2018 Impact Summit

Data disaggregation, in this context, is the process of breaking out large racial and ethnic groups like the five main groups used by the U.S. Census Bureau (American Indian, Black or African American, Asian, Latinx, and White) into smaller, more nuanced cultural, national, regional, or other sub-groups. Data disaggregation can help to inform questions about who has (or doesn’t have) access to various social services or amenities, how different groups of people experience a certain intervention or event, and/or how program outcomes or impacts vary by subgroup. Often, we need disaggregated data to help us understand complex social issues, because large race/ethnic categories just don’t give us enough information.
The purpose of this presentation is to illustrate how organizations use disaggregated data to inform their policies and programs, using real examples.  

Access to affordable, high-quality early care and education (ECE) is important for families and communities because of the dual role of ECE in facilitating employment of parents and in supporting child development. As understanding of the importance of early childhood learning environments expands, disparities in access to high-quality ECE have become of increasing concern to policymakers. To ensure adequate access to child care and early education, particularly for low-income families, policymakers and planners need accurate measures of access in order to target resources to locations and families with greatest need. Most studies of ECE access rely on counts of licensed capacity of child care providers within specific geographic areas such as census tracts or ZIP codes. These indicators ignore the reality that families can cross administrative boundaries to find the nearest or preferred child care provider.

In this presentation, we introduce new family-centered indicators of access to early care and education (ECE) services with respect to quantity, cost, and quality.  We present three types of indicators to capture three different and important dimensions of access: quantity, cost and quality.

The findings show that conclusions one draws about child care accessibility in different communities can sharply differ depending on how access measures are calculated. These improved indicators of accessibility yield better understanding of unmet childcare needs of different communities across the state.

The Sundance Research Cohort is a partnership between the Sundance Family Foundation and Wilder Research to measure impact of 12 Youth Social Entrepreneurship (YSE) programs. The Sundance Research Cohort has developed a set of indicators that measure the personal agency of youth to complement their Work Readiness Survey (modified from the Department of Labor’s WIA Tool) that measures 14 indicators of work readiness.  A self-administered Youth Retrospective Survey measures key indicators of youth’s social emotional development, such as confidence, trust and personal goal identification.  These indicators measure both the impact of programs on youth and are important for youth's future success.

This presentation will describe the YSE model: a trifecta of social/emotional development, community engagement, workforce readiness & agile thinking, outline the challenges of measuring future youth success, review tools to measure success, and discuss how those tools can best be used by other nonprofits seeking to measure youth personal agency.

The Arkansas Community Foundation created Aspire Arkansas to provide its communities with a tool to help drive improved charitable giving and inform positive community change across the state. It is meant to be both a yardstick of current state progress and a compass to help focus and drive efforts for improvement. To accomplish this, the Community Foundation needed interactive and holistic indicators that reported not only the overall state of affairs, but also examined disparities by geography, race and gender. The Aspire Arkansas site makes these multi-layered indicators approachable and digestible allowing community members to gain insights that are both broad (how the state ranks nationally) and deep (how the issue impacts different subgroups and each of the 75 counties differently). Creating these indicators requires intensive data collection from a multitude of sources and a synthesizing analysis that helps make meaning of how the different pieces of each indicator fit together to tell an overarching story.

For the past two decades, MetroTransit and Metropolitan Council have used transit market areas to plan and deploy appropriate transit service in the Twin Cities metro area. Over time, the determination of transit market areas has become more analytic. The transit market index introduced by Council staff in 2010 considered local population concentration, employment concentration, and transit dependency. These factors, measured and summarized for Census block groups, were combined into a simple index intended to represent local level preconditions and potential for transit service.
Metro Transit uses the transit market index and transit market areas to target investment and service deployment. High-frequency local route services and dedicated transitways are deployed in local areas with the greatest local potential for ridership response. The transit market index may be a useful device for understanding local factors that cumulatively make transit service viable.

At The Improve Group, we have experienced the effectiveness of using values to frame decisions for data collection. We will draw on 3 recent statewide surveys that illustrate how a set of core principles needs to shape outreach in order to achieve better representation. We have found this framework to be of particular importance in longitudinal, individual-level data collection – such as you may use to chart changes in community indicators over time.

Community Indicators efforts often use action and moving the needle as a rallying point for launching their web sites. However, when it comes to using data to drive decision making and measure progress, finding the right data can seem like a Goldilocks endeavor- some data have too large an estimation error, others have too small a sample, and yet others have too long a period between measurements. Finding the data source that is "just right" can take considerable community collaboration and data sharing. We share lessons learned from the experience of Central New York as it learned of its number 1 national ranking for concentrated poverty and then sought to create a local measure that could be used to identify progress. The result has been a work in progress scale the includes data routinely collected and mapped from a series of partners.

Natural disasters often impose significant and long-lasting stress on financial, social and ecological systems. Across the nation, there is a recognition that the benefits of creating environments resilient to adverse climate events helps promote and sustain county and community success over time. The challenge for communities is in finding ways to balance the need to preserve the socio-ecological systems on which they depend in the face of constantly changing natural hazard threats. The Climate Resilience Screening Index (CRSI) has been developed as an endpoint for characterizing county and community resilience outcomes that are based on risk profiles and responsive to changes in governance, societal, built and natural system characteristics. The Climate Resilience Screening Index (CRSI) framework serves as a conceptual roadmap showing how acute climate events impact resilience after factoring in the county and community characteristics. By evaluating the factors that influence vulnerability and recoverability, an estimation of resilience can quantify how changes in these characteristics will impact resilience given specific hazard profiles. Ultimately, this knowledge will help communities identify potential areas to target for increasing resilience to acute climate events.

It is well understood that patient and community health is impacted by the health of the surrounding
environment, including the impacts that our healthcare facilities have on the external environment. Stemming from the presenter’s 2017 doctoral dissertation that developed environmental key performance indicators for the long term care sector, this presentation will look at why indicators developed for hospitals did not work well for long term care facilities.  Individual examples will be presented, as well as a discussion about the dangers of over-generalizing key environmental performance indicators for a sector as large and as broad as healthcare.

In summary, participants will learn that without appropriately developed environmental key performance
indicators, we cannot effectively move the dial on demonstrating measurable, meaningful, and targeted environmental improvements. Overgeneralizations such as “reducing waste” and “reducing energy” will no longer define our work; we must be attentive, nuanced, and rigorous in our methods to find those categories of performance that can be influenced and ultimately improved. 

Sharing a perspective similar to the social determinants of health, the Canadian Index of Wellbeing (CIW) monitors progress in wellbeing over time and considers the complexity of interactions among the many factors that contribute to wellbeing. In addition, the CIW examines how progress in wellbeing varies among sub-groups within the population based on characteristics such as age, income, and gender.

This study explored where any differences between male and female Canadians on several indicators of wellbeing exist and whether these differences have narrowed, widened, or remained the same over time. By doing so, we can highlight those areas where success in moving towards greater gender equity is occurring, but importantly, identify areas where we can do better in ensuring that women have the same opportunities to thrive.

We need strengths-based indicators to fully understand community life and issues, make informed decisions about policies and programs, and track progress toward sustainable and equitable communities. However, most indicator data on child, family, and community well-being available from the Census, state agencies, and other sources mostly measure and highlight deficits and problems. Panel members will draw on their experiences producing numerous collections of family and community “well-being” indicators and engage the audience in devising strategies for measuring and promoting asset-based indicators.

While local planners know about issues in their communities to some extent, many jurisdictions lack the capacity to collect and analyze extensive data to systematically evaluate the needs of various parts of their communities. This project intends to fill this gap by providing a baseline description of the types of changes taking place in different suburban neighborhoods.  
Understanding changes taking place within communities helps inform regional policies and have a proactive approach to future planning challenges or planning opportunities, rather than being reactive to community needs. It also provides opportunities for local communities to learn from each other as they plan for the future of neighborhoods experiencing similar types of change.
The analysis reveals five characteristics that are used in clustering neighborhoods and shows that, in different combinations, changes in these variables produce seven distinct types of neighborhood change._

Social isolation is an absence of meaningful social relationships, which can negatively impact health and well-being. It is measured and defined in a variety of ways reflecting the complexity of the individual, community and societal interactions. Despite the vast amount of literature and research on this topic, no single state-level measure of social isolation exists. We created a multi-factorial measure of risk for social isolation informed by AARP’s framework and a literature review. We included six factors for adults aged 65 and older in our risk of social isolation measure using American Community Survey 5 year estimates, 2012-2016. Prevalence estimates of our six selected risk factors are reported by state. The overall risk is presented as a percentile based on the mean of z-scores for six risk factors for social isolation in adults aged 65 and over. Quintile maps are used to display the percentiles by state and county. Risk of social isolation varies widely by state with a concentrated area of high risk from the mid-South through the Appalachian Mountain states. Risk of social isolation by county shows high risk areas in central southern states, along the southeast Atlantic coast and in pockets of Texas, New Mexico, and California.  Our new measure of risk of social isolation permits states and counties to explore the extent and variation of the risk and to evaluate its use for targeting public and private programs to address risk factors for social isolation.