1
|
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.
Collapse
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.
| | | |
Collapse
|
2
|
Simkin J, Dummer TJB, Erickson AC, Otterstatter MC, Woods RR, Ogilvie G. Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package. Front Oncol 2022; 12:833265. [PMID: 36338766 PMCID: PMC9627310 DOI: 10.3389/fonc.2022.833265] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 09/26/2022] [Indexed: 09/28/2023] Open
Abstract
INTRODUCTION There is an increasing interest in small area analyses in cancer surveillance; however, technical capacity is limited and accessible analytical approaches remain to be determined. This study demonstrates an accessible approach for small area cancer risk estimation using Bayesian hierarchical models and data visualization through the smallareamapp R package. MATERIALS AND METHODS Incident lung (N = 26,448), female breast (N = 28,466), cervical (N = 1,478), and colorectal (N = 25,457) cancers diagnosed among British Columbia (BC) residents between 2011 and 2018 were obtained from the BC Cancer Registry. Indirect age-standardization was used to derive age-adjusted expected counts and standardized incidence ratios (SIRs) relative to provincial rates. Moran's I was used to assess the strength and direction of spatial autocorrelation. A modified Besag, York and Mollie model (BYM2) was used for model incidence counts to calculate posterior median relative risks (RR) by Community Health Service Areas (CHSA; N = 218), adjusting for spatial dependencies. Integrated Nested Laplace Approximation (INLA) was used for Bayesian model implementation. Areas with exceedance probabilities (above a threshold RR = 1.1) greater or equal to 80% were considered to have an elevated risk. The posterior median and 95% credible intervals (CrI) for the spatially structured effect were reported. Predictive posterior checks were conducted through predictive integral transformation values and observed versus fitted values. RESULTS The proportion of variance in the RR explained by a spatial effect ranged from 4.4% (male colorectal) to 19.2% (female breast). Lung cancer showed the greatest number of CHSAs with elevated risk (Nwomen = 50/218, Nmen = 44/218), representing 2357 total excess cases. The largest lung cancer RRs were 1.67 (95% CrI = 1.06-2.50; exceedance probability = 96%; cases = 13) among women and 2.49 (95% CrI = 2.14-2.88; exceedance probability = 100%; cases = 174) among men. Areas with small population sizes and extreme SIRs were generally smoothed towards the null (RR = 1.0). DISCUSSION We present a ready-to-use approach for small area cancer risk estimation and disease mapping using BYM2 and exceedance probabilities. We developed the smallareamapp R package, which provides a user-friendly interface through an R-Shiny application, for epidemiologists and surveillance experts to examine geographic variation in risk. These methods and tools can be used to estimate risk, generate hypotheses, and examine ecologic associations while adjusting for spatial dependency.
Collapse
Affiliation(s)
- Jonathan Simkin
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Trevor J. B. Dummer
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Anders C. Erickson
- Office of the Provincial Health Officer, Government of British Columbia, Victoria, BC, Canada
| | - Michael C. Otterstatter
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Ryan R. Woods
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Gina Ogilvie
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Women’s Health Research Institute, BC Women’s Hospital + Health Centre, Vancouver, BC, Canada
| |
Collapse
|
3
|
Forbes O, Hosking R, Mokany K, Lal A. Bayesian spatio-temporal modelling to assess the role of extreme weather, land use change and socio-economic trends on cryptosporidiosis in Australia, 2001-2018. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 791:148243. [PMID: 34412375 DOI: 10.1016/j.scitotenv.2021.148243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/23/2021] [Accepted: 05/29/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Intensification of land use threatens to increase the emergence and prevalence of zoonotic diseases, with an adverse impact on human wellbeing. Understanding how the interaction between agriculture, natural systems, climate and socioeconomic drivers influence zoonotic disease distribution is crucial to inform policy planning and management to limit the emergence of new infections. OBJECTIVES Here we assess the relative contribution of environmental, climatic and socioeconomic factors influencing reported cryptosporidiosis across Australia from 2001 to 2018. METHODS We apply a Bayesian spatio-temporal analysis using Integrated Nested Laplace Approximation (INLA). RESULTS We find that area-level risk of reported disease are associated with the proportions of the population under 5 and over 65 years of age, socioeconomic disadvantage, annual rainfall anomaly, and the proportion of natural habitat remaining. This combination of multiple factors influencing cryptosporidiosis highlights the benefits of a sophisticated spatio-temporal statistical approach. Two key findings from our model include: an estimated 4.6% increase in the risk of reported cryptosporidiosis associated with 22.8% higher percentage of postal area covered with original habitat; and an estimated 1.8% increase in disease risk associated with a 77.99 mm increase in annual rainfall anomaly at the postal area level. DISCUSSION These results provide novel insights regarding the predictive effects of extreme rainfall and the proportion of remaining natural habitat, which add unique explanatory power to the model alongside the variance associated with other predictive variables and spatiotemporal variation in reported disease. This demonstrates the importance of including perspectives from land and water management experts for policy making and public health responses to manage environmentally mediated diseases, including cryptosporidiosis.
Collapse
Affiliation(s)
- Owen Forbes
- Research School of Population Health, Australian National University, Acton, Australia; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Brisbane, Australia
| | - Rose Hosking
- Research School of Population Health, Australian National University, Acton, Australia
| | - Karel Mokany
- Macroecological Modelling, CSIRO Land & Water, Black Mountain Laboratories, Canberra, ACT, Australia
| | - Aparna Lal
- Research School of Population Health, Australian National University, Acton, Australia.
| |
Collapse
|
4
|
Saha D, Alluri P, Gan A, Wu W. Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models. ACCIDENT; ANALYSIS AND PREVENTION 2018; 118:166-177. [PMID: 29477462 DOI: 10.1016/j.aap.2018.02.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 02/14/2018] [Accepted: 02/14/2018] [Indexed: 06/08/2023]
Abstract
The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.
Collapse
Affiliation(s)
- Dibakar Saha
- Collaborative Sciences Center for Road Safety, School of Urban and Regional Planning, Florida Atlantic University, 777 Glades Road, SO 376, Boca Raton, 33431, FL, United States.
| | - Priyanka Alluri
- Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3680, Miami, 33174, FL, United States
| | - Albert Gan
- Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3680, Miami, 33174, FL, United States
| | - Wanyang Wu
- Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3680, Miami, 33174, FL, United States
| |
Collapse
|
5
|
Goicoa T, Adin A, Etxeberria J, Militino AF, Ugarte MD. Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns. Stat Methods Med Res 2017; 28:384-403. [PMID: 28847210 DOI: 10.1177/0962280217726802] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
In this paper age-space-time models based on one and two-dimensional P-splines with B-spline bases are proposed for smoothing mortality rates, where both fixed relative scale and scale invariant two-dimensional penalties are examined. Model fitting and inference are carried out using integrated nested Laplace approximations, a recent Bayesian technique that speeds up computations compared to McMC methods. The models will be illustrated with Spanish breast cancer mortality data during the period 1985-2010, where a general decline in breast cancer mortality has been observed in Spanish provinces in the last decades. The results reveal that mortality rates for the oldest age groups do not decrease in all provinces.
Collapse
Affiliation(s)
- T Goicoa
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain.,3 Research Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - A Adin
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| | - J Etxeberria
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain.,4 Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Spain
| | - A F Militino
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| | - M D Ugarte
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| |
Collapse
|
6
|
Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. Biom J 2017; 59:531-549. [DOI: 10.1002/bimj.201500263] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 09/04/2016] [Accepted: 10/02/2016] [Indexed: 01/09/2023]
|
7
|
Meredith CS, Budy P, Hooten MB, Prates MO. Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach. Biol Invasions 2016. [DOI: 10.1007/s10530-016-1322-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
8
|
Ugarte MD, Adin A, Goicoa T. Two-level spatially structured models in spatio-temporal disease mapping. Stat Methods Med Res 2016; 25:1080-100. [DOI: 10.1177/0962280216660423] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This work focuses on extending some classical spatio-temporal models in disease mapping. The objective is to present a family of flexible models to analyze real data naturally organized in two different levels of spatial aggregation like municipalities within health areas or provinces, or counties within states. Model fitting and inference will be carried out using integrated nested Laplace approximations. The performance of the new models compared to models including a single spatial random effect is assessed by simulation. Results show good behavior of the proposed two-level spatially structured models in terms of several criteria. Brain cancer mortality data in the municipalities of two regions in Spain will be analyzed using the new model proposals. It will be shown that a model with two-level spatial random effects overcomes the usual single-level models.
Collapse
Affiliation(s)
- María Dolores Ugarte
- Department of Statistics and Operations Research, Public University of Navarre, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| | - Aritz Adin
- Department of Statistics and Operations Research, Public University of Navarre, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| | - Tomás Goicoa
- Department of Statistics and Operations Research, Public University of Navarre, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
- Research Network on Health Services in Chronic Diseases (REDISSEC), Spain
| |
Collapse
|
9
|
Goicoa T, Ugarte MD, Etxeberria J, Militino AF. Age-space-time CAR models in Bayesian disease mapping. Stat Med 2016; 35:2391-405. [PMID: 26814019 DOI: 10.1002/sim.6873] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Revised: 11/17/2015] [Accepted: 12/22/2015] [Indexed: 12/25/2022]
Abstract
Mortality counts are usually aggregated over age groups assuming similar effects of both time and region, yet the spatio-temporal evolution of cancer mortality rates may depend on changing age structures. In this paper, mortality rates are analyzed by region, time period and age group, and models including space-time, space-age, and age-time interactions are considered. The integrated nested Laplace approximation method, known as INLA, is adopted for model fitting and inference in order to reduce computing time in comparison with Markov chain Monte Carlo (McMC) methods. The methodology provides full posterior distributions of the quantities of interest while avoiding complex simulation techniques. The proposed models are used to analyze prostate cancer mortality data in 50 Spanish provinces over the period 1986-2010. The results reveal a decline in mortality since the late 1990s, particularly in the age group [65,70), probably because of the inclusion of the PSA (prostate-specific antigen) test and better treatment of early-stage disease. The decline is not clearly observed in the oldest age groups. Copyright © 2016 John Wiley & Sons, Ltd.
Collapse
Affiliation(s)
- T Goicoa
- Department of Statistics and O. R. Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain.,Institute for Advanced Materials (INAMAT), Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain.,Research Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - M D Ugarte
- Department of Statistics and O. R. Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain.,Institute for Advanced Materials (INAMAT), Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain
| | - J Etxeberria
- Department of Statistics and O. R. Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain.,Institute for Advanced Materials (INAMAT), Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Spain
| | - A F Militino
- Department of Statistics and O. R. Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain.,Institute for Advanced Materials (INAMAT), Universidad Pública de Navarra, Campus de Arrosadia, Pamplona, 31006, Spain
| |
Collapse
|
10
|
Tosas Auguet O, Betley JR, Stabler RA, Patel A, Ioannou A, Marbach H, Hearn P, Aryee A, Goldenberg SD, Otter JA, Desai N, Karadag T, Grundy C, Gaunt MW, Cooper BS, Edgeworth JD, Kypraios T. Evidence for Community Transmission of Community-Associated but Not Health-Care-Associated Methicillin-Resistant Staphylococcus Aureus Strains Linked to Social and Material Deprivation: Spatial Analysis of Cross-sectional Data. PLoS Med 2016; 13:e1001944. [PMID: 26812054 PMCID: PMC4727805 DOI: 10.1371/journal.pmed.1001944] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 12/11/2015] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Identifying and tackling the social determinants of infectious diseases has become a public health priority following the recognition that individuals with lower socioeconomic status are disproportionately affected by infectious diseases. In many parts of the world, epidemiologically and genotypically defined community-associated (CA) methicillin-resistant Staphylococcus aureus (MRSA) strains have emerged to become frequent causes of hospital infection. The aim of this study was to use spatial models with adjustment for area-level hospital attendance to determine the transmission niche of genotypically defined CA- and health-care-associated (HA)-MRSA strains across a diverse region of South East London and to explore a potential link between MRSA carriage and markers of social and material deprivation. METHODS AND FINDINGS This study involved spatial analysis of cross-sectional data linked with all MRSA isolates identified by three National Health Service (NHS) microbiology laboratories between 1 November 2011 and 29 February 2012. The cohort of hospital-based NHS microbiology diagnostic services serves 867,254 usual residents in the Lambeth, Southwark, and Lewisham boroughs in South East London, United Kingdom (UK). Isolates were classified as HA- or CA-MRSA based on whole genome sequencing. All MRSA cases identified over 4 mo within the three-borough catchment area (n = 471) were mapped to small geographies and linked to area-level aggregated socioeconomic and demographic data. Disease mapping and ecological regression models were used to infer the most likely transmission niches for each MRSA genetic classification and to describe the spatial epidemiology of MRSA in relation to social determinants. Specifically, we aimed to identify demographic and socioeconomic population traits that explain cross-area extra variation in HA- and CA-MRSA relative risks following adjustment for hospital attendance data. We explored the potential for associations with the English Indices of Deprivation 2010 (including the Index of Multiple Deprivation and several deprivation domains and subdomains) and the 2011 England and Wales census demographic and socioeconomic indicators (including numbers of households by deprivation dimension) and indicators of population health. Both CA-and HA-MRSA were associated with household deprivation (CA-MRSA relative risk [RR]: 1.72 [1.03-2.94]; HA-MRSA RR: 1.57 [1.06-2.33]), which was correlated with hospital attendance (Pearson correlation coefficient [PCC] = 0.76). HA-MRSA was also associated with poor health (RR: 1.10 [1.01-1.19]) and residence in communal care homes (RR: 1.24 [1.12-1.37]), whereas CA-MRSA was linked with household overcrowding (RR: 1.58 [1.04-2.41]) and wider barriers, which represent a combined score for household overcrowding, low income, and homelessness (RR: 1.76 [1.16-2.70]). CA-MRSA was also associated with recent immigration to the UK (RR: 1.77 [1.19-2.66]). For the area-level variation in RR for CA-MRSA, 28.67% was attributable to the spatial arrangement of target geographies, compared with only 0.09% for HA-MRSA. An advantage to our study is that it provided a representative sample of usual residents receiving care in the catchment areas. A limitation is that relationships apparent in aggregated data analyses cannot be assumed to operate at the individual level. CONCLUSIONS There was no evidence of community transmission of HA-MRSA strains, implying that HA-MRSA cases identified in the community originate from the hospital reservoir and are maintained by frequent attendance at health care facilities. In contrast, there was a high risk of CA-MRSA in deprived areas linked with overcrowding, homelessness, low income, and recent immigration to the UK, which was not explainable by health care exposure. Furthermore, areas adjacent to these deprived areas were themselves at greater risk of CA-MRSA, indicating community transmission of CA-MRSA. This ongoing community transmission could lead to CA-MRSA becoming the dominant strain types carried by patients admitted to hospital, particularly if successful hospital-based MRSA infection control programmes are maintained. These results suggest that community infection control programmes targeting transmission of CA-MRSA will be required to control MRSA in both the community and hospital. These epidemiological changes will also have implications for effectiveness of risk-factor-based hospital admission MRSA screening programmes.
Collapse
Affiliation(s)
- Olga Tosas Auguet
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Jason R. Betley
- Illumina, Cambridge Limited, Chesterford Research Park, Little Chesterford, Essex, United Kingdom
| | - Richard A. Stabler
- Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Amita Patel
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Avgousta Ioannou
- Illumina, Cambridge Limited, Chesterford Research Park, Little Chesterford, Essex, United Kingdom
| | - Helene Marbach
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Pasco Hearn
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Anna Aryee
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Simon D. Goldenberg
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Jonathan A. Otter
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Nergish Desai
- Department of Medical Microbiology, King's Hospital NHS Foundation Trust, London, United Kingdom
| | - Tacim Karadag
- Department of Microbiology, University Hospital Lewisham, Lewisham and Greenwich NHS Trust, London, United Kingdom
| | - Chris Grundy
- Department of Social and Environmental Health Research, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Michael W. Gaunt
- Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ben S. Cooper
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
- Mahidol Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand
| | - Jonathan D. Edgeworth
- Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, King's College London and Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Theodore Kypraios
- School of Mathematical Sciences, University Park, University of Nottingham, Nottingham, United Kingdom
| |
Collapse
|
11
|
Bauer C, Wakefield J, Rue H, Self S, Feng Z, Wang Y. Bayesian penalized spline models for the analysis of spatio-temporal count data. Stat Med 2015; 35:1848-65. [PMID: 26530705 DOI: 10.1002/sim.6785] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 10/02/2015] [Accepted: 10/10/2015] [Indexed: 11/11/2022]
Abstract
In recent years, the availability of infectious disease counts in time and space has increased, and consequently, there has been renewed interest in model formulation for such data. In this paper, we describe a model that was motivated by the need to analyze hand, foot, and mouth disease surveillance data in China. The data are aggregated by geographical areas and by week, with the aims of the analysis being to gain insight into the space-time dynamics and to make short-term predictions, which will aid in the implementation of public health campaigns in those areas with a large predicted disease burden. The model we develop decomposes disease-risk into marginal spatial and temporal components and a space-time interaction piece. The latter is the crucial element, and we use a tensor product spline model with a Markov random field prior on the coefficients of the basis functions. The model can be formulated as a Gaussian Markov random field and so fast computation can be carried out using the integrated nested Laplace approximation approach. A simulation study shows that the model can pick up complex space-time structure and our analysis of hand, foot, and mouth disease data in the central north region of China provides new insights into the dynamics of the disease.
Collapse
Affiliation(s)
- Cici Bauer
- Department of Biostatistics, Brown University, Providence, RI, U.S.A
| | - Jon Wakefield
- Department of Statistics, University of Washington, Seattle, WA, U.S.A
| | - Håvard Rue
- Norwegian University of Science and Technology, Trondheim, Norway
| | - Steve Self
- Fred Hutchinson Cancer Research Center, Seattle, WA, U.S.A
| | - Zijian Feng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yu Wang
- Chinese Center for Disease Control and Prevention, Beijing, China
| |
Collapse
|
12
|
Ugarte MD, Adin A, Goicoa T, Casado I, Ardanaz E, Larrañaga N. Temporal evolution of brain cancer incidence in the municipalities of Navarre and the Basque Country, Spain. BMC Public Health 2015; 15:1018. [PMID: 26438178 PMCID: PMC4594739 DOI: 10.1186/s12889-015-2354-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 09/23/2015] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Brain cancer incidence rates in Spain are below the European's average. However, there are two regions in the north of the country, Navarre and the Basque Country, ranked among the European regions with the highest incidence rates for both males and females. Our objective here was two-fold. Firstly, to describe the temporal evolution of the geographical pattern of brain cancer incidence in Navarre and the Basque Country, and secondly, to look for specific high risk areas (municipalities) within these two regions in the study period (1986-2008). METHODS A mixed Poisson model with two levels of spatial effects is used. The model also included two levels of spatial effects (municipalities and local health areas). Model fitting was carried out using penalized quasi-likelihood. High risk regions were detected using upper one-sided confidence intervals. RESULTS Results revealed a group of high risk areas surrounding Pamplona, the capital city of Navarre, and a few municipalities with significant high risks in the northern part of the region, specifically in the border between Navarre and the Basque Country (Gipuzkoa). The global temporal trend was found to be increasing. Differences were also observed among specific risk evolutions in certain municipalities. CONCLUSIONS Brain cancer incidence in Navarre and the Basque Country (Spain) is still increasing with time. The number of high risk areas within those two regions is also increasing. Our study highlights the need of continuous surveillance of this cancer in the areas of high risk. However, due to the low percentage of cases explained by the known risk factors, primary prevention should be applied as a general recommendation in these populations.
Collapse
Affiliation(s)
- María Dolores Ugarte
- Department of Statistics and O.R., Public University of Navarre, Campus de Arrosadía, Pamplona, 31006, Spain.
- Institute for Advanced Materials (INAMAT), Public University of Navarre, Campus de Arrosadía, Pamplona, 31006, Spain.
| | - Aritz Adin
- Department of Statistics and O.R., Public University of Navarre, Campus de Arrosadía, Pamplona, 31006, Spain.
- Institute for Advanced Materials (INAMAT), Public University of Navarre, Campus de Arrosadía, Pamplona, 31006, Spain.
| | - Tomás Goicoa
- Department of Statistics and O.R., Public University of Navarre, Campus de Arrosadía, Pamplona, 31006, Spain.
- Institute for Advanced Materials (INAMAT), Public University of Navarre, Campus de Arrosadía, Pamplona, 31006, Spain.
- Research Network on Health Services in Chronic Diseases (REDISSEC), Madrid, Spain.
| | - Itziar Casado
- Navarre Public Health Institute, Calle Leyre 15, Pamplona, 31006, Spain.
| | - Eva Ardanaz
- Navarre Public Health Institute, Calle Leyre 15, Pamplona, 31006, Spain.
- CIBER of Epidemiology an Public Health CIBERESP, Madrid, Spain.
| | - Nerea Larrañaga
- CIBER of Epidemiology an Public Health CIBERESP, Madrid, Spain.
- Public Health Division of Gipuzkoa, BIODonostia Research Institute, Government of the Basque Country, Nafarroa hiribidea 4, Donostia-San Sebastián, 20013, Spain.
| |
Collapse
|
13
|
Sauter R, Held L. Network meta-analysis with integrated nested Laplace approximations. Biom J 2015; 57:1038-50. [PMID: 26360927 DOI: 10.1002/bimj.201400163] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 03/23/2015] [Accepted: 05/27/2015] [Indexed: 11/11/2022]
Abstract
Analyzing the collected evidence of a systematic review in form of a network meta-analysis (NMA) enjoys increasing popularity and provides a valuable instrument for decision making. Bayesian inference of NMA models is often propagated, especially if correlated random effects for multiarm trials are included. The standard choice for Bayesian inference is Markov chain Monte Carlo (MCMC) sampling, which is computationally intensive. An alternative to MCMC sampling is the recently suggested approximate Bayesian method of integrated nested Laplace approximations (INLA) that dramatically saves computation time without any substantial loss in accuracy. We show how INLA apply to NMA models for summary level as well as trial-arm level data. Specifically, we outline the modeling of multiarm trials and inference for functional contrasts with INLA. We demonstrate how INLA facilitate the assessment of network inconsistency with node-splitting. Three applications illustrate the use of INLA for a NMA.
Collapse
Affiliation(s)
- Rafael Sauter
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zürich, Switzerland
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zürich, Switzerland
| |
Collapse
|
14
|
Kang SY, McGree J, Baade P, Mengersen K. A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models. AUST NZ J STAT 2015. [DOI: 10.1111/anzs.12127] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Su Yun Kang
- Mathematical Sciences School; Queensland University of Technology; GPO Box 2434 Brisbane QLD 4001 Australia
- CRC for Spatial Information; 204 Lygon Street Carlton Vic. 3053 Australia
| | - James McGree
- Mathematical Sciences School; Queensland University of Technology; GPO Box 2434 Brisbane QLD 4001 Australia
- CRC for Spatial Information; 204 Lygon Street Carlton Vic. 3053 Australia
| | - Peter Baade
- Viertel Centre for Research in Cancer Control; Cancer Council Queensland; Gregory Terrace Fortitude Valley Australia
- School of Public Health; Queensland University of Technology; GPO Box 2434 Brisbane QLD 4001 Australia
- Griffith Health Institute; Griffith University; Brisbane QLD 4001 Australia
| | - Kerrie Mengersen
- Mathematical Sciences School; Queensland University of Technology; GPO Box 2434 Brisbane QLD 4001 Australia
- CRC for Spatial Information; 204 Lygon Street Carlton Vic. 3053 Australia
| |
Collapse
|
15
|
Iftimi A, Montes F, Santiyán AM, Martínez-Ruiz F. Space-time airborne disease mapping applied to detect specific behaviour of varicella in Valencia, Spain. Spat Spatiotemporal Epidemiol 2015; 14-15:33-44. [PMID: 26530821 DOI: 10.1016/j.sste.2015.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Revised: 05/14/2015] [Accepted: 07/09/2015] [Indexed: 10/23/2022]
Abstract
Airborne diseases are one of humanity's most feared sicknesses and have regularly caused concern among specialists. Varicella is an airborne disease which usually affects children before the age of 10. Because of its nature, varicella gives rise to interesting spatial, temporal and spatio-temporal patterns. This paper studies spatio-temporal exploratory analysis tools to detect specific behaviour of varicella in the city of Valencia, Spain, from 2008 to 2013. These methods have shown a significant association between the spatial and the temporal component, confirmed by the space-time models applied to the data. High relative risk of varicella is observed in economically disadvantaged regions, areas less involved in vaccination programmes.
Collapse
|
16
|
Ugarte MD, Adin A, Goicoa T, López-Abente G. Analyzing the evolution of young people's brain cancer mortality in Spanish provinces. Cancer Epidemiol 2015; 39:480-5. [PMID: 25907644 DOI: 10.1016/j.canep.2015.03.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 03/03/2015] [Accepted: 03/31/2015] [Indexed: 01/19/2023]
Abstract
OBJECTIVES To analyze the spatio-temporal evolution of brain cancer relative mortality risks in young population (under 20 years of age) in Spanish provinces during the period 1986-2010. METHODS A new and flexible conditional autoregressive spatio-temporal model with two levels of spatial aggregation was used. RESULTS Brain cancer relative mortality risks in young population in Spanish provinces decreased during the last years, although a clear increase was observed during the 1990s. The global geographical pattern emphasized a high relative mortality risk in Navarre and a low relative mortality risk in Madrid. Although there is a specific Autonomous Region-time interaction effect on the relative mortality risks this effect is weak in the final estimates when compared to the global spatial and temporal effects. CONCLUSIONS Differences in mortality between regions and over time may be caused by the increase in survival rates, the differences in treatment or the availability of diagnostic tools. The increase in relative risks observed in the 1990s was probably due to improved diagnostics with computerized axial tomography and magnetic resonance imaging techniques.
Collapse
Affiliation(s)
- M D Ugarte
- Department of Statistics and O.R., Public University of Navarre, Spain; Institute for Advanced Materials (INAMAT), Public University of Navarre, Spain.
| | - A Adin
- Department of Statistics and O.R., Public University of Navarre, Spain
| | - T Goicoa
- Department of Statistics and O.R., Public University of Navarre, Spain; Institute for Advanced Materials (INAMAT), Public University of Navarre, Spain; Research Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - G López-Abente
- Environmental and Cancer Epidemiology Unit, National Centre for Epidemiology, Carlos III Institute of Health, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Madrid, Spain
| |
Collapse
|
17
|
Assareh H, Ou L, Chen J, Hillman K, Flabouris A, Hollis SJ. Geographic variation of failure-to-rescue in public acute hospitals in New South Wales, Australia. PLoS One 2014; 9:e109807. [PMID: 25310260 PMCID: PMC4195695 DOI: 10.1371/journal.pone.0109807] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Accepted: 09/14/2014] [Indexed: 12/21/2022] Open
Abstract
Despite the wide acceptance of Failure-to-Rescue (FTR) as a patient safety indicator (defined as the deaths among surgical patients with treatable complications), no study has explored the geographic variation of FTR in a large health jurisdiction. Our study aimed to explore the spatiotemporal variations of FTR rates across New South Wales (NSW), Australia. We conducted a population-based study using all admitted surgical patients in public acute hospitals during 2002-2009 in NSW, Australia. We developed a spatiotemporal Poisson model using Integrated Nested Laplace Approximation (INLA) methods in a Bayesian framework to obtain area-specific adjusted relative risk. Local Government Area (LGA) was chosen as the areal unit. LGA-aggregated covariates included age, gender, socio-economic and remoteness index scores, distance between patient residential postcode and the treating hospital, and a quadratic time trend. We studied 4,285,494 elective surgical admissions in 82 acute public hospitals over eight years in NSW. Around 14% of patients who developed at least one of the six FTR-related complications (58,590) died during hospitalization. Of 153 LGAs, patients who lived in 31 LGAs, accommodating 48% of NSW patients at risk, were exposed to an excessive adjusted FTR risk (10% to 50%) compared to the state-average. They were mostly located in state's centre and western Sydney. Thirty LGAs with a lower adjusted FTR risk (10% to 30%), accommodating 8% of patients at risk, were mostly found in the southern parts of NSW and Sydney east and south. There were significant spatiotemporal variations of FTR rates across NSW over an eight-year span. Areas identified with significantly high and low FTR risks provide potential opportunities for policy-makers, clinicians and researchers to learn from the success or failure of adopting the best care for surgical patients and build a self-learning organisation and health system.
Collapse
Affiliation(s)
- Hassan Assareh
- Simpson Centre for Health Services Research, Australian Institute of Health Innovation & South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
- Epidemiology, Western Sydney Local Health District, Sydney, New South Wales, Australia
| | - Lixin Ou
- Simpson Centre for Health Services Research, Australian Institute of Health Innovation & South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Jack Chen
- Simpson Centre for Health Services Research, Australian Institute of Health Innovation & South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Kenneth Hillman
- Simpson Centre for Health Services Research, Australian Institute of Health Innovation & South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Arthas Flabouris
- Intensive Care Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Stephanie J. Hollis
- Simpson Centre for Health Services Research, Australian Institute of Health Innovation & South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| |
Collapse
|
18
|
Chen C, Wakefield J, Lumely T. The use of sampling weights in Bayesian hierarchical models for small area estimation. Spat Spatiotemporal Epidemiol 2014; 11:33-43. [PMID: 25457595 DOI: 10.1016/j.sste.2014.07.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Revised: 05/22/2014] [Accepted: 07/12/2014] [Indexed: 10/24/2022]
Abstract
Hierarchical modeling has been used extensively for small area estimation. However, design weights that are required to reflect complex surveys are rarely considered in these models. We develop computationally efficient, Bayesian spatial smoothing models that acknowledge the design weights. Computation is carried out using the integrated nested Laplace approximation, which is fast. An extensive simulation study is presented that considers the effects of non-response and non-random selection of individuals, allowing examination of the impact of ignoring the design weights and the benefits of spatial smoothing. The results show that, when compared with standard approaches, mean squared error can be greatly reduced with the proposed methods. Bias reduction occurs through the inclusion of the design weights, with variance reduction being achieved through hierarchical smoothing. We analyze data from the Washington State 2006 Behavioral Risk Factor Surveillance System. The models are easily and quickly fitted within the R environment, using existing packages.
Collapse
Affiliation(s)
- Cici Chen
- Department of Biostatistics, Brown University, USA.
| | - Jon Wakefield
- Department of Statistics, University of Washington, USA; Department Biostatistics, University of Washington, USA.
| | - Thomas Lumely
- Department of Statistics, University of Auckland, New Zealand
| |
Collapse
|
19
|
Affiliation(s)
- Andrew B. Lawson
- Department of Public Health Sciences Medical University of South Carolina Charleston SC USA
| |
Collapse
|
20
|
Kang SY, McGree J, Mengersen K. The choice of spatial scales and spatial smoothness priors for various spatial patterns. Spat Spatiotemporal Epidemiol 2014; 10:11-26. [PMID: 25113587 DOI: 10.1016/j.sste.2014.05.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2014] [Revised: 04/17/2014] [Accepted: 05/29/2014] [Indexed: 11/26/2022]
Abstract
Given the drawbacks for using geo-political areas in mapping outcomes unrelated to geo-politics, a compromise is to aggregate and analyse data at the grid level. This has the advantage of allowing spatial smoothing and modelling at a biologically or physically relevant scale. This article addresses two consequent issues: the choice of the spatial smoothness prior and the scale of the grid. Firstly, we describe several spatial smoothness priors applicable for grid data and discuss the contexts in which these priors can be employed based on different aims. Two such aims are considered, i.e., to identify regions with clustering and to model spatial dependence in the data. Secondly, the choice of the grid size is shown to depend largely on the spatial patterns. We present a guide on the selection of spatial scales and smoothness priors for various point patterns based on the two aims for spatial smoothing.
Collapse
Affiliation(s)
- Su Yun Kang
- Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; CRC for Spatial Information, 204 Lygon Street, Carlton, Victoria 3053, Australia.
| | - James McGree
- Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; CRC for Spatial Information, 204 Lygon Street, Carlton, Victoria 3053, Australia
| | - Kerrie Mengersen
- Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; CRC for Spatial Information, 204 Lygon Street, Carlton, Victoria 3053, Australia
| |
Collapse
|
21
|
Ugarte MD, Adin A, Goicoa T, Militino AF. On fitting spatio-temporal disease mapping models using approximate Bayesian inference. Stat Methods Med Res 2014; 23:507-30. [PMID: 24713158 DOI: 10.1177/0962280214527528] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Spatio-temporal disease mapping comprises a wide range of models used to describe the distribution of a disease in space and its evolution in time. These models have been commonly formulated within a hierarchical Bayesian framework with two main approaches: an empirical Bayes (EB) and a fully Bayes (FB) approach. The EB approach provides point estimates of the parameters relying on the well-known penalized quasi-likelihood (PQL) technique. The FB approach provides the posterior distribution of the target parameters. These marginal distributions are not usually available in closed form and common estimation procedures are based on Markov chain Monte Carlo (MCMC) methods. However, the spatio-temporal models used in disease mapping are often very complex and MCMC methods may lead to large Monte Carlo errors and a huge computation time if the dimension of the data at hand is large. To circumvent these potential inconveniences, a new technique called integrated nested Laplace approximations (INLA), based on nested Laplace approximations, has been proposed for Bayesian inference in latent Gaussian models. In this paper, we show how to fit different spatio-temporal models for disease mapping with INLA using the Leroux CAR prior for the spatial component, and we compare it with PQL via a simulation study. The spatio-temporal distribution of male brain cancer mortality in Spain during the period 1986-2010 is also analysed.
Collapse
Affiliation(s)
| | - Aritz Adin
- Department of Statistics and O. R., Public University of Navarre, Spain
| | - Tomas Goicoa
- Department of Statistics and O. R., Public University of Navarre, Spain Research Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | | |
Collapse
|
22
|
Affiliation(s)
- Craig Anderson
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QQ, UK
| | - Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QQ, UK
| | - Nema Dean
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QQ, UK
| |
Collapse
|
23
|
Blangiardo M, Cameletti M, Baio G, Rue H. Spatial and spatio-temporal models with R-INLA. Spat Spatiotemporal Epidemiol 2013; 7:39-55. [DOI: 10.1016/j.sste.2013.07.003] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
24
|
|
25
|
The analysis--hierarchical models: past, present and future. Prev Vet Med 2013; 113:304-12. [PMID: 24176136 DOI: 10.1016/j.prevetmed.2013.10.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Revised: 09/10/2013] [Accepted: 10/01/2013] [Indexed: 11/23/2022]
Abstract
This paper discusses statistical modelling for data with a hierarchical structure, and distinguishes in this context between three different meanings of the term hierarchical model: to account for clustering, to investigate variability and separate predictive equations at different hierarchical levels (multi-level analysis), and in a Bayesian framework to involve multiple layers of data or prior information. Within each of these areas, the paper reviews both past developments and the present state, and offers indications of future directions. In a worked example, previously reported data on piglet lameness are reanalyzed with multi-level methodology for survival analysis, leading to new insights into the data structure and predictor effects. In our view, hierarchical models of all three types discussed have much to offer for data analysis in veterinary epidemiology and other disciplines.
Collapse
|
26
|
Kang SY, McGree J, Mengersen K. The impact of spatial scales and spatial smoothing on the outcome of bayesian spatial model. PLoS One 2013; 8:e75957. [PMID: 24146799 PMCID: PMC3795684 DOI: 10.1371/journal.pone.0075957] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2013] [Accepted: 08/19/2013] [Indexed: 12/02/2022] Open
Abstract
Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matérn correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.
Collapse
Affiliation(s)
- Su Yun Kang
- Mathematical Sciences School, Queensland University of Technology, Brisbane, Queensland, Australia
- Cooperative Research Centre for Spatial Information, Melbourne, Victoria, Australia
| | - James McGree
- Mathematical Sciences School, Queensland University of Technology, Brisbane, Queensland, Australia
- Cooperative Research Centre for Spatial Information, Melbourne, Victoria, Australia
| | - Kerrie Mengersen
- Mathematical Sciences School, Queensland University of Technology, Brisbane, Queensland, Australia
- Cooperative Research Centre for Spatial Information, Melbourne, Victoria, Australia
| |
Collapse
|
27
|
Manitz J, Höhle M. Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany. Biom J 2013; 55:509-26. [DOI: 10.1002/bimj.201200141] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Revised: 11/29/2012] [Accepted: 02/11/2013] [Indexed: 11/12/2022]
Affiliation(s)
- Juliane Manitz
- Centre for Statistics; University of Göttingen; Platz der Göttinger Sieben 5; 37073 Göttingen; Germany
| | - Michael Höhle
- Department for Infectious Disease Epidemiology; Robert Koch Institute, DGZ-Ring 1; 13086 Berlin; Germany
| |
Collapse
|
28
|
Blangiardo M, Cameletti M, Baio G, Rue H. Spatial and spatio-temporal models with R-INLA. Spat Spatiotemporal Epidemiol 2013; 4:33-49. [DOI: 10.1016/j.sste.2012.12.001] [Citation(s) in RCA: 158] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Revised: 11/28/2012] [Accepted: 12/05/2012] [Indexed: 10/27/2022]
|
29
|
Ancelet S, Abellan JJ, Del Rio Vilas VJ, Birch C, Richardson S. Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources. Biom J 2013; 54:385-404. [PMID: 22685004 DOI: 10.1002/bimj.201000106] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
When analyzing the geographical variations of disease risk, one common problem is data sparseness. In such a setting, we investigate the possibility of using Bayesian shared spatial component models to strengthen inference and correct for any spatially structured sources of bias, when distinct data sources on one or more related diseases are available. Specifically, we apply our models to analyze the spatial variation of risk of two forms of scrapie infection affecting sheep in Wales (UK) using three surveillance sources on each disease. We first model each disease separately from the combined data sources and then extend our approach to jointly analyze diseases and data sources. We assess the predictive performances of several nested joint models through pseudo cross-validatory predictive model checks.
Collapse
Affiliation(s)
- Sophie Ancelet
- AgroParisTech/INRA UMR, Department of Applied Mathematics and Informatics, MORSE team, Paris, France.
| | | | | | | | | |
Collapse
|
30
|
Wang XF. Joint generalized models for multidimensional outcomes: a case study of neuroscience data from multimodalities. Biom J 2012; 54:264-80. [PMID: 22522380 DOI: 10.1002/bimj.201100041] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper is motivated from the analysis of neuroscience data in a study of neural and muscular mechanisms of muscle fatigue. Multidimensional outcomes of different natures were obtained simultaneously from multiple modalities, including handgrip force, electromyography (EMG), and functional magnetic resonance imaging (fMRI). We first study individual modeling of the univariate response depending on its nature. A mixed-effects beta model and a mixed-effects simplex model are compared for modeling the force/EMG percentages. A mixed-effects negative-binomial model is proposed for modeling the fMRI counts. Then, I present a joint modeling approach to model the multidimensional outcomes together, which allows us to not only estimate the covariate effects but also to evaluate the strength of association among the multiple responses from different modalities. A simulation study is conducted to quantify the possible benefits by the new approaches in finite sample situations. Finally, the analysis of the fatigue data is illustrated with the use of the proposed methods.
Collapse
Affiliation(s)
- Xiao-Feng Wang
- Department of Quantitative Health Sciences/Biostatistics Section, Cleveland Clinic Foundation, Cleveland, OH 44195, USA.
| |
Collapse
|
31
|
Ruiz-Cárdenas R, Krainski ET, Rue H. Direct fitting of dynamic models using integrated nested Laplace approximations — INLA. Comput Stat Data Anal 2012. [DOI: 10.1016/j.csda.2011.10.024] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
32
|
Riebler A, Held L, Rue H. Estimation and extrapolation of time trends in registry data—Borrowing strength from related populations. Ann Appl Stat 2012. [DOI: 10.1214/11-aoas498] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
33
|
Schrödle B, Held L, Rue H. Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases. Biometrics 2011; 68:736-44. [PMID: 22171626 DOI: 10.1111/j.1541-0420.2011.01717.x] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Linking information on a movement network with space-time data on disease incidence is one of the key challenges in infectious disease epidemiology. In this article, we propose and compare two statistical frameworks for this purpose, namely, parameter-driven (PD) and observation-driven (OD) models. Bayesian inference in PD models is done using integrated nested Laplace approximations, while OD models can be easily fitted with existing software using maximum likelihood. The predictive performance of both formulations is assessed using proper scoring rules. As a case study, the impact of cattle trade on the spatiotemporal spread of Coxiellosis in Swiss cows, 2004-2009, is finally investigated.
Collapse
Affiliation(s)
- Birgit Schrödle
- Division of Biostatistics, Institute for Social and Preventive Medicine, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland
| | | | | |
Collapse
|
34
|
Bisanzio D, Giacobini M, Bertolotti L, Mosca A, Balbo L, Kitron U, Vazquez-Prokopec GM. Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont Region, Italy. Parasit Vectors 2011; 4:230. [PMID: 22152822 PMCID: PMC3251540 DOI: 10.1186/1756-3305-4-230] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2011] [Accepted: 12/09/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND West Nile Virus (WNV) transmission in Italy was first reported in 1998 as an equine outbreak near the swamps of Padule di Fucecchio, Tuscany. No other cases were identified during the following decade until 2008, when horse and human outbreaks were reported in Emilia Romagna, North Italy. Since then, WNV outbreaks have occurred annually, spreading from their initial northern foci throughout the country. Following the outbreak in 1998 the Italian public health authority defined a surveillance plan to detect WNV circulation in birds, horses and mosquitoes. By applying spatial statistical analysis (spatial point pattern analysis) and models (Bayesian GLMM models) to a longitudinal dataset on the abundance of the three putative WNV vectors [Ochlerotatus caspius (Pallas 1771), Culex pipiens (Linnaeus 1758) and Culex modestus (Ficalbi 1890)] in eastern Piedmont, we quantified their abundance and distribution in space and time and generated prediction maps outlining the areas with the highest vector productivity and potential for WNV introduction and amplification. RESULTS The highest abundance and significant spatial clusters of Oc. caspius and Cx. modestus were in proximity to rice fields, and for Cx. pipiens, in proximity to highly populated urban areas. The GLMM model showed the importance of weather conditions and environmental factors in predicting mosquito abundance. Distance from the preferential breeding sites and elevation were negatively associated with the number of collected mosquitoes. The Normalized Difference Vegetation Index (NDVI) was positively correlated with mosquito abundance in rice fields (Oc. caspius and Cx. modestus). Based on the best models, we developed prediction maps for the year 2010 outlining the areas where high abundance of vectors could favour the introduction and amplification of WNV. CONCLUSIONS Our findings provide useful information for surveillance activities aiming to identify locations where the potential for WNV introduction and local transmission are highest. Such information can be used by vector control offices to stratify control interventions in areas prone to the invasion of WNV and other mosquito-transmitted pathogens.
Collapse
Affiliation(s)
- Donal Bisanzio
- Department of Animal Production, Epidemiology and Ecology, Faculty of Veterinary Medicine, University of Torino, Italy.
| | | | | | | | | | | | | |
Collapse
|
35
|
Corberán-Vallet A, Lawson AB. Conditional predictive inference for online surveillance of spatial disease incidence. Stat Med 2011; 30:3095-116. [PMID: 21898522 DOI: 10.1002/sim.4340] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2010] [Accepted: 06/16/2011] [Indexed: 11/11/2022]
Abstract
This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina.
Collapse
Affiliation(s)
- Ana Corberán-Vallet
- Division of Biostatistics and Epidemiology, Medical University of South Carolina, 135 Cannon St, Suite 303, Charleston, SC 29425, USA.
| | | |
Collapse
|
36
|
|