arXiv subjects

A. Adin

Publications and source records attributed to A. Adin.

8 recordsLinked to original sources

arXiv2025

Improving Disease Risk Estimation in Small Areas by Accounting for Spatiotemporal Local Discontinuities

This work proposes a two-step method to enhance disease risk estimation in small areas by integrating spatiotemporal cluster detection within a Bayesian hierarchical spatiotemporal model. First, we introduce an efficient scan-statistic-based clustering algorithm that employs a greedy search within the scan window, enabling flexible cluster detection across large spatial domains. We then integrate these detected clusters into a Bayesian spatiotemporal model to estimate relative risks, explicitly accounting for identified risk discontinuities. We apply this methodology to large-scale cancer mortality data at the municipality level across continental Spain. Our results show our method offers superior cluster detection accuracy compared to SaTScan. Furthermore, integrating cluster information into a Bayesian spatiotemporal model significantly improves model fit and risk estimate performance, as evidenced by better DIC, WAIC, and logarithmic scores than SaTScan-based or standard BYM2 models. This methodology provides a powerful tool for epidemiological analysis, offering a more precise identification of high- and low-risk areas and enhancing the accuracy of risk estimation models.

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arXiv2025

Suicide Mortality in Spain (2010-2022): Temporal Trends, Spatial Patterns, and Risk Factors

Background: Suicide remains a major public health concern worldwide, responsible for more than 700,000 deaths in 2021, accounting for approximately 1.1\% of all global deaths. While many high-income countries have reported declines in age-standardized suicide rates over the past two decades, recent evidence from Spain indicates increasing mortality among women, whereas suicide rates among men have remained relatively stable. To better understand these patterns and their potential underlying determinants, this study examines the spatial and temporal patterns of age-stratified suicide mortality across Spanish provinces from 2010 to 2022, with particular attention to sex-specific differences. Methods: Mixed Poisson models were applied to analyze provincial- and temporal-level suicide mortality rates, stratified by age and sex. The models accounted for spatial and temporal confounding effects and examined associations with various socioeconomic and contextual factors, including rurality and unemployment. Results: Findings highlight the influence of rurality and unemployment on suicide mortality, with distinct gender-specific patterns. A 10$\%$ increase in the proportion of residents living in rural areas was associated with more than a 5$\%$ rise in male suicide mortality, while a 1$\%$ increase in the annual unemployment rate was linked to a 2.4$\%$ increase in female suicide mortality. Although male suicide rates remained consistently higher than female rates, a notable and steady upward trend was observed in female suicide mortality over the study period. Conclusions: The use of sophisticated statistical models permits the detection of underlying patterns, revealing both geographic and temporal disparities in suicide mortality across Spanish provinces.

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arXiv2023

Automatic cross-validation in structured models: Is it time to leave out leave-one-out?

Standard techniques such as leave-one-out cross-validation (LOOCV) might not be suitable for evaluating the predictive performance of models incorporating structured random effects. In such cases, the correlation between the training and test sets could have a notable impact on the model's prediction error. To overcome this issue, an automatic group construction procedure for leave-group-out cross validation (LGOCV) has recently emerged as a valuable tool for enhancing predictive performance measurement in structured models. The purpose of this paper is (i) to compare LOOCV and LGOCV within structured models, emphasizing model selection and predictive performance, and (ii) to provide real data applications in spatial statistics using complex structured models fitted with INLA, showcasing the utility of the automatic LGOCV method. First, we briefly review the key aspects of the recently proposed LGOCV method for automatic group construction in latent Gaussian models. We also demonstrate the effectiveness of this method for selecting the model with the highest predictive performance by simulating extrapolation tasks in both temporal and spatial data analyses. Finally, we provide insights into the effectiveness of the LGOCV method in modelling complex structured data, encompassing spatio-temporal multivariate count data, spatial compositional data, and spatio-temporal geospatial data.

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arXiv2023

A scalable approach for short-term disease forecasting in high spatial resolution areal data

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatio-temporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed ``divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatio-temporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error and interval score when forecasting cancer mortality one, two and three years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace (INLA) estimation technique.

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arXiv2022

High-dimensional order-free multivariate spatial disease mapping

Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of small areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region. We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal permits the analysis of big data sets and provides better results than fitting a single multivariate model.

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arXiv2022

Big problems in spatio-temporal disease mapping: methods and software

Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal count data, with special emphasis on mortality/incidence relative risk estimation. We present a pragmatic and simple idea that permits to fit hierarchical spatio-temporal models when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming and even unfeasible. Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users. Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.

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arXiv2021

Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India

Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects, while obtaining good model predictions. In particular, restricted regression and an apparently -- though in fact not -- equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India.

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arXiv2020

Scalable Bayesian modeling for smoothing disease risks in large spatial data sets

Several methods have been proposed in the spatial statistics literature for the analysis of big data sets in continuous domains. However, new methods for analyzing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modeling approach for smoothing mortality (or incidence) risks in high-dimensional data, that is, when the number of small areas is very large. The method is implemented in the R add-on package bigDM. Model fitting and inference is based on the idea of "divide and conquer" and use integrated nested Laplace approximations and numerical integration. We analyze the proposal's empirical performance in a comprehensive simulation study that consider two model-free settings. Finally, the methodology is applied to analyze male colorectal cancer mortality in Spanish municipalities showing its benefits with regard to the standard approach in terms of goodness of fit and computational time.

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