Pub Date : 2024-05-09DOI: 10.1038/s44220-024-00229-x
Shamal Lalvani, Sumra Bari, Nicole L. Vike, Leandros Stefanopoulos, Byoung-Woo Kim, Martin Block, Nicos Maglaveras, Aggelos K. Katsaggelos, Hans C. Breiter
The prediction of suicidal thought and behavior has met with mixed results. This study of 3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of four suicidal thought and behavior (STB) variables using a short reward/aversion judgment task and a limited set of demographic and mental health surveys. The focus was to produce a simple, quick and objective framework for assessing STB that might be automatable, without the use of big data. A balanced random forest classifier performed better than a Gaussian mixture model and four standard machine learning classifiers for predicting passive suicide ideation, active suicide ideation, suicide planning and planning for safety. Accuracies ranged from 78% to 92% (optimal area under the curve between 0.80 and 0.95) without overfitting, and peak performance was observed for predicting suicide planning. The relative importance of features for prediction showed distinct weighting across judgment variables, contributing between 40% and 64% to prediction per Gini scores. Mediation/moderation analyses showed that depression, anxiety, loneliness and age variables moderated the judgment variables, indicating that the interaction of judgment with mental health and demographic indices is fundamental for the high-accuracy prediction of STB. These findings suggest the feasibility of an efficient and highly scalable system for suicide assessment, without requiring psychiatric records or neural measures. The findings suggest that STB might be understood within a cognitive framework for judgment with quantitative variables whose unique constellation separates passive and active suicidal thought (ideation) from suicide planning and planning for safety. Applying machine learning to an objective framework for suicidality, the authors demonstrate that four suicidal thought and behavior variables can be predicted with high accuracy and may present a scalable system for suicide risk assessment.
{"title":"Predicting suicidality with small sets of interpretable reward behavior and survey variables","authors":"Shamal Lalvani, Sumra Bari, Nicole L. Vike, Leandros Stefanopoulos, Byoung-Woo Kim, Martin Block, Nicos Maglaveras, Aggelos K. Katsaggelos, Hans C. Breiter","doi":"10.1038/s44220-024-00229-x","DOIUrl":"10.1038/s44220-024-00229-x","url":null,"abstract":"The prediction of suicidal thought and behavior has met with mixed results. This study of 3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of four suicidal thought and behavior (STB) variables using a short reward/aversion judgment task and a limited set of demographic and mental health surveys. The focus was to produce a simple, quick and objective framework for assessing STB that might be automatable, without the use of big data. A balanced random forest classifier performed better than a Gaussian mixture model and four standard machine learning classifiers for predicting passive suicide ideation, active suicide ideation, suicide planning and planning for safety. Accuracies ranged from 78% to 92% (optimal area under the curve between 0.80 and 0.95) without overfitting, and peak performance was observed for predicting suicide planning. The relative importance of features for prediction showed distinct weighting across judgment variables, contributing between 40% and 64% to prediction per Gini scores. Mediation/moderation analyses showed that depression, anxiety, loneliness and age variables moderated the judgment variables, indicating that the interaction of judgment with mental health and demographic indices is fundamental for the high-accuracy prediction of STB. These findings suggest the feasibility of an efficient and highly scalable system for suicide assessment, without requiring psychiatric records or neural measures. The findings suggest that STB might be understood within a cognitive framework for judgment with quantitative variables whose unique constellation separates passive and active suicidal thought (ideation) from suicide planning and planning for safety. Applying machine learning to an objective framework for suicidality, the authors demonstrate that four suicidal thought and behavior variables can be predicted with high accuracy and may present a scalable system for suicide risk assessment.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 7","pages":"773-786"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00229-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1038/s44220-024-00232-2
Jina Suh, Sachin R. Pendse, Robert Lewis, Esther Howe, Koustuv Saha, Ebele Okoli, Judith Amores, Gonzalo Ramos, Jenny Shen, Judith Borghouts, Ashish Sharma, Paola Pedrelli, Liz Friedman, Charmain Jackman, Yusra Benhalim, Desmond C. Ong, Sameer Segal, Tim Althoff, Mary Czerwinski
The impact of technology on mental health has become a core concern for researchers and practitioners from the clinical science, public health and technology sectors. One factor that influences this impact is the large gap between the silos of technologies explicitly designed as mental health support tools and the everyday technologies that inadvertently affect mental health. Here we ground our position on findings from a workshop that brought together over 60 experts and emphasize the importance of a multi-sectoral collaboration across these silos to address the complexities of technology’s impact on mental health. Our specific recommendations include a push to align stakeholders, incentives and governance, adopting person-centered and proactive mental health evaluation, and empowering users and clinicians (and their interactions) through mental health and technology literacy. We highlight sector-specific adaptations that can support greater collaborations, toward a future in which users have consistently positive interactions between technology use and their mental health. In this Perspective, the authors make recommendations on better aligning stakeholders, including those in technology, practitioners and researchers, to increase collaboration and governance in technology and mental health.
{"title":"Rethinking technology innovation for mental health: framework for multi-sectoral collaboration","authors":"Jina Suh, Sachin R. Pendse, Robert Lewis, Esther Howe, Koustuv Saha, Ebele Okoli, Judith Amores, Gonzalo Ramos, Jenny Shen, Judith Borghouts, Ashish Sharma, Paola Pedrelli, Liz Friedman, Charmain Jackman, Yusra Benhalim, Desmond C. Ong, Sameer Segal, Tim Althoff, Mary Czerwinski","doi":"10.1038/s44220-024-00232-2","DOIUrl":"10.1038/s44220-024-00232-2","url":null,"abstract":"The impact of technology on mental health has become a core concern for researchers and practitioners from the clinical science, public health and technology sectors. One factor that influences this impact is the large gap between the silos of technologies explicitly designed as mental health support tools and the everyday technologies that inadvertently affect mental health. Here we ground our position on findings from a workshop that brought together over 60 experts and emphasize the importance of a multi-sectoral collaboration across these silos to address the complexities of technology’s impact on mental health. Our specific recommendations include a push to align stakeholders, incentives and governance, adopting person-centered and proactive mental health evaluation, and empowering users and clinicians (and their interactions) through mental health and technology literacy. We highlight sector-specific adaptations that can support greater collaborations, toward a future in which users have consistently positive interactions between technology use and their mental health. In this Perspective, the authors make recommendations on better aligning stakeholders, including those in technology, practitioners and researchers, to increase collaboration and governance in technology and mental health.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 5","pages":"478-488"},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140919355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1038/s44220-024-00253-x
Jinkai Li, Qiuzhen Ren, Erga Luo, Chuanlong Ma, Chengfang Liu
The COVID-19 pandemic has caused profound mental health problems among left-behind children (LBC). Here we discuss the challenges that LBC are facing in the post-COVID-19 era and the potential underlying mechanisms, and provide recommendations for future policy priorities.
{"title":"A call for immediate action to improve the mental health of left-behind children in the post-COVID-19 era","authors":"Jinkai Li, Qiuzhen Ren, Erga Luo, Chuanlong Ma, Chengfang Liu","doi":"10.1038/s44220-024-00253-x","DOIUrl":"10.1038/s44220-024-00253-x","url":null,"abstract":"The COVID-19 pandemic has caused profound mental health problems among left-behind children (LBC). Here we discuss the challenges that LBC are facing in the post-COVID-19 era and the potential underlying mechanisms, and provide recommendations for future policy priorities.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 5","pages":"466-468"},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140919382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1038/s44220-024-00241-1
Michael L. Platt, Peter Sterling
Increasing inequality and social fragmentation may give rise to a collective state of despair that may not only diminish the desire to live but also dampen the drive to reproduce, resulting in shrinking fertility and population decline.
{"title":"Declining human fertility and the epidemic of despair","authors":"Michael L. Platt, Peter Sterling","doi":"10.1038/s44220-024-00241-1","DOIUrl":"10.1038/s44220-024-00241-1","url":null,"abstract":"Increasing inequality and social fragmentation may give rise to a collective state of despair that may not only diminish the desire to live but also dampen the drive to reproduce, resulting in shrinking fertility and population decline.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 5","pages":"463-465"},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140919356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1038/s44220-024-00233-1
Joshua C. Black, Andrew A. Monte, Nabarun Dasgupta, Jennifer S. Jewell, Karilynn M. Rockhill, Richard A. Olson, Richard C. Dart
The encouraging results of late-phase clinical trials investigating psychedelic-assisted psychotherapy suggests that US Food and Drug Administration approval and subsequent expansion of use is imminent in the USA. Without fit-for-purpose postmarket surveillance to proactively monitor utilization by patients and providers, there is a risk that the real-world benefits of psychedelic-assisted psychotherapy will not be realized. Incorrect conclusions, such as misattribution of adverse events to illicit psychedelics, may result from ill-designed surveillance programs. A successful surveillance program should monitor appropriate, equitable access for patients and inform reasonable limitations to improve patient safety. Multiple domains, including environmental factors, personal factors and relevant effectiveness and safety outcomes, should be incorporated. Current data systems that monitor drug use are generally ill-suited to address the unique needs for psychedelic surveillance. An intentionally designed mosaic of data systems is required to monitor the safety and effectiveness of psychedelic surveillance. In this Perspective, authors argue for a more robust and comprehensive postmarket surveillance program of psychedelic-assisted psychotherapy to better ensure safe, appropriate and equitable care for patients.
{"title":"Optimizing real-world benefit and risk of new psychedelic medications: the need for innovative postmarket surveillance","authors":"Joshua C. Black, Andrew A. Monte, Nabarun Dasgupta, Jennifer S. Jewell, Karilynn M. Rockhill, Richard A. Olson, Richard C. Dart","doi":"10.1038/s44220-024-00233-1","DOIUrl":"10.1038/s44220-024-00233-1","url":null,"abstract":"The encouraging results of late-phase clinical trials investigating psychedelic-assisted psychotherapy suggests that US Food and Drug Administration approval and subsequent expansion of use is imminent in the USA. Without fit-for-purpose postmarket surveillance to proactively monitor utilization by patients and providers, there is a risk that the real-world benefits of psychedelic-assisted psychotherapy will not be realized. Incorrect conclusions, such as misattribution of adverse events to illicit psychedelics, may result from ill-designed surveillance programs. A successful surveillance program should monitor appropriate, equitable access for patients and inform reasonable limitations to improve patient safety. Multiple domains, including environmental factors, personal factors and relevant effectiveness and safety outcomes, should be incorporated. Current data systems that monitor drug use are generally ill-suited to address the unique needs for psychedelic surveillance. An intentionally designed mosaic of data systems is required to monitor the safety and effectiveness of psychedelic surveillance. In this Perspective, authors argue for a more robust and comprehensive postmarket surveillance program of psychedelic-assisted psychotherapy to better ensure safe, appropriate and equitable care for patients.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 5","pages":"469-477"},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140919367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.1038/s44220-024-00237-x
M. Tanveer, T. Goel, R. Sharma, A. K. Malik, I. Beheshti, J. Del Ser, P. N. Suganthan, C. T. Lin
Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by Alzheimer’s disease in the brain. Diagnosing Alzheimer’s disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer’s disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer’s disease diagnosis. These models combine several deep neural networks to increase a prediction’s robustness. Here we revisit key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess where our knowledge in this area stands. In this Review, the authors cover the latest understanding of ensemble deep learning models as a means to complement Alzheimer’s disease diagnosis.
{"title":"Ensemble deep learning for Alzheimer’s disease characterization and estimation","authors":"M. Tanveer, T. Goel, R. Sharma, A. K. Malik, I. Beheshti, J. Del Ser, P. N. Suganthan, C. T. Lin","doi":"10.1038/s44220-024-00237-x","DOIUrl":"10.1038/s44220-024-00237-x","url":null,"abstract":"Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by Alzheimer’s disease in the brain. Diagnosing Alzheimer’s disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer’s disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer’s disease diagnosis. These models combine several deep neural networks to increase a prediction’s robustness. Here we revisit key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess where our knowledge in this area stands. In this Review, the authors cover the latest understanding of ensemble deep learning models as a means to complement Alzheimer’s disease diagnosis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 6","pages":"655-667"},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.1038/s44220-024-00245-x
Perline A. Demange, Dorret I. Boomsma, Elsje van Bergen, Michel G. Nivard
We investigate the causal relationship between educational attainment (EA) and mental health conditions using two research designs. Here we first compare the relationship between EA and 18 psychiatric diagnoses within-sibship in Dutch national registry data (N = 1.7 million), thereby controlling for unmeasured familial factors. Second, we apply two-sample Mendelian randomization, which uses genetic variants related to EA or psychiatric diagnosis as instrumental variables, to test whether there is a causal relation in either direction. Our results suggest that lower levels of EA causally increase the risk of major depressive disorder, attention-deficit/hyperactivity disorder, alcohol dependence, generalized anxiety disorder and post-traumatic stress disorder diagnoses. We also find evidence of a causal effect of attention-deficit/hyperactivity disorder on EA. For schizophrenia, anorexia nervosa, obsessive–compulsive disorder and bipolar disorder, the results were inconsistent across the different approaches, highlighting the importance of using multiple research designs to understand complex relationships, such as between EA and mental health conditions. Analyzing national registry data, the authors use within-sibling design and two-sample Mendelian randomization to identify bidirectional causal relationships between educational attainment (EA) and mental health conditions, demonstrating that lower levels of EA were differentially associated with some disorders, such as major depressive disorder, but that attention-deficit/hyperactivity disorder causally affected EA.
我们采用两种研究设计来探讨教育程度（EA）与精神健康状况之间的因果关系。在这里，我们首先比较了荷兰全国登记数据（N = 170 万）中 EA 与同胞兄弟姐妹中 18 种精神疾病诊断之间的关系，从而控制了未测量的家族因素。其次，我们采用双样本孟德尔随机化方法，将与 EA 或精神病诊断相关的基因变异作为工具变量，检验两者之间是否存在因果关系。我们的研究结果表明，较低的 EA 水平会增加患重度抑郁症、注意力缺陷/多动症、酒精依赖症、广泛性焦虑症和创伤后应激障碍的风险。我们还发现了注意力缺陷/多动障碍对 EA 产生因果效应的证据。对于精神分裂症、神经性厌食症、强迫症和双相情感障碍，不同方法得出的结果并不一致，这凸显了使用多种研究设计来理解复杂关系的重要性，例如 EA 与精神健康状况之间的关系。作者通过分析全国登记数据，采用同胞设计和双样本孟德尔随机法来确定教育程度（EA）与精神健康状况之间的双向因果关系，证明较低的教育程度与某些疾病（如重度抑郁症）有不同程度的关联，但注意力缺陷/多动症对教育程度有因果影响。
{"title":"Educational attainment and psychiatric diagnoses: a national registry data and two-sample Mendelian randomization study","authors":"Perline A. Demange, Dorret I. Boomsma, Elsje van Bergen, Michel G. Nivard","doi":"10.1038/s44220-024-00245-x","DOIUrl":"10.1038/s44220-024-00245-x","url":null,"abstract":"We investigate the causal relationship between educational attainment (EA) and mental health conditions using two research designs. Here we first compare the relationship between EA and 18 psychiatric diagnoses within-sibship in Dutch national registry data (N = 1.7 million), thereby controlling for unmeasured familial factors. Second, we apply two-sample Mendelian randomization, which uses genetic variants related to EA or psychiatric diagnosis as instrumental variables, to test whether there is a causal relation in either direction. Our results suggest that lower levels of EA causally increase the risk of major depressive disorder, attention-deficit/hyperactivity disorder, alcohol dependence, generalized anxiety disorder and post-traumatic stress disorder diagnoses. We also find evidence of a causal effect of attention-deficit/hyperactivity disorder on EA. For schizophrenia, anorexia nervosa, obsessive–compulsive disorder and bipolar disorder, the results were inconsistent across the different approaches, highlighting the importance of using multiple research designs to understand complex relationships, such as between EA and mental health conditions. Analyzing national registry data, the authors use within-sibling design and two-sample Mendelian randomization to identify bidirectional causal relationships between educational attainment (EA) and mental health conditions, demonstrating that lower levels of EA were differentially associated with some disorders, such as major depressive disorder, but that attention-deficit/hyperactivity disorder causally affected EA.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 6","pages":"668-679"},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141020690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.1038/s44220-024-00247-9
Kasey G. Creswell, Garrett C. Hisler, Greta Lyons, Francisco A. Carrillo-Álvarez, Catharine E. Fairbairn, Aidan G. C. Wright
Most past studies examining changes in alcohol use from before to during the COVID-19 pandemic used cross-sectional designs that required participants to retrospectively report on their pre-pandemic alcohol consumption. The few longitudinal studies conducted so far also commonly relied on retrospective reports of pre-pandemic alcohol use, and no previous longitudinal studies included multiple assessments that occurred both prior to and after the onset of the pandemic. Here, in 234 heavy drinking young adults (aged 21–29 years), we (1) prospectively examined within-person changes in alcohol consumption/patterns and alcohol problems assessed at multiple timepoints before and after the pandemic onset (February 2018 to March 2022), to examine trajectories of changes in alcohol involvement after the start of COVID in the context of deviations from pre-COVID trajectories (using individual growth models fitted in a multilevel structural equation modeling framework), and (2) tested theoretically informed mechanisms (that is, changes in negative affectivity, coping-motivated drinking and solitary drinking) in explaining pandemic-associated changes in alcohol consumption/patterns and alcohol problems using correlated slopes models. The results showed significant reductions in alcohol use quantity and frequency, as well as alcohol problems, from pre- to post-pandemic onset, which were largely driven by significant decreases in weekend (versus weekday) drinking quantity and frequency and drinks per drinking day. Negative affectivity significantly decreased, and solitary drinking significantly increased, from pre- to post-pandemic onset, with no significant change to coping drinking motives; changes in these variables were not related to decreases in alcohol involvement, and the magnitude of changes in all variables from pre- to post-pandemic onset did not generally differ for males and females. The results indicate that alcohol use and concomitant alcohol-related problems significantly decreased in these heavy drinking young adults during the pandemic, and these decreases were evident up to two years post pandemic onset. In this prospective longitudinal study of alcohol consumption and patterns in heavy drinking young adults, significant reductions in alcohol use quantity, frequency and problems were observed from pre- to post-pandemic onset.
{"title":"Changes in alcohol consumption and alcohol problems before and after the COVID-19 pandemic: a prospective study in heavy drinking young adults","authors":"Kasey G. Creswell, Garrett C. Hisler, Greta Lyons, Francisco A. Carrillo-Álvarez, Catharine E. Fairbairn, Aidan G. C. Wright","doi":"10.1038/s44220-024-00247-9","DOIUrl":"10.1038/s44220-024-00247-9","url":null,"abstract":"Most past studies examining changes in alcohol use from before to during the COVID-19 pandemic used cross-sectional designs that required participants to retrospectively report on their pre-pandemic alcohol consumption. The few longitudinal studies conducted so far also commonly relied on retrospective reports of pre-pandemic alcohol use, and no previous longitudinal studies included multiple assessments that occurred both prior to and after the onset of the pandemic. Here, in 234 heavy drinking young adults (aged 21–29 years), we (1) prospectively examined within-person changes in alcohol consumption/patterns and alcohol problems assessed at multiple timepoints before and after the pandemic onset (February 2018 to March 2022), to examine trajectories of changes in alcohol involvement after the start of COVID in the context of deviations from pre-COVID trajectories (using individual growth models fitted in a multilevel structural equation modeling framework), and (2) tested theoretically informed mechanisms (that is, changes in negative affectivity, coping-motivated drinking and solitary drinking) in explaining pandemic-associated changes in alcohol consumption/patterns and alcohol problems using correlated slopes models. The results showed significant reductions in alcohol use quantity and frequency, as well as alcohol problems, from pre- to post-pandemic onset, which were largely driven by significant decreases in weekend (versus weekday) drinking quantity and frequency and drinks per drinking day. Negative affectivity significantly decreased, and solitary drinking significantly increased, from pre- to post-pandemic onset, with no significant change to coping drinking motives; changes in these variables were not related to decreases in alcohol involvement, and the magnitude of changes in all variables from pre- to post-pandemic onset did not generally differ for males and females. The results indicate that alcohol use and concomitant alcohol-related problems significantly decreased in these heavy drinking young adults during the pandemic, and these decreases were evident up to two years post pandemic onset. In this prospective longitudinal study of alcohol consumption and patterns in heavy drinking young adults, significant reductions in alcohol use quantity, frequency and problems were observed from pre- to post-pandemic onset.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 6","pages":"728-739"},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141020822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.1038/s44220-024-00250-0
Shanquan Chen, Miaoqing Yang, Hannah Kuper
This study investigates the association between price inflation and mental health conditions in the general population during the post-coronavirus disease 2019 (COVID-19) era in England, beginning from April 2022. Here, utilizing data from the Office for National Statistics and the National Health Service, we examined the association between price inflation, reflected by an official index ‘Consumer Prices Index including owner occupiers’ housing costs’ and the number of people in contact with mental health services across different age groups. Our findings revealed that, compared with the pre-COVID-19 period (August 2016 to February 2020), significant associations emerged between specific living costs (including costs for ‘food and non-alcoholic beverages’, ‘housing, water and fuels’ and ‘miscellaneous goods and services’) and mental health service utilization during the post-COVID-19 era. This association was particularly noted for adults aged 19–64 years and the elderly population aged 65 years and over. The results highlight the importance of addressing the potential causes of mental health issues in the context of rising living costs and can inform targeted social and economic policies, such as financial subsidies for food and non-alcoholic beverages and the need to scale up mental health services. In this study, the authors investigate the association between price inflation and mental health service uptake in the United Kingdom, demonstrating that increasing costs of living exacerbate mental health needs, particularly among adults and older populations.
{"title":"Investigating inflation, living costs and mental health service utilization in post-COVID-19 England","authors":"Shanquan Chen, Miaoqing Yang, Hannah Kuper","doi":"10.1038/s44220-024-00250-0","DOIUrl":"10.1038/s44220-024-00250-0","url":null,"abstract":"This study investigates the association between price inflation and mental health conditions in the general population during the post-coronavirus disease 2019 (COVID-19) era in England, beginning from April 2022. Here, utilizing data from the Office for National Statistics and the National Health Service, we examined the association between price inflation, reflected by an official index ‘Consumer Prices Index including owner occupiers’ housing costs’ and the number of people in contact with mental health services across different age groups. Our findings revealed that, compared with the pre-COVID-19 period (August 2016 to February 2020), significant associations emerged between specific living costs (including costs for ‘food and non-alcoholic beverages’, ‘housing, water and fuels’ and ‘miscellaneous goods and services’) and mental health service utilization during the post-COVID-19 era. This association was particularly noted for adults aged 19–64 years and the elderly population aged 65 years and over. The results highlight the importance of addressing the potential causes of mental health issues in the context of rising living costs and can inform targeted social and economic policies, such as financial subsidies for food and non-alcoholic beverages and the need to scale up mental health services. In this study, the authors investigate the association between price inflation and mental health service uptake in the United Kingdom, demonstrating that increasing costs of living exacerbate mental health needs, particularly among adults and older populations.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 6","pages":"712-716"},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00250-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141023118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1038/s44220-024-00249-7
Mark Wade, Margaret A. Sheridan, Stacy S. Drury, Florin Tibu, Charles H. Zeanah, Nathan A. Fox, Charles A. Nelson, Katie A. McLaughlin
Early psychosocial deprivation is associated with alterations in stress-response system development and later psychopathology. Using data from the Bucharest Early Intervention Project, we examined whether blunted reactivity to social stress served as a mechanism linking early deprivation to later psychopathology in 135 youths, 89 of whom were raised in institutions during early childhood (46 randomly assigned to foster care intervention). At 12 and 16 years, cortisol and sympathetic nervous system reactivity were assessed using the Trier Social Stress Test. Bifactor scores of general and specific psychopathology were estimated from caregiver and teacher reports. Blunted cortisol reactivity at 12 years mediated the association between deprivation and general psychopathology at 16 years, whereas blunted sympathetic nervous system reactivity mediated externalizing-specific problems. Increased stress reactivity did not mediate intervention effects on psychopathology. Early deprivation may shape stress-response system development in a way that confers broad risk for mental health problems during adolescence. Wade and colleagues analyze data from the Bucharest Early Intervention Project to examine whether stress reactivity measured at age 12 may serve as a mechanism linking early institutional deprivation with psychopathology at age 16.
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