1
|
De Witte D, Abad AA, Molenberghs G, Verbeke G, Sanchez L, Mas-Bermejo P, Neyens T. A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba. Spat Spatiotemporal Epidemiol 2023; 45:100588. [PMID: 37301587 DOI: 10.1016/j.sste.2023.100588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 06/12/2023]
Abstract
To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.
Collapse
Affiliation(s)
| | - Ariel Alonso Abad
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| | - Geert Molenberghs
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| | - Geert Verbeke
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| | - Lizet Sanchez
- Cuban National Group of Epidemiology and Modeling of the COVID-19 Pandemic, Center of Molecular Immunology, Havana, 11 600, Cuba
| | - Pedro Mas-Bermejo
- Cuban National Group of Epidemiology and Modeling of the COVID-19 Pandemic, Institute "Pedro Kouri", Havana, 11 600, Cuba
| | - Thomas Neyens
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| |
Collapse
|
2
|
Maranzano P, Otto P, Fassò A. Adaptive LASSO estimation for functional hidden dynamic geostatistical models. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2023; 37:1-23. [PMID: 37362848 PMCID: PMC10189237 DOI: 10.1007/s00477-023-02466-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/01/2023] [Indexed: 06/28/2023]
Abstract
We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models. Supplementary Information The online version contains supplementary material available at 10.1007/s00477-023-02466-5.
Collapse
Affiliation(s)
- Paolo Maranzano
- Department of Economics, Management and Statistics (DEMS), University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
- Fondazione Eni Enrico Mattei (FEEM), Corso Magenta 63, 20123 Milano, Italy
| | - Philipp Otto
- Insitute of Cartography and Geoinformatics (IKG), Leibniz University of Hannover, Appelstrasse 9a, 30167 Hannover, Lower Saxony Germany
| | - Alessandro Fassò
- Department of Economics, University of Bergamo, Via dei Caniana 2, 24127 Bergamo, Italy
| |
Collapse
|
3
|
Lee D, Robertson C, Marques D. Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity. SPATIAL STATISTICS 2022; 49:100508. [PMID: 33868908 PMCID: PMC8035810 DOI: 10.1016/j.spasta.2021.100508] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/31/2021] [Accepted: 03/31/2021] [Indexed: 05/24/2023]
Abstract
Modelling the small-area spatio-temporal dynamics of the Covid-19 pandemic is of major public health importance, because it allows health agencies to better understand how and why the virus spreads. However, in Scotland during the first wave of the pandemic testing capacity was severely limited, meaning that large numbers of infected people were not formally diagnosed as having the virus. As a result, data on confirmed cases are unlikely to represent the true infection rates, and due to the small numbers of positive tests these data are not available at the small-area level for confidentiality reasons. Therefore to estimate the small-area dynamics in Covid-19 incidence this paper analyses the spatio-temporal trends in telehealth data relating to Covid-19, because during the first wave of the pandemic the public were advised to call the national telehealth provider NHS 24 if they experienced symptoms of the virus. Specifically, we propose a multivariate spatio-temporal correlation model for modelling the proportions of calls classified as either relating to Covid-19 directly or having related symptoms, and provide software for fitting the model in a Bayesian setting using Markov chain Monte Carlo simulation. The model was developed in partnership with the national health agency Public Health Scotland, and here we use it to analyse the spatio-temporal dynamics of the first wave of the Covid-19 pandemic in Scotland between March and July 2020, specifically focusing on the spatial variation in the peak and the end of the first wave.
Collapse
Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8SQ, Scotland, United Kingdom
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, G1 1XH, Scotland, United Kingdom
- Public Health Scotland, Meridian Court, 5 Cadogan Street, Glasgow G2 6QE, Scotland, United Kingdom
| | - Diogo Marques
- Public Health Scotland, Meridian Court, 5 Cadogan Street, Glasgow G2 6QE, Scotland, United Kingdom
| |
Collapse
|
4
|
Stoppa G, Mensi C, Fazzo L, Minelli G, Manno V, Consonni D, Biggeri A, Catelan D. Spatial Analysis of Shared Risk Factors between Pleural and Ovarian Cancer Mortality in Lombardy (Italy). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063467. [PMID: 35329152 PMCID: PMC8949464 DOI: 10.3390/ijerph19063467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/11/2022] [Accepted: 03/12/2022] [Indexed: 01/04/2023]
Abstract
Background: Asbestos exposure is a recognized risk factor for ovarian cancer and malignant mesothelioma. There are reports in the literature of geographical ecological associations between the occurrence of these two diseases. Our aim was to further explore this association by applying advanced Bayesian techniques to a large population (10 million people). Methods: We specified a series of Bayesian hierarchical shared models to the bivariate spatial distribution of ovarian and pleural cancer mortality by municipality in the Lombardy Region (Italy) in 2000–2018. Results: Pleural cancer showed a strongly clustered spatial distribution, while ovarian cancer showed a less structured spatial pattern. The most supported Bayesian models by predictive accuracy (widely applicable or Watanabe–Akaike information criterion, WAIC) provided evidence of a shared component between the two diseases. Among five municipalities with significant high standardized mortality ratios of ovarian cancer, three also had high pleural cancer rates. Wide uncertainty was present when addressing the risk of ovarian cancer associated with pleural cancer in areas at low background risk of ovarian cancer. Conclusions: We found evidence of a shared risk factor between ovarian and pleural cancer at the small geographical level. The impact of the shared risk factor can be relevant and can go unnoticed when the prevalence of other risk factors for ovarian cancer is low. Bayesian modelling provides useful information to tailor epidemiological surveillance.
Collapse
Affiliation(s)
- Giorgia Stoppa
- Unit of Biostatistics, Epidemiology and Public Health, DCTVPH, University of Padova, 35131 Padova, Italy; (A.B.); (D.C.)
- Correspondence: (G.S.); (C.M.)
| | - Carolina Mensi
- Occupational Health Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
- Correspondence: (G.S.); (C.M.)
| | - Lucia Fazzo
- Department of Environment and Health, Istituto Superiore di Sanità, 00100 Rome, Italy;
| | - Giada Minelli
- Statistical Service, Istituto Superiore di Sanità, 00100 Roma, Italy; (G.M.); (V.M.)
| | - Valerio Manno
- Statistical Service, Istituto Superiore di Sanità, 00100 Roma, Italy; (G.M.); (V.M.)
| | - Dario Consonni
- Occupational Health Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Annibale Biggeri
- Unit of Biostatistics, Epidemiology and Public Health, DCTVPH, University of Padova, 35131 Padova, Italy; (A.B.); (D.C.)
| | - Dolores Catelan
- Unit of Biostatistics, Epidemiology and Public Health, DCTVPH, University of Padova, 35131 Padova, Italy; (A.B.); (D.C.)
| |
Collapse
|
5
|
Kline D, Hepler SA. Estimating the burden of the opioid epidemic for adults and adolescents in Ohio counties. Biometrics 2021; 77:765-775. [PMID: 32413155 PMCID: PMC7666653 DOI: 10.1111/biom.13295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 03/19/2020] [Indexed: 11/30/2022]
Abstract
Quantifying the opioid epidemic at the local level is a challenging problem that has important consequences on resource allocation. Adults and adolescents may exhibit different spatial trends and require different interventions and resources so it is important to examine the problem for each age group. In Ohio, surveillance data are collected at the county level for each age group on measurable outcomes of the opioid epidemic, overdose deaths, and treatment admissions. However, our interest lies in quantifying the unmeasurable construct, representing the burden of the opioid epidemic, which drives rates of the outcomes. We propose jointly modeling adult and adolescent surveillance outcomes through a multivariate spatial factor model. A generalized spatial factor model within each age group quantifies a latent factor related to the number of opioid-associated treatment admissions and deaths. By assuming a multivariate conditional autoregressive model for the spatial factors of adults and adolescents, we allow the adolescent model to borrow strength from the adult model (and vice versa), improving estimation. We also incorporate county-level covariates to help explain spatial heterogeneity in each of the factors. We apply this approach to the state of Ohio and discuss the findings. Our framework provides a coherent approach for synthesizing information across multiple outcomes and age groups to better understand the spatial epidemiology of the opioid epidemic.
Collapse
Affiliation(s)
- David Kline
- Department of Biomedical Informatics, Center for Biostatistics, The Ohio State University, Columbus, Ohio
| | - Staci A Hepler
- Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, North Carolina
| |
Collapse
|
6
|
Wah W, Ahern S, Earnest A. A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality. Int J Public Health 2020; 65:673-682. [PMID: 32449006 DOI: 10.1007/s00038-020-01384-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/26/2020] [Accepted: 05/02/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES This study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality. METHODS This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018. RESULTS A total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization. CONCLUSIONS Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.
Collapse
Affiliation(s)
- Win Wah
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Susannah Ahern
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Arul Earnest
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| |
Collapse
|
7
|
Carroll R, Lawson AB, Zhao S. Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping. Biostatistics 2019; 20:666-680. [PMID: 29939209 DOI: 10.1093/biostatistics/kxy023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 03/08/2018] [Accepted: 04/24/2018] [Indexed: 11/15/2022] Open
Abstract
The introduction of spatial and temporal frailty parameters in survival models furnishes a way to represent unmeasured confounding in the outcome of interest. Using a Bayesian accelerated failure time model, we are able to flexibly explore a wide range of spatial and temporal options for structuring frailties as well as examine the benefits of using these different structures in certain settings. A setting of particular interest for this work involved using temporal frailties to capture the impact of events of interest on breast cancer survival. Our results suggest that it is important to include these temporal frailties when there is a true temporal structure to the outcome and including them when a true temporal structure is absent does not sacrifice model fit. Additionally, the frailties are able to correctly recover the truth imposed on simulated data without affecting the fixed effect estimates. In the case study involving Louisiana breast cancer-specific mortality, the temporal frailty played an important role in representing the unmeasured confounding related to improvements in knowledge, education, and disease screenings as well as the impacts of Hurricane Katrina and the passing of the Affordable Care Act. In conclusion, the incorporation of temporal, in addition to spatial, frailties in survival analysis can lead to better fitting models and improved inference by representing both spatially and temporally varying unmeasured risk factors and confounding that could impact survival. Specifically, we successfully estimated changes in survival around the time of events of interest.
Collapse
Affiliation(s)
- Rachel Carroll
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr., Research Triangle Park, NC, USA
| | - Andrew B Lawson
- Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St., Charleston, SC, USA
| | - Shanshan Zhao
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr., Research Triangle Park, NC, USA
| |
Collapse
|
8
|
Carroll R, Zhao S. Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All. Clin Colorectal Cancer 2019; 18:e261-e274. [PMID: 30713133 PMCID: PMC7983285 DOI: 10.1016/j.clcc.2018.12.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 12/01/2018] [Accepted: 12/06/2018] [Indexed: 11/27/2022]
Abstract
BACKGROUND Colorectal cancer (CRC) is common worldwide, with 140,250 diagnoses and 50,630 deaths estimated for the United States in 2018. Guidelines current to the most recent individuals in our analysis suggested regular screenings beginning at age 50 have reduced the incidence of CRC. However, the incidence continues to rise among those under 50. Less is known about survival following CRC diagnosis, but research has suggested that younger cases may also have worse survival. However, we hypothesize that younger individuals are generally healthier with fewer comorbidities, leading to the potential for better survival following diagnosis. MATERIALS AND METHODS We utilized the Surveillance, Epidemiology, and End Results data to estimate and assess both spatial and temporal variation in age-specific colorectal cancer incidence and survival in Iowa. RESULTS Both overall and older-onset colorectal cancer incidence began to decline in the early 2000s, whereas younger-onset incidences decreased until the late 1980s but then increased steeply through the 2000s. The risk for those younger than 50 years of age first exceeded the risk for those 50 years or older in 2007. Survival times did increase for overall CRC, older-onset CRC, and young-onset CRC throughout the study period, with young-onset CRC increasing at a higher rate. The spatial variation assessment indicated that the survival was positively associated with several variables of interest, most notably disparities including better access to healthcare and higher sociodemographic status. CONCLUSION In conclusion, results suggest that regular colorectal screenings could reduce incidence and mortality in people under 50.
Collapse
Affiliation(s)
- Rachel Carroll
- National Institute of Environmental Health Sciences, Durham, NC.
| | - Shanshan Zhao
- National Institute of Environmental Health Sciences, Durham, NC
| |
Collapse
|
9
|
Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14050503. [PMID: 28486417 PMCID: PMC5451954 DOI: 10.3390/ijerph14050503] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 05/03/2017] [Accepted: 05/05/2017] [Indexed: 11/16/2022]
Abstract
Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.
Collapse
|