Women who experienced depression during pregnancy were about four times as likely to experience PPD compared to women with no depression during pregnancy, with greater risk associated with depression during the first and third trimesters.
Childbirth-related PTSD is relatively common; routine screening with a survey of PTSD symptoms may identify women in need of specific PTSD interventions.
Machine learning can be used to analyze data contained within the electronic health record to accurately predict which individuals are at increased risk for postpartum depression.
Compared to individuals with histories of psychiatric illness prior to pregnancy, perinatal individuals without a prepregnancy psychiatric history were less likely to be screened, referred, and treated for depression.
Imagine if we could use machine learning to enhance the care we deliver, analyzing a larger body of patient-specific factors in order to make more reliable predictions of risk for perinatal mood and anxiety disorders.
Last month, the Alliance for Innovation on Maternal Health (AIM) launched a new Patient Safety Bundle addressing perinatal mental health conditions. The current revision addresses gaps in the safety bundle previously released in 2016. Patient [...]
About 60% of women with mood disorders experience premenstrual exacerbation or PME of their mood during the premenstrual phase of their cycle.
In a webinar put together by 2020 Mom, presenters provide an overview of maternal suicide, review risk factors for suicide in this population, and discuss efforts to generate accurate data on maternal suicide.
Greater attention to eating behaviors and screening eating disorders during pregnancy is the first step in improving outcomes in this population.
Screening, Brief Intervention and Referral to Treatment or SBIRT has been shown to be a quick and effective means of identifying and intervening with patients whose patterns of use put them at risk for, or who already have, substance-related health problems.
