Amanda J Baxter1, Fiona J Charlson2, Hui G Cheng3, Rahul Shidhaye4, Alize J Ferrari2, Harvey A Whiteford2. 1. University of Queensland, School of Public Health, Herston, QLD, Australia; Queensland Centre for Mental Health Research, Wacol, QLD, Australia; University of Washington, Institute for Health Metrics and Evaluation, Seattle, WA, USA. Electronic address: amanda_baxter@qcmhr.uq.edu.au. 2. University of Queensland, School of Public Health, Herston, QLD, Australia; Queensland Centre for Mental Health Research, Wacol, QLD, Australia; University of Washington, Institute for Health Metrics and Evaluation, Seattle, WA, USA. 3. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 4. Public Health Foundation of India, New Delhi, India; CAPHRI School for Public Health and Primary Care, Maastricht University, Netherlands.
Abstract
BACKGROUND: Population-representative prevalence data for mental, neurological, and substance use disorders are essential for evidence-based decision making. As a background to the China-India Mental Health Alliance Series, we aim to examine the availability of data and report prevalence for the most common mental, neurological, and substance use disorders in China and India from the Global Burden of Disease study 2013 (GBD 2013). METHODS: In this systematic analysis, data sources were identified from GBD 2013 for the prevalence of mental, neurological, and substance use disorders in China and India published up to Dec 31, 2013. We calculated the proportion of the population represented by the data with the adjusted population coverage (APC) method adjusting for age, sex, and population size. We developed prevalence models with DisMod-MR 2.0, a Bayesian meta-regression instrument used to pool population-representative epidemiological data as part of GBD 2013. We report estimates and 95% uncertainly intervals (95% UI) for 15 mental, neurological, and substance use disorders for China and India in 1990 and 2013, and benchmark these against those for other BRICS countries (Brazil, Russia, and South Africa) in 2013. FINDINGS: Few population-representative data were found for the disorders, with an average coverage of 15% of the population of the Chinese mainland and 1% of the population of India. For men in both China and India, major depressive disorder, anxiety disorders, and alcohol dependence were the most common mental, neurological, and substance use disorders. Prevalence of major depressive disorder was 2·2% (95% UI 1·5-2·8) in Chinese men and 3·5% (2·4-4·6) in Indian men; prevalence of anxiety disorders was 2·0% (1·1-3·2) and 1·9% (1·2-2·3), respectively. For women, anxiety disorders, major depressive disorder, and dysthymia were the most common. Prevalence of major depressive disorder was 3·3% (2·3-4·1) in Chinese women and 4·7% (95% UI 3·3-6·2) in Indian women; prevalence of anxiety disorders was 3·3% (1·6-5·3) and 4·1% (3·3-5·0), respectively. Schizophrenia was more prevalent in China (0·5%, 95% UI 0·4-0·5) than in India (0·2%; 0·2-0·2). INTERPRETATION: More data for mental, neurological, and substance use disorders are needed for India and China but the large population and geographic scale of these countries present challenges to population-representative data collection. FUNDING: China-India Mental Health Alliance, China Medical Board.
BACKGROUND: Population-representative prevalence data for mental, neurological, and substance use disorders are essential for evidence-based decision making. As a background to the China-India Mental Health Alliance Series, we aim to examine the availability of data and report prevalence for the most common mental, neurological, and substance use disorders in China and India from the Global Burden of Disease study 2013 (GBD 2013). METHODS: In this systematic analysis, data sources were identified from GBD 2013 for the prevalence of mental, neurological, and substance use disorders in China and India published up to Dec 31, 2013. We calculated the proportion of the population represented by the data with the adjusted population coverage (APC) method adjusting for age, sex, and population size. We developed prevalence models with DisMod-MR 2.0, a Bayesian meta-regression instrument used to pool population-representative epidemiological data as part of GBD 2013. We report estimates and 95% uncertainly intervals (95% UI) for 15 mental, neurological, and substance use disorders for China and India in 1990 and 2013, and benchmark these against those for other BRICS countries (Brazil, Russia, and South Africa) in 2013. FINDINGS: Few population-representative data were found for the disorders, with an average coverage of 15% of the population of the Chinese mainland and 1% of the population of India. For men in both China and India, major depressive disorder, anxiety disorders, and alcohol dependence were the most common mental, neurological, and substance use disorders. Prevalence of major depressive disorder was 2·2% (95% UI 1·5-2·8) in Chinese men and 3·5% (2·4-4·6) in Indian men; prevalence of anxiety disorders was 2·0% (1·1-3·2) and 1·9% (1·2-2·3), respectively. For women, anxiety disorders, major depressive disorder, and dysthymia were the most common. Prevalence of major depressive disorder was 3·3% (2·3-4·1) in Chinese women and 4·7% (95% UI 3·3-6·2) in Indian women; prevalence of anxiety disorders was 3·3% (1·6-5·3) and 4·1% (3·3-5·0), respectively. Schizophrenia was more prevalent in China (0·5%, 95% UI 0·4-0·5) than in India (0·2%; 0·2-0·2). INTERPRETATION: More data for mental, neurological, and substance use disorders are needed for India and China but the large population and geographic scale of these countries present challenges to population-representative data collection. FUNDING: China-India Mental Health Alliance, China Medical Board.
Authors: Christopher G Kemp; Leslie C M Johnson; Rajesh Sagar; Subramani Poongothai; Nikhil Tandon; Ranjit Mohan Anjana; Sosale Aravind; Gumpeny R Sridhar; Shivani A Patel; Karl Emmert-Fees; Deepa Rao; K M V Narayan; Viswanathan Mohan; Mohammed K Ali; Lydia A Chwastiak Journal: Gen Hosp Psychiatry Date: 2021-11-27 Impact factor: 3.238
Authors: Fabian Termorshuizen; Els van der Ven; Ilaria Tarricone; Hannah E Jongsma; Charlotte Gayer-Anderson; Antonio Lasalvia; Sarah Tosato; Diego Quattrone; Caterina La Cascia; Andrei Szöke; Domenico Berardi; Pierre-Michel Llorca; Lieuwe de Haan; Eva Velthorst; Miguel Bernardo; Julio Sanjuán; Manuel Arrojo; Robin M Murray; Bart P Rutten; Peter B Jones; Jim van Os; James B Kirkbride; Craig Morgan; Jean-Paul Selten Journal: Psychol Med Date: 2020-09-22 Impact factor: 10.592