| Literature DB >> 32869916 |
Somava Saha1,2,3, Bruce B Cohen4,5, Julia Nagy4,6, Marianne E McPHERSON4,6, Robert Phillips5,7.
Abstract
Policy Points Well-being In the Nation (WIN) offers the first parsimonious set of vetted common measures to improve population health and social determinants across sectors at local, state, and national levels and is driven by what communities need to improve health, well-being, and equity. The WIN measures were codesigned with more than 100 communities, federal agencies, and national organizations across sectors, in alignment with the National Committee on Vital and Health Statistics, the Foundations for Evidence-Based Policymaking Act, and Healthy People 2030. WIN offers a process for a collaborative learning measurement system to drive a learning health and well-being system across sectors at the community, state, and national levels. The WIN development process identified critical gaps and opportunities in equitable community-level data infrastructure, interoperability, and protections that could be used to inform the Federal Data Strategy.Entities:
Mesh:
Year: 2020 PMID: 32869916 PMCID: PMC7482388 DOI: 10.1111/1468-0009.12477
Source DB: PubMed Journal: Milbank Q ISSN: 0887-378X Impact factor: 4.911
Decision Criteria, Adapted From the National Quality Forum Criteria for Evaluating a Measure
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Domain Subdomain Proposed metric Source of metric Link to website for more information Level of data available (national, state, county, subcounty, zip code, community, etc.) |
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Potential to improve health Potential to improve social drivers of well‐being Potential to improve equity Aligned with major national/global strategy Potential to develop new knowledge about what creates well‐being Strong evidence that this improves health, well‐being, and equity Valid Reliable Benchmarking available |
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Data already collected, analyzed, and reported Cost of additional collection/availability of resources to support collection Burden of collection and reporting Groups ready to adopt |
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Time‐frame data changes (rating: 3 if less than quarterly, 2 if less than yearly, 1 if yearly, 0 if more than yearly) Timeliness of data availability (rating: 3 if less than quarterly, 2 if less than yearly, 1 if yearly, 0 if more than yearly) Usefulness to communities Usefulness to researchers/national stakeholders Meaningfulness to people with lived experience Currently used by/could be used by (name initiatives, organizations actively using) Level of data availability |
Well‐Being in the Nation (WIN) Process
| Process | Output | |
|---|---|---|
| Landscape analysis | Identification of both measurement efforts and measures. | 500+ measures and 50+ measurement efforts and implementation efforts identified. |
| Engagement | Leads from major measurement efforts and implementation efforts identified. Formation of Stewardship Group, Measure Development, and Measure Implementation groups. | 100+ organizations and communities engaged. |
| Delphi Cycle 1: Identification of missing candidate measures | Participants suggested additions to the list of candidate measures, derived from their expertise or familiarity. | Complete list of candidate measures generated. |
| Delphi Cycle 2: Prioritization of candidate measures | In each domain, participants prioritized 10 measures for inclusion in each of the national and community measure sets based on the measure's importance, value/usefulness, and usability to stakeholders. | Approximately 20 of the most selected measures per domain at each national and community level. |
| Delphi Cycle 3: Evaluation of candidate measures | In each domain, participants prioritized five measures for inclusion in each of the national and community measure sets and then evaluated the measures’ importance, feasibility, usability, and value on a scale of 1 (least) to 3 (most). | Parsimonious set of measures at national and community levels. |
| Delphi Cycle 4: Expert validation of candidate measures | Two to six experts in each domain/sector of the framework evaluated Cycle 3 outputs. Measures were then categorized into Leading Indicators and Flexible Expanded Set based on importance and data availability. | Modified parsimonious set of measures: Core Measures, Leading Indicators, and Flexible Expanded Set. |
| Delphi Cycle 5: Alignment of measures with existing initiatives | Outputs of the expert validation cycle (Cycle 4) were compared with measures used in other major initiatives and reviewed with implementers. The major gaps and alignment opportunities were also addressed. | Refined Core Measures, Leading Indicators, and Flexible Expanded Set integrated into existing initiatives and measurement efforts. |
| Formation of WIN Network and “Living Library” process | Core body of implementers engaged to actively lead implementation to learn together and make WIN a “living library of measures” that is refined as the field learns together. | Collaborative WIN network chosen by implementers to advance implementation, refine the measures with added testing, and add policy and narrative strategies. |
Well‐Being in the Nation (WIN) Measures
| Description | Measure Organization | Example | |
|---|---|---|---|
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| Nine measures to be used across initiatives. |
People reported well‐being Life expectancy Child poverty Healthy community indices aligned with the framework Differences in well‐being Years of life lost Income inequality High school graduation rates Demographic variables to use in a standard way for equity analysis |
County Health Rankings & Roadmaps USNWR Healthiest Communities Rankings |
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| Highly recommended measures related to determinants of health and well‐being of people, places, and equity that have readily available data. |
Community vitality Economy Education Environment and infrastructure Equity Food and agriculture Health Housing Public safety Transportation Well‐being Demographics |
Social‐emotional support Perception of racial inclusion Unemployment rates Availability and quality of affordable housing Availability and quality of healthful food Self‐perceived health Deaths of despair Juvenile incarceration Availability of transportation |
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| Highly promising, valid measures that require additional adoption and research or lack data availability; promising as a future leading indicator. | Organized in same categories as leading indicators. |
Everyday Discrimination Scale Sense of purpose and meaning Social isolation |
Ways That WIN Measures Are Being Used in Communities
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Downtown Women's Center (DWC), the first housing services provider dedicated to serving women on skid row in Los Angeles, has a small clinic dedicated to meeting the health needs of women experiencing homelessness. The center adapted the Diabetes Prevention Program in partnership with these women and used a combination of clinical measures (A1C, BMI, blood pressure) and WIN measures to evaluate its progress. Within six months, compared with a control group and controlling for housing placement, the center observed that 30% more women were thriving and fewer were suffering, with demonstrated accompanying improvements in clinical outcomes. In addition to a number of small clinics like the DWC, a number of large health systems, such as Kaiser Permanente, Health Partners, Providence St. Joseph, Methodist Healthcare Ministries, and Adventist, have adopted the WIN measures to assess their impact on their patients and the community. In Delaware, the Division of Substance Abuse and Mental Health (DSAMH) has convened a multisector collaborative across state agencies (police, corrections, social service, foster care, etc.) and community providers (emergency rooms, addiction providers, hospitals, etc.) to meet the needs of people with addictions. DSAMH is using a combination of WIN measures such as overall well‐being, deaths of despair, years of life lost/gained, employment, housing, other social needs met, and legal issues resolved to support people in real time and to focus and evaluate their impact across sectors. In Fox Cities, Wisconsin, community leaders across sectors came together to look at intergenerational well‐being, community vitality, and basic needs. Because they had stratified their data and used powerful measures that everyone could understand, they learned that 80% to 92% of their communities of color were struggling or suffering. This discovery led to communitywide dialogues about inclusion as well as consideration of policy and system changes to support racial and economic inclusion. |