11 results on '"Ratsimbazafy, Francis"'
Search Results
2. Association of step counts over time with the risk of chronic disease in the All of Us Research Program
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Master, Hiral, Annis, Jeffrey, Huang, Shi, Beckman, Joshua A., Ratsimbazafy, Francis, Marginean, Kayla, Carroll, Robert, Natarajan, Karthik, Harrell, Frank E., Roden, Dan M., Harris, Paul, and Brittain, Evan L.
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- 2022
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3. The All of Us Research Program: Data quality, utility, and diversity
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Ahmedani, Brian, Cole Johnson, Christine D., Ahsan, Habib, Antoine-LaVigne, Donna, Singleton, Glendora, Anton-Culver, Hoda, Topol, Eric, Baca-Motes, Katie, Steinhubl, Steven, Wade, James, Begale, Mark, Jain, Praduman, Sutherland, Scott, Lewis, Beth, Korf, Bruce, Behringer, Melissa, Gharavi, Ali G., Goldstein, David B., Hripcsak, George, Bier, Louise, Boerwinkle, Eric, Brilliant, Murray H., Murali, Narayana, Hebbring, Scott Joseph, Farrar-Edwards, Dorothy, Burnside, Elizabeth, Drezner, Marc K., Taylor, Amy, Channamsetty, Veena, Montalvo, Wanda, Sharma, Yashoda, Chinea, Carmen, Jenks, Nancy, Cicek, Mine, Thibodeau, Steve, Holmes, Beverly Wilson, Schlueter, Eric, Collier, Ever, Winkler, Joyce, Corcoran, John, D’Addezio, Nick, Daviglus, Martha, Winn, Robert, Wilkins, Consuelo, Roden, Dan, Denny, Joshua, Doheny, Kim, Nickerson, Debbie, Eichler, Evan, Jarvik, Gail, Funk, Gretchen, Philippakis, Anthony, Rehm, Heidi, Lennon, Niall, Kathiresan, Sekar, Gabriel, Stacey, Gibbs, Richard, Gil Rico, Edgar M., Glazer, David, Grand, Joannie, Greenland, Philip, Harris, Paul, Shenkman, Elizabeth, Hogan, William R., Igho-Pemu, Priscilla, Pollan, Cliff, Jorge, Milena, Okun, Sally, Karlson, Elizabeth W., Smoller, Jordan, Murphy, Shawn N., Ross, Margaret Elizabeth, Kaushal, Rainu, Winford, Eboni, Wallace, Febe, Khatri, Parinda, Kheterpal, Vik, Ojo, Akinlolu, Moreno, Francisco A., Kron, Irving, Peterson, Rachele, Menon, Usha, Lattimore, Patricia Watkins, Leviner, Noga, Obedin-Maliver, Juno, Lunn, Mitchell, Malik-Gagnon, Lynda, Mangravite, Lara, Marallo, Adria, Marroquin, Oscar, Visweswaran, Shyam, Reis, Steven, Marshall, Gailen, Jr., McGovern, Patrick, Mignucci, Deb, Moore, John, Munoz, Fatima, Talavera, Gregory, O'Connor, George T., O'Donnell, Christopher, Ohno-Machado, Lucila, Orr, Greg, Randal, Fornessa, Theodorou, Andreas A., Reiman, Eric, Roxas-Murray, Mercedita, Stark, Louisa, Tepp, Ronnie, Zhou, Alicia, Topper, Scott, Trousdale, Rhonda, Tsao, Phil, Weidman, Lisa, Weiss, Scott T., Wellis, David, Whittle, Jeffrey, Wilson, Amanda, Zuchner, Stephan, Zwick, Michael E., Ramirez, Andrea H., Sulieman, Lina, Schlueter, David J., Halvorson, Alese, Qian, Jun, Ratsimbazafy, Francis, Loperena, Roxana, Mayo, Kelsey, Basford, Melissa, Deflaux, Nicole, Muthuraman, Karthik N., Natarajan, Karthik, Kho, Abel, Xu, Hua, Clark, Cheryl R., Cohn, Elizabeth, Schully, Sheri D., Ahmedani, Brian K., Argos, Maria, Cronin, Robert M., O’Donnell, Christopher, Fouad, Mona, Hebbring, Scott J., Smoller, Jordan W., Sodeke, Stephen, Wilbanks, John, Hentges, Justin, Mockrin, Stephen, Lunt, Christopher, Devaney, Stephanie A., Gebo, Kelly, Denny, Joshua C., Carroll, Robert J., Harris, Paul A., and Roden, Dan M.
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- 2022
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4. Using ORBIS to Build a Global Database of Firms with State Participation
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Dall'Olio, Andrea, Goodwin, Tanja, Martinez Licetti, Martha, Orlowski, Jan, Patiño Peña, Fausto, Ratsimbazafy, Francis, and Sanchez-Navarro, Dennis
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PUBLIC ENTERPRISES ,GOVERNANCE OF STATE-OWNED ENTERPRISES ,FIRMS ,BUSINESSES OF THE STATE DATABASE ,STATE PARTICIPATION ,STATE-OWNED ENTERPRISE DATABASE - Abstract
This paper develops a novel methodology to construct a harmonized cross-country database of the state’s footprint in markets: the Businesses of the State database. The methodology of the database is built on three criteria—(i) a harmonized definition of state-owned enterprises, (ii) identification of direct and indirect state ownership linkages at the national and subnational levels across the corporate sector, and (iii) classification of economic activities depending on their efficiency rationale—which conceptualize a framework to trace state presence in the corporate sector across economic activities. The database is constructed leveraging different firm-level data sources including the ORBIS Global Database, as the primary data source, which is then complemented with supplementary data sources (EMIS Intelligence, Factiva, Worldscope, Pitchbook, among others) to mitigate ORBIS’s data limitations across countries and regions. The Businesses of the State database identifies an unprecedented number of firms with state participation across countries and economic activities, as well as providing novel insights on financial performance, economic performance, and governance of state-owned enterprises. A deep-dive analysis of 36 countries within the Businesses of the State database shows that 69 percent of state-owned enterprises operate in competitive activities (low efficiency-rationale for state participation), 16% are in partially contestable industries (moderate efficiency rationale), and 15 percent are natural monopolies (strong efficiency rationale). Furthermore, this analysis suggests that performance-based productivity of state-owned enterprises (revenue per worker) is negatively correlated with government control variables, such as government shareholding percentage and direct versus indirect government ownership.
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- 2022
5. Design and Implementation of the All of Us Research Program COVID-19 Participant Experience (COPE) Survey.
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Schulkey, Claire E, Litwin, Tamara R, Ellsworth, Genevieve, Sansbury, Heather, Ahmedani, Brian K, Choi, Karmel W, Cronin, Robert M, Kloth, Yasmin, Ashbeck, Alan W, Sutherland, Scott, Mapes, Brandy M, Begale, Mark, Bhat, Geeta, King, Paula, Marginean, Kayla, Wolfe, Keri Ann, Kouame, Aymone, Raquel, Carmina, Ratsimbazafy, Francis, and Bornemeier, Zach
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COVID-19 ,SOCIAL determinants of health ,SUBSTANCE abuse ,COVID-19 vaccines ,MENTAL health ,PUBLIC health ,CULTURAL pluralism ,EXPERIENCE ,SURVEYS ,PHYSICAL activity ,HUMAN services programs ,LONELINESS ,COVID-19 testing ,COVID-19 pandemic - Abstract
In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources. [ABSTRACT FROM AUTHOR]
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- 2023
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6. Achieving a Representative Sample of Asian Americans in Biomedical Research Through Community-Based Approaches: Comparing Demographic Data in the All of Us Research Program With the American Community Survey.
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Randal, Fornessa T., Lozano, Paula, Qi, Siya, Maene, Chieko, Shah, Sameep, Mo, Yicklun, Ratsimbazafy, Francis, Boerwinkle, Eric, Cicek, Mine, Clark, Cheryl R., Cohn, Elizabeth, Gebo, Kelly, Loperena, Roxana, Mayo, Kelsey, Mockrin, Stephen, Ohno-Machado, Lucila, Schully, Sheri, Ramirez, Andrea H., Aschebrook-Kilfoy, Briseis, and Ahsan, Habibul
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HUMAN research subjects ,PATIENT selection ,ASIANS ,PSYCHOSOCIAL factors ,DESCRIPTIVE statistics ,RESEARCH funding ,RESEARCH bias ,HEALTH equity ,MEDICAL research - Abstract
Background: Underrepresented persons are often not included in biomedical research. It is unknown if the general Asian American population is being represented in All of Us. The purpose of this study was to compare the Asian demographic data in the All of Us cohort with the Asian nationally representative data from the American Community Survey. Method: Demographic characteristics and health literacy of Asians in All of Us were examined. Findings were qualitatively compared with the Asian data in the 2019 American Community Survey 1-year estimate. Results: Compared with the national composition of Asians, less All of Us participants were born outside the United States (64% vs 79%), were younger, and had higher levels of education (76% vs 52%). Over 60% of All of Us participants reported high levels of health literacy. Conclusion: This study had implications for the development of strategies that ensure diverse populations are represented in biomedical research. [ABSTRACT FROM AUTHOR]
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- 2023
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7. Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence.
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Miller, Cheryl N., Althoff, Keri N., Schlueter, David J., Anton-Culver, Hoda, Qingxia Chen, Garbett, Shawn, Ratsimbazafy, Francis, Thomsen, Isaac, Karlson, Elizabeth W., Cicek, Mine, Pinto, Ligia A., Malin, Bradley A., Ohno-Machado, Lucila, Williams, Carolyn, Goldstein, David, Kouame, Aymone, Ramirez, Andrea, Gebo, Kelly A., and Schully, Sheri D.
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- 2022
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8. An Overview of Cancer in the First 315,000 All of Us Participants.
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Aschebrook-Kilfoy, Briseis, Zakin, Paul, Craver, Andrew, Shah, Sameep, Kibriya, Muhammad G., Stepniak, Elizabeth, Ramirez, Andrea, Clark, Cheryl, Cohn, Elizabeth, Ohno-Machado, Lucila, Cicek, Mine, Boerwinkle, Eric, Schully, Sheri D., Mockrin, Stephen, Gebo, Kelly, Mayo, Kelsey, Ratsimbazafy, Francis, Sanders, Alan, Shah, Raj C., and Argos, Maria
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ETHNICITY ,ELECTRONIC health records ,RACE ,EARLY detection of cancer ,CANCER research - Abstract
Introduction: The NIH All of Us Research Program will have the scale and scope to enable research for a wide range of diseases, including cancer. The program's focus on diversity and inclusion promises a better understanding of the unequal burden of cancer. Preliminary cancer ascertainment in the All of Us cohort from two data sources (self-reported versus electronic health records (EHR)) is considered. Materials and methods: This work was performed on data collected from the All of Us Research Program's 315,297 enrolled participants to date using the Researcher Workbench, where approved researchers can access and analyze All of Us data on cancer and other diseases. Cancer case ascertainment was performed using data from EHR and self-reported surveys across key factors. Distribution of cancer types and concordance of data sources by cancer site and demographics is analyzed. Results and discussion: Data collected from 315,297 participants resulted in 13,298 cancer cases detected in the survey (in 89,261 participants), 23,520 cancer cases detected in the EHR (in 203,813 participants), and 7,123 cancer cases detected across both sources (in 62,497 participants). Key differences in survey completion by race/ethnicity impacted the makeup of cohorts when compared to cancer in the EHR and national NCI SEER data. Conclusions: This study provides key insight into cancer detection in the All of Us Research Program and points to the existing strengths and limitations of All of Us as a platform for cancer research now and in the future. [ABSTRACT FROM AUTHOR]
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- 2022
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9. Hypertension prevalence in the All of Us Research Program among groups traditionally underrepresented in medical research.
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Chandler, Paulette D., Clark, Cheryl R., Zhou, Guohai, Noel, Nyia L., Achilike, Confidence, Mendez, Lizette, Ramirez, Andrea H., Loperena-Cortes, Roxana, Mayo, Kelsey, Cohn, Elizabeth, Ohno-Machado, Lucila, Boerwinkle, Eric, Cicek, Mine, Qian, Jun, Schully, Sheri, Ratsimbazafy, Francis, Mockrin, Stephen, Gebo, Kelly, Dedier, Julien J., and Murphy, Shawn N.
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HYPERTENSION ,MEDICAL research ,PUBLIC health ,ELECTRONIC health records ,ANTIHYPERTENSIVE agents - Abstract
The All of Us Research Program was designed to enable broad-based precision medicine research in a cohort of unprecedented scale and diversity. Hypertension (HTN) is a major public health concern. The validity of HTN data and definition of hypertension cases in the All of Us (AoU) Research Program for use in rule-based algorithms is unknown. In this cross-sectional, population-based study, we compare HTN prevalence in the AoU Research Program to HTN prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES). We used AoU baseline data from patient (age ≥ 18) measurements (PM), surveys, and electronic health record (EHR) blood pressure measurements. We retrospectively examined the prevalence of HTN in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED) codes and blood pressure medications recorded in the EHR. We defined HTN as the participant having at least 2 HTN diagnosis/billing codes on separate dates in the EHR data AND at least one HTN medication. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18–39, 40–59, and ≥ 60). Among the 185,770 participants enrolled in the AoU Cohort (mean age at enrollment = 51.2 years) available in a Researcher Workbench as of October 2019, EHR data was available for at least one SNOMED code from 112,805 participants, medications for 104,230 participants, and 103,490 participants had both medication and SNOMED data. The total number of persons with SNOMED codes on at least two distinct dates and at least one antihypertensive medication was 33,310 for a crude prevalence of HTN of 32.2%. AoU age-adjusted HTN prevalence was 27.9% using 3 groups compared to 29.6% in NHANES. The AoU cohort is a growing source of diverse longitudinal data to study hypertension nationwide and develop precision rule-based algorithms for use in hypertension treatment and prevention research. The prevalence of hypertension in this cohort is similar to that in prior population-based surveys. [ABSTRACT FROM AUTHOR]
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- 2021
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10. The All of Us Research Program: Data quality, utility, and diversity.
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Ramirez AH, Sulieman L, Schlueter DJ, Halvorson A, Qian J, Ratsimbazafy F, Loperena R, Mayo K, Basford M, Deflaux N, Muthuraman KN, Natarajan K, Kho A, Xu H, Wilkins C, Anton-Culver H, Boerwinkle E, Cicek M, Clark CR, Cohn E, Ohno-Machado L, Schully SD, Ahmedani BK, Argos M, Cronin RM, O'Donnell C, Fouad M, Goldstein DB, Greenland P, Hebbring SJ, Karlson EW, Khatri P, Korf B, Smoller JW, Sodeke S, Wilbanks J, Hentges J, Mockrin S, Lunt C, Devaney SA, Gebo K, Denny JC, Carroll RJ, Glazer D, Harris PA, Hripcsak G, Philippakis A, and Roden DM
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The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools., Competing Interests: The authors declare no competing interests.
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- 2022
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11. Predictive Analytics for Glaucoma Using Data From the All of Us Research Program.
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Baxter SL, Saseendrakumar BR, Paul P, Kim J, Bonomi L, Kuo TT, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark CR, Cohn E, Gebo K, Mayo K, Mockrin S, Schully SD, Ramirez A, and Ohno-Machado L
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- Aged, Aged, 80 and over, Female, Humans, Information Storage and Retrieval methods, Logistic Models, Machine Learning, Male, Middle Aged, Models, Statistical, Neural Networks, Computer, ROC Curve, Databases, Factual statistics & numerical data, Electronic Health Records statistics & numerical data, Filtering Surgery methods, Glaucoma, Open-Angle diagnosis, Glaucoma, Open-Angle surgery
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Purpose: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research., Design: Development and evaluation of machine learning models., Methods: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall., Results: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests)., Conclusions: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research., (Published by Elsevier Inc.)
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- 2021
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