The prevalence of depression varies widely across nations, but we do not yet understand what underlies this variation. Here we use estimates from the Global Burden of Disease study to analyze the correlates of depression across 195 countries and territories. We begin by identifying potential cross-correlates of depression using past clinical and cultural psychology literature. We then take a data-driven approach to modeling which factors correlate with depression in zero-order analyses, and in a multiple regression model that controls for covariation between factors. Our findings reveal several potential correlates of depression, including cultural individualism, daylight hours, divorce rate, and GDP per capita. Cultural individualism is the only factor that remains significant across all our models, even when adjusting for spatial autocorrelation, mental healthcare workers per capita, multicollinearity, and outliers. These findings shed light on how depression varies around the world, the sociocultural and environmental factors that underlie this variation, and potential future directions for the study of culture and mental illness.

抑郁症的患病率在各个国家/地区差异很大，但我们尚不了解这种差异的原因。在这里，我们使用全球疾病负担研究的估计值来分析 195 个国家和地区的抑郁症相关性。我们首先使用过去的临床和文化心理学文献确定抑郁症的潜在交叉相关性。然后，我们采用数据驱动的方法对零阶分析中哪些因素与抑郁症相关，以及在控制因素间协变的多元回归模型中进行建模。我们的研究结果揭示了抑郁症的几个潜在相关因素，包括文化个人主义、日照时间、离婚率和人均 GDP。文化个人主义是唯一在我们所有模型中仍然重要的因素，即使在调整空间自相关时，人均精神卫生工作者、多重共线性和异常值。这些发现阐明了抑郁症在世界范围内的变化、造成这种变化的社会文化和环境因素，以及未来文化和精神疾病研究的潜在方向。