Feasibility and Acceptability of a Prevention-Focused Screener for Perinatal Depression Risk: Mixed Methods Cohort...

Area of Science:

• Perinatal mental health
• Machine learning applications in healthcare
• Preventive medicine

Background:

• Perinatal depression affects over 20% of women, with suicide being a leading cause of maternal death.
• Early identification of at-risk individuals is crucial for targeted preventive care during pregnancy.

Purpose of the Study:

• To evaluate women's receptivity to a machine learning-based predictive screener.
• To identify asymptomatic women in early pregnancy at risk for later moderate to severe depression.

Main Methods:

• Adult pregnant women with negative first-trimester depression screens were recruited.
• A 6-question predictive screener was delivered via patient portal; 255 women completed it.
• Chi-square and Mann-Whitney U tests analyzed acceptability, perceived benefits, concerns, and desired resources.

Main Results:

• The screener identified 20% of participants as being at risk for perinatal depression.
• Participants found the screener easy to complete and felt comfortable sharing answers.
• Key benefits included opportunities for preventive care and depression risk education.

Conclusions:

• The machine learning screener was acceptable to pregnant women via patient portal.
• Commonly endorsed preventive care options included counseling and mind-body interventions.
• Concerns about knowing future depression risk were voiced by a minority but were addressable.