A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda

INTRODUCTION

Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions.

RESULTS

The findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda.

CONCLUSION

The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.

Geographical Inequality on Cataract Surgery Uptake in 200,000 Australians: Findings from the “45 and Up Study”

Using a geographical information system (GIS), we investigated the spatiotemporal evolution of a cataract surgery service and its association with socioeconomic factors and private insurance, based on 10-year real-world medical claim data in an Australian population. The data collected cover a decade (2007–2016) from the “45 and Up Study”. A total of 234,201 participants within the cataract surgery service were grouped into 88 Statistical Area Level 3 (SA3s) according to their residential postcodes in New South Wales Australia. We analyzed the spatiotemporal variations and geographical distribution inequality in cataract surgery incidence and its respect to socioeconomic status (SES) and private health insurance coverage by Spearman correlation analysis and Moran's I test. Then these variations were intuitive displayed by six-quartile maps and a local indicator of spatial association (LISA) maps based on GIS. The average cumulative age-gender-standardized of the incidence of cataract surgery (ICS) was 8.85% (95% CI, 5.33–15.6). Spatial variation was significant (univariate Moran's I = 0.45, P = 0.001) with incidence gradually decreasing from the coastal regions to the north-western inland regions, suggesting inequality in the cataract surgery service across the state of New South Wales. Notably, clustering of the low incidence areas had gradually disappeared over the decade, suggesting that the cataract surgery service has improved over time. Low scores on the “index of socioeconomic disadvantages” (IRSD) and high private health insurance coverage were significantly associated with a higher incidence of cataract surgery (bivariate Moran's I = −0.13 and 0.23, P < 0.01; Spearman correlation r = 0.25 and −0.25, P = 0.02), which is displayed on the map visually and obviously. Spatiotemporal variations in the incidence of cataract surgery are significant, but the low incidence area had gradually disappeared over time. High socioeconomic status and private insurance contribute to a higher incidence of cataract surgery in Australia.

Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England

The overwhelming spatio-temporal nature of the spread of the ongoing Covid-19 pandemic demands urgent attention of data analysts and model developers. Modelling results obtained from analytical tool development are essential to understand the ongoing pandemic dynamics with a view to helping the public and policy makers. The pandemic has generated data on a huge number of interesting statistics such as the number of new cases, hospitalisations and deaths in many spatio-temporal resolutions for the analysts to investigate. The multivariate nature of these data sets, along with the inherent spatio-temporal dependencies, poses new challenges for modellers. This article proposes a two-stage hierarchical Bayesian model as a joint bivariate model for the number of cases and deaths observed weekly for the different local authority administrative regions in England. An adaptive model is proposed for the weekly Covid-19 death rates as part of the joint bivariate model. The adaptive model is able to detect possible step changes in death rates in neighbouring areas. The joint model is also used to evaluate the effects of several socio-economic and environmental covariates on the rates of cases and deaths. Inclusion of these covariates points to the presence of a north-south divide in both the case and death rates. Nitrogen dioxide, the only air pollution measure used in the model, is seen to be significantly positively associated with the number cases, even in the presence of the spatio-temporal random effects taking care of spatio-temporal dependencies present in the data. The proposed models provide excellent fits to the observed data and are seen to perform well for predicting the location specific number of deaths a week in advance. The structure of the models is very general and the same framework can be used for modelling other areally aggregated temporal statistics of the pandemics, e.g. the rate of hospitalisation.

Mapping the Relative Risk of Congenital Hypothyroidism Incidence via Spatial Zero-Inflated Poisson Model in Guilan Province, Iran

Background:

Congenital hypothyroidism (CH) is one of the most prevalent preventable causes of mental retardation. Studies show that the incidence rate of CH is very high in Iran. Disease mapping is a tool for visually expressing the frequency, incidence, or relative risk of illness. The present study aimed to model CH counts considering the effects of the neighborhood in towns and perform mapping based on the relative risk.

Methods:

In this historical cohort study, data of all neonates diagnosed with CH with TSH level ≥5 mIU/L between March 21, 2017, and March 20, 2018, in health centers in Guilan, Iran were used. The number of neonates with CH was zero in most towns of Guilan Province. The Bayesian spatial zero-inflated Poisson (ZIP) regression model was employed to investigate the effect of the town's neighborhood on the relative risk of CH incidence. Then, the map of the posterior mean of the relative risk for CH incidence was provided. The analysis was performed using OpenBUGS and Arc GIS software programs.

Results:

The relative risk of CH incidence was high in the West of Guilan. Moreover, the goodness-of-fit criterion indicated that it is more appropriate to fit the Bayesian spatial ZIP model to these data than the common model.

Conclusions:

Considering the high relative risk of CH in the Western towns of Guilan Province, it is better to check important risk factors in this region.

Joint space-time Bayesian disease mapping via quantification of disease risk association

Alzheimer's disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer's disease risk in order to preclude its inception in patients. Characterizing Alzheimer's disease risk can be accomplished at the population-level by the space-time modeling of Alzheimer's disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer's disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer's disease risk over space and time. From an application of these models to real-world Alzheimer's disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer's disease risk.

A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data

Spatiotemporal models for count data are required in a wide range of scientific fields, and they have become particularly crucial today because of their ability to analyze COVID-19-related data. The main objective of this paper is to present a review describing the most important approaches, and we monitor their performance under the same dataset. For this review, we focus on the three R-packages that can be used for this purpose, and the different models assessed are representative of the two most widespread methodologies used to analyze spatiotemporal count data: the classical approach and the Bayesian point of view. A COVID-19-related case study is analyzed as an illustration of these different methodologies. Because of the current urgent need for monitoring and predicting data in the COVID-19 pandemic, this case study is, in itself, of particular importance and can be considered the secondary objective of this work. Satisfactory and promising results have been obtained in this second goal. With respect to the main objective, it has been seen that, although the three models provide similar results in our case study, their different properties and flexibility allow us to choose the model depending on the application at hand.

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

Background

Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018.

Methods

This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05.

Results

The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found.

Conclusion

The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11157-1.

A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany

The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany.

The Association between Community Water Fluoridation and Bone Diseases: A Natural Experiment in Cheongju, Korea

The present study aimed to investigate the association between bone diseases and community water fluoridation (CWF). An ecological study with a natural experiment design was conducted in Cheongju, South Korea, from 1 January 2004 to 31 December 2013. The community water fluoridation program was implemented in Cheongju and divided into CWF and non-CWF areas. To observe adverse health effects related to bone diseases, we conducted a spatio-temporal analysis of the prevalence of hip fracture, osteoporosis, and bone cancer in residents who have lived in CWF and non-CWF areas using National Health Insurance Service data. First, we used standardized incidence ratios to estimate the disease risk. Second, the hierarchical Bayesian Poisson spatio-temporal regression model was used to investigate the association between the selected bone diseases and CWF considering space and time interaction. The method for Bayesian estimation was based on the R-integrated nested Laplace approximation (INLA). Comparing the CWF area with the non-CWF area, there was no clear evidence that exposure to CWF increased health risks at the town level in Cheongju since CWF was terminated after 2004. The posterior relative risks (RR) of hip fracture was 0.95 (95% confidence intervals 0.87, 1.05) and osteoporosis was 0.94 (0.87, 1.02). The RR in bone cancer was a little high because the sample size very small compared to the other bone diseases (RR = 1.20 (0.89, 1.61)). The relative risk of selected bone diseases (hip fractures, osteoporosis, and bone cancer) increased over time but did not increase in the CWF area compared to non-CWF areas. CWF has been used to reduce dental caries in all population groups and is known for its cost-effectiveness. These findings suggest that CWF is not associated with adverse health risks related to bone diseases. This study provides scientific evidence based on a natural experiment design. It is necessary to continue research on the well-designed epidemiological studies and develop public health prevention programs to help in make suitable polices.

Evaluating the impact of a small number of areas on spatial estimation

Background

There is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study.

Methods

Both the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight different sizes of areal grids, ranging from 4 to 2500 areas, and two different levels of both spatial autocorrelation and disease counts. Model goodness-of-fit measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered.

Results

The simulation study showed that model performance varied under different scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G = 2$$\end{document}G=2 models performed similarly and better than the independent and localised \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G = 3$$\end{document}G=3 models. However, when the number of areas were at least 100, all models performed differently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G = 3 was a better choice.

Conclusion

Detecting spatial patterns can be difficult when there are very few areas. Understanding the characteristics of the data and the relative influence of alternative conditional autoregressive priors is essential in selecting an appropriate Bayesian spatial model.

Determinants of subnational disparities in antenatal care utilisation: a spatial analysis of demographic and health survey data in Kenya

BACKGROUND

The spatial variation in antenatal care (ANC) utilisation is likely associated with disparities observed in maternal and neonatal deaths. Most maternal deaths are preventable through services offered during ANC; however, estimates of ANC coverage at lower decision-making units (sub-county) is mostly lacking. In this study, we aimed to estimate the coverage of at least four ANC (ANC4) visits at the sub-county level using the 2014 Kenya Demographic and Health Survey (KDHS 2014) and identify factors associated with ANC utilisation in Kenya.

METHODS

Data from the KDHS 2014 was used to compute sub-county estimates of ANC4 using small area estimation (SAE) techniques which relied on spatial relatedness to yield precise and reliable estimates at each of the 295 sub-counties. Hierarchical mixed-effect logistic regression was used to identify factors influencing ANC4 utilisation. Sub-county estimates of factors significantly associated with ANC utilisation were produced using SAE techniques and mapped to visualise disparities.

RESULTS

The coverage of ANC4 across sub-counties was heterogeneous, ranging from a low of 17% in Mandera West sub-county to over 77% in Nakuru Town West and Ruiru sub-counties. Thirty-one per cent of the 295 sub-counties had coverage of less than 50%. Maternal education, household wealth, place of delivery, marital status, age at first marriage, and birth order were all associated with ANC utilisation. The areas with low ANC4 utilisation rates corresponded to areas of low socioeconomic status, fewer educated women and a small number of health facility deliveries.

CONCLUSION

Suboptimal coverage of ANC4 and its heterogeneity at sub-county level calls for urgent, focused and localised approaches to improve access to antenatal care services. Policy formulation and resources allocation should rely on data-driven strategies to guide national and county governments achieve equity in access and utilisation of health interventions.

Small-area spatial-temporal changes in pre-exposure prophylaxis (PrEP) use in the general population and among men who have sex with men in the United States between 2012 and 2018

PURPOSE

Oral emtricitabine/tenofovir disoproxil fumarate was approved for use as pre-exposure prophylaxis (PrEP) by the U.S. Food and Drug Administration in 2012. We used national pharmacy data to examine trends of PrEP use in U.S. counties from 2012 to 2018.

METHODS

Using multi-level small-area spatio-temporal modeling, we calculated the estimated annual percentage change (EAPC) in prevalence of PrEP use in the general population from 2012 to 2018. We also used a proxy measure for prevalence of PrEP use among men who have sex with men (MSM) to evaluate trends of use among MSM, the PrEP use-to-MSM ratio (PmR) or number of male PrEP users per 1000 MSM population.

RESULTS

The prevalence of PrEP use and PmR increased (EAPC range: (+26.9%, +71.0%) and (+28.4%, +158.7%), respectively) in all counties with varying magnitude of increase. Counties of the Midwest and the upper South and upper West had the slowest increase in prevalence of PrEP use (EAPC range: (+26.9%; +52.9%)). Counties of the northern part of the South had the lowest PmR (EAPC range: (+28.4%; +76.0%)). Counties of the most populous core-based statistical areas had a relatively faster increase in population prevalence of PrEP use but slower increase in PmR.

CONCLUSIONS

All counties in the U.S. have witnessed an increase in PrEP use with important geographic variabilities. Identifying areas with slow improvement in PrEP use, as well as "model counties" with the fastest pace of progress in PrEP coverage, is critical to inform local and state-level policies and program evaluation for PrEP scale up, particularly among MSM at higher risk for HIV.

Impact of fast-food outlet density on incidence of myocardial infarction in the Hunter region

BACKGROUND

There is an established association between fast-food consumption and metabolic diseases. Some studies also suggest that calorie-dense food promotes a proinflammatory response, which is itself linked with myocardial infarction (MI). Whether increased fast-food availability is a risk factor for MI remains unknown.

AIM

To investigate the role of fast-food outlet density (FFD) as a novel environmental risk factor for MI in the Hunter region, New South Wales (NSW).

METHODS

We conducted a retrospective cohort study using a database of all MI events between 1996 and 2013, extracted from the Hunter Cardiac and Stroke Outcomes unit. FFD was calculated for each local government area (LGA) of the Hunter region, allowing for a comparative analysis. Stratification by fast-food outlet data and LGA resulted in a total of 3070 cases. Weighted linear regression was used to investigate the role of FFD on incidence of MI in regional and rural Australia.

RESULTS

FFD was positively correlated with rates of MI, remaining consistent in both single and multivariate predictor models adjusting for age, obesity, hyperlipidaemia, hypertension, smoking status, diabetes and socioeconomic status (P < 0.001). An increase of one fast-food outlet corresponded with four additional cases of MI per 100 000 people per year (4.07, 95% confidence interval, 3.86-4.28).

CONCLUSIONS

FFD was positively associated with incidence of MI in both rural and metropolitan areas of NSW. This relationship remained consistent after multivariate adjustment for standard cardiovascular risk factors, highlighting the importance of an individual's food environment as a potential contributor towards their health.

Bayesian Space-Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010-2013

Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space–time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010–2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010–2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74–0.91) to 1.26 (95% CI 1.13–1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region’s average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

What calls for service tell us about suicide: A 7-year spatio-temporal analysis of neighborhood correlates of suicide-related calls

Previous research has shown that neighborhood-level variables such as social deprivation, social fragmentation or rurality are related to suicide risk, but most of these studies have been conducted in the U.S. or northern European countries. The aim of this study was to analyze the spatio-temporal distribution of suicide in a southern European city (Valencia, Spain), and determine whether this distribution was related to a set of neighborhood-level characteristics. We used suicide-related calls for service as an indicator of suicide cases (n = 6,537), and analyzed the relationship of the outcome variable with several neighborhood-level variables: economic status, education level, population density, residential instability, one-person households, immigrant concentration, and population aging. A Bayesian autoregressive model was used to study the spatio-temporal distribution at the census block group level for a 7-year period (2010–2016). Results showed that neighborhoods with lower levels of education and population density, and higher levels of residential instability, one-person households, and an aging population had higher levels of suicide-related calls for service. Immigrant concentration and economic status did not make a relevant contribution to the model. These results could help to develop better-targeted community-level suicide prevention strategies.

A Bayesian latent process spatiotemporal regression model for areal count data

Model-based approaches for the analysis of areal count data are commonplace in spatiotemporal analysis. In Bayesian hierarchical models, a latent process is incorporated in the mean function to account for dependence in space and time. Typically, the latent process is modelled using a conditional autoregressive (CAR) prior. The aim of this paper is to offer an alternative approach to CAR-based priors for modelling the latent process. The proposed approach is based on a spatiotemporal generalization of a latent process Poisson regression model developed in a time series setting. Spatiotemporal dependence in the autoregressive model for the latent process is modelled through its transition matrix, with a structured covariance matrix specified for its error term. The proposed model and its parameterizations are fitted in a Bayesian framework implemented via MCMC techniques. Our findings based on real-life examples show that the proposed approach is at least as effective as CAR-based models.

Smoothed Temporal Atlases of Age-Gender All-Cause Mortality in South Africa

Most mortality maps in South Africa and most contried of the sub-Saharan region are static, showing aggregated count data over years or at specific years. Lack of space and temporral dynamanics in these maps may adversely impact on their use and application for vigorous public health policy decisions and interventions. This study aims at describing and modeling sub-national distributions of age-gender specific all-cause mortality and their temporal evolutions from 1997 to 2013 in South Africa. Mortality information that included year, age, gender, and municipality administrative division were obtained from Statistics South Africa for the period. Individual mortality level data were grouped by three ages groups (0-14, 15-64, and 65 and over) and gender (male, female) and aggregated at each of the 234 municipalities in the country. The six age-gender all-cause mortality rates may be related due to shared common social deprivation, health and demographic risk factors. We undertake a joint analysis of the spatial-temporal variation of the six age-gender mortality risks. This is done within a shared component spatial model construction where age-gender common and specific spatial and temporal trends are estiamted using a hierarchical Bayesian spatial model. The results show municipal and temporal differentials in mortality risk profiles between age and gender groupings. High rates were seen in 2005, especially for the 15-64 years age group for both males and females. The dynamic geographical and time distributions of subnational age-gender all-cause mortality contribute to a better understanding of the temporal evolvement and geographical variations in the relationship between demographic composition and burden of diseases in South Africa. This provides useful information for effective monitoring and evaluation of public health policies and programmes targeting mortality reduction across time and sub-populations in the country.

Spatio-Temporal Analysis of Suicide-Related Emergency Calls

Considerable effort has been devoted to incorporate temporal trends in disease mapping. In this line, this work describes the importance of including the effect of the seasonality in a particular setting related with suicides. In particular, the number of suicide-related emergency calls is modeled by means of an autoregressive approach to spatio-temporal disease mapping that allows for incorporating the possible interaction between both temporal and spatial effects. Results show the importance of including seasonality effect, as there are differences between the number of suicide-related emergency calls between the four seasons of each year.