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Jonathan W. Kowalski

Bio: Jonathan W. Kowalski is an academic researcher from Allergan. The author has contributed to research in topics: Population & Bimatoprost. The author has an hindex of 24, co-authored 54 publications receiving 5967 citations.


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Journal ArticleDOI
TL;DR: Longer diabetes duration and poorer glycemic and blood pressure control are strongly associated with DR, and these data highlight the substantial worldwide public health burden of DR and the importance of modifiable risk factors in its occurrence.
Abstract: OBJECTIVE To examine the global prevalence and major risk factors for diabetic retinopathy (DR) and vision-threatening diabetic retinopathy (VTDR) among people with diabetes. RESEARCH DESIGN AND METHODS A pooled analysis using individual participant data from population-based studies around the world was performed. A systematic literature review was conducted to identify all population-based studies in general populations or individuals with diabetes who had ascertained DR from retinal photographs. Studies provided data for DR end points, including any DR, proliferative DR, diabetic macular edema, and VTDR, and also major systemic risk factors. Pooled prevalence estimates were directly age-standardized to the 2010 World Diabetes Population aged 20–79 years. RESULTS A total of 35 studies (1980–2008) provided data from 22,896 individuals with diabetes. The overall prevalence was 34.6% (95% CI 34.5–34.8) for any DR, 6.96% (6.87–7.04) for proliferative DR, 6.81% (6.74–6.89) for diabetic macular edema, and 10.2% (10.1–10.3) for VTDR. All DR prevalence end points increased with diabetes duration, hemoglobin A 1c , and blood pressure levels and were higher in people with type 1 compared with type 2 diabetes. CONCLUSIONS There are approximately 93 million people with DR, 17 million with proliferative DR, 21 million with diabetic macular edema, and 28 million with VTDR worldwide. Longer diabetes duration and poorer glycemic and blood pressure control are strongly associated with DR. These data highlight the substantial worldwide public health burden of DR and the importance of modifiable risk factors in its occurrence. This study is limited by data pooled from studies at different time points, with different methodologies and population characteristics.

3,282 citations

Journal ArticleDOI
TL;DR: The study provides summary data on the prevalence of RVO and suggests that approximately 16 million people may have this condition and research on preventive and treatment strategies for this sight-threatening eye disease is needed.

899 citations

Journal ArticleDOI
TL;DR: Visual acuity generally improved in eyes with BRVO without intervention, although clinically significant improvement beyond 20/40 was uncommon, and the best available evidence from the literature indicated this was uncommon.

542 citations

Journal ArticleDOI
TL;DR: Hyperhidrosis affects a much larger proportion of the US population than previously reported and more than half of these individuals have axillary hyperhidrosis, in which sweating can result in occupational, emotional, psychological, social, and physical impairment.
Abstract: Background The current epidemiologic data on hyperhidrosis are scarce and insufficient to provide precise prevalence or impact estimates. Objective We sought to estimate the prevalence of hyperhidrosis in the US population and assess the impact of sweating on those affected by axillary hyperhidrosis. Methods A nationally representative sample of 150,000 households was screened by mailed survey for hyperhidrosis and projected to the US population based on US census data. Ascertainment of hyperhidrosis was based on a question that asked whether participants experienced excessive or abnormal/unusual sweating. Results The prevalence of hyperhidrosis in the survey sample was 2.9% (6800 individuals). The projected prevalence of hyperhidrosis in the United States is 2.8% (7.8 million individuals), and 50.8% of this population (4.0 million individuals) reported that they have axillary hyperhidrosis (1.4% of the US population). Only 38% had discussed their sweating with a health care professional. Approximately one third of individuals with axillary hyperhidrosis (0.5% of the US population or 1.3 million individuals) reported that their sweating is barely tolerable and frequently interferes, or is intolerable and always interferes, with daily activities. Conclusion Hyperhidrosis affects a much larger proportion of the US population than previously reported. More than half of these individuals have axillary hyperhidrosis, in which sweating can result in occupational, emotional, psychological, social, and physical impairment.

509 citations


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Journal ArticleDOI
04 Sep 2013-JAMA
TL;DR: The estimated prevalence of diabetes among a representative sample of Chinese adults was 11.6% and the prevalence of prediabetes was 50.1%, which indicates the importance of diabetes as a public health problem in China.
Abstract: Importance Noncommunicable chronic diseases have become the leading causes of mortality and disease burden worldwide. Objective To investigate the prevalence of diabetes and glycemic control in the Chinese adult population. Design, Setting, and Participants Using a complex, multistage, probability sampling design, we conducted a cross-sectional survey in a nationally representative sample of 98 658 Chinese adults in 2010. Main Outcomes and Measures Plasma glucose and hemoglobin A 1c levels were measured after at least a 10-hour overnight fast among all study participants, and a 2-hour oral glucose tolerance test was conducted among participants without a self-reported history of diagnosed diabetes. Diabetes and prediabetes were defined according to the 2010 American Diabetes Association criteria; whereas, a hemoglobin A 1c level of Results The overall prevalence of diabetes was estimated to be 11.6% (95% CI, 11.3%-11.8%) in the Chinese adult population. The prevalence among men was 12.1% (95% CI, 11.7%-12.5%) and among women was 11.0% (95% CI, 10.7%-11.4%). The prevalence of previously diagnosed diabetes was estimated to be 3.5% (95% CI, 3.4%-3.6%) in the Chinese population: 3.6% (95% CI, 3.4%-3.8%) in men and 3.4% (95% CI, 3.2%-3.5%) in women. The prevalence of undiagnosed diabetes was 8.1% (95% CI, 7.9%-8.3%) in the Chinese population: 8.5% (95% CI, 8.2%-8.8%) in men and 7.7% (95% CI, 7.4%-8.0%) in women. In addition, the prevalence of prediabetes was estimated to be 50.1% (95% CI, 49.7%-50.6%) in Chinese adults: 52.1% (95% CI, 51.5%-52.7%) in men and 48.1% (95% CI, 47.6%-48.7%) in women. The prevalence of diabetes was higher in older age groups, in urban residents, and in persons living in economically developed regions. Among patients with diabetes, only 25.8% (95% CI, 24.9%-26.8%) received treatment for diabetes, and only 39.7% (95% CI, 37.6%-41.8%) of those treated had adequate glycemic control. Conclusions and Relevance The estimated prevalence of diabetes among a representative sample of Chinese adults was 11.6% and the prevalence of prediabetes was 50.1%. Projections based on sample weighting suggest this may represent up to 113.9 million Chinese adults with diabetes and 493.4 million with prediabetes. These findings indicate the importance of diabetes as a public health problem in China.

2,337 citations

Journal ArticleDOI
Seth Flaxman1, Rupert R A Bourne2, Serge Resnikoff3, Serge Resnikoff4, Peter Ackland5, Tasanee Braithwaite6, Maria V Cicinelli, Aditi Das7, Jost B. Jonas8, Jill E Keeffe9, John H. Kempen10, Janet L Leasher11, Hans Limburg, Kovin Naidoo12, Kovin Naidoo3, Konrad Pesudovs13, Alexander J Silvester, Gretchen A Stevens14, Nina Tahhan4, Nina Tahhan3, Tien Yin Wong15, Hugh R. Taylor16, Rupert R A Bourne2, Aries Arditi, Yaniv Barkana, Banu Bozkurt17, Alain M. Bron, Donald L. Budenz18, Feng Cai, Robert J Casson19, Usha Chakravarthy20, Jaewan Choi, Maria Vittoria Cicinelli, Nathan Congdon20, Reza Dana21, Rakhi Dandona22, Lalit Dandona23, Iva Dekaris, Monte A. Del Monte24, Jenny deva25, Laura E. Dreer26, Leon B. Ellwein27, Marcela Frazier26, Kevin D. Frick28, David S. Friedman28, João M. Furtado29, H. Gao30, Gus Gazzard31, Ronnie George32, Stephen Gichuhi33, Victor H. Gonzalez, Billy R. Hammond34, Mary Elizabeth Hartnett35, Minguang He16, James F. Hejtmancik, Flavio E. Hirai36, John J Huang37, April D. Ingram38, Jonathan C. Javitt28, Jost B. Jonas8, Charlotte E. Joslin39, John H Kempen10, Moncef Khairallah, Rohit C Khanna9, Judy E. Kim40, George N. Lambrou41, Van C. Lansingh, Paolo Lanzetta42, Jennifer I. Lim43, Kaweh Mansouri, Anu A. Mathew44, Alan R. Morse, Beatriz Munoz, David C. Musch24, Vinay Nangia, Maria Palaiou10, Maurizio Battaglia Parodi, Fernando Yaacov Pena, Tunde Peto20, Harry A. Quigley, Murugesan Raju45, Pradeep Y. Ramulu46, Zane Rankin15, Dana Reza21, Alan L. Robin23, Luca Rossetti47, Jinan B. Saaddine46, Mya Sandar15, Janet B. Serle48, Tueng T. Shen23, Rajesh K. Shetty49, Pamela C. Sieving27, Juan Carlos Silva50, Rita S. Sitorus51, Dwight Stambolian52, Gretchen Stevens14, Hugh Taylor16, Jaime Tejedor, James M. Tielsch28, Miltiadis K. Tsilimbaris53, Jan C. van Meurs, Rohit Varma54, Gianni Virgili55, Ya Xing Wang56, Ningli Wang56, Sheila K. West, Peter Wiedemann57, Tien Wong15, Richard Wormald6, Yingfeng Zheng15 
Imperial College London1, Anglia Ruskin University2, Brien Holden Vision Institute3, University of New South Wales4, International Agency for the Prevention of Blindness5, Moorfields Eye Hospital6, York Hospital7, Heidelberg University8, L V Prasad Eye Institute9, Massachusetts Eye and Ear Infirmary10, Nova Southeastern University11, University of KwaZulu-Natal12, National Health and Medical Research Council13, World Health Organization14, National University of Singapore15, University of Melbourne16, Selçuk University17, University of Miami18, University of Adelaide19, Queen's University Belfast20, Harvard University21, The George Institute for Global Health22, University of Washington23, University of Michigan24, Universiti Tunku Abdul Rahman25, University of Alabama at Birmingham26, National Institutes of Health27, Johns Hopkins University28, University of São Paulo29, Henry Ford Health System30, University College London31, Sankara Nethralaya32, University of Nairobi33, University of Georgia34, University of Utah35, Federal University of São Paulo36, Yale University37, Alberta Children's Hospital38, University of Illinois at Chicago39, Medical College of Wisconsin40, Novartis41, University of Udine42, University of Illinois at Urbana–Champaign43, Royal Children's Hospital44, University of Missouri45, Centers for Disease Control and Prevention46, University of Milan47, Icahn School of Medicine at Mount Sinai48, Mayo Clinic49, Pan American Health Organization50, University of Indonesia51, University of Pennsylvania52, University of Crete53, University of Southern California54, University of Florence55, Capital Medical University56, Leipzig University57
TL;DR: A series of regression models were fitted to estimate the proportion of moderate or severe vision impairment and blindness by cause, age, region, and year, and found that world regions varied markedly in the causes of blindness and vision impairment in this age group.

1,909 citations

01 Feb 2009
TL;DR: eMedicine创建于1996年,由近万名临床医师作为作者或编辑参与此临校医学知识库。
Abstract: eMedicine创建于1996年,由近万名临床医师作为作者或编辑参与此临床医学知识库的建设,其中编辑均是来自美国哈佛、耶鲁、斯坦福、芝加哥、德克萨斯、加州大学等各分校医学院的教授或副教授。

1,459 citations

Journal ArticleDOI
12 Dec 2017-JAMA
TL;DR: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases.
Abstract: Importance A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures Use of a deep learning system. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

1,309 citations