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LaMonte, Michael J.

https://orcid.org/0000-0002-6669-5242

Mayo Clinic

University of Minnesota

Citations 3

Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations
Williams, Brent A. , Voyce, Stephen , Sidney, Stephen , Roger, Véronique L. , Plante, Timothy B. , Larson, Sharon , LaMonte, Michael J. , Labarthe, Darwin R. , DeBarmore, Bailey M. , Chang, Alex R. , Chamberlain, Alanna M. , Benziger, Catherine P. , Chamberlain, Alanna M , Benziger, Catherine P , Chang, Alexander R

Journal of the American Heart Association | April 12, 2022 | Volume: 11 | Issue: 8 | Pages: No page available

10.1161/jaha.121.024409 Get It

Cardiovascular disease surveillance involves quantifying the evolving population‐level burden of cardiovascular outcomes and risk factors as a data‐driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more “national” surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care–seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information‐gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.
Chronic Recreational Physical Inactivity and Epithelial Ovarian Cancer Risk: Evidence from the Ovarian Cancer Association Consortium
Cannioto, Rikki , LaMonte, Michael J. , Sucheston‐Campbell, Lara, Eng, Kevin H., Szender, J. Brian, Chang‐Claude, Jenny, Schmalfeldt, Barbara , Klapdor, R, Gower, Emily, Minlikeeva, Albina N. , Zirpoli, Gary , Edwards, Robert P., Goode, Ellen L. , Goodman, Marc T. , Høgdall, Estrid , Hosono, Satoyo , Jensen, Allan , Jordan, Susan J. , Group, on behalf of The Australian Ovarian Cancer Study, Matsuo, Keitaro , Ness, Roberta B., Olsen, Catherine M. , Olson, Sara H. , Pearce, Celeste Leigh , Pike, Malcolm C. , Rossing, Mary Anne, Szamreta, Elizabeth A. , Thompson, Pamela J., Tseng, Chiu-Chen , Vierkant, Robert A. , Webb, Penelope M. , Wentzensen, Nicolas , Wicklund, Kristine G. , Winham, Stacey J. , Wu, Anna H. , Schildkraut, Joellen M. , Terry, Kathryn L. , Kelemen, Linda E. , Moysich, Kirsten B. , Risch, Harvey A. , Hong, Chi‐Chen , Bandera, Elisa V. , Berchuck, Andrew , Cramer, Daniel W. , Doherty, Jennifer A. , Fridley, Brooke L. , Group, on behalf of The Australian Ovarian Cancer Study, Kjær, Susanne K. , Modugno, Francesmary , Winham, Stacey J , Webb, Penelope M , Jensen, Allan , Vierkant, Robert A , Moysich, Kirsten , Matsuo, Keitaro , Høgdall, Estrid , Zirpoli, Gary , Goodman, Marc T

Cancer Epidemiology, Biomarkers & Prevention | May 7, 2016 | Volume: 25 | Issue: 7 | Pages: 1114-1124

10.1158/1055-9965.epi-15-1330 Get It

Abstract Background: Despite a large body of literature evaluating the association between recreational physical activity and epithelial ovarian cancer (EOC) risk, the extant evidence is inconclusive, and little is known about the independent association between recreational physical inactivity and EOC risk. We conducted a pooled analysis of nine studies from the Ovarian Cancer Association Consortium to investigate the association between chronic recreational physical inactivity and EOC risk. Methods: In accordance with the 2008 Physical Activity Guidelines for Americans, women reporting no regular, weekly recreational physical activity were classified as inactive. Multivariable logistic regression was utilized to estimate the ORs and 95% confidence intervals (CI) for the association between inactivity and EOC risk overall and by subgroups based upon histotype, menopausal status, race, and body mass index. Results: The current analysis included data from 8,309 EOC patients and 12,612 controls. We observed a significant positive association between inactivity and EOC risk (OR = 1.34; 95% CI, 1.14–1.57), and similar associations were observed for each histotype. Conclusions: In this large pooled analysis examining the association between recreational physical inactivity and EOC risk, we observed consistent evidence of an association between chronic inactivity and all EOC histotypes. Impact: These data add to the growing body of evidence suggesting that inactivity is an independent risk factor for cancer. If the apparent association between inactivity and EOC risk is substantiated, additional work via targeted interventions should be pursued to characterize the dose of activity required to mitigate the risk of this highly fatal disease. Cancer Epidemiol Biomarkers Prev; 25(7); 1114–24. ©2016 AACR.