Skip to main content
Download PDF
Download ePub
  • Study protocol
  • Open access
  • Published:

A cluster randomized stepped wedge implementation trial of scale-up approaches to ending pregnancy-related and -associated morbidity and mortality disparities in 12 Michigan counties: rationale and study protocol

Implementation Science Communications volume 6, Article number: 19 (2025) Cite this article

Abstract

Background

Hospital-focused maternal health safety and quality guidelines have been found to reduce pregnancy-related and -associated morbidity and mortality (PRAMM). Unfortunately, quality of obstetric care can improve without affecting disparities. This project is the first controlled implementation trial to test approaches to implementing safety guidelines that: (1) target PRAMM disparities; and (2) focus on community care (care provided outside hospitals in outpatient and other community settings, and coordination among care settings), where most deaths occur. It is also one of the first to test scale-up or sustainment implementation approaches to addressing maternal morbidity and mortality disparities.

Methods

This project, one of three in the federally funded Multilevel Interventions for Raci.a.l Equity (MIRACLE) Maternal Health Research Center of Excellence, will develop and evaluate an implementation approach for scaling up bundled equity-focused maternal health safety guidelines in community care settings county-wide. The scale-up approach will be co-developed with partners, and then tested using a cluster randomized stepped-wedge trial of 12 Michigan counties with a total population of nearly 6 million. Randomization occurs at the county level; birthing people and providers are clustered within counties. PRAMM outcomes (individual level; primary) will be extracted from a pre-existing statewide linked dataset that includes Medicaid claims and vital records data. The sample will include all Medicaid insured individuals in the 12 counties observed during pregnancy, at birth, and up to 1 year postpartum during the project period (~ 151,920 births, including ~ 49,110 births to Black and/or Hispanic mothers). Implementation outcomes (provider level) will be collected using annual provider (n = 600) surveys and will include scale-up (penetration, reach, control for delivery, and intervention effectiveness at scale) and sustainment (maintenance of fidelity to core elements, health benefits, and capacity to deliver core elements over time) of bundles and cost-effectiveness of implementation approaches.

Discussion

This implementation trial will be the first to evaluate an implementation approach to scaling community health equity-focused maternal safety guidelines, addressing an understudied aspect of implementation science (i.e., scale-up). The study will also provide information about implementation cost-effectiveness needed to drive policy decisions.

Trial registration

The study was prospectively registered on Clinicaltrials.gov (NCT06541951) on August 6, 2024. The first participant has not yet been recruited. The url for the trial registration is: https://clinicaltrials.gov/study/NCT06541951?locStr=Flint,%20MI&country=United%20States&state=Michigan&city=Flint&rank=1.

Peer Review reports

Background

Disparities in pregnancy-related and -associated morbidity and mortality

Pregnancy-related and -associated morbidity and mortality (PRAMM) is a critical problem in the United States (U.S.) The U.S. maternal mortality rate is the highest among high-income countries [1, 2]. Around 80% of these deaths are preventable [3, 4]. Pregnancy-related deaths are deaths during pregnancy or 1 year postpartum caused or triggered by the physical effects of pregnancy. Pregnancy-associated deaths (e.g., homicide, suicide, overdose) are common and increasing [5, 6]. In Michigan (the location of this project), pregnancy-associated deaths make up an additional 4 times as many deaths as pregnancy-related deaths [7]. Severe maternal morbidity (SMM) affects another 60,000 in the U.S. every year [2, 8, 9]. Finally, non-severe maternal morbidity (NSMM) is defined as “any health condition attributed to and/or complicating pregnancy and childbirth that negatively impacts a woman’s well-being and functioning” during pregnancy and postpartum [10, 11]. NSMM includes direct (e.g., delivery complications, hypertensive disorders of pregnancy); indirect (e.g. mental health disorders, endocrine, nutritional, and metabolic diseases); and co-incidental (e.g., partner violence, sexual assault) diagnoses and procedures [12]. Although these co-occurring conditions are classified as “non-severe,” they are experienced by a large number of pregnant and postpartum people (~ 30%), involve suffering and impairment, and increase the burden of disease during pregnancy and postpartum [13]. We use the term “PRAMM” to encompass pregnancy-related and pregnancy associated deaths, SMM, and NSMM during pregnancy and through 12 months postpartum.

There are strong racial/ethnic disparities in PRAMM. Black birthing people are 3–4 times more likely to have pregnancy-related complications and die [14] compared to non-Hispanic white (NHW) counterparts nationally [15, 16]. In addition, Black individuals experience pregnancy associated intimate partner homicide at more than 3–5 times the rate of NHW people and more than 8 times the rate of their non-pregnant peers [6, 17]. Black individuals also have higher rates of SMM [8, 18] compared to their NHW counterparts [18, 19]. In fact, Black individuals have the highest rates for 22 of 25 SMM indicators used by the Center for Disease Control and Prevention (CDC) [19]. Black individuals also have higher rates of many NSMM conditions than their NHW counterparts, including elevated rates of perinatal depression and anxiety disorders [20], hypertension [21], preeclampsia [22], and delivery complications [23], among others. Although Hispanic people do not have elevated rates of maternal death, they do have 55% higher rates of SMM than their NHW counterparts [13]. Disparities across PRAMM mean significant disparities in preventable suffering, disability, and death.

The importance of focusing safety and quality efforts on equity

Implementing safety and quality improvement initiatives is a high priority for addressing maternal morbidity and mortality [24]. Practice efforts in California have demonstrated that widespread use of hospital-focused maternal safety bundles (i.e., small sets of evidence-based guidelines) [25] are an important part of successful efforts to reduce maternal morbidity and mortality [24]. In fact, California’s maternal mortality rates decreased from 1999 to 2013, while rates in the rest of the country doubled [24, 26].

However, overall quality of obstetric care can improve without any effect on disparities [27, 28]. For example, California’s efforts reduced maternal mortality but did not reduce maternal mortality disparities. [26] The National Institutes of Health (NIH) has stated that, “Disparities in maternal outcomes persist after controlling for patient characteristics (e.g., health behaviors, preconception health) and health care system factors (e.g., site and overall quality of care), suggesting that additional factors may be contributing to maternal morbidity.” [27] Therefore, it is important to develop, use, and evaluate quality and safety approaches specifically targeting racial and ethnic disparities [27].

Another important aspect of reducing PRAMM is addressing safety and equity in community care. Community care consists of care provided outside the hospital in outpatient and other community settings and coordination of care across settings. Given that only 17% of U.S. pregnancy-related and pregnancy-associated deaths occur around the time of delivery (meaning that 83% take place during pregnancy or postpartum) [29], outpatient and community efforts are important.

The first community care safety bundles focusing on maternal health equity have been developed recently. The national Council on Patient Safety in Women’s Health Care previously created patient safety bundles (or care guidelines) related to inpatient obstetric care [30]. With funding from the U.S. Health Resources and Services Administration, the National Health Start Association launched a new initiative (the Alliance for Innovation in Maternal Health Community Care Initiative; AIM-CCI). AIM-CCI has developed 5 equity-focused maternal safety bundles for community-based organizations and outpatient clinical settings using an equity-centered inclusive process shaped by birthing people, providers, community partners and agencies [31]. These bundles (Table 1) constitute the set of equity best practices to be scaled up in this project.

Table 1 Example guidelines from the AIM-CCI equity focused maternal safety bundles [32]
Full size table

Addressing maternal morbidity and mortality disparities is of high priority to policymakers, and use of implementation science has been recommended. For example, the U.S. House of Representatives Black Maternal Health Caucus proposed the Black Maternal Health Momnibus Act [33]. The Caucus also advocated for the now-funded NIH IMPROVE (Implementing a Maternal Health and Pregnancy Outcomes Vision for Everyone) maternal health equity initiative [34]. NIH, including the NIH plan to improve the health of women [24], has emphasized the need for implementation science research to address maternal morbidity and mortality disparities [24, 27, 35,36,37]. This project answers that call.

The science of scale-up and sustainment

Innovations have limited impact on societal health if they are not scaled up and sustained. Yet scale-up and sustainment are under-studied aspects of implementation science. This is a common problem bemoaned in the literature [38,39,40,41,42,43,44,45,46,47,48,49,50,51,52] and by our health and community partners. Fitting for the scope of the problem of PRAMM disparities, this study focuses focus on scale-up and sustainment.

This project uses the Zamboni et al. [39] definition of scalability as “the ability of a health intervention shown to be efficacious on a small scale or under controlled conditions to be expanded under real-world conditions to reach a greater proportion of the eligible population, while retaining effectiveness.” [39, 50, 51] This definition encompasses three themes: (1) expansion of coverage and penetration, the potential reach of an intervention within and across organizations; (2) transferring control for delivery from initial implementers or innovators to local actors and embedding the intervention in local delivery systems; (3) retaining effectiveness as the intervention is scaled up [39, 50, 51]. Sustainment occurs when a program’s core elements are continued at sufficient fidelity, health benefits continue (i.e., effectiveness), and capacity for continuation of core elements is maintained [40,41,42]. Implementation measures are taken from these definitions.

Scale-up approach development steps are taken from these definitions and the Institute for Healthcare Improvement Framework for Going to Full Scale (IHI-FS) [47, 53]. Few if any extensively tested models of scale-up exist. We chose to test the IHI-FS scale-up model because: (1) it is well-described and was developed from a synthesis of existing scale-up models; [47] and (2) although not previously tested in controlled trials, the IHI-FS has been used as a guiding framework to successfully scale maternal and child health interventions nationally in other countries [47]. Given that there are few extensively tested models of scale-up and scalability, our controlled test of an implementation approach based on this model is innovative and will contribute to the implementation science literature.

Contributions of this project

This is the first controlled implementation trial of which we are aware to test approaches to implementing safety bundles that focus on: (1) PRAMM disparities; and (2) community care, where most deaths during pregnancy and postpartum take place [29]. This will also be the first study to develop and test a scale-up (or sustainment)-focused implementation approach for addressing maternal morbidity/mortality disparities, and will advance the science of scale-up and sustainment. Because the study tests scale-up, we wanted to include as large a population and number/variety of agencies as possible. Therefore, the trial cluster randomizes 12 counties (with a combined population of 6 million) in 4 steps. It uses a stepped wedge design to improve power, to allow each county to serve as both control and intervention (reducing potential randomization imbalances), and because we anticipate that systems changes made during the trial will persist. County-wide health outcomes will be assessed using Medicaid data. Provider-level implementation outcomes will be assessed using annual provider (i.e., nurse, doula, midwife, physician, home visiting staff, community health worker, perinatal social worker, etc.) surveys and study team tracking. Specific aims of this project are to:

  1. Aim 1.

    Develop a county-wide scale-up focused implementation approach for equity-focused, community care maternal safety bundles with community and clinical partners.

  2. Aim 2.

    Test the scale-up focused implementation approach using a stepped-wedge trial of 12 Michigan counties, with the following outcomes:

  1. a.

    Health impact on Black/Hispanic:

  • i. PRAMM (overall [primary] & relative to NHW])

  • ii. Severe maternal morbidity and pregnancy-associated mortality only (up to 1-year postpartum; overall & relative to NHW).

  1. b.

    Implementation outcomes (provider-level): scale-up [39] (reflected by penetration, reach, control for delivery, and intervention effectiveness at scale) and sustainment [40, 42] (reflected by maintenance of fidelity to core elements, health benefits, and capacity to deliver core elements over time).

  2. c.

    Costs and cost-effectiveness of implementation approaches.

Methods

This project is one of three linked projects and two cores addressing maternal morbidity and mortality disparities that make up the Maternal Health Multilevel Intervention/s for Racial Equity (MIRACLE) Center (U54 HD113291), one of the Maternal Health Research Centers of Excellence funded through NIH’s IMPROVE Initiative [34].

Community engagement

This project engages at least two communities. The first includes Black and Hispanic (B/H) people, including those who are pregnant or postpartum. The second “community” consists of the clinicians and healthcare/community agencies whose practices, patterns, and behaviors we hope to influence to address maternal health disparities. Often, these communities overlap (i.e., our health system partner representatives are B/H women). We take our community engagement model from Shediak-Rizkallah and Bone [42], who suggest that participation ➔ ownership ➔ improved capacity ➔ sustained program.

We have worked with our partners to create a project (and a Center) that is equitable both in content and in process. For example, each project and the Center itself are led by joint academic and community/clinical Principal Investigators (PIs) from our target communities. The project builds on years of work by Center Health/Community PI Ms. Vander Meulen, who has been part of the national AIM-CCI bundle development effort and has been the lead of one of the first six sites nationally to implement and test the bundles. Project idea. Ms. Vander Meulen suggested that the AIM-CCI bundles are a critical component of achieving maternal health equity, providing part of the focus for this project. Project Health/Community PI Ms. Clement provided the other part of the focus for this project: the importance of scale-up and sustainment. Project development. After the core idea of the project was developed, Center academic and health/community investigators and partners met weekly (and sometimes more often) to hone project ideas, approaches, and development. Of the 15 core members of the group that meet weekly to develop the Center, all but 3 were women and more than half identified as Black or Hispanic. About a third were from the university, about a third were from the large health systems, and about a third were from community-based organizations. During proposal preparation, they met at least weekly, discussed their interests in the project, and agreed to leverage their social capital to help meet study aims. Project conduct. Members of all these communities are valued members of the study team and meet regularly to inform the study implementation intervention and study procedures. Dissemination for this project and for the Center is intricately partnered and will leverage social networks.

Aim 1: development of the scale-up implementation approach

Previous work

Kent County Healthy Start at Corewell Health (one of our partners) has been the lead of 6 national pilot communities developing and implementing the AIM-CCI community care bundles since 2020. The Community Principal Investigator of the Center has been leading this effort for Kent County. Bundles have also been implemented in Genesee County, Michigan as part of another of our projects [54]. Implementation of AIM-CCI bundles in these two Michigan counties represents the “set up” and first part of the “build the scalable unit” steps of the IHI-FS (Table 2). This experience has yielded (1) structured and detailed implementation process notes [45, 55,56,57] of later stage AIM-CCI bundle implementation in Kent County and earlier stage AIM-CCI bundle implementation in Genesee County; (2) implementation experience with the bundles; and (3) relevant partnerships, all all of which will be used in the scale-up approach development process. The current project will use implementation data and experiences from these initial efforts to create a scale-up focused implementation intervention, which will be tested in a 12-county implementation trial.

Table 2 Operationalization of the IHI Model for Going to Full Scale in implementation approach development in the current study
Full size table

Development tasks in current project

Table 2 shows how the IHI-FS is operationalized in this project. The national AIM-CCI team has already addressed Step 1 (creating and packaging the AIM-CCI bundles to be scalable). Our prior work has already addressed the first half of Step 2 of the IHI-FS (define and measure scale-up objectives and beginning to build the scalable unit; see Table 2). For this project, development and manualization of the scale-up implementation approach will follow the second half of Step 2 (“integrate impact and process evaluation”) described in Table 2, leveraging data and manuals from previous implementation and partners described in Table 3.

Table 3 Aim 1 assets and partners
Full size table

Our approach to change package development will use an Implementation Mapping [58] approach within the IHI-FS framework. Implementation Mapping involves five tasks [58]. The first task is to identify program adopters and implementers and conduct an implementation needs assessment. We will begin with a list of Medicaid providers. We will refine and augment this list in partnership with state, regional, and local partners listed in the right column of Table 3 to identify relevant perinatal and community agencies in the 12 target counties. The study team will verify contact information, addresses, and help to identify the most appropriate agency representatives. The baseline (pre-implementation intervention) provider survey, which evaluates provider use of and familiarity with the guidelines will constitute the needs assessment.

The second task is to identify implementation outcomes, performance objectives, determinants, and change objectives. Implementation outcomes are already identified. Determinants will be identified through qualitative and quantitative analysis of implementation process and outcome data in Kent and Genesee Counties (described in the left column of Table 3). We will conduct thematic analysis of the structured process notes to identify determinants, including barriers and facilitators. We will also conduct exploratory analyses of frequency of use of each implementation strategy in each clinic/setting as predictors of adoption and sustainment in those agencies. Although these analyses are exploratory, they should provide initial insights. We will then convene a series of working groups with the organizations listed in Table 3 to review the data from Tasks 1 and 2 (i.e., the needs assessment and quantitative and qualitative analyses) to provide additional insight into determinants, and to develop and hone performance and change objectives.

The third task is to select frameworks and design implementation strategies. Our framework, the IHI-FS is already selected. We are using the determinants and performance/change objectives identified in the previous task to collaboratively (with partners listed on the right side of Table 3) select approaches/strategies to address each of the adoption mechanisms/support systems requirements outlined by the IHI-FS in the right column of Table 2 (i.e., maximize reach, shift control for delivery to local actors, ensure capacity to conduct the program effectively, create/provide infrastructure for scale-up, collect and share data with staff and leaders, design for sustainability, enroll and train leaders, communicate about value of the intervention, support through policy, and create a culture of urgency and persistence). Consistent with the IHI-FS and with AIM-CCI recommendations, implementation strategies will include learning collaboratives. Therefore, at least one of the manualized implementation approaches will be to work with community providers to convene local collaborative learning groups of community prenatal and postnatal providers who meet regularly to share ideas and experiences to support one another in implementing the bundles, learning from each other, and providing feedback to the implementation team. We will work with partners listed on the right column of Table 3 to determine how best to structure and leverage these groups. As noted in the left column of Table 3, we already have several sustainment strategies manualized and tested [45]. These will be integrated into the scale-up strategies identified in this process.

The fourth task is to produce implementation protocols and materials. To reduce burden on our partners, our team will take the lead on this, but will iteratively review drafts with members of the organizations listed in Table 3 to ensure they seem appropriate and meet the needs of our target group of clinical and community providers.

The fifth task is to evaluate implementation outcomes. This task can be iterative [58], fitting well with the IHI-FS, in which implementation/scale-up is iteratively tested, first in “set up” (i.e., implementation and assessment of implementation outcomes in two counties - Kent and Genesee), then through “testing scale-up” (in 12 counties in Aim 2 of this project), and then by “going to full-scale” (i.e., all counties, future efforts; see Table 2) [47].

Following IHI-FS and other recommendations for scale-up, planned development of the scale-up implementation approach: (1) integrates impact and process evaluation; [39, 47] (2) uses learning collaboratives and workgroups to manualize and hone implementation blueprints [59, 61] to test in Aim 2; [47] and (3) combines subject matter experts with application experts to help organizations select, test, and implement changes [62].

Aim 2: Test the scale-up focused implementation approach in a 12-county stepped wedge trial

Counties

After the scale-up implementation approach is developed and manualized, we will test the approach throughout the 12 counties using a stepped wedge design (Fig. 1; Table 4). We chose the 11 counties in Michigan with the highest number of annual Medicaid-covered births to B/H mothers, excluding the two counties (Kent and Genesee) that have been implementing the bundles in R01 MD016003, plus one rural county to examine interactions with an intervention led by a different Center project.

Fig. 1
figure 1

Map and estimated characteristics of counties and county randomization blocks

Full size image
Table 4 Study timeline
Full size table

Randomization and assignment to conditions

Birthing people and providers are clustered within counties. Counties have been assigned to four blocks to balance number of Medicaid-covered births to B/H people and region of Michigan (Fig. 1). Blocks and estimated annual number of Medicaid-covered births to B/H people include: Wayne County (n = 5,502); Oakland, Ingham, and Isabella counties (n = 864); Macomb, Muskegon, Calhoun, and Jackson counties (n = 988); and Saginaw, Kalamazoo, Berrien, and Washtenaw counties (n = 831). The study statistician will randomize the four blocks to the four steps of this stepped wedge trial. We anticipate that providers within counties will largely be the same over time, and that birthing people within counties will largely be different (though subsequent births or moves within the project period are possible).

Care as usual comparison condition

Our comparison condition is usual care available to Medicaid insured pregnant and postpartum people in Michigan prior to implementation of the AIM-CCI bundles. Analyses by our team suggest that among Medicaid-covered birthing individuals in Michigan, approximately 76% receive adequate prenatal care, 49% use the emergency department at least once during pregnancy, and 59% receive postpartum care within 60 days after birth [63]. None of the 12 counties in the stepped wedge (and only a small handful in the entire country) have tried to implement the AIM-CCI bundles.

Characterization of the scale-up implementation intervention

Every implementation activity will be documented in an electronic implementation case note. The case note will include: encounter length, time spent on operational vs. clinical support, a checklist of implementation strategies used (taken from Powell et al., 2015) [59] as recommended in recent guidelines for specifying and reporting implementation strategies [60], a checklist of discussion topics, and free response sections to describe staff’s responses and anything not covered by the checklists. We will assess the frequency with which each of the Powell strategies occur. This will allow us to characterize the timing and type of support at each site, to examine (1) fidelity to the implementation framework; (2) the association of these variables with scale-up and sustainment outcomes; and (3) agency participation or non-participation in offered supports.

Sources of data and study samples

Health outcomes

We will rely on the linked Michigan Department of Health and Human Services (MDHHS) Health Services Data Warehouse maintained by MDHHS to assess health outcomes. The linked sources of data include: (1) complete Medicaid pregnancy, and postpartum (typically 12 months after birth) medical claims; (2) monthly Medicaid eligibility during pregnancy and postpartum; (3) birth records, including data on pre-pregnancy and pregnancy risk factors (e.g. chronic disease, prior preterm births); (4) maternal death records; (5) census tract data; and (6) some prenatal and postpartum screening data.

The study population for health outcomes will consist of all pregnant individuals with Medicaid while pregnant, in the month of birth, or during the 12 months postpartum who give birth during the first 6 years of the study period in the 12 counties, as identified in the MDHHS Health Services Data Warehouse. Maternal race will be assessed based on birth records. County of residence at delivery will be used to identify birthing people in intervention and control counties. County of delivery will be used to assign mothers to project counties in sensitivity analyses.

Implementation outcomes

The study sample for implementation outcomes will consist of health care providers (of all kinds, including nurses, doulas, midwives, physicians, home visiting staff, community health workers, perinatal social workers) and agencies serving pregnant or postpartum people with Medicaid in the target counties, identified through Medicaid directories and internet searches. We will identify relevant clinical and community programs that touch birthing persons (such as the Michigan Maternal Infant Health Program; Healthy Start; other home visiting programs; etc.) through internet searches and through state and local partners. In the last 3 months of Years 1–6 (for 6 total surveys), we will send a brief annual survey to all providers in the study sample in all 12 counties (~ 600 providers total) to assess implementation outcomes (see Table 5). The survey will be offered through both electronic and paper forms and will use best practices to maximize provider survey responses [64].

Table 5 Implementation outcomes and measures
Full size table

Sample size and power

Given the available sample size, we determined the effect sizes detectable with power of .80 in two-sided tests at .05 level of significance for all study hypotheses.

Health outcomes

Analyses will be at the pregnant person level, with ~ 151,920 births, including ~ 49,110 births to B/H mothers/birthing people (~ 8,185 births per year for each of the 6 assessment years). The unequal cluster (county) size will be accounted for at the individual level with the appropriate weights incorporated into standard errors. Because Wayne County (which includes Detroit) is an outlier in terms of its size (N = 5,502 B/H births per year), we conducted power analyses with and without Wayne County to ensure that primary and sensitivity analyses have sufficient power for tests of hypotheses.

PRAMM

There are more than 120 PRAMM conditions, but each person will have a limited number of them. Following recent data [13], we assumed a distribution with 70% zeros and decreasing probabilities for higher counts resulting in a control (pre-intervention) mean of 1.03 and standard deviation 2.11. The estimated intraclass correlation coefficient (ICC) was 0.003 and the coefficient of variation 0.10 based on 2019 MDHHS Data Warehouse data. We calculated power for example effect sizes (Cohen’s d; Table 6) using the Hussey and Hughes approach [65].

Table 6 Power and detectable effect sizes for health outcomes (intervention vs. control)
Full size table

SMM

For the secondary yes/no outcome of SMM, based on the literature [13], the rate is 4.35% among Medicaid-insured Black individuals, 2.99% among Hispanic individuals, and 1.3% among non-Hispanic White. Based on demographic characteristics in the sample, the control (pre-intervention) SMM rate is 3.98% for B/H participants in the entire sample of 12 counties, and 3.94% for counties other than Wayne. Projected post-intervention rates detectable as significantly different from control were computed. Even very small reductions in PRAMM and SMM are detectable given the proposed sample size, with or without Wayne County (see Table 6).

Implementation outcomes

For implementation outcomes, assuming 28% response rate of 600 (~ 50/county) providers, the number of observations across 12 counties each year is n = 168. We used a conservative upper range of ICCs for clinicians (0.05–0.1) and correlation of 0.6 between pairs of repeated measures for clinicians over time in a cohort design to determine power to detect example small to medium effect sizes (Table 7).

Table 7 Power and detectable effect sizes for implementation outcomes (intervention vs. control)
Full size table

Assessments

Health outcomes

Our primary health outcome is Black/Hispanic PRAMM (overall & relative to NHW). PRAMM is a count variable reflecting overall number of diagnoses/procedures/incidents from NSMM, SMM, and pregnancy-associated mortality (which includes pregnancy-related mortality). Components of this composite measure are defined below. Our secondary health outcome is SMM/pregnancy-associated mortality (overall & relative to NHW), a yes/no outcome that = 1 if there is SMM and/or mortality and 0 otherwise.

Non-severe maternal morbidity (NSMM), is defined by the WHO’s Maternal Morbidity Working Group (MMWG) [10] as ”any health condition attributed to and/or complicating pregnancy and childbirth that has a negative impact on the woman’s well-being and functioning” during pregnancy and postpartum [12, 66, 67]. Most NSMM are measurable using International Classification of Diseases (ICD-10) diagnostic and procedure codes [11] with standard definitions adopted by the WHO [11, 12]. To capture multimorbidity in our sample, the overall NSMM indicator will be the sum of WHO-delineated NSMM diagnoses and procedures [11, 12] identified in a woman’s Medicaid claims during pregnancy and up to 12 months postpartum. We will also assess the following WHO-defined subcategories: (1) number of direct NSMM diagnoses and procedures [12]; (2) number of indirect NSMM diagnoses and procedures; and (3) number co-incidental NSMM diagnoses and procedures [12]. Disparities will be defined as the percent difference between non-Hispanic Black-NHW and Hispanic-NHW outcomes.

We will assess severe maternal morbidity (SMM) [68] using CDC’s list of 21 SMM indicators based on ICD-10 diagnosis and procedure codes [69] that occur antepartum, intrapartum, and up to 12 months postpartum [9]. Pregnancy-associated death will be defined as a death during pregnancy or within one year of delivery or termination of pregnancy [70], which may be from a cause related [2] or unrelated to pregnancy and will be considered present if a death record is linked to the pregnancy or birth record. Disparities will be defined as the percent difference between non-Hispanic Black-NHW and Hispanic-NHW outcomes.

Implementation outcomes

Most implementation outcomes will be measured using the annual provider survey. The same survey will be given to the same providers in all 12 counties each year (i.e., all measures will be collected in all counties at all 6 time points). The survey will list the evidence-based practices in all targeted AIM-CCI bundles (with a short definition), and ask (i) whether the provider is using the practice (penetration); (ii) approximately how many people the provider has served using the practice in the past year (reach); (iii) whether the provider currently has the capacity to offer the practice; (iv) whether the provider currently offers the practice as defined (fidelity); and (v) the extent to which the practice is embedded in local delivery systems and workflow (control for delivery). We will also collect brief information on provider (demographics, discipline, scope and years of practice) and setting (kind of setting, how many served per year, main funding sources). Effectiveness will be measured using the PRAMM and SMM analyses described above. Continuation of health benefits over time will be operationalized as the slope of the PRAMM outcome (annually) once the county moves into the “intervention” phase of the stepped wedge design.

Implementation cost-effectiveness

Our grant accounting will capture the costs of providing the scale-up implementation approach. The primary cost-effectiveness measure will be NSMM. For NSMM, we will calculate intervention costs per point of NSMM score reduction. Secondary cost-effectiveness measures will be maternal mortality and SMM. Prevented SMM will be also monetized using Medicaid claims data by calculating the difference between Medicaid delivery expenditures between women/birthing persons with SMM and without SMM using our own claims data and prior estimates [9]. The value of a statistical life, currently around $10 million, will be used to monetize prevented maternal deaths. Costs (and savings) in future years will be discounted to present value in the year of treatment initiation using the recommended discount rate.

Analyses

Tests will be two-sided (p < = 0.05) and we will report measures of clinical significance (i.e., effect sizes).

Missing data

As of 2023, most Medicaid-insured birthing people in Michigan retain coverage for 12 months after birth, which will ensure minimal missing claims for calculating PRAMM and SMM outcomes. Health outcomes will be defined per the number of months with Medicaid coverage. For implementation outcomes, a conservative response rate of 28% was factored in power considerations,

Health outcomes

Health outcomes will be analyzed at the individual birthing person level, within county-level clusters. Correct standard errors for the parameter estimate (difference between intervention and control means) will be derived from the generalized linear mixed effects models with appropriately distributed errors and fixed effects of time in years and intervention condition. Site (county) will be treated as a random effect to account for nesting of participants within sites. We will examine implementation intervention effects for: (1) PRAMM among Black people and then among Hispanic people, (2) PRAMM disparities (non-Hispanic Black vs. NHW; Hispanic vs. NHW), (3) SMM among Black and then among Hispanic people, and (4) SMM disparities (non-Hispanic Black vs. NHW; Hispanic vs. NHW).

Implementation outcomes

Effectiveness will be measured using the PRAMM and SMM analyses described above; intervention vs. comparison condition). Continuation of health benefits over time will be operationalized as the slope of` the PRAMM outcome (annually) once the county moves into the “intervention” phase of the stepped wedge design. Implementation outcomes collected from clinicians will be analyzed using generalized mixed models with fixed effects of time and intervention condition. In addition to the random effect for nesting of clinicians within counties, another random effect will account for measures obtained from the same clinician over time. Separate analyses will address: (1) penetration (count of recommended bundle practices the provider has used in the past year? [primary]; (2) reach (approximately how many people the provider has served using each bundle practice in the past year – practice by # of people composite total); [71] (3) capacity over time (slope of capacity annually after county moves into “intervention” phase of stepped wedge); (4) fidelity (number of bundle practices offered as defined); and (5) control for delivery (sum of across practices of a Likert item per bundle practice that ranges from 1 = practice is fully embedded in local delivery systems and workflow to 5 = practice entirely relies on outside efforts/support).

Implementation cost-effectiveness

We will use a comparative cost effectiveness analyses of the implementation approach relative to status quo in the 12 study counties. Following widely accepted guidelines [72,73,74], our analyses will adopt a societal perspective, considering all costs regardless of source. If direct cost savings exceed the program costs, the program is said to offer net cost savings. We describe our plan for determining mean change in and standard deviations of these measures above. We will calculate incremental cost-effectiveness ratios as the ratio of the incremental costs over the incremental effectiveness. Sensitivity analysis will examine cost-effectiveness ratios at 1%, 3%, 5% and 8% discount rates.

Mechanisms and moderators

We will explore implementation outcomes (penetration, reach, control for delivery, fidelity, and capacity for intervention delivery) as mechanisms of effects of the scale-up approach on PRAMM. Average implementation outcomes across county and previous time period will be tested as potential mediators in predicting individual-level PRAMM. We will use the Preacher and Hayes [75, 76] approach to estimate direct and indirect (through the mediator) effects of the implementation intervention on PRAMM, with county as a cluster. We will examine provider (discipline, scope and years of practice) and setting (kind of setting, size, funding) characteristics as moderators of the effect of the scale-up intervention on implementation and PRAMM outcomes.

Discussion

This trial is the first study to develop and test a scale-up focused implementation approach for maternal health equity bundles. Several methodological features are uncommon and make this study interesting. These include efforts to implement across health and community organizations county-wide and then evaluating population-level (i.e., county-wide) health outcomes, use of the IHI Framework for Going to Full Scale to guide implementation approach development, and a focus on scale-up outcomes, an understudied aspect of implementation science. Implementing guidelines for improving health equity and use of equitable research processes also make this trial protocol notable.

This project adheres to rigorous standards for causal inference methods (randomized stepped wedge design that accounts for nesting of participants within sites) [77]. Rigor and reproducibility are also ensured by clear research questions, an appropriate comparison condition, a priori data analysis plans, use of a large and pre-existing linked Medicaid dataset, clearly defined measures, transparent statistical and power analyses, sufficient power, valid statistical methods to deal with missing data, sensitivity analyses, detailed intervention manuals, and fidelity evaluation.

This project builds on extensive, directly related previous work, increasing feasibility and likely success of the proposed scale-up approach and research methods. The study is well-powered and can yield evaluable data even if provider response rates are lower than expected. Examining mechanisms and moderators will provide information that may help target future scale-up approaches whether or not our scale-up approach yields anticipated main effects [78].

The evidence generated by this study has significant potential to speed scale-up of evidence-based maternal health equity practices to end racial and ethnic disparities in maternal mortality and morbidity. Results will also advance the science of scale-up and sustainment generally. This is important because we need to reach birthing people at scale and because scale-up is an understudied aspect of implementation science.

A list of abbreviations is provided in Table 8.

Table 8 Terms and abbreviations
Full size table

Data availability

The pre-existing statewide linked Medicaid dataset (individual level) is available from the Michigan Department of Health and Human Services, upon request, and with a new data use agreement to be initiated by the requesting individuals. Restrictions apply to the availability of these data, which were used under license for this study. De-identified implementation outcomes (provider level) data collected by the project will be available in appropriate national repositories and as well as by request from the corresponding author (JEJ).

References

  1. Kassebaum NJ, Barber RM, Bhutta ZA, et al. Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the global burden of Disease Study 2015. Lancet. 2016;388:1775–812.

    Article  Google Scholar 

  2. Pregnancy Mortality Surveillance System. Centers for Disease Control and Prevention. 2023. https://www.cdc.gov/reproductivehealth/maternal-mortality/pregnancy-mortality-surveillance-system.htm. Accessed 1 May 2020.

  3. Petersen EE, Davis NL, Goodman D, et al. Vital signs: pregnancy-related deaths, United States, 2011–2015, and strategies for prevention, 13 states, 2013–2017. Morb Mortal Wkly Rep. 2019;68:423.

    Google Scholar 

  4. Four in 5 pregnancy-related deaths in the U.S. are preventable. CDC Newsroom. 2022. https://www.cdc.gov/media/releases/2022/p0919-pregnancy-related-deaths.html#:~:text=More%20than%2080%25%20of%20pregnancy,identify%20recommendations%20to%20prevent%20future. Accessed 7 Nov 2022.

  5. Collier A, Molina RL. Maternal mortality in the United States: updates on trends, causes, and solutions. Neoreviews. 2019;20:561–74.

    Article  Google Scholar 

  6. Margerison CE, Roberts MH, Gemmill A, Goldman-Mellor S. Pregnancy-associated deaths due to drugs, suicide, and homicide in the United States, 2010–2019. Obstet Gynecol. 2022;139:172–80.

    PubMed  PubMed Central  Google Scholar 

  7. Michigan issues maternal mortality report; new plan to help state reach vision of zero preventable deaths, zero disparities. State of Michigan. 2018. https://www.michigan.gov/mdhhs/inside-mdhhs/newsroom/2018/11/08/michigan-issues-maternal-mortality-report-new-plan-to-help-state-reach-vision-of-zero-preventable-d. Accessed 10 May 2022.

  8. Howell EA, Janevic T, Hebert PL, Egorova NN, Balbierz A, Zeitlin J. Differences in morbidity and mortality rates in black, white, and Hispanic very preterm infants among New York City hospitals. JAMA Pediatr. 2018;172:269–77.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Vesco KK, Ferrante S, Chen Y, Rhodes T, Black CM, Allen-Ramey F. Costs of severe maternal morbidity during pregnancy in US commercially insured and Medicaid populations: an observational study. Matern Child Health J. 2020;24:30–8.

    Article  PubMed  Google Scholar 

  10. Firoz T, McCaw-Binns A, Filippi V, et al. A framework for healthcare interventions to address maternal morbidity. Int J Gynecol Obstet. 2018;141:61–8.

    Article  Google Scholar 

  11. World Health Organization. The WHO application of ICD-10 to deaths during pregnancy, childbirth and puerperium: ICD MM. https://www.who.int/reproductivehealth/publications/monitoring/9789241548458/en/. Accessed 10 May 2022.

  12. Chou D, Tunçalp Ö, Firoz T, et al. Constructing maternal morbidity–towards a standard tool to measure and monitor maternal health beyond mortality. BMC Pregnancy Childbirth. 2016;16:45.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Declercq E, Zephyrin L. Severe maternal morbidity in the United States: A primer. 2021. Advancing Health Equity. https://www.commonwealthfund.org/publications/issue-briefs/2021/oct/severe-maternal-morbidity-united-states-primer. Accessed 31 Oct 2022.

  14. Howell EA. Reducing disparities in severe maternal morbidity and mortality. Clin Obstet Gynecol. 2018;61:387.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Pregnancy Mortality Surveillance System. Centers for Disease Control and Prevention. 2023. https://www.cdc.gov/reproductivehealth/maternal-mortality/pregnancy-mortality-surveillance-system.htm. Accessed 11 Feb 2018.

  16. Grossi G. Black and Hispanic Women had Significant Rise in Maternal Mortality in. 2020. https://www.hcplive.com/view/black-hispanic-women-significant-rise-maternal-mortality-2020. Accessed 7 Nov 2022.

  17. Kivisto A, Mills S, Elwood LS. Racial disparities in pregnancy-associated intimate Partner Homicide. J Interpers Violence. 2022;37:10938–61.

    Article  Google Scholar 

  18. Howell EA, Zeitlin J. Improving hospital quality to reduce disparities in severe maternal morbidity and mortality. Semin Perinatol. 2017. https://doiorg.publicaciones.saludcastillayleon.es/10.1053/j.semperi.2017.04.002.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Howell E. Reducing disparities in severe maternal morbidity and mortality. Clin Obstet Gynecol. 2018;61:387–99.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Matthews K, Morgan I, Davis K, Estriplet T, Perez S, Crear-Perry JA. Pathways To Equitable And Antiracist Maternal Mental Health Care: Insights From Black Women Stakeholders. Health Aff. 2021;40(10). https://doiorg.publicaciones.saludcastillayleon.es/10.1377/hlthaff.2021.00808. Accessed 10 May 2022.

  21. Miranda ML, Swamy GK, Edwards S, Maxson P, Gelfand A, James S. Disparities in maternal hypertension and pregnancy outcomes: evidence from North Carolina, 1994–2003. Public Health Rep. 2010;125:579–87.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Minhas AS, Ogunwole M, Vaught AJ, Wu P, Mamas MA, Gulati M, Zhao D, Hayws AG, Michos ED. Racial disparities in Cardiovascular complications with pregnancy-Induced Hypertension in the United States. Hypertension. 2021;78:480–8.

    Article  CAS  PubMed  Google Scholar 

  23. Laditka SB, Ladkitka JN, Probst JC. Racial and ethnic disparities in potentially avoidable delivery complications among pregnant Medicaid Beneficiaries in South Carolina. Matern Child Health J. 2006;10:339–50.

    Article  PubMed  Google Scholar 

  24. National Institutes of Health. Perspectives on advancing NIH research to inform and improve the health of women. 2022. https://orwh.od.nih.gov/sites/orwh/files/docs/ORWH_WHC_ExecutiveSummary508.pdf. Accessed 10 May 2024.

  25. Alliance_for_Innovation_on_Maternal_Health. AIM Patient Safety Bundles. https://saferbirth.org/patient-safety-bundles/. Accessed 14 Oct 2022.

  26. Main EK, Markow C, Gould J. Addressing maternal mortality and morbidity in California through public-private partnerships. Health Aff. 2018;37:1484–93.

    Article  Google Scholar 

  27. National Institutes of Health. Addressing racial disparities in maternal mortality and morbidity (R01 clinical trial optional). https://grants.nih.gov/grants/guide/rfa-files/RFA-MD-20-008.html. Accessed 30 Apr 2020.

  28. U.S. Department of Health and Human Services. Addressing Racial Disparities in Maternal Mortality and Morbidity NIH. https://grants.nih.gov/grants/guide/rfa-files/RFA-MD-20-008.html. Accessed 1 May 2020.

  29. Declercq E, Zephyrin L. Maternal mortality in the United States: A Primer. 2020. https://doiorg.publicaciones.saludcastillayleon.es/10.26099/ta1q-mw24. Accessed 10 May 2022.

  30. Council on Patient Safety in Women’s Health Care. Patient Safety Bundles. https://safehealthcareforeverywoman.org/patient-safety-bundles/#link_tab-maternal. Accessed 2 May 2020.

  31. Alliance for Innovation on Maternal Health - Community Care Initiative (AIM-CCI). Our approach. 12-1-22, https://www.aimcci.org/our-approach/. Accessed 10 May 2024.

  32. Alliance for Innovation on Maternal Health Community Care Initiative. Maternal safety bundles. AIM-CCI. https://www.aimcci.org/bundles/. Accessed 14 Oct 2022.

  33. United States House of Representatives. Congresswoman Alma Adams & Congresswoman Lauren Underwood. Black Maternal Health Caucus. https://blackmaternalhealthcaucus-underwood.house.gov/Momnibus. Accessed 8 Mar 2024.

  34. United States House of Representatives. Congresswoman Alma Adams & Congresswoman Lauren Underwood. Black Maternal Health Caucus Priorities. https://blackmaternalhealthcaucus-underwood.house.gov/appropriations-priorities. Accessed 10 May 2024.

  35. National Institutes of Health. Maternal Health Research Centers of Excellence (U54 Clinical Trial Optional). National_Institutes_of_Health. https://grants.nih.gov/grants/guide/rfa-files/RFA-HD-23-035.html. Accessed 24 Oct 2022.

  36. National Institutes of Health, Notice of Special Interest (NOSI). : Implementation Science to Advance Maternal Health and Maternal Health Equity for the IMPROVE initiative. https://grants.nih.gov/grants/guide/notice-files/NOT-HD-22-043.html. Accessed 4 Nov 2022.

  37. National Institutes of Health. Maternal Health Research Centers of Excellence Implementation Science Hub/Resource Center. https://grants.nih.gov/grants/guide/rfa-files/rfa-hd-23-037.html. Accessed 4 Nov 2022.

  38. Dearing JW, Kee KF, Peng T-Q. Historical roots of dissemination and implementation science. In: Brownson RC, Colditz GA, Proctor EK, editors. Dissemination and implementation research in health. Oxford University Press; 2023. pp. 69–85.

    Chapter  Google Scholar 

  39. Zamboni K, Schellenberg J, Hanson C, Betran AP, Dumont A. Assessing scalability of an intervention: why, how, and who? Health Policy Plann. 2019;34:544–52.

    Article  Google Scholar 

  40. Wiltsey-Stirman S, Kimberly J, Cook N, Calloway A, Castro F, Charns M. The sustainability of new programs and innovations: a review of the empirical literature and recommendations for future research. Implement Sci. 2012;7:17.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Scheirer M. Is sustainability possible? A review and commentary on empirical studies of program sustainability. Am J Eval. 2005;23:320–47.

    Article  Google Scholar 

  42. Shediac-Rizkallah M, Bone LR. Planning for the sustainability of community-based health programs: conceptual frameworks and future directions for research, practice and policy. Health Educ Res. 1998;13:87–108.

    Article  CAS  PubMed  Google Scholar 

  43. Proctor E, Luke D, Calhoun A, McMillen C, Brownson R, McCrary S, Padek M. Sustainability of evidence-based healthcare: research agenda, methodological advances, and infrastructure support. Implement Sci. 2015;10:88.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hailemariam M, Bustos T, Montgomery B, Barajas R, Evans LB. Drahota, A. evidence-based intervention sustainability strategies: a systematic review. Implement Sci. 2019;14:57.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Johnson JE, Wiltsey-Stirman S, Sikorskii A, et al. Protocol for the ROSE sustainment (ROSES) study, a sequential multiple assignment randomized trial to determine the minimum necessary intervention to maintain a postpartum depression prevention program in prenatal clinics serving low-income women. Implement Sci. 2018. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-018-0807-9.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Chambers D. Building a lasting impact: Implementation Science and Sustainability. presented at: NCI Implementation Science Webinar;, July. 23, 2013 2013; http://www.youtube.com/watch?v=J8vp1wKt_YI. Accessed 10 May 2024.

  47. Barker PM, Reid A, Schall MW. A framework for scaling up health interventions: lessons from large-scale improvement initiatives in Africa. Implement Sci. 2016;11:12.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Larson R, Dearing JW, Backer TE. Scaling up social programs: pathways, partnerships, and fidelity. The Wallace Foundation; 2017.

    Book  Google Scholar 

  49. Mangham LJ, Hanson K. Scaling up international health: what are the key issues? Health Policy Plann. 2010;25:85–96.

    Article  Google Scholar 

  50. Milat A, Bauman A, Redman S. Narrative review of models and success factors for scaling up public health interventions. Implement Sci. 2015;10:113.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Milat A, Bauman A, Redman S. The concept of scalability: increasing the scale and potential adoption of health promotion interventions into policy and practice. Health Promot Int. 2013;28:285–98.

    Article  PubMed  Google Scholar 

  52. Milat A, Lee K, Conte K, Grunseit A, Wolfenden L, Nassau F, Orr N, Sreeram P, Bauman A. Intervention Scalability Assessment Tool: a decision support tool for health policy makers and implementers. Health Res Policy Sy. 2020;18:1.

    Article  Google Scholar 

  53. Parry G. Evolving the IHI Scale-up Framework. Accessed 10-2-2020, 2020. ihi.org/communities/blogs/evolving-the-ihi-scale-up-framework

  54. Johnson JE, Roman L, Key KD, Vander Muelen M, Raffo JE, Luo Z, Margerison CE, Olomu A, Johnson-Lawrence V, McCoy White J, Meghea C. Study protocol: the Maternal Health Multilevel Intervention for Racial Equity (maternal Health MIRACLE) Project. Contemp Clin Trials. 2022;120:106894.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Johnson JE, Stout RL, Miller TR, Zlotnick C, Cerbo LA, Andrade JT, Nargiso J, Bonner J, Wiltsey-Stirman S. Randomized cost-effectiveness trial of group interpersonal psychotherapy (IPT) for prisoners with major depression. J Consult Clin Psychol. 2019;87:392–406.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Johnson JE, Miller T, Stout RL, Zlotnick C, Cerbo LA, Andrade JT, Wiltsey-Stirman S. Study protocol: hybrid type I cost-effectiveness and implementation study of interpersonal psychotherapy (IPT) for men and women prisoners with major depression. Contemp Clin Trials. 2016;47:266–74.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Johnson JE, Hailemariam M, Zlotnick C, Richie F, Sinclair J, Chuong A, Wiltsey Stirman S. Mixed methods analysis of implementation of interpersonal psychotherapy (IPT) for major depressive disorder in prisons in a hybrid type I randomized trial. Adm Policy Ment Hlth. 2020;47:410–26.

    Article  Google Scholar 

  58. Fernandez ME, ten Hoor GA, Lieshout S, Rodriguez SA, Beidas RS, Parcel G, Ruiter RAC, Markham CM, Kok G. Implementation mapping: using intervention mapping to develop implementation strategies. Front Public Health. 2019;7:158.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Powell BJ, Waltz TJ, Chinman MJ et al. A refined compilation of implementation strategies: results from the Expert recommendations for Implementing Change (ERIC) project. Implement Sci. Feb 12 2015;10:21. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-015-0209-1

  60. Proctor EK, Powell BJ, McMillen JC. Implementation strategies: recommendations for specifying and reporting. Implement Sci Dec 01. 2013;8:139. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1748-5908-8-139.

    Article  Google Scholar 

  61. Proctor EK, Powell BJ, McMillen JC. Implementation strategies: recommendations for specifying and reporting. Journal article. Implement Sci Dec. 2013;01(1):139. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1748-5908-8-139.

    Article  Google Scholar 

  62. Institute for Healthcare Improvement. The breakthrough series: IHI’s collaborative model for achieving breakthrough improvement. Diabetes Spectr. 2004;17(2):97–101.

    Article  Google Scholar 

  63. Roman L, Raffo JE, Strutz KL, Luo Z, Johnson ME, Vander Meulen P, Henning S, Baker D, Titcombe C, Meghea C. The impact of a Population-based system of care intervention on enhanced prenatal care and service utilization among Medicaid-insured pregnant women. Am J Prev Med. 2022;62:117–27.

  64. Dillman D, Smyth JD, Christian TM. Internet, mail, and mixed-Mode surveys: the tailored design method. Wiley; 2009.

    Google Scholar 

  65. Hughes J. University of Washington. http://faculty.washington.edu/jphughes/pubs.html. Accessed 11 Nov 2022.

  66. Firoz T, Chou D, von Dadelszen P, et al. Measuring maternal health: focus on maternal morbidity. Bull World Health Organ. 2013;91:794–96.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Say L, Chou D, Group WMMW. Maternal morbidity: time for reflection, recognition, and action. Int J Gynaecol Obstet. 2018;141:1.

    Article  PubMed  PubMed Central  Google Scholar 

  68. American College of Obstetricians Gynecologists. Severe maternal morbidity: screening and review. Am J Obstet Gynecol. 2016;215:17–22.

    Article  Google Scholar 

  69. Centers for Disease Control and Prevention. Reproductive Health. How does CDC Identify Severe Maternal Morbidity? https://www.cdc.gov/reproductivehealth/maternalinfanthealth/smm/severe-morbidity-ICD.htm. Accessed 1 May 2020.

  70. Centers for Disease Control and Prevention. State strategies for preventing pregnancy-related deaths: A guide for moving matneral mortality review committee data to action. 2022. https://www.cdc.gov/reproductivehealth/maternal-mortality/docs/pdf/state-strategies-508.pdf. Accessed 10 May 2024.

  71. Shwartz MR, Rosen JD. Composite measures of health care provider performance: a description of approaches. Milbank Q. 2015;93:788–825.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Steuerle, E., and Jackson, L M. (editors). Committee on the Use of Economic Evidence to Inform Investments in Children, Youth, and Families; Board on Children, Youth, and Families; Division of Behavioral and Social Sciences and Education; National Academies of Sciences, Engineering, and Medicine. Advancing the Power of Economic Evidence to Inform Investments in children. Youth, and Families. National Academies; 2016.

    Google Scholar 

  73. Neumann P. Using cost-effectiveness analysis to improve health care. Oxford University Press; 2005.

    Google Scholar 

  74. Neumann P, Sanders GD, Russell LB, Siegel JE, Ganiats TG, editors. Cost-effectiveness in health and medicine, Second Edition. Oxford University Press; 2016.

  75. Preacher KJ, Selig JP. Advantages of Monte Carlo confidence intervals for indirect effects. Commun Methods Meas. 2012;6:77–98.

    Article  Google Scholar 

  76. Preacher K, Hays A. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum Comput. 2004;36:717–31.

    Article  PubMed  Google Scholar 

  77. Patient-Centered Outcomes Research Institute. PCORI, Methodology Standards. February. 2019. https://www.pcori.org/sites/default/files/PCORI-Methodology-Standards.pdf. Accessed 10 May 2024.

  78. National Institute of Mental Health. Psychosocial Research at NIMH: a primer. 2016. https://www.nimh.nih.gov/research/research-funded-by-nimh/psychosocial-research-at-nimh-a-primer. Accessed 18 Nov 2022.

  79. National Institutes of Health. NIH establishes maternal health research centers of excellence. National Institutes of Health. https://www.nih.gov/news-events/news-releases/nih-establishes-maternal-health-research-centers-excellence. Accessed 8 Mar 2024.

  80. Alliance for Innovation on Maternal Health Community Care Initiative. Community care for postpartum safety and wellness. AIM-CCI. https://www.aimcci.org/available-bundles/community-care-for-postpartum-safety-and-wellness/. Accessed 14 Oct 2022.

  81. Dearing J. Applying diffusion of innovation theory to intervention development. Res Social Work Prac. 2009;19:503–18.

    Article  Google Scholar 

  82. Collins C, Sapiano TN. Lessons learned from dissemination of evidence-based interventions for HIV prevention. Am J Prev Med. 2016;51:140–7.

    Article  Google Scholar 

  83. Blue Ridge Institute for Medical Research. BRIMR rankings of NIH funded in 2023 as compiled by Robert Roskoski Jr. and Tristram G. Parslow. https://brimr.org/brimr-rankings-of-nih-funding-in-2023/. Accessed 10 May 2024.

Download references

Acknowledgements

This project would not be possible without the efforts, knowledge, and partnerships of the other Center Academic and Health/Community investigators, including Kimberly-Dawn Wisdom, Athena McKay, Celeste Sanchez-Lloyd, Sharon Saddler, E. Hill De Loney, Steven Ondersma, Claire Margerison, Jaime Slaughter-Acey, Richard Leach, our growing Community Partnership Consortium, and the research staff and students working on the project, Aisling Nolan, Malak Ismail, Ally Rooker, Lyndsey Braman, Meghana Atmakur, Taegan Byers, Alyssa Gill, and Ranya Srour.

Authors’ information

Additional information about the MIRACLE Center can be found at: https://obgyn.msu.edu/the-miracle-center. In 2023, Michigan State University had the #1 most NIH-funded Department of Obstetrics and Gynecology in the U.S., per the Blue Ridge Reports [83]. Dr. Johnson is the Founding Chair of Michigan State University’s Charles Stewart Mott Department of Public Health, which is based in Flint, Michigan, and is one of the only university departments of which we are aware to be co-developed and co-led by community members.

Funding

This is one of three linked projects that make up the Maternal health Multilevel Intervention/s for Racial Equity (MIRACLE) Center (U54 HD113291), funded by the U.S. National Institutes of Health. Project Principal Investigators are Dr. Johnson, Ms. Clement, and Dr. Loree. Center Principal Investigators are Dr. Meghea, Dr. Johnson, and Ms. Vander Meulen. The funder had no role in the conceptualization or design of this study or in the preparation of the manuscript.

Author information

Authors and Affiliations

  1. Charles Stewart Mott Department of Public Health, College of Human Medicine, Michigan State University, 200 East 1st Street, Flint, MI, 48502, USA

    Jennifer E. Johnson & Jonne McCoy White

  2. Henry Ford Health, One Ford Place – 5C, Detroit, MI, 48202, USA

    Jaye Clement

  3. Department of Psychiatry, College of Osteopathic Medicine, Michigan State University, East Fee Hall, 965 Wilson Rd A233, East Lansing, MI, 48824, USA

    Alla Sikorskii

  4. Center for Health Policy & Health Services Research, Henry Ford Health, 1 Ford Place, Detroit, 48202, MI, USA

    Amy Loree

  5. Strong Beginnings - Healthy Start at Corewell Health, 751 Lafayette Ave. NE, Grand Rapids, 49503, MI, USA

    Margaret Vander Meulen

  6. Department of Obstetrics, Gynecology, and Reproductive Medicine, College of Human Medicine, Michigan State University, 965 Fee Road, East Lansing, MI, 48824, USA

    LeeAnne Roman

  7. Department of Communication, Michigan State University, 404 Wilson Rd #473, East Lansing, MI, USA

    James W. Dearing

  8. Department of Obstetrics, Gynecology and Reproductive Biology, Michigan State University, 965 Wilson Rd, A630, East Lansing, MI, USA

    Hannah Bolder

  9. Department of Obstetrics and Gynecology and Physiology, Wayne State University School of Medicine, 3990 John R. Street, 7 Brush North, Detroit, MI, USA

    Robert Sokol

  10. Department of Obstetrics, Gynecology, and Reproductive Biology, College of Human Medicine, Michigan State University, 965 Wilson Rd, East Lansing, MI, 48824, USA

    Jennifer E. Johnson, Hannah Bolder & Cristian Meghea

Authors
  1. Jennifer E. Johnson
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jaye Clement
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Alla Sikorskii
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Amy Loree
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Margaret Vander Meulen
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. LeeAnne Roman
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. James W. Dearing
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Hannah Bolder
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Jonne McCoy White
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Robert Sokol
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Cristian Meghea
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conceptualization of this project was led by JJ and JC. Study methods were designed by JJ (implementation approach development, implementation science methods, trial methods, outcomes, overall design), AS (statistical and power analyses, design implications), and CM (health outcomes, choice of 12 counties). All authors (i.e., JEJ, JC, AS, MVM, LR, JD, HB, JMW, RS, and CM) except AL were involved in funding acquisition; after the project was funded, AL joined the project as a third principal investigator. JJ, JC, AL, MVM, JMW, RS, and CM gathered and/or represent the health and community partners who helped to conceptualize the project and will assist with implementation approach development and implementation. MVM has been involved in developing and pilot testing the safety bundles that will be implemented. JJ wrote the original draft of the paper. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Jennifer E. Johnson.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Michigan State University Biomedical Institutional Review Board (FWA00004556). Informed consent will be obtained from provider participants. Health data is obtained through a limited use administrative dataset.

Consent for publication

NA.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Johnson, J.E., Clement, J., Sikorskii, A. et al. A cluster randomized stepped wedge implementation trial of scale-up approaches to ending pregnancy-related and -associated morbidity and mortality disparities in 12 Michigan counties: rationale and study protocol. Implement Sci Commun 6, 19 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s43058-024-00677-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s43058-024-00677-7

Keywords

Implementation Science Communications

ISSN: 2662-2211