2011).

    Google Scholar 

  34. Kirkbride, J. B. et al. The social determinants of mental health and disorder: evidence, prevention and recommendations. World Psychiatry 23, 58–90 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Sussell, A. Suicide rates by industry and occupation—National Vital Statistics System, United States, 2021. MMWR Morb. Mortal. Wkly Rep. https://doi.org/10.15585/mmwr.mm7250a2 (2023).

  36. Chen, Q. et al. Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression. Sci. Total Environ. 572, 450–466 (2016).

    Article  PubMed  Google Scholar 

  37. CDC/ATSDR SVI Data and Documentation Download | Place and Health (CDC and ATSDR, accessed 6 May 2023); https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

  38. Liu, S. et al. Social vulnerability and risk of suicide in US adults, 2016–2020. JAMA Netw. Open 6, e239995 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Bundy, J. D. et al. Social determinants of health and premature death among adults in the USA from 1999 to 2018: a national cohort study. Lancet Public Health 8, e422–e431 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Masulli, F. & Rovetta, S. Clustering high-dimensional data. In Proc. Clustering High-Dimensional Data: First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers (eds Masulli, F. et al.) 1–13 (Springer, 2015).

  41. Zhang, C.-H. & Huang, J. The sparsity and bias of the Lasso selection in high-dimensional linear regression. Ann. Stat. 36, 1567–1594 (2008).

    Article  Google Scholar 

  42. Mann, J. J., Michel, C. A. & Auerbach, R. P. Improving suicide prevention through evidence-based strategies: a systematic review. Am. J. Psychiatry https://doi.org/10.1176/appi.ajp.2020.20060864 (2021).

  43. Mock, C., Quansah, R., Krishnan, R., Arreola-Risa, C. & Rivara, F. Strengthening the prevention and care of injuries worldwide. Lancet 363, 2172–2179 (2004).

    Article  PubMed  Google Scholar 

  44. Thomson, R. M. et al. How do income changes impact on mental health and wellbeing for working-age adults? A systematic review and meta-analysis. Lancet Public Health 7, e515–e528 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Blumenthal, D., Fowler, E. J., Abrams, M. & Collins, S. R. Covid-19—implications for the health care system. N. Engl. J. Med. 383, 1483–1488 (2020).

    Article  PubMed  Google Scholar 

  46. Beseran, E. et al. Deaths of despair: a scoping review on the social determinants of drug overdose, alcohol-related liver disease and suicide. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph191912395 (2022).

  47. Ramchand, R., Gordon, J. A. & Pearson, J. L. Trends in suicide rates by race and ethnicity in the United States. JAMA Netw. Open 4, e2111563 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kolak, M., Bhatt, J., Park, Y. H., Padrón, N. A. & Molefe, A. Quantification of neighborhood-level social determinants of health in the continental United States. JAMA Netw. Open 3, e1919928 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Sit, H. F., Hall, B. J. & Li, S. X. Cultural adaptation of interventions for common mental disorders. Lancet Psychiatry 10, 374–376 (2023).

    Article  PubMed  Google Scholar 

  50. Talukder, S. et al. Urban health in the post-2015 agenda. Lancet 385, 769 (2015).

    Article  PubMed  Google Scholar 

  51. Romanello, M. et al. The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. Lancet 398, 1619–1662 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bennett, K. J., Borders, T. F., Holmes, G. M., Kozhimannil, K. B. & Ziller, E. What is rural? Challenges and implications of definitions that inadequately encompass rural people and places. Health Aff. 38, 1985–1992 (2019).

    Article  Google Scholar 

  53. Dwyer-Lindgren, L. et al. Cause-specific mortality by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities. Lancet https://doi.org/10.1016/S0140-6736(23)01088-7 (2023).

  54. Imbens, G. Understanding and misunderstanding randomized controlled trials: a commentary on Deaton and Cartwright. Soc. Sci. Med. 210, 50–52 (2018).

    Article  PubMed  Google Scholar 

  55. Deaton, A. & Cartwright, N. Understanding and misunderstanding randomized controlled trials. Soc. Sci. Med. 210, 2–21 (2018).

    Article  PubMed  Google Scholar 

  56. Lund, C. et al. Social determinants of mental disorders and the Sustainable Development Goals: a systematic review of reviews. Lancet Psychiatry 5, 357–369 (2018).

    Article  PubMed  Google Scholar 

  57. Frank, J. et al. Work as a social determinant of health in high-income countries: past, present, and future. Lancet 402, 1357–1367 (2023).

    Article  PubMed  Google Scholar 

  58. Bhugra, D. & Ventriglio, A. Commercial determinants of mental health. Int. J. Soc. Psychiatry 69, 1065–1067 (2023).

    Article  PubMed  Google Scholar 

  59. Dwyer-Lindgren, L. et al. Life expectancy by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities. Lancet 400, 25–38 (2022).

    Article  Google Scholar 

  60. Social Determinants of Health (SDOH) (AHRQ, accessed 1 May 2023); https://www.ahrq.gov/sdoh/index.html

  61. Hwong, A. R. et al. Climate change and mental health research methods, gaps, and priorities: a scoping review. Lancet Planet. Health 6, e281–e291 (2022).

    Article  PubMed  Google Scholar 

  62. Helbich, M. et al. Natural environments and suicide mortality in the Netherlands: a cross-sectional, ecological study. Lancet Planet. Health 2, e134–e139 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. International Statistical Classification of Diseases and Related Health Problems: 10th Revision (ICD-10) (World Health Organization, accessed 10 January 2024); https://icd.who.int/browse10/2019/en

  64. About Multiple Cause of Death, 1999–2020 (CDC, accessed 4 September 2023); https://wonder.cdc.gov/mcd-icd10.html

  65. Multiple Cause of Death 1999–2020 (CDC, accessed 4 September 2023); https://wonder.cdc.gov/wonder/help/mcd.html#Assurance%20of%20Confidentiality

  66. Müllner, D. Modern hierarchical, agglomerative clustering algorithms. Preprint at https://arxiv.org/abs/1109.2378 (2011).

  67. Murtagh, F. & Legendre, P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J. Classif. 31, 274–295 (2014).

    Article  Google Scholar 

  68. Laszlo, A. M., Hulman, A., Csicsman, J., Bari, F. & Nyari, T. A. The use of regression methods for the investigation of trends in suicide rates in Hungary between 1963 and 2011. Soc. Psychiatry Psychiatr. Epidemiol. 50, 249–256 (2015).

    Article  PubMed  Google Scholar 

  69. Lindén, A. & Mäntyniemi, S. Using the negative binomial distribution to model overdispersion in ecological count data. Ecology 92, 1414–1421 (2011).

    Article  PubMed  Google Scholar 

  70. Bouwmeester, W. et al. Prediction models for clustered data: comparison of a random intercept and standard regression model. BMC Med. Res. Methodol. 13, 19 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Santosa, F. & Symes, W. W. Linear inversion of band-limited reflection seismograms. SIAM J. Sci. Stat. Comput. 7, 1307–1330 (1986).

    Article  Google Scholar 

  72. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996).

    Article  Google Scholar 

  73. Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning (Springer, 2001); https://doi.org/10.1007/978-0-387-21606-5

  74. Ismail, N. & Zamani, H. Estimation of claim count data using negative binomial, generalized Poisson, zero-inflated negative binomial and zero-inflated generalized Poisson regression models. Casualty Actuarial Society E-Forum 41, 1–28 (2013).

    Google Scholar 

  75. Chai, T. & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (MAE). Geosci. Model Dev. Discuss. 7, 1525–1534 (2014).

    Google Scholar 

  76. Rural Classifications (USDA ERS, accessed 8 September 2023); https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/

Download references

Acknowledgments

This study was supported by funding from the US National Institute of Mental Health (RF1MH134649; Y.X.), American Foundation for Suicide Prevention (YIG-2-133-22; Y.X.), Mental Health Research Network (Y.X.), Google (Y.X.), the US National Institute on Drug Abuse Center for Health Economics of Treatment Interventions for Substance Use Disorder, HCV, and HIV (CHERISH, Y.X.), the US National Institute on Drug Abuse (K24DA061696-01; A.C.T.) and the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD), a program of the US National Institutes of Health (1OT2OD032581-02-259; Y.X., J.P.). The sponsor had no role in the design and conduct of the study, the collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication. We would like to acknowledge Dr L. Snowden for his invaluable contributions to this work. Dr Snowden was engaged in the conceptualization and early writing of this manuscript prior to his passing in January 2025. As a pioneering scholar and leader in mental health services research, his intellectual guidance shaped this paper and many others in the field. We are honored to carry forward his legacy through this work.

Author information

Author notes

  1. These authors contributed equally: Yunyu Xiao, Yuan Meng.

Authors and Affiliations

  1. Department of Population Health Sciences, Weill Cornell Medicine, NewYork-Presbyterian, New York, NY, USA

    Yunyu Xiao, Yuan Meng & Jyotishman Pathak

  2. School of Public Health, University of California, Berkeley, CA, USA

    Timothy T. Brown & Lonnie R. Snowden

  3. Center for Global Health and Mongan Institute, Massachusetts General Hospital, Boston, MA, USA

    Alexander C. Tsai

  4. Harvard Medical School, Boston, MA, USA

    Alexander C. Tsai

  5. School of Social Welfare, University of California, Berkeley, CA, USA

    Julian Chun-Chung Chow

  6. Departments of Psychiatry and Radiology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA

    J. John Mann

  7. Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA

    J. John Mann

Authors
  1. Yunyu Xiao
    View author publications

    Search author on:PubMed Google Scholar

  2. Yuan Meng
    View author publications

    Search author on:PubMed Google Scholar

  3. Timothy T. Brown
    View author publications

    Search author on:PubMed Google Scholar

  4. Alexander C. Tsai
    View author publications

    Search author on:PubMed Google Scholar

  5. Lonnie R. Snowden
    View author publications

    Search author on:PubMed Google Scholar

  6. Julian Chun-Chung Chow
    View author publications

    Search author on:PubMed Google Scholar

  7. Jyotishman Pathak
    View author publications

    Search author on:PubMed Google Scholar

  8. J. John Mann
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.X., Y.M. and J.J.M. carried out concept and design. Y.X., Y.M., T.T.B., A.C.T., L.R.S., J.C.-C.C., J.P. and J.J.M. carried out acquisition, analysis or interpretation of data. Y.X. and Y.M. drafted the paper. Y.X., Y.M., T.T.B., A.C.T., L.R.S., J.C.-C.C., J.P. and J.J.M. provided critical revision of the paper for important intellectual content. Y.X. and Y.M. carried out statistical analysis. Y.X. obtained funding. Y.X. provided administrative, technical or material support. Y.X. and Y.M. had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. Y.X. and Y.M. have made equal contributions.

Corresponding author

Correspondence to Yunyu Xiao.

Ethics declarations

Competing interests

A.C.T. reports an honorarium from Elsevier for his work as co-editor in chief of the Elsevier-owned journal SSM-Mental Health. J.J.M. reports royalties for commercial use of the C-SSRS from the Research Foundation for Mental Hygiene and for the Columbia Psychiatry Pathways App from Columbia University. All other authors report no conflicts of interest.

Peer review

Peer review information

Nature Mental Health thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9, Tables 1–20, Results 1–3 and Methods 1–7.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, Y., Meng, Y., Brown, T.T. et al. Machine learning to investigate policy-relevant social determinants of health and suicide rates in the United States. Nat. Mental Health 3, 675–684 (2025). https://doi.org/10.1038/s44220-025-00424-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44220-025-00424-4

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing