Abstract
Purpose
In May 2012, the Association of Maternal and Child Health (MCH) Programs initiated a project to develop indicators for use at a state or community level to assess, monitor, and evaluate the application of life course principles to public health.
Description
Using a developmental framework established by a national expert panel, teams of program leaders, epidemiologists, and academicians from seven states proposed indicators for initial consideration. More than 400 indicators were initially proposed, 102 were selected for full assessment and review, and 59 were selected for final recommendation as Maternal and Child Health (MCH) life course indicators.
Assessment
Each indicator was assessed on five core features of a life course approach: equity, resource realignment, impact, intergenerational wellness, and life course evidence. Indicators were also assessed on three data criteria: quality, availability, and simplicity.
Conclusion
These indicators represent a major step toward the translation of the life course perspective from theory to application. MCH programs implementing program and policy changes guided by the life course framework can use these initial measures to assess and influence their approaches.
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Significance
Although current public health surveillance systems provide data that can be used to assess life course health components, this is the first multistate consensus on indicators to define and monitor life course health at the state level.
Introduction
The life course approach to maternal and child health (MCH) includes the full spectrum of factors that influence an individual’s health through all stages of life. The life course approach to MCH is grounded in life course theory. Life course theory first emerged in the fields of sociology and developmental psychology in the early 1900s and resulted in appeals for longitudinal approaches to research.Footnote 1 Later health researchers began to observe the relationship between early life experiences and subsequent health outcomes; particularly pioneering for life course theory within health research was the work on fetal origins of adult health.Footnote 2 Research informed by life course theory was applied early on in MCH to racial disparities in birth outcomesFootnote 3 and has evolved over time into a lifecourse health development model which defines health through understanding dynamic, emergent processes and interactions between risk and protective influences throughout the lifespan.Footnote 4
In recent years, corresponding with—and in response to—the development of a lifecourse health development model, there has been expanding interest in life course approaches to public health practice among health departments and community partners across states and within communities.Footnote 5 , Footnote 6 As an operational concept for MCH public health practice, life course theory has been used as a framework explaining the relationship between health trends and disparities by focusing on the biological, social, economic, and environmental factors underlying population health experiences and outcomes.Footnote 7 As more stakeholders examine health through a life course lens, assessment and evaluation tools are required to help assess risk and resilience factors; quantify and illustrate the connected community structure needed to support a life course approach to public health; and aid in the planning of comprehensive, integrated systems and programs.
Currently, there are no nationally standardized population-based metrics for measuring a life course approach to MCH. In response, the Association of Maternal and Child Health Programs (AMCHP), an association of state health department Title V MCH programs, launched a project designed to identify and recommend a set of state-level life course indicators that can be used to assess, monitor, evaluate, and advocate for programs and policies for MCH populations. This article describes the multistate collaborative methodology used to develop the proposed indicators, presents a list of indicators selected from currently available national surveys and data systems, and explores the strengths and limitations of the selected indicators.
Methods
Organizing Framework
Throughout early 2012, 25 national thought leaders from academia and public health practice were convened as part of the Life Course Metrics National Expert Panel. The panel developed an operational definition for “life course approach” for the overall project, recommended a four-part framework to use in proposing indicators, and suggested initial criteria for the screening and evaluation of possible indicators.
As defined by the national expert panel:
A life course approach is based on a theoretical model that takes into consideration the full spectrum of factors that impact an individual’s health, not just at one stage of life (e.g., adolescence), but through all stages of life (e.g., infancy, childhood, adolescence, childbearing age, elderly age). Life course theory shines light on health and disease patterns—particularly health disparities—across populations and over time. Life course theory also points to broad family, social, economic, and environmental factors as underlying causes of persistent inequalities in health for a wide range of diseases and conditions across population groups.
Table 1 contains core components of a life course approach.
Based on this definition, a four-part framework was recommended to help states think broadly about potential indicators that move beyond traditional performance measures. The national expert panel envisioned a set of indicators that captured the role of MCH programs across four areas: minimizing risk, improving outcomes, providing services, and maintaining or expanding capacity. Translated to a framework, these four elements are defined as (1) Risks—the experiences and exposures that indicate risk for future life course outcomes; (2) Outcomes—the health and social outcomes that reflect or summarize an adverse life course trajectory; (3) Services—the risk reduction and health promotion from services provided over time to MCH populations; and (4) Capacity—the capacity of communities and organizations to address health through a life course perspective.
The initial four-part framework created challenges because of overlap in the concepts that define risk and outcome indicators, as well as the concepts that define services and capacity indicators. During indicator selection, the initial four-part framework was condensed into two overarching categories: Risk/Outcome and Capacity/Services. In condensing the four-part framework to two large categories, the teams still needed a pragmatic way to organize the final 59 indicators. To meet this need, the indicators were assigned to 12 categories that describe the scope and diversity captured in the set while avoiding disease- or population-specific identifiers. The evolution of our organization framework is represented in Fig. 1.
State Teams
Life course theory is an extensive, complex, and multifaceted approach, and identification of life course indicators was therefore best served through a collaborative, multiorganizational effort engaging state teams inclusive of experts from state public health programs, state epidemiology and data programs, community health and social service providers, public health academics, and other cross-sector partners. Seven state-based teams were selected to lead indicator selection through a competitive process in which applicant teams were asked to describe current commitment to a life course approach to MCH and describe a working team that represents the multidimensional aspects of life course. In August 2012, the selected teams began the process of developing and rating state MCH life course indicators. After discussions with the national expert panel, the state teams finalized the organizing framework. The teams used the framework as a platform to generate indicator proposals and develop the final set of criteria to rate the indicators.
Indicator Criteria
Criteria were established to help screen, evaluate, and determine the strength of potential life course indicators. Each indicator was assessed on five core features of a life course approach: equity, resource realignment, impact, intergenerational wellness, and life course evidence. Indicators were also assessed on three data criteria: quality, availability, and simplicity. Expanded definitions of these criteria are included in Table 2.
The five core features criteria were also used to evaluate how well each selected indicator incorporates components of a life course approach to MCH and to argue for why it should be considered an appropriate life course indicator. For example, infant mortality, though a sentinel indicator of the health of populations, was not included in the final set of indicators. When considering the life course criteria, state teams decided an appropriate life course indicator would illuminate the risk and protective factors that influence infant mortality and affect child development. The final set of indicators does include important risk and interim outcome components of infant mortality, such as preterm birth, small for gestational age, maternal education, experiences of discrimination, and economic measures.
Indicator Selection
AMCHP facilitated an eight-step process to support state teams in selecting the recommended indicators. State teams review the expert panel’s work and the proposed selection process and approved with minor modifications. The process was implemented with small modifications based on previous experiences with public health indicator selection, including the preconception health indicators and the chronic disease indicators.Footnote 8 , Footnote 9The process is outlined below.
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1.
Call for indicator proposals. The state team and national expert panel members issued a call for proposals. In addition, a call was issued to the general public and publicized on the AMCHP website, through AMCHP publications, and through partner networks.
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2.
Initial screen of indicator proposals. Via email, members of the seven state teams rated each indicator based on how well it met the defined criteria. During a 2-day, in-person meeting, representatives from the state teams voted “yes/no” on further consideration of each proposed indicator. To make it onto the initial list of selected indicators, each indicator had to be approved by supermajority—at least five of the seven team representative votes.
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3.
Development of indicator description sheets. State team members or AMCHP staff constructed a description for each indicator selected in the initial screening round.
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4.
Final screen and vote on indicator proposals. After reviewing and considering indicator description sheets, state teams provided overall ratings of indicators. State team representatives met for a second, in-person meeting to discuss the indicator proposals and select a final set of indicators through vote of the supermajority.
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5.
Release of final indicator selections for public comment. Public comment was solicited through a variety of channels, including listserves, targeted emails, webinars, and special presentations to interested groups. State and local health departments, federal agency representatives, state and national nonprofit organizations, and a number of interested individuals submitted comments.
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6.
Refinement of final indicators based on feedback. The indicator set was refined based on feedback from the public comment period. Proposals to drop or replace particular indicators and to make changes to numerator, denominator, or data source were made. Each proposal was presented to the state teams for consideration, and modifications were made accordingly. Ultimately, no indicators were added or dropped. The comments were used to refine indicator definitions and to develop and strengthen information supporting each indicator in the final set.
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7.
Dissemination of final indicator set. After revisions were made based on public comment, the final indicator set was disseminated through the AMCHP website. An online indicator tool provides indicator information, including the expanded indicator description sheets where numerator, denominator, possible modifiers, national comparison data (when available), and notes on calculation are summarized alongside descriptions of how the indicator meets the data and life course criteria.
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8.
Development of tools for use. In addition to the online indicator tool mentioned above, tools were developed to promote the use of the indicators and make them accessible to a variety of stakeholders.
Results
State teams, national expert panel members, and the public submitted proposals for 413 indicators using the organizing framework. The first round of rating, discussing, and voting resulted in the selection of 102 indicators for consideration through research and development of indicator description sheets.
After the final round, the teams recommended a final set of 59 indicators. Recommended indicators are drawn from 28 separate data sources, with 40 of the 59 indicators drawn from eight data sources (summarized in Table 3) and twenty indicators drawing from a unique data source., The 59 final indicators were organized into the 12 descriptive categories: 3—Childhood Experiences; 2—Community Health Policy; 6—Community Wellbeing; 5—Discrimination and Segregation; 3—Early Life Services; 3—Economic Experiences; 11—Family Wellbeing; 8—Health Care Access and Quality; 4—Mental Health; 3—Organizational Measurement Capacity; 8—Reproductive Life Experiences; and 3—Social Capital.
The predominant reasons why potential indicators were excluded during the selection process were the following: indicator data were frequently not available at a state level for the majority of US states and the District of Columbia; indicator sensitivity, specificity, positive predictive value, reliability, and consistency across jurisdictions were not of the desired quality; or the indicator was too complex to calculate and/or explain to professionals and the public when balanced with the value gained from its calculation. In addition, state teams considered duplication or similarity of indicator focus and alignment with current life course science. A list of potential indicators that were not selected was made available to inform stakeholders of the scope of indicators considered and to advocate for the development of surveys and data systems to address identified gaps.
Critical issues emerged when applying the life course criteria, in particular two of the criteria—implications for equity and impact across the lifespan. State team members opted to use a broad definition of equity that did not focus solely on racial and ethnic differences, and they adopted the perspective that any population disparity in a risk factor or health outcome should be viewed as an inequity. A number of indicators were initially proposed as being important across the lifespan, but further discussion revealed that for each of these indicators, one or more critical and sensitive life stages had the most impact for a person’s life trajectory. State teams were asked to examine global indicators critically to determine whether they should be revised to focus on the most critical/sensitive life stages.
Table 4 provides a brief description of the set of 59 recommended life course indicators, organized by descriptive category. Project resources, including an online tool that provides in-depth information about each indicator, were released in the fall of 2013 and are available on the AMCHP website.Footnote 10
Discussion
Within the final indicator set, there is overlap with existing public health measures. This overlap demonstrates the synergy of a life course approach with other public health approaches and programs. Furthermore, this agreement across initiatives illustrates how the reframing of MCH through a life course approach does not require starting from scratch. Rather, the data that are already collected are integrated and can provide a starting point into this new framing of MCH to identify opportunities for investment in and applications of life course. The overlap also provides a helpful opportunity for engaging with new partners who may not be familiar with life course by identifying the life course components of current initiatives. Sixteen of the recommended indicators are current Title V performance measures,Footnote 11 8 are core state preconception health indicators,Footnote 12 36 align with federal Healthy People topic areas and objectives,Footnote 13 14 are national Chronic Disease Indicators,Footnote 14 6 align with the Center for Disease Control and Prevention’s Winnable Battles initiative,Footnote 15 and 9 are measures endorsed by the National Quality Forum.Footnote 16
In addition to the existing measures used in MCH, there are indicators not as commonly used for MCH programs. Examples include fluoridation, concentrated disadvantage, homelessness, perceived experiences of discrimination, racial residential segregation, organizational data measurement capacity, and voter registration. These indicators help expand the focus of MCH programs to incorporate broader economic and social opportunities, community capacity and policy, and the living and working conditions experienced by individuals.
The indicator criteria favored the selection of an indicator set that builds bridges among partners to articulate a shared vision and promotes novel approaches to building capacity, improving services, and reducing exposure to risk factors. The final indicator set has the ability to help MCH programs leverage new and existing partnerships through the inclusion of nontraditional MCH indicators. Using these indicators to define assessment and evaluation of a life course approach to MCH will require considering a breadth of investments and partners influencing health. To achieve measurable change within any of these indicators, multi-sector partnerships among agencies at the federal, state, and local levels, as well as schools, urban planners, community- and faith-based organizations, national-to-local initiatives, and more must work together within a collective impact framework.Footnote 17
Despite the strengths of the final indicator set, there are also limitations based on current data availability. Specifically, the lack of indicators measuring resiliency and protective factors. Although life course theory includes resiliency factors in addition to risk factors, current public health practice is primarily focused on risk measurement. From an epidemiology perspective, tracking disease prevalence and mortality has been the prevailing public health approach; most standard measures in epidemiology tend to be risk-based. True resiliency measures, however, are not necessarily the opposite of risk measures. Further work is needed to identify factors that truly support or counterbalance risks in the life course approach to MCH. Despite this challenge, the recommended indicator set offers a few examples of resiliency measures, including Fourth Grade Proficiency (LC-57), Voter Registration (LC-59), and multiple measures of receipt of immunizations and/or preventive care.
Another major weakness is the lack of indicators based on longitudinal data. The operating assumption for selecting the indicators was that they could be used immediately when released. The current availability of data at the state and local levels limited what could be considered as indicators; the lack of readily available longitudinal data is one example of how this restriction creates gaps in the set. Potential longitudinal indicators include having measures that examine the combination of various risk and/or resilience factors. Lastly, the complexity of some of the proposed indicators posed a challenge for the simplicity criteria. A proposed indicator may have truly captured the life course implications for how an economic factor influences health, but if it was so complex to calculate and explain that no one could easily use or understand it, it was not considered an appropriate life course indicator.
With an overall lack of available, longitudinal data within state public health data systems the final indicator set is also limited by the cross-sectional nature of the indicators included. Critical developmental periods is a key aspect of life course science and the life course development model.Footnote 18 , Footnote 19 , Footnote 20 The cross-section indicators within the final set, therefore, cannot be specifically tied back to related critical periods as defined through research on life course development. While this is a limitation for the operationalization of the final set, the accompanying indicator description available for each indicator on the AMCHP website provides more thorough discussion for each indicator on the relation of the cross-section measure to critical developmental periods and processes.
Conclusion
Life course theory provides a rich and layered understanding of the development of an individual’s health over time and across generations. The theory emphasizes the role of timeline, timing, risks, resiliency, environment, and equity on individual health.Footnote 21 The components of life course theory require public health practitioners to emphasize the linking and integration of programs; promote integrated multi-sector service systems; ensure the availability of services at critical and sensitive periods throughout the lifespan; incorporate whole person, whole family, and whole community approaches into all work; and address health equity through working toward elimination of health disparities.Footnote 22 Several state and local MCH programs and initiatives are using the life course theory to form priorities and develop plans for public health programs. Participants from the multistate collaborative are beginning to use the indicators and resources to help align initiatives with a life course approach, broaden their collaborations through engagement of new stakeholders, leverage new partnerships, and develop data-to-action plans. Specific examples are summarized in Table 5. Although current public health surveillance systems provide data that can be used to assess life course health components, this is the first multistate consensus on indicators to define and monitor life course health at the state level.
Notes
Russ et al. [1].
Barker et al. [2].
Lu and Halfon [3].
Halfon et al. [4].
Frey et al. [5].
Shirmali et al. [6].
Fine and Kotelchuck [7].
Broussard et al. [8].
Centers for Disease Control and Prevention [9].
Association of Maternal and Child Health Programs [10].
Health Resources and Services Administration [11].
Broussard et al. [12].
US Department of Health and Human Services [13].
Centers for Disease Control and Prevention [14].
Centers for Disease Control and Prevention [15].
National Quality Forum [16].
Kania and Kramer [17].
Scott [18].
Power and Hertzman [19].
Hertzman et al. [20].
Fine and Kotelchuck [21].
Fine and Kotelchuck [21].
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Acknowledgments
This work was funded by a grant from W.K. Kellogg Foundation. We would also like to acknowledge all of the members of the national expert panel and each member of the state teams that participated in the Life Course Metrics Project. Members of Life Course Metrics Project state teams are listed below. Florida Cheryl Clark, DrPH, RHIA, Florida Department of Health; Kris-Tena Albers, CNM, MN, Florida Department of Health; Trina Thompson, MA, Florida Department of Health; Carol Brady, MANE, Florida Healthy Start Coalition; Leisa Stanley, PhD, Healthy Start Coalition of Hillsborough County; Isaac Eberstein, PhD, Florida State University; Javier Vazquez, MPH, Florida Department of Health; Kelli Stannard, RN, BSN, Florida Department of Health; Susan Redmon, RN, MPH, Florida Department of Health; Shairi R. Turner, MD, MPH, Florida Department of Health. Iowa: Gretchen Hageman, MA, Iowa Department of Public Health; Kimberly Noble Piper, BS, RN, CPH, CPHG, Iowa Department of Public Health; Debra Waldron, MD, MPH, FAAP, Child Health Specialty Clinics; Mary Mincer-Hansen, PhD, RN, Des Moines University; DeAnn Decker, Iowa Department of Public Health; Dawn Gentsch, Iowa Primary Care Association; Denise Wheeler CNM, MS, ARNP, Iowa Department of Public Health; Abby Kremer, MPH, Iowa Department of Public Health. Massachusetts Karin Downs, RN, MPH, Massachusetts Department of Public Health; Suzanne H. Gottlieb, Massachusetts Department of Public Health; Deborah Allen, ScD, Boston Public Health Commission; Eugene Declercq, PhD, Boston University School of Public Health; Candice Belanoff, ScD, MPH, Boston University School of Public Health; Milton Kotelchuck, PhD, MPH, Massachusetts General Hospital Center for Child & Adolescent Health Research and Policy and Harvard Medical School; Olivia Sappenfield, MPH, Massachusetts Department of Public Health; Susan E. Manning, MD, MPH, Massachusetts Department of Public Health; Emily Lu, MPH, Massachusetts Department of Public Health; Jill Clark, MPH, Massachusetts Department of Public Health; John A. Zdanowicz, DMD, MPH, Harvard School of Dental Medicine. Michigan Brenda Fink, MSW, ACSW, Michigan Department of Community Health; Kevin Dombkowski, DrPH, MS, University of Michigan; Chris Fussman, MS, Michigan Department of Community Health; Julia Heany, PhD, Michigan Public Health Institute; Monica Kwasnik, MA, Michigan Department of Community Health; Cassandre Larrieux, MPH, Ingham County Health Department; Mary Ludtke, MA, Department of Community Health; Nancy Peeler, EdM, Michigan Department of Community Health; Carrie Tarry, MPH, Michigan Department of Community Health. Nebraska Paula Eurek, Nebraska Department of Health and Human Services; Jennifer Severe-Oforah, MCRP, Nebraska Department of Health and Human Services; Mary Balluf, MS, RD, LMNT, Douglas County Health Department; Cathy Dillon, MA, Nebraska Department of Health and Human Services; Holly Dingman, MS, Nebraska Department of Health and Human Services; Rosa Gofin, MD, MPH, College of Public Health, University of Nebraska Medical Center; Mihaela Johnson, PhD, Nebraska Department of Health and Human Services; Colleen Svoboda, MPH, Nebraska Department of Health and Human Services; Shirley Terry, RN, BSN, Lincoln-Lancaster County Health Department. North Carolina Alvina Long Valentin, RN, MPH, North Carolina Women’s and Children’s Health Section; Deborah Carroll, North Carolina Women’s and Children’s Health Section; Najmul Chowdhury, MB, BS, MPH, North Carolina Women’s and Children’s Health Section; Julie De Clerque, DrPH, MPH, Sheps Center for Health Services Research; Kathleen Jones-Vessey, MS, North Carolina State Center for Health Statistics; Kathy Lamb, MS, RD, North Carolina Women’s and Children’s Health Section; Debbie Mason, MPH, Forsyth County Department of Public Health; Judy Ruffin, MPA, North Carolina Women’s and Children’s Health Section. Louisiana Amy Zapata, MPH, Louisiana Office of Public Health; Geoff Nagle, PhD, MPH, MSW, LCSW, Louisiana’s Early Childhood Advisory Council; Michelle Alletto, MPA, Louisiana Birth Outcomes Initiative; Nicole Richmond, MS, Louisiana Office of Public Health; Nkenge H. Jones-Jack, MPH, Louisiana Office of Health and Hospitals; Allen Schulenberg, MPA, Louisiana Department of Education; Petrice Abiodun-Sams, PhD, Lindy Boggs National Center for Community Literacy; Allison Plyer, MBA, ScD, Nonprofit Knowledge Works Greater New Orleans Community Data Center; Monisha Shah, MPH, Tulane University; Shokufeh Ramirez, MPH, Tulane University; Rebecca Gurvich, MPH, Tulane University; Lisanne Brown, PhD, Louisiana Public Health Institute; Janna Knight, MPH, Louisiana Public Health Institute; David Kulick, MPH, Louisiana Public Health Institute; Snigdha Mukherjee, MPH, Louisiana Public Health Institute.
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Callahan, T., Stampfel, C., Cornell, A. et al. From Theory to Measurement: Recommended State MCH Life Course Indicators. Matern Child Health J 19, 2336–2347 (2015). https://doi.org/10.1007/s10995-015-1767-1
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DOI: https://doi.org/10.1007/s10995-015-1767-1