Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation.

BACKGROUND: Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations. METHODS: Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings. RESULTS: By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis. CONCLUSION: While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.

Socioeconomic Factors Influence Health Information Seeking and Trust Over Time: Evidence From a Cross-Sectional, Pooled Analyses of HINTS Data.

PURPOSE: Assessed socioeconomic factors in health information seeking behavior and trust of information sources from 2007 to 2017. DESIGN: Pooled cross-sectional survey data. SETTING: Health Information National Trends Survey. PARTICIPATION: Data included 6 iterations of U.S. adults (Pooled: N = 19,496; 2007: N = 3,593; 2011: N = 3,959; 2013: N = 3,185; FDA 2015: N = 3,738; 2017: N = 3,285; and FDA 2017: N = 1,736). MEASURES: Outcome variables were health information seeking, high confidence, and high trust of health information from several sources. Independent variables were education and income group, controlling for other sociodemographic variables. ANALYSIS: Weighted descriptive and multivariate logistic regression for the pooled sample assessed associations by education and income. Fully interacted models with education/income-survey year interactions compared differences in outcomes between years. RESULTS: We found information seeking, confidence, and trust were associated with income and education, which supported previously reported findings. Additionally, our findings indicated low-and medium-income groups had significantly lower odds of seeking health information compared to those in a high-income group. Regarding trust of information, a high school education was associated with higher odds of trust in family and friends. We also found that, over time, information seeking, confidence, and trust behavior differed by income and education, with some differences persisting. CONCLUSION: Disparities by income and education in trust of information sources remained across time. Understanding optimal information sources, their reach, and their credibility among groups could enable more targeted interventions and health messaging. We also describe the implications for our findings in the context of COVID-19.

Visualizing Opportunity Index Data Using a Dashboard Application: A Tool to Communicate Infant Mortality-Based Area Deprivation Index Information.

BACKGROUND: An area deprivation index (ADI) is a geographical measure that accounts for socioeconomic factors (e.g., crime, health, and education). The state of Ohio developed an ADI associated with infant mortality: Ohio Opportunity Index (OOI). However, a powerful tool to present this information effectively to stakeholders was needed. OBJECTIVES: We present a real use-case by documenting the design, development, deployment, and training processes associated with a dashboard solution visualizing ADI data. METHODS: The Opportunity Index Dashboard (OID) allows for interactive exploration of the OOI and its seven domains-transportation, education, employment, housing, health, access to services, and crime. We used a user-centered design approach involving feedback sessions with stakeholders, who included representatives from project sponsors and subject matter experts. We assessed the usability of the OID based on the effectiveness, efficiency, and satisfaction dimensions. The process of designing, developing, deploying, and training users in regard to the OID is described. RESULTS: We report feedback provided by stakeholders for the OID categorized by function, content, and aesthetics. The OID has multiple, interactive components: choropleth map displaying OOI scores for a specific census tract, graphs presenting OOI or domain scores between tracts to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. Changes based on parameter and filter selections are described using a general use-case. In the usability evaluation, the median task completion success rate was 83% and the median system usability score was 68. CONCLUSION: The OID could assist health care leaders in making decisions that enhance care delivery and policy decision making regarding infant mortality. The dashboard helps communicate deprivation data across domains in a clear and concise manner. Our experience building this dashboard presents a template for developing dashboards that can address other health priorities.