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Aswi A, Rahardiantoro S, Kurnia A, Sartono B, Handayani D, Nurwan N, Cramb S. Childhood stunting in Indonesia: assessing the performance of Bayesian spatial conditional autoregressive models. GEOSPATIAL HEALTH 2024; 19. [PMID: 39371042 DOI: 10.4081/gh.2024.1321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Accepted: 09/15/2024] [Indexed: 10/08/2024]
Abstract
Stunting continues to be a significant health issue, particularly in developing nations, with Indonesia ranking third in prevalence in Southeast Asia. This research examined the risk of stunting and influencing factors in Indonesia by implementing various Bayesian spatial conditional autoregressive (CAR) models that include covariates. A total of 750 models were run, including five different Bayesian spatial CAR models (Besag-York-Mollie (BYM), CAR Leroux and three forms of localised CAR), with 30 covariate combinations and five different hyperprior combinations for each model. The Poisson distribution was employed to model the counts of stunting cases. After a comprehensive evaluation of all model selection criteria utilized, the Bayesian localised CAR model with three covariates were preferred, either allowing up to 2 clusters with a variance hyperprior of inverse-gamma (1, 0.1) or allowing 3 clusters with a variance hyperprior of inverse-gamma (1, 0.01). Poverty and recent low birth weight (LBW) births are significantly associated with an increased risk of stunting, whereas child diet diversity is inversely related to the risk of stunting. Model results indicated that Sulawesi Barat Province has the highest risk of stunting, with DKI Jakarta Province the lowest. These areas with high stunting require interventions to reduce poverty, LBW births and increase child diet diversity.
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Affiliation(s)
- Aswi Aswi
- Statistics Department, Universitas Negeri Makassar, Makassar.
| | | | | | | | | | - Nurwan Nurwan
- Statistics Department, Universitas Negeri Makassar, Makassar.
| | - Susanna Cramb
- Australian Centre for Health Services Innovation & Centre for Healthcare Transformation, Queensland University of Technology.
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Whitworth KW, Rector-Houze AM, Chen WJ, Ibarluzea J, Swartz M, Symanski E, Iniguez C, Lertxundi A, Valentin A, González-Safont L, Vrijheid M, Guxens M. Relation of prenatal and postnatal PM 2.5 exposure with cognitive and motor function among preschool-aged children. Int J Hyg Environ Health 2024; 256:114317. [PMID: 38171265 DOI: 10.1016/j.ijheh.2023.114317] [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: 08/15/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 01/05/2024]
Abstract
The literature informing susceptible periods of exposure on children's neurodevelopment is limited. We evaluated the impacts of pre- and postnatal fine particulate matter (PM2.5) exposure on children's cognitive and motor function among 1303 mother-child pairs in the Spanish INMA (Environment and Childhood) Study. Random forest models with temporal back extrapolation were used to estimate daily residential PM2.5 exposures that we averaged across 1-week lags during the prenatal period and 4-week lags during the postnatal period. The McCarthy Scales of Children's Abilities (MSCA) were administered around 5 years to assess general cognitive index (GCI) and several subscales (verbal, perceptual-performance, memory, fine motor, gross motor). We applied distributed lag nonlinear models within the Bayesian hierarchical framework to explore periods of susceptibility to PM2.5 on each MSCA outcome. Effect estimates were calculated per 5 μg/m3 increase in PM2.5 and aggregated across adjacent statistically significant lags using cumulative β (βcum) and 95% Credible Intervals (95%CrI). We evaluated interactions between PM2.5 with fetal growth and child sex. We did not observe associations of PM2.5 exposure with lower GCI scores. We found a period of susceptibility to PM2.5 on fine motor scores in gestational weeks 1-9 (βcum = -2.55, 95%CrI = -3.53,-1.56) and on gross motor scores in weeks 7-17 (βcum = -2.27,95%CrI = -3.43,-1.11) though the individual lags for the latter were only borderline statistically significant. Exposure in gestational week 17 was weakly associated with verbal scores (βcum = -0.17, 95%CrI = -0.26,-0.09). In the postnatal period (from age 0.5-1.2 years), we observed a window of susceptibility to PM2.5 on lower perceptual-performance (β = -2.42, 95%CrI = -3.37,-1.46). Unexpected protective associations were observed for several outcomes with exposures in the later postnatal period. We observed no evidence of differences in susceptible periods by fetal growth or child sex. Preschool-aged children's motor function may be particularly susceptible to PM2.5 exposures experienced in utero whereas the first year of life was identified as a period of susceptibility to PM2.5 for children's perceptual-performance.
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Affiliation(s)
- Kristina W Whitworth
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA; Center for Precision Environmental Health, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA.
| | - Alison M Rector-Houze
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA; Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, 1200 Pressler St., Houston, TX, 77030, USA
| | - Wei-Jen Chen
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Jesus Ibarluzea
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain; Environmental Epidemiology and Child Development, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, 20014, Donostia-San Sebastian, Spain; Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, Av. Navarra, 4, 20013, Donostia-San Sebastian, Spain; Faculty of Psychology, Universidad del País Vasco (UPV/EHU), Campus Gipuzkoa, Av. Tolosa, 70, 20018, Donostia-San Sebastian, Spain
| | - Michael Swartz
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, 1200 Pressler St., Houston, TX, 77030, USA
| | - Elaine Symanski
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA; Center for Precision Environmental Health, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Carmen Iniguez
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain; Department of Statistics and Operational Research, Universitat de València, Calle Dr Moliner, 50, 46100, València, Spain; Epidemiology and Environmental Health Joint Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Universitat Jaume I-Universitat de València, Av. De Catalunya, 21, 46020, València, Spain
| | - Aitana Lertxundi
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain; Environmental Epidemiology and Child Development, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, 20014, Donostia-San Sebastian, Spain; Department of Preventive Medicine and Public Health, Universidad del País Vasco (UPV/EHU), Barrio Sarriena, s/n, 48940, Leioa, Spain
| | - Antonia Valentin
- Barcelona Institute of Global Health (ISGlobal), C/del Dr. Aiguader, 88, 08003, Barcelona, Spain
| | - Llucia González-Safont
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain; Epidemiology and Environmental Health Joint Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Universitat Jaume I-Universitat de València, Av. De Catalunya, 21, 46020, València, Spain; Nursing and Chiropody Faculty of Valencia University, Av. De Blasko Ibanez, 13, 46010, Valencia, Spain
| | - Martine Vrijheid
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain; Barcelona Institute of Global Health (ISGlobal), C/del Dr. Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Placa de la Merce, 12, 08002, Barcelona, Spain
| | - Monica Guxens
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain; Barcelona Institute of Global Health (ISGlobal), C/del Dr. Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Placa de la Merce, 12, 08002, Barcelona, Spain; Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre (Erasmus MC), Dr. Moleaterplein 40, 30115 GD, Rotterdam, Netherlands
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Chen WJ, Rector-Houze AM, Guxens M, Iñiguez C, Swartz MD, Symanski E, Ibarluzea J, Valentin A, Lertxundi A, González-Safont L, Sunyer J, Whitworth KW. Susceptible windows of prenatal and postnatal fine particulate matter exposures and attention-deficit hyperactivity disorder symptoms in early childhood. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168806. [PMID: 38016567 DOI: 10.1016/j.scitotenv.2023.168806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 11/30/2023]
Abstract
Few prior studies have explored windows of susceptibility to fine particulate matter (PM2.5) in both the prenatal and postnatal periods and children's attention-deficit/hyperactivity disorder (ADHD) symptoms. We analyzed data from 1416 mother-child pairs from the Spanish INMA (INfancia y Medio Ambiente) Study (2003-2008). Around 5 years of age, teachers reported the number of ADHD symptoms (i.e., inattention, hyperactivity/impulsivity) using the ADHD Diagnostic and Statistical Manual of Mental Disorders. Around 7 years of age, parents completed the Conners' Parent Rating Scales, from which we evaluated the ADHD index, cognitive problems/inattention, hyperactivity, and oppositional subscales, reported as age- and sex-standardized T-scores. Daily residential PM2.5 exposures were estimated using a two-stage random forest model with temporal back-extrapolation and averaged over 1-week periods in the prenatal period and 4-week periods in the postnatal period. We applied distributed lag non-linear models within the Bayesian hierarchical model framework to identify susceptible windows of prenatal or postnatal exposure to PM2.5 (per 5-μg/m3) for ADHD symptoms. Models were adjusted for relevant covariates, and cumulative effects were reported by aggregating risk ratios (RRcum) or effect estimates (βcum) across adjacent susceptible windows. A similar susceptible period of exposure to PM2.5 (1.2-2.9 and 0.9-2.7 years of age, respectively) was identified for hyperactivity/impulsivity symptoms assessed ~5 years (RRcum = 2.72, 95% credible interval [CrI] = 1.98, 3.74) and increased hyperactivity subscale ~7 years (βcum = 3.70, 95% CrI = 2.36, 5.03). We observed a susceptibility period to PM2.5 on risk of hyperactivity/impulsivity symptoms ~5 years in gestational weeks 16-22 (RRcum = 1.36, 95% CrI = 1.22, 1.52). No associations between PM2.5 exposure and other ADHD symptoms were observed. We report consistent evidence of toddlerhood as a susceptible window of PM2.5 exposure for hyperactivity in young children. Although mid-pregnancy was identified as a susceptible period of exposure on hyperactivity symptoms in preschool-aged children, this association was not observed at the time children were school-aged.
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Affiliation(s)
- Wei-Jen Chen
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Alison M Rector-Houze
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA; Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, TX, USA
| | - Mònica Guxens
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; ISGlobal, Barcelona, Spain; Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre (Erasmus MC), Rotterdam, the Netherlands
| | - Carmen Iñiguez
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Department of Statistics and Operational Research, Universitat de València, València, Spain; Epidemiology and Environmental Health Joint Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Universitat Jaume I-Universitat de València, València, Spain
| | - Michael D Swartz
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, TX, USA
| | - Elaine Symanski
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA; Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA
| | - Jesús Ibarluzea
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Group of Environmental Epidemiology and Child Development, Biodonostia Health Research Institute, San Sebastian, Spain; Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, 20013 San Sebastian, Spain; Faculty of Psychology, Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
| | - Antonia Valentin
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; ISGlobal, Barcelona, Spain
| | - Aitana Lertxundi
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Group of Environmental Epidemiology and Child Development, Biodonostia Health Research Institute, San Sebastian, Spain; Department of Preventive Medicine and Public Health, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Llúcia González-Safont
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Epidemiology and Environmental Health Joint Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Universitat Jaume I-Universitat de València, València, Spain; Nursing and Chiropody Faculty of Valencia University, Valencia, Spain
| | - Jordi Sunyer
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; ISGlobal, Barcelona, Spain
| | - Kristina W Whitworth
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA; Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA.
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Wilson EC, Cousins S, Etter DR, Humphreys JM, Roloff GJ, Carter NH. Habitat and climatic associations of climate-sensitive species along a southern range boundary. Ecol Evol 2023; 13:e10083. [PMID: 37214615 PMCID: PMC10191803 DOI: 10.1002/ece3.10083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/24/2023] Open
Abstract
Climate change and habitat loss are recognized as important drivers of shifts in wildlife species' geographic distributions. While often considered independently, there is considerable overlap between these drivers, and understanding how they contribute to range shifts can predict future species assemblages and inform effective management. Our objective was to evaluate the impacts of habitat, climatic, and anthropogenic effects on the distributions of climate-sensitive vertebrates along a southern range boundary in Northern Michigan, USA. We combined multiple sources of occurrence data, including harvest and citizen-science data, then used hierarchical Bayesian spatial models to determine habitat and climatic associations for four climate-sensitive vertebrate species (American marten [Martes americana], snowshoe hare [Lepus americanus], ruffed grouse [Bonasa umbellus] and moose [Alces alces]). We used total basal area of at-risk forest types to represent habitat, and temperature and winter habitat indices to represent climate. Marten associated with upland spruce-fir and lowland riparian forest types, hares with lowland conifer and aspen-birch, grouse with lowland riparian hardwoods, and moose with upland spruce-fir. Species differed in climatic drivers with hares positively associated with cooler annual temperatures, moose with cooler summer temperatures and grouse with colder winter temperatures. Contrary to expectations, temperature variables outperformed winter habitat indices. Model performance varied greatly among species, as did predicted distributions along the southern edge of the Northwoods region. As multiple species were associated with lowland riparian and upland spruce-fir habitats, these results provide potential for efficient prioritization of habitat management. Both direct and indirect effects from climate change are likely to impact the distribution of climate-sensitive species in the future and the use of multiple data types and sources in the modelling of species distributions can result in more accurate predictions resulting in improved management at policy-relevant scales.
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Affiliation(s)
- Evan C. Wilson
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMichiganUSA
| | - Stella Cousins
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMichiganUSA
| | | | - John M. Humphreys
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichiganUSA
- United States Department of Agriculture, Agricultural Research ServiceSidneyMontanaUSA
| | - Gary J. Roloff
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichiganUSA
| | - Neil H. Carter
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMichiganUSA
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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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Chen WJ, Rector AM, Guxens M, Iniguez C, Swartz MD, Symanski E, Ibarluzea J, Ambros A, Estarlich M, Lertxundi A, Riano-Galán I, Sunyer J, Fernandez-Somoano A, Chauhan SP, Ish J, Whitworth KW. Susceptible windows of exposure to fine particulate matter and fetal growth trajectories in the Spanish INMA (INfancia y Medio Ambiente) birth cohort. ENVIRONMENTAL RESEARCH 2023; 216:114628. [PMID: 36279916 PMCID: PMC9847009 DOI: 10.1016/j.envres.2022.114628] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/13/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
While prior studies report associations between fine particulate matter (PM2.5) exposure and fetal growth, few have explored temporally refined susceptible windows of exposure. We included 2328 women from the Spanish INMA Project from 2003 to 2008. Longitudinal growth curves were constructed for each fetus using ultrasounds from 12, 20, and 34 gestational weeks. Z-scores representing growth trajectories of biparietal diameter, femur length, abdominal circumference (AC), and estimated fetal weight (EFW) during early (0-12 weeks), mid- (12-20 weeks), and late (20-34 weeks) pregnancy were calculated. A spatio-temporal random forest model with back-extrapolation provided weekly PM2.5 exposure estimates for each woman during her pregnancy. Distributed lag non-linear models were implemented within the Bayesian hierarchical framework to identify susceptible windows of exposure for each outcome and cumulative effects [βcum, 95% credible interval (CrI)] were aggregated across adjacent weeks. For comparison, general linear models evaluated associations between PM2.5 averaged across multi-week periods (i.e., weeks 1-11, 12-19, and 20-33) and fetal growth, mutually adjusted for exposure during each period. Results are presented as %change in z-scores per 5 μg/m3 in PM2.5, adjusted for covariates. Weeks 1-6 [βcum = -0.77%, 95%CrI (-1.07%, -0.47%)] were identified as a susceptible window of exposure for reduced late pregnancy EFW while weeks 29-33 were positively associated with this outcome [βcum = 0.42%, 95%CrI (0.20%, 0.64%)]. A similar pattern was observed for AC in late pregnancy. In linear regression models, PM2.5 exposure averaged across weeks 1-11 was associated with reduced late pregnancy EFW and AC; but, positive associations between PM2.5 and EFW or AC trajectories in late pregnancy were not observed. PM2.5 exposures during specific weeks may affect fetal growth differentially across pregnancy and such associations may be missed by averaging exposure across multi-week periods, highlighting the importance of temporally refined exposure estimates when studying the associations of air pollution with fetal growth.
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Affiliation(s)
- Wei-Jen Chen
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Alison M Rector
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA; Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, TX, USA
| | - Monica Guxens
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; ISGlobal, Barcelona, Spain; Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre (Erasmus MC), Rotterdam, the Netherlands
| | - Carmen Iniguez
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Department of Statistics and Operational Research, Universitat de València, València, Spain; Epidemiology and Environmental Health Joint Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Universitat Jaume I-Universitat de València, València, Spain
| | - Michael D Swartz
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, TX, USA
| | - Elaine Symanski
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA; Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA
| | - Jesús Ibarluzea
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Group of Environmental Epidemiology and Child Development, Biodonostia Health Research Institute, San Sebastian, Spain; Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, 20013, San Sebastian, Spain; Faculty of Psychology, Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
| | - Albert Ambros
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; ISGlobal, Barcelona, Spain
| | - Marisa Estarlich
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Epidemiology and Environmental Health Joint Research Unit, The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Universitat Jaume I-Universitat de València, València, Spain; Faculty of Nursing and Chiropody, Universitat de València, València, Spain
| | - Aitana Lertxundi
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Group of Environmental Epidemiology and Child Development, Biodonostia Health Research Institute, San Sebastian, Spain; Department of Preventive Medicine and Public Health, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Isolina Riano-Galán
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain; Servicio de Pediatría, Endocrinología pediátrica, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain
| | - Jordi Sunyer
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; ISGlobal, Barcelona, Spain
| | - Ana Fernandez-Somoano
- Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain; IUOPA-Área de Medicina Preventiva y Salud Pública, Departamento de Medicina, Universidad de Oviedo, Oviedo, Spain
| | - Suneet P Chauhan
- Department of Obstetrics, Gynecology and Reproductive Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Jennifer Ish
- Epidemiology Branch, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Durham, NC, USA
| | - Kristina W Whitworth
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA; Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA.
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Nazia N, Butt ZA, Bedard ML, Tang WC, Sehar H, Law J. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:8267. [PMID: 35886114 PMCID: PMC9324591 DOI: 10.3390/ijerph19148267] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 02/04/2023]
Abstract
The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Melanie Lyn Bedard
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Wang-Choi Tang
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Hibah Sehar
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Jane Law
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
- School of Planning, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada
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8
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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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9
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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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10
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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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11
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Rifkin JL, Cao G, Rausher MD. Genetic architecture of divergence: the selfing syndrome in Ipomoea lacunosa. AMERICAN JOURNAL OF BOTANY 2021; 108:2038-2054. [PMID: 34648660 DOI: 10.1002/ajb2.1749] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 05/28/2021] [Accepted: 05/29/2021] [Indexed: 06/13/2023]
Abstract
PREMISE Highly selfing plant species frequently display a distinctive suite of traits termed the selfing syndrome. Here we tested the hypothesis that these traits are grouped into correlated evolutionary modules and determined the degree of independence between such modules. METHODS We evaluated phenotypic correlations and QTL overlaps in F2 offspring of a cross between the morning glories Ipomoea lacunosa and I. cordatotriloba and investigated how traits clustered into modules at both the phenotypic and genetic level. We then compared our findings to other QTL studies of the selfing syndrome. RESULTS In the I. lacunosa selfing syndrome, traits grouped into modules that displayed correlated evolution within but not between modules. QTL overlap predicted phenotypic correlations, and QTLs affecting the same trait module were significantly physically clustered in the genome. The genetic architecture of the selfing syndrome varied across systems, but the pattern of stronger within- than between-module correlation was widespread. CONCLUSIONS The genetic architecture we observe in the selfing syndrome is consistent with a growing understanding of floral morphological integration achieved via pleiotropy in clustered traits. This view of floral evolution is consistent with resource limitation or predation driving the evolution of the selfing syndrome, but invites further research into both the selective causes of the selfing syndrome and how genetic architecture itself evolves in response to changes in mating system.
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Affiliation(s)
- Joanna L Rifkin
- Department of Ecology and Evolutionary Biology, The University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada
| | - Gongyuan Cao
- Department of Biology, Duke University, 124 Science Drive, Durham, NC, 27701, USA
| | - Mark D Rausher
- Department of Biology, Duke University, 124 Science Drive, Durham, NC, 27701, USA
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Briz-Redón Á, Iftimi A, Correcher JF, De Andrés J, Lozano M, Romero-García C. A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 36:271-282. [PMID: 34421343 PMCID: PMC8371601 DOI: 10.1007/s00477-021-02077-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/07/2021] [Indexed: 06/13/2023]
Abstract
Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.
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Affiliation(s)
- Álvaro Briz-Redón
- Statistics Office, City Council of Valencia, Carrer de l’Arquebisbe Mayoral, 1, 46002 Valencia, Spain
| | - Adina Iftimi
- Department of Statistics and Operations Research, University of Valencia, Valencia, Spain
| | | | - Jose De Andrés
- Anesthesia Unit - Surgical Specialties Department, University of Valencia, Valencia, Spain
- Department of Anesthesia, Critical Care and Pain Unit, General University Hospital, Valencia, Spain
| | - Manuel Lozano
- Department of Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine, University of Valencia, Valencia, Spain
| | - Carolina Romero-García
- Department of Anesthesia, Critical Care and Pain Unit, General University Hospital, Valencia, Spain
- Division of Research Methodology, European University of Valencia, Valencia, Spain
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Li X, Guindani M, Ng CS, Hobbs BP. A Bayesian nonparametric model for textural pattern heterogeneity. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12469] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Xiao Li
- Personalized Healthcare Genentech, Inc. South San Francisco CA USA
| | | | - Chaan S. Ng
- Department of Diagnostic Radiology The University of Texas MD Anderson Cancer Center Houston TX USA
| | - Brian P. Hobbs
- Dell Medical School The University of Texas at Austin Austin TX USA
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Perez-Panades J, Botella-Rocamora P, Martinez-Beneito MA. Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies. Int J Health Geogr 2020; 19:54. [PMID: 33276785 PMCID: PMC7716592 DOI: 10.1186/s12942-020-00251-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 11/19/2020] [Indexed: 11/30/2022] Open
Abstract
Background Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR) the omnipresent risk indicator for small areas epidemiologic studies. Methods To deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age–space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates. Results Three case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of considering age–space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered. Conclusion Our age–space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas.
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Affiliation(s)
- Jordi Perez-Panades
- Direcció General de Salut Pública i Addiccions, Conselleria de Sanitat Universal i Salut Pública, Avda/Cataluña, 21, 46020, Valencia, Spain.
| | - Paloma Botella-Rocamora
- Direcció General de Salut Pública i Addiccions, Conselleria de Sanitat Universal i Salut Pública, Avda/Cataluña, 21, 46020, Valencia, Spain
| | - Miguel Angel Martinez-Beneito
- Departament d'Estadística i Investigació Operativa, Universitat de València, C/Dr. Moliner, 50, 46100, Burjassot, Valencia, Spain
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