Introduction Postpartum depression impacts 6.5–12.9% of U.S. women. Postpartum depression is associated with impaired bonding and development, marital discord, suicide, and infanticide. However, the current standard of care is to not screen women for postpartum depression. This study modeled the cost-effectiveness of physicians screening for and treating postpartum depression and psychosis in partnership with a psychiatrist. Methods This study follows a hypothetical cohort of 1,000 pregnant women experiencing one live birth over a two-year time horizon. We used a decision tree model to obtain the outcomes of screening for and treating postpartum depression and psychosis using the Edinburgh Postnatal Depression Scale. We use a Medicaid payer perspective because they cover approximately 50% of births in the U.S. The cost-effectiveness of the intervention is measured in cost per remission achieved and cost per quality-adjusted life-year (QALY) gained. We conducted both deterministic and probabilistic sensitivity analyses. Results Screening for and treating postpartum depression and psychosis produced 29 more healthy women at a cost of $943 per woman. The incremental cost-effectiveness ratios of the intervention branch compared to usual care were $13,857 per QALY gained (below the commonly accepted willingness to pay threshold of $50,000/QALY gained) and $10,182 per remission achieved. These results were robust in both the deterministic and probabilistic sensitivity analyses of input parameters. Discussion Screening for and treating postpartum depression is a cost-effective intervention and should be considered as part of usual postnatal care, which aligns with the recently proposed recommendations from the U.S. Preventive Services Task Force.

Public health practitioners can use Behavior Over Time (BOT) graphs to spur discussion and systems thinking around complex challenges. Multiple large systems, such as health care, the economy, and education, affect chronic disease rates in the United States. System thinking tools can build public health practitioners’ capacity to understand these systems and collaborate within and across sectors to improve population health. BOT graphs show a variable, or variables (y axis) over time (x axis). Although analyzing trends is not new to public health, drawing BOT graphs, annotating the events and systemic forces that are likely to influence the depicted trends, and then discussing the graphs in a diverse group provides an opportunity for public health practitioners to hear each other’s perspectives and creates a more holistic understanding of the key factors that contribute to a trend. We describe how BOT graphs are used in public health, how they can be used to generate group discussion, and how this process can advance systems-level thinking. Then we describe how BOT graphs were used with groups of maternal and child health (MCH) practitioners and partners (N = 101) during a training session to advance their thinking about MCH challenges. Eighty-six percent of the 84 participants who completed an evaluation agreed or strongly agreed that they would use this BOT graph process to engage stakeholders in their home states and jurisdictions. The BOT graph process we describe can be applied to a variety of public health issues and used by practitioners, stakeholders, and researchers.

Objectives System Dynamics (SD) is a promising decision support modeling approach for growing shared understanding of complex maternal and child health (MCH) trends. We sought to inventory published applications of SD to MCH topics and introduce the MCH workforce to these approaches through examples to support further iteration and use. Methods We conducted a systematic search (1958–2018) for applications of SD to MCH topics and characterized identified articles, following PRISMA guidelines. Pairs of experts abstracted information on SD approach and MCH relevance. Results We identified 101 articles describing applications of SD to MCH topics. Approach: 27 articles present qualitative diagrams, 10 introduce concept models that begin to quantify dynamics, and 67 present more fully tested/analyzed models. Purpose: The most common purposes described were to increase understanding (n = 55) and support strategic planning (n = 26). While the majority of studies (n = 53) did not involve stakeholders, 40 included what we considered to be a high level of stakeholder engagement – a strength of SD for MCH. Topics: The two Healthy People 2020 topics addressed most frequently were early and middle childhood (n = 30) and access to health services (n = 26). The most commonly addressed SDG goals were “End disease epidemics” (n = 26) and “End preventable deaths” (n = 26). Conclusions for Practice While several excellent examples of the application of SD in MCH were found, SD is still underutilized in MCH. Because SD is particularly well-suited to studying and addressing complex challenges with stakeholders, its expanded use by the MCH workforce could inform an understanding of contemporary MCH challenges.