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Emerging Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and produce early results that are consistent with the objectives of the activities and thus indicate effectiveness.
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White Paper/Brief
This paper serves as a foundational look into how structural racism and social determinants of health impact communities of color — particularly Black and Latino communities — in Massachusetts. This document uses local Massachusetts and national data sources to examine multiple factors for health inequities among racial minorities within the state. The primer covers demographic profiles, social drivers of health, access to coverage and care, service utilization, health outcomes, and the disparate impact of COVID-19 with infographics across multiple areas of health.
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Promising Practices that show evidence of effectiveness in improving public health outcomes in a specific real-life setting, as indicated by achievement of aims consistent with the objectives of the activities, and are suitable for adaptation by other communities.
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Summary Report/Recommendations
This article explores how the relationships between vaccine site density, vaccination rates, and social vulnerability are connected across metropolitan and non-metropolitan areas in the U.S. The study uses CDC Social Vulnerability Index data combined with vaccination site density data to examine how vaccination site placement can benefit highly vulnerable populations. The results determined that while areas with higher socioeconomic vulnerability contain a large density of vaccination sites, this does not affect the low vaccination rates found in these communities. Other methods besides vaccination site placement must be considered to overcome these barriers in vaccination rates.
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Novel Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and are in the process of generating evidence of effectiveness or may not yet be tested.
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Commentary
This article outlines a framework for partnering with Indigenous nations in research and data collection and calls for the need for equitable data use agreements. The framework guidelines include (1) incorporating respect and collaboration early in negotiations; (2) recognition that specificity of terms is key to trust-building; (3) remembering that good data stewardship entails safeguarding; and (4) building sustainable relationships.
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Novel Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and are in the process of generating evidence of effectiveness or may not yet be tested.
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Summary Report/Recommendations
This article shows efforts of six grant-funded regional partners to create a usable public health analytic system to address health inequities among COVID-19 positive cases on an individual patient level. The article highlights the many challenges of this Multistate Data Strategy, including lack of standardization across data sources, missing data fields, and different state-level reporting requirements. However, the ability to produce this analytic system in real time, including a standardized COVID-19 data dictionary, demonstrates the necessity for healthcare administrators to utilize deidentified patient-level data in order to provide better care for state residents, particularly in disadvantaged communities.
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Promising Practices that show evidence of effectiveness in improving public health outcomes in a specific real-life setting, as indicated by achievement of aims consistent with the objectives of the activities, and are suitable for adaptation by other communities.
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Systematic Review/Meta-Analysis
This article explores how health data technology tools such as Artificial Intelligence (AI) and Machine Learning (ML) tools can be implemented and adapted to assist in better responses and outcomes to the COVID-19 pandemic, as well as future epidemics. This literature review focuses on peer-reviewed articles concerning four themes: COVID-19 and the need for AI; utility of AI in COVID-19 screening, contract tracing, and diagnosis; use of AI in COVID-19 patient monitoring and drug development; AI beyond COVID-19 and opportunities for Low-Middle Income Communities (LMIC). This review contains examples of ways healthcare systems have implemented AI and ML to predict and treat outcomes of COVID-19, as well as potential capacities for AI.
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