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- 2020 International Society of Hypertension Global Hypertension Practice Guidelines.Hypertension. 2020; 75: 1334-1357https://doi.org/10.1161/HYPERTENSIONAHA.120.15026
- The PCORnet Blood Pressure Control Laboratory: a platform for surveillance and efficient trials.Circ Cardiovasc Qual Outcomes. 2020; 13e006115https://doi.org/10.1161/CIRCOUTCOMES.119.006115
- Trends in blood pressure control among U.S. adults with hypertension, 1999-2000 to 2017-2018.JAMA. 2020; 324: 1190-1200https://doi.org/10.1001/jama.2020.14545
- Long-term and recent trends in hypertension awareness, treatment, and control in 12 high-income countries: an analysis of 123 nationally representative surveys.Lancet. 2019; 394: 639-651https://doi.org/10.1016/S0140-6736(19)31145-6
- Type 2 diabetes and hypertension.Circ Res. 2019; 124: 930-937https://doi.org/10.1161/CIRCRESAHA.118.314487
- Impact of major depression on cardiovascular outcomes for individuals with hypertension: prospective survival analysis in UK Biobank.BMJ Open. 2019; 9e024433https://doi.org/10.1136/bmjopen-2018-024433
- Association of anxiety and depression with hypertension control: a U.S. multidisciplinary group practice observational study.J Hypertens. 2015; 33: 2215-2222https://doi.org/10.1097/HJH.0000000000000693
- Blood pressure values and depression in hypertensive individuals at high cardiovascular risk.BMC Cardiovasc Disord. 2014; 14: 109https://doi.org/10.1186/1471-2261-14-109
- Multimorbidity trends in United States adults, 1988-2014.J Am Board Fam Med. 2018; 31: 503-513https://doi.org/10.3122/jabfm.2018.04.180008
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- Trends in antihypertensive medication monotherapy and combination use among U.S. adults, National Health and Nutrition Examination Survey 2005-2016.Hypertension. 2020; 75: 973-981https://doi.org/10.1161/HYPERTENSIONAHA.119.14360
- Trends in antihypertensive medication use among U.S. patients with resistant hypertension, 2008 to 2014.Hypertension. 2016; 68: 1349-1354https://doi.org/10.1161/HYPERTENSIONAHA.116.08128
- Evaluating the representativeness of U.S. centricity electronic medical records with reports from Centers for Disease Control and Prevention: office visits and cardiometabolic conditions.JMIR Med Inform. 2020; 8: e17174https://doi.org/10.2196/preprints.17174
- Comparison of GE centricity electronic medical record database and National Ambulatory Medical Care Survey findings on the prevalence of major conditions in the United States.Popul Health Manag. 2010; 13: 139-150https://doi.org/10.1089/pop.2009.0036
Brixner D, Said Q, Kirkness C, Oberg B, Ben-Joseph R, Oderda G. Assessment of cardiometabolic risk factors in a national primary care electronic health record database. Value Health. 2007;10(s1):S29‒S36. doi:10.1111/j.1524-4733.2006.00152.x.
- Long-term sustainability of glycaemic achievements with second-line antidiabetic therapies in patients with type 2 diabetes: a real-world study.Diabetes Obes Metab. 2018; 20: 1722-1731https://doi.org/10.1111/dom.13288
- Data mining approach to estimate the duration of drug therapy from longitudinal electronic medical records.Open Bioinforma J. 2017; 10: 1-15https://doi.org/10.2174/1875036201709010001
- Long-term trends in antidiabetes drug usage in the U.S.: real-world evidence in patients newly diagnosed with type 2 diabetes.Diabetes Care. 2018; 41: 69-78https://doi.org/10.2337/dc17-1414
- Cardiovascular risk factor burden in people with incident type 2 diabetes in the U.S. receiving antidiabetic and cardioprotective therapies.Diabetes Care. 2019; 42: 644-650https://doi.org/10.2337/dc18-1865
- Data mining approach to identify disease cohorts from primary care electronic medical records: a case of diabetes mellitus.Open Bioinforma J. 2017; 10: 16-27https://doi.org/10.2174/1875036201710010016
- Defining major depressive disorder cohorts using the EHR: multiple phenotypes based on ICD-9 codes and medication orders.Neurol Psychiatry Brain Res. 2020; 36: 18-26https://doi.org/10.1016/j.npbr.2020.02.002
- Using electronic health records to characterize prescription patterns: focus on antidepressants in nonpsychiatric outpatient settings.JAMIA Open. 2018; 1: 233-245https://doi.org/10.1093/jamiaopen/ooy037
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Mental illness. National Institute of Mental Health. https://www.nimh.nih.gov/health/statistics/mental-illness.shtml#part_154785. Accessed July 31, 2019.
- Statistical challenges in analysing large longitudinal patient-level data: the danger of misleading clinical inferences with imputed data.J Indian Soc Agric Stat. 2014; 68: 39-54
- Hypertension cascade: hypertension prevalence, treatment and control estimates among us adults aged 18 years and older applying the criteria from the American College of Cardiology and American Heart Association’s 2017 Hypertension Guideline-NHANES 2013–2016.HHS, Atlanta, GA2019https://millionhearts.hhs.gov/data-reports/hypertension-prevalence.htmlDate accessed: June 15, 2021
- Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: a validation study from a U.S. electronic health record database.Pharmacoepidemiol Drug Saf. 2017; 26: 963-972https://doi.org/10.1002/pds.4234