The Measuring Up project works to identify promotive and protective factors (circumstances) that help children develop optimally and equitably. The project focuses on identifying these factors for the prenatal period (before birth) to age 3. We also identify risk factors that can prevent very young children’s optimal development. In addition, we identify policies and programs that support optimal development for kids. Child Trends is partnering on this work with several other organizations: the National Center on Birth Defects and Developmental Disabilities (NCBDDD) at the Centers for Disease Control and Prevention (CDC), the Georgia Department of Public Health (GA-DPH), the U.S. Health Resources and Services Administration (HRSA), and the Marcus Autism Center (MAC). This group of partners is referred to as “the Collaborative.”
The ultimate goal of the Measuring Up project is to help policymakers, researchers, and other individuals and organizations with an interest in early childhood development assess key policies, programs, and practices that affect the well-being of children from the prenatal period through age 3—both in terms of their strengths and the areas in which they need to improve.
Key Findings from the Measuring Up Project
- Existing measures of infant and toddler well-being focus heavily on the child’s physical health, and little attention is given to their cognitive (i.e., brain) and social-emotional-behavioral health.
- Many existing indicators come from one of three data collection systems supported by the U.S. Department of Health and Human Services (HHS): the National Immunization Survey (NIS), the National Survey of Children’s Health (NSCH), and the National Vital Statistics System (NVSS).
- Most indicators focus on contextual, or environmental, influences on a child’s development (for example, health care settings and family and caregiver environments) rather than on the actual outcomes (i.e., results) experienced by that child.
- Indicators that examine outcomes for individual children frequently focus on the absence of positive development (in other words, problems or delays in development) rather than the presence of positive development.
- Data sources generally do not collect enough data to allow useful breakdowns of findings by race and ethnicity at state or local levels. This means that data do not consistently report how outcomes may be different for children of different races and ethnicities. This drawback prevents decision makers from understanding whether specific programs and policies benefit all groups of children.
Child development is multi-dimensional, meaning that the different areas of prenatal health, cognitive and language development, social-emotional-behavioral development, and motor development and physical health are interwoven.
As a first step, the Measuring Up project articulated a conceptual model (see Figure 1). This model draws heavily from Urie Bronfenbrenner’s social-ecological model (1995; 1999), which recognizes that the child develops within multiple contexts. These contexts include the following:
- Microsystems, including relationships with families and other primary caregivers in settings such as the home, child care, or school
- Exosystems, which affect the child indirectly; for example, parents’ workplaces have direct effects on parental well-being and, through them, on the child
- Mesosystem, or connections or relationships among various settings that can affect the child directly or indirectly
- Macrosystem, which refers to contexts at the societal level that broadly extend beyond any individual or family, such as culture and subcultures, economic forces, infrastructure and environmental features, beliefs and prejudices, and public policies
In our version of this well-known ecological model, the child and their contexts can be visualized as a series of concentric circles, moving outward from the child level (closest) to the more distant systems. The model’s time dimension—shown as an arrow cutting across the concentric circles—symbolizes that individual development evolves over the life course, as does a child’s context, both objectively and in their impacts on the child. These systems interact, as indicated by the multiple white bidirectional arrows.
In addition, developmental science has identified a number of risk, promotive, and protective factors that can, respectively, threaten, promote, or protect from threat the likelihood of optimal well-being (ASPE, 2013; Institute of Medicine, 2000; Moore, Murphey, Beltz, Martin, Bartlett, & Caal, 2016). These factors are at play at all contextual levels. The balance of risk, promotive, and protective factors within a child’s social ecology helps determine their well-being.
Figure 1: Early Childhood Social-Ecological Contexts of the Child
We take a systematic approach to our work, which progresses from (1.) taking inventory of available population-level indicators of well-being for children from birth to age 3; to (2.) exploring policies and programs with the potential to improve the lives of infants and toddlers; to (3.) studying the link between public policies and child outcomes; to (4.) concluding with policymaker-focused dissemination efforts.
Figure 2: Measuring Up Project Approach
To date, we have completed step #1 and are in the process of completing steps #2 and #3. Specifically the project has accomplished the following: identified risk factors and protective or promotive factors that are associated with important child development outcomes; developed partnerships with other organizations (like Prenatal to Three Policy Impact Center and the Zero to Three State of Babies Yearbook team); consulted with experts on child development; and identified state-level data on policies and child outcomes to use in a rigorous multivariate analysis (i.e., a statistical way of measuring what does and doesn’t work). Our first set of analyses will examine whether certain policy characteristics might have some influence on child maltreatment rates—in other words, which types of policies most help families avoid child maltreatment. These analyses will help us design future work that focuses on additional key outcomes associated with child well-being. Results and recommendations will be shared (step #4) with state and national policymakers, and other individuals and organizations with an interest in better understanding how to improve early outcomes for children. We also hope to replicate the analytic model—meaning, re-create our approach to studying child maltreatment—to help us learn more about other important child well-being outcomes.
This brief describes the need for more comprehensive data that describe children’s overall health—including their cognitive and social-emotional-behavioral development—to better support infants and toddlers, as well as their families, service providers, and policymakers working to foster their development. We provide recommendations for improving federal data collection efforts.
Suggested citation: Darling, K. & Ryberg, R. (2022). Better Data Needed for Monitoring and Promoting Infant and Toddler Well-Being. Child Trends. Available at: https://www.childtrends.org/publications/better-data-needed-for-monitoring-and-promoting-infant-and-toddler-well-being
In this peer-reviewed, open-access paper, we review currently available indicators of well-being from the prenatal period to age 3. Most of the child-level indicators identified were in the physical health domain; relatively fewer were found in the early cognition and language or social-emotional-behavioral domains. While some states are making progress toward developing integrated early childhood data systems, more work is needed to provide robust data on infant and toddler development. These results highlight the need to develop a broader range of indicators of infant and toddler well-being and improve measurement sources to better inform policies and programs that advance population health.
Suggested citation: Ryberg, R., Wiggins, L., Daily, S., Moore, K. A., Piña, G., & Klin, A. (2022). Measuring state-level infant and toddler well-being in the United : Gaps in data lead to gaps in understanding. Child Indicators Research. Available at: https://link.springer.com/article/10.1007/s12187-021-09902-4
Measuring Up Collaborative Members and Funders