Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data

Background Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility. Methods Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021. Results We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination. Conclusions While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.

Spatially varying effects of measured confounding variables on disease risk

Background The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence. Methods We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina. Results The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender. Conclusion The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.

Light at night and risk of breast cancer: a systematic review and dose–response meta-analysis

Breast cancer is the most common malignancy in women and the second leading cause of cancer death overall. Besides genetic, reproductive, and hormonal factors involved in disease onset and progression, greater attention has focused recently on the etiologic role of environmental factors, including exposure to artificial lighting such as light-at-night (LAN). We investigated the extent to which LAN, including outdoor and indoor exposure, affects breast cancer risk. We performed a systematic review of epidemiological evidence on the association between LAN exposure and breast cancer risk, using a dose–response meta-analysis to examine the shape of the relation. We retrieved 17 eligible studies through September 13, 2021, including ten cohort and seven case–control studies. In the analysis comparing highest versus lowest LAN exposure, we found a positive association between exposure and disease risk (risk ratio [RR] 1.11, 95% confidence interval-CI 1.07–1.15), with comparable associations in case–control studies (RR 1.14, 95% CI 0.98–1.34) and cohort studies (RR 1.10, 95% CI 1.06–1.15). In stratified analyses, risk was similar for outdoor and indoor LAN exposure, while slightly stronger risks were observed for premenopausal women (premenopausal: RR 1.16, 95% CI 1.04–1.28; postmenopausal: 1.07, 95% CI 1.02–1.13) and for women with estrogen receptor (ER) positive breast cancer (ER + : RR 1.09, 95% CI 1.02–1.17; ER–: RR 1.07, 95% CI 0.92–1.23). The dose–response meta-analysis, performed only in studies investigating outdoor LAN using comparable exposure assessment, showed a linear relation up to 40 nW/cm2/sr after which the curve flattened, especially among premenopausal women. This first assessment of the dose–response relation between LAN and breast cancer supports a positive association in selected subgroups, particularly in premenopausal women.

Differences in depressive symptoms by rurality in Japan: a cross-sectional multilevel study using different aggregation units of municipalities and neighborhoods (JAGES)

Background Rurality can reflect many aspects of the community, including community characteristics that may be associated with mental health. In this study, we focused on geographical units to address multiple layers of a rural environment. By evaluating rurality at both the municipality and neighborhood (i.e., a smaller unit within a municipality) levels in Japan, we aimed to elucidate the relationship between depression and rurality. To explore the mechanisms linking rurality and depression, we examined how the association between rurality and depression can be explained by community social capital according to geographical units. Methods We used cross-sectional data from the 2016 wave of the Japan Gerontological Evaluation Study involving 144,822 respondents aged 65 years or older residing in 937 neighborhoods across 39 municipalities. The population density quintile for municipality-level rurality and the quintile for the time required to reach densely inhabited districts for neighborhood-level rurality were used. We calculated the prevalence ratios of depressive symptoms by gender using a three-level (individual, neighborhood, and municipality) Poisson regression. Community social capital was assessed using three components: civic participation, social cohesion, and reciprocity. Results The prevalence of depressive symptoms was higher in municipalities with lower population density than those with the highest population density; the ratios were 1.22 (95% confidence intervals: 1.15, 1.30) for men and 1.22 (1.13, 1.31) for women. In contrast, when evaluating rurality at the neighborhood level, the prevalence of depressive symptoms was 0.9 times lower for men in rural areas; no such association was observed for women. In rural municipalities, community civic participation was associated with an increased risk of depressive symptoms. In rural neighborhoods, community social cohesion and reciprocity were linked to a lower risk of depressive symptoms. Conclusions The association between rurality and depression varied according to geographical unit. In rural municipalities, the risk of depression may be higher for both men and women, and the presence of an environment conducive to civic participation may contribute to a higher risk of depression, as observed in this study. The risk of depression in men may be lower in rural neighborhoods in Japan, which may be related to high social cohesion and reciprocity.

Joint mapping of cardiovascular diseases: comparing the geographic patterns in incident acute myocardial infarction, stroke and atrial fibrillation, a Danish register-based cohort study 2014–15

Background Disease mapping aims at identifying geographic patterns in disease. This may provide a better understanding of disease aetiology and risk factors as well as enable targeted prevention and allocation of resources. Joint mapping of multiple diseases may lead to improved insights since e.g. similarities and differences between geographic patterns may reflect shared and disease-specific determinants of disease. The objective of this study was to compare the geographic patterns in incident acute myocardial infarction (AMI), stroke and atrial fibrillation (AF) using the unique, population-based Danish register data. Methods Incident AMI, stroke and AF was modelled by a multivariate Poisson model including a disease-specific random effect of municipality modelled by a multivariate conditionally autoregressive (MCAR) structure. Analyses were adjusted for age, sex and income. Results The study included 3.5 million adults contributing 6.8 million person-years. In total, 18,349 incident cases of AMI, 28,006 incident cases of stroke, and 39,040 incident cases of AF occurred. Estimated municipality-specific standardized incidence rates ranged from 0.76 to 1.35 for AMI, from 0.79 to 1.38 for stroke, and from 0.85 to 1.24 for AF. In all diseases, geographic variation with clusters of high or low risk of disease after adjustment was seen. The geographic patterns displayed overall similarities between the diseases, with stroke and AF having the strongest resemblances. The most notable difference was observed in Copenhagen (high risk of stroke and AF, low risk of AMI). AF showed the least geographic variation. Conclusion Using multiple-disease mapping, this study adds to the results of previous studies by enabling joint evaluation and comparison of the geographic patterns in AMI, stroke and AF. The simultaneous mapping of diseases displayed similarities and differences in occurrence that are non-assessable in traditional single-disease mapping studies. In addition to reflecting the fact that AF is a strong risk factor for stroke, the results suggested that AMI, stroke and AF share some, but not all environmental risk factors after accounting for age, sex and income (indicator of lifestyle and health behaviour).

Development of a Web GIS for small-scale detection and analysis of COVID-19 (SARS-CoV-2) cases based on volunteered geographic information for the city of Cologne, Germany, in July/August 2020

Background Various applications have been developed worldwide to contain and to combat the coronavirus disease-19 (COVID-19) pandemic. In this context, spatial information is always of great significance. The aim of this study is to describe the development of a Web GIS based on open source products for the collection and analysis of COVID-19 cases and its feasibility in terms of technical implementation and data protection. Methods With the help of this Web GIS, data on this issue were collected voluntarily from the Cologne area. Using house perimeters as a data basis, it was possible to check, in conjunction with the Official Topographic Cartographic Information System object type catalog, whether buildings with certain functions, for example residential building with trade and services, have been visited more frequently by infected persons than other types of buildings. In this context, data protection and ethical and legal issues were considered. Results The results of this study show that the development of a Web GIS for the generation and evaluation of volunteered geographic information (VGI) with the help of open source software is possible. Furthermore, there are numerous data protection and ethical and legal aspects to consider, which not only affect VGI per se but also affect IT security. Conclusions From a data protection perspective, more attention needs to be paid to the intervention and post-processing of data. In addition, official data must always be used as a reference for the actual spatial consideration of the number of infections. However, VGI provides added value at a small-scale level, so that valid information can also be reliably derived in the context of health issues. The creation of guidelines for the consideration of data protection, ethical aspects, and legal requirements in the context of VGI-based applications must also be considered. Trial registration The article does not report the results of a health care intervention for human participants

Impact of imprecise household location on effective coverage estimates generated through linking household and health provider data by geographic proximity: a simulation study

Background Geographic proximity is often used to link household and health provider data to estimate effective coverage of health interventions. Existing household surveys often provide displaced data on the central point within household clusters rather than household location. This may introduce error into analyses based on the distance between households and providers. Methods We assessed the effect of imprecise household location on quality-adjusted effective coverage of child curative services estimated by linking sick children to providers based on geographic proximity. We used data on care-seeking for child illness and health provider quality in Southern Province, Zambia. The dataset included the location of respondent households, a census of providers, and data on the exact outlets utilized by sick children included in the study. We displaced the central point of each household cluster point five times. We calculated quality-adjusted coverage by assigning each sick child to a provider’s care based on three measures of geographic proximity (Euclidean distance, travel time, and geographic radius) from the household location, cluster point, and displaced cluster locations. We compared the estimates of quality-adjusted coverage to each other and estimates using each sick child’s true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores. Results Fewer children were linked to their true source of care using cluster locations than household locations. Effective coverage estimates produced using undisplaced or displaced cluster points did not vary significantly from estimates produced using household location data or each sick child’s true source of care. However, the sensitivity analyses simulating greater variability in provider quality showed bias in effective coverage estimates produced with the geographic radius and travel time method using imprecise location data in some scenarios. Conclusions Use of undisplaced or displaced cluster location reduced the proportion of children that linked to their true source of care. In settings with minimal variability in quality within provider categories, the impact on effective coverage estimates is limited. However, use of imprecise household location and choice of geographic linking method can bias estimates in areas with high variability in provider quality or preferential care-seeking.

Improving spatial data in health geographics: a practical approach for testing data to measure children’s physical activity and food environments using Google Street View

Background Geographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children’s health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided ‘as is’, and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand. Methods We used the ‘streetview’ python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets. Results A total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be ‘complete’. A total of 664 (55%) food outlets were identified and temporally validated. Conclusions Our research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.