Back

Association Between Depression and All-Cause Mortality : A Cohort Study Based on NHANES Data

Yao, F.; Zhang, J.; Wang, H.

2025-05-01 public and global health
10.1101/2025.04.29.25326650 medRxiv
Show abstract

BackgroundDepression is a common mental disorder that significantly influences mental health outcomes and increases the risk of all-cause mortality. However, the dose-response relationship between depression and all-cause mortality remains unclear. MethodsA total of 36,193 participants from the National Health and Nutrition Examination Survey (NHANES) 2005-2018 were included in this study. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) scale, and all-cause mortality data were obtained from the National Death Index. Weighted Cox regression models were applied to evaluate the association between depression and all-cause mortality, with special attention to non-linear relationships. ResultsAmong the study population, 8.74% were identified as having depression. The mean follow-up duration was 90.53 months, during which 9.87% of participants died from all causes. A significant non-linear association was observed between PHQ-9 scores and all-cause mortality. All-cause mortality increased markedly with depression scores [&le;] 7 (HR: 1.068, 95% CI: 1.042-1.095, P < 0.0001) and plateaued when scores approached 7. ConclusionsElevated depressive symptoms, even at mild levels, are associated with a substantial increase in all-cause mortality. These findings underline the necessity for early identification and intervention for depressive symptoms to reduce long-term adverse health outcomes. This research provides critical evidence to inform public health strategies targeting the reduction of depression-related mortality.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.