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Dandona R, Khan M. Engagement With Death Registration and Cause-of-Death Reporting to Strengthen Suicide Statistics. CRISIS 2024; 45:249-253. [PMID: 39138983 DOI: 10.1027/0227-5910/a000962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Affiliation(s)
- Rakhi Dandona
- Public Health Foundation of India, New Delhi, India
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Centre for Mental Health and Community Wellbeing, Melbourne School of Population and Global Health, The University of Melbourne, VIC, Australia
| | - Murad Khan
- Department of Psychiatry and Brain and Mind Institute, Aga Khan University, Karachi, Pakistan
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Sainbayar A, Gombojav D, Lundeg G, Byambaa B, Meier J, Dünser MW, Mendsaikhan N. Out-of-hospital deaths in Mongolia: a nationwide cohort study on the proportion, causes, and potential impact of emergency and critical care services. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 39:100867. [PMID: 37927992 PMCID: PMC10625029 DOI: 10.1016/j.lanwpc.2023.100867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/30/2023] [Accepted: 07/17/2023] [Indexed: 11/07/2023]
Abstract
Background Little is known about the proportion and causes of out-of-hospital deaths in Mongolia. In this study, we aimed to determine the proportion and causes of out-of-hospital deaths in Mongolia during a six-month observation period before the COVID-19 pandemic. Methods In a retrospective study, the Mongolian National Death Registry was screened for all deaths occurring from 01 to 06/2020. The proportion and causes of out-of-hospital deaths, causes of out-of-hospital deaths likely treatable by emergency/critical care interventions, as well as sex, regional and seasonal differences in the proportion and causes of out-of-hospital deaths were determined. The primary endpoint was the proportion and causes of out-of-hospital death in children and adults. Descriptive statistical methods, the Fisher's Exact, multirow Chi2-or Mann-Whitney-U-rank sum tests were used for data analysis. Findings Five-thousand-five-hundred-fifty-three of 7762 deaths (71.5%) occurred outside of a hospital. The proportion of out-of-hospital deaths was lower in children than adults (39.3% vs. 74.8%, p < 0.001). Trauma, chronic neurological diseases, lower respiratory tract infections, congenital birth defects, and neonatal disorders were the causes of out-of-hospital deaths resulting in most years of life lost in children. In adults, chronic heart diseases, trauma, liver cancer, poisonings, and self-harm caused the highest burden of premature mortality. The proportion of out-of-hospital deaths did not differ between females and males (70.5% vs. 72.2%, p = 0.09). The proportion (all, p < 0.001; adults, p < 0.001; children, p < 0.001) and causes (adults, p < 0.001; children, p < 0.001) of out-of-hospital deaths differed between Mongolian regions and Ulaanbaatar. The proportion of out-of-hospital deaths was higher during winter than spring/summer months (72.3% vs. 69.9%, p = 0.03). An expert panel estimated that 49.3% of out-of-hospital deaths were likely treatable by emergency/critical care interventions. Interpretation With regional and seasonal variations, about 75% of Mongolian adults and 40% of Mongolian children died outside of a hospital. Heart diseases, trauma, cancer, and poisonings resulted in most years of life lost. About half of the causes of out-of-hospital deaths could be treated by emergency/critical care interventions. Funding Institutional funding.
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Affiliation(s)
- Altanchimeg Sainbayar
- Department of Critical Care and Anesthesia, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
- Intensive Care Unit, Mongolia Japan Hospital, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
| | - Davaa Gombojav
- Department of Epidemiology and Biostatistics, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
| | - Ganbold Lundeg
- Department of Critical Care and Anesthesia, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
- Intensive Care Unit, Mongolia Japan Hospital, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
| | - Boldbaatar Byambaa
- Department of Health Statistics, Centre for Health Development, Ulaanbaatar, Mongolia
| | - Jens Meier
- Department of Anesthesiology and Intensive Care Medicine, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - Martin W. Dünser
- Department of Anesthesiology and Intensive Care Medicine, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - Naranpurev Mendsaikhan
- Department of Critical Care and Anesthesia, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
- Intensive Care Unit, Mongolia Japan Hospital, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
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Huang L, Li OZ, Yin X. Inferring China's excess mortality during the COVID-19 pandemic using online mourning and funeral search volume. Sci Rep 2023; 13:15665. [PMID: 37730765 PMCID: PMC10511516 DOI: 10.1038/s41598-023-42979-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 09/17/2023] [Indexed: 09/22/2023] Open
Abstract
We construct a mourning and funeral index, using online search volume for "wreath and elegiac couplet", "obituary", "mortuary house", "cinerary casket", "cremation" and "pass away", to infer excess cases of mortality in China during the COVID-19 pandemic. During the 3-month period (December 2022-February 2023) after China ended its Zero-COVID policy, there were around 712 thousand excess cases of mortality. These excess cases of mortality, bench marked against the 2-year period preceding the pandemic, could be directly or indirectly related to COVID-19. During the 35-month Zero-COVID regime (January 2020-November 2022), the excess death toll was a negative 1480 thousand. Overall, by delaying the surge in infections, China might have saved 767 thousand lives. While these estimates are based on various assumptions and can be imprecise, China's COVID-19 experience could reasonably be characterized by a sharp surge in deaths after its departure from Zero-COVID and a steady pattern of lives saved during the Zero-COVID regime.
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Affiliation(s)
- Li Huang
- Shanghai Jiao Tong University, Shanghai, China
| | - Oliver Zhen Li
- Shanghai Lixin University of Accounting and Finance, Shanghai, China.
- National University of Singapore, Singapore, Singapore.
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Nahar Q, Alam A, Mahmud K, Sathi SS, Chakraborty N, Siddique AB, Rahman AE, Streatfield PK, Jamil K, El Arifeen S. Levels and trends in mortality and causes of death among women of reproductive age in Bangladesh: Findings from three national surveys. J Glob Health 2023; 13:07005. [PMID: 37616128 PMCID: PMC10449030 DOI: 10.7189/jogh.13.07005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023] Open
Abstract
Background Information on the mortality rate and proportional cause-specific mortality is essential for identifying diseases of public health importance, design programmes, and formulating policies, but such data on women of reproductive age in Bangladesh is limited. Methods We analysed secondary data from the 2001, 2010, and 2016 rounds of the nationally representative Bangladesh Maternal Mortality and Health Care Survey (BMMS) to estimate mortality rates and causes of death among women aged 15-49 years. We collected information on causes of death three years prior to each survey using a country-adapted version of the World Health Organization (WHO) verbal autopsy (VA) questionnaire. Trained physicians independently reviewed the VA questionnaire and assigned a cause of death using the International Classification of Diseases (ICD) codes. The analysis included mortality rates and proportional mortality showing overall and age-specific causes of death. Results The overall mortality rates for women aged 15-49 years decreased over time, from 190 per 100 000 years of observation in the 2001 BMMS, to 121 per 100 000 in the 2010 BMMS, to 116 per 100 000 in the 2016 BMMS. Age-specific mortality showed a similar downward pattern. The three diseases contributing the most to mortality were maternal causes (13-20%), circulatory system diseases (15-23%), and malignancy (14-24%). The relative position of these three diseases changed between the three surveys. From the 2001 BMMS to the 2010 BMMS and subsequently to the 2016 BMMS, the number of deaths from non-communicable diseases (e.g. cardiovascular diseases and malignancies) increased from 29% to 38% to 48%. Maternal causes led to the highest proportion of deaths among 20-34-year-olds in all three surveys (25-32%), while suicide was the number one cause of death for teenagers (19-22%). Circulatory system diseases and malignancy were the two leading causes of death for older women aged 35-49 years (40%-67%). Conclusions There was a gradual shift in the causes of death from communicable to non-communicable diseases among women of reproductive age in Bangladesh. Suicide as the primary cause of death among teenage girls demands urgent attention for prevention.
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Affiliation(s)
- Quamrun Nahar
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Anadil Alam
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | | | - Nitai Chakraborty
- Data for Impact, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | | | | | | | - Kanta Jamil
- Independent Consultant, Melbourne, Australia
| | - Shams El Arifeen
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
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Tu WJ, Zhao Z, Yin P, Cao L, Zeng J, Chen H, Fan D, Fang Q, Gao P, Gu Y, Tan G, Han J, He L, Hu B, Hua Y, Kang D, Li H, Liu J, Liu Y, Lou M, Luo B, Pan S, Peng B, Ren L, Wang L, Wu J, Xu Y, Xu Y, Yang Y, Zhang M, Zhang S, Zhu L, Zhu Y, Li Z, Chu L, An X, Wang L, Yin M, Li M, Yin L, Yan W, Li C, Tang J, Zhou M, Wang L. Estimated Burden of Stroke in China in 2020. JAMA Netw Open 2023; 6:e231455. [PMID: 36862407 PMCID: PMC9982699 DOI: 10.1001/jamanetworkopen.2023.1455] [Citation(s) in RCA: 154] [Impact Index Per Article: 154.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
IMPORTANCE Stroke is the leading cause of death in China. However, recent data about the up-to-date stroke burden in China are limited. OBJECTIVE To investigate the urban-rural disparity of stroke burden in the Chinese adult population, including prevalence, incidence, and mortality rate, and disparities between urban and rural populations. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study was based on a nationally representative survey that included 676 394 participants aged 40 years and older. It was conducted from July 2020 to December 2020 in 31 provinces in mainland China. MAIN OUTCOMES AND MEASURES Primary outcome was self-reported stroke verified by trained neurologists during a face-to-face interviews using a standardized protocol. Stroke incidence were assessed by defining first-ever strokes that occurred during 1 year preceding the survey. Strokes causing death that occurred during the 1 year preceding the survey were considered as death cases. RESULTS The study included 676 394 Chinese adults (395 122 [58.4%] females; mean [SD] age, 59.7 [11.0] years). In 2020, the weighted prevalence, incidence, and mortality rates of stroke in China were 2.6% (95% CI, 2.6%-2.6%), 505.2 (95% CI, 488.5-522.0) per 100 000 person-years, and 343.4 (95% CI, 329.6-357.2) per 100 000 person-years, respectively. It was estimated that among the Chinese population aged 40 years and older in 2020, there were 3.4 (95% CI, 3.3-3.6) million incident cases of stroke, 17.8 (95% CI, 17.5-18.0) million prevalent cases of stroke, and 2.3 (95% CI, 2.2-2.4) million deaths from stroke. Ischemic stroke constituted 15.5 (95% CI, 15.2-15.6) million (86.8%) of all incident strokes in 2020, while intracerebral hemorrhage constituted 2.1 (95% CI, 2.1-2.1) million (11.9%) and subarachnoid hemorrhage constituted 0.2 (95% CI, 0.2-0.2) million (1.3%). The prevalence of stroke was higher in urban than in rural areas (2.7% [95% CI, 2.6%-2.7%] vs 2.5% [95% CI, 2.5%-2.6%]; P = .02), but the incidence rate (485.5 [95% CI, 462.8-508.3] vs 520.8 [95% CI, 496.3-545.2] per 100 000 person-years; P < .001) and mortality rate (309.9 [95% CI, 291.7-328.1] vs 369.7 [95% CI, 349.1-390.3] per 100 000 person-years; P < .001) were lower in urban areas than in rural areas. In 2020, the leading risk factor for stroke was hypertension (OR, 3.20 [95% CI, 3.09-3.32]). CONCLUSIONS AND RELEVANCE In a large, nationally representative sample of adults aged 40 years or older, the estimated prevalence, incidence, and mortality rate of stroke in China in 2020 were 2.6%, 505.2 per 100 000 person-years, and 343.4 per 100 000 person-years, respectively, indicating the need for an improved stroke prevention strategy in the general Chinese population.
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Affiliation(s)
- Wen-Jun Tu
- The General Office of Stroke Prevention Project Committee, National Health Commission of the People's Republic of China, Beijing, China
- Department of Radiobiology, Institute of Radiation Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhenping Zhao
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lei Cao
- The General Office of Stroke Prevention Project Committee, National Health Commission of the People's Republic of China, Beijing, China
| | - Jingsheng Zeng
- Department of Neurology, the First Affiliated Hospital of Sun Yat–sen University, Guangzhou, China
| | - Huisheng Chen
- Department of Neurology, The General Hospital of Northern Theater Command of the Chinese People’s Liberation Army, Shenyang, China
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, Beijing, China
| | - Qi Fang
- Department of Neurology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Pei Gao
- Peking University School of Public Health, Beijing, China
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital Fudan University, Shanghai, China
| | - Guojun Tan
- Department of Neurology, the Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianfeng Han
- Department of Neurology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi’an, China
| | - Li He
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Bo Hu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Hua
- Department of Ultrasound Vascular, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Dezhi Kang
- Department of Neurosurgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Hongyan Li
- Department of Neurology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Jianmin Liu
- Department of Neurosurgery, Shanghai Changhai Hospital, Shanghai, China
| | - Yuanli Liu
- School of Health and Health Management Policy, Peking Union Medical College, Beijing, China
| | - Min Lou
- Department of Neurology, the Second Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Benyan Luo
- Department of Neurology, the First Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Suyue Pan
- Department of Neurology, Nanfang Hospital of Southern Medical University, Guangzhou, China
| | - Bin Peng
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Lijie Ren
- Department of Neurology, Shenzhen Second Hospital, Shenzhen, China
| | - Lihua Wang
- Department of Neurology, the Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jian Wu
- Department of Neurology, Beijing Tsinghua Changgung Memoria Hospital, Beijing, China
| | - Yuming Xu
- Department of Neurology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yun Xu
- Department of Neurology, Drum Tower Hospital Affiliated to Nanjing University School of Medicine, China
| | - Yi Yang
- Department of Neurology, the First Bethune Hospital of Jilin University, Changchun, China
| | - Meng Zhang
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China
| | - Shu Zhang
- Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Liangfu Zhu
- Department of Cerebrovascular Disease, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yicheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lan Chu
- Department of Neurology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xiuli An
- Department of Neurology, Harbin Second Hospital, Harbin, China
| | - Lingxiao Wang
- The General Office of Stroke Prevention Project Committee, National Health Commission of the People's Republic of China, Beijing, China
| | - Meng Yin
- The General Office of Stroke Prevention Project Committee, National Health Commission of the People's Republic of China, Beijing, China
| | - Mei Li
- Chronic Noncommunicable Disease Prevention and Control Institute, Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang, China
| | - Li Yin
- Department of Chronic Disease, Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Wei Yan
- Chronic Noncommunicable Disease Prevention and Control Institute, Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, China
| | - Chuan Li
- Chronic Noncommunicable Disease Prevention and Control Institute, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Junli Tang
- Chronic Noncommunicable Disease Prevention and Control Institute, Shandong Provincial Center for Disease Control and Prevention, Jinan, China
| | - Maigeng Zhou
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Longde Wang
- The General Office of Stroke Prevention Project Committee, National Health Commission of the People's Republic of China, Beijing, China
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Chen L, Xia T, Rampatige R, Li H, Adair T, Joshi R, Gu Z, Yu H, Fang B, McLaughlin D, Lopez AD, Wang C, Yuan Z. Assessing the Diagnostic Accuracy of Physicians for Home Death Certification in Shanghai: Application of SmartVA. Front Public Health 2022; 10:842880. [PMID: 35784257 PMCID: PMC9247331 DOI: 10.3389/fpubh.2022.842880] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Approximately 30% of deaths in Shanghai either occur at home or are not medically attended. The recorded cause of death (COD) in these cases may not be reliable. We applied the Smart Verbal Autopsy (VA) tool to assign the COD for a representative sample of home deaths certified by 16 community health centers (CHCs) from three districts in Shanghai, from December 2017 to June 2018. The results were compared with diagnoses from routine practice to ascertain the added value of using SmartVA. Overall, cause-specific mortality fraction (CSMF) accuracy improved from 0.93 (93%) to 0.96 after the application of SmartVA. A comparison with a “gold standard (GS)” diagnoses obtained from a parallel medical record review investigation found that 86.3% of the initial diagnoses made by the CHCs were assigned the correct COD, increasing to 90.5% after the application of SmartVA. We conclude that routine application of SmartVA is not indicated for general use in CHCs, although the tool did improve diagnostic accuracy for residual causes, such as other or ill-defined cancers and non-communicable diseases.
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Affiliation(s)
- Lei Chen
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Tian Xia
- Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Rasika Rampatige
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Hang Li
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Tim Adair
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Rohina Joshi
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Faculty of Medicine, The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
- The George Institute for Global Health, New Delhi, India
| | - Zhen Gu
- Vital Strategies, New York, NY, United States
| | - Huiting Yu
- Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Bo Fang
- Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Deirdre McLaughlin
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Alan D. Lopez
- Department of Health Metrics Sciences, IHME, University of Washington, Seattle, WA, United States
| | - Chunfang Wang
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Zheng'an Yuan
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
- *Correspondence: Zheng'an Yuan
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Firth SM, Hart JD, Reeve M, Li H, Mikkelsen L, Sarmiento DC, Bo KS, Kwa V, Qi JL, Yin P, Segarra A, Riley I, Joshi R. Integrating community-based verbal autopsy into civil registration and vital statistics: lessons learnt from five countries. BMJ Glob Health 2021; 6:bmjgh-2021-006760. [PMID: 34728477 PMCID: PMC8565529 DOI: 10.1136/bmjgh-2021-006760] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/12/2021] [Indexed: 01/09/2023] Open
Abstract
This paper describes the lessons from scaling up a verbal autopsy (VA) intervention to improve data about causes of death according to a nine-domain framework: governance, design, operations, human resources, financing, infrastructure, logistics, information technologies and data quality assurance. We use experiences from China, Myanmar, Papua New Guinea, Philippines and Solomon Islands to explore how VA has been successfully implemented in different contexts, to guide other countries in their VA implementation. The governance structure for VA implementation comprised a multidisciplinary team of technical experts, implementers and staff at different levels within ministries. A staged approach to VA implementation involved scoping and mapping of death registration processes, followed by pretest and pilot phases which allowed for redesign before a phased scale-up. Existing health workforce in countries were trained to conduct the VA interviews as part of their routine role. Costs included training and compensation for the VA interviewers, information technology (IT) infrastructure costs, advocacy and dissemination, which were borne by the funding agency in early stages of implementation. The complexity of the necessary infrastructure, logistics and IT support required for VA increased with scale-up. Quality assurance was built into the different phases of the implementation. VA as a source of cause of death data for community deaths will be needed for some time. With the right technical and political support, countries can scale up this intervention to ensure ongoing collection of quality and timely information on community deaths for use in health planning and better monitoring of national and global health goals.
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Affiliation(s)
- Sonja Margot Firth
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - John D Hart
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Matthew Reeve
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hang Li
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Lene Mikkelsen
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Khin Sandar Bo
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Viola Kwa
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jin-Lei Qi
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Agnes Segarra
- Epidemiological Bureau, Republic of the Philippines Department of Health, Manila, Philippines
| | - Ian Riley
- School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rohina Joshi
- The George Institute for Global Health, Newtown, New South Wales, Australia,The George Institute for Global Health India, New Delhi, Delhi, India
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