High-quality data are an important tool to help us understand the health and well-being of American Indian and Alaska Native (AIAN) people during pregnancy, childbirth, and postpartum. Data can identify areas of strength and spot where additional supports may be needed. Policies and programs grounded in high-quality data can also support improved maternal outcomes and family well-being. However, finding information about Native maternal health is not straightforward: Relevant information is spread across health care data systems, public health surveillance systems, reports and data tools, and research studies.

Understanding what available data can tell us about Native maternal health requires attention to how the data were collected, who and what they represent, and whether they are appropriate for the question(s) being asked. AIAN people are often undercounted or misclassified in health data, and small population sizes can limit the information that is available or reported. Data sources also vary in how they collect information on and classify race and ethnicity, and in what geographic areas and populations they represent and which measures they make available. While national data may identify broad patterns, they may not answer questions at the state or regional level, and information available at any of these levels may not reflect the experiences, priorities, or needs of a particular Tribal Nation or Native community.

Commonly Used Maternal Health Data Sources

Several established data systems and tools are commonly used to understand maternal health in the United States. However, the availability and usefulness of information about Native people within these sources vary, based on how the data are collected, analyzed, and reported.

Healthcare Cost and Utilization Project (HCUP)

The Healthcare Cost and Utilization Project (HCUP) is a family of health care databases developed from hospital administrative data contributed by the project’s state partner organizations. HCUP data can be used to examine hospital-based maternal health care and outcomes, including delivery hospitalizations, diagnoses and procedures, severe maternal morbidity, health care utilization, and payer information.

HCUP includes information on patients’ race and ethnicity, but how race and ethnicity are collected and reported varies across states and hospitals. HCUP standardizes information received from participating states into common race and ethnicity categories, and those decisions can affect how Native people are represented in AIAN-specific estimates. For example, in HCUP’s primary uniform race variable, Hispanic ethnicity overrides racial classifications, and individuals with multiracial identities are often classified as “Other.” Race and ethnicity information may also be missing or suppressed due to small numbers. The presence of an AIAN category therefore does not necessarily indicate that Native people are fully or consistently represented in the resulting estimates.

The Pregnancy Risk Assessment Monitoring System (PRAMS)

The Pregnancy Risk Assessment Monitoring System (PRAMS) is a population-based surveillance system[1] led by the Centers for Disease Control and Prevention (CDC) in collaboration with participating health departments. PRAMS collects information directly from people with recent live births about their health, behaviors, and experiences before, during, and after pregnancy. Participating jurisdictions draw samples from birth certificate records and can oversample particular populations, including by maternal race and ethnicity. PRAMS provides information on prenatal and postpartum care, participation in social services, breastfeeding, maternal mental health, substance use, and social and economic factors that may not be captured in vital records or hospital administrative data.

The availability and usefulness of PRAMS data for understanding Native maternal health varies across jurisdictions. Sampling plans are developed by individual jurisdictions, including decisions about which populations, if any, to oversample. Oversampling Native respondents can help support AIAN-specific estimates, but these approaches vary, and small sample sizes can still limit estimates, particularly for less common maternal health outcomes. Recent disruptions to PRAMS highlight the importance of sustained data collection and access. In 2025, disruptions to the federal PRAMS infrastructure interrupted national data collection and researcher access, creating gaps in recent data and uncertainty about the continuity of PRAMS.

Some jurisdictions have adapted PRAMS to extend its usefulness. Tribal PRAMS, for example, uses the PRAMS model to better serve Native communities. Tribal Epidemiology Centers and partners have used the PRAMS framework to collect information specifically from Native mothers and to incorporate questions that are responsive to Tribal and community priorities. As another example, PRAMS data have also been used alongside other sources to examine factors affecting maternal health that extend beyond individual health outcomes. A South Dakota Advisory Committee report drew on PRAMS and Tribal PRAMS data and testimony to examine barriers to prenatal care, along with broader issues such as transportation, housing and infrastructure, access to health care, and discrimination. Adapting an established public health data system or combining its data with other information can help identify factors shaping Native maternal health that are not visible in health outcomes alone.

Maternal Mortality Review committees (MMRCs)

Maternal Mortality Review committees (MMRCs) are multidisciplinary committees that review deaths that occur during pregnancy and in the year following pregnancy to understand the circumstances surrounding each death and identify opportunities for prevention. MMRCs combine information from multiple sources to determine whether a death was pregnancy-related, identify causes and contributing factors, assess preventability, and recommend changes to prevent future deaths. The CDC’s Maternal Mortality Review Information Application (MMRIA) provides a standardized system for documenting information from these reviews.

MMRCs have contributed important information about maternal deaths among AIAN people. Data compiled across 45 MMRCs, for example, provided national findings on AIAN pregnancy-related deaths in 2022, including underlying causes, contributing circumstances, and opportunities for prevention. Analyses of these data show how information on underlying causes, contributing circumstances, and preventability can provide a more nuanced understanding of Native maternal deaths and potential points of prevention.

The usefulness of MMRCs for understanding Native maternal mortality also depends on how records are identified and whose knowledge informs the review. Racial misclassification can affect whether AIAN deaths are accurately identified, and Tribal representation on state MMRCs remains limited. The National Indian Health Board reported in 2025 that very few state MMRCs included Tribal representation; the organization is currently working with Tribal partners to explore the development of Tribally led MMRCs. Tribally led approaches would create space for Tribal authority, establish data protection protocols, and allow cultural and community knowledge to more directly shape the review of maternal deaths and development of recommendations.

March of Dimes PeriStats

The March of Dimes PeriStats is an online tool developed by the March of Dimes Perinatal Data Center that brings together maternal and infant health information from multiple sources and presents it via reports, maps, graphs, and tables. Depending on the indicator, the underlying data come from a range of sources, including the National Center for Health Statistics, PRAMS, HCUP, or other federal and state data systems. Many indicators can be examined by race and ethnicity and at different geographic levels.

PeriStats can make maternal health information easier to access and compare, but the information available about Native people is shaped both by the underlying data sources and by decisions about which findings are analyzed and communicated. Some AIAN estimates may be unavailable or suppressed because of small sample sizes or event counts, and definitions of race and ethnicity vary across sources.

The means of communicating available data can also affect the visibility of Native maternal health inequities. In 2026, a group of Indigenous maternal health professionals, advocates, and allies wrote to March of Dimes after a national statement highlighted racial disparities in maternal mortality but did not mention the AIAN maternal mortality rate—even though available CDC data showed that AIAN women had the highest maternal mortality rate among the racial and ethnic groups reported. The authors called for AIAN maternal health statistics to be clearly and consistently included in national reporting and for Indigenous expertise to inform how the data are interpreted and communicated.

Sources Focused on Native Populations and Communities

In contrast to data sources and tools focused more broadly on the overall population, some sources and approaches focus more specifically on AIAN populations and communities. Information focused on Native populations and communities can reveal variation, context, strengths, and priorities that may not be visible in broader population-level data.

Indian Health Service (IHS) reports

The Indian Health Service (IHS) has published reports on the health of AIAN people, including Trends in Indian Health and Regional Differences in Indian Health. These reports include indicators that are relevant to maternal health, such as birth rates, low birthweight, prenatal care in the first trimester, and diabetes in pregnancy.

The population(s) represented in IHS reports varies depending on the indicator and data source. For example, Trends in Indian Health draws on IHS administrative data for some measures, while the birth and death statistics presented in the report are based on vital records collected by states and reported through the National Center for Health Statistics. IHS uses geographic service areas to identify AIAN vital events associated with populations served by IHS because vital records do not indicate whether a person is an IHS user. Population estimates used to calculate rates also come from U.S. Census Bureau data. Therefore, statistics presented in an IHS report should not automatically be interpreted as measures derived from IHS patient records or as representing only people who receive care through IHS.

IHS reports can provide a Native-focused view of health patterns nationally and across IHS geographic areas. Regional comparisons can reveal variation within the Native population, including areas of relative strength, that may be obscured in national AIAN estimates. Some IHS areas, for example, have maternal health indicators that compare favorably with overall U.S. estimates. However, IHS reports have not been updated regularly, limiting their usefulness for understanding current maternal health patterns. Their interpretation also requires attention to the underlying source, population, and geography for each measure. The reports document limitations that affect the interpretation of Native health data, including racial misclassification in vital records and differences between Census-based population estimates and populations served by IHS.

Urban Indian Health Institute (UIHI) Community Health Profiles

The Urban Indian Health Institute (UIHI), a public health and research organization in the Pacific Northwest that also serves as one of 12 Tribal Epidemiology Centers, has developed a series of Community Health Profiles to describe the health of AIAN people who live in areas served by Urban Indian Organizations (UIOs). The profiles bring together information from multiple data systems across more than 50 indicators, including maternal and child health measures such as prenatal care, maternal smoking during pregnancy, preterm birth, low birthweight, breastfeeding, and infant mortality. Individual profiles were developed for 45 UIO service areas covering 125 counties across the United States, including areas such as Denver, Helena, and Los Angeles.

Like the IHS reports, the Community Health Profiles draw on multiple data sources and represent different populations depending on the measure. UIHI analyzes information from sources that include the American Community Survey, National Vital Statistics System, Behavioral Risk Factor Surveillance System, and other national data systems. Multiple years of data are combined to produce more stable estimates for relatively small populations, and the AIAN population is generally defined as AIAN alone (as opposed to AIAN in combination with other races or ethnicities) unless otherwise indicated.

UIHI organizes and analyzes information from existing data systems to support health planning and decision making for communities that are served by UIOs. The Community Health Profiles can help communities identify health priorities, allocate resources, plan programs, identify data gaps, and guide research. UIHI also notes that national public health surveillance systems may not include information about the specific strengths and concerns of communities served by UIOs and points out that local data can add important context.

Qualitative, community-engaged, and place-based research

Quantitative data sources can provide important information about Native maternal health, but qualitative, community-engaged, and place-based research can add context and insight that may not be present in those data. Interviews, storytelling approaches, and other qualitative methods can deepen understanding of Native people’s experiences during pregnancy, childbirth, and postpartum in ways that administrative or public health surveillance systems may not. Qualitative research can examine how health care, historical and contemporary conditions, culture, and family and community contexts shape maternal health and well-being.

A qualitative study with eight United Keetoowah Band mothers in Oklahoma, for example, used story inquiry to understand participants’ experiences of becoming mothers and their postpartum mental health. The study identified maternal mental health challenges and inadequacies in perinatal care and documented the roles of historical trauma, colonization, and cultural strengths in shaping participants’ experiences. The research was conducted with Tribal support and included community involvement in interpreting and validating the findings. At the Tribe’s request, the underlying data were not made publicly available because they included protected cultural knowledge, an example of data access being governed by Tribal priorities.

Other place-based studies can identify strengths and practices that may be difficult to spot in population-level data. One case series used a national survey to identify rural hospitals serving racially diverse communities and developed case studies of three such hospitals. The Yukon-Kuskokwim Delta Regional Hospital in Alaska—operated by the Yukon-Kuskokwim Health Corporation (a Tribal Organization that administers a comprehensive health care delivery system for 58 rural communities in southwest Alaska on behalf of the IHS)—was selected for its approach to providing obstetric care across a geographically challenging region while supporting Alaska Native cultural values.

Findings from qualitative research are grounded in the specific people and contexts with which the research was conducted; these findings should not automatically be generalized to other Native populations. Context-specific findings can be particularly valuable when questions concern the experiences, priorities, or strengths of a particular Tribal Nation or Native community. Research design also shapes what can be learned, as does who develops the questions, how communities are engaged, and whose knowledge informs interpretation.

Strengthening Information About Native Maternal Health

No single data source can answer all questions about Native maternal health. Established maternal health systems provide important information about health outcomes, health care use, experiences, and maternal deaths, but their usefulness for Native populations depends on how Native people are represented, the populations and geographic levels they cover, and how their data are analyzed, interpreted, and communicated. It’s especially important to understand what an estimate represents when small populations, racial misclassification, inconsistent definitions, or data suppression affect what information is available.

Strengthening the information available about Native maternal health can occur through building on existing data infrastructure while also increasing the role of Native expertise, priorities, and leadership in collecting, interpreting, communicating, and using information. Established public health surveillance and administrative systems can better represent Native people, and approaches such as PRAMS can be adapted to incorporate Tribal and community priorities. Native organizations can bring together and interpret information from existing systems around questions important to the communities they serve. Tribally led review processes and community-engaged research can contribute cultural, community, and contextual knowledge that existing systems may not include.

Sustained investment is needed in both established public health infrastructure and in Native-led data capacity and approaches; equally important is attention to the level at which information is needed. National data can identify patterns and inform federal policy, while regional, state, Tribal, and community information can reveal differences that national estimates obscure and inform decisions closer to where people live and receive care. Connecting sources and forms of knowledge can strengthen the foundation for identifying priorities, developing policy and program responses, and advancing the health and well-being of Native mothers, families, and communities.

Notes

[1] In public health, surveillance refers to the ongoing, systematic collection, analysis, interpretation, and use of health information to understand population health and inform public health actions.