The dataset contains information on medical history, residential information and demographic information on CATHGEN participants as well as modeled PM2.5 values at participants' residence. This dataset is not publicly accessible because: The data are human subjects data containing potential identifiable information (PII) and therefore access is restricted to the study investigators. Because base data are owned by other entities, these data need to be requested directly from Duke University. It can be accessed through the following means: These data can accessed upon request to the CATHGEN steering committee at Duke University. Format: Data are stored as SAS files on secure EPA drives. This dataset is associated with the following publication: Weaver, A., L. McGuinn, L. Neas, J. Mirowsky, R. Devlin, R. Dhingra, C. Ward-Caviness, W. Cascio, W. Kraus, E. Hauser, Q. Di, J. Schwartz, and D. Diaz-Sanchez. Neighborhood sociodemographic effects on the associations between long-term PM2.5 exposure and cardiovascular outcomes and diabetes. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 3(1): e038, (2019).

Data include individual-level health data, including results from cardiovascular tests and medical history. This is linked to air quality data at participants' residence. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Data may be requested through the Jackson Heart Study. Format: Data include individual-level health data, including results from cardiovascular tests and medical history. This is linked to air quality data at participants' residence. Since these data contain PII, they cannot be released to ScienceHub. This dataset is associated with the following publications: Weaver, A., A. Bidulescu, G. Wellenius, D. Hickson, M. Sims, A. Vaidyanathan, W. Wu, A. Correa, and Y. Wang. Associations between Air Pollution Indicators and Prevalent and Incident Diabetes among African American Participants in the Jackson Heart Study. Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 5(3): e140, (2021). Weaver, A., Y. Wang, G. Wellenius, A. Bidulescu, M. Sims, A. Vaidyanathan, D. Hickson, D. Shimbo, M. Abdalla, K. Diaz, and S. Seals. Long-Term Air Pollution and Blood Pressure in an African American Cohort: The Jackson Heart Study. American Journal of Preventive Medicine. Elsevier B.V., Amsterdam, NETHERLANDS, 60(3): 397-405, (2021).

Data include individual-level health data, including results from cardiovascular tests and medical history. This is linked to air quality data at participants' residence. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Data may be requested through the Jackson Heart Study. Format: Data include individual-level health data, including results from cardiovascular tests and medical history. This is linked to air quality data at participants' residence. Since these data contain PII, they cannot be released to ScienceHub. This dataset is associated with the following publications: Weaver, A., A. Bidulescu, G. Wellenius, D. Hickson, M. Sims, A. Vaidyanathan, W. Wu, A. Correa, and Y. Wang. Associations between Air Pollution Indicators and Prevalent and Incident Diabetes among African American Participants in the Jackson Heart Study. Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 5(3): e140, (2021). Weaver, A., Y. Wang, G. Wellenius, A. Bidulescu, M. Sims, A. Vaidyanathan, D. Hickson, D. Shimbo, M. Abdalla, K. Diaz, and S. Seals. Long-Term Air Pollution and Blood Pressure in an African American Cohort: The Jackson Heart Study. American Journal of Preventive Medicine. Elsevier B.V., Amsterdam, NETHERLANDS, 60(3): 397-405, (2021).

This data set provides de-identified population data for Hypertension and Hyperlipidemia comorbidity prevalence. The data is provided by three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) and represents their insured population for the 2015 and 2016 calendar years. Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Background: Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods. Methods: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002–2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12 km spatial resolutions, and satellite-based models at 10 km and 1 km resolutions. Case status was either a coronary artery disease (CAD) index>23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. Results: We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12 km-CMAQ models. Conclusions: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Clinical data are located in: C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Cathgen Satellite data are located in : C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\New Ikm Satellite Data C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Satellite Data CMAQ data are located in C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\CMAQ Data. Format: There are two types of datasets used in this study: clinical data taken from patient records at the Duke Medical Center; and air pollution data (PM2.5) taken from a federal reference monitor located in Raleigh, CMAQ data obtained from collaborators at Georgia Tech and NERL/ORD, and satellite data obtained from collaborators at Harvard. Metadata are in the form of Excel spreadsheets that contain columns of data that specify clinical and exposure information for each individual participating in the study. This dataset is associated with the following publication: McGuinn, L., C. Ward-Caviness, A. Schneider, Q. Di, A. Chudnovsky, J. Schwartz, P. Koutrakis, A. Russell, V. Garcia, W. Krause, E. Hauser, L. Neas, W. Cascio, D. Diaz-Sanchez, and R. Devlin. Fine Particulate Matter and Cardiovascular Disease: Comparison of Assessment Methods for Long-term Exposure. ENVIRONMENTAL RESEARCH. Academic Press Incorporated, Orlando, FL, USA, 159: 16-23, (2017).

Background: Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods. Methods: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002–2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12 km spatial resolutions, and satellite-based models at 10 km and 1 km resolutions. Case status was either a coronary artery disease (CAD) index>23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. Results: We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12 km-CMAQ models. Conclusions: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Clinical data are located in: C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Cathgen Satellite data are located in : C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\New Ikm Satellite Data C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\Satellite Data CMAQ data are located in C:\Users\rdevlin\OneDrive - Environmental Protection Agency (EPA)\Excel Files\CMAQ Data. Format: There are two types of datasets used in this study: clinical data taken from patient records at the Duke Medical Center; and air pollution data (PM2.5) taken from a federal reference monitor located in Raleigh, CMAQ data obtained from collaborators at Georgia Tech and NERL/ORD, and satellite data obtained from collaborators at Harvard. Metadata are in the form of Excel spreadsheets that contain columns of data that specify clinical and exposure information for each individual participating in the study. This dataset is associated with the following publication: McGuinn, L., C. Ward-Caviness, A. Schneider, Q. Di, A. Chudnovsky, J. Schwartz, P. Koutrakis, A. Russell, V. Garcia, W. Krause, E. Hauser, L. Neas, W. Cascio, D. Diaz-Sanchez, and R. Devlin. Fine Particulate Matter and Cardiovascular Disease: Comparison of Assessment Methods for Long-term Exposure. ENVIRONMENTAL RESEARCH. Academic Press Incorporated, Orlando, FL, USA, 159: 16-23, (2017).

These datasets provide de-identified insurance data for hyperlipidemia. The data is provided by three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) and represents their insured population for the 2015 and 2016 calendar years. Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

The dataset is tabular data on DNA methylation assessment, residential history, individual-level socioeconomic status, and neighborhood built environment for participants in the Detroit Neighborhood Health Study. This dataset is not publicly accessible because: The data was not generated by the EPA, is not owned by the EPA, and contains protected information such as residential history. It can be accessed through the following means: The data can be accessed by contacting the data manager - Dr. Chantel Martin. Format: The data is tabular data containing information on DNA methylation, residential history, disease diagnoses, built environment, and individual-level socioeconomic data. This dataset is associated with the following publication: Ward-Caviness, C., S. Pu, C. Martin, S. Galea, M. Uddin, D. Wildman, K. Koenen, and A. Aiello. Epigenetic predictors of all-cause mortality are associated with objective measures of neighborhood disadvantage in an urban population. Clinical Epigenetics. BioMed Central Ltd, London, UK, 12(44): 1, (2020).

The data contains location information, DNA methylation (transformed into epigenetic aging biomakers), dates of examination, demographics, disease diagnoses, and traffic-related air pollution exposures. It is stored as a series of data frames suitable for use in the R programming language. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: This data can be accessed by contacting Dr. Cavin Ward-Caviness and describing your needs for the analysis, completing the necessary ethics trainings, and gaining approval on the appropriate IRB applications as well as by the CATHGEN Steering Committee. Format: The data contains location information, DNA methylation (transformed into epigenetic aging biomakers), dates of examination, demographics, disease diagnoses, and traffic-related air pollution exposures. It is stored as a series of data frames suitable for use in the R programming language. This dataset is associated with the following publication: Ward-Caviness, C., A. Russell, A. Weaver, E. Slawsky, R. Dhingra, L. Coulter Kwee, R. Jiang, L. Neas, D. Diaz-Sanchez, R. Devlin, W. Cascio, E. Hauser, S. Shah, W. Kraus, and K. Olden. Accelerated epigenetic age as a biomarker of cardiovascular sensitivity to traffic-related air pollution. Aging. Impact Journals, LLC, Orchard Park, NY, USA, 12(23): 24141-24155, (2020).

The data contains location information, DNA methylation (transformed into epigenetic aging biomakers), dates of examination, demographics, disease diagnoses, and traffic-related air pollution exposures. It is stored as a series of data frames suitable for use in the R programming language. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: This data can be accessed by contacting Dr. Cavin Ward-Caviness and describing your needs for the analysis, completing the necessary ethics trainings, and gaining approval on the appropriate IRB applications as well as by the CATHGEN Steering Committee. Format: The data contains location information, DNA methylation (transformed into epigenetic aging biomakers), dates of examination, demographics, disease diagnoses, and traffic-related air pollution exposures. It is stored as a series of data frames suitable for use in the R programming language. This dataset is associated with the following publication: Ward-Caviness, C., A. Russell, A. Weaver, E. Slawsky, R. Dhingra, L. Coulter Kwee, R. Jiang, L. Neas, D. Diaz-Sanchez, R. Devlin, W. Cascio, E. Hauser, S. Shah, W. Kraus, and K. Olden. Accelerated epigenetic age as a biomarker of cardiovascular sensitivity to traffic-related air pollution. Aging. Impact Journals, LLC, Orchard Park, NY, USA, 12(23): 24141-24155, (2020).