An intuitive Bayesian spatial model for disease mapping that accounts for scaling

The real-time infection hospitalisation and fatality risk across the COVID-19 pandemic in England

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution.

Impacts of social deprivation on mortality and protective effects of greenness exposure in Hong Kong, 1999-2018: A spatiotemporal perspective

Addressing health inequality is crucial for fostering healthy city development. However, there is a dearth of literature simultaneously investigating the effects of social deprivation and greenness exposure on mortality risks, as well as how greenness exposure may mitigate the adverse effect of social deprivation on mortality risks from a spatiotemporal perspective. Drawing on socioeconomic, remote sensing, and mortality record data, this study presents spatiotemporal patterns of social deprivation, population weighted greenness exposure, and all-cause and cause-specific mortality in Hong Kong. A Bayesian regression model was applied to investigate the impacts of social deprivation and greenness exposure on mortality and examine how socioeconomic inequalities in mortality may vary across areas with different greenness levels in Hong Kong from 1999 to 2018. We observed a decline in social deprivation (0.67-0.56), and an increase in greenness exposure (0.34-0.41) in Hong Kong during 1999-2018. Areas with high mortality gradually clustered in the Kowloon Peninsula and the northern regions of Hong Kong Island. Adverse impacts of social deprivation on all-cause mortality weakened in recent years (RR from 2009 to 2013: 1.103, 95%CI: 1.051-1.159, RR from 2014 to 2018: 1.041 95%CI: 0.950-1.139), while the protective impacts of greenness exposure consistently strengthened (RR from 1999 to 2003: 0.903, 95%CI: 0.827-0.984, RR from 2014 to 2018: 0.859, 95%CI: 0.763-0.965). Moreover, the adverse effects of social deprivation on mortality risks were found to be higher in areas with lower greenness exposure. These findings provide evidence of associations between social deprivation, greenness exposure, and mortality risks in Hong Kong over the past decades, and highlight the potential of greenness exposure to mitigate health inequalities. Our study provides valuable implications for policymakers to develop a healthy city.

Identifying areas of Australia with high out-of-hospital cardiac arrest incidence and low bystander cardiopulmonary resuscitation rates: A retrospective, observational study

AIM

This study aims to explore regional variation and identify regions within Australia with high incidence of out-of-hospital cardiac arrest (OHCA) and low rates of bystander cardiopulmonary resuscitation (CPR).

METHOD

Adult OHCAs of presumed medical aetiology occurring across Australia between 2017 and 2019 were mapped onto local government areas (LGA) using the location of arrest coordinates. Bayesian spatial models were applied to provide "smoothed" estimates of OHCA incidence and bystander CPR rates (for bystander-witnessed OHCAs) for each LGA. For each state and territory, high-risk LGAs were defined as those with an incidence rate greater than the state or territory's 75th percentile and a bystander CPR rate less than the state or territory's 25th percentile.

RESULTS

A total of 62,579 OHCA cases attended by emergency medical services across 543 LGAs nationwide were included in the study. Nationally, the OHCA incidence rate across LGA ranged from 58.5 to 198.3 persons per 100,000, while bystander CPR rates ranged from 45% to 75%. We identified 60 high-risk LGAs, which were predominantly located in the state of New South Wales. Within each region, high-risk LGAs were typically located in regional and remote areas of the country, except for four metropolitan areas-two in Adelaide and two in Perth.

CONCLUSIONS

We have identified high-risk LGAs, characterised by high incidence and low bystander CPR rates, which are predominantly in regional and remote areas of Australia. Strategies for reducing OHCA and improving bystander response may be best targeted at these regions.

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.

Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021

BACKGROUND

Ghana is among the top 10 highest malaria burden countries, with about 20,000 children dying annually, 25% of which were under five years. This study aimed to produce interactive web-based disease spatial maps and identify the high-burden malaria districts in Ghana.

METHODS

The study used 2016-2021 data extracted from the routine health service nationally representative and comprehensive District Health Information Management System II (DHIMS2) implemented by the Ghana Health Service. Bayesian geospatial modelling and interactive web-based spatial disease mapping methods were employed to quantify spatial variations and clustering in malaria risk across 260 districts. For each district, the study simultaneously mapped the observed malaria counts, district name, standardized incidence rate, and predicted relative risk and their associated standard errors using interactive web-based visualization methods.

RESULTS

A total of 32,659,240 malaria cases were reported among children < 5 years from 2016 to 2021. For every 10% increase in the number of children, malaria risk increased by 0.039 (log-mean 0.95, 95% credible interval = - 13.82-15.73) and for every 10% increase in the number of males, malaria risk decreased by 0.075, albeit not statistically significant (log-mean - 1.82, 95% credible interval = - 16.59-12.95). The study found substantial spatial and temporal differences in malaria risk across the 260 districts. The predicted national relative risk was 1.25 (95% credible interval = 1.23, 1.27). The malaria risk is relatively the same over the entire year. However, a slightly higher relative risk was recorded in 2019 while in 2021, residing in Keta, Abuakwa South, Jomoro, Ahafo Ano South East, Tain, Nanumba North, and Tatale Sanguli districts was associated with the highest malaria risk ranging from a relative risk of 3.00 to 4.83. The district-level spatial patterns of malaria risks changed over time.

CONCLUSION

This study identified high malaria risk districts in Ghana where urgent and targeted control efforts are required. Noticeable changes were also observed in malaria risk for certain districts over some periods in the study. The findings provide an effective, actionable tool to arm policymakers and programme managers in their efforts to reduce malaria risk and its associated morbidity and mortality in line with the Sustainable Development Goals (SDG) 3.2 for limited public health resource settings, where universal intervention across all districts is practically impossible.

Extended excess hazard models for spatially dependent survival data

Relative survival represents the preferred framework for the analysis of population cancer survival data. The aim is to model the survival probability associated with cancer in the absence of information about the cause of death. Recent data linkage developments have allowed for incorporating the place of residence into the population cancer databases; however, modeling this spatial information has received little attention in the relative survival setting. We propose a flexible parametric class of spatial excess hazard models (along with inference tools), named "Relative Survival Spatial General Hazard," that allows for the inclusion of fixed and spatial effects in both time-level and hazard-level components. We illustrate the performance of the proposed model using an extensive simulation study, and provide guidelines about the interplay of sample size, censoring, and model misspecification. We present a case study using real data from colon cancer patients in England. This case study illustrates how a spatial model can be used to identify geographical areas with low cancer survival, as well as how to summarize such a model through marginal survival quantities and spatial effects.

Tropical tree ectomycorrhiza are distributed independently of soil nutrients

Mycorrhizae, a form of plant-fungal symbioses, mediate vegetation impacts on ecosystem functioning. Climatic effects on decomposition and soil quality are suggested to drive mycorrhizal distributions, with arbuscular mycorrhizal plants prevailing in low-latitude/high-soil-quality areas and ectomycorrhizal (EcM) plants in high-latitude/low-soil-quality areas. However, these generalizations, based on coarse-resolution data, obscure finer-scale variations and result in high uncertainties in the predicted distributions of mycorrhizal types and their drivers. Using data from 31 lowland tropical forests, both at a coarse scale (mean-plot-level data) and fine scale (20 × 20 metres from a subset of 16 sites), we demonstrate that the distribution and abundance of EcM-associated trees are independent of soil quality. Resource exchange differences among mycorrhizal partners, stemming from diverse evolutionary origins of mycorrhizal fungi, may decouple soil fertility from the advantage provided by mycorrhizal associations. Additionally, distinct historical biogeographies and diversification patterns have led to differences in forest composition and nutrient-acquisition strategies across three major tropical regions. Notably, Africa and Asia's lowland tropical forests have abundant EcM trees, whereas they are relatively scarce in lowland neotropical forests. A greater understanding of the functional biology of mycorrhizal symbiosis is required, especially in the lowland tropics, to overcome biases from assuming similarity to temperate and boreal regions.

Visible minority status and occupation were associated with increased COVID-19 infection in Greater Vancouver British Columbia between June and November 2020: an ecological study

Background

The COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada.

Methods

Provincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated.

Results

We found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline.

Conclusion

Despite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

Spatial distribution and determinants of tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study

INTRODUCTION

Mozambique is a high-burden country for tuberculosis (TB). International studies show that TB is a disease that tends to cluster in specific regions, and different risk factors (HIV prevalence, migration, overcrowding, poverty, house condition, temperature, altitude, undernutrition, urbanization, and inadequate access to TB diagnosis and treatment) are reported in the literature to be associated with TB incidence. Although Mozambique has a higher burden of TB, the spatial distribution, and determinants of TB incidence at the sub-national level have not been studied yet for the whole country. Therefore, we aimed to analyze the spatial distribution and determinants of tuberculosis incidence across all 154 districts of Mozambique and identify the hotspot areas.

METHOD

We conducted an ecological study with the district as our unit of analysis, where we included all cases of tuberculosis diagnosed in Mozambique between 2016 and 2020. We obtained the data from the Mozambique Ministry of Health and other publicly available open sources. The predictor variables were selected based on the literature review and data availability at the district level in Mozambique. The parameters were estimated through Bayesian hierarchical Poisson regression models using Markov Chain Monte Carlo simulation.

RESULTS

A total of 512 877 people were diagnosed with tuberculosis in Mozambique during our five-year study period. We found high variability in the spatial distribution of tuberculosis incidence across the country. Sixty-two districts out of 154 were identified as hotspot areas. The districts with the highest incidence rate were concentrated in the south and the country's central regions. In contrast, those with lower incidence rates were mainly in the north. In the multivariate analysis, we found that TB incidence was positively associated with the prevalence of HIV (RR: 1.23; 95 % CrI 1.13 to 1.34) and negatively associated with the annual average temperature (RR: 0.83; 95 % CrI 0.74 to 0.94).

CONCLUSION

The incidence of tuberculosis is unevenly distributed across the country. Lower average temperature and high HIV prevalence seem to increase TB incidence. Targeting interventions in higher-risk areas and strengthening collaboration between HIV and TB programs is paramount to ending tuberculosis in Mozambique, as established by the WHO's End TB strategy and the Sustainable Development Goals.

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.

Lead and delinquency rates; A spatio-temporal perspective

Juvenile delinquency has significant social costs for perpetrators, victims, and communities. To understand the distribution of delinquency offenses this study considers the spatial clustering of juvenile delinquency with lead, race, and neighborhood deprivation using a longitudinal ecological design (N = 4390) and a hierarchical model implemented in a Bayesian methodology that allows space-time interaction. The results show lead exposure is positively related to delinquency offense rates, and over time delinquency rates have become more concentrated in areas with higher levels of lead exposure and shares of Black or African American residents. The study emphasizes the isolation of neighborhoods with social problems and the importance of monitoring patterns of lead and crime at local levels as communities implement lead exposure mitigation programs.

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.

Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam

Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue's distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue's expansion throughout Vietnam.

Trends and disparities in China's cardiovascular disease burden from 1990 to 2019

BACKGROUND AND AIMS

In order to find the exact strategies in the prevention of cardiovascular diseases (CVD), it is necessary to assess their risk factors systematically. Here, we used the Global Burden of Disease (GBD) to review the long-term trends and epidemiological characteristics among Chinese.

METHODS AND RESULTS

We comprehensively analyzed the burden of CVD for the Chinese population using GBD 2019, including prevalence, incidence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs). Then, we analyzed trends over time, and predicted mortality and morbidity, using joinpoint regression, age-period-cohort (APC) model, and Bayesian APC approach. Finally, we analyzed the attributable burden of CVD. In 2019, the prevalence of CVD in China was 120 million, representing a 140.02% increase since 1990. The number of DALYs attributed to CVD increased by 52.56% compared to 1990. Joinpoint showed a fluctuating incidence downward, while mortality significantly declined. The APC fitting results indicated that recent generations have a higher prevalence than the past, and the prevalence has increased among individuals of the same age group. The BAPC predicted that CVD's prevalence and mortality in the Chinese would stabilize and decline between 2020 and 2030, with a significant decline among males. The main CVD-attributable burdens in 2019 were metabolic risks, especially high blood pressure.

CONCLUSION

Given China's large and rapidly aging population, the burden of CVD is a major concern. Practical strategies to prevent and manage CVD are urgently needed to address this public health challenge.

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.

Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England

In the early phases of growth, resurgent epidemic waves of SARS-CoV-2 incidence have been characterised by localised outbreaks. Therefore, understanding the geographic dispersion of emerging variants at the start of an outbreak is key for situational public health awareness. Using telecoms data, we derived mobility networks describing the movement patterns between local authorities in England, which we have used to inform the spatial structure of a Bayesian BYM2 model. Surge testing interventions can result in spatio-temporal sampling bias, and we account for this by extending the BYM2 model to include a random effect for each timepoint in a given area. Simulated-scenario modelling and real-world analyses of each variant that became dominant in England were conducted using our BYM2 model at local authority level in England. Simulated datasets were created using a stochastic metapopulation model, with the transmission rates between different areas parameterised using telecoms mobility data. Different scenarios were constructed to reproduce real-world spatial dispersion patterns that could prove challenging to inference, and we used these scenarios to understand the performance characteristics of the BYM2 model. The model performed better than unadjusted test positivity in all the simulation-scenarios, and in particular when sample sizes were small, or data was missing for geographical areas. Through the analyses of emerging variant transmission across England, we found a reduction in the early growth phase geographic clustering of later dominant variants as England became more interconnected from early 2022 and public health interventions were reduced. We have also shown the recent increased geographic spread and dominance of variants with similar mutations in the receptor binding domain, which may be indicative of convergent evolution of SARS-CoV-2 variants.

A statewide study of disparities in local policies and tobacco, vape, and cannabis retail environments

The current study: (1) assesses sociodemographic disparities in local policies related to tobacco and cannabis retail, and (2) examines the cross-sectional association between policy strength and retailer densities of tobacco, e-cigarette (vape), and cannabis retailers within California cities and county unincorporated areas (N = 539). We combined (a) American Community Survey data (2019 5-year estimates), (b) 2018 tobacco, vape, and cannabis retailer locations from a commercial data provider, (c) 2017 tobacco and vape retail environment policy data from American Lung Association, and (d) 2018 cannabis policy data from California Cannabis Local Laws Database. Conditional autoregressive models examined policy strength associations with sociodemographic composition and retailer density in California jurisdictions. Jurisdictions with larger percentages of Black and foreign-born residents had stronger tobacco and vape policies. For cannabis policy, only income had a small, significant positive association with policy strength. Contrary to hypothesis, tobacco/vape policies were not significantly associated with retailer density, but cannabis policy strength was associated with lower cannabis retailer density (relative rate = 0.58, 95% Uncertainty Interval 0.47-0.70)-this effect was completely driven by storefront bans. Thus, storefront cannabis bans were the only policy studied that was associated with lower cannabis retailer density. Further research is needed to understand policies and disparities in retail environments for tobacco, vape, and cannabis, including data on the prospective association between policy implementation and subsequent retailer density, and the role of enforcement.

Race, Class, and Place Modify Mortality Rates for the Leading Causes of Death in the United States, 1999-2021

BACKGROUND

Race and ethnicity, socioeconomic class, and geographic location are well-known social determinants of health in the US. Studies of population mortality often consider two, but not all three of these risk factors.

OBJECTIVES

To disarticulate the associations of race (whiteness), class (socioeconomic status), and place (county) with risk of cause-specific death in the US.

DESIGN

We conducted a retrospective analysis of death certificate data. Bayesian regression models, adjusted for age and race/ethnicity from the American Community Survey and the county Area Deprivation Index, were used for inference.

MAIN MEASURES

County-level mortality for 11 leading causes of death (1999-2019) and COVID-19 (2020-2021).

KEY RESULTS

County "whiteness" and socioeconomic status modified death rates; geospatial effects differed by cause of death. Other factors equal, a 20% increase in county whiteness was associated with 5-8% increase in death from three causes and 4-15% reduction in death from others, including COVID-19. Other factors equal, advantaged counties had significantly lower death rates, even when juxtaposed with disadvantaged ones. Patterns of residual risk, measured by spatial county effects, varied by cause of death; for example: cancer and heart disease death rates were better explained by age, socioeconomic status, and county whiteness than were COVID-19 and suicide deaths.

CONCLUSIONS

There are important independent contributions from race, class, and geography to risk of death in the US.

Genomic epidemiology unveils the dynamics and spatial corridor behind the Yellow Fever virus outbreak in Southern Brazil

Despite the considerable morbidity and mortality of yellow fever virus (YFV) infections in Brazil, our understanding of disease outbreaks is hampered by limited viral genomic data. Here, through a combination of phylogenetic and epidemiological models, we reconstructed the recent transmission history of YFV within different epidemic seasons in Brazil. A suitability index based on the highly domesticated Aedes aegypti was able to capture the seasonality of reported human infections. Spatial modeling revealed spatial hotspots with both past reporting and low vaccination coverage, which coincided with many of the largest urban centers in the Southeast. Phylodynamic analysis unraveled the circulation of three distinct lineages and provided proof of the directionality of a known spatial corridor that connects the endemic North with the extra-Amazonian basin. This study illustrates that genomics linked with eco-epidemiology can provide new insights into the landscape of YFV transmission, augmenting traditional approaches to infectious disease surveillance and control.

Community factors and excess mortality in the COVID-19 pandemic in England, Italy and Sweden

BACKGROUND

Analyses of coronavirus disease 19 suggest specific risk factors make communities more or less vulnerable to pandemic-related deaths within countries. What is unclear is whether the characteristics affecting vulnerability of small communities within countries produce similar patterns of excess mortality across countries with different demographics and public health responses to the pandemic. Our aim is to quantify community-level variations in excess mortality within England, Italy and Sweden and identify how such spatial variability was driven by community-level characteristics.

METHODS

We applied a two-stage Bayesian model to quantify inequalities in excess mortality in people aged 40 years and older at the community level in England, Italy and Sweden during the first year of the pandemic (March 2020-February 2021). We used community characteristics measuring deprivation, air pollution, living conditions, population density and movement of people as covariates to quantify their associations with excess mortality.

RESULTS

We found just under half of communities in England (48.1%) and Italy (45.8%) had an excess mortality of over 300 per 100 000 males over the age of 40, while for Sweden that covered 23.1% of communities. We showed that deprivation is a strong predictor of excess mortality across the three countries, and communities with high levels of overcrowding were associated with higher excess mortality in England and Sweden.

CONCLUSION

These results highlight some international similarities in factors affecting mortality that will help policy makers target public health measures to increase resilience to the mortality impacts of this and future pandemics.

Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022

Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the COVID-19 pandemic's mortality impact. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating factors, and inform response efforts. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019. An estimated 1,179,024 excess deaths occurred during the first 2 years of the pandemic (first: 634,830; second: 544,194). Overall, excess mortality decreased in large metropolitan counties but increased in nonmetropolitan counties. Despite the initial concentration of mortality in large metropolitan Northeastern counties, nonmetropolitan Southern counties had the highest cumulative relative excess mortality by July 2021. These results highlight the need for investments in rural health as the pandemic's rural impact grows.

Geographic patterns and hotspots of pediatric tuberculosis: the role of socioeconomic determinants

OBJECTIVE

Children are an important demographic group for understanding overall tuberculosis epidemiology, and monitoring of childhood tuberculosis is essential for appropriate prevention. The present study sought to characterize the spatial distribution of childhood tuberculosis notification rates in continental Portugal; identify high-risk areas; and evaluate the association between childhood tuberculosis notification rates and socioeconomic deprivation.

METHODS

Using hierarchical Bayesian spatial models, we analyzed the geographic distribution of pediatric tuberculosis notification rates across 278 municipalities between 2016 and 2020 and determined high-risk and low-risk areas. We used the Portuguese version of the European Deprivation Index to estimate the association between childhood tuberculosis and area-level socioeconomic deprivation.

RESULTS

Notification rates ranged from 1.8 to 13.15 per 100,000 children under 5 years of age. We identified seven high-risk areas, the relative risk of which was significantly above the study area average. All seven high-risk areas were located in the metropolitan area of Porto or Lisbon. There was a significant relationship between socioeconomic deprivation and pediatric tuberculosis notification rates (relative risk = 1.16; Bayesian credible interval, 1.05-1.29).

CONCLUSIONS

Identified high-risk and socioeconomically deprived areas should constitute target areas for tuberculosis control, and these data should be integrated with other risk factors to define more precise criteria for BCG vaccination.

Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: a spatial temporal analysis

Background

Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique.

Methods

We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial-temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors.

Results

A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37-5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01-1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61-0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30-2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014-1.054]) and having electricity (0.979 [0.967-0.992]) and sharing toilet facilities (0.957 [0.924-0.991]) significantly reduced malaria risk.

Conclusion

Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.

Spatiotemporal bayesian modelling of scorpionism and its risk factors in the state of São Paulo, Brazil

BACKGROUND

Scorpion stings in Brazil represent a major public health problem due to their incidence and their potential ability to lead to severe and often fatal clinical outcomes. A better understanding of scorpionism determinants is essential for a precise comprehension of accident dynamics and to guide public policy. Our study is the first to model the spatio-temporal variability of scorpionism across municipalities in São Paulo (SP) and to investigate its relationship with demographic, socioeconomic, environmental, and climatic variables.

METHODOLOGY

This ecological study analyzed secondary data on scorpion envenomation in SP from 2008 to 2021, using the Integrated Nested Laplace Approximation (INLA) to perform Bayesian inference for detection of areas and periods with the most suitable conditions for scorpionism.

PRINCIPAL FINDINGS

From the spring of 2008 to 2021, the relative risk (RR) increased eight times in SP, from 0.47 (95%CI 0.43-0.51) to 3.57 (95%CI 3.36-3.78), although there has been an apparent stabilization since 2019. The western, northern, and northwestern parts of SP showed higher risks; overall, there was a 13% decrease in scorpionism during winters. Among the covariates considered, an increase of one standard deviation in the Gini index, which captures income inequality, was associated with a 11% increase in scorpion envenomation. Maximum temperatures were also associated with scorpionism, with risks doubling for temperatures above 36°C. Relative humidity displayed a nonlinear association, with a 50% increase in risk for 30-32% humidity and reached a minimum of 0.63 RR for 75-76% humidity.

CONCLUSIONS

Higher temperatures, lower humidity, and social inequalities were associated with a higher risk of scorpionism in SP municipalities. By capturing local and temporal relationships across space and time, authorities can design more effective strategies that adhere to local and temporal considerations.

Bayesian spatial modeling of childhood overweight and obesity prevalence in Costa Rica

BACKGROUND

Childhood overweight and obesity levels are rising and becoming a concern globally. In Costa Rica, the prevalence of these conditions has reached alarming values. Spatial analyses can identify risk factors and geographical patterns to develop tailored and effective public health actions in this context.

METHODS

A Bayesian spatial mixed model was built to understand the geographic patterns of childhood overweight and obesity prevalence in Costa Rica and their association with some socioeconomic factors. Data was obtained from the 2016 Weight and Size Census (6 - 12 years old children) and 2011 National Census.

RESULTS

Average years of schooling increase the levels of overweight and obesity until reaching an approximate value of 8 years, then they start to decrease. Moreover, for every 10-point increment in the percentage of homes with difficulties to cover their basic needs and in the percentage of population under 14 years old, there is a decrease of 7.7 and 14.0 points, respectively, in the odds of obesity. Spatial patterns show higher values of prevalence in the center area of the country, touristic destinations, head of province districts and in the borders with Panama.

CONCLUSIONS

Especially for childhood obesity, the average years of schooling is a non-linear factor, describing a U-inverted curve. Lower percentages of households in poverty and population under 14 years old are slightly associated with higher levels of obesity. Districts with high commercial and touristic activity present higher prevalence risk.

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

BACKGROUND AND OBJECTIVE

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 areal data, with special emphasis on mortality/incidence relative risk estimation.

METHODS

We present a pragmatic and simple idea that permits hierarchical spatio-temporal models to be fitted 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 or even unfeasible.

RESULTS

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.

CONCLUSIONS

Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.

Spatial-temporal distribution of incidence, mortality, and case-fatality ratios of coronavirus disease 2019 and its social determinants in Brazilian municipalities

The COVID-19 pandemic caused impact on public health worldwide. Brazil gained prominence during the pandemic due to the magnitude of disease. This study aimed to evaluate the spatial-temporal dynamics of incidence, mortality, and case fatality of COVID-19 and its associations with social determinants in Brazilian municipalities and epidemiological week. We modeled incidence, mortality, and case fatality rates using spatial-temporal Bayesian model. "Bolsa Família Programme" (BOLSAFAM) and "proportional mortality ratio" (PMR) were inversely associated with the standardized incidence ratio (SIR), while "health insurance coverage" (HEALTHINSUR) and "Gini index" were directly associated with the SIR. BOLSAFAM and PMR were inversely associated with the standardized mortality ratio (SMR) and standardized case fatality ratio (SCFR). The highest proportion of excess risk for SIR and the SMR started in the North, expanding to the Midwest, Southeast, and South regions. The highest proportion of excess risk for the SCFR outcome was observed in some municipalities in the North region and in the other Brazilian regions. The COVID-19 incidence and mortality in municipalities that most benefited from the cash transfer programme and with better social development decreased. The municipalities with a higher proportion of non-whites had a higher risk of becoming ill and dying from the disease.

Geographic and temporal trends in fentanyl-detected deaths in Connecticut, 2009-2019

PURPOSE

Since 2012 fentanyl-detected fatal overdoses have risen from 4% of all fatal overdoses in Connecticut to 82% in 2019. We aimed to investigate the geographic and temporal trends in fentanyl-detected deaths in Connecticut during 2009-2019.

METHODS

Data on the dates and locations of accidental/undetermined opioid-detected fatalities were obtained from Connecticut Office of the Chief Medical Examiner. Using a Bayesian space-time binomial model, we estimated spatiotemporal trends in the proportion of fentanyl-detected deaths.

RESULTS

During 2009-2019, a total of 6,632 opioid deaths were identified. Among these, 3234 (49%) were fentanyl-detected. The modeled spatial patterns suggested that opioid deaths in northeastern Connecticut had higher probability of being fentanyl-detected, while New Haven and its neighboring towns and the southwestern region of Connecticut, primarily Greenwich, had a lower risk. Model estimates also suggested fentanyl-detected deaths gradually overtook the preceding non-fentanyl opioid-detected deaths across Connecticut. The estimated temporal trend showed the probability of fentanyl involvement increased substantially since 2014.

CONCLUSIONS

Our findings suggest that geographic variation exists in the probability of fentanyl-detected deaths, and areas at heightened risk are identified. Further studies are warranted to explore potential factors contributing to the geographic heterogeneity and continuing dispersion of fentanyl-detected deaths in Connecticut.

Using mortality to predict incidence for rare and lethal cancers in very small areas

Incidence and mortality figures are needed to get a comprehensive overview of cancer burden. In many countries, cancer mortality figures are routinely recorded by statistical offices, whereas incidence depends on regional cancer registries. However, due to the complexity of updating cancer registries, incidence numbers become available 3 or 4 years later than mortality figures. It is, therefore, necessary to develop reliable procedures to predict cancer incidence at least until the period when mortality data are available. Most of the methods proposed in the literature are designed to predict total cancer (except nonmelanoma skin cancer) or major cancer sites. However, less frequent lethal cancers, such as brain cancer, are generally excluded from predictions because the scarce number of cases makes it difficult to use univariate models. Our proposal comes to fill this gap and consists of modeling jointly incidence and mortality data using spatio-temporal models with spatial and age shared components. This approach allows for predicting lethal cancers improving the performance of individual models when data are scarce by taking advantage of the high correlation between incidence and mortality. A fully Bayesian approach based on integrated nested Laplace approximations is considered for model fitting and inference. A validation process is also conducted to assess the performance of alternative models. We use the new proposals to predict brain cancer incidence rates by gender and age groups in the health units of Navarre and Basque Country (Spain) during the period 2005-2008.

Influence of anthropic changes and environmental characteristics on the occurrence of Tegumentary Leishmaniasis in Montes Claros, Minas Gerais, Brazil, between 2012 and 2019

This is an ecological study that investigated the influence of environmental, socioeconomic characteristics and changes in land use and cover on the occurrence of Tegumentary Leishmaniasis (TL) in the city of Montes Claros. The relationships between the number of cases of TL, which occurred between 2012 and 2019, in each census sector and the standardized covariates (Number of properties, altitude, Brazilian Deprivation Index, Normalized Difference Vegetation Index (NDVI), proportion of sector (PS) deforested, PS that underwent other anthropic alterations and unaltered PS) were evaluated with ecological Bayesian Models. Four multivariate models were constructed, with similar quality of adjustments, but Model 1 was the most parsimonious. Model 1 revealed that for each one-unit increase of standard deviation (SD) in the log of the number of properties, at the altitude and root of the deforested PS, corresponds to an increase of 44%, 34% and 24.5% in the number of cases of TL, respectively. The variable NDVI, included in models 3 and 4, was positively associated with the increase in the number of TL cases, being that for each one-unit increase in the NDVI was verified an increase of 21.3% and 20.2% respectively in the models. This study showed that the spatial distribution of TL cases in the city of Montes Claros occurs in a heterogeneous way and our findings support the hypothesis that socio-environmental characteristics and deforestation influence the occurrence of this disease in the studied area. Thus, these factors must be considered for the development of disease control strategies.

Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam

Bayesian space-time regression models are helpful tools to describe and predict the distribution of infectious disease outbreaks and to delineate high-risk areas for disease control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts across spatial units. Structured spatial effects capture correlations in case counts amongst neighboring provinces arising from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of connectivity due to long-distance movement, but we often lack data on host movements. Phylogeographic models inferring routes of viral dissemination across a region could serve as a proxy for patterns of population connectivity. The objective of this study was to investigate whether the effects of population connectivity in space-time regressions of case counts were better captured by spatial adjacency or by inferences from phylogeographic analyses. To compare these two approaches, we used foot-and-mouth disease virus (FMDV) outbreak data from across Vietnam as an example. We identified that accounting for virus movement through phylogeographic analysis serves as a better proxy for population connectivity than spatial adjacency in spatial-temporal risk models. This approach may contribute to design surveillance activities in countries lacking movement data.

Differences Between Reported COVID-19 Deaths and Estimated Excess Deaths in Counties Across the United States, March 2020 to February 2022

Accurate and timely tracking of COVID-19 deaths is essential to a well-functioning public health surveillance system. The extent to which official COVID-19 death tallies have captured the true toll of the pandemic in the United States is unknown. In the current study, we develop a Bayesian hierarchical model to estimate monthly excess mortality in each county over the first two years of the pandemic and compare these estimates to the number of deaths officially attributed to Covid-19 on death certificates. Overall, we estimated that 268,176 excess deaths were not reported as Covid-19 deaths during the first two years of the Covid-19 pandemic, which represented 23.7% of all excess deaths that occurred. Differences between excess deaths and reported COVID-19 deaths were substantial in both the first and second year of the pandemic. Excess deaths were less likely to be reported as COVID-19 deaths in the Mountain division, in the South, and in nonmetro counties. The number of excess deaths exceeded COVID-19 deaths in all Census divisions except for the New England and Middle Atlantic divisions where there were more COVID-19 deaths than excess deaths in large metro areas and medium or small metro areas. Increases in excess deaths not assigned to COVID-19 followed similar patterns over time to increases in reported COVID-19 deaths and typically preceded or occurred concurrently with increases in reported COVID-19 deaths. Estimates from this study can be used to inform targeting of resources to areas in which the true toll of the COVID-19 pandemic has been underestimated.

Direct and indirect effects of the COVID-19 pandemic on mortality in Switzerland

The direct and indirect impact of the COVID-19 pandemic on population-level mortality is of concern to public health but challenging to quantify. Using data for 2011-2019, we applied Bayesian models to predict the expected number of deaths in Switzerland and compared them with laboratory-confirmed COVID-19 deaths from February 2020 to April 2022 (study period). We estimated that COVID-19-related mortality was underestimated by a factor of 0.72 (95% credible interval [CrI]: 0.46-0.78). After accounting for COVID-19 deaths, the observed mortality was -4% (95% CrI: -8 to 0) lower than expected. The deficit in mortality was concentrated in age groups 40-59 (-12%, 95%CrI: -19 to -5) and 60-69 (-8%, 95%CrI: -15 to -2). Although COVID-19 control measures may have negative effects, after subtracting COVID-19 deaths, there were fewer deaths in Switzerland during the pandemic than expected, suggesting that any negative effects of control measures were offset by the positive effects. These results have important implications for the ongoing debate about the appropriateness of COVID-19 control measures.

Intra-urban spatial variability of breast and cervical cancer mortality in the city of São Paulo: analysis of associated factors

ABSTRACT Objective: To identify spatial variability of mortality from breast and cervical cancer and to assess factors associated in the city of São Paulo. Methods: Between 2009 and 2016, 10,124 deaths from breast cancer and 2,116 deaths from cervical cancer were recorded in the Mortality Information System among women aged 20 years and over. The records were geocoded by address of residence and grouped according to Primary Health Care coverage areas. A spatial regression modeling was put together using the Bayesian approach with a Besag-York-Mollié structure to verify the association of deaths with selected indicators. Results: Mortality rates from these types of cancer showed inverse spatial patterns. These variables were associated with breast cancer mortality: travel time between one and two hours to work (RR – relative risk: 0.97; 95%CI – credible interval: 0.93–1.00); women being the head of the household (RR 0.97; 95%CI 0.94–0.99) and deaths from breast cancer in private health institutions (RR 1.04; 95%CI 1.00–1.07). The following variables were associated with mortality from cervical cancer: travel time to work between half an hour and one hour (RR 0.92; 95%CI 0.87–0.98); per capita household income of up to 3 minimum wages (RR 1.27; 95%CI 1.18–1.37) and ratio of children under one year of age related to the female population aged 15 to 49 years (RR 1.09; 95%CI 1.01–1.18). Conclusion: The predicted RR for mortality from these cancers were calculated and associated with the socioeconomic conditions of the areas covered.

Revisiting Gaussian Markov random fields and Bayesian disease mapping

We revisit several conditionally formulated Gaussian Markov random fields, known as the intrinsic conditional autoregressive model, the proper conditional autoregressive model, and the Leroux et al. conditional autoregressive model, as well as convolution models such as the well known Besag, York and Mollie model, its (adaptive) re-parameterization, and its scaled alternatives, for their roles of modelling underlying spatial risks in Bayesian disease mapping. Analytic and simulation studies, with graphic visualizations, and disease mapping case studies, present insights and critique on these models for their nature and capacities in characterizing spatial dependencies, local influences, and spatial covariance and correlation functions, and in facilitating stabilized and efficient posterior risk prediction and inference. It is illustrated that these models are Gaussian (Markov) random fields of different spatial dependence, local influence, and (covariance) correlation functions and can play different and complementary roles in Bayesian disease mapping applications.

Exposure to industrial hog and poultry operations and urinary tract infections in North Carolina, USA

An increasing share of urinary tract infections (UTIs) are caused by extraintestinal pathogenic Escherichia coli (ExPEC) lineages that have also been identified in poultry and hogs with high genetic similarity to human clinical isolates. We investigated industrial food animal production as a source of uropathogen transmission by examining relationships of hog and poultry density with emergency department (ED) visits for UTIs in North Carolina (NC). ED visits for UTI in 2016-2019 were identified by ICD-10 code from NC's ZIP code-level syndromic surveillance system and livestock counts were obtained from permit data and aerial imagery. We calculated separate hog and poultry spatial densities (animals/km²) by Census block with a 5 km buffer on the block perimeter and weighted by block population to estimate mean ZIP code densities. Associations between livestock density and UTI incidence were estimated using a reparameterized Besag-York-Mollié (BYM2) model with ZIP code population offsets to account for spatial autocorrelation. We excluded metropolitan and offshore ZIP codes and assessed effect measure modification by calendar year, ZIP code rurality, and patient sex, age, race/ethnicity, and health insurance status. In single-animal models, hog exposure was associated with increased UTI incidence (rate ratio [RR]: 1.21, 95 % CI: 1.07-1.37 in the highest hog-density tertile), but poultry exposure was associated with reduced UTI rates (RR: 0.86, 95 % CI: 0.81-0.91). However, the reference group for single-animal poultry models included ZIP codes with only hogs, which had some of the highest UTI rates; when compared with ZIP codes without any hogs or poultry, there was no association between poultry exposure and UTI incidence. Hog exposure was associated with increased UTI incidence in areas that also had medium to high poultry density, but not in areas with low poultry density, suggesting that intense hog production may contribute to increased UTI incidence in neighboring communities.

Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention

Background

The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies.

Methods

This study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015.

Results

The results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions.

Conclusions

The results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies.

Malaria is an infectious disease that is widespread in many African countries. Malaria transmission within a country can vary between regions, so tailored interventions for malaria control and elimination targeted to different regions are necessary. To achieve this, it is important to measure and monitor the frequency of malaria infections, its risk, and trends at different geographic administrative scales. This study analysed over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. The results showed an increasing national trend in malaria risk and malaria infection frequency and identified differences between health facility and district trends. These findings support a flexible approach when implementing and expanding national malaria monitoring, control and elimination strategies, especially in areas bordering countries where malaria is widespread, cross-border movement is common, and cross-border initiatives could be beneficial.

Lubinda et al. analyse over 32 million health-facility reported malaria cases in Zambia (2009–15) to examine spatially-structured temporal trends. They observe overall increasing trends in risk and rates and highlight the potential benefits of using an adaptive scaling approach in national malaria strategies, and a need for cross-border initiatives.

Spatiotemporal variation of childhood hyperthyroidism: a 10-year nationwide study

OBJECTIVE

Childhood hyperthyroidism is mostly caused by Graves' disease, a rare autoimmune disease in children. Epidemiological data are scarce and the variability of within-region incidence is unknown. We aimed to provide the first description of temporal trends in pediatric hyperthyroidism in France and to explore spatial trends, with a view to identifying possible environmental triggers.

DESIGN AND METHODS

We performed an observational population-based study on data collected from the National Health Data System, covering the 2008-2017 period and the whole of France. We identified patients with an indicator reflecting incident cases of treated hyperthyroidism, in children aged 6 months-17.9 years, localized at the scale of the département (equivalent to a county) of residence. We performed descriptive analyses of incidence rate by sex, age, and year, and used a spatiotemporal model for estimation at département level.

RESULTS

We identified 4734 incident cases: 3787 girls (80%) and 947 boys (20%). The crude incidence rate was 3.35 (95% CI: 3.26; 3.45) per 100 000 person-years over the study period. We estimated the increase in incidence between 2008 and 2017 at 30.1% (19.0%; 42.3%). Annual incidence rate increased linearly over the 10-year period in both girls and boys, rising similarly in all age groups and in all départements. The spatial model highlighted marked heterogeneity in the risk of childhood hyperthyroidism across France.

CONCLUSION

The trend toward increasing incidence observed may reflect changes in genetic and environmental interactions, and the marked spatial heterogeneity may reflect localized ethnic or environmental factors worthy of further investigation.

Identifying regions of excess injury risks associated with distracted driving: A case study in Central Ohio, USA

This study examines the latent influence of spatial locations on the relative risks of crash injuries associated with distracted driving (DD) and identifies regions of excess risks for policy intervention. Using a sample of aggregated injury and fatal DD crash records for the period 2015–2019 across 1,024 census block groups in Central Ohio (i.e., the Columbus Metropolitan Area) in the United States, we investigate the role of latent effects along with several covariates such as land-use mix, sociodemographic features, and the built environment. To this end, we specifically leverage a full Bayesian hierarchical formulation with conditional autoregressive priors to account for uncertainty (i.e., spatially structured random effects) stemming from adjacent census block groups. Furthermore, we consider uncorrelated random effects from upper-level administrative units within which each block group is nested (i.e., census tracts and counties). Our analysis reveals that (1) addressing spatial correlation improves the model's performance, (2) block-group-level variability substantially explains the residual random fluctuation, and (3) intersection density appears negatively associated with the relative risks of crash injuries, while more diversified land use can increase injury risk. Based on these findings, we present spatial clusters with twice the relative risks compared to other block groups, suggesting that policies be devised to mitigate severe injuries due to DD and therefore enhance public health.

•

Crash injuries associated with distracted driving are investigated.

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Spatial correlation accounts for residual variation in relative injury risks.

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Intersection density appears to reduce the risks of crash injuries.

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Diversified land use leads to an elevated injury risk.

•

We identify small areas with excess injury risks.

Lethal violence against women in southern Brazil: Spatial analysis and associated factors

OBJECTIVE

estimate the risk for the occurrence of lethal violence against women and to identify the associated factors in the state of Paraná.

METHOD

ecological study of deaths of women aged between 15 and 59 years, victims of aggression. The units of analysis were the cities of Paraná. Latent Bayesian Gaussian models with negative binomial probability distribution were used. The modeling considered intercept, spatial random effects and covariates, performed with the deterministic Integrated Nested Laplace Approximations approach.

RESULTS

There was a positive association between lethal violence against women and the percentage of mothers who were heads of households. Finally, male homicide rates, rates of non-lethal violence against women and the cities with women mayors and councilors were also associated.

CONCLUSION

This type of violence was associated with low education, structural violence and the participation of women in politics.

Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network

As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability-including environmental determinants of COVID-19 infection-into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for "what-if" analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.

Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package

INTRODUCTION

There is an increasing interest in small area analyses in cancer surveillance; however, technical capacity is limited and accessible analytical approaches remain to be determined. This study demonstrates an accessible approach for small area cancer risk estimation using Bayesian hierarchical models and data visualization through the smallareamapp R package.

MATERIALS AND METHODS

Incident lung (N = 26,448), female breast (N = 28,466), cervical (N = 1,478), and colorectal (N = 25,457) cancers diagnosed among British Columbia (BC) residents between 2011 and 2018 were obtained from the BC Cancer Registry. Indirect age-standardization was used to derive age-adjusted expected counts and standardized incidence ratios (SIRs) relative to provincial rates. Moran's I was used to assess the strength and direction of spatial autocorrelation. A modified Besag, York and Mollie model (BYM2) was used for model incidence counts to calculate posterior median relative risks (RR) by Community Health Service Areas (CHSA; N = 218), adjusting for spatial dependencies. Integrated Nested Laplace Approximation (INLA) was used for Bayesian model implementation. Areas with exceedance probabilities (above a threshold RR = 1.1) greater or equal to 80% were considered to have an elevated risk. The posterior median and 95% credible intervals (CrI) for the spatially structured effect were reported. Predictive posterior checks were conducted through predictive integral transformation values and observed versus fitted values.

RESULTS

The proportion of variance in the RR explained by a spatial effect ranged from 4.4% (male colorectal) to 19.2% (female breast). Lung cancer showed the greatest number of CHSAs with elevated risk (Nwomen = 50/218, Nmen = 44/218), representing 2357 total excess cases. The largest lung cancer RRs were 1.67 (95% CrI = 1.06-2.50; exceedance probability = 96%; cases = 13) among women and 2.49 (95% CrI = 2.14-2.88; exceedance probability = 100%; cases = 174) among men. Areas with small population sizes and extreme SIRs were generally smoothed towards the null (RR = 1.0).

DISCUSSION

We present a ready-to-use approach for small area cancer risk estimation and disease mapping using BYM2 and exceedance probabilities. We developed the smallareamapp R package, which provides a user-friendly interface through an R-Shiny application, for epidemiologists and surveillance experts to examine geographic variation in risk. These methods and tools can be used to estimate risk, generate hypotheses, and examine ecologic associations while adjusting for spatial dependency.

Modeling infectious disease dynamics: Integrating contact tracing-based stochastic compartment and spatio-temporal risk models

Major infectious diseases such as COVID-19 have a significant impact on population lives and put enormous pressure on healthcare systems globally. Strong interventions, such as lockdowns and social distancing measures, imposed to prevent these diseases from spreading, may also negatively impact society, leading to jobs losses, mental health problems, and increased inequalities, making crucial the prioritization of riskier areas when applying these protocols. The modeling of mobility data derived from contact-tracing data can be used to forecast infectious trajectories and help design strategies for prevention and control. In this work, we propose a new spatial-stochastic model that allows us to characterize the temporally varying spatial risk better than existing methods. We demonstrate the use of the proposed model by simulating an epidemic in the city of Valencia, Spain, and comparing it with a contact tracing-based stochastic compartment reference model. The results show that, by accounting for the spatial risk values in the model, the peak of infected individuals, as well as the overall number of infected cases, are reduced. Therefore, adding a spatial risk component into compartment models may give finer control over the epidemic dynamics, which might help the people in charge to make better decisions.

Population-Based Disparities in U.S. Urban Heat Exposure from 2003 to 2018

Previous studies have shown, in the United States (U.S.), that communities of color are exposed to significantly higher temperatures in urban environments than complementary White populations. Studies highlighting this disparity have generally been cross-sectional and are therefore "snapshots" in time. Using surface urban heat island (SUHI) intensity data, U.S. Census 2020 population counts, and a measure of residential segregation, this study performs a comparative analysis between census tracts identified as prevalent for White, Black, Hispanic and Asian populations and their thermal exposure from 2003 to 2018. The analysis concentrates on the top 200 most populous U.S. cities. SUHI intensity is shown to be increasing on average through time for the examined tracts. However, based on raw observations the increase is only statistically significant for White and Black prevalent census tracts. There is a 1.25 K to ~2.00 K higher degree of thermal exposure on average for communities of color relative to White prevalent areas. When examined on an inter-city basis, White and Black prevalent tracts had the largest disparity, as measured by SUHI intensity, in New Orleans, LA, by <6.00 K. Hispanic (>7.00 K) and Asian (<6.75 K) prevalent tracts were greatest in intensity in San Jose, CA. To further explore temporal patterns, two models were developed using a Bayesian hierarchical spatial temporal framework. One models the effect of varying the percentages of each population group relative to SUHI intensity within all examined tracts. Increases in percentages of Black, Hispanic, and Asian populations contributed to statistically significant increases in SUHI intensity. White increases in population percentage witnessed a lowering of SUHI intensity. Throughout all modeled tracts, there is a statistically significant 0.01 K per year average increase in SUHI intensity. A second model tests the effect of residential segregation on thermal inequity across all examined cities. Residential segregation, indeed, has a statistically significant positive association with SUHI intensity based on this portion of the analysis. Similarly, there is a statistically significant 0.01 K increase in average SUHI intensity per year for all cities. Results from this study can be used to guide and prioritize intervention strategies and further urgency related to social, climatic, and environmental justice concerns.

A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping

Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity. However, conventional approaches require the spatial structure to be fully defined prior to model fitting. By applying penalized smoothing splines to coordinates, we create two-dimensional smooth surfaces describing the spatial structure of the data while making minimal assumptions about the structure. The result is a non-stationary surface which is setting specific. These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects. Through simulation studies, we show that the splines can be applied to any symmetric continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data. Using Bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease. These models were found to perform at least as well as existing modelling frameworks, while allowing for future extensions and multiple sources of spatial connectivity.

Impact of mass vaccination on the spatiotemporal dynamics of FMD outbreaks in India, 2008-2016

Foot-and-mouth disease (FMD) is endemic in India, where circulation of serotypes O, A and Asia1 is frequent. Here, we provide an epidemiological assessment of the ongoing mass vaccination programs in regard to post-vaccination monitoring and outbreak occurrence. The objective of this study was assessing the contribution of mass vaccination campaigns in reducing the risk of FMD in India from 2008 to 2016 by evaluating sero-monitoring data and modelling the spatiotemporal dynamics of reported outbreaks. Through analyzing antibody titre data from >1 million animals sampled as part of pre- and post-vaccination monitoring, we show that the percent of animals with inferred immunological protection (based on ELISA) was highly variable across states but generally increased through time. In addition, the number of outbreaks in a state was negatively correlated with the percent of animals with inferred protection. We then analyzed the distribution of reported FMD outbreaks across states using a Bayesian space-time model. This approach provides better acuity to disentangle the effect of mass vaccination programs on outbreak occurrence, while accounting for other factors that contribute to spatiotemporal variability in outbreak counts, notably proximity to international borders and inherent spatiotemporal correlations in incidence. This model demonstrated a ∼50% reduction in the risk of outbreaks in states that were part of the vaccination program. In addition, after controlling for spatial autocorrelation in the data, states that had international borders experienced heightened risk of FMD outbreaks. These findings help inform risk-based control strategies for India as the country progresses towards reducing reported clinical disease.