Reducing maternal health disparities: the rural context

Mental Health Within 24 Months After Delivery Among Women with Common Pregnancy Conditions

Objective: The aim of this study is to estimate the risk of a new mental health diagnosis within the first 24 months postpartum among women with common pregnancy conditions, overall and by rurality. Materials and Methods: This longitudinal population-based study used the Maine Health Data Organization's All-Payer Claims Data to estimate the cumulative risk of a new mental health disorder diagnosis in the first 24 months postpartum among women with deliveries during 2007-2019 and who did not have a mental health diagnosis before pregnancy. Cox models were used to estimate hazard ratios for common pregnancy conditions (prenatal depression, gestational diabetes [GDM], and hypertensive disorders of pregnancy [HDP]) on the new diagnosis of five mental health conditions, separately. Models were adjusted for maternal demographics and pregnancy characteristics. Results: Of the 123,125 deliveries, the cumulative risk of being diagnosed in the first 24 months postpartum with depression was 28%, anxiety 25%, bipolar disorder 3%, post-traumatic stress disorder 6%, and schizophrenia/psychotic disorder 1%. Women with prenatal depression were at higher risk of having a postpartum mental health diagnosis compared with women without prenatal depression (adjusted hazard ratios [aHRs] ranged from 2.5 [for anxiety] to 4.1 [for postpartum depression]). Risk of having postpartum depression was modestly higher among women with HDP, as was the risk of postpartum bipolar disorder among those with GDM. Findings were generally similar between women living in rural versus urban areas. Conclusions: Effective interventions to prevent, screen, and treat mental health conditions among women with pregnancy complications for an extended time postpartum are warranted.

The role of evaluation in iterative learning and implementation of quality of care interventions

Background: The Lancet Global Health Commission (LGHC) has argued that quality of care (QoC) is an emergent property that requires an iterative process to learn and implement. Such iterations are required given that health systems are complex adaptive systems.Objective: This paper explores the multiple roles that evaluations need to play in order to help with iterative learning and implementation. We argue evaluation needs to shift from a summative focus toward an approach that promotes learning in complex systems. A framework is presented to help guide the iterative learning, and includes the dimensions of clinical care, person-centered care, continuum of care, and 'more than medicine. Multiple roles of evaluation corresponding to each of the dimensions are discussed.Methods: This paper is informed by reviews of the literature on QoC and the roles of evaluation in complex systems. The proposed framework synthesizes the multiple views of QoC. The recommendations of the roles of evaluation are informed both by review and experience in evaluating multiple QoC initiatives.Results: The specific roles of different evaluation approaches, including summative, realist, developmental, and participatory, are identified in relationship to the dimensions in our proposed framework. In order to achieve the potential of LGHC, there is a need to discuss how different evaluation approaches can be combined in a coherent way to promote iterative learning and implementation of QoC initiatives.Conclusion: One of the implications of the QoC framework discussed in the paper is that time needs to be spent upfront in recognizing areas in which knowledge of a specific intervention is not complete at the outset. This, of course, implies taking stock of areas of incompleteness in knowledge of context, theory of change, support structures needed in order for the program to succeed in specific settings. The role of evaluation should not be limited to only providing an external assessment, but an important goal in building evaluation capacity should be to promote adaptive management among planners and practitioners. Such iterative learning and adaptive management are needed to achieve the goals of sustainable development goals.