Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting

The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65  years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.

Contrasting Methods of Measurement in Spatial Analyses Examining the Alcohol Environment and Child Maltreatment

Child physical abuse is a major public health issue in the United States. Environmental child welfare research has focused on neighborhood characteristics and the influence of alcohol and marijuana establishments. To our knowledge, child welfare studies have singularly examined the outcome in terms of victims, that is, at the level of child population, and have not considered the parent population. Thus, in this exploratory study, we use spatial scan statistics to analyze patterns of child physical abuse at the child and household level, and we use Bayesian hierarchical spatial conditional autoregressive models to determine the relative influence of alcohol availability and other environmental factors. We find that household clusters are nested in child clusters and that controlling for alcohol establishments reduces cluster size. In the Bayesian regression models, alcohol availability increased risk slightly, while neighborhood diversity (measured using Blau's Index) elevated risk considerably. Immediate implications for child welfare agencies are discussed.

An Evaluation of Geo-located Twitter Data for Measuring Human Migration

This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.

Anemia Among Children Under 5 Years in India: A District-Level Spatial Analysis Using NFHS-5 Data

The 2015/2016 National Family Health Survey (NFHS-4) revealed that the prevalence of anemia among children under 5 years is 58% in India. Lack of nutritional supplementation and lack of health care facilities are found to be important influential factors of anemia among children. We aimed to examine district-level spatial heterogeneity and clustering of associated factors with childhood anemia in India. Geographically weighted regression was applied on the NFHS-5 data for 335 districts. Factors such as prevalence of nutritional supplementation in children and mothers, birth order, antenatal care, diarrhea in children, and stunting were found to be significantly associated. Spatial scan statistics technique identified 3 significant local spatial clusters of anemia. This study provides findings based on the latest available data which can further assist in the design and execution of tailor-made policies.

Spatial heterogeneity of KRAS mutations in colorectal cancers in northern France

Background

Somatic mutations in the KRAS gene are the most common oncogenic mutations found in human cancers. However, no clinical features have been linked to KRAS mutations in colorectal cancer [CRC].

Purpose

In this study, we attempted to identify the potential geographical population clusters of KRAS mutations in CRC patients in northern France.

Patients and methods

All patients with CRC who were identified to have KRAS mutations between 2008 and 2014 at the Regional Molecular Biology Platform at Lille University Hospital were included. 2,486 patients underwent a KRAS status available, with 40.9% of CRC with KRAS mutations in northern France. We retrospectively collected demographic and geographic data from these patients. The proportions of KRAS mutation were smoothed to take into account the variability related to low frequencies and spatial autocorrelation. Geographical clusters were searched using spatial scan statistical models.

Results

A mutation at KRAS codon 12 or 13 was found in 1,018 patients [40.9%]. We report 5 clusters of over-incidence but only one elongated cluster that was statistically significant [Cluster 1; proportion of KRAS mutation among CRC: 0.4570; RR=1.29; P=0.0314]. We made an ecological study which did not highlight a significant association between KRAS mutations and the distance to the Closest Waste Incineration Plant, and between KRAS mutations and The French Ecological Deprivation Index but few socio-economic and environmental data were available.

Conclusion

There was a spatial heterogeneity and a greater frequency of KRAS mutations in some areas close to major highways and big cities in northern France. These data demand deeper epidemiological investigations to identify environmental factors such as air pollution as key factors in the occurrence of KRAS mutations.

Do sedentary behavior and physical activity spatially cluster? Analysis of a population-based sample of Boston adolescents

Sedentary behavior and lack of physical activity are key modifiable behavioral risk factors for chronic health problems, such as obesity and diabetes. Little is known about how sedentary behavior and physical activity among adolescents spatially cluster. The objective was to detect spatial clustering of sedentary behavior and physical activity among Boston adolescents. Data were used from the 2008 Boston Youth Survey Geospatial Dataset, a sample of public high school students who responded to a sedentary behavior and physical activity questionnaire. Four binary variables were created: 1) TV watching (>2 hours/day), 2) video games (>2 hours/day), 3) total screen time (>2 hours/day); and 4) 20 minutes/day of physical activity (≥5 days/week). A spatial scan statistic was utilized to detect clustering of sedentary behavior and physical activity. One statistically significant cluster of TV watching emerged among Boston adolescents in the unadjusted model. Students inside the cluster were more than twice as likely to report > 2 hours/day of TV watching compared to respondents outside the cluster. No significant clusters of sedentary behavior and physical activity emerged. Findings suggest that TV watching is spatially clustered among Boston adolescents. Such findings may serve to inform public health policymakers by identifying specific locations in Boston that could provide opportunities for policy intervention. Future research should examine what is linked to the clusters, such as neighborhood environments and network effects.

Lung cancer mortality clusters in Shandong Province, China: how do they change over 40 years?

Lung cancer has long been a major health problem in China. This study aimed to examine the temporal trend and spatial pattern of lung cancer mortality in Shandong Province from 1970 to 2013. Lung cancer mortality data were obtained from Shandong Death Registration System and three nationwide retrospective cause-of-death surveys. A Purely Spatial Scan Statistics method with Discrete Poisson models was used to detect possible high-risk spatial clusters. The results show that lung cancer mortality rate in Shandong Province increased markedly from 1970-1974 (7.22 per 100,000 person-years) to 2011-2013 (56.37/100, 000). This increase was associated with both demographic and non-demographic factors. Several significant spatial clusters with high lung cancer mortality were identified. The most likely cluster was located in the northern region of Shandong Province during both 1970-1974 and 2011-2013. It appears the spatial pattern remained largely consistent over the last 40 years despite the absolute increase in the mortality rates. These findings will help develop intervention strategies to reduce lung cancer mortality in this large Chinese population.

Geospatial analysis of nonmedical vaccine exemptions and pertussis outbreaks in the United States

Because of increased numbers of recorded pertussis cases in the United States, this study sought to understand the role of nonmedical vaccine exemptions and waning immunity may have had on the resurgence of pertussis in the United States at the community level. We used geospatial scan statistics, SaTScan, version 9.4, to analyze nonmedical vaccine exemptions of children entering kindergarten in 2011 and 2012 and reported pertussis cases in 2012 for children in age groups 5 years and younger and 10 to 14 years. Eight statistically significant clusters of nonmedical vaccine exemptions in kindergarteners and 11 statistically significant clusters of pertussis cases in children and adolescents were identified and geospatially linked. Forty-five percent of the counties in the study had high rates of nonmedical vaccine exemptions. The proportion of kindergarteners with nonmedical vaccine exemptions was 2.8 times larger in the identified exemption clusters. In addition, 31 counties had geographic clusters of high rates of pertussis in children ages 10 to 14 years old, consistent with waning immunity. Our findings are consistent with the view that geographic clusters of nonmedical vaccine exemptions and waning immunity may have been factors contributing to community-level pertussis outbreaks.

A geographic study of West Nile virus in humans, dead corvids and mosquitoes in Ontario using spatial scan statistics with a survival time application

Surveillance of West Nile virus (WNv) in Ontario has included passive reporting of human cases and testing of trapped mosquitoes and dead birds found by the public. The dead bird surveillance programme was limited to testing within a public health unit (PHU) until a small number of birds test positive. These dead corvid and mosquito surveillance programmes have not been compared for their ability to provide early warning in geographic areas where human cases occur each year. Spatial scan statistics were applied to time-to-event survival data based on first cases of WNv in found dead corvids, mosquitoes and humans. Clusters identified using raw data were compared to clusters based on model-adjusted survival times to evaluate whether geographic and sociodemographic factors influenced their distribution. Statistically significant (p < .05) space-time clusters of PHUs with faster time to detection were found using each surveillance data stream. During 2002-2004, the corvid surveillance programme outperformed the mosquito programme in terms of time to WNv detection, while the clusters of first-positive mosquito pools were more spatially similar to first human cases. In 2006, a cluster of first-positive dead corvids was located in northern PHUs and preceded a cluster of early human cases that was identified after controlling for the influence of geographic region and sociodemographic profile.

Spatial epidemiology of Escherichia coli O157:H7 in dairy cattle in relation to night roosts Of Sturnus vulgaris (European Starling) in Ohio, USA (2007-2009)

The goal of our study was to use spatial scan statics to determine whether the night roosts of European starlings (Sturnus vulgaris) act as point sources for the dissemination of Escherichia coli O157:H7 among dairy farms. From 2007 to 2009, we collected bovine faecal samples (n = 9000) and starling gastrointestinal contents (n = 430) from 150 dairy farms in northeastern Ohio, USA. Isolates of E. coli O157:H7 recovered from these samples were subtyped using multilocus variable-number tandem repeat analysis (MLVA). Generated MLVA types were used to construct a dendrogram based on a categorical multistate coefficient and unweighted pair-group method with arithmetic mean (UPGMA). Using a focused spatial scan statistic, we identified statistically significant spatial clusters among dairy farms surrounding starling night roosts, with an increased prevalence of E. coli O157:H7-positive bovine faecal pats, increased diversity of distinguishable MLVA types and a greater number of isolates with MLVA types from bovine-starling clades versus bovine-only clades. Thus, our findings are compatible with the hypothesis that starlings have a role in the dissemination of E. coli O157:H7 among dairy farms, and further research into starling management is warranted.

Analysis of the spatial variation of hospitalization admissions for hypertension disease in Shenzhen, China

In China, awareness about hypertension, the treatment rate and the control rate are low compared to developed countries, even though China's aging population has grown, especially in those areas with a high degree of urbanization. However, limited epidemiological studies have attempted to describe the spatial variation of the geo-referenced data on hypertension disease over an urban area of China. In this study, we applied hierarchical Bayesian models to explore the spatial heterogeneity of the relative risk for hypertension admissions throughout Shenzhen in 2011. The final model specification includes an intercept and spatial components (structured and unstructured). Although the road density could be used as a covariate in modeling, it is an indirect factor on the relative risk. In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters. The results showed that the relative risk for hospital admission for hypertension has high-value clusters in the south and southeastern Shenzhen. This study aimed to identify some specific regions with high relative risk, and this information is useful for the health administrators. Further research should address more-detailed data collection and an explanation of the spatial patterns.

Using the Flow of People in Cluster Detection and Inference

Objective

We present a new approach to the circular scan method [1] that uses the flow of people to detect and infer clusters of regions with high incidence of some event randomly distributed in a map. We use a real database of homicides cases in Minas Gerais state, in southeast Brazil to compare our proposed method with the original circular scan method in a study of simulated clusters and the real situation.

Introduction

The traditional SaTScan algorithm[1],[2] uses the euclidean distance between centroids of the regions in a map to assemble a connected (in the sense that two connected regions share a physical border) sets of regions. According to the value of the respective logarithm of the likelihood ratio (LLR) a connected set of regions can be classified as a statistically significant detected cluster. Considering the study of events like contagious diseases or homicides we consider using the flow of people between two regions in order to build up a set of regions (zone) with high incidence of cases of the event. In this sense the regions will be closer as the greater the flow of people between them. In a cluster of regions formed according to the criterion of proximity due to the flow of people, the regions will be not necessarily connected to each other.

Methods

We consider a study map with a number of observed cases and risk population for each region. The original circular scan algorithm randomly chooses one region as the first zone and calculates its respective LLR. In the next step a new zone is created including the first region and the region closest to it according the euclidean distance between their centroids and the respective LLR is calculated. This process is repeated until the zone population exceeds a certain percentage of the total population of the map. In our spatial flow scan algorithm everything works in the same manner except that the degree of proximity of two regions is given by the flow of people between them, the higher the flow between the regions closest one is the other. Instead of considering an order of increasing distances to add a region and create a new zone our algorithm uses a decreasing flow of people. In this way we can obtain a zone/cluster candidate composed of a number of non necessarily connected regions.

Results

Minas Gerais state is located in Brazil south-eastern region composed of 853 municipalities or regions with an estimated population of 19,150,344 in 2005. All data were obtained from the Brazilian Ministry of Health (WWW.DATASUS.GOV.BR) and Brazilian Institute of Geography and Statistics (WWW.IBGE.GOV.BR). In the period of 2003 to 2008 were recorded 20,912 homicides at a rate of 22 cases per 100,000. To measure the flow of people between the cities we obtain the data of bus round trips between all the 853 Minas Gerais municipalities from state department of highways (www.der.mg.gov.br). As a large number of pairs of cities have zero bus trips between them we use a gravity model [3] to estimate the flow of people. We use 30% as upper percentage for a zone population. With the real data of homicides cases the original circular scan found a significant cluster containing the city of Belo Horizonte which is the Minas Gerais state capital and large urban area that include Belo Horizonte and 22 more cities totalizing a population of about 3.5 milion people. Our adapted spatial scan algorithm also found a similar cluster including the capital Belo Horizonte but with two small cities less.

Conclusions

In simulation studies where the real cluster is known we observe that our spatial flow scan algorithm has a performance similar to the circular scan concerning detection power and slightly worse in relation to the positive predicted value (PPV) and the sensitivity when the real cluster is regular. However, the performance of our algorithm is clearly better with regard to the sensitivity and the PPV when the real cluster is irregular and or non-connected.