Improving preconception health is recognized as being crucial to improving reproductive health outcomes for women and infants. At the same time, there is increasing pressure on public health and clinical medicine programs to have evidence that documents positive health impact for continued support for program implementation and policy change. In the field of preconception health and health care, there is a growing body of evidence to support the implementation of public health programs and clinical practice. One current challenge is the unavailability of a comprehensive surveillance system providing data to demonstrate the need for such programs and to monitor the impact of programs and services. There is no single source of data or evidence for policy and financing support for preconception care; however, there are a number of related data resources that can be used to inform and support such programs. We describe national and state-level data sources from which data relevant to preconception health and health care can be extracted as well as steps that can be taken to improve the quantity and quality of preconception health data.
There is an increased call for the fields of public health and clinical medicine to invest in programs and practice models that are evidence based and have been shown to have measurable impacts on health, especially in times of scarce resources to fund scientific research. Developing health policies and financing strategies to support best practices and programs requires relevant, accurate data for successful implementation and evaluation. Preconception health and health care is 1 area in which there is a growing body of evidence to support the implementation of public health programs and clinical practice. The concept of preconception health and health care has been part of both public health and clinical practice landscapes for >20 years (
Moos and Cefalo, 1987
). Since 2004, there has been renewed and increased interest in preconception health and health care as both a conceptual framework and a model for improving the health of women and improving pregnancy outcomes for mothers and infants (Johnson et al., 2006
). One key component of the evidence base needed to support clinical practice and public health programs is the availability of reliable and relevant surveillance data. Data are critical to developing, implementing, and sustaining policies that will make preconception health and health care a standard of care in both clinical services and public health programs. Although there is no single source of data or evidence for policy and financing support for preconception care, there are many data resources that can be used to inform and support changes that will result in improved health of women, and in turn their offspring. This paper describes efforts to identify existing data sources and systems that can contribute to the evidence necessary to support the development and expansion of preconception health and health care programs and policies.- Johnson K.
- Posner S.F.
- Biermann J.
- Cordero J.F.
- Atrash H.K.
- Parker C.S.
- et al.
Recommendations to improve preconception health and health care—United States: A report of the CDC/ATSDR Preconception Care Work Group and the Select Panel on Preconception Care.
Morbidity and Mortality Weekly Reports: Reports and Recommendations. 2006; 55: 1-30
We know that what gets measured gets noticed. Healthy People 2000 (
Centers for Disease Control and Prevention (CDC), 2000
) included an objective (14.3) for preconception care for 60% of primary care physicians to provide age-appropriate preconception care. This objective was deleted from the next version of the Healthy People publication in part because it was not able to be measured. In Healthy People 2010 (CDC, 2000), there are no objectives specific to preconception health; however, there are several objectives that are specified that relate to preconception health.The growing evidence base to support the implementation of preconception health and health care, which includes maternal risk assessment, educational/behavioral interventions, vaccinations, screening, treatment, and health promotion programs, has not reached maturity (
Korenbrot et al., 2002
). The majority of evidence currently published focuses on specific risk behaviors and conditions rather than integrated approaches or programs that address all the needs of an individual woman. For some risk factors, the evidence for effective interventions, such as folic acid and the prevention of neural tube defects, and the protocol to reduce the transmission of group B Strep from mother to infant, is well-established, whereas in other areas, such as postpartum programs to promote appropriate weight loss, there is less evidence or information (Centers for Disease Control and Prevention (CDC), 2002
, Centers for Disease Control and Prevention (CDC), 2005
, Korenbrot et al., 2002
, Werler et al., 1993
). As ongoing research evaluates more comprehensive programs, one of the current limitations in the existing literature is that measurable indicators of preconception health and health care are not readily available and thus not do not provide the solid evidence base needed to form an integrated and comprehensive set of health care policies to improve the health of women and families across the lifespan.Several steps necessary for the capacity of data systems to build the evidence base needed to support policy change include expanding the coverage and scope of existing surveillance systems, conducting clinical trials, and increasing program evaluation (
Posner et al., 2006
). Putting these recommendations into action is expensive and time consuming. A call has been made to address the critical first step in such an undertaking, which is to define the content and domains of preconception health and health care and identify potential sources of surveillance data. The Public Health Work Group (PHWG) of the CDC Preconception Health and Healthcare Steering Committee has been active in exploring ways to improve surveillance systems and build data analysis capacity. The PHWG has prioritized the need for developing a library of indicators available in specific data systems for different populations, selecting the most important indicators to measure, and determining which indicators are not being measured. Concurrently, the Clinical Work Group of this CDC Steering Committee has been instrumental in this process by working to clarify and define the content of clinical care. The Clinical Work Group has identified key preconception care domains, including chronic diseases; infectious diseases; medication use; genetic/inherited conditions; adverse past pregnancy outcomes such as maternal morbidity, low birth weight, premature birth, and infant death; and personal behaviors and exposures such as obesity and smoking, as well as other preconception health risk factors that have been shown to affect maternal health and pregnancy outcomes (Atrash et al., 2006
). The Policy and Finance Work Group has discussed and described the importance of including public and private payers, including Medicaid, and health maintenance organizations, in the process of defining appropriate indicators. The components of preconception care addressed by public health programs and by consumer groups are equally important for improving the health of women and families. Public health programs and consumer awareness are a critical component of health promotion and disease prevention in communities to educate and help the general public improve their health and well-being.Under the auspices of the PHWG, dedicated effort is being put forth by representatives from 7 states (Delaware, Florida, Michigan, North Carolina, Texas, California, and Utah) to reach consensus on relevant preconception health and health care domains and associated surveillance indicators. This team has broadly addressed preconception health, including interconception health and health care, with a focus on vulnerable populations. The multistate team's initial work is 3-fold: 1) developing a library of state level indicators currently available in specific public data systems; 2) identifying the most important indicators to measure; and 3) highlighting those indicators that are currently not being measured at the state level. To date, 11 available data systems (Appendix A) have been identified that may provide state health departments with data to monitor preconception health and health care outcomes. In addition to these data sources, the team has outlined a conceptual framework for the broad domains of indicators (Table 1). These domains measure a broad range of topics useful to public program implementation, evaluation, and surveillance as well as consumer and clinical audiences. The conceptual framework will be used to identify specific indicators by the State Workgroup and include behavior, medical, chronic disease management, and social context indicators that have been identified as being important to preconception health and health care. Although current efforts are focused on what is most relevant to state health departments, additional effort for indicators being measured at the national and local levels is anticipated.
Table 1Conceptual framework for core state preconception health surveillance indicator domains for pre- and interconception health and health care
| General health status (self-rated health) and life satisfaction |
|---|
| Social determinants of health |
| Poverty |
| Housing |
| Income |
| Education |
| Life expectancy |
| Health care |
| Health insurance or Medicaid |
| Identified primary care provider |
| Access to dental care |
| Barriers to care |
| Adequacy of care |
| Satisfaction with care |
| Reproductive health/family planning |
| Previous low birth weight |
| Previous preterm birth |
| Prior fetal death (stillbirth, miscarriage) |
| Postpartum depression |
| Interpregnancy interval/birth spacing |
| Pregnancy intention |
| Pregnancy wantedness |
| Use of contraception |
| Tobacco, alcohol, and substance use |
| Nutrition and physical activity |
| Fruit and vegetable consumption |
| Obesity and overweight |
| Folic acid supplementation |
| Multivitamin consumption |
| Mental health |
| Mental distress |
| Stress |
| Anxiety and depression |
| Emotional/social support |
| Physical abuse |
| Mental abuse |
| Adequacy of support |
| Chronic conditions |
| Infections |
| HIV testing |
| Prevalence and/or Incidence of STDs |
| Immunizations |
| Genetics/epigenetics |
Although a single, comprehensive data system could ideally provide the information needed from a single source, this is not practical or feasible in the current environment. In a time when there are limited resources and infrastructures to support the existing surveillance systems, it is impractical to develop an entirely new surveillance system that can provide data to local, state, and federal researchers, program planners, and policy makers. Furthermore, the reach and coverage of a comprehensive system would require substantial resources and be duplicative of many of the existing systems. Many of the existing systems can be used to collect the relevant data by adding or modifying existing data collection instruments. Integrating data from multiple existing data systems can provide comparable results and data from a range of populations not currently covered by any 1 system. Existing data sources that could provide data for these purposes are underutilized. Adequate support for existing systems is likely to be more cost efficient than building another system from the ground up.
State-level systems, such as the Pregnancy Risk Assessment and Monitoring System (PRAMS) and state-specific surveys such as the California Health Interview Survey, Maternal and Infant Health Assessment, and Women's Health Survey, can serve as models for data collection for specific populations. Although generally not thought of as a data source for preconception care, there are a number of relevant preconception health and health care indicators currently available in PRAMS. These include prepregnancy body mass index, tobacco use, health insurance status, pregnancy planning, and health care seeking behavior. Two recent publications summarizing these indicators have resulted in an increased interest in using these data for measuring preconception health (
Centers for Disease Control and Prevention (CDC), 2007
, D'Angelo et al., 2007
). The PRAMS survey tool currently is undergoing its 5-year revision and the inclusion of additional preconception care indicators is under consideration. The challenge in working with this data system for policy change is that it represents a select population—women who recently had a live birth—and it is operative in only 39 states. In addition, states must choose to include preconception health modules as special topics among their optional state questions because many potential preconception health indicators are not a part of the core PRAMS questionnaire that all PRAMS states are required to use. Recent revisions of the California Maternal and Infant Health Assessment also have included new preconception health and health care indicators.- D'Angelo D.
- Williams L.
- Morrow B.
- Cox S.
- Harris N.
- Harrison L.
- et al.
Preconception and interconception health status of women who recently gave birth to a live-born infant- Pregnancy Risk Assessment Monitoring System (PRAMS), United States, 26 reporting areas, 2004.
Morbidity and Mortality Weekly Report Surveillance Summary. 2007; 56: 1-35
The Behavioral Risk Factor Surveillance System (BRFSS) can provide state-level data as well as national data. The BRFSS also captures local-level data for selected larger metropolitan areas. The strength of the BRFSS system is that it includes adult women, regardless of pregnancy status, and men, allowing for the assessment of selected indicators in the general population. The system is used primarily to monitor health conditions and high-risk behaviors that contribute to morbidity and mortality, and has not consistently included questions about pregnancy intention or family planning, which are useful for obtaining a complete picture of chronic disease risks, health behaviors, and unintended pregnancy. Although the BRFSS assesses a broad array of health conditions and behaviors, not all of the data elements that are needed to completely understand the burden and impact of existing conditions are included. Another limitation in the BRFSS data system is the potential for small numbers of participants in specific subgroups (e.g., pregnant women or women of reproductive age), which might undermine statistical power. Many BRFSS topics are not collected annually. Topics may rotate or sporadically appear, making surveillance on these topics more difficult. Furthermore, BRFSS is limited to households that have a landline telephone and excludes those without telephones or who only have wireless phone service.
The Youth Risk Behavior Surveillance System (YRBSS) can also provide state and national, as well as local, data for selected larger metropolitan areas. The YRBSS is a school-based survey of students in grades 9–12 that represents the general population of students. Both girls and boys are included. Similar to the BRFSS, the YRBSS is used primarily to monitor high-risk behaviors that contribute to morbidity and mortality. Although the system tracks a broad range of risk-taking behaviors among adolescents, including but not limited to smoking and drinking behaviors, states may choose to omit questions on sexual behaviors and use of family planning. Pregnancy data are also inconsistently ascertained, although the entire target population is considered to be at reproductive age. Although there may seem to be substantial overlap in some measures assessed by the YRBSS and the BRFSS, the surveillance populations are entirely different. The YRBSS also provides a unique opportunity to examine differential health behaviors between sexually active and non-sexually active youth.
Nationally representative data systems such as the National Health and Nutrition Examination Survey (NHANES) also can provide data for understanding what is needed in this field. For example, management of chronic diseases and contraindicated contraceptive use can be examined using NHANES data. Such analysis would provide information for clinical practice and public health programs on management of chronic disease among women of reproductive age and gaps in health providers’ and public health programs’ knowledge about contraindications for contraceptive use, which could result in adverse health events for women. The National Survey of Family Growth offers similar opportunities for examining preconception health issues. However, these data sets are unable to provide state or local estimates, thus limiting their ability to address state- or local-specific concerns.
The National Children's Study began in 2008, and was conceptualized as a comprehensive national study of child health. As children grow up in families and because this study starts by enrolling women before they become pregnant, there is an opportunity to measure risk and protective factors before pregnancy and then to assess their impact on a woman's health as well the health of her offspring. New guidance and investments are needed to include preconception health and health care measures in this new study.
One of the primary challenges of working with data from multiple sources is the issue of comparability across surveillance systems. Many research, program, and policy decisions require a number of topics that may or may not be available from specific populations and thus limit the availability of evidence to support decisions. Identification of such limitations can help to guide the modification of existing data systems and/or areas of new research. The identification of appropriate indicators in this field is a dynamic process and will change as the field matures, proven interventions are identified, and content areas are modified. The differences can also be an asset because different indicators are relevant to different populations. For example, PRAMS and BRFSS survey very different populations and relevant indicators, such as postpartum depression measured in PRAMS or chronic disease management among women not currently using an effective method of contraception in BRFSS. PRAMS is limited to those who have recently had a live birth and the depression measure is relevant to interconception health and health care, whereas the BRFSS chronic disease management assessment provides information on the general health status of those who might become pregnant. This measure could help to identify the need for interventions before pregnancy.
As opportunities arise for developing and implementing new measures and new systems, there are important issues to consider. To date, much of the data collection has focused on the burden of specific chronic disease and risk factors such as diabetes and tobacco use. Although it is important to know the prevalence of a disease, a measure that includes the proportion with the disease that is appropriately managed or the type of management received is critical to truly understanding the impact of these conditions on health outcomes. Similarly, for women with a previous poor pregnancy outcome, measurement of interconception health and health care is important to help define and intervene on factors that reduce a woman's risk of having a subsequent poor pregnancy outcome (
Biermann et al., 2006
). In addition, systems such as PRAMS and BRFSS measure the broader sociocultural context with items including social stressors and perceived racism. These have important implications for the development and implementation of public health programs with a special focus on health equity, social justice, and the elimination of disparities. Research to understand the protective behaviors of women who are able to successfully manage high-risk conditions are important to the design of effective interventions. Evaluation of the health and economic impact of effective interventions is critical to develop the evidence base needed to affect public health programs and policies that promote the integration of these interventions into the standard of care.Using a health promotion framework can facilitate the integration of preconception health and health care activities into existing clinical and public health programs and services. The State of California's recent preconception health efforts have taken this approach through a model program entitled “Every Woman, Every Time,” which aims to change the preconceptional care practices of health care providers (
Cullum, 2003
). This framework expands preconception health beyond traditional family planning and pregnancy outcomes to a broad range of activities designed to improve women's health across the lifespan and facilitate proactive decision making about childbearing. Taking advantage of existing clinical and public health systems is a way to facilitate policy change, identify mechanisms for payment, and offer comprehensive services. The health promotion infrastructures also support systems for both summarizing existing data and collecting new data needed for effecting necessary policy changes.Next Steps
There are a number of important actions and activities that are in process and planned for the coming years. It is important to recognize that all of the work to date on the data indicators subcommittee has been done by volunteers who recognize the importance of this activity. For the purposes of sustainability, the project would benefit from some dedicated resources. The State Working Group continues their activities to identify specific indicators from each of the data systems and has a small expert advisory committee with representation from federal, state, and academic organizations. When this work on state-level data indicators is completed, the next step is to conduct a formal gap analysis. Part of this effort will be to identify indicators that have previously been developed or develop new indicators.
Future activities include the development of a parallel data library for the national level data systems. This will follow a similar process and build on the work done by the State Working Group. The combination of the state- and national-level data systems reviews will be the basis for the open access library of data indicators. At this time, resources have not been identified for where this will be housed or for regular updating of the library. The intent of doing this work is to change surveillance systems so regular updates will be required. With the establishment of the library, it is expected that the workgroups will serve as resources for technical assistance on an as needed basis. This library of indicators will facilitate the development of state report cards or identification of indicators to be included in other programs that can be used by public health programs and policy makers for decision-making purposes.
Summary
The identification of existing data systems and their components, which can provide information needed to inform public health programs, clinical practice, and policy development, is a reasonable strategy, especially in times of scarce resources. In the absence of 1 comprehensive preconception health and health care data system, creative use of extant data systems is needed, such as integration of data across systems and expansion to include additional indicators. PRAMS, BRFSS, and the new National Children's Study are examples of existing systems that provide relevant data. In addition to the use and adaptation of existing data systems, investment in new data systems at the local, state, and national levels would allow for the development, implementation, and evaluation of preconception health and health care programs, services and policies—all of which are critical to efforts aimed at improving the health of women, children, and families.
Data on preconception health and health care will inform clinical health practice as well as public health programs and systems on the factors that influence outcomes for women, children, and families. Opportunities to support the development and expansion of preconception health and health care data systems should be taken advantage of. Efforts to include preconception health and health care indicators in existing data sets are needed. States have the opportunity to do this by choosing optional modules in existing data systems that include preconception health and health care indicators. Identifying critical data elements and including a broader set of data elements in existing and new data systems will provide opportunities to improve our understanding of the relationship between preconception health and health care and pregnancy outcomes for both mothers and their infants.
Appendix A.
Tabled
1Data systems providing state level preconception health and health care measures
| Data system | Source agency |
|---|---|
| Behavioral Risk Factor Surveillance System (BRFSS) | Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Adult and Community Health |
| Birth defects surveillance | Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities |
| National Immunization Survey (NIS) | Centers for Disease Control and Prevention, National Center for Health Statistics |
| National Program of Cancer Registries | Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Cancer Prevention and Control |
| National Vital Statistics System Birth Data | Centers for Disease Control and Prevention, National Center for Health Statistics |
| National Vital Statistics System Fetal Death Data | Centers for Disease Control and Prevention, National Center for Health Statistics |
| Pediatric Nutrition Surveillance System (PedNSS) | Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition and Physical Activity |
| Pregnancy Risk Assessment Monitoring System (PRAMS) | Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Reproductive Health |
| Pregnancy Nutrition Surveillance System (PNSS) | Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition and Physical Activity |
| Sexually Transmitted Disease Surveillance | Centers for Disease Control and Prevention, National Center for HIV, STD, and TB Prevention, Division of STD Prevention |
| Treatment Episode Data Set (TEDS) | Substance Abuse & Mental Health Services Administration (SAMHSA), Office of Applied Studies Drug and Alcohol Services Information System (DASIS) |
References
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Biography
Samuel Posner is the Associate Director for Science, National Center for Chronic Disease Prevention and Health Promotion at the Centers for Disease Control and Prevention.
Danielle Broussard is a CDC/CSTE Applied Epidemiology Fellow in Maternal Child Health assigned to the Florida Department of Health.
William Sappenfield is the State Maternal and Child Health Epidemiologist at the Florida Department of Health.
Nan Streeter is the Director, Maternal and Child Health Bureau at the Utah Department of Health Division of Community and Family Health Services.
Lauren B. Zapata is a research scientist in the Division of Reproductive Health at the Centers for Disease Control and Prevention.
Magda Peck is the CityMatCH Founder and Senior Advisor, is Professor of Pediatrics and Public Health at the University of Nebraska Medical Center in Omaha NE.
Article info
Publication history
Accepted:
July 10,
2008
Received in revised form:
July 9,
2008
Received:
April 28,
2008
Footnotes
The authors have no direct financial interests that might pose a conflict of interest in connection with the submitted manuscript.
The findings and conclusions of this article are those of the authors and do not necessarily represent those of the Centers for Disease Control and Prevention, the Council of State and Territorial Epidemiologists, the Florida Department of Health, the Utah Department of Health, the University of Nebraska Medical Center, or CityMatCH.
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Copyright
© 2008 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.
