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Zhang J, Zhong M, Huang J, Deng W, Li P, Yao Z, Ye X, Zhong X. Spatiotemporal patterns and socioeconomic determinants of pulmonary tuberculosis in Dongguan city, China, during 2011-2020: an ecological study. BMJ Open 2024; 14:e085733. [PMID: 39260857 PMCID: PMC11409261 DOI: 10.1136/bmjopen-2024-085733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/13/2024] Open
Abstract
OBJECTIVE Pulmonary tuberculosis (PTB) is a critical challenge worldwide, particularly in China. This study aimed to explore the spatiotemporal transmission patterns and socioeconomic factors of PTB in Dongguan city, China. METHODS/DESIGN An ecological study based on the reported new PTB cases between 2011 and 2020 was conducted in Dongguan city, China. The spatiotemporal analysis methods were used to explore the long-term trend, spatiotemporal transmission pattern and socioeconomic factors of PTB. MAIN OUTCOME MEASURES The number of new PTB cases. PARTICIPANTS We collected 35 756 new PTB cases, including 23 572 males and 12 184 females. RESULTS The seasonal-trend decomposition indicated a significant downward trend for PTB with a significant peak in 2017 and 2018, and local spatial autocorrelation showed more and more high-high clusters in the central and north-central towns with high incidence. The multivariate spatial time series analysis revealed that the endemic component had a leading role in driving PTB transmission, with a high total effect value being 189.40 (95% CI: 171.65-207.15). A Bayesian spatiotemporal model revealed that PTB incidence is positively associated with the agricultural population ratio (relative risk (RR) =1.074), gender ratio (RR=1.104) and the number of beds in medical institutions (RR=1.028). CONCLUSIONS These findings revealed potential spatiotemporal variability and spatial aggregation of PTB, so targeted preventive strategies should be made in different towns based on spatiotemporal transmission patterns and risk factors.
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Affiliation(s)
- Jingfeng Zhang
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Minghao Zhong
- Department of Prevention and Health Care, The Sixth People's Hospital of Dongguan City, Dongguan, China
| | - Jiayin Huang
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Wenjun Deng
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Pingyuan Li
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - ZhenJiang Yao
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Xiaohua Ye
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Xinguang Zhong
- Department of Prevention and Health Care, The Sixth People's Hospital of Dongguan City, Dongguan, China
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Hogg J, Staples K, Davis A, Cramb S, Patterson C, Kirkland L, Gourley M, Xiao J, Sun W. Improving the spatial and temporal resolution of burden of disease measures with Bayesian models. Spat Spatiotemporal Epidemiol 2024; 49:100663. [PMID: 38876559 DOI: 10.1016/j.sste.2024.100663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 05/16/2024] [Accepted: 05/23/2024] [Indexed: 06/16/2024]
Abstract
This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources.
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Affiliation(s)
- James Hogg
- Centre for Data Science, School of Mathematical Sciences, Queensland University of Technology (QUT), 2 George Street, Brisbane City, 4000, Australia.
| | - Kerry Staples
- Epidemiology Directorate, Western Australia Department of Health (WADOH), 189 Royal Street, East Perth, 6004, Australia.
| | - Alisha Davis
- Epidemiology Directorate, Western Australia Department of Health (WADOH), 189 Royal Street, East Perth, 6004, Australia.
| | - Susanna Cramb
- Centre for Data Science, School of Mathematical Sciences, Queensland University of Technology (QUT), 2 George Street, Brisbane City, 4000, Australia; Australian Centre for Health Services Innovation, School of Public Health and Social Work, QUT, Brisbane, Australia.
| | - Candice Patterson
- Epidemiology Directorate, Western Australia Department of Health (WADOH), 189 Royal Street, East Perth, 6004, Australia.
| | - Laura Kirkland
- Epidemiology Directorate, Western Australia Department of Health (WADOH), 189 Royal Street, East Perth, 6004, Australia.
| | - Michelle Gourley
- Australian Institute of Health and Welfare (AIHW), Australian Government, 1 Thynne Street, Bruce, 2617, Australia.
| | - Jianguo Xiao
- Epidemiology Directorate, Western Australia Department of Health (WADOH), 189 Royal Street, East Perth, 6004, Australia.
| | - Wendy Sun
- Epidemiology Directorate, Western Australia Department of Health (WADOH), 189 Royal Street, East Perth, 6004, Australia.
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Cruz RFD, Ruberti JA, Mota TS, Silveira LVDA, Chiaravalloti-Neto F. Spatiotemporal Bayesian modeling of the risk of congenital syphilis in São Paulo, SP, Brazil. Spat Spatiotemporal Epidemiol 2024; 49:100651. [PMID: 38876564 DOI: 10.1016/j.sste.2024.100651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/03/2024] [Accepted: 04/10/2024] [Indexed: 06/16/2024]
Abstract
The aim of this study is to analyze the spatiotemporal risk of congenital syphilis (CS) in high-prevalence areas in the city of São Paulo, SP, Brazil, and to evaluate its relationship with socioeconomic, demographic, and environmental variables. An ecological study was conducted based on secondary CS data with spatiotemporal components collected from 310 areas between 2010 and 2016. The data were modeled in a Bayesian context using the integrated nested Laplace approximation (INLA) method. Risk maps showed an increasing CS trend over time and highlighted the areas that presented the highest and lowest risk in each year. The model showed that the factors positively associated with a higher risk of CS were the Gini index and the proportion of women aged 18-24 years without education or with incomplete primary education, while the factors negatively associated were the proportion of women of childbearing age and the mean per capita income.
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Affiliation(s)
- Renato Ferreira da Cruz
- Institute of Exact and Earth Sciences, Araguaia University Campus - Unit II, Federal University of Mato Grosso, 6390 Valdon Varjão Avenue, Barra do Garca̧s, Mato Grosso, 78605-091, Brazil.
| | | | | | - Liciana Vaz de Arruda Silveira
- Institute of Biosciences, Department of Biostatistics, São Paulo State University Júlio de Mesquita Filho, Botucatu, São Paulo, Brazil.
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Yoneda K, Shinjo D, Takahashi N, Fushimi K. Geographical distribution of antimicrobial exposure among very preterm and very low birth weight infants: A nationwide database study in Japan. PLoS One 2024; 19:e0295528. [PMID: 38271353 PMCID: PMC10810499 DOI: 10.1371/journal.pone.0295528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 11/21/2023] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES To examine spatial effects in neonatal care, we conducted a retrospective cohort study to investigate the geographical distribution of antimicrobial exposure among very preterm and very low birth weight infants in Japan. STUDY DESIGN We utilized a nationwide claims database in Japan to extract prescriptions of injectable antimicrobials for 41,423 very preterm and very low birth weight infants admitted within the first two days of life from April 2010 to March 2021. We identified frequently prescribed antimicrobials, revealed early neonatal exposure and neonatal exposure to each antimicrobial agent by 47 prefectures in Japan, and evaluated their spatial autocorrelation using global and local Moran's I statistics. We then scrutinized regional disparities in antimicrobial drug prescriptions. RESULTS The top 10 antimicrobials prescribed to very preterm and very low birth weight infants in Japan were ampicillin, amikacin, gentamicin, cefotaxime, fluconazole, ampicillin combination, micafungin, cefmetazole, cefazolin, and vancomycin. We identified northern cold spots for fluconazole exposure and southern hot spots for ampicillin, amikacin, gentamicin, and cefmetazole exposure. Geographical heterogeneity in the selection of antibacterial and antimycotic agents was observed. CONCLUSION Our study revealed the geographical distribution of antimicrobial exposure among very preterm and very low birth weight infants in Japan, thus disclosing its spatial effects. Further research addressing the spatial effects of neonatal care is needed to understand how drug exposure affects the outcomes of preterm infants.
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Affiliation(s)
- Kota Yoneda
- Department of Health Policy and Informatics, Tokyo Medical and Dental University Graduate School, Tokyo, Japan
- Department of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan
| | - Daisuke Shinjo
- Department of Health Policy and Informatics, Tokyo Medical and Dental University Graduate School, Tokyo, Japan
| | - Naoto Takahashi
- Department of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan
| | - Kiyohide Fushimi
- Department of Health Policy and Informatics, Tokyo Medical and Dental University Graduate School, Tokyo, Japan
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Smith MJ, Charlton ME, Oleson JJ. Causal decomposition maps: An exploratory tool for designing area-level interventions aimed at reducing health disparities. Biom J 2023; 65:e2200213. [PMID: 37338305 PMCID: PMC10795519 DOI: 10.1002/bimj.202200213] [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: 08/02/2022] [Revised: 01/25/2023] [Accepted: 05/04/2023] [Indexed: 06/21/2023]
Abstract
Methods for decomposition analyses have been developed to partition between-group differences into explained and unexplained portions. In this paper, we introduce the concept of causal decomposition maps, which allow researchers to test the effect of area-level interventions on disease maps before implementation. These maps quantify the impact of interventions that aim to reduce differences in health outcomes between groups and illustrate how the disease map might change under different interventions. We adapt a new causal decomposition analysis method for the disease mapping context. Through the specification of a Bayesian hierarchical outcome model, we obtain counterfactual small area estimates of age-adjusted rates and reliable estimates of decomposition quantities. We present two formulations of the outcome model, with the second allowing for spatial interference of the intervention. Our method is utilized to determine whether the addition of gyms in different sets of rural ZIP codes could reduce any of the rural-urban difference in age-adjusted colorectal cancer incidence rates in Iowa ZIP codes.
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Affiliation(s)
- Melissa J. Smith
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mary E. Charlton
- Department of Epidemiology, University of Iowa, Iowa City, IA, USA
| | - Jacob J. Oleson
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
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Jia Y, Xu Q, Zhu Y, Li C, Qi C, She K, Liu T, Zhang Y, Li X. Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China. BMC Public Health 2023; 23:1422. [PMID: 37491220 PMCID: PMC10369697 DOI: 10.1186/s12889-023-16350-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/19/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Measles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has increased, posing a challenge to the global goal of measles eradication. This study estimated the relationship between meteorological factors and measles using spatiotemporal Bayesian model, aiming to provide scientific evidence for public health policy to eliminate measles. METHODS Descriptive statistical analysis was performed on monthly data of measles and meteorological variables in 136 counties of Shandong Province from 2009 to 2017. Spatiotemporal Bayesian model was used to estimate the effects of meteorological factors on measles, and to evaluate measles risk areas at county level. Case population was divided into multiple subgroups according to gender, age and occupation. The effects of meteorological factors on measles in subgroups were compared. RESULTS Specific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation. Taking lowest value (Q1) as reference, RR (95%CI) for higher temperatures (Q2-Q4) were 0.79 (0.69-0.91), 0.54 (0.44-0.65), and 0.48 (0.38-0.61), respectively; RR (95%CI) for higher relative humidity (Q2-Q4) were 0.76 (0.66-0.88), 0.56 (0.47-0.67), and 0.49 (0.38-0.63), respectively; RR (95%CI) for higher wind velocity (Q2-Q4) were 1.43 (1.25-1.64), 1.85 (1.57-2.18), 2.00 (1.59-2.52), respectively. 22 medium-to-high risk counties were identified, mainly in northwestern, southwestern and central Shandong Province. The trend was basically same in the effects of meteorological factors on measles in subgroups, but the magnitude of the effects was different. CONCLUSIONS Meteorological factors have an important impact on measles. It is crucial to integrate these factors into public health policies for measles prevention and control in China.
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Affiliation(s)
- Yan Jia
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Qing Xu
- Institute of Immunization and Preventive Management, Shandong Center for Disease Control and Prevention, Jinan, 250014, China
| | - Yuchen Zhu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Chunyu Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Chang Qi
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Kaili She
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Tingxuan Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Ying Zhang
- Faculty of Medicine and Health, School of Public Health, University of Sydney, Camperdown, NSW, 2006, Australia
| | - Xiujun Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
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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: 2] [Impact Index Per Article: 2.0] [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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Mohamad MS, Abdul Maulud KN, Faes C. A practical illustration of spatial smoothing methods for disconnected regions with INLA: spatial survey on overweight and obesity in Malaysia. Int J Health Geogr 2023; 22:14. [PMID: 37344913 DOI: 10.1186/s12942-023-00336-5] [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: 01/25/2023] [Accepted: 06/01/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND National prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5-17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates. RESULTS The spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight. CONCLUSION This study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east-west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.
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Affiliation(s)
- Maria Safura Mohamad
- Faculty of Social Sciences, Unit of Health Sciences, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland.
| | - Khairul Nizam Abdul Maulud
- Department of Civil Engineering, Faculty of Engineering & Built Environment, National University of Malaysia, 43600, Bangi, Selangor, Malaysia
- Earth Observation Centre, Institute of Climate Change, National University of Malaysia, 43600, Bangi, Selangor, Malaysia
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
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Zeng Q, Wang Q, Zhang K, Wong SC, Xu P. Analysis of the injury severity of motor vehicle-pedestrian crashes at urban intersections using spatiotemporal logistic regression models. ACCIDENT; ANALYSIS AND PREVENTION 2023; 189:107119. [PMID: 37235968 DOI: 10.1016/j.aap.2023.107119] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 04/18/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023]
Abstract
This paper conducted a comprehensive study on the injury severity of motor vehicle-pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.
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Affiliation(s)
- Qiang Zeng
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China.
| | - Qianfang Wang
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
| | - Keke Zhang
- Human Provincial Communications Planning, Survey & Design Institute Co., Ltd, Changsha, China
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.
| | - Pengpeng Xu
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China.
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Baldoquín Rodríguez W, Mirabal M, Van der Stuyft P, Gómez Padrón T, Fonseca V, Castillo RM, Monteagudo Díaz S, Baetens JM, De Baets B, Toledo Romaní ME, Vanlerberghe V. The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba. Trop Med Infect Dis 2023; 8:tropicalmed8040230. [PMID: 37104355 PMCID: PMC10143650 DOI: 10.3390/tropicalmed8040230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 04/28/2023] Open
Abstract
To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010-2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.
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Affiliation(s)
| | - Mayelin Mirabal
- Unidad de Información y Biblioteca, Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | | | - Tania Gómez Padrón
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Viviana Fonseca
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Rosa María Castillo
- Unidad Provincial de Vigilancia y Lucha Antivectorial, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Sonia Monteagudo Díaz
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Cienfuegos 55100, Cuba
| | - Jan M Baetens
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | | | - Veerle Vanlerberghe
- Public Health Department, Institute of Tropical Medicine, Nationalestraat 155, 2000 Antwerp, Belgium
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Tesema GA, Tessema ZT, Heritier S, Stirling RG, Earnest A. A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5295. [PMID: 37047911 PMCID: PMC10094468 DOI: 10.3390/ijerph20075295] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/13/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.
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Affiliation(s)
- 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 196, Ethiopia
| | - 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 196, Ethiopia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Rob G. Stirling
- Department of Respiratory Medicine, Alfred Health, Melbourne, VIC 3004, Australia
- Faculty of Medicine, Nursing and Health Sciences, Central Clinical School, 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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12
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Debusho LK, Bedaso NG. Bayesian Spatial Modelling of HIV Prevalence in Jimma Zone, Ethiopia. Diseases 2023; 11:diseases11010046. [PMID: 36975595 PMCID: PMC10047877 DOI: 10.3390/diseases11010046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/28/2023] [Accepted: 03/05/2023] [Indexed: 03/11/2023] Open
Abstract
Background: Although the human immunodeficiency virus (HIV) is spatially heterogeneous in Ethiopia, current regional estimates of HIV prevalence hide the epidemic’s heterogeneity. A thorough examination of the prevalence of HIV infection using district-level data could assist to develop HIV prevention strategies. The aims of this study were to examine the spatial clustering of HIV prevalence in Jimma Zone at district level and assess the effects of patient characteristics on the prevalence of HIV infection. Methods: The 8440 files of patients who underwent HIV testing in the 22 Districts of Jimma Zone between September 2018 and August 2019 were the source of data for this study. The global Moran’s index, Getis–Ord Gi* local statistic, and Bayesian hierarchical spatial modelling approach were applied to address the research objectives. Results: Positive spatial autocorrelation was observed in the districts and the local indicators of spatial analysis using the Getis–Ord statistic also identified three districts, namely Agaro, Gomma and Nono Benja, as hotspots, and two districts, namely Mancho and Omo Beyam, as coldspots with 95% and 90% confidence levels, respectively, for HIV prevalence. The results also showed eight patient-related characteristics that were considered in the study were associated with HIV prevalence in the study area. Furthermore, after accounting for these characteristics in the fitted model, there was no spatial clustering of HIV prevalence suggesting the patient characteristics had explained most of the heterogeneity in HIV prevalence in Jimma Zone for the study data. Conclusions: The identification of hotspot districts and the spatial dynamic of HIV infection in Jimma Zone at district level may allow health policymakers in the zone or Oromiya region or at national level to develop geographically specific strategies to prevent HIV transmission. Because clinic register data were used in the study, it is important to use caution when interpreting the results. The results are restricted to Jimma Zone districts and may not be generalizable to Ethiopia or the Oromiya region.
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Affiliation(s)
- Legesse Kassa Debusho
- Department of Statistics, College of Science, Engineering and Technology, University of South Africa, Private Bag X6, Florida 1710, South Africa
- Correspondence:
| | - Nemso Geda Bedaso
- Department of Statistics, College of Natural and Computational Science, Madda Walabu University, Bale Robe P.O. Box 247, Ethiopia
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13
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Puvvula J, Poole JA, Gwon Y, Rogan EG, Bell JE. Role of social determinants of health in differential respiratory exposure and health outcomes among children. BMC Public Health 2023; 23:119. [PMID: 36650500 PMCID: PMC9847182 DOI: 10.1186/s12889-022-14964-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Attributes defining the Social Determinants of Health (SDoH) are associated with disproportionate exposures to environmental hazards and differential health outcomes among communities. The dynamics between SDoH, disproportionate environmental exposures, and differential health outcomes are often specific to micro-geographic areas. METHODS This study focused on children less than 20 years of age who lived in Douglas County, Nebraska, during 2016-2019. To assess the role of SDoH in differential exposures, we evaluated the association between SDoH metrics and criteria pollutant concentrations and the association between SDoH and pediatric asthma exacerbations to quantify the role of SDoH in differential pediatric asthma outcomes. The Bayesian Poisson regression model with spatial random effects was used to evaluate associations. RESULTS We identified significant positive associations between the annual mean concentration of criteria pollutants (carbon monoxide, particulate matter2.5, nitrogen dioxide, sulfur dioxide) with race (Non-Hispanic Black and Hispanic/Latino), financial stability, and literacy. Additionally, there were significant positive associations between higher rates of pediatric asthma emergency department visits and neighborhoods with more Non-Hispanic Black children, children without health insurance coverage, and households without access to a vehicle. CONCLUSIONS Non-Hispanic Black and Hispanic/Latino children living in Douglas County, NE experience disproportionately higher exposure to criteria pollutant concentrations. Additionally, higher rates of asthma exacerbations among Non-Hispanic Black children could be due to reduced access to respiratory care that is potentially the result of financial instability and vehicle access. These results could inform city planners and health care providers to mitigate respiratory risks among these higher at-risk populations.
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Affiliation(s)
- Jagadeesh Puvvula
- Department of Environmental, Agricultural, and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Jill A. Poole
- grid.266813.80000 0001 0666 4105Division of Allergy and Immunology, Department of Medicine, University of Nebraska Medical Center, Omaha, NE USA
| | - Yeongjin Gwon
- grid.266813.80000 0001 0666 4105Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE USA
| | - Eleanor G. Rogan
- grid.266813.80000 0001 0666 4105Department of Environmental, Agricultural, and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE USA
| | - Jesse E. Bell
- grid.266813.80000 0001 0666 4105Department of Environmental, Agricultural, and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE USA ,grid.24434.350000 0004 1937 0060School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE USA ,grid.24434.350000 0004 1937 0060Daugherty Water for Food Global Institute, University of Nebraska, Lincoln, NE USA
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14
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MacNab YC. Revisiting Gaussian Markov random fields and Bayesian disease mapping. Stat Methods Med Res 2023; 32:207-225. [PMID: 36317373 PMCID: PMC9814028 DOI: 10.1177/09622802221129040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We revisit several conditionally formulated Gaussian Markov random fields, known as the intrinsic conditional autoregressive model, the proper conditional autoregressive model, and the Leroux et al. conditional autoregressive model, as well as convolution models such as the well known Besag, York and Mollie model, its (adaptive) re-parameterization, and its scaled alternatives, for their roles of modelling underlying spatial risks in Bayesian disease mapping. Analytic and simulation studies, with graphic visualizations, and disease mapping case studies, present insights and critique on these models for their nature and capacities in characterizing spatial dependencies, local influences, and spatial covariance and correlation functions, and in facilitating stabilized and efficient posterior risk prediction and inference. It is illustrated that these models are Gaussian (Markov) random fields of different spatial dependence, local influence, and (covariance) correlation functions and can play different and complementary roles in Bayesian disease mapping applications.
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Affiliation(s)
- Ying C MacNab
- School of Population and Public Health, 8166University of British Columbia, Vancouver, Canada
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15
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Zhang Y, Lin X, Yao Z, Sun D, Lin X, Wang X, Yang C, Song J. Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome. Comput Struct Biotechnol J 2022; 21:176-184. [PMID: 36544473 PMCID: PMC9755226 DOI: 10.1016/j.csbj.2022.12.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 12/01/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
The spatial transcriptome has enabled researchers to resolve transcriptome expression profiles while preserving information about cell location to better understand the complex biological processes that occur in organisms. Due to technical limitations, the current high-throughput spatial transcriptome sequencing methods (known as next-generation sequencing with spatial barcoding methods or spot-based methods) cannot achieve single-cell resolution. A single measurement site, called a spot, in these technologies frequently contains multiple cells of various types. Computational tools for determining the cellular composition of a spot have emerged as a way to break through these limitations. These tools are known as deconvolution tools. Recently, a couple of deconvolution tools based on different strategies have been developed and have shown promise in different aspects. The resulting single-cell resolution expression profiles and/or single-cell composition of spots will significantly affect downstream data mining; thus, it is crucial to choose a suitable deconvolution tool. In this review, we present a list of currently available tools for spatial transcriptome deconvolution, categorize them based on the strategies they employ, and explain their advantages and limitations in detail in order to guide the selection of these tools in future studies.
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Affiliation(s)
- Yingkun Zhang
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Xinrui Lin
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Zhixian Yao
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Di Sun
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xin Lin
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,Chemistry and Materials Science College, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyu Wang
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Chaoyong Yang
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,Corresponding author.
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16
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Jaya IGNM, Folmer H, Lundberg J. A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden. THE ANNALS OF REGIONAL SCIENCE 2022; 72:1-34. [PMID: 36465998 PMCID: PMC9707215 DOI: 10.1007/s00168-022-01191-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
The three closely related COVID-19 outcomes of incidence, intensive care (IC) admission and death, are commonly modelled separately leading to biased estimation of the parameters and relatively poor forecasts. This paper presents a joint spatiotemporal model of the three outcomes based on weekly data that is used for risk prediction and identification of hotspots. The paper applies a pure spatiotemporal model consisting of structured and unstructured spatial and temporal effects and their interaction capturing the effects of the unobserved covariates. The pure spatiotemporal model limits the data requirements to the three outcomes and the population at risk per spatiotemporal unit. The empirical study for the 21 Swedish regions for the period 1 January 2020-4 May 2021 confirms that the joint model predictions outperform the separate model predictions. The fifteen-week-ahead spatiotemporal forecasts (5 May-11 August 2021) show a significant decline in the relative risk of COVID-19 incidence, IC admission, death and number of hotspots. Supplementary Information The online version contains supplementary material available at 10.1007/s00168-022-01191-1.
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Affiliation(s)
- I Gede Nyoman Mindra Jaya
- Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands
- Statistics Department, Padjadjaran University, Bandung, Indonesia
| | - Henk Folmer
- Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands
- Statistics Department, Padjadjaran University, Bandung, Indonesia
| | - Johan Lundberg
- Department of Economics and Centre for Regional Science (CERUM), Umeå University, 901 87 Umeå, Sweden
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17
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Hughes J. A unified Gaussian copula methodology for spatial regression analysis. Sci Rep 2022; 12:15915. [PMID: 36151389 PMCID: PMC9508247 DOI: 10.1038/s41598-022-20171-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
Spatially referenced data arise in many fields, including imaging, ecology, public health, and marketing. Although principled smoothing or interpolation is paramount for many practitioners, regression, too, can be an important (or even the only or most important) goal of a spatial analysis. When doing spatial regression it is crucial to accommodate spatial variation in the response variable that cannot be explained by the spatially patterned explanatory variables included in the model. Failure to model both sources of spatial dependence-regression and extra-regression, if you will-can lead to erroneous inference for the regression coefficients. In this article I highlight an under-appreciated spatial regression model, namely, the spatial Gaussian copula regression model (SGCRM), and describe said model's advantages. Then I develop an intuitive, unified, and computationally efficient approach to inference for the SGCRM. I demonstrate the efficacy of the proposed methodology by way of an extensive simulation study along with analyses of a well-known dataset from disease mapping.
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Affiliation(s)
- John Hughes
- Lehigh University, Bethlehem, PA, 18015, USA.
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18
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Lee SA, Economou T, Lowe R. A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping. J R Soc Interface 2022; 19:20220440. [PMID: 36128702 PMCID: PMC9490350 DOI: 10.1098/rsif.2022.0440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity. However, conventional approaches require the spatial structure to be fully defined prior to model fitting. By applying penalized smoothing splines to coordinates, we create two-dimensional smooth surfaces describing the spatial structure of the data while making minimal assumptions about the structure. The result is a non-stationary surface which is setting specific. These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects. Through simulation studies, we show that the splines can be applied to any symmetric continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data. Using Bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease. These models were found to perform at least as well as existing modelling frameworks, while allowing for future extensions and multiple sources of spatial connectivity.
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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
| | - Theodoros Economou
- Climate and Atmosphere Research Centre, The Cyprus Institute, Nicosia, Cyprus
| | - 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
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
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19
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Ma Y, Zhou X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol 2022; 40:1349-1359. [PMID: 35501392 PMCID: PMC9464662 DOI: 10.1038/s41587-022-01273-7] [Citation(s) in RCA: 121] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 03/07/2022] [Indexed: 12/16/2022]
Abstract
Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference. CARD can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference. Applications to four datasets, including a pancreatic cancer dataset, identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity and compartmentalization of pancreatic cancer.
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Affiliation(s)
- Ying Ma
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
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20
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Kazemi M, Hushmandi K, Vahedian-Azimi A, Moayyed M, Karimi L, Goharrizi MASB, Salesi M, Parastouei K, Raei M. Spatial patterning of occupational stress and its related factors in Iranian critical care nurses using a hierarchical Bayesian technique. Work 2022; 72:1409-1419. [DOI: 10.3233/wor-210284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND: In each community, health problems’ patterns and geographical changes are of prime importance to determine high and low-risk areas. OBJECTIVES: This study aimed to investigate the Spatial patterning of occupational stress and its related factors in Iranian critical care nurses using a hierarchical Bayesian technique METHODS: The current research was a cross-sectional descriptive-analytical study. The data includes the number of critical care unit nurses who show a high stress level based on a questionnaire. We used variables such as age, gender, collaboration status, working time, marital status, clinical experience, education, supervisor support, stress score, and working on holiday days for this study. The survey participants had to be at least 18 years old, a registered nurse, and working in the intensive care unit (ICU). OpenBUGS version 3.2.3 was used to implement the Bayesian hierarchical Poisson model and find partial patterning of occupational stress and its related factors. RESULTS: The final sample size was 17414 nurses. The overall prevalence of occupational stress in ICU nurses was estimated at 70%. The lowest and highest prevalence was 65.8% in the North Khorasan province and 75.2% in Golestan province. Occupational stress had a statistically significant association with collaboration status, but with demographic variables, shift work, supportive supervisor, and working on holidays had no statistically significant association. CONCLUSIONS: According to the findings, it is necessary to eliminate or reduce job stress and increase efficiency in Iranian nurses, encourage teamwork and collaboration as an essential element of a healthy workplace environment.
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Affiliation(s)
- Morteza Kazemi
- Department of Biostatistics and Epidemiology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Kiavash Hushmandi
- Department of Food Hygiene and QualityControl, Division of Epidemiology and Zoonoses, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Majid Moayyed
- Department of Economic Studies, School of Business, University of Dundee, Dundee, UK
| | - Leila Karimi
- Behavioral Sciences Research Center, Life Style Institute, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | | | - Mahmood Salesi
- Chemical Injuries Research Center, Systems Biologyand Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Karim Parastouei
- Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mehdi Raei
- Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
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21
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MacNab YC. Bayesian disease mapping: Past, present, and future. SPATIAL STATISTICS 2022; 50:100593. [PMID: 35075407 PMCID: PMC8769562 DOI: 10.1016/j.spasta.2022.100593] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analysing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.
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Affiliation(s)
- Ying C MacNab
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
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22
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Geographical variation of COVID-19 vaccination coverage, ethnic diversity and population composition in Flanders. Vaccine X 2022; 11:100194. [PMID: 35855061 PMCID: PMC9281465 DOI: 10.1016/j.jvacx.2022.100194] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 01/24/2023] Open
Abstract
The vaccination coverage in Flanders is high, but some regions show lower vaccination willingness as compared to the overall vaccination coverage. Beginning November of 2021, the vaccination rate in Flanders was above 93% in age groups above 45 years, and around 85% in the age groups 12 to 44 years. Apart from Flanders as a whole, focus here is on the health sector Maasland, which has a slightly lower vaccination rate, especially in the age groups 12 to 44 years. In the Maasland region, located on the eastern border of Flanders, there are between 1% and 10% less vaccinated individuals than expected according to the vaccination rate in the whole of Flanders, with lowest vaccination rates in the south of the Maasland region. We study the impact of ethnic diversity in the population, population composition with respect to the ethnicity of individuals (in the sense of how the local population composition differs from the Flemish average), and socio-economic status on the vaccination rate at the level of the statistical sector, apart from the effect of age. We explain the statistical methods to investigate geographical differences and illustrate how one can deal with incomplete information in vaccination registries. Ethnic diversity in a region is associated with lower vaccination rates, as is a lower regional socio-economic status. The composition of the population in Maasland is associated with a 35% reduction in the odds to get vaccinated as compared to the overall Flemish population.
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23
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Baron JN, Chevalier V, Ly S, Duong V, Dussart P, Fontenille D, Peng YS, Martínez-López B. Accessibility to rabies centers and human rabies post-exposure prophylaxis rates in Cambodia: A Bayesian spatio-temporal analysis to identify optimal locations for future centers. PLoS Negl Trop Dis 2022; 16:e0010494. [PMID: 35771752 PMCID: PMC9491732 DOI: 10.1371/journal.pntd.0010494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 05/12/2022] [Indexed: 11/19/2022] Open
Abstract
Rabies is endemic in Cambodia. For exposed humans, post-exposure prophylaxis
(PEP) is very effective in preventing this otherwise fatal disease. The Institut
Pasteur du Cambodge (IPC) in Phnom Penh was the primary distributor of PEP in
Cambodia until 2018. Since then, and to increase distribution of PEP, two new
centers have been opened by IPC in the provinces of Battambang and Kampong Cham.
Data on bitten patients, who sometimes bring the head of the biting animal for
rabies analyses, have been recorded by IPC since 2000. However, human cases are
not routinely recorded in Cambodia, making it difficult to establish a human
burden of disease and generate a risk map of dog bites to inform the selection
of future PEP center locations in high-risk areas. Our aim was to assess the
impact of accessibility to rabies centers on the yearly rate of PEP patients in
the population and generate a risk map to identify the locations where new
centers would be the most beneficial to the Cambodian population. To accomplish
this, we used spatio-temporal Bayesian regression models with the number of PEP
patients as the outcome. The primary exposure variable considered was travel
time to the nearest IPC center. Secondary exposure variables consisted of travel
time to a provincial capital and urban proportion of the population. Between
2000 and 2016, a total of 293,955 PEP patient records were identified. Our
results showed a significant negative association between travel time to IPC and
the rate of PEP patients: an increase in one hour travel time from the living
location to IPC PEP centers leads to a reduction in PEP rate of 70% to 80%. Five
provinces were identified as the most efficient locations for future centers to
maximize PEP accessibility: Banteay Meanchey, Siem Reap, Takeo, Kampot and Svay
Rieng. Adding a PEP center in every provincial capital would increase the
proportion of Cambodians living within 60 minutes of a PEP center from 26.6% to
64.9%, and living within 120 minutes from 52.8% to 93.3%, which could save
hundreds of lives annually. Rabies is a fatal viral disease that affects the nervous system. It is endemic in
many countries in Africa and Asia where free roaming dogs form a reservoir.
Transmission to humans occurs most often through a dog bite. However,
post-exposure prophylaxis (PEP), if administered before symptom onset, is highly
effective at preventing the disease. In Cambodia, a few number of centers offer
PEP, with the Institut Pasteur du Cambodge in Phnom Penh being the main one.
These few locations lead to limited accessibility for rural areas distant from
Phnom Penh and underestimations of the dog-bite burden and PEP needs. Through
statistical modelling, we measured the impact of accessibility on the number of
PEP patients and predicted the impact of opening new centers in other locations.
We found that travel time was significantly associated with the rate of PEP
patients. IPC opened new rabies centers in Battambang and Kampong Cham provinces
in 2018 and 2019, respectively, and we identified four provinces where future
openings would be the most beneficial: Banteay Meanchey, Siem Reap, Takeo,
Kampot and Svay Rieng. This study is part of a broader drive to eradicate rabies
in Cambodia by 2030 through increased PEP infrastructure and control measures in
the dog population.
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Affiliation(s)
- Jerome N. Baron
- Center for Animal Disease Modeling and Surveillance (CADMS), Department
of Medicine & Epidemiology, School of Veterinary Medicine, University of
California, Davis, California, United States of America
- * E-mail:
| | - Véronique Chevalier
- CIRAD, UMR ASTRE, Phnom Penh, Cambodia
- ASTRE, Univ Montpellier, CIRAD, INRA, Montpellier,
France
- Epidemiology and Public Health Unit, Institut Pasteur du Cambodge, Phnom
Penh, Cambodia
| | - Sowath Ly
- Epidemiology and Public Health Unit, Institut Pasteur du Cambodge, Phnom
Penh, Cambodia
| | - Veasna Duong
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh,
Cambodia
| | - Philippe Dussart
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh,
Cambodia
| | | | - Yik Sing Peng
- Epidemiology and Public Health Unit, Institut Pasteur du Cambodge, Phnom
Penh, Cambodia
| | - Beatriz Martínez-López
- Center for Animal Disease Modeling and Surveillance (CADMS), Department
of Medicine & Epidemiology, School of Veterinary Medicine, University of
California, Davis, California, United States of America
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24
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Feng C. Spatial-temporal generalized additive model for modeling COVID-19 mortality risk in Toronto, Canada. SPATIAL STATISTICS 2022; 49:100526. [PMID: 34249608 PMCID: PMC8257405 DOI: 10.1016/j.spasta.2021.100526] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 06/03/2021] [Accepted: 06/25/2021] [Indexed: 06/13/2023]
Abstract
This article presents a spatial-temporal generalized additive model for modeling geo-referenced COVID-19 mortality data in Toronto, Canada. A range of factors and spatial-temporal terms are incorporated into the model. The non-linear and interactive effects of the neighborhood-level factors, i.e., population density and average of income, are modeled as a two-dimensional spline smoother. The change of spatial pattern over time is modeled as a three-dimensional tensor product smoother. By fitting this model, the space-time effect can uncover the underlying spatial-temporal pattern that is not explained by the covariates. The performance of the modeling method based on the individual data is also compared to the modeling methods based on the aggregated data in terms of in-sample and out-of-sample predictive checking. The results suggest that the individual-level based analysis provided a better overall model fit and higher predictive accuracy for detecting epidemic peaks in this application as compared to the analysis based on the aggregated data.
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Affiliation(s)
- Cindy Feng
- Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada, B3H 1V7
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Jahan F, Kennedy DW, Duncan EW, Mengersen KL. Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data. PLoS One 2022; 17:e0268130. [PMID: 35622835 PMCID: PMC9140259 DOI: 10.1371/journal.pone.0268130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 04/24/2022] [Indexed: 12/01/2022] Open
Abstract
Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.
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Affiliation(s)
- Farzana Jahan
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Daniel W. Kennedy
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Earl W. Duncan
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie L. Mengersen
- School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
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Ladislav K, Marek B. The geographical epidemiology of smoking-related premature mortality: a registry-based small-area analysis of the Czech death statistics. Spat Spatiotemporal Epidemiol 2022; 41:100501. [DOI: 10.1016/j.sste.2022.100501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 12/04/2021] [Accepted: 03/05/2022] [Indexed: 11/26/2022]
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Adin A, Congdon P, Santafé G, Ugarte MD. Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 36:2995-3010. [PMID: 35075346 PMCID: PMC8771626 DOI: 10.1007/s00477-022-02175-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/07/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.
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Affiliation(s)
- A. Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - P. Congdon
- School of Geography, Queen Mary University of London, London, UK
| | - G. Santafé
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - M. D. Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
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28
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Adin A, Congdon P, Santafé G, Ugarte MD. Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 36:2995-3010. [PMID: 35075346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.
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Affiliation(s)
- A Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - P Congdon
- School of Geography, Queen Mary University of London, London, UK
| | - G Santafé
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - M D Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
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29
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Gerogiannis G, Tranmer M, Lee D, Valente T. A Bayesian spatio‐network model for multiple adolescent adverse health behaviours. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | | | - Thomas Valente
- University of South California Los Angeles California USA
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Abstract
Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local.
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Acharya B. Spatiotemporal Analysis of Overall Health in the United States Between 2010 and 2018. Cureus 2021; 13:e18295. [PMID: 34692359 PMCID: PMC8526084 DOI: 10.7759/cureus.18295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/26/2021] [Indexed: 11/07/2022] Open
Abstract
Background Although many previous studies have documented spatial heterogeneity in health outcomes across the United States at different geographic scales, spatiotemporal analyses to understand overall health are scant. Methodology We used the County Health Rankings (CHR) data to analyze the three types of health outcomes, viz., overall health, length of life, and quality of life for 2010-2018 in the contiguous United States employing hierarchal Bayesian methods. Composite scores were created to proxy these outcomes utilizing predefined weights of several variables as recommended by CHR. Our methods assumed a convolution of spatially structured and unstructured errors to model the overall spatial error. Spatial effects were modeled using conditional autoregressive distribution. Results The substantial disparity in these health outcomes was evident, with counties having poorer health outcomes mostly concentrated in the southeastern United States. Models that incorporated county-level demographic and socioeconomic characteristics partially explained the observed spatial heterogeneity in health outcomes. Interestingly, there was no time effect in any of the outcomes suggesting a perpetuation of health disparity over the years. Conclusions County-specific health policy interventions that take into account the contextual factors might be beneficial in improving population health and breaking the perpetuation of health disparity.
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Affiliation(s)
- Binod Acharya
- Urban Health Collaborative, Drexel University, Philadelphia, USA
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32
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Warren JL, Cai J, Johnson NP, Deziel NC. A discrete kernel stick‐breaking model for detecting spatial boundaries in hydraulic fracturing wastewater disposal well placement across Ohio. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Joshua L. Warren
- Department of Biostatistics Yale University New Haven Connecticut USA
| | - Jiachen Cai
- Department of Biostatistics Yale University New Haven Connecticut USA
| | - Nicholaus P. Johnson
- Department of Environmental Health Sciences Yale University New Haven Connecticut USA
| | - Nicole C. Deziel
- Department of Environmental Health Sciences Yale University New Haven Connecticut USA
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Radon K, Bakuli A, Pütz P, Le Gleut R, Guggenbuehl Noller JM, Olbrich L, Saathoff E, Garí M, Schälte Y, Frahnow T, Wölfel R, Pritsch M, Rothe C, Pletschette M, Rubio-Acero R, Beyerl J, Metaxa D, Forster F, Thiel V, Castelletti N, Rieß F, Diefenbach MN, Fröschl G, Bruger J, Winter S, Frese J, Puchinger K, Brand I, Kroidl I, Wieser A, Hoelscher M, Hasenauer J, Fuchs C. From first to second wave: follow-up of the prospective COVID-19 cohort (KoCo19) in Munich (Germany). BMC Infect Dis 2021; 21:925. [PMID: 34493217 PMCID: PMC8423599 DOI: 10.1186/s12879-021-06589-4] [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: 05/16/2021] [Accepted: 08/19/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In the 2nd year of the COVID-19 pandemic, knowledge about the dynamics of the infection in the general population is still limited. Such information is essential for health planners, as many of those infected show no or only mild symptoms and thus, escape the surveillance system. We therefore aimed to describe the course of the pandemic in the Munich general population living in private households from April 2020 to January 2021. METHODS The KoCo19 baseline study took place from April to June 2020 including 5313 participants (age 14 years and above). From November 2020 to January 2021, we could again measure SARS-CoV-2 antibody status in 4433 of the baseline participants (response 83%). Participants were offered a self-sampling kit to take a capillary blood sample (dry blood spot; DBS). Blood was analysed using the Elecsys® Anti-SARS-CoV-2 assay (Roche). Questionnaire information on socio-demographics and potential risk factors assessed at baseline was available for all participants. In addition, follow-up information on health-risk taking behaviour and number of personal contacts outside the household (N = 2768) as well as leisure time activities (N = 1263) were collected in summer 2020. RESULTS Weighted and adjusted (for specificity and sensitivity) SARS-CoV-2 sero-prevalence at follow-up was 3.6% (95% CI 2.9-4.3%) as compared to 1.8% (95% CI 1.3-3.4%) at baseline. 91% of those tested positive at baseline were also antibody-positive at follow-up. While sero-prevalence increased from early November 2020 to January 2021, no indication of geospatial clustering across the city of Munich was found, although cases clustered within households. Taking baseline result and time to follow-up into account, men and participants in the age group 20-34 years were at the highest risk of sero-positivity. In the sensitivity analyses, differences in health-risk taking behaviour, number of personal contacts and leisure time activities partly explained these differences. CONCLUSION The number of citizens in Munich with SARS-CoV-2 antibodies was still below 5% during the 2nd wave of the pandemic. Antibodies remained present in the majority of SARS-CoV-2 sero-positive baseline participants. Besides age and sex, potentially confounded by differences in behaviour, no major risk factors could be identified. Non-pharmaceutical public health measures are thus still important.
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Affiliation(s)
- Katja Radon
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, 80336, Munich, Germany.
- Center for International Health (CIH), University Hospital, LMU Munich, 80336, Munich, Germany.
- Comprehensive Pneumology Center (CPC) Munich, German Center for Lung Research (DZL), 89337, Munich, Germany.
| | - Abhishek Bakuli
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Peter Pütz
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Faculty of Business Administration and Economics, Bielefeld University, 33615, Bielefeld, Germany
| | - Ronan Le Gleut
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Core Facility Statistical Consulting, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | | | - Laura Olbrich
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Elmar Saathoff
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Mercè Garí
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
| | - Turid Frahnow
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Faculty of Business Administration and Economics, Bielefeld University, 33615, Bielefeld, Germany
| | - Roman Wölfel
- German Center for Infection Research (DZIF), partner site, Munich, Germany
- Bundeswehr Institute of Microbiology, 80937, Munich, Germany
| | - Michael Pritsch
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Camilla Rothe
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Michel Pletschette
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Raquel Rubio-Acero
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Jessica Beyerl
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Dafni Metaxa
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Felix Forster
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, 80336, Munich, Germany
- Comprehensive Pneumology Center (CPC) Munich, German Center for Lung Research (DZL), 89337, Munich, Germany
| | - Verena Thiel
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Noemi Castelletti
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Friedrich Rieß
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Maximilian N Diefenbach
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Günter Fröschl
- Center for International Health (CIH), University Hospital, LMU Munich, 80336, Munich, Germany
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Jan Bruger
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Simon Winter
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Jonathan Frese
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Kerstin Puchinger
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Isabel Brand
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Inge Kroidl
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Andreas Wieser
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Michael Hoelscher
- Center for International Health (CIH), University Hospital, LMU Munich, 80336, Munich, Germany
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
- Interdisciplinary Research Unit Mathematics and Life Sciences, University of Bonn, 53113, Bonn, Germany
| | - Christiane Fuchs
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Faculty of Business Administration and Economics, Bielefeld University, 33615, Bielefeld, Germany
- Core Facility Statistical Consulting, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
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Jaya IGNM, Folmer H. Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia. JOURNAL OF REGIONAL SCIENCE 2021; 61:849-881. [PMID: 34230688 PMCID: PMC8250786 DOI: 10.1111/jors.12533] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/30/2021] [Accepted: 03/26/2021] [Indexed: 05/16/2023]
Abstract
The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.
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Affiliation(s)
- I. Gede Nyoman M. Jaya
- Department of Economic Geography, Faculty of Spatial SciencesGroningen UniversityGroningenThe Netherlands
- Department of StatisticsPadjadjaran UniversityBandungIndonesia
| | - Henk Folmer
- Department of Economic Geography, Faculty of Spatial SciencesGroningen UniversityGroningenThe Netherlands
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Fofana D, George EO, Bowman D. Combining assumptions and graphical network into gene expression data analysis. JOURNAL OF STATISTICAL DISTRIBUTIONS AND APPLICATIONS 2021. [DOI: 10.1186/s40488-021-00126-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Analyzing gene expression data rigorously requires taking assumptions into consideration but also relies on using information about network relations that exist among genes. Combining these different elements cannot only improve statistical power, but also provide a better framework through which gene expression can be properly analyzed.
Material and methods
We propose a novel statistical model that combines assumptions and gene network information into the analysis. Assumptions are important since every test statistic is valid only when required assumptions hold. So, we propose hybrid p-values and show that, under the null hypothesis of primary interest, these p-values are uniformly distributed. These proposed hybrid p-values take assumptions into consideration. We incorporate gene network information into the analysis because neighboring genes share biological functions. This correlation factor is taken into account via similar prior probabilities for neighboring genes.
Results
With a series of simulations our approach is compared with other approaches. Area Under the ROC Curves (AUCs) are constructed to compare the different methodologies; the AUC based on our methodology is larger than others. For regression analysis, AUC from our proposed method contains AUCs of Spearman test and of Pearson test. In addition, true negative rates (TNRs) also known as specificities are higher with our approach than with the other approaches. For two group comparison analysis, for instance, with a sample size of n=10, specificity corresponding to our proposed methodology is 0.716146 and specificities for t-test and rank sum are 0.689223 and 0.69797, respectively. Our method that combines assumptions and network information into the analysis is shown to be more powerful.
Conclusions
These proposed procedures are introduced as a general class of methods that can incorporate procedure-selection, account for multiple-testing, and incorporate graphical network information into the analysis. We obtain very good performance in simulations, and in real data analysis.
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Odhiambo JN, Kalinda C, Macharia PM, Snow RW, Sartorius B. Spatial and spatio-temporal methods for mapping malaria risk: a systematic review. BMJ Glob Health 2021; 5:bmjgh-2020-002919. [PMID: 33023880 PMCID: PMC7537142 DOI: 10.1136/bmjgh-2020-002919] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 12/21/2022] Open
Abstract
Background Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA). Methods A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion. Results One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach. Conclusions Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.
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Affiliation(s)
| | - Chester Kalinda
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Faculty of Agriculture and Natural Resources, University of Namibia, Windhoek, Namibia
| | - Peter M Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Benn Sartorius
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Association between environmental and climatic risk factors and the spatial distribution of cystic and alveolar echinococcosis in Kyrgyzstan. PLoS Negl Trop Dis 2021; 15:e0009498. [PMID: 34161356 PMCID: PMC8259979 DOI: 10.1371/journal.pntd.0009498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 07/06/2021] [Accepted: 05/20/2021] [Indexed: 11/24/2022] Open
Abstract
Background Cystic and alveolar echinococcosis (CE and AE) are neglected tropical diseases caused by Echinococcus granulosus sensu lato and E. multilocularis, and are emerging zoonoses in Kyrgyzstan. In this country, the spatial distribution of CE and AE surgical incidence in 2014-2016 showed marked heterogeneity across communities, suggesting the presence of ecological determinants underlying CE and AE distributions. Methodology/Principal findings For this reason, in this study we assessed potential associations between community-level confirmed primary CE (no.=2359) or AE (no.=546) cases in 2014-2016 in Kyrgyzstan and environmental and climatic variables derived from satellite-remote sensing datasets using conditional autoregressive models. We also mapped CE and AE relative risk. The number of AE cases was negatively associated with 10-year lag mean annual temperature. Although this time lag should not be considered as an exact measurement but with associated uncertainty, it is consistent with the estimated 10–15-year latency following AE infection. No associations were detected for CE. We also identified several communities at risk for CE or AE where no disease cases were reported in the study period. Conclusions/Significance Our findings support the hypothesis that CE is linked to an anthropogenic cycle and is less affected by environmental risk factors compared to AE, which is believed to result from spillover from a wild life cycle. As CE was not affected by factors we investigated, hence control should not have a geographical focus. In contrast, AE risk areas identified in this study without reported AE cases should be targeted for active disease surveillance in humans. This active surveillance would confirm or exclude AE transmission which might not be reported with the present passive surveillance system. These areas should also be targeted for ecological investigations in the animal hosts. Cystic and alveolar echinococcosis (CE and AE) are parasitic zoonoses that cause a substantial disease burden in Kyrgyzstan. The etiologic agents of these diseases are parasites in the genus Echinococcus. These parasites have complex life cycles which include mammalian definitive and intermediate hosts and a free-living egg stage in the environment. Consequently, environmental and climatic factors can affect the prevalence and geographical distribution of these diseases because such factors influence the parasites’ eggs survival and longevity, and can affect suitable habitats for the intermediate and definitive hosts. In this geographic correlation study, we assessed environmental and climatic determinants of the spatial distributions of CE and AE in Kyrgyzstan. We found that 10-year lag annual temperature plays an important role in AE distribution, whilst none of the variables assessed was found to significantly affect that of CE. Moreover, communities at risk where these diseases are potentially under- or misdiagnosed were identified. Our findings provide vital information for targeted, area-specific interventions in Kyrgyzstan, and add to the body of knowledge on the ecology of these neglected parasitic diseases that are emerging and reemerging in several regions in North America, Europe and Asia.
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Zheng Z, Pitzer VE, Warren JL, Weinberger DM. Community factors associated with local epidemic timing of respiratory syncytial virus: A spatiotemporal modeling study. SCIENCE ADVANCES 2021; 7:7/26/eabd6421. [PMID: 34162556 PMCID: PMC8221622 DOI: 10.1126/sciadv.abd6421] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 05/10/2021] [Indexed: 05/29/2023]
Abstract
Respiratory syncytial virus (RSV) causes a large burden of morbidity in young children and the elderly. Spatial variability in the timing of RSV epidemics provides an opportunity to probe the factors driving its transmission, including factors that influence epidemic seeding and growth rates. Using hospitalization data from Connecticut, New Jersey, and New York, we estimated epidemic timing at the ZIP code level using harmonic regression and then used a Bayesian meta-regression model to evaluate correlates of epidemic timing. Earlier epidemics were associated with larger household size and greater population density. Nearby localities had similar epidemic timing. Our results suggest that RSV epidemics grow faster in areas with more local contact opportunities, and that epidemic spread follows a spatial diffusion process based on geographic proximity. Our findings can inform the timing of delivery of RSV extended half-life prophylaxis and maternal vaccines and guide future studies on the transmission dynamics of RSV.
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Affiliation(s)
- Zhe Zheng
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT 06520, USA.
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT 06520, USA
| | - Joshua L Warren
- Department of Biostatistics and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT 06520, USA
| | - Daniel M Weinberger
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT 06520, USA
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39
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Kelling C, Graif C, Korkmaz G, Haran M. Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods. JOURNAL OF QUANTITATIVE CRIMINOLOGY 2021; 37:481-516. [PMID: 34149156 PMCID: PMC8210633 DOI: 10.1007/s10940-020-09454-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
OBJECTIVES Our goal is to understand the social dynamics affecting domestic and sexual violence in urban areas by investigating the role of connections between area nodes, or communities. We use innovative methods adapted from spatial statistics to investigate the importance of social proximity measured based on connectedness pathways between area nodes. In doing so, we seek to extend the standard treatment in the neighborhoods and crime literature of areas like census blocks as independent analytical units or as interdependent primarily due to geographic proximity. METHODS In this paper, we develop techniques to incorporate two types of proximity, geographic proximity and commuting proximity in spatial generalized linear mixed models (SGLMM) in order to estimate domestic and sexual violence in Detroit, Michigan and Arlington County, Virginia. Analyses are based on three types of CAR models (the Besag, York, and Mollié (BYM), Leroux, and the sparse SGLMM models) and two types of SAR models (the spatial lag and spatial error models) to examine how results vary with different model assumptions. We use data from local and federal sources such as the Police Data Initiative and American Community Survey. RESULTS Analyses show that incorporating information on commuting ties, a non-spatially bounded form of social proximity, to spatial models contributes to better deviance information criteria (DIC) scores (a metric which explicitly accounts for model fit and complexity) in Arlington for sexual and domestic crime as well as overall crime. In Detroit, the fit is improved only for overall crime. The distinctions in model fit are less pronounced when using cross-validated mean absolute error (MAE) as a comparison criteria. CONCLUSION Overall, the results indicate variations across crime type, urban contexts, and modeling approaches. Nonetheless, in important contexts, commuting ties among neighborhoods are observed to greatly improve our understanding of urban crime. If such ties contribute to the transfer of norms, social support, resources, and behaviors between places, they may then transfer also the effects of crime prevention efforts.
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Affiliation(s)
- Claire Kelling
- 330B Thomas Building, University Park, PA 16802
- Department of Statistics, Pennsylvania State University, University Park, PA
| | - Corina Graif
- Department of Sociology and Criminology, Pennsylvania State University, University Park, PA
| | - Gizem Korkmaz
- Biocomplexity Institute & Initiative, University of Virginia, 1100 Wilson Blvd., Arlington, VA
| | - Murali Haran
- Department of Statistics, Pennsylvania State University, University Park, PA
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40
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Piel FB, Parkes B, Hambly P, Roca-Barceló A, McCallion M, Leonardi G, Strosnider H, Yip F, Elliott P, Hansell AL. Software application profile: the Rapid Inquiry Facility 4.0: an open access tool for environmental public health tracking. Int J Epidemiol 2021; 49 Suppl 1:i38-i48. [PMID: 32293011 PMCID: PMC7158065 DOI: 10.1093/ije/dyz094] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2019] [Indexed: 12/02/2022] Open
Abstract
The Rapid Inquiry Facility 4.0 (RIF) is a new user-friendly and open-access tool, developed by the UK Small Area Health Statistics Unit (SAHSU), to facilitate environment public health tracking (EPHT) or surveillance (EPHS). The RIF is designed to help public health professionals and academics to rapidly perform exploratory investigations of health and environmental data at the small-area level (e.g. postcode or detailed census areas) in order to identify unusual signals, such as disease clusters and potential environmental hazards, whether localized (e.g. industrial site) or widespread (e.g. air and noise pollution). The RIF allows the use of advanced disease mapping methods, including Bayesian small-area smoothing and complex risk analysis functionalities, while accounting for confounders. The RIF could be particularly useful to monitor spatio-temporal trends in mortality and morbidity associated with cardiovascular diseases, cancers, diabetes and chronic lung diseases, or to conduct local or national studies on air pollution, flooding, low-magnetic fields or nuclear power plants.
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Affiliation(s)
- Frédéric B Piel
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.,MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Brandon Parkes
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Peter Hambly
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Aina Roca-Barceló
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Martin McCallion
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Giovanni Leonardi
- Environmental Epidemiology Group, Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton, UK
| | - Heather Strosnider
- Environmental Public Health Tracking Program, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, US
| | - Fuyuen Yip
- Environmental Public Health Tracking Program, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, US
| | - Paul Elliott
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.,MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Anna L Hansell
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.,Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
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Utazi CE, Nilsen K, Pannell O, Dotse‐Gborgbortsi W, Tatem AJ. District-level estimation of vaccination coverage: Discrete vs continuous spatial models. Stat Med 2021; 40:2197-2211. [PMID: 33540473 PMCID: PMC8638675 DOI: 10.1002/sim.8897] [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: 06/29/2020] [Revised: 01/10/2021] [Accepted: 01/15/2021] [Indexed: 01/29/2023]
Abstract
Health and development indicators (HDIs) such as vaccination coverage are regularly measured in many low- and middle-income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model-based approaches for producing subnational estimates of HDIs using survey data, particularly cluster-level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district-level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district-level data with continuous Gaussian process (GP) models that utilize geolocated cluster-level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015-16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between-cluster variation in the continuous GP models did not have any real effect on the district-level estimates. Our results provide guidance to practitioners on the reliability of these model-based approaches for producing estimates of vaccination coverage and other HDIs.
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Affiliation(s)
- C. Edson Utazi
- WorldPop, School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK
- Southampton Statistical Sciences Research InstituteUniversity of SouthamptonSouthamptonUK
| | - Kristine Nilsen
- WorldPop, School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK
| | - Oliver Pannell
- WorldPop, School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK
| | | | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK
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42
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Zhelyazkova M, Yordanova R, Mihaylov I, Kirov S, Tsonev S, Danko D, Mason C, Vassilev D. Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data. Front Genet 2021; 12:642991. [PMID: 33763122 PMCID: PMC7983949 DOI: 10.3389/fgene.2021.642991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/02/2021] [Indexed: 12/18/2022] Open
Abstract
The steady elaboration of the Metagenomic and Metadesign of Subways and Urban Biomes (MetaSUB) international consortium project raises important new questions about the origin, variation, and antimicrobial resistance of the collected samples. CAMDA (Critical Assessment of Massive Data Analysis, http://camda.info/) forum organizes annual challenges where different bioinformatics and statistical approaches are tested on samples collected around the world for bacterial classification and prediction of geographical origin. This work proposes a method which not only predicts the locations of unknown samples, but also estimates the relative risk of antimicrobial resistance through spatial modeling. We introduce a new component in the standard analysis as we apply a Bayesian spatial convolution model which accounts for spatial structure of the data as defined by the longitude and latitude of the samples and assess the relative risk of antimicrobial resistance taxa across regions which is relevant to public health. We can then use the estimated relative risk as a new measure for antimicrobial resistance. We also compare the performance of several machine learning methods, such as Gradient Boosting Machine, Random Forest, and Neural Network to predict the geographical origin of the mystery samples. All three methods show consistent results with some superiority of Random Forest classifier. In our future work we can consider a broader class of spatial models and incorporate covariates related to the environment and climate profiles of the samples to achieve more reliable estimation of the relative risk related to antimicrobial resistance.
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Affiliation(s)
- Maya Zhelyazkova
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Roumyana Yordanova
- Department of Mathematics, Hokkaido University, Sapporo, Japan.,Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Sofia, Bulgaria
| | - Iliyan Mihaylov
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Stefan Kirov
- Bristol-Myers Squibb, Pennington, NJ, United States
| | - Stefan Tsonev
- Department of Molecular Genetics, AgroBioInstitute, Sofia, Bulgaria
| | - David Danko
- Department of Computational Informatics, Weill Cornell Medical College, New York, NY, United States
| | | | - Dimitar Vassilev
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
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Huang G, Brown PE. Population-weighted exposure to air pollution and COVID-19 incidence in Germany. SPATIAL STATISTICS 2021; 41:100480. [PMID: 33163351 PMCID: PMC7606077 DOI: 10.1016/j.spasta.2020.100480] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 10/23/2020] [Accepted: 10/26/2020] [Indexed: 05/20/2023]
Abstract
Many countries have enforced social distancing to stop the spread of COVID-19. Within countries, although the measures taken by governments are similar, the incidence rate varies among areas (e.g., counties, cities). One potential explanation is that people in some areas are more vulnerable to the coronavirus disease because of their worsened health conditions caused by long-term exposure to poor air quality. In this study, we investigate whether long-term exposure to air pollution increases the risk of COVID-19 infection in Germany. The results show that nitrogen dioxide (NO 2 ) is significantly associated with COVID-19 incidence, with a 1 μ g m - 3 increase in long-term exposure to NO 2 increasing the COVID-19 incidence rate by 5.58% (95% credible interval [CI]: 3.35%, 7.86%). This result is consistent across various models. The analyses can be reproduced and updated routinely using public data sources and shared R code.
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Affiliation(s)
- Guowen Huang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Global Health Research, St Michael's Hospital, Toronto, ON, Canada
| | - Patrick E Brown
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Global Health Research, St Michael's Hospital, Toronto, ON, Canada
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Amiri L, Torabi M, Deardon R, Pickles M. Spatial modeling of individual-level infectious disease transmission: Tuberculosis data in Manitoba, Canada. Stat Med 2021; 40:1678-1704. [PMID: 33469942 DOI: 10.1002/sim.8863] [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: 10/28/2019] [Revised: 10/28/2020] [Accepted: 12/10/2020] [Indexed: 11/10/2022]
Abstract
Geographically dependent individual level models (GD-ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete-time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptible and infectious individuals. A key feature of GD-ILMs is that they take into account the spatial location of the individuals in addition to the distance between susceptible and infectious individuals. As a motivation of this article, we consider tuberculosis (TB) data which is an infectious disease which can be transmitted through individuals. It is also known that certain areas/demographics/communities have higher prevalent of TB (see Section 4 for more details). It is also of interest of policy makers to identify those areas with higher infectivity rate of TB for possible preventions. Therefore, we need to analyze this data properly to address those concerns. In this article, the expectation conditional maximization algorithm is proposed for estimating the parameters of GD-ILMs to be able to predict the areas with the highest average infectivity rates of TB. We also evaluate the performance of our proposed approach through some simulations. Our simulation results indicate that the proposed method provides reliable estimates of parameters which confirms accuracy of the infectivity rates.
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Affiliation(s)
- Leila Amiri
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Mahmoud Torabi
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,Department of Statistics, Faculty of Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Rob Deardon
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, Alberta, Canada
| | - Michael Pickles
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
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45
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Jalilian A, Mateu J. A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 35:797-812. [PMID: 33776559 PMCID: PMC7985594 DOI: 10.1007/s00477-021-02003-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/08/2021] [Indexed: 05/07/2023]
Abstract
The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany.
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Affiliation(s)
- Abdollah Jalilian
- Department of Statistics, Razi University, Kermanshah, 67149-67346 Iran
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46
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Schündeln MM, Lange T, Knoll M, Spix C, Brenner H, Bozorgmehr K, Stock C. Methods of spatial cluster detection in rare childhood cancers: Benchmarking data and results from a simulation study on nephroblastoma. Data Brief 2020; 34:106683. [PMID: 33426242 PMCID: PMC7779737 DOI: 10.1016/j.dib.2020.106683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 12/17/2020] [Indexed: 11/03/2022] Open
Abstract
The potential existence of spatial clusters in childhood cancer incidence is a debated topic. Identification of rare disease clusters in general may help to better understand disease etiology and develop preventive strategies against such entities. The incidence of newly diagnosed childhood malignancies under 15 years of age is 140/1,000,000. In this context, the subgroup of nephroblastoma represents an extremely rare entity with an annual incidence of 7/1,000,000. We evaluated widely used statistical approaches for spatial cluster detection in childhood cancer (Ref. Schündeln et al., 2021, Cancer Epidemiology). For the simulation study, random high risk clusters of 1 to 50 adjacent districts (NUTS-level 3, nomenclature des unités territoriales statistiques) were generated on the basis of the 402 German administrative districts. Each cluster was simulated with different relative risk levels (1 to 100). For each combination of cluster size and risk level 2000 iterations were performed. Simulated data was then analyzed by three local clustering tests: Besag-Newell method, spatial scan statistic and the Bayesian Besag-York-Mollié approach (fit by Integrated Nested Laplace Approximation). The performance characteristics of all three methods were systematically documented (sensitivity, specificity, positive/negative predictive values, exact- and minimum power, correct classification, positive/negative diagnostic likelihood and false positive/negative rate). This data article links to a Mendeley online repository which includes the raw data of simulated high-risk clusters and simulated cases on the district level for an all-childhood-malignancy scenario as well as for cases of nephroblastoma. These data was used for the evaluation of the three cluster detection methods. The R code for simulation and analysis are available from GitHub. The article also includes analyzed data summarizing the performance of the cluster detection tests in very rare disease entities, using the example of simulated nephroblastoma cases. The raw data from the study can be used for benchmarking analyses applying different spatial statistical methods systematically and evaluating their performance characteristics comparatively. The analyzed data from the nephroblastoma example can be useful to interpret the performance of the three applied local cluster detection tests in the setting of extremely rare disease entities. As a practical application, data and R code can be used for performance analyses when planning to establish surveillance systems for rare disease entities.
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Affiliation(s)
- Michael M Schündeln
- Pediatric Hematology and Oncology, Department of Pediatrics III, University Hospital Essen and the University of Duisburg-Essen, Essen, Germany
| | - Toni Lange
- Center for Evidence-based Healthcare, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Germany
| | - Maximilian Knoll
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Claudia Spix
- German Childhood Cancer Registry, Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kayvan Bozorgmehr
- Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - Christian Stock
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, Germany
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Schündeln MM, Lange T, Knoll M, Spix C, Brenner H, Bozorgmehr K, Stock C. Statistical methods for spatial cluster detection in childhood cancer incidence: A simulation study. Cancer Epidemiol 2020; 70:101873. [PMID: 33360605 DOI: 10.1016/j.canep.2020.101873] [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: 08/21/2020] [Revised: 11/15/2020] [Accepted: 11/29/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND AND OBJECTIVE The potential existence of spatial clusters in childhood cancer incidence is a debated topic. Identification of such clusters may help to better understand etiology and develop preventive strategies. We evaluated widely used statistical approaches to cluster detection in this context. METHODS Incidence of newly diagnosed childhood cancer (140/1,000,000 children under 15 years) and nephroblastoma (7/1,000,000) was simulated. Clusters of defined size (1-50) were randomly assembled on the district level in Germany. Each cluster was simulated with different relative risk levels (1-100). For each combination 2000 iterations were done. Simulated data was then analyzed by three local clustering tests: Besag-Newell method, spatial scan statistic and Bayesian Besag-York-Mollié with Integrated Nested Laplace Approximation approach. The operating characteristics (sensitivity, specificity, predictive values, power and correct classification) of all three methods were systematically described. RESULTS Performance varied considerably within and between methods, depending on the simulated setting. Sensitivity of all methods was positively associated with increasing size, incidence and RR of the high-risk area. Besag-York-Mollié showed highest specificity for minimally increased RR in most scenarios. The performance of all methods was lower in the nephroblastoma scenario compared with the scenario including all cancer cases. CONCLUSION This study illustrates the challenge to make reliable inferences on the existence of spatial clusters based on single statistical approaches in childhood cancer. Application of multiple methods, ideally with known operating characteristics, and a critical discussion of the joint evidence seems recommendable when aiming to identify high-risk clusters.
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Affiliation(s)
- Michael M Schündeln
- Pediatric Hematology and Oncology, Department of Pediatrics III, University Hospital Essen and the University of Duisburg-Essen, Essen, Germany.
| | - Toni Lange
- Center for Evidence-based Healthcare, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Germany
| | - Maximilian Knoll
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Claudia Spix
- German Childhood Cancer Registry, Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kayvan Bozorgmehr
- Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - Christian Stock
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, Germany
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Kigozi SP, Kigozi RN, Sebuguzi CM, Cano J, Rutazaana D, Opigo J, Bousema T, Yeka A, Gasasira A, Sartorius B, Pullan RL. Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019. BMC Public Health 2020; 20:1913. [PMID: 33317487 PMCID: PMC7737387 DOI: 10.1186/s12889-020-10007-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 12/04/2020] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. METHODS Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. RESULTS An estimated 38.8 million (95% Credible Interval [CI]: 37.9-40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9-21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7-9.4) to 36.6 (95% CI: 35.7-38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0-50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran's I = 0.3 (p < 0.001) and districts Moran's I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central - Busoga regions. CONCLUSION Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.
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Affiliation(s)
- Simon P Kigozi
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. .,Infectious Diseases Research Collaboration, PO Box 7475, Kampala, Uganda.
| | - Ruth N Kigozi
- USAID's Malaria Action Program for Districts, PO Box 8045, Kampala, Uganda
| | - Catherine M Sebuguzi
- Infectious Diseases Research Collaboration, PO Box 7475, Kampala, Uganda.,National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda
| | - Jorge Cano
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Damian Rutazaana
- National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda
| | - Jimmy Opigo
- National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda
| | - Teun Bousema
- Department of Medical Microbiology, Radboud University, Nijmegen, Netherlands
| | - Adoke Yeka
- Department of Disease Control and Environmental Health, College of Health Sciences, School of Public Health, Makerere University, PO Box 7072, Kampala, Uganda
| | - Anne Gasasira
- African Leaders Malaria Alliance (ALMA), Kampala, Uganda
| | - Benn Sartorius
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Rachel L Pullan
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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Dong N, Meng F, Zhang J, Wong SC, Xu P. Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes. ACCIDENT; ANALYSIS AND PREVENTION 2020; 148:105777. [PMID: 33011425 DOI: 10.1016/j.aap.2020.105777] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/17/2020] [Accepted: 09/09/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods. METHODS Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared. RESULTS Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes. CONCLUSIONS The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.
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Affiliation(s)
- Ni Dong
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China; Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, United States
| | - Fanyu Meng
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China; Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China
| | - Jie Zhang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Pengpeng Xu
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.
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50
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Saha D, Dumbaugh E, Merlin LA. A conceptual framework to understand the role of built environment on traffic safety. JOURNAL OF SAFETY RESEARCH 2020; 75:41-50. [PMID: 33334491 DOI: 10.1016/j.jsr.2020.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 01/24/2020] [Accepted: 07/27/2020] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Many U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics. METHODS Three years (2011-2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data. RESULTS Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less. CONCLUSIONS This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.
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Affiliation(s)
- Dibakar Saha
- Department of Urban and Regional Planning, Charles E. Schmidt College of Science, Florida Atlantic University, 777 Glades road, SO 284, Boca Raton, FL 33431, United states.
| | - Eric Dumbaugh
- Department of Urban and Regional Planning, Charles E. Schmidt College of Science, Florida Atlantic University, 777 Glades road, SO 284, Boca Raton, FL 33431, United states.
| | - Louis A Merlin
- Department of Urban and Regional Planning, Charles E. Schmidt College of Science, Florida Atlantic University, 777 Glades road, SO 284, Boca Raton, FL 33431, United states.
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