Spatiotemporal Trends of Birth Defects in North Carolina, 2003-2015.

Birth defects are a leading cause of infant mortality in the United States, but little is known about causes of many types of birth defects. Spatiotemporal disease mapping to identify high-prevalence areas, is a potential strategy to narrow the search for potential environmental and other causes that aggregate over space and time. We described the spatial and temporal trends of the prevalence of birth defects in North Carolina during 2003-2015, using data on live births obtained from the North Carolina Birth Defects Monitoring Program. By employing a Bayesian space-time Poisson model, we estimated spatial and temporal trends of non-chromosomal and chromosomal birth defects. During 2003-2015, 52,524 (3.3%) of 1,598,807 live births had at least one recorded birth defect. The prevalence of non-chromosomal birth defects decreased from 3.8% in 2003 to 2.9% in 2015. Spatial modeling suggested a large geographic variation in non-chromosomal birth defects at census-tract level, with the highest prevalence in southeastern North Carolina. The strong spatial heterogeneity revealed in this work allowed to identify geographic areas with higher prevalence of non-chromosomal birth defects in North Carolina. This variation will help inform future research focused on epidemiologic studies of birth defects to identify etiologic factors.

Discrepancies between the Spatial Distribution of Cancer Incidence and Mortality as an Indicator of Unmet Needs in Cancer Prevention and/or Treatment in Hungary.

There is a rich body of literature on the distribution of cancer incidence and mortality in socioeconomically different world regions, but none of the studies has compared the spatial distribution of mortality and incidence to see if they are consistent with each other. All malignant neoplasms combined and cervical, colorectal, breast, pancreatic, lung, and oral cancers separately were studied in the Hungarian population aged 25-64 years for 2007-2018 at the municipality level by sex. In each case, the spatial distribution of incidence and mortality were compared with each other and with the level of deprivation using disease mapping, spatial regression, risk analysis, and spatial scan statistics. A positive association between deprivation and mortality was found for each type of cancer, but there was no significant association for male colorectal cancer (relative risk (RR) 1.00; 95% credible interval (CI) 0.99-1.02), pancreatic cancer (RR: 1.01; 95%CI 0.98-1.04), and female colorectal cancer incidence (RR: 1.01; 95%CI 0.99-1.03), whereas a negative association for breast cancer (RR: 0.98; 95%CI 0.96-0.99) was found. Disease mapping analyses showed only partial overlap between areas of high incidence and mortality, often independent of deprivation. Our results highlight not only the diverse relationship between cancer burden and deprivation, but also the inconsistent relationship between cancer incidence and mortality, pointing to areas with populations that require special public health attention.

Social inequalities in health within the City of Milan (Lombardy Region, Northern Italy): An ecological assessment.

OBJECTIVES: to document existing geographical inequalities in health in the city of Milan (Lombardy Region, Northern Italy), examining the association between area socioeconomic disadvantage and health outcomes, with the aim to suggest policy action to tackle them. DESIGN: the analysis used an ecological framework; multiple health indicators were considered in the analysis; socioeconomic disadvantage was measured through indicators such as low education, unemployment, immigration status, and housing crowding. For each municipal statistical area, Bayesian Relative Risks of the outcomes (using the Besag-Yorkand-Mollié model) were plotted on the city map. To evaluate the association between social determinants and health outcomes, Spearman correlation coefficients were estimated. SETTING AND PARTICIPANTS: residents in the City of Milan aged between 30 and 75 years who were residing in Milan as of 01.01.2019, grouped in 88 statistical areas. MAIN OUTCOMES MEASURES: all-cause mortality, type-2 diabetes mellitus, hypertension, neoplasms, respiratory diseases, metabolic syndrome, antidepressants use, polypharmacy, and multimorbidity. RESULTS: the results consistently demonstrated a significant association between socioeconomic disadvantage and various health outcomes, with low education exhibiting the strongest correlations. Neoplasms displayed an inverse social gradient, while the relationship with antidepressant use varied. CONCLUSIONS: these findings provide valuable insights into the distribution of health inequalities in Milan and contribute to the existing literature on the social determinants of health. The study highlights the need for targeted interventions to address disparities and promote equitable health outcomes. The results can serve to inform the development of effective public health strategies and policies aimed at reducing health inequalities in the city.

Improving the spatial and temporal resolution of burden of disease measures with Bayesian models.

This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources.

Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020-2022 pandemic.

The gap between the reported and actual COVID-19 infection cases has been an issue of concern. Here, we present Bayesian hierarchical spatiotemporal disease mapping models for state-level predictions of COVID-19 infection risks and (under)reporting rates among people aged 65 and above during the first two years of the pandemic in the United States. With prior elicitation based on recent prevalence studies, the study suggests that the median state-level reporting rate of COVID-19 infection was 90% (interquartile range: [78%, 96%]). Our study uncovers spatiotemporal variations and dynamics in state-level infection risks and (under)reporting rates, suggesting time-varying associations between higher population density, higher percentage of minorities, and higher percentage of vaccination and increased risks of COVID-19 infection, as well as an association between more easily accessible tests and higher reporting rates. With sensitivity analyses, we highlight the impact and importance of incorporating covariates information and objective prior references for evaluating the issue of underreporting.

Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission.

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination.

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

Non-stationary Bayesian spatial model for disease mapping based on sub-regions.

This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model's ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior to the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package fbesag equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary. Additionally, we model different causes of death in Brazil, where we use the new model to investigate the spatial stationarity of these causes.

Spatial Heterogeneity of Nontuberculous Mycobacterial Pulmonary Disease in Shanghai: Insights from a Ten-Year Population-Based Study.

OBJECTIVE: To investigate the spatial heterogeneity of nontuberculous mycobacterial pulmonary disease (NTM-PD) in Shanghai. METHODS: A population-based retrospective study was conducted using presumptive pulmonary tuberculosis surveillance data of Shanghai between 2010 and 2019. The study described the spatial distribution of NTM-PD notification rates, employing hierarchical Bayesian mapping for high-risk areas and the Getis-Ord Gi* statistic to identify hot spots and explore associated factors. RESULTS: Of 1652 NTM-PD cases, the most common species was Mycobacterium kansasii complex (MKC) (41.9%), followed by Mycobacterium avium complex (MAC) (27.1%) and Mycobacterium abscessus complex (MABC) (16.2%). MKC-PD patients were generally younger males with a higher incidence of pulmonary cavities, while MAC-PD patients were more often farmers or had a history of tuberculosis treatment. MKC-PD hot spots were primarily located in the areas alongside the Huangpu River, while MAC-PD hot spots were mainly in the western agricultural areas. Patients with MKC-PD and MAC-PD exhibited a higher risk of spatial clustering compared to those with MABC-PD. CONCLUSIONS: Different types of NTM-PD exhibit distinct patterns of spatial clustering and are associated with various factors. These findings underscore the importance of environmental and host factors in the epidemic of NTM-PD.

Spatio-temporal clustering analysis of COVID-19 cases in Johor.

At the end of the year 2019, a virus named SARS-CoV-2 induced the coronavirus disease, which is very contagious and quickly spread around the world. This new infectious disease is called COVID-19. Numerous areas, such as the economy, social services, education, and healthcare system, have suffered grave consequences from the invasion of this deadly virus. Thus, a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster. In this research, the daily reported COVID-19 cases in 92 sub-districts in Johor state, Malaysia, as well as the population size associated to each sub-district, are used to study the propagation of COVID-19 disease across space and time in Johor. The time frame of this research is about 190 days, which started from August 5, 2021, until February 10, 2022. The clustering technique known as spatio-temporal clustering, which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level. The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre (Bandar Johor Bahru), and during the festive season. These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays. On the other hand, the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.

Psychosis prevalence in London neighbourhoods; A case study in spatial confounding.

Analysis of impacts of neighbourhood risk factors on mental health outcomes frequently adopts a disease mapping approach, with unknown neighbourhood influences summarised by random effects. However, such effects may show confounding with observed predictors, especially when such predictors have a clear spatial pattern. Here, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.

Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling.

This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability & Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths. We utilize COVID-19 cases and deaths data for the state of Indiana from the Regenstrief Institute in Indianapolis, Indiana, from March 1, 2020, to March 31, 2021. We then aggregate the COVID-19 data to the census tract level, obtain the input variables, domains (components), and composite measures of both CDC SVI and SoVI data to create a Bayesian spatial-temporal ecological regression model. We compare the resulting spatial-temporal patterns and relative risk (RR) of SARS-CoV-2 infection (COVID-19 cases) and associated death. Results show there are discernable spatial-temporal patterns for SARS-CoV-2 infections and deaths with the largest contiguous hotspot for SARS-CoV-2 infections found in the southwest of the Indianapolis metropolitan area. We also observed one large contiguous hotspot for deaths that stretches across Indiana from the Cincinnati area in the southeast to just east and north of Terre Haute (southeast to west central). The spatial-temporal Bayesian model shows that a 1-percentile increase in CDC SVI was significantly (p ≤ 0.05) associated with an increased risk of SARS-CoV-2 infection by 6 % (RR = 1.06, 95 %CI = 1.04 -1.08). Whereas a 1-percentile increase in SoVI was significantly predicted to increase the risk of COVID-19 death by 45 % (RR = 1.45, 95 %CI =1.38 - 1.53). Domain-specific variables related to socioeconomic status, age, and race/ethnicity were shown to increase the risk of SARS-CoV-2 infections and deaths. There were notable differences in the relative risk estimates for SARS-CoV-2 infections and deaths when each of the two indices were incorporated in the model. Observed differences between the two social vulnerability indices and infection and death are likely due to alternative methodologies of formation and differences in input variables. The findings add to the growing literature on the relationship between social vulnerability and COVID-19 and further the development of COVID-19-specific vulnerability indices by illustrating the utility of local spatial-temporal analysis.

An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates.

This article introduces bayesian spatial smoothing models for disease mapping-a specific application of small area estimation where the full universe of data is known-to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York, and Mollié (BYM) Poisson spatial and space-time smoothing models were fitted to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0 to 24.81 deaths per 10 000 people. However, the highest mortality rate was highly unstable, based on only 2 deaths in a population of approximately 800, and 80.5% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06 to 4.05 deaths per 10 000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gain precision from adjacent neighbors and their own counts in adjacent years. bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R software have made fitting these models more accessible to researchers.

Relative risk of childhood and adolescence cancer in Iran: spatiotemporal analysis from 1999 to 2016.

OBJECTIVE: Cancer is the third leading cause of death in the world with increasing trends in Iran. The study of epidemiology, trend, and geospatial distribution of pediatric cancers provides important information for screening as well as early detection of cancer and policy making. We aimed to assess the spatio-temporal disparity of childhood and adolescence cancer risk among provinces of Iran. METHODS: In this retrospective study, we estimated geospatial relative risk (RR) of childhood cancer in provinces of Iran using data from 29198 cases. We used BYM and its extended spatiotemporal model in Bayesian setting. This hierarchical model takes spatial and temporal effects into account in the incidence rate estimation simultaneously. RESULTS: The relative risk of cancer was > 1 for 45% of the provinces, where 27% of provinces had significantly ascending trend. North Khorasan, Yazd and Qazvin provinces had the highest risk rates while Sistan-Baluchistan province showed the lowest risk of cancer. However, the differential trends was highest in Sistan-Baluchistan, Bushehr, Hormozgan, and Kohgilouyeh-Boyerahmad. Both the point estimate and the trend of risk was high in Tehran. CONCLUSION: The geographic pattern and trend of cancer in children seems to be different from that in adults that urges further studies. This could lead to increased health system capacity and facilitate the access to effective detection, research, care and treatment of childhood cancer.

Joint quantile disease mapping with application to malaria and G6PD deficiency.

Statistical analysis based on quantile methods is more comprehensive, flexible and less sensitive to outliers when compared to mean methods. Joint disease mapping is useful for inferring correlation between different diseases. Most studies investigate this link through multiple correlated mean regressions. We propose a joint quantile regression framework for multiple diseases where different quantile levels can be considered. We are motivated by the theorized link between the presence of malaria and the gene deficiency G6PD, where medical scientists have anecdotally discovered a possible link between high levels of G6PD and lower than expected levels of malaria initially pointing towards the occurrence of G6PD inhibiting the occurrence of malaria. Thus, the need for flexible joint quantile regression in a disease mapping framework arises. Our model can be used for linear and nonlinear effects of covariates by stochastic splines since we define it as a latent Gaussian model. We perform Bayesian inference using the R integrated nested Laplace approximation, suitable even for large datasets. Finally, we illustrate the model's applicability by considering data from 21 countries, although better data are needed to prove a significant relationship. The proposed methodology offers a framework for future studies of interrelated disease phenomena.

Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions.

Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.

Joint spatial modeling of the risks of co-circulating mosquito-borne diseases in Ceará, Brazil.

Mosquito-borne diseases such as dengue and chikungunya have been co-circulating in the Americas, causing great damage to the population. In 2021, for instance, almost 1.5 million cases were reported on the continent, being Brazil the responsible for most of them. Even though they are transmitted by the same mosquito, it remains unclear whether there exists a relationship between both diseases. In this paper, we model the geographic distributions of dengue and chikungunya over the years 2016 to 2021 in the Brazilian state of Ceará. We use a Bayesian hierarchical spatial model for the joint analysis of two arboviruses that includes spatial covariates as well as specific and shared spatial effects that take into account the potential autocorrelation between the two diseases. Our findings allow us to identify areas with high risk of one or both diseases. Only 7% of the areas present high relative risk for both diseases, which suggests a competition between viruses. This study advances the understanding of the geographic patterns and the identification of risk factors of dengue and chikungunya being able to help health decision-making.

Mapping the prevalence of cancer risk factors at the small area level in Australia.

BACKGROUND: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. METHODS: Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. RESULTS: We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. CONCLUSIONS: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

Spatio-temporal evaluation of respiratory disease based on the information provided by patients admitted to a medical college hospital in Bangladesh using geographic information system.

In Bangladesh respiratory illnesses are one of the leading risk factors for death and disability. Limited access to healthcare services, indoor and outdoor air pollution, large-scale use of smoking materials, allergens, and lack of awareness are among the known leading factors contributing to respiratory illness in Bangladesh. Key initiatives taken by the government to handle respiratory illnesses include, changing of respiratory health policy, building awareness, enhancing healthcare facility, and promoting prevention measures. Despite all these efforts, the number of individuals suffering from respiratory diseases has increased steadily during the recent years. This study aims at examining the distribution pattern of respiratory diseases over space and time using Geographic Information System, which is expected to contribute to the better understand of the factors contributing to respiratory illness development. To achieve the aims of the study two interviews were conducted among patients with respiratory sickness in the medicine and respiratory medicine units of Rajshahi Medical College Hospital between January and April of 2019 and 2020 following the guidelines provided by the Ethics Committee, Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh (ethical approval reference number: 2018/08). Principal component extraction and spatial statistical analyses were performed to identify the key respiratory illnesses and their geographical distribution pattern respectively. The results indicate, during January-February the number of patients was a lot higher compared to March-April. The patients were hospitalized mainly due to four respiratory diseases (chronic obstructive pulmonary disease, asthma, pneumonia, and pulmonary hypertension). Geographical distribution pattern of respiratory disease cases also varied considerably between the years as well as months of the years. This information seems reasonable to elucidate the spatio-temporal distribution of respiratory disease and thus improve the existing prevention, control, and cure practices of respiratory illness of the study area. Approach used in this study to elicit spatio-temporal distribution of repertory disease can easily be implemented in other areas with similar geographical settings and patients' illness information from hospital.

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 spatiotemporal 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 spatiotemporal 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 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace estimation technique.