Sapien Labs, Arlington, Texas, USA.
Sapien Labs, Arlington, Texas, USA
BMJ Open. 2024 Mar 15;14(3):e075095. doi: 10.1136/bmjopen-2023-075095.
To understand the extent to which various demographic and social determinants predict mental health status and their relative hierarchy of predictive power in order to prioritise and develop population-based preventative approaches.
Cross-sectional analysis of survey data.
Internet-based survey from 32 countries across North America, Europe, Latin America, Middle East and North Africa, Sub-Saharan Africa, South Asia and Australia, collected between April 2020 and December 2021.
270 000 adults aged 18-85+ years who participated in the Global Mind Project.
We used 120+ demographic and social determinants to predict aggregate mental health status and scores of individuals (mental health quotient (MHQ)) and determine their relative predictive influence using various machine learning models including gradient boosting and random forest classification for various demographic stratifications by age, gender, geographical region and language. Outcomes reported include model performance metrics of accuracy, precision, recall, F1 scores and importance of individual factors determined by reduction in the squared error attributable to that factor.
Across all demographic classification models, 80% of those with negative MHQs were correctly identified, while regression models predicted specific MHQ scores within ±15% of the position on the scale. Predictions were higher for older ages (0.9+ accuracy, 0.9+ F1 Score; 65+ years) and poorer for younger ages (0.68 accuracy, 0.68 F1 Score; 18-24 years). Across all age groups, genders, regions and language groups, lack of social interaction and sufficient sleep were several times more important than all other factors. For younger ages (18-24 years), other highly predictive factors included cyberbullying and sexual abuse while not being able to work was high for ages 45-54 years.
Social determinants of traumas, adversities and lifestyle can account for 60%-90% of mental health challenges. However, additional factors are at play, particularly for younger ages, that are not included in these data and need further investigation.
了解各种人口统计学和社会决定因素在多大程度上预测心理健康状况及其相对预测能力的层次结构，以便确定优先事项并制定基于人群的预防方法。
横断面分析调查数据。
2020 年 4 月至 2021 年 12 月，在北美、欧洲、拉丁美洲、中东和北非、撒哈拉以南非洲、南亚和澳大利亚的 32 个国家进行的基于互联网的调查。
27 万 18-85 岁以上的成年人参加了全球心理项目。
我们使用了 120 多个人口统计学和社会决定因素来预测总体心理健康状况和个体的分数（心理健康商数（MHQ）），并使用各种机器学习模型，包括梯度提升和随机森林分类，确定它们的相对预测影响，针对年龄、性别、地理位置和语言等不同人口统计学进行分层。报告的结果包括准确性、精确性、召回率、F1 分数等模型性能指标，以及通过减少归因于该因素的平方误差来确定的个别因素的重要性。
在所有人口统计学分类模型中，80%的 MHQ 为负值的个体被正确识别，而回归模型预测了特定 MHQ 分数，其位置在该量表上的±15%范围内。对于年龄较大的人群（0.9 以上的准确性，0.9 以上的 F1 得分；65 岁以上），预测结果较高，而对于年龄较小的人群（0.68 的准确性，0.68 的 F1 得分；18-24 岁），预测结果较低。在所有年龄组、性别、地区和语言群体中，缺乏社会互动和充足睡眠比其他所有因素重要几倍。对于年龄较小的人群（18-24 岁），其他高度预测因素包括网络欺凌和性虐待，而对于 45-54 岁的人群，无法工作是一个重要因素。
创伤、逆境和生活方式的社会决定因素可以解释 60%-90%的心理健康挑战。然而，还有其他因素在起作用，特别是对于年轻人群，这些因素在这些数据中没有体现，需要进一步研究。
