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Cameron E, Mo J, Yu C. A health inequality analysis of childhood asthma prevalence in urban Australia. J Allergy Clin Immunol 2024; 154:285-296. [PMID: 38483422 DOI: 10.1016/j.jaci.2024.01.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 01/12/2024] [Accepted: 01/18/2024] [Indexed: 08/09/2024]
Abstract
BACKGROUND Long-standing health inequalities in Australian society that were exposed by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were described as "fault lines" in a recent call to action by a consortium of philanthropic organizations. With asthma a major contributor to childhood disease burden, studies of its spatial epidemiology can provide valuable insights into the emergence of health inequalities early in life. OBJECTIVE The aims of this study were to characterize the spatial variation of asthma prevalence among children living within Australia's 4 largest cities and quantify the relative contributions of climatic and environmental factors, outdoor air pollution, and socioeconomic status in determining this variation. METHODS A Bayesian model with spatial smoothing was developed to regress ecologic health status data from the 2021 Australian Census against groups of explanatory covariates intended to represent mechanistic pathways. RESULTS The prevalence of asthma in children aged 5 to 14 years averages 7.9%, 8.2%, 8.5%, and 7.6% in Sydney, Melbourne, Brisbane, and Perth, respectively. This small inter-city variation contrasts against marked intracity variation at the small-area level, which ranges from 6% to 12% between the least and most affected locations in each. Statistical variance decomposition on a subsample of Australian-born, nonindigenous children attributes 66% of the intracity spatial variation to the assembled covariates. Of these covariates, climatic and environmental factors contribute 30%, outdoor air pollution contributes 19%, and areal socioeconomic status contributes the remaining 51%. CONCLUSION Geographic health inequalities in the prevalence of childhood asthma within Australia's largest cities reflect a complex interplay of factors, among which socioeconomic status is a principal determinant.
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Affiliation(s)
- Ewan Cameron
- School of Population Health, Curtin University, Bentley, Australia; Geospatial Health and Development, Telethon Kids Institute, Nedlands, Australia.
| | - Joyce Mo
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Australia
| | - Charles Yu
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Australia
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Khan JR, Lingam R, Owens L, Chen K, Shanthikumar S, Oo S, Schultz A, Widger J, Bakar KS, Jaffe A, Homaira N. Social deprivation and spatial clustering of childhood asthma in Australia. Glob Health Res Policy 2024; 9:22. [PMID: 38910250 PMCID: PMC11194868 DOI: 10.1186/s41256-024-00361-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/28/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Asthma is the most common chronic respiratory illness among children in Australia. While childhood asthma prevalence varies by region, little is known about variations at the small geographic area level. Identifying small geographic area variations in asthma is critical for highlighting hotspots for targeted interventions. This study aimed to investigate small area-level variation, spatial clustering, and sociodemographic risk factors associated with childhood asthma prevalence in Australia. METHODS Data on self-reported (by parent/carer) asthma prevalence in children aged 0-14 years at statistical area level 2 (SA2, small geographic area) and selected sociodemographic features were extracted from the national Australian Household and Population Census 2021. A spatial cluster analysis was used to detect hotspots (i.e., areas and their neighbours with higher asthma prevalence than the entire study area average) of asthma prevalence. We also used a spatial Bayesian Poisson model to examine the relationship between sociodemographic features and asthma prevalence. All analyses were performed at the SA2 level. RESULTS Data were analysed from 4,621,716 children aged 0-14 years from 2,321 SA2s across the whole country. Overall, children's asthma prevalence was 6.27%, ranging from 0 to 16.5%, with significant hotspots of asthma prevalence in areas of greater socioeconomic disadvantage. Socioeconomically disadvantaged areas had significantly higher asthma prevalence than advantaged areas (prevalence ratio [PR] = 1.10, 95% credible interval [CrI] 1.06-1.14). Higher asthma prevalence was observed in areas with a higher proportion of Indigenous individuals (PR = 1.13, 95% CrI 1.10-1.17). CONCLUSIONS We identified significant geographic variation in asthma prevalence and sociodemographic predictors associated with the variation, which may help in designing targeted asthma management strategies and considerations for service enhancement for children in socially deprived areas.
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Affiliation(s)
- Jahidur Rahman Khan
- School of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia.
| | - Raghu Lingam
- School of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia
- Sydney Children's Hospital Network, Randwick, NSW, Australia
| | - Louisa Owens
- School of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia
- Sydney Children's Hospital Network, Randwick, NSW, Australia
| | - Katherine Chen
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
- The Royal Children's Hospital, Melbourne, VIC, Australia
| | - Shivanthan Shanthikumar
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
- The Royal Children's Hospital, Melbourne, VIC, Australia
| | - Steve Oo
- Perth Children's Hospital, Perth, WA, Australia
| | - Andre Schultz
- Perth Children's Hospital, Perth, WA, Australia
- University of Western Australia, Perth, WA, Australia
| | - John Widger
- Women's and Children's Hospital, Adelaide, SA, Australia
| | - K Shuvo Bakar
- Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia
| | - Adam Jaffe
- School of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia
- Sydney Children's Hospital Network, Randwick, NSW, Australia
| | - Nusrat Homaira
- School of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia
- Sydney Children's Hospital Network, Randwick, NSW, Australia
- James P. Grant School of Public Health, BRAC University, Dhaka, Bangladesh
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Zhang L, Wei L, Fang Y. Spatial-temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM 2.5). BMC Public Health 2024; 24:550. [PMID: 38383335 PMCID: PMC10882846 DOI: 10.1186/s12889-024-17986-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/04/2024] [Indexed: 02/23/2024] Open
Abstract
OBJECTIVE This study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011-2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China. METHODS This study utilized CHARLS follow-up data for ≥ 45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression. RESULTS PCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas. CONCLUSION PM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.
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Affiliation(s)
- Liangwen Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Linjiang Wei
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China.
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Guo X, Zhao B, Chen T, Hao B, Yang T, Xu H. Multimorbidity in the elderly in China based on the China Health and Retirement Longitudinal Study. PLoS One 2021; 16:e0255908. [PMID: 34352011 PMCID: PMC8341534 DOI: 10.1371/journal.pone.0255908] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/26/2021] [Indexed: 11/19/2022] Open
Abstract
This study aimed to investigate the spatial distribution and patterns of multimorbidity among the elderly in China. Data on the occurrence of 14 chronic diseases were collected for 9710 elderly participants in the 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). Web graph, Apriori algorithm, age-adjusted Charlson comorbidity index (AAC), and Spatial autocorrelation were used to perform the multimorbidity analysis. The multimorbidity prevalence rate was estimated as 49.64% in the elderly in China. Three major multimorbidity patterns were identified: [Asthma/Chronic lungs diseases]: (Support (S) = 6.17%, Confidence (C) = 63.77%, Lift (L) = 5.15); [Asthma, Arthritis, or rheumatism/ Chronic lungs diseases]: (S = 3.12%, C = 64.03%, L = 5.17); [Dyslipidemia, Hypertension, Arthritis or rheumatism/Heart attack]: (S = 3.96%, C = 51.56, L = 2.69). Results of the AAC analysis showed that the more chronic diseases an elderly has, the lower is the 10-year survival rate (P < 0.001). Global spatial autocorrelation showed a positive spatial correlation distribution for the prevalence of the third multimorbidity pattern in China (P = 0.032). The status of chronic diseases and multimorbidity among the elderly with a spatial correlation is a significant health issue in China.
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Affiliation(s)
- Xiaorong Guo
- Department of Vascular Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnosis, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnosis, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Bin Hao
- Department of Vascular Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Tao Yang
- Department of Vascular Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Huimin Xu
- Department of Vascular Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
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Johnson O, Gatheral T, Knight J, Giorgi E. A modelling framework for developing early warning systems of COPD emergency admissions. Spat Spatiotemporal Epidemiol 2021; 36:100392. [PMID: 33509425 DOI: 10.1016/j.sste.2020.100392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 10/22/2020] [Accepted: 11/06/2020] [Indexed: 11/26/2022]
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.
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Affiliation(s)
- Olatunji Johnson
- CHICAS Research Group, Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK.
| | - Tim Gatheral
- Respiratory Medicine, Royal Lancaster Infirmary, Lancaster, UK
| | - Jo Knight
- CHICAS Research Group, Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK
| | - Emanuele Giorgi
- CHICAS Research Group, Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK
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Pishgar E, Fanni Z, Tavakkolinia J, Mohammadi A, Kiani B, Bergquist R. Mortality rates due to respiratory tract diseases in Tehran, Iran during 2008-2018: a spatiotemporal, cross-sectional study. BMC Public Health 2020; 20:1414. [PMID: 32943045 PMCID: PMC7495408 DOI: 10.1186/s12889-020-09495-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/03/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Tehran, the 22nd most populous city in the world, has the highest mortality rate due to respiratory system diseases (RSDs) in Iran. This study aimed to investigate spatiotemporal patterns of mortality due to these diseases in Tehran between 2008 and 2018. METHODS We used a dataset available from Tehran Municipality including all cases deceased due RSDs in this city between 2008 and 2018. Global Moran's I was performed to test whether the age-adjusted mortality rates were randomly distributed or had a spatial pattern. Furthermore, Anselin Local Moran's I was conducted to identify potential clusters and outliers. RESULTS During the 10-year study, 519,312 people died in Tehran, 43,177 because of RSDs, which corresponds to 831.1 per 10,000 deaths and 5.0 per 10,000 population. The death rate was much higher in men (56.8%) than in women (43.2%) and the highest occurred in the > 65 age group (71.2%). Overall, three diseases dominated the mortality data: respiratory failure (44.2%), pneumonia (15.9%) and lung cancer (10.2%). The rates were significantly higher in the central and southeastern parts of the city and lower in the western areas. It increased during the period 2008-2018 and showed a clustered spatial pattern between 2008 and 2013 but presented a random geographical pattern afterwards. CONCLUSIONS This study provides a first report of the spatial distribution of mortality due to RSDs in Tehran and shows a significant increase in respiratory disease mortality in the last ten years. Effective control of the excess fatality rates would warrant a combination of urban prevention and treatment strategies including environmental health plans.
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Affiliation(s)
- Elahe Pishgar
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Zohre Fanni
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran.
| | - Jamileh Tavakkolinia
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Robert Bergquist
- Ingerod, Brastad, Sweden (formerly with the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases, World Health Organization), Geneva, Switzerland
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Niyonsenga T, Carroll SJ, Coffee NT, Taylor AW, Daniel M. Are changes in depressive symptoms, general health and residential area socio-economic status associated with trajectories of waist circumference and body mass index? PLoS One 2020; 15:e0227029. [PMID: 31914169 PMCID: PMC6948738 DOI: 10.1371/journal.pone.0227029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 12/11/2019] [Indexed: 11/18/2022] Open
Abstract
Objective This study sought to assess whether changes in depressive symptoms, general health, and area-level socio-economic status (SES) were associated to changes over time in waist circumference and body mass index (BMI). Methods A total of 2871 adults (18 years or older), living in Adelaide (South Australia), were observed across three waves of data collection spanning ten years, with clinical measures of waist circumference, height and weight. Participants completed the Centre for Epidemiologic Studies Depression (CES-D) and Short Form 36 health questionnaires (SF-36 general health domain). An area-level SES measure, relative location factor, was derived from hedonic regression models using residential property features but blind to location. Growth curve models with latent variables were fitted to data. Results Waist circumference, BMI and depressive symptoms increased over time. General health and relative location factor decreased. Worsening general health and depressive symptoms predicted worsening waist circumference and BMI trajectories in covariate-adjusted models. Diminishing relative location factor was negatively associated with waist circumference and BMI trajectories in unadjusted models only. Conclusions Worsening depressive symptoms and general health predict increasing adiposity and suggest the development of unhealthful adiposity might be prevented by attention to negative changes in mental health and overall general health.
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Affiliation(s)
- Theo Niyonsenga
- Health Research Institute, Faculty of Health, University of Canberra, Canberra, Australia
- * E-mail:
| | - Suzanne J. Carroll
- Health Research Institute, Faculty of Health, University of Canberra, Canberra, Australia
| | - Neil T. Coffee
- Health Research Institute, Faculty of Health, University of Canberra, Canberra, Australia
- School of Architecture and Built Environment, Healthy Cities Research Group, The University of Adelaide, South Australia, Australia
| | - Anne W. Taylor
- Discipline of Medicine, The University of Adelaide, South Australia, Australia
| | - Mark Daniel
- Health Research Institute, Faculty of Health, University of Canberra, Canberra, Australia
- Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Fitzroy, Australia
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Ding H, Fatehi F, Maiorana A, Bashi N, Hu W, Edwards I. Digital health for COPD care: the current state of play. J Thorac Dis 2019; 11:S2210-S2220. [PMID: 31737348 DOI: 10.21037/jtd.2019.10.17] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) imposes a huge burden to our healthcare systems and societies. To alleviate the burden, digital health-"the use of digital technologies for health"-has been recognized as a potential solution for improving COPD care at scale. The aim of this review is to provide an overview of digital health interventions in COPD care. We accordingly reviewed recent and emerging evidence on digital transformation approaches for COPD care focusing on (I) self-management, (II) in-hospital care, (III) post-discharge care, (IV) hospital-at-home, (V) ambient environment, and (VI) public health surveillance. The emerging approaches included digital-technology-enabled homecare programs, electronic records, big data analytics, and environment-monitoring applications. The digital health approaches of telemonitoring, telehealth and mHealth support the self-management, post-discharge care, and hospital-at-home strategy, with prospective effects on reducing acute COPD exacerbations and hospitalizations. Electronic records and classification tools have been implemented; and their effectiveness needs to be further evaluated in future studies. Air pollution concentrations in the ambient environment are associated with declined lung functions and increased risks for hospitalization and mortality. In all the digital transformation approaches, clinical evidence on reducing mortality, the ultimate goal of digital health intervention, is often inconsistent or insufficient. Digital health transformation provides great opportunities for clinical innovations and discovery of new intervention strategies. Further research remains needed for achieving reliable improvements in clinical outcomes and cost-benefits in future studies.
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Affiliation(s)
- Hang Ding
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Farhad Fatehi
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia.,School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Andrew Maiorana
- Allied Health Department and Advanced Heart Failure and Cardiac Transplant Service, Fiona Stanley Hospital, Perth, Australia.,School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia
| | - Nazli Bashi
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health & Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Iain Edwards
- Department of Community Health, Peninsula Health, Melbourne, Australia
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