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Wang C, Gao Y, Zhu L, Huang M, Wu Y, Xuan J. Treatment Patterns in Patients With Newly Diagnosed Type 2 Diabetes in China: A Retrospective, Longitudinal Database Study. Clin Ther 2019; 41:1440-1452. [PMID: 31155146 DOI: 10.1016/j.clinthera.2019.05.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/06/2019] [Accepted: 05/07/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE The objectives of this study were to examine the patterns of antihyperglycemic drug (AHD) therapy among patients with newly diagnosed type 2 diabetes mellitus (T2DM) in the general Chinese population, stratified by initial hemoglobin (Hb) A1c level, and to assess whether treatment patterns are consistent with the recommendations published in the China Diabetes Society's clinical treatment guideline. METHODS A retrospective database analysis was conducted, and data were obtained from the SuValue database. Prescribing patterns for diabetes treatments were determined from data obtained from the Nanhai District-based electronic medical records database, a subset of the SuValue database. Data from patients newly diagnosed with T2DM who also had at least 2 prescriptions for AHD medications after diagnosis and at least 1 HbA1c test result during the 12 months prior to AHD treatment initiation, between January 1, 2004, and July 22, 2018, were included in the analysis. ANOVA, χ2 test, and Kaplan-Meier survival analysis were used to examine differences between 4 initial-HbA1c groups (<7%, 7%-<8%, 8%-<9%, and ≥9%). FINDINGS A total of 4712 patients were included, with women accounting for 47.8%; the mean age (SD) of the study population was 56.44 (12.57) years. Men were more likely to have had a higher HbA1c level at initial AHD treatment (P < 0.0001). The first-line therapies most frequently prescribed were metformin combination (29.5%), followed by insulin-including treatment (25.9%), and metformin monotherapy (19.2%). Metformin monotherapy (29.5%) was most commonly prescribed in patients with an HbA1c level of <7%; metformin combination (31.7%), in patients with an HbA1c level of 7%-<8%; and insulin-containing treatment, in patients with HbA1c levels of 8%-<9% (28.1%) and ≥9% (38.4%). Insulin-including treatment was more commonly prescribed than was metformin combination in patients with an initial HbA1c level of ≥8% after initial treatment. In third- and fourth-line treatments, patients with an HbA1c level of ≥8% more prevalently were prescribed metformin combination and insulin-including treatment, while metformin combination and "other" treatment were more generally prescribed in patients with an HbA1c level of ≤8%. However, 8.8% of patients with an HbA1c level of <7% were prescribed insulin-including treatment as first-line therapy. In all lines of treatment, the percentages of patients prescribed insulin were increased with initial HbA1c levels. A similar pattern was seen with dipeptidyl peptidase 4 inhibitors after first-line treatment. Overall, the median time to treatment switch was shorter than 3 months. IMPLICATIONS The findings from the present study depict a comprehensive overview of AHD-treatment patterns in patients stratified by HbA1c level. The current treatment practices observed were inconsistent the published guideline, in terms of recommendations on metformin monotherapy and insulin use in first-line therapy.
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Affiliation(s)
- Chunping Wang
- School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Yue Gao
- Shanghai Centennial Scientific Co Ltd., Shanghai, China
| | - Lifeng Zhu
- Shanghai Suvalue Health Scientific Ltd., Shanghai, China
| | - Min Huang
- School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Yin Wu
- Shanghai Suvalue Health Scientific Ltd., Shanghai, China
| | - Jianwei Xuan
- School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, China.
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Sarasua SM, Li J, Hernandez GT, Ferdinand KC, Tobin JN, Fiscella KA, Jones DW, Sinopoli A, Egan BM. Opportunities for improving cardiovascular health outcomes in adults younger than 65 years with guideline-recommended statin therapy. J Clin Hypertens (Greenwich) 2017; 19:850-860. [PMID: 28480530 DOI: 10.1111/jch.13004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 02/01/2017] [Accepted: 02/13/2017] [Indexed: 10/19/2022]
Abstract
The impact of age, race/ethnicity, healthcare insurance, and selected clinical variables on statin-preventable ASCVD were quantified in adults aged 21 to 79 years from National Health and Nutrition Examination Surveys 2007-2012 using the 2013 American College of Cardiology/American Heart Association guideline on the treatment of cholesterol. Among ≈42.4 million statin-eligible, untreated adults, 52.6% were hypertensive and 71% were younger than 65 years. Of ≈232 000 statin-preventable ASCVD events annually, most occur in individuals younger than 65 years, with higher proportions in blacks and Hispanics than whites (73.0% and 69.2% vs 56.9%, respectively; P<.01). Among adults younger than 65 years, the ratio of statin-eligible but untreated to statin-treated adults was higher in blacks and Hispanics than whites (3.0 and 2.9 vs 1.3, respectively; P<.01), and blacks, men, hypertensives, and cigarette smokers were more likely to be statin eligible than their statin-ineligible counterparts by multivariable logistic regression. Two thirds of untreated statin-eligible adults had two or more healthcare visits per year. Identifying and treating more statin-eligible adults in the healthcare system could improve cardiovascular health equity.
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Affiliation(s)
- Sara M Sarasua
- Care Coordination Institute, Greenville, SC, USA.,Clemson University, School of Nursing, Clemson, SC, USA
| | - Jiexiang Li
- Department of Mathematics, College of Charleston, Charleston, SC, USA
| | - German T Hernandez
- Department of Internal Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA
| | - Keith C Ferdinand
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Jonathan N Tobin
- Clinical Directors Network (CDN), New York, NY, USA.,Center for Clinical and Translational Science, The Rockefeller University, New York, NY, USA.,Department of Epidemiology and Population Health, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
| | - Kevin A Fiscella
- Department of Family Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Daniel W Jones
- Departments of Medicine and Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Angelo Sinopoli
- Care Coordination Institute, Greenville, SC, USA.,University of South Carolina School of Medicine-Greenville, Greenville, SC, USA
| | - Brent M Egan
- Care Coordination Institute, Greenville, SC, USA.,University of South Carolina School of Medicine-Greenville, Greenville, SC, USA
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Dinkler JM, Sugar CA, Escarce JJ, Ong MK, Mangione CM. Does Age Matter? Association Between Usual Source of Care and Hypertension Control in the US Population: Data From NHANES 2007-2012. Am J Hypertens 2016; 29:934-40. [PMID: 26884134 PMCID: PMC5006109 DOI: 10.1093/ajh/hpw010] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 01/15/2016] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The positive role of having a usual source of care (USOC) on the receipt of preventative services is known. However, associations between USOC and hypertension control and the differential association across age groups is unknown in the US population. METHODS We used data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012. Multivariable logistic regression was used to evaluate the association between having a USOC and hypertension control. The differential effect of USOC on hypertension control by age was assessed using predicted marginal effects across age groups in the multivariable logistic model. RESULTS In adjusted analyses, those with a USOC had higher odds of hypertension control (odds ratio = 3.89, 95% confidence interval (CI): 2.15-6.98). The marginal effect of having a USOC is associated with a 30 percentage point higher probability of controlled blood pressure compared to those without a USOC (marginal probability = 0.30, 95% CI: 0.19-0.41). The marginal effect of USOC on hypertension control varied by age groups, with a statistically significantly lower marginal effect of USOC on hypertension seen among those older than 74 years of age (marginal probability = 0.27, 95% CI: 0.18-0.36) and younger than 35 years of age (marginal probability = 0.23, 95% CI: 0.14-0.33). CONCLUSION Having a USOC is significantly associated with improved hypertension control in the US population. The variation in the association across age groups has important implications in targeting age-specific antihypertensive strategies to reduce the burden of hypertension in the US population.
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Affiliation(s)
- John M Dinkler
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, California, USA; Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California, USA;
| | - Catherine A Sugar
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, California, USA
| | - José J Escarce
- Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California, USA; Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
| | - Michael K Ong
- Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA; VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Carol M Mangione
- Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California, USA; Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
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7
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Howard G, Moy CS, Howard VJ, McClure LA, Kleindorfer DO, Kissela BM, Judd SE, Unverzagt FW, Soliman EZ, Safford MM, Cushman M, Flaherty ML, Wadley VG. Where to Focus Efforts to Reduce the Black-White Disparity in Stroke Mortality: Incidence Versus Case Fatality? Stroke 2016; 47:1893-8. [PMID: 27256672 PMCID: PMC4927373 DOI: 10.1161/strokeaha.115.012631] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 04/18/2016] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND PURPOSE At age 45 years, blacks have a stroke mortality ≈3× greater than their white counterparts, with a declining disparity at older ages. We assess whether this black-white disparity in stroke mortality is attributable to a black-white disparity in stroke incidence versus a disparity in case fatality. METHODS We first assess if black-white differences in stroke mortality within 29 681 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort reflect national black-white differences in stroke mortality and then assess the degree to which black-white differences in stroke incidence or 30-day case fatality after stroke contribute to the disparities in stroke mortality. RESULTS The pattern of stroke mortality within the study mirrors the national pattern, with the black-to-white hazard ratio of ≈4.0 at age 45 years decreasing to ≈1.0 at age 85 years. The pattern of black-to-white disparities in stroke incidence shows a similar pattern but no evidence of a corresponding disparity in stroke case fatality. CONCLUSIONS These findings show that the black-white differences in stroke mortality are largely driven by differences in stroke incidence, with case fatality playing at most a minor role. Therefore, to reduce the black-white disparity in stroke mortality, interventions need to focus on prevention of stroke in blacks.
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Affiliation(s)
- George Howard
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.).
| | - Claudia S Moy
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Virginia J Howard
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Leslie A McClure
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Dawn O Kleindorfer
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Brett M Kissela
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Suzanne E Judd
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Fredrick W Unverzagt
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Elsayed Z Soliman
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Monika M Safford
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Mary Cushman
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Matthew L Flaherty
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
| | - Virginia G Wadley
- From the Departments of Biostatistics (G.H., S.E.J.) and Epidemiology (V.J.H.), University of Alabama at Birmingham School of Public Health; Office of Clinical Research, NINDS/NIH, Bethesda, MD (C.S.M.); Department of Epidemiology and Biostatistics, Dornsife School of Public Health at Drexel University, Philadelphia, PA (L.A.M.); Department of Neurology, University of Cincinnati, OH (D.O.K., B.M.K., M.L.F.); Department of Psychology, Indiana University, Indianapolis (F.W.U.); Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, NC (E.Z.S.); Department of General Internal Medicine, Weill Cornell School of Medicine, New York, NY (M.M.S., V.G.W.); and Department of Medicine, University of Vermont School of Medicine, Burlington (M.C.)
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Egan BM, Li J, Hutchison FN, Ferdinand KC. Hypertension in the United States, 1999 to 2012: progress toward Healthy People 2020 goals. Circulation 2014; 130:1692-9. [PMID: 25332288 DOI: 10.1161/circulationaha.114.010676] [Citation(s) in RCA: 161] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND To reduce the cardiovascular disease burden, Healthy People 2020 established US hypertension goals for adults to (1) decrease the prevalence to 26.9% and (2) raise treatment to 69.5% and control to 61.2%, which requires controlling 88.1% on treatment. METHODS AND RESULTS To assess the current status and progress toward these Healthy People 2020 goals, time trends in National Health and Nutrition Examination Surveys 1999 to 2012 data in 2-year blocks were assessed in adults ≥18 years of age age-adjusted to US 2010. From 1999 to 2000 to 2011 to 2012, prevalent hypertension was unchanged (30.1% versus 30.8%, P=0.32). Hypertension treatment (59.8% versus 74.7%, P<0.001) and proportion of treated adults controlled (53.3%-68.9%, P=0.0015) increased. Hypertension control to <140/<90 mm Hg rose every 2 years from 1999 to 2000 to 2009 to 2010 (32.2% versus 53.8%, P<0.001) before declining to 51.2% in 2011 to 2012. Modifiable factor(s) significant in multivariable logistic regression modeling include: (1) increasing body mass index with prevalent hypertension (odds ratio [OR], 1.44); (2) lack of health insurance (OR, 1.68) and <2 healthcare visits per year (OR, 4.24) with untreated hypertension; (3) healthcare insurance (OR, 1.69), ≥2 healthcare visits per year (OR, 3.23), and cholesterol treatment (OR, 1.90) with controlled hypertension. CONCLUSIONS The National Health and Nutrition Examination Survey 1999 to 2012 analysis suggests that Healthy People 2020 goals for hypertension ([1] prevalence shows no progress, [2] treatment was exceeded, and [3] control) have flattened below target. Findings are consistent with evidence that (1) obesity prevention and treatment could reduce prevalent hypertension, and (2) healthcare insurance, ≥2 healthcare visits per year, and guideline-based cholesterol treatment could improve hypertension control.
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Affiliation(s)
- Brent M Egan
- From the Care Coordination Institute and University of South Carolina School of Medicine-Greenville, Greenville Health System, Greenville, SC (B.M.E.); Department of Mathematics, College of Charleston, Charleston, SC (J.L.); Medical University of South Carolina and Ralph H. Johnson Veterans Affairs Medical Center, Charleston, SC (F.N.H.); and Tulane University School of Medicine, New Orleans, LA (K.C.F.).
| | - Jiexiang Li
- From the Care Coordination Institute and University of South Carolina School of Medicine-Greenville, Greenville Health System, Greenville, SC (B.M.E.); Department of Mathematics, College of Charleston, Charleston, SC (J.L.); Medical University of South Carolina and Ralph H. Johnson Veterans Affairs Medical Center, Charleston, SC (F.N.H.); and Tulane University School of Medicine, New Orleans, LA (K.C.F.)
| | - Florence N Hutchison
- From the Care Coordination Institute and University of South Carolina School of Medicine-Greenville, Greenville Health System, Greenville, SC (B.M.E.); Department of Mathematics, College of Charleston, Charleston, SC (J.L.); Medical University of South Carolina and Ralph H. Johnson Veterans Affairs Medical Center, Charleston, SC (F.N.H.); and Tulane University School of Medicine, New Orleans, LA (K.C.F.)
| | - Keith C Ferdinand
- From the Care Coordination Institute and University of South Carolina School of Medicine-Greenville, Greenville Health System, Greenville, SC (B.M.E.); Department of Mathematics, College of Charleston, Charleston, SC (J.L.); Medical University of South Carolina and Ralph H. Johnson Veterans Affairs Medical Center, Charleston, SC (F.N.H.); and Tulane University School of Medicine, New Orleans, LA (K.C.F.)
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