Editor’s Note:This post is a part of our Family Support Needs Assessment blog post series, in which PolicyLab’s home visiting team discusses multiple aspects of the collaborative report, including how to utilize its components, share with networks and more. Check out the first post in this series, which explores how stakeholders can utilize key sections of the Family Support Needs Assessment to support their work.
Maternal-child health (MCH) is a public health priority for the United States. The well-being of children and their caregivers determines the health of the next generation, and can help predict future public health challenges for families, communities and the health care system. Improving MCH relies on a deep understanding of the contributors to disparities in health outcomes among this population. Therefore, as one component of PolicyLab’s recently released Pennsylvania 2020 Family Support Needs Assessment (FSNA), we created a set of metrics, or indices, that measure the health outcomes of mothers and children, such as maternal depression, and the social and environmental factors that influence their health, like how much of a person’s income is going to pay their rent or the number of libraries and parks in their area. These indices rank the level of community need for supportive interventions as “low,” “moderate” or “elevated” depending on how counties fare in comparison to one another.
Bringing these data together using this novel approach—which we outlined in a recent peer-reviewed paper—helped us gain a complete picture of community needs and opportunities for support across the state. Now, we want to be sure local leaders can utilize this resource as a starting point to improve MCH in their communities, so we put together the guide below to help describe the information available and how it can be used.
How did we identify MCH needs across all Pennsylvania counties?
In an effort to provide a comprehensive view of the landscape of family and community well-being throughout the state, we selected 66 indicators, or metrics, and organized them into six domains to create MCH need indices for each of Pennsylvania’s 67 counties (see below figure for the list of domains and sample indicators). Most indicators originated from publicly available administrative data (e.g., National Vital Statistics System) and survey data (e.g., National Survey of Drug Use and Health).
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For each indicator, we gave counties their need scores by comparing a county’s indicator estimate with the rest of the state. We then calculated need scores for each domain, taking into account differences between indicators in data quality, how recently the data was collected and influence on MCH. Lastly, we categorized each domain need score into a need index: a county was considered to have “elevated need” if the county’s overall need score ranked within the top 25% of all Pennsylvania counties, as “low need” if it was within the bottom 25%, and as “moderate need” for the middle 50% of counties.
What were the goals for creating the MCH need indices?
The needs assessment offers a systematized way to understand the MCH landscape in local communities and to identify the geographic disparities in health influenced by deep-rooted structural issues in order to:
build awareness of the broad range of factors that impact the health of women, children and families in Pennsylvania communities,
provide communities with a reliable and transparent source of local data to support county-level efforts aimed at improving MCH, and
engage and activate local leaders from many sectors in improving MCH and addressing social and environmental factors influencing health disparities.
Who will find these MCH need indices helpful?
We know that addressing determinants of MCH requires a multi-sector coalition. The needs indices can support efforts from a variety of stakeholders to identify and prioritize communities for strategic investment and programmatic supports across a range of factors. Program administrators, service providers, local policymakers, regional planners, and advocacy groups can also use these indices to inform tailored programmatic and policy responses across many public sectors that support the health and well-being of children and families. In the first blog post in this series, our team also shared examples of ways different stakeholder groups can use the overall data included in the FSNA.
What makes this a unique resource for stakeholders supporting children & families?
Our team sought to create a user-friendly resource for communities by simplifying a large amount of information to clearly identify needs across key issues impacting MCH. In addition to providing synthesized, straightforward details on MCH outcomes, this resource is unique in several ways:
We developed the indices at different geographic levels. While county-level need indices are helpful for planners who work for the state or county level, zip-code-level need indices may better facilitate efforts of local planners working at the municipal or community level.
While most previous MCH needs assessments focus on proximal health outcomes when selecting metrics, we emphasized resource-based indicators that reflect the economic and social contexts of communities where families live.
We used publicly available data for the majority of the indicators included in the need indices. This means that stakeholders will be able to access the data from public sources without any cost and can update or filter the data to fit their needs.
We hope that these need indices will provide a reliable, accessible resource to support local leaders from a variety of sectors in improving MCH and addressing social and environmental factors influencing the health and well-being of families in their communities. We envision this tool can also inform numerous efforts, from submitting grants and hosting community conversations, to advocating for local cooperation and developing thriving local partnerships. If you’d like to learn more about the need indices and how to use them, view our new video (see below) created as part of PolicyLab’s “Research in Motion” series.