Spatial epidemiology: current approaches and future challenges

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.

Regional Variation in Restorative Treatment Need among Finnish Young People

Aim

To evaluate the regional variation in restorative treatment need among Finnish young people based on the socioeconomic factors.

Materials and Methods

This cross-sectional study was conducted in 20 garrisons of the Finnish Defence Forces in January and July 2011. The study population comprised 13,819 Finnish conscripts born in the beginning of 1990s, including females. A computer-based survey was done together with clinical oral examination to gather background information, e.g., educational status. Furthermore, average annual income of the conscript's residence municipality was achieved from the Statistics of Finland. The zip code of the place of residence of each conscript was later extracted from the Mildoc^® system. Georeferenced place of residence and income status were merged as information on provinces' level in a dataset. The association between the outcome variable and explanatory variables was determined by using the generalized linear mixed model, and geomaps were constructed.

Results

Mean D value was 1.41 ranging from 0.89 (Kymenlaakso) to 2.33 (Kainuu). Higher education and high-income level were protective factors for restorative treatment need. Restorative treatment need was also low in those areas with high (OR: 0.70, 95% CI: 0.56–0.87) and medium (OR: 0.79, 95% CI: 0.70–0.89) yearly income compared to low yearly income. The high odds for the need of restorative treatment were discovered in Northern Ostrobothnia (OR: 2.26, 95% CI: 1.53–3.33) followed by Central Ostrobothnia (OR: 2.08, 95% CI: 1.17–3.70), Uusimaa (OR: 1.55, 95% CI: 1.16–2.08), and Central Finland (OR: 1.54, 95% CI: 1.10–2.16) compared to Varsinais-Suomi.

Conclusion

In conclusion, there is a significant regional variation in restorative treatment need among Finnish young people in their twenties based on the socioeconomic factors.

Spatial epidemiologic analysis of the liver cancer and gallbladder cancer incidence and its determinants in South Korea

BACKGROUND

There have been reports on regional variation in prevalence of hepatitis B and C, and Clonorchis sinensis (C. sinensis) infection, which indicates potential of spatial variation in liver cancer and gallbladder cancer incidence in Korea. Therefore, we aimed to assess the regional variation of liver and gallbladder cancer incidence and its determinants based on the regional distribution of risk factors, including hepatitis B infection in Korea.

METHODS

This study used an ecological study design and district-level cancer incidence statistics generated by the National Cancer Center. Spatial clusters of liver and gallbladder cancer incidence were detected based on spatial scan statistics using SaTScan™ software. We set the size of maximum spatial scanning window of 25 and 35% of the population at risk for analyses of liver and gallbladder cancer, respectively. Significance level of 0.05 was used to reject the null hypothesis of no cluster. We fitted the Besag-York-Mollie model using integrated nested Laplace approximations to assess factors that influence the regional variation in cancer incidence.

RESULTS

Spatial clusters with high liver cancer incidence rates were detected in the southwestern and southeastern regions of Korea. High gallbladder cancer incidence rates are clustered in the southeastern region. Regional liver cancer incidence can be accounted for the prevalence of high household income (coefficient, - 0.10; 95% credible interval [CI], - 0.18 to - 0.02), marital status (coefficient, - 0.14; 95% CI, - 0.25 to - 0.03), the incidence of hepatitis B (coefficient, 0.87; 95% CI, 0.29 to 1.44), and liver cancer screening (coefficient, 0.06; 95% CI, 0.00 to 0.12), while gallbladder cancer incidence was related to the prevalence of high household income (coefficient, - 0.03; 95% CI, - 0.05 to 0.00) and living close to a river with a high prevalence of liver fluke infection (coefficient, 0.55; 95% CI, 0.14 to 0.96).

CONCLUSIONS

This study demonstrated geographic variation in liver and gallbladder cancer incidence, which can be explained by determinants such as hepatitis B, income, marital status, and living near a river.

Associations between online food outlet access and online food delivery service use amongst adults in the UK: a cross-sectional analysis of linked data

BACKGROUND

Online food delivery services facilitate 'online' access to food outlets that typically sell lenergy-dense nutrient-poor food. Greater online food outlet access might be related to the use of this purchasing format and living with excess bodyweight, however, this is not known. We aimed to investigate the association between aspects of online food outlet access and online food delivery service use, and differences according to customer sociodemographic characteristics, as well as the association between the number of food outlets accessible online and bodyweight.

METHODS

In 2019, we used an automated data collection method to collect data on all food outlets in the UK registered with the leading online food delivery service Just Eat (n = 33,204). We linked this with contemporaneous data on food purchasing, bodyweight, and sociodemographic information collected through the International Food Policy Study (analytic sample n = 3067). We used adjusted binomial logistic, linear, and multinomial logistic regression models to examine associations.

RESULTS

Adults in the UK had online access to a median of 85 food outlets (IQR: 34-181) and 85 unique types of cuisine (IQR: 64-108), and 15.1% reported online food delivery service use in the previous week. Those with the greatest number of accessible food outlets (quarter four, 182-879) had 71% greater odds of online food delivery service use (OR: 1.71; 95% CI: 1.09, 2.68) compared to those with the least (quarter one, 0-34). This pattern was evident amongst adults with a university degree (OR: 2.11; 95% CI: 1.15, 3.85), adults aged between 18 and 29 years (OR: 3.27, 95% CI: 1.59, 6.72), those living with children (OR: 1.94; 95% CI: 1.01; 3.75), and females at each level of increased exposure. We found no association between the number of unique types of cuisine accessible online and online food delivery service use, or between the number of food outlets accessible online and bodyweight.

CONCLUSIONS

The number of food outlets accessible online is positively associated with online food delivery service use. Adults with the highest education, younger adults, those living with children, and females, were particularly susceptible to the greatest online food outlet access. Further research is required to investigate the possible health implications of online food delivery service use.

Environmental influence on <em>Triatoma vitticeps</em> occurrence and <em>Trypanosoma cruzi</em> infection in the Atlantic Forest of south-eastern Brazil

Trypanosoma cruzi requires a triatomine insect vector for its life cycle, which can be complex in different enzootic scenarios, one of which is the unique transmission network in the Atlantic Forest of south-eastern Brazil. In Espírito Santo (ES) State, highly infected Triatoma vitticeps are frequently reported invading domiciles. However, triatomines were not found colonizing residences and mammals in the surrounding areas did not present T. cruzi infection. To date, the biotic and abiotic variables that modulate T. vitticeps occurrence and T. cruzi infection in ES State are still unknown. The aim of this study was to identify the environmental variables that modulate their occurrence. Local thematic maps were generated for two response variables: T. vitticeps occurrence and T. cruzi infection. The following explanatory variables were tested: climate (temperature, relative air humidity and rainfall), altitude elevation, mammalian species richness as well as soil and vegetation types. Spatiotemporal distribution patterns and correlation levels between response and explanatory variables were assessed through spatial statistics and map algebra modelling. The central and southern mesoregions presented higher T. vitticeps and T. cruzi distributions and can be considered transmission hotspots. The explanatory variables that can explain these phenomena were relative air humidity, average temperature, soil type, altitude elevation and mammalian species richness. Algebra map modelling demonstrated that central and southern mesoregions presented the environmental conditions needed for T. vitticeps occurrence and T. cruzi infection. The consideration of environmental variables is essential for understanding the T. cruzi transmission cycle. Cartographic and statistical methodologies used in parasitology have been demonstrated to be reliable and enlightening tools that should be incorporated routinely to expand the understanding of vector-borne parasite transmission.

Evaluation of the Relationship Between Geographic Proximity and Treatment for People Referred to a Metropolitan Multidisciplinary Pain Clinic

OBJECTIVE

This study examined which patient characteristics are associated with traveling further to attend a metropolitan, publicly funded pain management service, and whether travel distance was associated with differences in treatment profile, duration, and percentage of appointments attended.

DESIGN

Cross-sectional observational cohort study.

METHOD

Patients ≤70 years of age with a single referral between January 2014 and June 2018 who had not died within 12 months of their first appointment and who had a usual place of residence were included (N = 1,684; mean age = 47.2 years; 55.5% female). Travel distance was calculated with the HERE Routing API on the basis of historical travel times for each scheduled appointment.

RESULTS

Median travel time was 27.5 minutes (Q1, Q3: 12.5, 46.2). Ordinal regression showed that women had 20% lower odds of traveling further, but people who were overweight or obese (odds ratio [OR] = 1.4-2.3), unemployed (OR = 1.27), or taking higher opioid dosages (OR = 1.79-2.82) had higher odds of traveling further. People traveling >60 minutes had fewer treatment minutes (median = 143 minutes) than people living within 15 minutes of the pain clinic (median = 440 minutes), and a smaller proportion of those traveling >60 minutes attended group programs vs. medical appointments only (n = 35, 17.0%) relative to those living within 15 minutes of their destination (n = 184, 32.6%). People living 16-30 minutes from the clinic missed the highest proportion of appointments.

CONCLUSIONS

Although people traveling further for treatment may be seeking predominantly medical treatment, particularly opioid medications, the present findings highlight the need to further explore patient triage and program models of care to ensure that people living with persistent disabling pain can access the same level of care, regardless of where they live.

Contextual Determinants in Alcohol, Tobacco and Cannabis Consumption, Mood and Bullying during Adolescence

The present study aimed to explore the differences in the consumption of alcohol, tobacco and cannabis, mood and bullying between adolescents. A cross-sectional study was carried out in five regions of Northern Spain (one in Galiza and four in central Catalonia) that share similar socioeconomic characteristics and encompass around 10,000 inhabitants each. Students living in Burela, Galiza (N = 71) were compared to those of Central Catalonia (N = 193). The independent variable was the municipality of residence. The dependent variables encompassed: weekly available pocket money, Family Affluence Scale, self-classified academic qualification, place of origin, alcohol consumption, tobacco and cannabis smoking, negative mood and bullying. The mean age and their 95% confidence intervals (95% CI) of participants were similar between the regions (Burela: 15.90 years (15.68-16.13) and Central Catalonia: 15.36 years (15.28-15.44)). More than half of the participants were females (Burela, Galiza (53.5%) and Catalonia (54.9%)). Prevalence ratios (PR) and their 95% CI were estimated using Poisson regression models. In comparison with adolescents from Burela (Galiza), those living in Central Catalonia had higher prevalence of diverse academic levels (adjusted PR = 3.92 (1.78-8.66)), tobacco consumption (adjusted PR = 2.41 (1.47-3.97)) and negative mood (adjusted PR = 5.97 (3.05-11.70)). Even when dealing with regions with similar socioeconomic characteristics and number of inhabitants, differences exist in terms of the socioeconomic level, tobacco consumption, mood and bullying, as reported by adolescents.

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Effectiveness of spatially targeted interventions for control of HIV, tuberculosis, leprosy and malaria: a systematic review

BACKGROUND

As infectious diseases approach global elimination targets, spatial targeting is increasingly important to identify community hotspots of transmission and effectively target interventions. We aimed to synthesise relevant evidence to define best practice approaches and identify policy and research gaps.

OBJECTIVE

To systematically appraise evidence for the effectiveness of spatially targeted community public health interventions for HIV, tuberculosis (TB), leprosy and malaria.

DESIGN

Systematic review.

DATA SOURCES

We searched Medline, Embase, Global Health, Web of Science and Cochrane Database of Systematic Reviews between 1 January 1993 and 22 March 2021.

STUDY SELECTION

The studies had to include HIV or TB or leprosy or malaria and spatial hotspot definition, and community interventions.

DATA EXTRACTION AND SYNTHESIS

A data extraction tool was used. For each study, we summarised approaches to identifying hotpots, intervention design and effectiveness of the intervention.

RESULTS

Ten studies, including one cluster randomised trial and nine with alternative designs (before-after, comparator area), satisfied our inclusion criteria. Spatially targeted interventions for HIV (one USA study), TB (three USA) and leprosy (two Brazil, one Federated States of Micronesia) each used household location and disease density to define hotspots followed by community-based screening. Malaria studies (one each from India, Indonesia and Kenya) used household location and disease density for hotspot identification followed by complex interventions typically combining community screening, larviciding of stagnant water bodies, indoor residual spraying and mass drug administration. Evidence of effect was mixed.

CONCLUSIONS

Studies investigating spatially targeted interventions were few in number, and mostly underpowered or otherwise limited methodologically, affecting interpretation of intervention impact. Applying advanced epidemiological methodologies supporting more robust hotspot identification and larger or more intensive interventions would strengthen the evidence-base for this increasingly important approach.

PROSPERO REGISTRATION NUMBER

CRD42019130133.

Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland

Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, and per capita GDP) on the Chinese mainland, we established a geographically weighted regression (GWR) model to analyze their impacts in different seasons and provinces. The GWR model successfully describes the spatial changes of the influence of potential risks, and shows greatly improved estimation performance compared with the ordinary linear regression (OLR) method. Our findings help to understand the seasonally and spatially relevant effects of natural environmental and socioeconomic factors on the I-HFMD, and can provide information to be used to develop effective prevention strategies against HFMD at different locations and in different seasons.

Association between environmental and climatic risk factors and the spatial distribution of cystic and alveolar echinococcosis in Kyrgyzstan

Background

Cystic and alveolar echinococcosis (CE and AE) are neglected tropical diseases caused by Echinococcus granulosus sensu lato and E. multilocularis, and are emerging zoonoses in Kyrgyzstan. In this country, the spatial distribution of CE and AE surgical incidence in 2014-2016 showed marked heterogeneity across communities, suggesting the presence of ecological determinants underlying CE and AE distributions.

Methodology/Principal findings

For this reason, in this study we assessed potential associations between community-level confirmed primary CE (no.=2359) or AE (no.=546) cases in 2014-2016 in Kyrgyzstan and environmental and climatic variables derived from satellite-remote sensing datasets using conditional autoregressive models. We also mapped CE and AE relative risk. The number of AE cases was negatively associated with 10-year lag mean annual temperature. Although this time lag should not be considered as an exact measurement but with associated uncertainty, it is consistent with the estimated 10–15-year latency following AE infection. No associations were detected for CE. We also identified several communities at risk for CE or AE where no disease cases were reported in the study period.

Conclusions/Significance

Our findings support the hypothesis that CE is linked to an anthropogenic cycle and is less affected by environmental risk factors compared to AE, which is believed to result from spillover from a wild life cycle. As CE was not affected by factors we investigated, hence control should not have a geographical focus. In contrast, AE risk areas identified in this study without reported AE cases should be targeted for active disease surveillance in humans. This active surveillance would confirm or exclude AE transmission which might not be reported with the present passive surveillance system. These areas should also be targeted for ecological investigations in the animal hosts.

Cystic and alveolar echinococcosis (CE and AE) are parasitic zoonoses that cause a substantial disease burden in Kyrgyzstan. The etiologic agents of these diseases are parasites in the genus Echinococcus. These parasites have complex life cycles which include mammalian definitive and intermediate hosts and a free-living egg stage in the environment. Consequently, environmental and climatic factors can affect the prevalence and geographical distribution of these diseases because such factors influence the parasites’ eggs survival and longevity, and can affect suitable habitats for the intermediate and definitive hosts. In this geographic correlation study, we assessed environmental and climatic determinants of the spatial distributions of CE and AE in Kyrgyzstan. We found that 10-year lag annual temperature plays an important role in AE distribution, whilst none of the variables assessed was found to significantly affect that of CE. Moreover, communities at risk where these diseases are potentially under- or misdiagnosed were identified. Our findings provide vital information for targeted, area-specific interventions in Kyrgyzstan, and add to the body of knowledge on the ecology of these neglected parasitic diseases that are emerging and reemerging in several regions in North America, Europe and Asia.

Assessment of Environmental Hazards to Public Health in Temperate Urban Argentina

Human health risk in urban areas depends on multiple environmental features. We performed a year-round survey in a highly urbanized district located in temperate Argentina (General San Martín, Buenos Aires) to establish baseline information about environmental hazards associated with health risks. Sampling was stratified into low and high hazardous zones according to estimated indexes previously developed for the area for four hazards: drinking water and air pollution, and mosquito and rodent infestation. Water from wells showed lower concentrations of aluminum, manganese and iron, and higher values of arsenic than tap samples, with the latter showing records above the maximum permitted for arsenic, aluminum and chromium. Benzene concentration in air was higher in summer than in winter, and in areas close to dumps and landfills, gas stations, high traffic pathways and industries with respect to low hazard areas. Adult mosquito collections were more abundant in high hazardous areas, three species from the genus Culex dominated the captures and the proportion of individuals from each species was variable seasonally and spatially. Rodent activity was recorded inside and outside dwellings, and its observed values did not differ between low and high hazardous areas. In the comparison between field data and estimated hazard maps, high accuracy was obtained for air pollution maps, intermediate accuracy for water pollution and mosquito infestation, and poor accuracy for rodent infestation. How to improve field surveys and estimated maps are both discussed, highlighting the need for dynamic feedback between GIS-based models and environmental monitoring.

Rurality and Origin-Destination Trajectories of Medical School Application and Matriculation in the United States

Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin-destination (O-D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural-urban gap in medical school applications, and reduce physician shortages in rural areas.

A Bayesian approach for estimating age-adjusted rates for low-prevalence diseases over space and time

Age-adjusted rates are frequently used by epidemiologists to compare disease incidence and mortality across populations. In small geographic regions, age-adjusted rates computed directly from the data are subject to considerable variability and are generally unreliable. Therefore, we desire an approach that accounts for the excessive number of zero counts in disease mapping datasets, which are naturally present for low-prevalence diseases and are further innated when stratifying by age group. Bayesian modeling approaches are naturally suited to employ spatial and temporal smoothing to produce more stable estimates of age-adjusted rates for small areas. We propose a Bayesian hierarchical spatio-temporal hurdle model for counts and demonstrate how age-adjusted rates can be estimated from the hurdle model. We perform a simulation study to evaluate the performance of the proposed model vs a traditional Poisson model on datasets with varying characteristics. The approach is illustrated using two applications to cancer mortality at the county level.

Combining Global Positioning System (GPS) with saliva collection among sexual minority adults: A feasibility study

BACKGROUND

This is the first study, of which we are aware, to evaluate the feasibility and accessibility of simultaneous use of Global Positioning System (GPS) and saliva collection for biomarker assessment as an objective measure of stress physiology among sexual minority (lesbian, gay, bisexual, queer, and other non-heterosexual identities) individuals. The principal motivation for pairing GPS and saliva collection was to investigate how characteristics of the built and social environments along with participants' daily activity paths affect stress. This can contribute to a better understanding of health and health behaviors in the sexual minority community.

METHODS

A convenience sample of enrolled participants (N = 124) from Houston, Texas was asked to complete questionnaires, carry with them a GPS unit daily, and collect and store 6 samples of saliva at specific times across the span of a day prior to a second visit around one week later.

RESULTS

Of 124 participants, 16 participants (12.90%) provided no useable GPS data and 98 (79.03%) provided at least 4 days of data. More than three-fourths (n = 98, 79.03%) also provided complete saliva samples.

CONCLUSIONS

Our results show that the simultaneous use of GPS and saliva collection to assess sexual minority individuals' activity paths and stress level is feasible.

Spatiotemporal Dynamics of Scrub Typhus in Jiangxi Province, China, from 2006 to 2018

Background: Scrub typhus (ST) has become a significant potential threat to public health in Jiangxi. Further investigation is essential for the control and management of the spatiotemporal patterns of the disease. Methods: Time-series analyses, spatial distribution analyses, spatial autocorrelation analysis, and space-time scan statistics were performed to detect spatiotemporal dynamics distribution of the incidence of ST. Results: From 2006 to 2018, a total of 5508 ST cases occurred in Jiangxi, covering 79 counties. The number of ST cases increased continuously from 2006 to 2018, and there was obvious seasonality during the variation process in each year, with a primary peak in autumn (September to October) and a smaller peak in summer (June to August). From 2007 to 2018, the spatial distribution of the ST epidemic was significant heterogeneity, and Nanfeng, Huichang, Xunwu, Anyuan, Longnan, and Xinfeng were hotspots. Seven spatiotemporal clusters were observed using Kulldorff's space-time scan statistic, and the most likely cluster only included one county, Nanfeng county. The high-risk areas of the disease were in the mountainous, hilly region of Wuyi and the southern mountainous region of Jiangxi. Conclusions: Targeted interventions should be executed in high-risk regions for the precise prevention and control of ST.

Plasmodiophora brassicae Inoculum Density and Spatial Patterns at the Field Level and Relation to Soil Characteristics

Clubroot, caused by Plasmodiophora brassicae, is an important soilborne disease of the Brassicaceae. Knowledge of the spatial dynamics of P. brassicae at the field level and the influence of soil properties on pathogen spatial patterns can improve understanding of clubroot epidemiology and management. To study the spatial patterns of P. brassicae inoculum density and their relationship to different soil properties, four clubroot-infested fields in central Alberta, Canada, were sampled in 2017 and 2019, and P. brassicae inoculum density, soil pH, and boron, calcium, and magnesium concentrations were quantified. Spatial autocorrelation of the inoculum density was estimated for each of the fields in both years with the Moran’s I and semi-variograms. A Bayesian hierarchical spatial approach was used to model the relationship between P. brassicae inoculum density and the soil parameters. Patchiness of the pathogen was detected, with most patches located at the field edges and adjacent to the entrance. Infested patches grew in size from 2017 to 2019, with an average increase in diameter of 221.3 m and with this growth determined by the maximum inoculum density and active dispersal methods such as movement by machinery and wind. Soil pH, boron, calcium, and magnesium concentrations were not found to have an important effect on the inoculum density of P. brassicae.

Geographic Information Systems in Spatial Epidemiology: Unveiling New Horizons in Dental Public Health

Objectives:

Research on the role of environment and place in various aspects of dental public health using geographic information systems (GIS) is escalating rapidly. Yet, the understanding of GIS and the analytical tools that it offers are still vaguely understood. This narrative review therefore draws from the utilization of GIS in the dental public health research.

Materials and Methods:

Electronic databases such as Google Scholar, PUBMED, and Scopus were searched using terms “spatial epidemiology,” “GIS,” “geographic information systems,” “health geography,” “environment public health tracking,” “spatial distribution,” “disease mapping,” “geographic correlation studies,” “cartography,” “big data,” and “disease clustering” through December 2019.

Results:

This review builds upon the prospects of GIS application in various aspects of dental public health. Studies were classified as: (1) GIS for mapping of disease, population at risk, and risk factors; (2) GIS in geographic correlation studies; (3) GIS for gauging healthcare accessibility and spatial distribution of healthcare providers. We also identified the commonly used GIS analytical techniques in oral epidemiology.

Conclusions:

We anticipate that this review will spur advancement in the utilization of spatial analytical techniques and GIS in the dental public health research.

Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions

Background

Previous studies from Ethiopia detected disease clustering using broader geographic settings, but limited information exists on the spatial distribution of the disease using residential locations. An assessment of predictors of spatial variations of TB at community level could fill the knowledge gaps, and helps in devising tailored interventions to improve TB control.

Objective

To assess the pattern of spatial distribution of pulmonary tuberculosis (PTB) based on geographic locations of individual cases in the Dale district and Yirga Alem town in southern Ethiopia.

Methods

The socio-demographic characteristics of PTB cases were collected using a structured questionnaire, and spatial information was collected using geographic position systems. We carried out Getis and Ord (Gi*) statistics and scan statistics to explore the pattern of spatial clusters of PTB cases, and geographically weighted regression (GWR) was used to assess the spatial heterogeneities in relationship between predictor variables and PTB case notification rates (CNRs).

Results

The distribution of PTB varied by enumeration areas within the kebeles, and we identified areas with significant hotspots in various areas ineach year. In GWR analysis, the disease distribution showed a geographic heterogeneity (non-stationarity) in relation to physical access (distance to TB control facilities) and population density (AICc = 5591, R² = 0.3359, adjusted R² = 0.2671). The model explained 27% of the variability in PTB CNRs (local R² ranged from 0.0002–0.4248 between enumeration areas). The GWR analysis showed that areas with high PTB CNRs had better physical accessibility to TB control facilities and high population density. The effect of physical access on PTB CNRs changed after the coverage of TB control facilities was improved.

Conclusion

We report a varying distribution of PTB in small and different areas over 10 years. Spatial and temporal analysis of disease distribution can be used to identify areas with a high burden of disease and predictors of clustering, which helps in making policy decisions and devising targeted interventions.

Childhood leukemia near nuclear sites in Belgium: An ecological study at small geographical level

BACKGROUND

A previous investigation of the occurrence of childhood acute leukemia around the Belgian nuclear sites has shown positive associations around one nuclear site (Mol-Dessel). In the following years, the Belgian Cancer Registry has made data available at the smallest administrative unit for which demographic information exists in Belgium, i.e. the statistical sector. This offers the advantage to reduce the potential misclassification due to large geographical scales.

METHODS

The current study performed for the period 2006-2016 uses Poisson models to investigate (i) the incidence of childhood acute leukemia within 20 km around the four Belgian nuclear sites, (ii) exposure-response relationships between cancer incidence and surrogate exposures from the nuclear sites (distance, wind direction frequency and exposure by hypothetical radioactive discharges taking into account historical meteorological conditions). All analyses are carried out at statistical sector level.

RESULTS

Higher incidence rate ratios were found for children <15 years (7 cases, RR = 3.01, 95% CI: 1.43;6.35) and children <5 years (< 5 cases, RR = 3.62, 95% CI: 1.35;9.74) living less than 5 km from the site of Mol-Dessel. In addition, there was an indication for positive exposure-response relationships with the different types of surrogate exposures.

CONCLUSION

Results confirm an increased incidence of acute childhood leukemia around Mol-Dessel, but the number of cases remains very small. Random variation cannot be excluded and the ecological design does not allow concluding on causality. These findings emphasize the need for more in-depth research into the risk factors of childhood leukemia, for a better understanding of the etiology of this disease.

Using multiple data sources to explore disease transmission risk between commercial poultry, backyard poultry, and wild birds in New Zealand

The movements of backyard poultry and wild bird populations are known to pose a disease risk to the commercial poultry industry. However, it is often difficult to estimate this risk due to the lack of accurate data on the numbers, locations, and movement patterns of these populations. The main aim of this study was to evaluate the use of three different data sources when investigating disease transmission risk between poultry populations in New Zealand including (1) cross-sectional survey data looking at the movement of goods and services within the commercial poultry industry, (2) backyard poultry sales data from the online auction site TradeMe®, and (3) citizen science data from the wild bird monitoring project eBird. The cross-sectional survey data and backyard poultry sales data were transformed into network graphs showing the connectivity of commercial and backyard poultry producers across different geographical regions. The backyard poultry network was also used to parameterise a Susceptible-Infectious (SI) simulation model to explore the behaviour of potential disease outbreaks. The citizen science data was used to create an additional map showing the spatial distribution of wild bird observations across New Zealand. To explore the potential for diseases to spread between each population, maps were combined into bivariate choropleth maps showing the overlap between movements within the commercial poultry industry, backyard poultry trades and, wild bird observations. Network analysis revealed that the commercial poultry network was highly connected with geographical clustering around the urban centres of Auckland, New Plymouth and Christchurch. The backyard poultry network was also a highly active trade network and displayed similar geographic clustering to the commercial network. In the disease simulation models, the high connectivity resulted in all suburbs becoming infected in 96.4 % of the SI simulations. Analysis of the eBird data included reports of over 80 species; the majority of which were identified as coastal seabirds or wading birds that showed little overlap with either backyard or commercial poultry. Overall, our study findings highlight how the spatial patterns of trading activity within the commercial poultry industry, alongside the movement of backyard poultry and wild birds, have the potential to contribute significantly to the spread of diseases between these populations. However, it is clear that in order to fully understand this risk landscape, further data integration is needed; including the use of additional datasets that have further information on critical variables such as environmental factors.

Spatial patterns of lower respiratory tract infections and their association with fine particulate matter

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM_2.5). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM_2.5 data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM_2.5. Subgroup analyses were performed to determine whether LRIs and PM_2.5 are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM_2.5 with a coefficient of 0.21 (95% CI 0.06-0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM_2.5 on LRI for children (0-14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries.

COVID-19 does not stop at open borders: Spatial contagion among local authority districts during England's first wave

Infectious diseases generate spatial dependence or contagion not only between individuals but also between geographical units. New infections in one local district do not just depend on properties of the district, but also on the strength of social ties of its population with populations in other districts and their own degree of infectiousness. We show that SARS-CoV-2 infections during the first wave of the pandemic spread across district borders in England as a function of pre-crisis commute to work streams between districts. Crucially, the strength of this spatial contagion depends on the phase of the epidemic. In the first pre-lockdown phase, the spread of the virus across district borders is high. During the lockdown period, the cross-border spread of new infections slows down significantly. Spatial contagion increases again after the lockdown is eased but not statistically significantly so.

Spatiotemporal Analysis of AIDS Incidence and Its Influencing Factors on the Chinese Mainland, 2005-2017

Acquired Immune Deficiency Syndrome (AIDS) has become one of the most severe public health issues and nowadays around 38 million people are living with the human immunodeficiency virus (HIV). Ensuring healthy lives and promoting well-being is one of 17 United Nations Sustainable Development Goals. Here, we used the Markov chain matrix and geospatial clustering to comprehensively quantify the trends of the AIDS epidemic at the provincial administrate level in the mainland of China from 2005 to 2017. The Geographically Weighted Regression (GWR) model was further adopted to explore four groups of potential influencing factors (i.e., economy, traffic and transportation, medical care, and education) of the AIDS incidence rate in 2017 and their spatially distributed patterns. Results showed that the AIDS prevalence in southeastern China had been dominant and become prevalent in the past decade. The AIDS intensity level had been increasing between 2008 and 2011 but been gradually decreasing afterward. The analysis of the Markov chain matrix indicated that the AIDS epidemic has been generally in control on the Chinese mainland. The economic development was closely related to the rate of AIDS incidence on the Chinese mainland. The GWR result further suggested that medical care and the education effects on AIDS incidence rate can vary with different regions, but significant conclusions cannot be directly demonstrated. Our findings contribute an analytical framework of understanding AIDS epidemic trends and spatial variability of potential underlying factors throughout a complex extent to customize scientific prevention.

Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017

Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.

Geographic Accessibility of HIV Preventive Services for Young Latino Men in Miami, Florida: A Cross-Sectional Study

ABSTRACT

The U.S. HIV incidence has decreased or stabilized among all subgroups except for young Latino men who have sex with men (YLMSM). Previous research has suggested that Latinos experience geographic accessibility barriers to YLMSM-friendly HIV prevention services. We aimed to characterize the geographic accessibility of young Latinos ages 15-29 years to HIV preventive services in Miami-Dade County, the domestic HIV epicenter. Using ArcMap, we created a density map of 18 YLMSM-friendly HIV programs, then used Network Analysis Tools to generate service areas describing time and travel distance for walking, public transit, and driving. Our results show that accessibility to YLMSM-friendly HIV prevention services by YLMSM varies by mode of transportation. Of YLMSM, HIV prevention services are available to 2% by walking, 19% by public transit, and 70% by driving. To increase accessibility, future public health interventions should use geographic information system and geodemographic data to identify areas for culturally appropriate service expansion.

Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.

Towards a Situated Spatial Epidemiology of Violence: A Placially-Informed Geospatial Analysis of Homicide in Alagoas, Brazil

This paper presents an empirically grounded call for a more nuanced engagement and situatedness with placial characteristics within a spatial epidemiology frame. By using qualitative data collected through interviews and observation to parameterise standard and spatial regression models, and through a critical interpretation of their results, we present initial inroads for a situated spatial epidemiology and an analytical framework for health/medical geographers to iteratively engage with data, modelling, and the context of both the subject and process of analysis. In this study, we explore the socioeconomic factors that influence homicide rates in the Brazilian state of Alagoas from a critical public health perspective. Informed by field observation and interviews with 24 youths in low-income neighbourhoods and prisons in Alagoas, we derive and critically reflect on three regression models to predict municipal homicide rates from 2016-2020. The model results indicate significant effects for the male population, persons without elementary school completion, households with reported income, divorced persons, households without piped water, and persons working outside their home municipality. These results are situated in the broader socioeconomic context, trajectories, and cycles of inequality in the study area and underscore the need for integrative and contextually engaged mixed method study design in spatial epidemiology.

A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

PURPOSE

This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States.

METHODS

We assemble a county-level data set that contains COVID-19-confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence.

RESULTS

The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties.

CONCLUSIONS

Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

The unknown denominator problem in population studies of disease frequency

Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address "unknown denominator problems": ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.

Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model

BACKGROUND

Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality.

METHODS

Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States).

RESULTS

The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran's I (0.44; p < 0.001) was 17.4 (10.3-26.9). The malarial endemic cluster was identified in Paju-si, Korea (p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified (p < 0.001).

CONCLUSIONS

As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.

Assessment of Relative Asthma Risk in Populations Living Near Incineration Facilities in Seoul, Korea

While incineration is among the most commonly used technologies for waste disposal, there is ongoing public concern regarding the adverse health impact. The aim of this study is thus to use health statistics to assess the relative risk of asthma-related hospitalization for those living in close proximity to incineration facilities. We also examine differences in asthma risk related to age demographics. The spatial relationship between incineration facilities and asthma-related hospital admissions in Seoul is analyzed for the period of 2009–2011 using the Rapid Inquiry Facility (RIF) and SaTScan software. The relative risk of asthma-related hospitalization decreased with increasing distance from incinerators, but increased among those living within a 2-km radius. The relative risks of asthma-related hospitalization were 1.13 (95% confidence interval (CI): 1.10–1.17), 1.12 (95% CI: 1.08–1.17), and 1.18 (95% CI: 1.10–1.27) for all ages, those aged below 15 years, and those aged 65 years and older, respectively. This study is the first to observe an increased risk of asthma-related hospitalization in relation to a person’s distance from an incinerator in Seoul, Korea. It is clear that asthma should be considered an adverse health outcome during health impact assessments of incineration plants.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany

Background

As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.

Methods

By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations.

Results

A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables’ partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure.

Conclusions

The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.

Modeling epidemics by the lattice Boltzmann method

In this paper, we demonstrate that the lattice Boltzmann method can be successfully adopted to investigate the dynamics of epidemics. Numerical simulations prove the excellent accuracy properties of the approach, which recovers the solution of the popular SIR model. Because spatial effects are naturally accounted for in the lattice Boltzmann formulation, the present scheme appears to be more competitive than traditional solution procedures. Interestingly, it allows us to simulate scenarios characterized by selective lockdown configurations.

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Background

This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data.

Methods

Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters.

Results

Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Epidemic investigations within an arm's reach - role of google maps during an epidemic outbreak

Epidemics such as novel Coronavirus 2019 (COVID-19) can be contained and the rate of infection reduced by public health measures such as epidemiologic inquiries and social distancing. Epidemiologic inquiry requires resources and time which may not be available or reduced when the outbreak is excessive. We evaluated the use of Google Maps Timeline (GMTL) for creating spatial epidemiologic timelines. The study compares locations, routes, and means of transport between GMTL and user recall for 17 suitable users who were recruited during March 2020. They were interviewed about their timeline using the Timeline Follow-Back (TLFB) method which was then compared to their GMTL and discrepancies between both methods were addressed. Interviewer conclusions were divided into categories: (1) participant recalled, (2) no recall (until shown). Categories were subdivided by GMTL accuracy: [a] GMTL accurate, [b] GMTL inaccurate, [c] GMTL data missing. A total of 362 locations were compared. Participants recalled 322 (88.95% SD = 8.55) locations compared with 40 (11.05%, SD = 2.05) locations not recalled. There were 304 locations found accurate on GMTL (83.98%, SD = 9.49), 29 (8.01%, SD = 1.11) inaccurate locations, and 29 (8.01%, SD = 0.54) missing locations. The total discrepancy between GMTL and TLFB recall was 95 cases (26.24%, SD = 3.25). Despite variations between users, Google Maps with GMTL technology may be useful in identifying potentially exposed individuals in a pandemic. It is especially useful when resources are limited. Further research is required with a larger number of users who are undergoing a real epidemiologic investigation to corroborate findings and establish further recommendations.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

Disease mapping of early- and late-stage cancer to monitor inequalities in early detection: a study of cutaneous malignant melanoma

We consider disease mapping of early- and late-stage cancer, in order to identify and monitor inequalities in early detection. Our method is demonstrated by mapping cancer incidence at high geographical resolution using data on 10,302 cutaneous malignant melanoma (CMM) cases within the 3.7 million population of South-West Sweden. The cases were geocoded into small-areas, each with a population size between 600 and 2600 and accessible socio-demographic data. Using the disease mapping application Rapid Inquiry Facility (RIF) 4.0, we produced regional maps to visualise spatial variations in stage I, II and III-IV CMM incidences, complemented by local maps to explore the variations within two urban areas. Pronounced spatial disparities in stage I CMM incidence were revealed by the regional and local maps. Stage I CMM incidence was markedly higher in wealthier small-areas, in particular within each urban area. A twofold higher stage I incidence was observed, on average, in the wealthiest small-areas (upper quintile) than in the poorest small-areas (lower quintile). We identified in the regional map of stage III-IV CMM two clusters of higher or lower than expected late-stage incidences which were quite distinct from those identified for stage I. In conclusion, our analysis of CMM incidences supported the use of this method of cancer stage incidence mapping for revealing geographical and socio-demographic disparities in cancer detection.

Lessons Learned From the Stakeholder Engagement in Research: Application of Spatial Analytical Tools in One Health Problems

Stakeholder engagement in research is widely advocated as a tool to integrate diverse knowledge and perspectives in the management of health threats while addressing potential conflicts of interest. Although guidelines for stakeholder engagement exist in public health and environmental sciences, the feasibility of actionable decisions based on scientific analyses and the lessons learned from the stakeholder engagement in the process co-creation of knowledge have been rarely discussed in One Health literature and veterinary sciences. Risk maps and risk regionalization using spatiotemporal epidemiological/analytical tools are known to improve risk perception and communication. Risk maps are useful when informing policy and management decisions on quarantine, vaccination, and surveillance intended to prevent or control threats to human, animal, or environmental health interface (i.e., One Health). We hypothesized that researcher-stakeholder engagement throughout the research process could enhance the utility of risk maps; while identifying opportunities to improve data collection, analysis, interpretation, and, ultimately, implementation of scientific/evidence-based management and policy measures. Three case studies were conducted to test this process of co-creation of scientific knowledge, using spatiotemporal epidemiological approaches, all related to One Health problems affecting Minnesota. Our interpretation of the opportunities, challenges, and lessons learned from the process are summarized from both researcher and stakeholder perspectives. By sharing our experience we intend to provide an understanding of the expectations, realizations, and “good practices” we learned through this slow-moving iterative process of co-creation of knowledge. We hope this contribution benefits the planning of future transdisciplinary research related to risk map-based management of One Health problems.

Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

Small-area methods for investigation of environment and health

Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographical scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast-growing computational capabilities, permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but has also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases.

Evolutionary ecology, taxonomy, and systematics of avian malaria and related parasites

Haemosporidian parasites of the genera Plasmodium, Leucocytozoon, and Haemoproteus are one of the most prevalent and widely studied groups of parasites infecting birds. Plasmodium is the most well-known haemosporidian as the avian parasite Plasmodium relictum was the original transmission model for human malaria and was also responsible for catastrophic effects on native avifauna when introduced to Hawaii. The past two decades have seen a dramatic increase in research on avian haemosporidian parasites as a model system to understand evolutionary and ecological parasite-host relationships. Despite haemosporidians being one the best studied groups of avian parasites their specialization among avian hosts and variation in prevalence amongst regions and host taxa are not fully understood. In this review we focus on describing the current phylogenetic and morphological diversity of haemosporidian parasites, their specificity among avian and vector hosts, and identifying the determinants of haemosporidian prevalence among avian species. We also discuss how these parasites might spread across regions due to global climate change and the importance of avian migratory behavior in parasite dispersion and subsequent diversification.

Spatiality in Health: The Distribution of Health Conditions Associated with Electronic Waste Processing Activities at Agbogbloshie, Accra

Background

A walk through the Agbogbloshie e-waste recycling site shows a marked heterogeneity in the spatial distribution of the different e-waste processing activities, which are likely to drive clustering of health conditions associated with the different activity type in each space.

Objective of study

To conduct a spatial assessment and analysis of health conditions associated with different e-waste activities at different activity spaces at Agbogbloshie.

Methods

A choropleth showing the various activity spaces at the Agbogbloshie e-waste site was produced by mapping boundaries of these spaces using Etrex GPS device and individuals working in each activity spaces were recruited and studied. Upon obtaining consent and agreeing to participate in the study, each subject was physically examined and assessed various health outcomes of interest via direct physical examination while characterizing and enumerating the scars, lacerations, abrasions, skin condition and cuts after which both systolic and diastolic blood pressure values were recorded alongside the administration of open and close ended questionnaires. All individuals working within each activity space and consented to participate were recruited; giving a total of one hundred and twelve (112) subjects in all.

Results

A study of the choropleth showed that health conditions associated e-waste processing activities were clustered in a fashion similar to the corresponding distribution of each activity. While a total of 96.2% of all the study subjects had cuts, the dismantlers had higher mix of scars, lacerations and abrasions. Abrasions were observed in 16.3% of the dismantlers. Scars were the most common skin condition and were observed on the skins of 93.6% of the subjects. Prevalence of burns among the study subjects was 23.1%. Developing hypertension was not associated with activity type and while a total of 90.2% of subjects had normal blood pressure and 9.8% of them were hypertensives. Finally, 98.2% of respondents felt the need to have a first aid clinic at the site with 96.4% and 97.3% willing to visit the clinic and pay for services respectively.

Conclusion

We conclude that while the observed injuries were random and were due purely to accidents without any role of spatial determinants such as the configuration, slope, topography and other subterranean features of the activity spaces, a strong association between the injuries and activity type was observed.