New national recognition for Jessica Clifton

 Jessica Clifton, PhD, faculty scientist in the Department of Medicine, was recently elected to the Mental Health Professional Group of the American Society for Reproductive Medicine. In addition, Dr. Clifton was appointed to the American Psychological Association’s Women’s Health Interest Group.

Congratulations to Jessica!

Amanda Kennedy on academic detailing

Amanda Kennedy, PharmD, Professor of Medicine, has earned much national attention for her work in the use of academic detailing to improve quality of care. Her latest work was recently published. Congratulations, Amanda!

 

Kennedy AG, Regier L, Fischer MA. Educating community clinicians using principles of academic detailing in an evolving landscape. Am J Health Syst Pharm. 2020 Nov 6:zxaa351. doi: 10.1093/ajhp/zxaa351. Epub ahead of print. PMID: 33155056.

 

FW: [HRS Data Alert] Recent HRS Data Releases and Updates

 

 

View this email in your browser   News and Announcements Recent HRS Data Releases and Updates The following data products are now available on the HRS website:   Public Data Releases The HRS COVID-19 Project data V1 is now available. The COVID-19 module of HRS 2020 is being administered to the 50% random subsample of households who were originally assigned to enhanced face-to-face interviewing (EFTF), in two random half groups, one starting in June and one in September. This special midterm release includes 3,266 respondents from the group released in June and was produced under an accelerated time frame to facilitate rapid access to HRS research data while the 2020 HRS data collection is still underway.  Updates will be made in the future for additional cases and additional psychosocial questionnaire returns.  Refer to the Data Description for more information.  The Epigenetic Clocks V1 file is now available. A number of researchers have identified portions of the genome where DNA methylation (DNAm) changes are related to either age or, more recently, to health outcomes linked to age. The resulting "methylation clocks" combine information for a small number of CpGs (typically 100-500) to produce indicators of epigenetic aging. Thirteen epigenetic clocks have been constructed using the HRS DNAm data collected in 2016 (n=4,018) through the 2016 Venous Blood Study (VBS).   Sensitive Health Data Releases The 2016 Biomarker Data V1 is now available. The 2016 Biomarker Data were part of the physical measures and biomarker portion of the 2016 Core data collection. The Dried Blood Spot (DBS) data have been collected from approximately half the sample in each year beginning in 2006. Detailed descriptions of the procedures for collection and assay of DBS based data as well as VBS blood-based markers beginning in 2016 are available on the HRS website. The 2016 VBS Sub Sample Data V1 is now available. These data contain information on homocysteine, clusterin, brain-derived neurotrophic factor (BDNF), and mtDNA copy number. Procedures for collection, assay, and descriptions of other blood-based variables are included in this documentation (link). These assays were done on a non-random subsample people who participated in the 2016 Venous Blood Study. The sample includes all the participants of the 2016 Healthy Cognitive Aging Project (HCAP) who provided blood samples, younger participants designated for future HCAP assessments, and a subsample of HCAP non-participants. This subsample fully represents the entire HRS sample when weighted. The 2016 VBS Supplemental File V1 is now available. The VBS 2016 Supplemental File contains additional assays that were measured on the 2016 Venous Blood Study sample. These data contain information on 5 cytokines, vitamin D and IGF-1. Procedures for collection, assay, and descriptions of other blood-based variables are included in the documentation Venous blood collection and assay protocol in the 2016 Health and Retirement Study.   Restricted Data Release and Updates The Respondent Cross-Year Benefits file V5.2 is derived from Master Beneficiary Record (MBR) data for primary beneficiary and other (secondary) insured and has been updated to include data through 2016.  The Respondent Cross-Year Summary Earnings file V5.2 contains Master Earnings File earnings data for respondents and has been updated to provide data through 2016. The Respondent Cross-Year Detail Earnings file V5.2 has been updated to include data through 2016. This dataset contains one record per year for each consenting respondent who received wages during the years 1978-2016 and whose records were available in the Master Earnings File.  Questions, comments or concerns about the above data products should be directed to the HRS Help Desk at hrsquestions@umich.edu. Health and Retirement Study Survey Research Center 426 Thompson Street Ann Arbor, MI 48104 Add us to your address book You are receiving this email because you opted in to email announcements at the HRS website. Unsubscribe Update your preferences     The Health and Retirement Study is supported by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration.

Clinical Research Oriented Workshop (CROW) Meeting: November 12, 2020

 

Present:   Levi Bonnell, Justine Dee, Juvena Hitt, Emily Houston, Ben Littenberg, Adam Sprouse-Blum, Connie van Eeghen, (7)

1.                   Warm Up: (tech issues – on radio silence at start up)

2.                   Adam: re-application to continue loan repayment program

a.       Little known about reviewers; might be admin only

                                                   i.      Success rate for 1^st for app is 50%

                                                 ii.      Success rate for reapplication is 70%

b.       Composition/organization

                                                   i.      Define the medical terms, e.g. Gastroparesis

                                                 ii.      State the general “need to fill this gap” up front, not at the end

                                               iii.      Use images/illustrations to support complex idea; duplication between documents is OK and even helpful

c.       Design

                                                   i.      Explain why excluding pregnant subjects

                                                 ii.      Consent methods: all with a face – in person or Zoom – this is a good method, but phone is faster and sometimes helpful.  REDCap consent is OK too.

                                               iii.      Include participant incentive as part of the consent procedure

                                               iv.      Evaluate figures for their content and value

                                                 v.      Describe plan to develop a bio-bank of samples

                                               vi.      Sample selection (which sample to analyze than more than one sample provided) depends on the logic model: which is the predictor, the microbiome or the migraine? Build the design around the model.

                                              vii.      Consider how other sources of data will be used in the design, e.g. the role of the food diary

                                            viii.      Make sure that the analytic method fits the data, using standard, accepted language

                                                ix.      Diagrams, if not intuitive, should be explained

1.       Use diagrams to tell a story, e.g. before/after sample analysis

2.       Define the panel – who is included

3.                   Next week:  Systematic Review with Emily or review of NIH website?