ArticleAJPM focus2023
Stakeholder Perspectives on Data-Driven Solutions to Address Cardiovascular Disease and Health Equity in New York City.
Article in AJPM focus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- Data-Driven Commissioning to Reduce Type 2 Diabetes Related Health Disparities in The Netherlands: Using Key Informant Group Interviews.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Introduction: There is growing recognition of the importance of addressing the social determinants of health in efforts to improve health equity. In dense urban environments such as New York City, disparities in chronic health conditions (e.g., cardiovascular disease) closely mimic inequities in social factors such as income, education, and housing. Although there is a wealth of data on these social factors in New York City, little is known about how to rapidly use available data sources to address health disparities. Methods: Semistructured interviews were conducted with key stakeholders (N=11) from across the public health landscape in New York City (health departments, healthcare delivery systems, and community-based organizations) to assess perspectives on how social determinants of health data can be used to address cardiovascular disease and health equity, what data-driven tools would be useful, and challenges to using these data sources and developing tools. A matrix analysis approach was used to analyze the interview data. Results: Stakeholders were optimistic about using social determinants of health data to address health equity by delivering holistic care, connecting people with additional resources, and increasing investments in under-resourced communities. However, interviewees noted challenges related to the quality and timeliness of social determinants of health data, interoperability between data systems, and lack of consistent metrics related to cardiovascular disease and health equity. Conclusions: Future research on this topic should focus on mitigating the barriers to using social determinants of health data, which includes incorporating social determinants of health data from other sectors. There is also a need to assess how data-driven solutions can be implemented within and across communities and organizations.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.