Spatial epidemiology: current approaches and future challenges

Residential Proximity to Oil and Gas Development and Mental Health in a North American Preconception Cohort Study: 2013-2023

Objectives. To evaluate associations between oil and gas development (OGD) and mental health using cross-sectional data from a preconception cohort study, Pregnancy Study Online. Methods. We analyzed baseline data from a prospective cohort of US and Canadian women aged 21 to 45 years who were attempting conception without fertility treatment (2013-2023). We developed residential proximity measures for active OGD during preconception, including distance from nearest site. At baseline, participants completed validated scales for perceived stress (10-item Perceived Stress Scale, PSS) and depressive symptoms (Major Depression Inventory, MDI) and reported psychotropic medication use. We used log-binomial regression and restricted cubic splines to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs). Results. Among 5725 participants across 37 states and provinces, residence at 2 km versus 20 to 50 km of active OGD was associated with moderate to high perceived stress (PSS ≥ 20 vs < 20: PR = 1.08; 95% CI = 0.98, 1.18), moderate to severe depressive symptoms (MDI ≥ 20 vs < 20: PR = 1.27; 95% CI = 1.11, 1.45), and psychotropic medication use (PR = 1.11; 95% CI = 0.97, 1.28). Conclusions. Among North American pregnancy planners, closer proximity to OGD was associated with adverse preconception mental health symptomatology. (Am J Public Health. Published online ahead of print July 11, 2024:e1-e12. https://doi.org/10.2105/AJPH.2024.307730).

Greenness, air pollution, and temperature exposure effects in predicting premature mortality and morbidity: A small-area study using spatial random forest model

BACKGROUND

Although studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework.

METHODS

Years of potential life lost reflecting premature mortality and comparative illness and disability ratio reflecting chronic morbidity from 1673 small areas covering Greater Manchester for the year 2008-2013 obtained. Average annual levels of NO2 concentration, normalized difference vegetation index (NDVI) representing greenness, and annual average air temperature were utilized to assess exposure in each area. These exposures were linked to health outcomes using non-spatial and spatial random forest (RF) models while accounting for spatial autocorrelation.

RESULTS

Spatial-RF models provided the best predictive accuracy when accounted for spatial autocorrelation. Among the exposures considered, air pollution emerged as the most influential in predicting mortality and morbidity, followed by NDVI and temperature exposure. Nonlinear exposure-response relations were observed, and interactions between exposures illustrated specific ranges or sweet and sour spots of exposure thresholds where combined effects either exacerbate or moderate health conditions.

CONCLUSION

Air pollution exposure had a greater negative impact on health compared to greenness and temperature exposure. Combined exposure effects may indicate the highest influence of premature mortality and morbidity burden.

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

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

Using a general practice research database to assess the spatio-temporal COVID-19 risk

BACKGROUND

In Flanders, general practitioners (GPs) were among the first ones to collect data regarding COVID-19 cases. Intego is a GPs' morbidity registry in primary care with data collected from the electronic medical records from a sample of general practices. The Intego database contain elaborate information regarding patient characteristics, such as comorbidities. At the national level, the Belgian Public Health Institute (Sciensano) recorded all test-confirmed COVID-19 cases, but without other patient characteristics.

METHODS

Spatio and spatio-temporal analyses were used to analyse the spread of COVID-19 incidence at two levels of spatial aggregation: the municipality and the health sector levels. Our study goal was to compare spatio-temporal modelling results based on the Intego and Sciensano data, in order to see whether the Intego database is capable of detecting epidemiological trends similar to those in the Sciensano data. Comparable results would allow researchers to use these Intego data, and their wealth of patient information, to model COVID-19-related processes.

RESULTS

The two data sources provided comparable results. Being a male decreased the odds of having COVID-19 disease. The odds for the age categories (17,35], (35,65] and (65,110] of being a confirmed COVID-19 case were significantly higher than the odds for the age category [0,17]. In the Intego data, having one of the following comorbidities, i.e., chronic kidney disease, heart and vascular disease, and diabetes, was significantly associated with being a COVID-19 case, increasing the odds of being diagnosed with COVID-19.

CONCLUSION

We were able to show how an alternative data source, the Intego data, can be used in a pandemic situation. We consider our findings useful for public health officials who plan intervention strategies aimed at monitor and control disease outbreaks such as that of COVID-19.

Secular trends and regional pattern in body height of Austrian conscripts born between 1961 and 2002

The human growth process is influenced not only by genetic factors but also by environmental factors. Therefore, regional differences in mean body heights may exist within a population or a state. In the present study, we described and evaluated the regional trends in mean body heights in the nine Austrian provinces over a period spanning more than four decades. Body height data of 1734569 male conscripts born in Austria with Austrian citizenship between 1961 and 2002 were anonymized and analyzed. From 1961 to 2002 birth cohorts, an overall increase in the mean body height of Austrian recruits was observed, although regional differences were evident. Regions with shorter body heights in the 1961-1963 birth cohorts showed a particularly pronounced increase in mean body heights. Meanwhile, the course of body height growth in the capital city, Vienna, was striking, where the highest body heights were documented for the 1961-1963 birth cohorts. In Vienna, mean body heights continued to decline until the 1984 birth cohort and increased again from the 1988 birth cohorts. In addition to economic factors, increased stress factors in an urban environment and a form of urban penalty are discussed as causes.

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

Applications and Best Practices for Geospatial Analysis Research in Dermatology

Dermatologic diseases often exhibit distinct geographic patterns, underscoring the significant role of regional environmental, genetic, and sociocultural factors in driving their prevalence and manifestations. Geographic information and geospatial analysis enable researchers to investigate the spatial distribution of adverse health outcomes and their relationship with socioeconomic and environmental risk factors that are inherently geographic. Health geographers and spatial epidemiologists have developed numerous geospatial analytical tools to collect, process, visualize, and analyze geographic data. These tools help provide vital spatial context to the comprehension of the underlying dynamics behind health outcomes. By identifying areas with high rates of dermatologic disease and areas with barriers to access to quality dermatologic care, findings from studies utilizing geospatial analysis can inform the design and targeting of policy and intervention to help improve dermatologic healthcare outcomes and promote health equity. This article emphasizes the significance of geospatial data and analysis in dermatology research. We explore the common processes in data acquisition, harmonization, and geospatial analytics while conducting spatially and dermatologically relevant research. The article also highlights the practical application of geospatial analysis through instances drawn from the dermatology literature.

Spatial Survival Model for COVID-19 in México

A spatial survival analysis was performed to identify some of the factors that influence the survival of patients with COVID-19 in the states of Guerrero, México, and Chihuahua. The data that we analyzed correspond to the period from 28 February 2020 to 24 November 2021. A Cox proportional hazards frailty model and a Cox proportional hazards model were fitted. For both models, the estimation of the parameters was carried out using the Bayesian approach. According to the DIC, WAIC, and LPML criteria, the spatial model was better. The analysis showed that the spatial effect influences the survival times of patients with COVID-19. The spatial survival analysis also revealed that age, gender, and the presence of comorbidities, which vary between states, and the development of pneumonia increase the risk of death from COVID-19.

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 high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy

Purpose

Despite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development.

Methods

We performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters.

Results

The most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters.

Conclusions

Results provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.

Epidemiology and geographic clustering of Erdheim-Chester disease in Italy and France

This geoepidemiological study, performed in Italy and France, shows that Erdheim-Chester disease is increasingly diagnosed and cases cluster in specific geographic areas, namely southern Italy and central France. Disease frequency inversely correlates with the Human Development Index.

Mapping spatial morbidity patterns for bronchiolitis related to socioeconomic estimators: A spatial epidemiology approach to identify health disparities in Puerto Madryn, Argentina

OBJECTIVES

To describe the frequency of hospitalizations of infants under 1 year of age with bronchiolitis in Puerto Madryn, Argentina, and to study the spatial distribution of cases throughout the city in relation to socioeconomic indicators. To visualize and better understand the underlying processes behind the local manifestation of the disease by creating a vulnerability map of the city.

METHODS

We performed a cross-sectional study of all patients discharged for bronchiolitis from the local public Hospital in 2017, considering length of hospital stay, readmission rate, patient age, home address and socioeconomic indicators (household overcrowding). To understand the local spatial distribution of the disease and its relationship to overcrowding, we used GIS and Moran's global and local spatial autocorrelation indices.

RESULTS

The spatial distribution of bronchiolitis cases was not random, but significantly aggregated. Of the 120 hospitalized children, 100 infants (83.33%) live in areas identified as having at least one unsatisfied basic need (UBN). We found a positive and statistically significant relationship between frequency of cases and percentage of overcrowded housing by census radius.

CONCLUSIONS

A clear association was found between bronchiolitis and neighborhoods with UBNs, and overcrowding is likely to be a particularly important explanatory factor in this association. By combining GIS tools, spatial statistics, geo-referenced epidemiological data, and population-level information, vulnerability maps can be created to facilitate visualization of priority areas for development and implementation of more effective health interventions. Incorporating the spatial and syndemic perspective into health studies makes important contributions to the understanding of local health-disease processes.

Lessons learned from identifying clusters of severe acute respiratory infections with influenza sentinel surveillance, Bangladesh, 2009-2020

Background

We explored whether hospital-based surveillance is useful in detecting severe acute respiratory infection (SARI) clusters and how often these events result in outbreak investigation and community mitigation.

Methods

During May 2009-December 2020, physicians at 14 sentinel hospitals prospectively identified SARI clusters (i.e., ≥2 SARI cases who developed symptoms ≤10 days of each other and lived <30 min walk or <3 km from each other). Oropharyngeal and nasopharyngeal swabs were tested for influenza and other respiratory viruses by real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We describe the demographic of persons within clusters, laboratory results, and outbreak investigations.

Results

Field staff identified 464 clusters comprising 1427 SARI cases (range 0-13 clusters per month). Sixty percent of clusters had three, 23% had two, and 17% had ≥4 cases. Their median age was 2 years (inter-quartile range [IQR] 0.4-25) and 63% were male. Laboratory results were available for the 464 clusters with a median of 9 days (IQR = 6-13 days) after cluster identification. Less than one in five clusters had cases that tested positive for the same virus: respiratory syncytial virus (RSV) in 58 (13%), influenza viruses in 24 (5%), human metapneumovirus (HMPV) in five (1%), human parainfluenza virus (HPIV) in three (0.6%), adenovirus in two (0.4%). While 102/464 (22%) had poultry exposure, none tested positive for influenza A (H5N1) or A (H7N9). None of the 464 clusters led to field deployments for outbreak response.

Conclusions

For 11 years, none of the hundreds of identified clusters led to an emergency response. The value of this event-based surveillance might be improved by seeking larger clusters, with stronger epidemiologic ties or decedents.

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach

Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R 2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%-96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

The Nova Scotia Community Cancer Matrix: A geospatial tool to support cancer prevention

Globally, cancer is a leading cause of death and morbidity and its burden is increasing worldwide. It is established that medical approaches alone will not solve this cancer crisis. Moreover, while cancer treatment can be effective, it is costly and access to treatment and health care is vastly inequitable. However, almost 50% of cancers are caused by potentially avoidable risk factors and are thus preventable. Cancer prevention represents the most cost-effective, feasible and sustainable pathway towards global cancer control. While much is known about cancer risk factors, prevention programs often lack consideration of how place impacts cancer risk over time. Maximizing cancer prevention investment requires an understanding of the geographic context for why some people develop cancer while others do not. Data on how community and individual level risk factors interact is therefore required. The Nova Scotia Community Cancer Matrix (NS-Matrix) study was established in Nova Scotia (NS), a small province in Eastern Canada with a population of 1 million. The study integrates small-area profiles of cancer incidence with cancer risk factors and socioeconomic conditions, to inform locally relevant and equitable cancer prevention strategies. The NS-Matrix Study includes over 99,000 incident cancers diagnosed in NS between 2001 and 2017, georeferenced to small-area communities. In this analysis we used Bayesian inference to identify communities with high and low risk for lung and bladder cancer: two highly preventable cancers with rates in NS exceeding the Canadian average, and for which key risk factors are high. We report significant spatial heterogeneity in lung and bladder cancer risk. The identification of spatial disparities relating to a community's socioeconomic profile and other spatially varying factors, such as environmental exposures, can inform prevention. Adopting Bayesian spatial analysis methods and utilizing high quality cancer registry data provides a model to support geographically-focused cancer prevention efforts, tailored to local community needs.

Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Human-Biting Ixodes scapularis Submissions to a Crowd-Funded Tick Testing Program Correlate with the Incidence of Rare Tick-Borne Disease: A Seven-Year Retrospective Study of Anaplasmosis and Babesiosis in Massachusetts

Tick-borne zoonoses pose a serious burden to global public health. To understand the distribution and determinants of these diseases, the many entangled environment-vector-host interactions which influence risk must be considered. Previous studies have evaluated how passive tick testing surveillance measures connect with the incidence of human Lyme disease. The present study sought to extend this to babesiosis and anaplasmosis, two rare tick-borne diseases. Human cases reported to the Massachusetts Department of Health and submissions to TickReport tick testing services between 2015 and 2021 were retrospectively analyzed. Moderate-to-strong town-level correlations using Spearman's Rho (ρ) were established between Ixodes scapularis submissions (total, infected, adult, and nymphal) and human disease. Aggregated ρ values ranged from 0.708 to 0.830 for anaplasmosis and 0.552 to 0.684 for babesiosis. Point observations maintained similar patterns but were slightly weaker, with mild year-to-year variation. The seasonality of tick submissions and demographics of bite victims also correlated well with reported disease. Future studies should assess how this information may best complement human disease reporting and entomological surveys as proxies for Lyme disease incidence in intervention studies, and how it may be used to better understand the dynamics of human-tick encounters.

Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot-and-mouth disease dissemination

Introduction

Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed.

Methods

To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination?

Results

Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties-including data circularity-were demonstrated. The emergent patterns helped identify 11 counties as 'disseminators' or 'facilitators' (F) and 264 counties as 'barriers' (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur.

Discussion

Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.

Refining acute human exposure assessment to pesticides in surface water: An integrated data-driven modeling approach

The substantial spatial and temporal variability of pesticides has led to large uncertainties when determining their peak aqueous concentrations. There is however a lack of large-scale studies dealing with accurate determination of annual maximum daily concentration (AMDC) across the landscape and over time based on the publicly available monitoring data. We developed a novel data-driven approach that firstly used time series modeling to generate AMDCs for qualified water monitoring sites in the conterminous U.S. With feature variables such as pesticide use and land cover compiled into the dataset, machine learning models using eXtreme Gradient Boosting (XGBoost) and Random Forest Regressor (RF) were then developed to estimate AMDCs in surface waters across the U.S. Both models exhibited significant predictability, while a hybrid model consisting of the average predictions by XGBoost and RF model had the highest prediction accuracy (mean absolute error (MAE): 1.23; R²: 0.61). The analysis of permutation variable importance indicated that pesticide use and drainage area were the two most important drivers. Partial dependence analysis revealed that pesticide use, precipitation, cultivated crop land cover and solubility exhibited concentration-promoting effects, whereas drainage area and molecular weight had concentration-demoting effects. Soil adsorption coefficient (Koc) showed nonmonotonic effects. The hybrid model was used to predict and map AMDCs of four example pesticides, including 2,4-dichlorophenoxyacetic acid (2,4-D), atrazine, glyphosate and imidacloprid during 2016-2019 at national scale. The predictive capability was validated using independent monitoring datasets. The fully evaluated approach significantly reduced the uncertainties in modeling annual peak concentrations and served as a valuable solution for conducting geographically oriented, highly refined exposure assessments for pesticides.

The Association between Spatial-Temporal Distribution of Prostate Cancer and Environmental Factors in Mainland China

BACKGROUND

In China, the incidence and mortality of prostate cancer are increasing. In this study, we analyzed the spatial-temporal distribution characteristics of prostate cancer incidence and mortality in China and explored the potential associations of socioeconomic, ecological, and meteorologic conditions.

METHODS

Spatial-temporal scan statistics were used to analyze the spatial-temporal patterns of prostate cancer in China from 2012 to 2016. Spatial regression models and the Geodetector method were used to explore the potential associations of anthropogenic and natural factors with prostate cancer.

RESULTS

The incidence and mortality of prostate cancer in China from 2012 to 2016 rapidly increased. The high incidence and mortality clusters were concentrated in the economically developed Yangtze River Delta region along the southeast coast. Among the 14 selected environmental factors, gross domestic product (GDP) per capita, population density, comprehensive index of environmental pollution discharge, accessibility of health care resources, urbanization rate, and nitrogen dioxide (NO2) had significant positive correlations with prostate cancer incidence and mortality. GDP per capita, urbanization rate, and population density had high explanatory power.

CONCLUSIONS

The high-concentration areas for prostate cancer are located in more economically developed cities. The index of environmental pollution discharge, NO2, and prostate cancer incidence and mortality were positively correlated. The government should advocate increasing the use of clean energy while strengthening the regulation of industrial production to reduce pollutant emissions.

IMPACT

To inform the development of prevention and control strategies for prostate cancer in China.

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R² of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO₂, PM₁₀, and O₃); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

Spatial Aspects of Health-Developing a Conceptual Framework

Numerous studies and models address the determinants of health. However, in existing models, the spatial aspects of the determinants are not or only marginally taken into account and a theoretical discussion of the association between space and the determinants of health is missing. The aim of this paper is to generate a framework that can be used to place the determinants of health in a spatial context. A screening of the current first serves to identify the relevant determinants and describes the current state of knowledge. In addition, spatial scales that are important for the spatial consideration of health were developed and discussed. Based on these two steps, the conceptual framework on the spatial determinants of health was derived and subsequently discussed. The results show a variety of determinants that are associated with health from a spatial point of view. The overarching categories are global driving forces, policy and governance, living and physical environment, socio-demographic and economic conditions, healthcare services and cultural and working conditions. Three spatial scales (macro, meso and micro) are further subdivided into six levels, such as global (e.g., continents), regional (e.g., council areas) or neighbourhood (e.g., communities). The combination of the determinants and spatial scales are presented within a conceptual framework as a result of this work. Operating mechanisms and pathways between the spatial levels were added schematically. This is the first conceptual framework that links the determinants of health with the spatial perspective. It can form the working basis for future analyses in which spatial aspects of health are taken into account.

Spatio-temporal analysis of the COVID-19 pandemic in Iran

Globally, the COVID-19 pandemic is a top-level public health concern. This paper is an attempt to identify and COVID-19 pandemic in Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases, deaths, recoveries, number of hospitals, hospital beds and population from March 2, 2019 to the end of November 2021 in 31 provinces of Iran from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS10.3 were utilized to analyze and evaluate COVID-19, including Geographic Weight Regression (GWR), Getis-OrdGi* (G-i-star) statistics (hot and cold spot), and Moran autocorrelation spatial analysis. Moran statistics, based on the GWR model, demonstrated that deaths and recoveries followed a clustering pattern for the confirmed cases index during the study period. The Moran Z-score for all three indicators confirmed cases, deaths, and recoveries, which was greater than 2.5 (95% confidence level). The Getis-OrdGi* (G-I-Star) (hot and cold spot) data revealed a wide range of levels for six variables (confirmed cases, deaths, recoveries, population, hospital beds, and hospital) across Iran's provinces. The overall number of deaths exceeded the population and the number of hospitals in the central and southern regions, including the provinces of Qom, Alborz, Tehran, Markazi, Isfahan, Razavi Khorasan, East Azerbaijan, Fars, and Yazd, which had the largest number and The Z-score for the deaths Index is greater than 14.314. The results of this research can pave the way for future studies.

Geographic information science and the United States opioid overdose crisis: A scoping review of methods, scales, and application areas

BACKGROUND

The Opioid Overdose Crisis (OOC) continues to generate morbidity and mortality in the United States, outpacing other prominent accident-related reasons. Multiple disciplines have applied geographic information science (GIScience) to understand geographical patterns in opioid-related health measures. However, there are limited reviews that assess how GIScience has been used.

OBJECTIVES

This scoping review investigates how GIScience has been used to conduct research on the OOC. Specific sub-objectives involve identifying bibliometric trends, the location and scale of studies, the frequency of use of various GIScience methodologies, and what direction future research can take to address existing gaps.

METHODS

The review was pre-registered with the Open Science Framework ((https://osf.io/h3mfx/) and followed the PRISMA-ScR guidelines. Scholarly research was gathered from the Web of Science Core Collection, PubMed, IEEE Xplore, ACM Digital Library. Inclusion criteria was defined as having a publication date between January 1999 and August 2021, using GIScience as a central part of the research, and investigating an opioid-related health measure.

RESULTS

231 studies met the inclusion criteria. Most studies were published from 2017 onward. While many (41.6%) of studies were conducted using nationwide data, the majority (58.4%) occurred at the sub-national level. California, New York, Ohio, and Appalachia were most frequently studied, while the Midwest, north Rocky Mountains, Alaska, and Hawaii lacked studies. The most common GIScience methodology used was descriptive mapping, and county-level data was the most common unit of analysis across methodologies.

CONCLUSIONS

Future research of GIScience on the OOC can address gaps by developing use cases for machine learning, conducting analyses at the sub-county level, and applying GIScience to questions involving illicit fentanyl. Research using GIScience is expected to continue to increase, and multidisciplinary research efforts amongst GIScientists, epidemiologists, and other medical professionals can improve the rigor of research.

An integrated system for the management of environmental data to support veterinary epidemiology

Environmental and climatic fluctuations can greatly influence the dynamics of infectious diseases of veterinary concern, or interfere with the implementation of relevant control measures. Including environmental and climatic aspects in epidemiological studies could provide policy makers with new insights to assign resources for measures to prevent or limit the spread of animal diseases, particularly those with zoonotic potential. The ever-increasing number of technologies and tools permits acquiring environmental data from various sources, including ground-based sensors and Satellite Earth Observation (SEO). However, the high heterogeneity of these datasets often requires at least some basic GIS (Geographic Information Systems) and/or coding skills to use them in further analysis. Therefore, the high availability of data does not always correspond to widespread use for research purposes. The development of an integrated data pre-processing system makes it possible to obtain information that could be easily and directly used in subsequent epidemiological analyses, supporting both research activities and the management of disease outbreaks. Indeed, such an approach allows for the reduction of the time spent on searching, downloading, processing and validating environmental data, thereby optimizing available resources and reducing any possible errors directly related to data collection. Although multitudes of free services that allow obtaining SEO data exist nowadays (either raw or pre-processed through a specific coding language), the availability and quality of information can be sub-optimal when dealing with very small scale and local data. In fact, some information sets (e.g., air temperature, rainfall), usually derived from ground-based sensors (e.g., agro-meteo station), are managed, processed and redistributed by agencies operating on a local scale which are often not directly accessible by the most common free SEO services (e.g., Google Earth Engine). The EVE (Environmental data for Veterinary Epidemiology) system has been developed to acquire, pre-process and archive a set of environmental information at various scales, in order to facilitate and speed up access by epidemiologists, researchers and decision-makers, also accounting for the integration of SEO information with locally sensed data.

Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties

COVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natural choice to model case counts, this approach fails to account for correlation due to spatial locations. Being a contagious disease and often spreading through community infections, the number of COVID-19 cases are inevitably spatially correlated as locations neighboring counties with a high COVID-19 case count are more likely to have a high case count. In this analysis, we combine generalized estimating equations (GEEs) for Poisson regression, a popular method for analyzing correlated data, with a semivariogram to model daily COVID-19 case counts in 67 Pennsylvania counties between March 20, 2020 to January 23, 2021 in order to study infection dynamics during the beginning of the pandemic. We use a semivariogram that describes the spatial correlation as a function of the distance between two counties as the working correlation. We further incorporate a zero-inflated model in our spatial GEE to accommodate excess zeros in reported cases due to logistical challenges associated with disease monitoring. By modeling time-varying holiday covariates, we estimated the effect of holiday timing on case count. Our analysis showed that the incidence rate ratio was significantly greater than one, 6-8 days after a holiday suggesting a surge in COVID-19 cases approximately one week after a holiday.

Drawings as tools to (re)imagine space in interdisciplinary global health research

Understanding the role of space in infectious diseases' dynamics in urban contexts is key to developing effective mitigation strategies. Urbanism, a discipline that both studies and acts upon the city, commonly uses drawings to analyze spatial patterns and their variables. This paper revisits drawings as analytical and integrative tools for interdisciplinary research. We introduce the use of drawings in two interdisciplinary projects conducted in the field of global public health: first, a study about the heterogeneous burden of tuberculosis and COVID-19 in Lima, Peru, and second, a study about urban malaria in Jimma, Ethiopia. In both cases, drawings such as maps, plans, and sections were used to analyze spatial factors present in the urban context at different scales: from the scale of the territory, the city, and the district, to the neighborhood and the household. We discuss the methodological approaches taken in both cases, considering the nature of the diseases being investigated as well as the natural and social context in which the studies took place. We contend that the use of drawings helps to reimagine space in public health research by adding a multidimensional perspective to spatial variables and contexts. The processes and products of drawing can help to (a) identify systemic relations within the spatial context, (b) facilitate integration of quantitative and qualitative data, and (c) guide the formulation of policy recommendations, informing public and urban health planning.

Colombian ocular inflammatory diseases epidemiology study (COIDES): prevalence, incidence and sociodemographic characterisation of Scleritis in Colombia, 2015–2020

Objective

To describe the epidemiological and demographic characteristics of scleritis in Colombia.

Methods and analysis

Population-based study using the national database from the Colombian Ministry of Health, using the International Classification of Diseases-10 code for Scleritis (H150) to estimate the prevalence and incidence from 2015 to 2019. Additionally, we evaluated the impact of the COVID-19 pandemic lockdown on the epidemiology of the disease during 2020, using the Gaussian Random Markov Field model (conditional autoregressive; CAR model). Finally, a standardised morbidity rate map was made to assess the geographic distribution of scleritis in the country.

Results

The 5-year average prevalence and incidence of scleritis in Colombia were 0.6 (95% CI 0.59 to 0.6) and 0.65 (95% CI 0.64 to 0.64) cases per 100 000 inhabitants, respectively. We found 1429 registers of scleritis throughout the country between 2015 and 2019. Women represented 64.3%. The age groups with most cases were between 40 and 69 years in both sexes. However, women between 30 and 39 years and men between 20 and 29 years presented the highest number of new cases. In 2020, the pandemic reduced approximately 0.23 points the incidence of scleritis. Bogotá, Valle del Cauca and Antioquia had most of the cases, the latter two with an increased risk over time.

Conclusion

Colombia has a lower incidence of scleritis than the reported in other latitudes, with a pattern of presentation at younger ages. Furthermore, the lockdown derived from the CODIV-19 pandemic affected the follow-up and diagnosis of patients with scleritis. This is the first epidemiological description of scleritis in a developing country and South America.

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.

Spatio-temporal clustering of amyotrophic lateral sclerosis in France: A population-based study

OBJECTIVE

To assess spatial aggregates of amyotrophic lateral sclerosis (ALS) incident cases, using a solid geo-epidemiological statistical method, in France.

METHODS

This population-based study (2003-2011) investigated 47.1 million person-years of follow-up (PYFU). Case ascertainment of incident ALS cases was based on multiple sources (ALS referral centers, hospital centres and health insurance data). Neurologists confirmed all ALS diagnoses. Exhaustiveness was estimated through capture-recapture. Aggregates were investigated in four steps: (a) geographical modelling (standardized incidence ratio (SIR) calculation), (b) analysis of the spatial distribution of incidence (Phothoff-Winttinghill's test, Global Moran's Index, Kulldorf's spatial scan statistic, Local Moran's Index), (c) classification of the level of certainty of spatial aggregates (i.e. definite cluster; probable over-incidence area; possible over-incidence area) and (d) evaluation of the robustness of the results.

RESULTS

The standardized incidence of ALS was 2.46/100,000 PYFU (95% CI 2.31-2.63, European population as reference) based on 1199 incident cases. We identified 13 areas of spatial aggregates: one cluster (stable in robustness analysis), five probable over-incidence areas (2 stable in robustness analysis) and seven possible over-incidence areas (including 4 stable areas in robustness analysis). A cluster was identified in the Rhône-Alpes region: 100 observed vs 54.07 expected cases for 2,411,514 PYFU, SIR: 1.85 (95% CI 1.50-2.25).

CONCLUSION

We report here one of the largest investigations of incidence and spatial aggregation of ALS ever performed in a western country. Using a solid methodology framework for case ascertainment and cluster analysis, we identified 13 areas that warrant further investigation.

Progress on application of spatial epidemiology in ophthalmology

Most ocular diseases observed with cataract, chlamydia trachomatis, diabetic retinopathy, and uveitis, have their associations with environmental exposures, lifestyle, and habits, making their distribution has certain temporal and spatial features based essentially on epidemiology. Spatial epidemiology focuses on the use of geographic information systems (GIS), global navigation satellite systems (GNSS), and spatial analysis to map spatial distribution as well as change the tendency of diseases and investigate the health services status of populations. Recently, the spatial epidemic approach has been applied in the field of ophthalmology, which provides many valuable key messages on ocular disease prevention and control. This work briefly reviewed the context of spatial epidemiology and summarized its progress in the analysis of spatiotemporal distribution, non-monitoring area data estimation, influencing factors of ocular diseases, and allocation and utilization of eye health resources, to provide references for its application in the prevention and control of ocular diseases in the future.

Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.

Geospatial analysis of cesarean section in Iran (2016-2020): exploring clustered patterns and measuring spatial interactions of available health services

BACKGROUND

The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services.

METHODS

This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space-time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services.

RESULTS

The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area.

CONCLUSIONS

CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity.

Epidemiology for public health practice: The application of spatial epidemiology

Spatial epidemiology is the description and analysis of geographic patterns and variations in disease risk factors, morbidity and mortality with respect to their distributions associated with demographic, socioeconomic, environmental, health behavior, and genetic risk factors, and time-varying changes. In the Letter to Editor, we had a brief description of the practice for the mortality and the space-time patterns of John Snow's map of cholera epidemic in London, United Kingdom in 1854. This map is one of the earliest public heath practices of developing and applying spatial epidemiology. In the early history, spatial epidemiology was predominantly applied in infectious disease and risk factor studies. However, since the recent decades, noncommunicable diseases have become the leading cause of death in both developing and developed countries, spatial epidemiology has been used in the study of noncommunicable disease. In the Letter, we addressed two examples that applied spatial epidemiology to cluster and identify stroke belt and diabetes belt across the states and counties in the United States. Similar to any other epidemiological study design and analysis approaches, spatial epidemiology has its limitations. We should keep in mind when applying spatial epidemiology in research and in public health practice.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Clustering and mapping the first COVID-19 outbreak in France

Background

With more than 160 000 confirmed COVID-19 cases and about 30 000 deceased people at the end of June 2020, France was one of the countries most affected by the coronavirus crisis worldwide. We aim to assess the efficiency of global lockdown policy in limiting spatial contamination through an in-depth reanalysis of spatial statistics in France during the first lockdown and immediate post-lockdown phases.

Methods

To reach that goal, we use an integrated approach at the crossroads of geography, spatial epidemiology, and public health science. To eliminate any ambiguity relevant to the scope of the study, attention focused at first on data quality assessment. The data used originate from official databases (Santé Publique France) and the analysis is performed at a departmental level. We then developed spatial autocorrelation analysis, thematic mapping, hot spot analysis, and multivariate clustering.

Results

We observe the extreme heterogeneity of local situations and demonstrate that clustering and intensity are decorrelated indicators. Thematic mapping allows us to identify five “ghost” clusters, whereas hot spot analysis detects two positive and two negative clusters. Our re-evaluation also highlights that spatial dissemination follows a twofold logic, zonal contiguity and linear development, thus determining a “metastatic” propagation pattern.

Conclusions

One of the most problematic issues about COVID-19 management by the authorities is the limited capacity to identify hot spots. Clustering of epidemic events is often biased because of inappropriate data quality assessment and algorithms eliminating statistical-spatial outliers. Enhanced detection techniques allow for a better identification of hot and cold spots, which may lead to more effective political decisions during epidemic outbreaks. 

Graphical Abstract

Artificial intelligence against the first wave of COVID-19: evidence from China

BACKGROUND

The COVID-19 pandemic unexpectedly broke out at the end of 2019. Due to the highly contagious, widespread, and risky nature of this disease, the pandemic prevention and control has been a tremendous challenge worldwide. One potentially powerful tool against the COVID-19 pandemic is artificial intelligence (AI). This study systematically assessed the effectiveness of AI in infection prevention and control during the first wave of COVID-19 in China.  METHODS: To better evaluate the role of AI in a pandemic emergency, we focused on the first-wave COVID-19 in the period from the early December 2019 to the end of April 2020 across 304 cities in China. We employed three sets of dependent variables to capture various dimensions of the effect of AI: (1) the time to the peak of cumulative confirmed cases, (2) the case fatality rate and whether there were severe cases, and (3) the number of local policies for work and production resumption and the time span to having the first such policy. The main explanatory variable was the local AI development measured by the number of AI patents. To fit the features of different dependent variables, we employed a variety of estimation methods, including the OLS, Tobit, Probit, and Poisson estimations. We included a large set of control variables and added interaction terms to test the mechanisms through which AI took an effect.

RESULTS

Our results showed that AI had highly significant effects on (1) screening and detecting the disease, and (2) monitoring and evaluating the epidemic evolution. Specifically, AI was useful to screen and detect the COVID-19 in cities with high cross-city mobility. Also, AI played an important role for production resumption in cities with high risk to reopen. However, there was limited evidence supporting the effectiveness of AI in the diagnosis and treatment of the disease.

CONCLUSIONS

These results suggested that AI can play an important role against the pandemic.

Association Between Neighborhood Disadvantage and Fertility Among Pregnancy Planners in the US

IMPORTANCE

Decades of inequitable policies in the US have yielded disparities in neighborhood quality, and some studies show that living in a socioeconomically disadvantaged neighborhood is associated with worse health outcomes, including reproductive health outcomes. However, no US studies to date have directly examined the association between residence in disadvantaged neighborhoods and fertility.

OBJECTIVE

To examine the association between residence in disadvantaged neighborhoods and fecundability, a sensitive marker of fertility with many health implications.

DESIGN, SETTING, AND PARTICIPANTS

This prospective preconception cohort study used the Pregnancy Study Online, for which baseline data were collected from June 19, 2013, through April 12, 2019. The study included 6356 participants who identified as female, were 21 to 45 years of age, were attempting conception without fertility treatment, and provided a valid residential address in the contiguous US at enrollment.

EXPOSURES

A standardized area deprivation index (ADI) derived at the census block group level applied to each residential address.

MAIN OUTCOMES AND MEASURES

Fecundability, the per-cycle probability of conception, via questionnaires that were completed every 8 weeks for 12 months, until conception or a censoring event. Proportional probabilities models were used to estimate fecundability ratios and 95% CIs for associations between ADI and fecundability. Restricted cubic splines were also implemented to examine nonlinearity. Models were adjusted for demographic characteristics and factors associated with fertility. The study's a priori hypothesis was that higher levels of neighborhood disadvantage would be associated with decreased fecundability.

RESULTS

Among 6356 participants, 3725 pregnancies were observed for 27 427 menstrual cycles of follow-up. The mean (SD) baseline age was 30.0 (4.1) years, and most participants were non-Hispanic White (5297 [83.3%]) and nulliparous (4179 [65.7%]). Comparing the top and bottom deciles of disadvantaged neighborhood status, adjusted fecundability ratios were 0.79 (95% CI, 0.66-0.96) for national-level ADI rankings and 0.77 (95% CI, 0.65-0.92) for within-state ADI rankings. Restricted cubic splines showed some evidence of nonlinearity in the association. Associations were slightly stronger among participants with lower annual incomes (<$50 000).

CONCLUSIONS AND RELEVANCE

In this cohort study, residence in a socioeconomically disadvantaged neighborhood was associated with moderately decreased fecundability. If confirmed in other studies, these results suggest that investments to reduce disadvantaged neighborhood status may yield positive cobenefits for fertility.

Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China

COVID-19, the disease caused by the novel coronavirus 2019, has caused grave woes across the globe since it was first reported in the epicentre of Wuhan, Hubei, China, in December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that rendered more than 900 million people housebound for more than two months since the lockdown of Wuhan, and elsewhere, on 23 January 2020. Here, we assess the impact of China’s massive lockdowns and travel restrictions reflected by the changes in mobility patterns across and within provinces, before and during the lockdown period. We calibrate movement flow between provinces with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicentre Hubei. Moreover, we show that synchronous lockdowns and consequent reduced mobility lag a certain time to elicit an actual impact on suppressing the spread. Such highly coordinated nationwide lockdowns, applied via a top-down approach along with high levels of compliance from the bottom up, are central to mitigating and controlling early-stage outbreaks and averting a massive health crisis.

A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico

spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.

Distributive justice in environmental health hazards from industrial contamination: A systematic review of national and near-national assessments of social inequalities

Communities where polluting human activities are sited often show disadvantage in terms of social and economic variables. Environmental distributive justice studies seek to identify common characteristics in exposed populations and highlight the presence of environmental inequalities. We have conducted a review of the existing literature about justice in the distribution of health hazards from industrial pollution. We included papers investigating associations between social disadvantage and contamination through assessments at national or macro-area level. From each study we extracted: indicators for the social determinants of exposed communities (classified according to PROGRESS-plus categories); definition and measurement of environmental hazard (as either proximity to contamination sources, exposure to emissions or health impacts from pollutants); study design and methods; significant results. We retrieved 45 eligible articles. Most publications were from USA and had a nationwide scope with data at municipal/sub-municipal scale. Socioeconomic position and race/ethnicity were the social determinants most often explored, followed by occupation and education; air pollution was the commonest sort of contamination, while proximity prevailed as measurement of hazard. All papers found significant associations between social dimensions and health hazard from industrial contamination: the majority of associations supported an increased burden on vulnerable categories, especially ethnic minorities and unemployed - however, several relationships were found in the opposite direction or in both ways, particularly with wealth and education, reflecting a mixed reality where potential discrimination in siting decisions coexists with socioeconomic benefits for nearby communities due to industrial development. Assessments of environmental distributive justice are lacking in most countries and those that are conducted show vast methodological heterogeneity. We recommend consistency in models and indicators, the inclusion of female-led households among indicators of social disadvantage, and the adoption of a small scale to elicit significant findings and provide meaningful policy action.

Spatio-temporal mixture process estimation to detect dynamical changes in population

Population monitoring is a challenge in many areas such as public health and ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data changes. Assuming that mixture models can correctly model populations, we propose a new version of the Expectation-Maximization (EM) algorithm to better estimate the number of clusters and their parameters at the same time. This algorithm is compared to existing methods on several simulated datasets. We then combine the algorithm with a temporal statistical model, allowing for the detection of dynamical changes in population distributions, and call the result a spatio-temporal mixture process (STMP). We test STMPs on synthetic data, and consider several different behaviors of the distributions, to fit this process. Finally, we validate STMPs on a real data set of positive diagnosed patients to coronavirus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes.

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis

OBJECTIVE

Countries in sub-Saharan Africa suffer the highest rates of child mortality worldwide. Urban areas tend to have lower mortality than rural areas, but these comparisons likely mask large within-city inequalities. We aimed to estimate rates of under-five mortality (U5M) at the neighbourhood level for Ghana's Greater Accra Metropolitan Area (GAMA) and measure the extent of intraurban inequalities.

METHODS

We accessed data on >700 000 women aged 25-49 years living in GAMA using the most recent Ghana census (2010). We summarised counts of child births and deaths by five-year age group of women and neighbourhood (n=406) and applied indirect demographic methods to convert the summaries to yearly probabilities of death before age five years. We fitted a Bayesian spatiotemporal model to the neighbourhood U5M probabilities to obtain estimates for the year 2010 and examined their correlations with indicators of neighbourhood living and socioeconomic conditions.

RESULTS

U5M varied almost five-fold across neighbourhoods in GAMA in 2010, ranging from 28 (95% credible interval (CrI) 8 to 63) to 138 (95% CrI 111 to 167) deaths per 1000 live births. U5M was highest in neighbourhoods of the central urban core and industrial areas, with an average of 95 deaths per 1000 live births across these neighbourhoods. Peri-urban neighbourhoods performed better, on average, but rates varied more across neighbourhoods compared with neighbourhoods in the central urban areas. U5M was negatively correlated with multiple indicators of improved living and socioeconomic conditions among peri-urban neighbourhoods. Among urban neighbourhoods, correlations with these factors were weaker or, in some cases, reversed, including with median household consumption and women's schooling.

CONCLUSION

Reducing child mortality in high-burden urban neighbourhoods in GAMA, where a substantial portion of the urban population resides, should be prioritised as part of continued efforts to meet the Sustainable Development Goal national target of less than 25 deaths per 1000 live births.

Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China

The COVID-19, the disease caused by the novel coronavirus 2019 (SARS-CoV-2), has caused graving woes across the globe since first reported in the epicenter Wuhan, Hubei, China, December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that put more than 900 million people housebound for more than two months since the lockdown of Wuhan on 23 January 2020 when other provinces in China followed suit. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns before and during the lockdown period. We quantify the synchrony of mobility patterns across provinces and within provinces. Using these mobility data, we calibrate movement flow between provinces in combination with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicenter Hubei. As such, infections can propagate further into other interconnected places both near and far, thereby necessitating synchronous lockdowns. Moreover, our data-driven modeling analysis shows that lockdowns and consequently reduced mobility lag a certain time to elicit an actual impact on slowing down the spreading and ultimately putting the epidemic under check. In spite of the vastly heterogeneous demographics and epidemiological characteristics across China, mobility data shows that massive travel restrictions have been applied consistently via a top-down approach along with high levels of compliance from the bottom up.

Large local variations in the use of health services in rural southern Ethiopia: An ecological study

Ethiopia is behind schedule in assuring accessible, equitable and quality health services. Understanding the geographical variability of the health services and adjusting small-area level factors can help the decision-makers to prioritize interventions and allocate scarce resources. There is lack of information on the degree of variation of health service utilisation at micro-geographic area scale using robust statistical tools in Ethiopia. Therefore, the objective of this study was to assess the health service utilisation and identify factors that account for the variation in health service utilisation at kebele (the smallest administrative unit) level in the Dale and Wonsho districts of the Sidama region. An exploratory ecological study design was employed on the secondary patient data collected from 1 July 2017 to 30 June 2018 from 65 primary health care units of the fifty-four kebeles in Dale and Wonsho districts, in the Sidama region. ArcGIS software was used to visualise the distribution of health service utilisation. SaTScan analysis was performed to explore the unadjusted and covariate-adjusted spatial distribution of health service utilisation. Linear regression was applied to adjust the explanatory variables and control for confounding. A total of 67,678 patients in 54 kebeles were considered for spatial analysis. The distribution of the health service utilisation varied across the kebeles with a mean of 0.17 visits per person per year (Range: 0.01-1.19). Five kebeles with health centres had a higher utilisation rate than other rural kebeles without health centres. More than half (57.4%) of the kebeles were within a 10 km distance from health centres. The study found that distance to the health centre was associated with the low health care utilisation. Improving the accessibility of health services by upgrading the primary health care units could increase the service use.

Mapping and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA)

Background

Using geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus. This study aimed to determine the spatial analysis of COVID-19 in Qom Province, using the local indicators of spatial association (LISA).

Methods

In a primary descriptive-analytical study, all individuals infected with COVID-19 in Qom Province from February 19^th, 2020 to September 30^th, 2020 were identified and included in the study. The spatial distribution in urban areas was determined using the Moran coefficient in geographic information systems (GIS); in addition, the spatial autocorrelation of the coronavirus in different urban districts of the province was calculated using the LISA method.

Results

The prevalence of COVID-19 in Qom Province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID-19 in Qom was clustered. District 3 (Imam Khomeini St.) and District 6 (Imamzadeh Ebrahim St.) were set in the High-High category of LISA: a high-value area surrounded by high-value areas as the two foci of COVID-19 in Qom Province. District 1 (Bajak) of urban districts was set in the Low-High category: a low-value area surrounded by high values. This district is located in a low-value area surrounded by high values.

Conclusions

According to the results, district 3 (Imam Khomeini St.) and district 6 (Imamzadeh Ebrahim St.) areas are key areas for preventing and controlling interventional measures. In addition, considering the location of District 1 (Bajak) as an urban district in the Low-High category surrounded by high values, it seems that distance and spatial proximity play a major role in the spread of the disease.