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Tessema ZT, Tesema GA, Ahern S, Earnest A. A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6277. [PMID: 37444123 PMCID: PMC10341419 DOI: 10.3390/ijerph20136277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.
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Affiliation(s)
- Zemenu Tadesse Tessema
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia
| | - Getayeneh Antehunegn Tesema
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia
| | - Susannah Ahern
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Arul Earnest
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
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Hossain S, Islam MM, Hasan MA, Chowdhury PB, Easty IA, Tusar MK, Rashid MB, Bashar K. Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach. Heliyon 2023; 9:e16053. [PMID: 37215791 PMCID: PMC10192530 DOI: 10.1016/j.heliyon.2023.e16053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 05/03/2023] [Accepted: 05/03/2023] [Indexed: 05/24/2023] Open
Abstract
Background In Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. Methods A total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg-1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. Results The mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. Conclusion The findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.
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Affiliation(s)
- Sorif Hossain
- Department of Statistics, Noakhali Science and Technology University, Bangladesh
| | - Md. Momin Islam
- Department of Meteorology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md. Abid Hasan
- Department of Oceanography, Noakhali Science and Technology University, Bangladesh
| | | | - Imtiaj Ahmed Easty
- Department of Oceanography, Noakhali Science and Technology University, Bangladesh
| | - Md. Kamruzzaman Tusar
- Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Bangladesh
| | | | - Kabirul Bashar
- Department of Zoology, Jahangirnagar University, Bangladesh
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Damtew YT, Tong M, Varghese BM, Anikeeva O, Hansen A, Dear K, Zhang Y, Morgan G, Driscoll T, Capon T, Bi P. Effects of high temperatures and heatwaves on dengue fever: a systematic review and meta-analysis. EBioMedicine 2023; 91:104582. [PMID: 37088034 PMCID: PMC10149186 DOI: 10.1016/j.ebiom.2023.104582] [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: 01/06/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND Studies have shown that dengue virus transmission increases in association with ambient temperature. We performed a systematic review and meta-analysis to assess the effect of both high temperatures and heatwave events on dengue transmission in different climate zones globally. METHODS A systematic literature search was conducted in PubMed, Scopus, Embase, and Web of Science from January 1990 to September 20, 2022. We included peer reviewed original observational studies using ecological time series, case crossover, or case series study designs reporting the association of high temperatures and heatwave with dengue and comparing risks over different exposures or time periods. Studies classified as case reports, clinical trials, non-human studies, conference abstracts, editorials, reviews, books, posters, commentaries; and studies that examined only seasonal effects were excluded. Effect estimates were extracted from published literature. A random effects meta-analysis was performed to pool the relative risks (RRs) of dengue infection per 1 °C increase in temperature, and further subgroup analyses were also conducted. The quality and strength of evidence were evaluated following the Navigation Guide systematic review methodology framework. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO). FINDINGS The study selection process yielded 6367 studies. A total of 106 studies covering more than four million dengue cases fulfilled the inclusion criteria; of these, 54 studies were eligible for meta-analysis. The overall pooled estimate showed a 13% increase in risk of dengue infection (RR = 1.13; 95% confidence interval (CI): 1.11-1.16, I2 = 98.0%) for each 1 °C increase in high temperatures. Subgroup analyses by climate zones suggested greater effects of temperature in tropical monsoon climate zone (RR = 1.29, 95% CI: 1.11-1.51) and humid subtropical climate zone (RR = 1.20, 95% CI: 1.15-1.25). Heatwave events showed association with an increased risk of dengue infection (RR = 1.08; 95% CI: 0.95-1.23, I2 = 88.9%), despite a wide confidence interval. The overall strength of evidence was found to be "sufficient" for high temperatures but "limited" for heatwaves. Our results showed that high temperatures increased the risk of dengue infection, albeit with varying risks across climate zones and different levels of national income. INTERPRETATION High temperatures increased the relative risk of dengue infection. Future studies on the association between temperature and dengue infection should consider local and regional climate, socio-demographic and environmental characteristics to explore vulnerability at local and regional levels for tailored prevention. FUNDING Australian Research Council Discovery Program.
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Affiliation(s)
- Yohannes Tefera Damtew
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia; College of Health and Medical Sciences, Haramaya University, P.O.BOX 138, Dire Dawa, Ethiopia.
| | - Michael Tong
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Canberra ACT, 2601, Australia.
| | - Blesson Mathew Varghese
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Olga Anikeeva
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Alana Hansen
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Keith Dear
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Ying Zhang
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia.
| | - Geoffrey Morgan
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia.
| | - Tim Driscoll
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia.
| | - Tony Capon
- Monash Sustainable Development Institute, Monash University, Melbourne, Victoria, Australia.
| | - Peng Bi
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
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Marina R, Ariati J, Anwar A, Astuti EP, Dhewantara PW. Climate and vector-borne diseases in Indonesia: a systematic literature review and critical appraisal of evidence. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1-28. [PMID: 36367556 DOI: 10.1007/s00484-022-02390-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/10/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Climate is widely known as an important driver to transmit vector-borne diseases (VBD). However, evidence of the role of climate variability on VBD risk in Indonesia has not been adequately understood. We conducted a systematic literature review to collate and critically review studies on the relationship between climate variability and VBD in Indonesia. We searched articles on PubMed, Scopus, and Google Scholar databases that are published until December 2021. Studies that reported the relationship of climate and VBD, such as dengue, chikungunya, Zika, and malaria, were included. For the reporting, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 66 out of 284 studies were reviewed. Fifty-two (78.8%) papers investigated dengue, 13 (19.7%) papers studied malaria, one (1.5%) paper discussed chikungunya, and no (0%) paper reported on Zika. The studies were predominantly conducted in western Indonesian cities. Most studies have examined the short-term effect of climate variability on the incidence of VBD at national, sub-national, and local levels. Rainfall (n = 60/66; 90.9%), mean temperature (Tmean) (n = 50/66; 75.8%), and relative humidity (RH) (n = 50/66; 75.8%) were the common climatic factors employed in the studies. The effect of climate on the incidence of VBD was heterogenous across locations. Only a few studies have investigated the long-term effects of climate on the distribution and incidence of VBD. The paucity of high-quality epidemiological data and variation in methodology are two major issues that limit the generalizability of evidence. A unified framework is required for future research to assess the impacts of climate on VBD in Indonesia to provide reliable evidence for better policymaking.
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Affiliation(s)
- Rina Marina
- Vector-borne and Zoonotic Diseases Research Group, Research Center for Public Health and Nutrition, Cibinong Science Center, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor KM.46, Bogor, West Java, 16915, Indonesia.
| | - Jusniar Ariati
- Center for Health Services Policy, Health Policy Agency, Ministry of Health of Indonesia, Jl. Percetakan Negara No. 29, Jakarta, 10560, Indonesia
| | - Athena Anwar
- Research Center for Climate and Atmosphere, National Agency for Research and Innovation, Jl. Djunjunan No. 133, Bandung, 40174, Indonesia
| | - Endang Puji Astuti
- Vector-borne and Zoonotic Diseases Research Group, Research Center for Public Health and Nutrition, Cibinong Science Center, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor KM.46, Bogor, West Java, 16915, Indonesia
| | - Pandji Wibawa Dhewantara
- Vector-borne and Zoonotic Diseases Research Group, Research Center for Public Health and Nutrition, Cibinong Science Center, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor KM.46, Bogor, West Java, 16915, Indonesia
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Polo G, Soler-Tovar D, Villamil Jimenez LC, Benavides-Ortiz E, Mera Acosta C. Bayesian spatial modeling of COVID-19 case-fatality rate inequalities. Spat Spatiotemporal Epidemiol 2022; 41:100494. [PMID: 35691638 PMCID: PMC8956344 DOI: 10.1016/j.sste.2022.100494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/04/2021] [Accepted: 02/28/2022] [Indexed: 11/17/2022]
Abstract
The ongoing outbreak of COVID-19 challenges the health systems and epidemiological responses of all countries worldwide. Although preventive measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we identify the spatial association of socioeconomic factors and the risk for dying from COVID-19 in Colombia. We confirm that from March 16 to October 04, 2020, the COVID-19 case-fatality rate and the multidimensional poverty index have a heterogeneous spatial distribution. Spatial analysis reveals that the risk of dying from COVID-19 increases in regions with a higher proportion of poor people with dwelling (RR 1.74 95%CI = 1.54–9.75), educational (RR 1.69 95%CI = 1.36–5.94), childhood/youth (RR 1.35 95%CI = 1.08–4.03), and health (RR 1.16 95%CI = 1.06–2.04) deprivations. These findings evidence the vulnerability of most disadvantaged members of society to dying in a pandemic and assist the spatial planning of preventive strategies focused on vulnerable communities.
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Hossain MP, Zhou W, Ren C, Marshall J, Yuan HY. Prediction of dengue annual incidence using seasonal climate variability in Bangladesh between 2000 and 2018. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000047. [PMID: 36962108 PMCID: PMC10021868 DOI: 10.1371/journal.pgph.0000047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 10/18/2021] [Indexed: 05/01/2023]
Abstract
The incidence of dengue has increased rapidly in Bangladesh since 2010 with an outbreak in 2018 reaching a historically high number of cases, 10,148. A better understanding of the effects of climate variability before dengue season on the increasing incidence of dengue in Bangladesh can enable early warning of future outbreaks. We developed a generalized linear model to predict the number of annual dengue cases based on monthly minimum temperature, rainfall and sunshine prior to dengue season. Variable selection and leave-one-out cross-validation were performed to identify the best prediction model and to evaluate the model's performance. Our model successfully predicted the largest outbreak in 2018, with 10,077 cases (95% CI: [9,912-10,276]), in addition to smaller outbreaks in five different years (2003, 2006, 2010, 2012 and 2014) and successfully identified the increasing trend in cases between 2010 and 2018. We found that temperature was positively associated with the annual incidence during the late winter months (between January and March) but negatively associated during the early summer (between April and June). Our results might be suggest an optimal minimum temperature for mosquito growth of 21-23°C. This study has implications for understanding how climate variability has affected recent dengue expansion in neighbours of Bangladesh (such as northern India and Southeast Asia).
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Affiliation(s)
- M. Pear Hossain
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Wen Zhou
- School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong
| | - Chao Ren
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong
| | - John Marshall
- Division of Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
| | - Hsiang-Yu Yuan
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
- * E-mail:
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Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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Abstract
This book explores the topic of resilience at the city level. The focus is more on outbreak events at the city level, or how cities should prepare and react in facing the larger events of epidemic and pandemic.
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