Stakeholder Perspectives on Data-Driven Solutions to Address Cardiovascular Disease and Health Equity in New York City
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PROVIDER: S-EPMC10546603 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
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Project description:We conducted a mixed methods pilot feasibility study of a Stakeholder and Equity Data-Driven Implementation (SEDDI) process to facilitate using healthcare data to identify patient groups experiencing gaps in the use of evidence-based interventions (EBIs) and rapidly adapt EBIs to achieve greater access and equitable outcomes. We evaluated the feasibility and acceptability of SEDDI in a pilot hybrid type 2 effectiveness-implementation trial of a paired colorectal cancer (CRC) and social needs screening intervention at four federally qualified community health centers (CHCs). An external facilitator partnered with CHC teams to support initial implementation, followed by the SEDDI phase focused on advancing health equity. Facilitation sessions were delivered over 8 months. Preliminary evaluation of SEDDI involved convergent mixed methods with quantitative survey and focus group data. CHCs used data to identify gaps in outreach and completion of CRC screening with respect to race/ethnicity, gender, age, and language. Adaptations to improve access and use of the intervention included cultural, linguistic, and health literacy tailoring. CHC teams reported that facilitation and systematic review of data were helpful in identifying and prioritizing gaps. None of the four CHCs completed rapid cycle testing of adaptations largely due to competing priorities during the COVID-19 response. SEDDI has the potential for advancing chronic disease prevention and management by providing a stakeholder and data-driven approach to identify and prioritize health equity targets and guide adaptations to improve health equity. ClinicalTrials.gov Identifier: NCT04585919.
| S-EPMC9530430 | biostudies-literature
Project description:Translational approaches to science have the potential to produce research that better meets the needs of community stakeholders and advances scientific understanding. Researchers involved in translational research make committed efforts to increased engagement and communication with stakeholders throughout the research process, from planning through implementation and evaluation. Referred to as solutions-driven research within the U.S. Environmental Protection Agency (EPA) Office of Research Development, this approach is being piloted on Cape Cod (Barnstable County), Massachusetts. EPA researchers are working in close coordination with community partners on the Cape to better understand and address challenges with managing nonpoint source nitrogen. The pilot also aims to assess the usefulness of solutions-driven research approaches for application in future EPA research efforts. Using semi-structured interviews with researchers and other stakeholders, we examined researchers' and stakeholders' perspectives on the impacts of intentional and intensive stakeholder engagement on research efforts to improve coastal water quality. This study provides a reflexive assessment of the perceived benefits and drawbacks for researchers and other stakeholders when there is an institutional expectation of an increased focus on engagement. We found that engagement has been truly intertwined with research in the pilot, participants perceived an improvement in research usefulness through developing valuable collaborative relationships, and that these relationships required significant time commitments to maintain. We also identified a need for an efficient infrastructure for developing and distributing communication materials for continued engagement with diverse stakeholders throughout the research process. The paper provides transferable practices for researchers seeking to use a solutions-driven research approach based on lessons learned thus far in how to support researchers and research planning in simultaneously prioritizing effective engagement and sound collaborative environmental science research to address a localized environmental challenge. This is an innovative approach in that interviews occurred as the implementation phase of the project began, with the goal of implementing the lessons learned outlined here in the ongoing project.Supplementary informationThe online version contains supplementary material available at 10.1007/s42532-022-00119-5.
| S-EPMC9281378 | biostudies-literature
Project description:National efforts to improve equitable teaching practices in biology education have led to an increase in research on the barriers to student participation and performance, as well as solutions for overcoming these barriers. Fewer studies have examined the extent to which the resulting data trends and effective strategies are generalizable across multiple contexts or are specific to individual classrooms, institutions, or geographic regions. To address gaps in our understanding, as well as to establish baseline information about students across contexts, a working group associated with a research coordination network (Equity and Diversity in Undergraduate STEM, EDU-STEM) convened in Las Vegas, Nevada, in November of 2019. We addressed the following objectives: 1) characterize the present state of equity and diversity in undergraduate biology education research; 2) address the value of a network of educators focused on science, technology, engineering, and mathematics equity; 3) summarize the status of data collection and results; 4) identify and prioritize questions and interventions for future collaboration; and 5) construct a recruitment plan that will further the efforts of the EDU-STEM research coordination network. The report that follows is a summary of the conclusions and future directions from our discussion.
| S-EPMC8693933 | biostudies-literature
Project description:Background Cardiovascular disease is the leading cause of morbidity and mortality worldwide. Prior research suggests that social determinants of health have a compounding effect on health and are associated with cardiovascular disease. This scoping review explores what and how social determinants of health data are being used to address cardiovascular disease and improve health equity. Methods and Results After removing duplicate citations, the initial search yielded 4110 articles for screening, and 50 studies were identified for data extraction. Most studies relied on similar data sources for social determinants of health, including geocoded electronic health record data, national survey responses, and census data, and largely focused on health care access and quality, and the neighborhood and built environment. Most focused on developing interventions to improve health care access and quality or characterizing neighborhood risk and individual risk. Conclusions Given that few interventions addressed economic stability, education access and quality, or community context and social risk, the potential for harnessing social determinants of health data to reduce the burden of cardiovascular disease remains unrealized.
| S-EPMC10727404 | biostudies-literature
Project description:Artificial intelligence and machine learning (AI/ML) tools have the potential to improve health equity. However, many historically underrepresented communities have not been engaged in AI/ML training, research, and infrastructure development. Therefore, AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) seeks to increase participation and engagement of researchers and communities through mutually beneficial partnerships. The purpose of this paper is to summarize feedback from listening sessions conducted by the AIM-AHEAD Coordinating Center in February 2022, titled the "AIM-AHEAD Community Building Convention (ACBC)." A total of six listening sessions were held over three days. A total of 977 people registered with AIM-AHEAD to attend ACBC and 557 individuals attended the listening sessions across stakeholder groups. Facilitators led the conversation based on a series of guiding questions, and responses were captured through voice and chat via the Slido platform. A professional third-party provider transcribed the audio. Qualitative analysis included data from transcripts and chat logs. Thematic analysis was then used to identify common and unique themes across all transcripts. Six main themes arose from the sessions. Attendees felt that storytelling would be a powerful tool in communicating the impact of AI/ML in promoting health equity, trust building is vital and can be fostered through existing trusted relationships, and diverse communities should be involved every step of the way. Attendees shared a wealth of information that will guide AIM-AHEAD's future activities. The sessions highlighted the need for researchers to translate AI/ML concepts into vignettes that are digestible to the larger public, the importance of diversity, and how open-science platforms can be used to encourage multi-disciplinary collaboration. While the sessions confirmed some of the existing barriers in applying AI/ML for health equity, they also offered new insights that were captured in the six themes.
| S-EPMC10313007 | biostudies-literature
Project description:BackgroundCOVID-19 has caused over 46,000 deaths in New York City, with a disproportional impact on certain communities. As part of the COVID-19 response, the city has directly administered over 6 million COVID-19 tests (in addition to millions of indirectly administered tests not covered in this analysis) at no cost to individuals, resulting in nearly half a million positive results. Given that the prevalence of testing, throughout the pandemic, has tended to be higher in more affluent areas, these tests were targeted to areas with fewer resources.ObjectiveThis study aimed to evaluate the impact of New York City's COVID-19 testing program; specifically, we aimed to review its ability to provide equitable testing in economically, geographically, and demographically diverse populations. Of note, in addition to the brick-and-mortar testing sites evaluated herein, this program conducted 2.1 million tests through mobile units to further address testing inequity.MethodsTesting data were collected from the in-house Microsoft SQL Server Management Studio 18 Clarity database, representing 6,347,533 total tests and 449,721 positive test results. These tests were conducted at 48 hospital system locations. Per capita testing rates by zip code tabulation area (ZCTA) and COVID-19 positivity rates by ZCTA were used as dependent variables in separate regressions. Median income, median age, the percentage of English-speaking individuals, and the percentage of people of color were used as independent demographic variables to analyze testing patterns across several intersecting identities. Negative binomial regressions were run in a Jupyter Notebook using Python.ResultsPer capita testing inversely correlated with median income geographically. The overall pseudo r2 value was 0.1101 when comparing hospital system tests by ZCTA against the selected variables. The number of tests significantly increased as median income fell (SE 1.00000155; P<.001). No other variables correlated at a significant level with the number of tests (all P values were >.05). When considering positive test results by ZCTA, the number of positive test results also significantly increased as median income fell (SE 1.57e-6; P<.001) and as the percentage of female residents fell (SE 0.957; P=.001). The number of positive test results by ZCTA rose at a significant level alongside the percentage of English-only speakers (SE 0.271; P=.03).ConclusionsNew York City's COVID-19 testing program was able to improve equity through the provision of no-cost testing, which focused on areas of the city that were disproportionately impacted by COVID-19 and had fewer resources. By detecting higher numbers of positive test results in resource-poor neighborhoods, New York City was able to deploy additional resources, such as those for contact tracing and isolation and quarantine support (eg, free food delivery and free hotel stays), early during the COVID-19 pandemic. Equitable deployment of testing is feasible and should be considered early in future epidemics or pandemics.
| S-EPMC11924968 | biostudies-literature
Project description:Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible-exposed-infectious-recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC's government's website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.
| S-EPMC8452065 | biostudies-literature
Project description:Background: Racial-ethnic inequity in type 1 diabetes technology use is well documented and contributes to disparities in glycemic and long-term outcomes. However, solutions to address technology inequity remain sparse and lack stakeholder input. Methods: We employed user-centered design principles to conduct workshop sessions with multidisciplinary panels of stakeholders, building off of our prior study highlighting patient-identified barriers and proposed solutions. Stakeholders were convened to review our prior findings and co-create interventions to increase technology use among underserved populations with type 1 diabetes. Stakeholders included type 1 diabetes patients who had recently onboarded to technology; endocrinology and primary care physicians; nurses; diabetes educators; psychologists; and community health workers. Sessions were recorded and analyzed iteratively by multiple coders for common themes. Results: We convened 7 virtual 2-h workshops for 32 stakeholders from 11 states in the United States. Patients and providers confirmed prior published studies highlighting patient barriers and generated new ideas by co-creating solutions. Common themes of proposed interventions included (1) prioritizing more equitable systems of offering technology, (2) using visual and hands-on approaches to increase accessibility of technology and education, (3) including peer and family support systems more, and (4) assisting with insurance navigation and social needs. Discussion: Our study furthers the field by providing stakeholder-endorsed intervention ideas that propose feasible changes at the patient, provider, and system levels to reduce inequity in diabetes technology use in type 1 diabetes. Multidisciplinary stakeholder engagement in disparities research offers unique insight that is impactful and acceptable to the target population.
| S-EPMC9208861 | biostudies-literature
Project description:18S rRNA gene V4 region amplicon sequencing of protists in NYC environmental samples
| PRJEB23950 | ENA
Project description:18S rRNA gene V9 region amplicon sequencing of protists in NYC environmental samples
| PRJEB26690 | ENA