Mental health service use among pregnant and early postpartum women

Lee-Carbon, Leonie; Nath, Selina; Trevillion, Kylee; Byford, Sarah; Howard, Louise; Challacombe, Fiona; Heslin, Margaret

doi:10.1007/s00127-022-02331-w

Cited by 16 publications

(11 citation statements)

References 44 publications

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“…11,12 Despite higher risk, parents' use of mental health services is low. [13][14][15][16] In a prospective study, only one-third of recent-birthing mothers with a diagnosable mental health disorder at their first prenatal appointment had contact with mental health services during pregnancy through 3 months post partum. 15 Similarly, only one-third of mothers with major depressive disorder at their first prenatal appointment received any professional treatment by 6 weeks post partum.…”

Section: Introductionmentioning

confidence: 99%

Mental Health Treatment Rates During Pregnancy and Post Partum in US Military Service Members

Heissel,

Healy

2024

JAMA Netw Open

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ImportanceAlthough new parents’ mental health is known to decline, less is known about changes in therapy attendance, especially among military service members.ObjectiveTo investigate changes in therapy attendance among new parents and by parental leave length.Design, Setting, and ParticipantsThis cohort study of US Army and Navy service members from January 1, 2013, to December 31, 2019, compared parents’ monthly therapy attendance with matched nonparents’ across childbirth and compared mothers’ weekly therapy attendance before vs after returning to work. Eligible monthly sample members included service members with first births from January 1, 2014, to December 31, 2017, and 12 months of data before to 24 months after birth and nonparents with 36 months of data. Eligible weekly sample members included mothers with first births from January 1, 2013, to June 30, 2019, and data from 12 months before to 6 months after birth and nonparents with 18 months of data. Data analysis was performed from July 1, 2023, to January 15, 2024.ExposureThose exposed to parenthood had no prior children, acquired a dependent younger than 1 year, and, for mothers, had an inpatient birth. Unexposed matches did not add a dependent younger than 1 year.Main Outcomes and MeasuresMonthly counts of mental health therapy sessions and any therapy sessions (weekly).ResultsThe monthly sample included 15 554 193 person-month observations, representing 321 200 parents and matches, including 10 193 mothers (3.2%; mean [SD] age, 25.0 [4.9] years), 50 865 nonmother matches (15.8%; mean [SD] age, 25.0 [5.0] years), 43 365 fathers (13.5%; mean [SD] age, 26.4 [4.8] years), and 216 777 nonfather matches (67.5%; mean [SD] age, 26.4 [4.8] years). The weekly sample included 17 464 mothers. Mothers went to 0.0712 fewer sessions at 1 month post partum (95% CI, −0.0846 to −0.0579) compared with 10 months before birth. Fathers went to 0.0154 fewer sessions in the month of birth (95% CI, −0.0194 to −0.0114) compared with 10 months before. Parents with preexisting treatment needs had larger decreases in treatment. Weekly therapy attendance increased by 0.555 percentage points (95% CI, 0.257-0.852) when mothers returned to work from 6 weeks of leave and 0.953 percentage points (95% CI, 0.610-1.297) after 12 weeks of leave.Conclusions and RelevanceIn this cohort study of new parents, therapy attendance decreased around childbirth, especially among parents with prior mental health needs and mothers with longer maternity leaves. These findings suggest that more accessible treatment, including home visits or telehealth appointments, is needed.

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Section: Introductionmentioning

confidence: 99%

Mental Health Treatment Rates During Pregnancy and Post Partum in US Military Service Members

Heissel,

Healy

2024

JAMA Netw Open

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“…If a person or a family member has sought help in the past, they usually have a better understanding of symptoms and the benefits of mental health services and how to access them. 17 – 27 …”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13] In addition to research that has identified factors related to diagnosis of PPD, 5,[14][15][16] several factors have been identified as prompting help-seeking. [17][18][19][20][21][22][23][24][25][26][27][28] Demographic factors such as income, education, and race or ethnicity can function as facilitators or barriers to care. Pregnant people with lower incomes may not be able to afford care because of lack of insurance coverage, less paid leave from work, childcare coverage, or transportation costs.…”

Section: Introductionmentioning

confidence: 99%

“…If a person or a family member has sought help in the past, they usually have a better understanding of symptoms and the benefits of mental health services and how to access them. [17][18][19][20][21][22][23][24][25][26][27] A 2009 study used Pregnancy Risk Assessment Monitoring System (PRAMS) data from Utah to examine factors associated with help-seeking among 337 people who had PPD symptoms. 19 They report that help-seeking during pregnancy, living in a rural setting, and having an infant in neonatal intensive care were associated with postpartum help-seeking.…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning to predict help-seeking among 2016–2018 Pregnancy Risk Assessment Monitoring System participants with postpartum depression symptoms

Fischbein

Cook

Baughman

et al. 2022

Womens Health (Lond Engl)

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Background: Despite the importance of early identification and treatment, postpartum depression often remains largely undiagnosed with unreported symptoms. While research has identified several factors as prompting help-seeking for postpartum depression symptoms, no research has examined help-seeking for postpartum depression using data from a multi-state/jurisdictional survey analyzed with machine learning techniques. Objectives: This study examines help-seeking among people with postpartum depression symptoms using and demonstrating the utility of machine learning techniques. Methods: Data from the 2016–2018 Pregnancy Risk Assessment Monitoring System, a cross-sectional survey matched with birth certificate data, were used. Six US states/jurisdictions included the outcome help-seeking for postpartum depression symptoms and were used in the analysis. An ensemble method, “Super Learner,” was used to identify the best combination of algorithms and most important variables that predict help-seeking among 1920 recently pregnant people who screen positive for postpartum depression symptoms. Results: The Super Learner predicted well and had an area under the receiver operating curve of 87.95%. It outperformed the highest weighted algorithms which were conditional random forest and stochastic gradient boosting. The following variables were consistently among the top 10 most important variables across the algorithms for predicting increased help-seeking: participants who reported having been diagnosed with postpartum depression, having depression during pregnancy, living in particular US states, being a White compared to Black or Asian American individual, and having a higher maternal body mass index at the time of the survey. Conclusion: These results show the utility of using ensemble machine learning techniques to examine complex topics like help-seeking. Healthcare providers should consider the factors identified in this study when screening and conducting outreach and follow-up for postpartum depression symptoms.

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“…Successful depression screening includes appropriate mental health follow-up for mothers with elevated scores. Rates of follow-up for evaluation and treatment are low in the general population of depressed mothers (Flynn et al, 2006; Lee-Carbon et al, 2022). Similarly low rates were reported from a study of 30 mothers screened for depression in the NICU, of which, 13 were identified as depressed and 4 were seen by a mental health professional (Vaughn & Hooper, 2020).…”

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confidence: 99%

Improving Maternal Depression Screening in the Neonatal Intensive Care Unit

McCabe,

Henderson,

Davila

et al. 2024

McN: The American Journal of Maternal/Child Nursing

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Purpose: To examine whether self-perceived benefits of mental health treatment differed between mothers of babies in the neonatal intensive care unit with and without a positive screen for depression based on their Edinburgh Postnatal Depression score. Study Design and Methods: Mothers were recruited in person pre-COVID-19 pandemic, and via phone call and online advertisement during the pandemic. Mothers completed a 10-item depression scale and whether they believed they would benefit from mental health treatment. A chi-square test determined the difference in perceived benefit between mothers who screened positively for depression and those who did not. Results: This secondary analysis included 205 mothers, with an average age of 29. Of the 68 mothers who screened positively for depression, 12 believed that would not benefit from mental health intervention. Of the 137 who screened negatively for depression, 18 believed they would benefit from mental health intervention. Mothers who screened negatively for depression were significantly less likely to believe they would benefit from mental health intervention. Clinical Implications: Depression screening scales offer guidance on which mothers to flag for follow-up, but neither on how a mother will respond nor how to effectively approach a mother about her mental health. Nurses can improve identification and follow-up of depressed mothers in the neonatal intensive care unit by asking mothers about their perceived need for mental health treatment.

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Mental health service use among pregnant and early postpartum women

Cited by 16 publications

References 44 publications

Mental Health Treatment Rates During Pregnancy and Post Partum in US Military Service Members

Mental Health Treatment Rates During Pregnancy and Post Partum in US Military Service Members

Using machine learning to predict help-seeking among 2016–2018 Pregnancy Risk Assessment Monitoring System participants with postpartum depression symptoms

Improving Maternal Depression Screening in the Neonatal Intensive Care Unit

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