Ecological fallacies and the analysis of areal census data

More than 'minority': Social tolerance and youth wellbeing at the intersection of ethnicity and neighbourhood segregation

Social tolerance is an indicator of healthy diverse societies, and is associated with individual well-being. However, previous studies have found that social tolerance varies between groups and is experienced differently through one's immediate social context. This lends to the plausibility of ethnicity and neighbourhood ethnic composition altering one's experience of living in their neighbourhood and the impact of well-being. Relying on 6 waves of nationally-representative panel data from young adults in Singapore, we investigate how ethnicity and neighbourhood ethnic composition influences the relationship between social tolerance and well-being. We find that this relationship is moderated by both factors in ways that deviates from the conventional majority-minority dichotomy found in literature. This indicates that efforts made to improve social tolerance may lead to varying outcomes, depending on one's ethnicity and social context.

The Spatial Scale and Spread of Child Victimization

Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K-function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions.

A simulated 'sandbox' for exploring the modifiable areal unit problem in aggregation and disaggregation

We present a spatial testbed of simulated boundary data based on a set of very high-resolution census-based areal units surrounding Guadalajara, Mexico. From these input areal units, we simulated 10 levels of spatial resolutions, ranging from levels with 5,515-52,388 units and 100 simulated zonal configurations for each level - totalling 1,000 simulated sets of areal units. These data facilitate interrogating various realizations of the data and the effects of the spatial coarseness and zonal configurations, the Modifiable Areal Unit Problem (MAUP), on applications such as model training, model prediction, disaggregation, and aggregation processes. Further, these data can facilitate the production of spatially explicit, non-parametric estimates of confidence intervals via bootstrapping. We provide a pre-processed version of these 1,000 simulated sets of areal units, meta- and summary data to assist in their use, and a code notebook with the means to alter and/or reproduce these data.

Mapping the transcriptome: Realizing the full potential of spatial data analysis

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Impacts of exposure to humidex on cardiovascular mortality: a multi-city study in Southwest China

BACKGROUND

Many studies have reported the association between ambient temperature and mortality from cardiovascular disease (CVD). However, the health effects of humidity are still unclear, much less the combined effects of temperature and humidity. In this study, we used humidex to quantify the effect of temperature and humidity combined on CVD mortality.

METHODS

Daily meteorological, air pollution, and CVD mortality data were collected in four cities in southwest China. We used a distributed lag non-linear model (DLNM) in the first stage to assess the exposure-response association between humidex and city-specific CVD mortality. A multivariate meta-analysis was conducted in the second stage to pool these effects at the overall level. To evaluate the mortality burden of high and low humidex, we determined the attributable fraction (AF). According to the abovementioned processes, stratified analyses were conducted based on various demographic factors.

RESULTS

Humidex and the CVD exposure-response curve showed an inverted "J" shape, the minimum mortality humidex (MMH) was 31.7 (77th percentile), and the cumulative relative risk (CRR) was 2.27 (95% confidence interval [CI], 1.76-2.91). At extremely high and low humidex, CRRs were 1.19 (95% CI, 0.98-1.44) and 2.52 (95% CI, 1.88-3.38), respectively. The burden of CVD mortality attributed to non-optimal humidex was 21.59% (95% empirical CI [eCI], 18.12-24.59%), most of which was due to low humidex, with an AF of 20.16% (95% eCI, 16.72-23.23%).

CONCLUSIONS

Low humidex could significantly increase the risk of CVD mortality, and vulnerability to humidex differed across populations with different demographic characteristics. The elderly (> 64 years old), unmarried people, and those with a limited level of education (1-9 years) were especially susceptible to low humidex. Therefore, humidex is appropriate as a predictor in a CVD early-warning system.

Satisfaction with urban trees associates with tree canopy cover and tree visibility around the home

Many world cities want to expand the number of urban trees. How this expansion occurs should consider what people expect from trees based on how they experience and perceive these trees. Therefore, we need a better understanding of how people perceptually respond to urban tree abundance. This research examined whether people's satisfaction with urban trees and satisfaction with the management of those trees were related to objective measures of greenery such as the Normalized Difference Vegetation Index (NDVI), percent tree canopy cover, and the Viewshed Greenness Visibility Index (VGVI) for trees. We used a demographically and geographically representative survey of 223 residents in Toronto, Canada, and calculated NDVI, canopy cover, and VGVI at three neighbourhood sizes. We analysed the data using generalized linear regression. We found that canopy cover and VGVI had a positive association with satisfaction with urban trees. The associations were comparatively stronger at larger neighbourhood scales than at smaller scales. There were no statistically significant associations with NDVI or satisfaction with the management of urban trees.

Do ethnic integration policies also improve socio-economic integration? A study of residential segregation in Singapore

Concerns over the negative impact of residential segregation have motivated desegregation policies around the world. Singapore’s Ethnic Integration Policy (EIP) is a desegregation policy perceived to be effective in reducing ethnic segregation. However, there is little clarity about how the Ethnic Integration Policy might affect socio-economic segregation, another important dimension of segregation. This study explores Singapore’s socio-economic and ethnic residential segregation patterns from 1990 until 2020, focussing on three scales of analysis: national, city district-level (subzone) and building-level. Ethnic and socio-economic segregation, which were generally low, fluctuated in opposite directions over the years. While public housing flats were exposed to less ethnic and socio-economic segregation than private housing, findings suggest a negative relationship between ethnic and socio-economic segregation for majority public housing subzones. This inverse relationship between socio-economic and ethnic segregation might be due to the Ethnic Integration Policy's distortionary effect on flat resale prices. These findings highlight the need for greater attentiveness to residential integration policies’ impact on both socio-economic and ethnic integration, and not to assume that policies aimed at improving one would be sufficient to address the other.

The epidemiology, healthcare and societal burden of basal cell carcinoma in Wales 2000-2018: a retrospective nationwide analysis

BACKGROUND

Basal cell carcinoma (BCC) represents the most commonly occurring cancer worldwide within the white population. Reports predict 298 308 cases of BCC in the UK by 2025, at a cost of £265-366 million to the National Health Service (NHS). Despite the morbidity, societal and healthcare pressures brought about by BCC, routinely collected healthcare data and global registration remain limited.

OBJECTIVES

To calculate the incidence of BCC in Wales between 2000 and 2018 and to establish the related healthcare utilization and estimated cost of care.

METHODS

The Secure Anonymised Information Linkage (SAIL) databank is one of the largest and most robust health and social care data repositories in the UK. Cancer registry data were linked to routinely collected healthcare databases between 2000 and 2018. Pathological data from Swansea Bay University Health Board (SBUHB) were used for internal validation.

RESULTS

A total of 61 404 histologically proven BCCs were identified within the SAIL Databank during the study period. The European age-standardized incidence for BCC in 2018 was 224.6 per 100 000 person-years. Based on validated regional data, a 45% greater incidence was noted within SBUHB pathology vs. matched regions within SAIL between 2016 and 2018. A negative association between deprivation and incidence was noted with a higher incidence in the least socially deprived and rural dwellers. Approximately 2% travelled 25-50 miles for dermatological services compared with 37% for plastic surgery. Estimated NHS costs of surgically managed lesions for 2002-2019 equated to £119.2-164.4 million.

CONCLUSIONS

Robust epidemiological data that are internationally comparable and representative are scarce for nonmelanoma skin cancer. The rising global incidence coupled with struggling healthcare systems in the post-COVID-19 recovery period serve to intensify the societal and healthcare impact. This study is the first to demonstrate the incidence of BCC in Wales and is one of a small number in the UK using internally validated large cohort datasets. Furthermore, our findings demonstrate one of the highest published incidences within the UK and Europe.

Spatial analysis of climatic factors and plasmodium falciparum malaria prevalence among children in Ghana

Malaria is a major public health problem especially in Africa where 94% of global malaria cases occur. Malaria prevalence and mortalities are disproportionately higher among children. In 2019, children accounted for 67% of malaria deaths globally. Recently, climatic factors have been acknowledged to influence the number and severity of malaria cases. Plasmodium falciparum-the most deadly malaria parasite, accounts for more than 95% of malaria infections among children in Ghana. Using the 2017 Ghana Demographic Health Survey data, we examined the local variation in the prevalence and climatic determinants of child malaria. The findings showed that climatic factors such as temperature, rainfall aridity and Enhanced Vegetation Index are significantly and positively associated with Plasmodium falciparum malaria prevalence among children in Ghana. However, there are local variations in how these climatic factors affect child malaria prevalence. Plasmodium falciparum malaria prevalence was highest among children in the south western, north western and northern Ghana.

Temperature-related mortality and associated vulnerabilities: evidence from Scotland using extended time-series datasets

BACKGROUND

Adverse health impacts have been found under extreme temperatures in many parts of the world. The majority of such research to date for the UK has been conducted on populations in England, whilst the impacts of ambient temperature on health outcomes in Scottish populations remain largely unknown.

METHODS

This study uses time-series regression analysis with distributed lag non-linear models to characterise acute relationships between daily mean ambient temperature and mortality in Scotland including the four largest cities (Aberdeen, Dundee, Edinburgh and Glasgow) and three regions during 1974-2018. Increases in mortality risk under extreme cold and heat in individual cities and regions were aggregated using multivariate meta-analysis. Cold results are summarised by comparing the relative risk (RR) of death at the 1st percentile of localised temperature distributions compared to the 10th percentile, and heat effects as the RR at the 99th compared to the 90th percentile.

RESULTS

Adverse cold effects were observed in all cities and regions, and heat effects were apparent in all cities and regions except northern Scotland. Aggregate all-cause mortality risk in Scotland was estimated to increase by 10% (95% confidence interval, CI: 7%, 13%) under extreme cold and 4% (CI: 2%, 5%) under extreme heat. People in urban areas experienced higher mortality risk under extreme cold and heat than those in rural regions. The elderly had the highest RR under both extreme cold and heat. Males experienced greater cold effects than females, whereas the reverse was true with heat effects, particularly among the elderly. Those who were unmarried had higher RR than those married under extreme heat, and the effect remained after controlling for age. The younger population living in the most deprived areas experienced higher cold and heat effects than in less deprived areas. Deaths from respiratory diseases were most sensitive to both cold and heat exposures, although mortality risk for cardiovascular diseases was also heightened, particularly in the elderly. Cold effects were lower in the most recent 15 years, which may be linked to policies and actions in preventing the vulnerable population from cold impacts. No temporal trend was found with the heat effect.

CONCLUSIONS

This study assesses mortality risk associated with extreme temperatures in Scotland and identifies those groups who would benefit most from targeted actions to reduce cold- and heat-related mortalities.

Spatial equity implications and neighborhood indicators of ridehailing trip frequency and vehicle miles traveled in the phoenix metro region

Early optimism for ridehailing services to complement existing public transit services and offer individuals another shared mobility service with reduced travel costs and improved travel times have largely proven to be unsubstantiated. This unwelcomed outcome, in part due to the popularity of ridehailing services among wealthier populations and restrictions on the less-expensive ridesharing service in some urban settings, has likely instead resulted in heightened disparities in access to this on-demand mobility option for historically-marginalized populations and under-resourced communities. This hypothesis is examined by estimating the macro-level socioeconomic and built environment determinants of ridehailing pick-ups and drop-offs in the Phoenix metro region with spatial lag of X modeling. A geographically weighted regression (GWR) model of vehicle miles traveled was then estimated using route-level ridehailing data from a third-party mileage tracking app to identify zonal attributes associated with this measure of vehicle-based exposure. Together, study findings highlight the benefits and drawbacks of greater ridehailing service activity, identifying a need for programs and interventions that safeguard and improve access to affordable high-quality mobility options for transportation disadvantaged neighborhoods.

Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface

Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity.

Measurement(s)	Population Density
Technology Type(s)	satellite imaging
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Environment	populated place
Sample Characteristic - Location	contiguous United States of America

Refined Urban Functional Zone Mapping by Integrating Open-Source Data

The determination of a reasonable spatial analysis unit is an essential step in urban functional zone (UFZ) division, which significantly affects the results. However, most studies on the division of functional zones are based on excessively large spatial units, such as blocks or traffic analysis zones (TAZs), which easily overlook the detailed characteristics of urban regions and introduce bias to the research conclusion. To address this issue, a refined zone segmentation method, namely, the Voronoi diagram for the polygon method, was proposed to generate refined spatial analysis units. Afterward, the functional topics of the spatial analysis unit were classified by a multiclass support vector machine (SVM) to produce the final UFZ map, where the functional topics of each spatial unit were obtained by coupling latent Dirichlet allocation (LDA). To verify the effectiveness of the proposed method, experiments were conducted in Beijing, China. The results indicated that the proposed segmentation method can generate fine-scale spatial units and provide fine-grained and higher accuracy UFZs (overall accuracy = 84%; kappa = 0.82).

Individual, interpersonal, and neighborhood measures associated with opioid use stigma: Evidence from a nationally representative survey

Despite growing awareness of opioid use disorder (OUD), fatal overdoses and downstream health conditions (e. g., hepatitis C and HIV) continue to rise in some populations. Various interrelated structural forces, together with social and economic determinants, contribute to this ongoing crisis; among these, access to medications for opioid use disorder (MOUD) and stigma towards people with OUD remain understudied. We combined data on methadone, buprenorphine, and naltrexone providers from SAMHSA’s 2019 directory, additional naltrexone providers from Vivitrol’s location finder service, with a nationally representative survey called “The AmeriSpeak survey on stigma toward people with OUD.” Integrating the social-ecological framework, we focus on individual characteristics, personal and family members’ experience with OUD, and spatial access to MOUD at the community level. We use nationally representative survey data from 3008 respondents who completed their survey in 2020. Recognizing that stigma is a multifaceted construct, we also examine how the process varies for different types of stigma, specifically perceived dangerousness and untrustworthiness, as well as social distancing measures under different scenarios. We found a significant association between stigma and spatial access to MOUD — more resources are related to weaker stigma. Respondents had a stronger stigma towards people experiencing current OUD (versus past OUD), and they were more concerned about OUD if the person would marry into their family (versus being their coworkers). Additionally, respondents’ age, sex, education, and personal experience with OUD were also associated with their stigma, and the association can vary depending on the specific type of stigma. Overall, stigma towards people with OUD was associated with both personal experiences and environmental measures.

A Monte Carlo analysis of false inference in spatial conflict event studies

Spatial event data is heavily used in contemporary research on political violence. Such data are oftentimes mapped onto grid-cells or administrative regions to draw inference about the determinants of conflict intensity. This setup can identify geographic determinants of violence, but is also prone to methodological issues. Problems resulting from spatial aggregation and dependence have been raised in methodological studies, but are rarely accounted for in applied research. As a consequence, we know little about the empirical relevance of these general problems and the trustworthiness of a popular research design. We address these questions by simulating conflict events based on spatial covariates from seven high-profile conflicts. We find that standard designs fail to deliver reliable inference even under ideal conditions at alarming rates. We also test a set of statistical remedies which strongly improve the results: Controlling for the geographic area of spatial units eliminates an important source of spurious correlation. In time-series analyses, the same result can be achieved with unit-level fixed effects. Under outcome diffusion, spatial lag models with area controls produce most reliable inference. When those are computationally intractable, geographically larger aggregations lead to similar improvements. Generally, all analyses should be performed at two separate levels of geographic aggregation. To facilitate future research into geographic methods, we release the Simple Conflict Event Generator (SCEG) developed for this analysis.

The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions

Spatial data are used in many scientific domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modifiable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities specific to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural field, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research.

Association between ambient temperature and varicella among adults in Qingdao, China during 2008-2019

Little concern has been paid to the relationship between temperature and varicella among adults. Daily meteorological data and varicella cases in Qingdao among adults from 1 January 2008 to 31 December 2019 were collected. A combination of quasi-Poisson generalized additive model (GAM) and distributed lag non-linear model (DLNM) was conducted to assess the temperature-lag-varicella relationship. We also estimated the lag-response curves for different temperatures and the exposure-response relationships for different lag days. The number of varicella cases was 10,296. Compared with the minimum-varicella temperature (25°C), we found the largest effect of temperature on varicella within 21 lag days was at 1°C (RR, 6.72; 95% CI, 2.90-15.57), and then the effect declined as the temperature increased. A similar trend of rising first and then falling was found in temperature-response curves for different lag days. A reverse U-shape lag pattern was found for different levels of temperatures. Temperature may affect varicella.

Racial and ethnic disparities in pregnancy complications and the protective role of greenspace: A retrospective birth cohort study

Greenspace may positively impact pregnancy health for racially and economically minoritized populations; few studies have examined local availability and accessibility of green/park space in reducing maternal morbidity. The objective of this retrospective birth cohort study was to examine the association between residential exposure to greenspace and adverse pregnancy health outcomes in a Southern US state characterized by high poverty and racial disparities in maternal health (2013-2017). National data from the Protected Area database - United States (PAD-US) and ParkServe estimated three publicly available and accessible residential greenspace measures-a more direct proxy than using remotely-sensed greenness indicators (e.g., normalized difference vegetation index (NDVI))-(a) percent area of greenspace (M1), (b) area of available greenspace per person (M2), (c) total population within a 10-minute walk (M3). Generalized Estimating Equations with logistic regression were used to examine the association between individual greenspace metrics and South Carolina hospital deliveries (n = 238,922 deliveries) for women with correlated maternal health outcomes for gestational hypertension (GHTN), gestational diabetes (GD), severe maternal morbidity (SMM), preeclampsia (PRE), mental disorders (MD), depressive disorders (DD), and preterm birth (PTB). Lowest compared to highest tertiles of all three metrics were associated with increased risk for MD, DD, and a monotonic increase in GD, particularly for black women. Women with the lowest access to M2 and M3 were more at risk for PRE, PTB, and MD. We observed that women in low-income, majority-black communities in the lowest versus highest tertile of M2 were more likely to experience a DD, MD, SMM, or PTB compared to primarily high-income majority-white communities. Available and accessible green/park space may present as an effective nature-based intervention to reduce maternal complications, particularly for gestational diabetes and other pregnancy health risks for which there are currently few known evidence-based primary prevention strategies.

The conspiracy of Covid-19 and 5G: Spatial analysis fallacies in the age of data democratization

In a context of mistrust in public health institutions and practices, anti-COVID/vaccination protests and the storming of Congress have illustrated that conspiracy theories are real and immanent threat to health and wellbeing, democracy, and public understanding of science. One manifestation of this is the suggested correlation of COVID-19 with 5G mobile technology. Throughout 2020, this alleged correlation was promoted and distributed widely on social media, often in the form of maps overlaying the distribution of COVID-19 cases with the instillation of 5G towers. These conspiracy theories are not fringe phenomena, and they form part of a growing repertoire for conspiracist activist groups with capacities for organised violence. In this paper, we outline how spatial data have been co-opted, and spatial correlations asserted by conspiracy theorists. We consider the basis of their claims of causal association with reference to three key areas of geographical explanation: (1) how social properties are constituted and how they exert complex causal forces, (2) the pitfalls of correlation with spatial and ecological data, and (3) the challenges of specifying and interpreting causal effects with spatial data. For each, we consider the unique theoretical and technical challenges involved in specifying meaningful correlation, and how their discarding facilitates conspiracist attribution. In doing so, we offer a basis both to interrogate conspiracists’ uses and interpretation of data from elementary principles and offer some cautionary notes on the potential for their future misuse in an age of data democratization. Finally, this paper contributes to work on the basis of conspiracy theories in general, by asserting how – absent an appreciation of these key methodological principles – spatial health data may be especially prone to co-option by conspiracist groups.

How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics

Cities worldwide adopted lockdown policies in response to the outbreak of coronavirus disease 2019 (COVID-19), significantly influencing people's travel behavior. In particular, micro-mobility, an emerging mode of urban transport, is profoundly shaped by this crisis. However, there is limited research devoted to understanding the rapidly evolving trip patterns of micro-mobility in response to COVID-19. To fill this gap, we analyze the changes in micro-mobility usage before and during the lockdown period exploiting high-resolution micro-mobility trip data collected in Zurich, Switzerland. Specifically, docked bike, docked e-bike, and dockless e-bike are evaluated and compared from the perspective of space, time and semantics. First, the spatial and temporal analysis results uncover that the number of trips decreased remarkably during the lockdown period. The striking difference between the normal and lockdown period is the decline in the peak hours of workdays. Second, the origin-destination flows are used to construct spatially embedded networks. The results suggest that the origin-destination pairs remain similar during the lockdown period, while the numbers of trips between each origin-destination pair is reduced due to COVID-19 pandemic. Finally, the semantic analysis is conducted to uncover the changes in trip purpose. It is revealed that the proportions of Home, Park, and Grocery activities increase, while the proportions of Leisure and Shopping activities decrease during the lockdown period. The above results can help planners and policymakers better make evidence-based policies regarding micro-mobility in the post-pandemic society.

Where do university graduates live? – A computer vision approach using satellite images

In this article, we examine to what extent the settlement of university graduates can be derived from satellite images. We apply a convolutional neural network (CNN) to grid images of a city and predict five density classes of university graduates at a micro level (250 m × 250 m grid size). The CNN reaches an accuracy rate of 40.5% (random approach: 20%). Furthermore, the accuracy increases to 78.3% when considering a one-class deviation compared to the true class. We also examine the predictability of inhabited and uninhabited grid cells, where we achieve an accuracy of 95.3% using the same CNN. From this, we conclude that there is information that correlates with graduate density that can be derived by analysing only satellite images. The findings show the high potential of computer vision for urban and regional economics. Particularly in data-poor regions, the approach utilised facilitates comparative analytics and provides a possible solution for the modifiable aerial unit (MAU) problem. The MAU problem is a statistical bias that can influence the results of a spatial data analysis of point-estimate data that is aggregated in districts of different shapes and sizes, distorting the results.

On the link between temperature and regional COVID-19 severity: Evidence from Italy

This study analyzes the link between temperature and COVID-19 incidence in a sample of Italian regions during the period that covers the first epidemic wave of 2020. To that end, Bayesian model averaging techniques are used to analyze the relevance of temperature together with a set of additional climatic, demographic, social, and health policy factors. The robustness of individual predictors is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by temperature given that it appears as one of the most relevant determinants reducing regional coronavirus disease 2019 (COVID-19) severity. The strong negative link observed in our baseline analysis is robust to the specification of priors, the scale of analysis, the correction of measurement errors in the data due to under-reporting, the time window considered, and the inclusion of spatial effects in the model. In a second step, we compute relative importance metrics that decompose the variability explained by the model. We find that cross-regional temperature differentials explain a large share of the observed variation on the number of infections.

Is urban green space associated with lower mental healthcare expenditure?

INTRODUCTION

While the evidence of mental health benefits from investing in green space accumulates, claims of reduced healthcare expenditure are rarely supported by evidence from analyses of actual healthcare data. Additionally, the question of 'who pays?' has been ignored. We addressed these gaps using person-level data in three Australian cities.

METHODS

55,339 participants with a mean follow-up time of 4.97 years in the Sax Institute's 45 and Up Study (wave 2, collected 2012-2015) were linked to fee-for-service records of antidepressant prescriptions and talking therapy subsidised by the Australian Government (including data on per unit fee, state subsidy, and individual co-payment). Total green space, tree canopy and open grass within 1.6 km road network distances were linked to each participant. Multilevel logistic, negative binomial, and generalised linear models with gamma distribution adjusted for demographic and socioeconomic confounders were used to assess association between each green space variable and prescribing/referral and costs of antidepressants and talking therapy.

RESULTS

Prescription of at least one course of antidepressants occurred for 20.01% (n = 11,071). Referral for at least one session of talking therapy occurred in 8.95% (n = 4954). 13,482 participants (24.4%) had either a prescription or a referral. A 10% increase in green space was associated with higher levels of antidepressant prescribing (e.g. incident rate ratio (IRR) = 1.06, 95%CI = 1.04-1.08). Tree canopy was not associated with antidepressant prescribing or referrals for talking therapy. Open grass was associated with higher odds (OR = 1.17, 95%CI = 1.13-1.20) and counts (IRR = 1.05, 95%CI = 1.02-1.08) of antidepressant prescriptions. Open grass was also associated with lower odds (OR = 0.87, 95%CI = 0.82-0.92) and counts (IRR = 0.93, 95%CI = 0.90-0.96) of talking therapy referrals. Open grass was associated with higher total and mean per-person levels of expenditure on antidepressant prescriptions.

CONCLUSION

Although green space supports mental health, these unexpected results provide pause for reflection on whether greening strategies will always result in purported reductions in mental healthcare expenditure.

Estimating multiple greenspace exposure types and their associations with neighbourhood premature mortality: A socioecological study

BACKGROUND

Greenspace exposures are often measured using single exposure metrics, which can lead to conflicting results. Existing methodologies are limited in their ability to estimate greenspace exposure comprehensively. We demonstrate new methods for estimating single and combined greenspace exposure metrics, representing multiple exposure types that combine impacts at various scales. We also investigate the association between those greenspace exposure types and premature mortality.

METHODS

We used geospatial data and spatial analytics to model and map greenspace availability, accessibility and eye-level visibility exposure metrics. These were harmonised and standardised to create a novel composite greenspace exposure index (CGEI). Using these metrics, we investigated associations between greenspace exposures and years of potential life lost (YPLL) for 1673 neighbourhoods applying spatial autoregressive models. We also investigated the variations in these associations in conjunction with levels of socioeconomic deprivation based on the index of multiple deprivations.

RESULTS

Our new CGEI metric provides the opportunity to estimate spatially explicit total greenspace exposure. We found that a 1-unit increase in neighbourhood CGEI was associated with approximately a 10-year reduction in YPLL. Meaning a 0.1 increment or 10% increase in the CGEI is associated with an approximately one year lower premature mortality value. A single 1-unit increase in greenspace availability was associated with a YPLL reduction of 9.8 years, whereas greenness visibility related to a reduction of 6.14 years. We found no significant association between greenspace accessibility and YPLL. Our results further identified divergent trends in the relations between greenspace exposure types (e.g. availability vs. accessibility) and levels of socioeconomic deprivation (e.g. least vs. most).

CONCLUSION

Our methods and metrics provide a novel approach to the assessment of multiple greenspace exposure types, and can be linked to the broader exposome framework. Our results showed that a higher composite greenspace exposure is associated with lower premature mortality.

Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

This article assesses whether ambient population is a more suitable population-at-risk measure for crime types with mobile targets than residential population for the purpose of intelligence-led policing applications. Specifically, the potential use of ambient population as a crime rate denominator and predictor for predictive policing models is evaluated, using mobile phone data (with a total of 9,397,473 data points) as a proxy. The results show that ambient population correlates more strongly with crime than residential population. Crime rates based on ambient population designate different problem areas than crime rates based on residential population. The prediction performance of predictive policing models can be improved by using ambient population instead of residential population. These findings support that ambient population is a more suitable population-at-risk measure, as it better reflects the underlying dynamics in spatiotemporal crime trends. Its use has therefore much as-of-yet unused potential not only for criminal research and theory testing, but also for intelligence-led policy and practice.

Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers

Several determinants are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated the effects of the most prominent determining factors on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany's National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy) or within residential areas, and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects leading to an increase in new COVID-19 cases for public mobility in retail and recreational areas or workplaces, awareness measured by searches for "corona" in Google, higher rainfall, and some socio-demographic factors. Non-pharmaceutical interventions were found to be effective in reducing case numbers. This comprehensive causal graph analysis of a variety of determinants affecting COVID-19 progression gives strong evidence for the driving forces of mobility, public awareness, and temperature, whose implications need to be taken into account for future decisions regarding pandemic management.

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data

BACKGROUND

The growth of geolocated data has opened the door to a wealth of new research opportunities in the health fields. One avenue of particular interest is the relationship between the spaces where people spend time and their health outcomes. This research model typically intersects individual data collected on a specific cohort with publicly available socioeconomic or environmental aggregate data. In spatial terms: individuals are represented as points on map at a particular time, and context is represented as polygons containing aggregated or modeled data from sampled observations. Uncertainty abounds in these kinds of complex representations.

METHODS

We present four sensitivity analysis approaches that interrogate the stability of spatial and temporal relationships between point and polygon data. Positional accuracy assesses the significance of assigning the point to the correct polygon. Neighborhood size investigates how the size of the context assumed to be relevant impacts observed results. Life course considers the impact of variation in contextual effects over time. Time of day recognizes that most people occupy different spaces throughout the day, and that exposure is not simply a function residential location. We use eight years of point data from a longitudinal study of children living in rural Pennsylvania and North Carolina and eight years of air pollution and population data presented at 0.5 mile (0.805 km) grid cells. We first identify the challenges faced for research attempting to match individual outcomes to contextual effects, then present methods for estimating the effect this uncertainty could introduce into an analysis and finally contextualize these measures as part of a larger framework on uncertainty analysis.

RESULTS

Spatial and temporal uncertainty is highly variable across the children within our cohort and the population in general. For our test datasets, we find greater uncertainty over the life course than in positional accuracy and neighborhood size. Time of day uncertainty is relatively low for these children.

CONCLUSIONS

Spatial and temporal uncertainty should be considered for each individual in a study since the magnitude can vary considerably across observations. The underlying assumptions driving the source data play an important role in the level of measured uncertainty.

Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa

Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.

Methodological challenges to confirmatory latent variable models of social vulnerability

Socially vulnerable communities experience disproportionately negative outcomes following natural disasters and underscoring a need for well-validated measures to identify those at risk. However, questions have surfaced regarding the factor structure, internal consistency, and generalizability of social vulnerability measures. A reliance on data-driven techniques, which are susceptible to sample-specific characteristics, has likely exacerbated the difficulty generalizing social vulnerability measures across contexts. This study sought to validate previously published structures of SoVI using confirmatory factor analysis (CFA). We fit CFA models of 28 sociodemographic variables frequently used to calculate a commonly used measure, the social vulnerability index (SoVI), drawn from the American Community Survey across 4162 census tracts in Florida. Confirmatory models generally did not support theory-driven pillars of SoVI that were previously used to study vulnerability in the New York metropolitan area. Modified models and alternative SoVI factor structures also failed to fit the data. Many of the input variables displayed little to no variability, limiting their utility and explanatory power. Taken together, our results highlight the poor generalizability of SoVI across contexts, but raise several important considerations for reliability and validity, as well as issues related to source data and scale. We discuss the implications of these findings for improved theory-driven measurement of social vulnerability.

A Generalized Additive Model Correlating Blacklegged Ticks With White-Tailed Deer Density, Temperature, and Humidity in Maine, USA, 1990-2013

Geographical range expansions of blacklegged tick [Ixodes scapularis Say (Acari: Ixodidae)] populations over time in the United States have been attributed to a mosaic of factors including 20th century reforestation followed by suburbanization, burgeoning populations of the white-tailed deer [Odocoileus virginianus Zimmerman (Artiodactyla: Cervidae)], and, at the northern edge of I. scapularis' range, climate change. Maine, a high Lyme disease incidence state, has been experiencing warmer and shorter winter seasons, and relatively more so in its northern tier. Maine served as a case study to investigate the interacting impacts of deer and seasonal climatology on the spatial and temporal distribution of I. scapularis. A passive tick surveillance dataset indexed abundance of I. scapularis nymphs for the state, 1990-2013. With Maine's wildlife management districts as the spatial unit, we used a generalized additive model to assess linear and nonlinear relationships between I. scapularis nymph abundance and predictors. Nymph submission rate increased with increasing deer densities up to ~5 deer/km2 (13 deer/mi2), but beyond this threshold did not vary with deer density. This corroborated the idea of a saturating relationship between I. scapularis and deer density. Nymphs also were associated with warmer minimum winter temperatures, earlier degree-day accumulation, and higher relative humidity. However, nymph abundance only increased with warmer winters and degree-day accumulation where deer density exceeded ~2 deer/km2 (~6/mi2). Anticipated increases in I. scapularis in the northern tier could be partially mitigated through deer herd management.

The temporal characteristics of the lag-response relationship and related key time points between ambient temperature and hand, foot and mouth disease: A multicity study from mainland China

BACKGROUND

Previous studies have thoroughly elucidated the exposure-response relationship between ambient temperature and hand, foot, and mouth disease (HFMD), whereas very little concern has been to the lag-response relationship and related key time points.

OBJECTIVES

We aimed to clarify the temporal characteristics of the lag-response relationship between ambient temperature and HFMD and how they may vary spatially.

METHODS

We retrieved the daily time series of meteorological variables and HFMD counts for 143 cities in mainland China between 2009 and 2014. We estimated the city-specific lag-response curve between ambient temperature and HFMD and related key time points by applying common distributed lag nonlinear models (DLNM) and Monte Carlo simulation methods. Then, we pooled the city-specific estimates by performing a meta-regression with the city-specific characteristics as meta-predictors to explain the potential spatial heterogeneity.

RESULTS

We found a robust lag pattern between temperature and HFMD for different levels of temperatures. The temporal change of risk obtained its maximum value on the current day but dropped sharply thereafter and then rebounded to a secondary peak, which implied the presence of a harvesting effect. By contrast, the estimation of key time points showed substantial heterogeneity, especially at high temperature (the I² statistics ranged from 47% to 80%). With one unit increase in the geographic index, the secondary peak would arrive 0.37 (0.02, 0.71) days later. With one unit increase in the economic index and climatic index, the duration time of the lag-response curve would be lengthened by 0.36 (0.1, 0.62) and 0.92 (0.54, 1.29) days, respectively.

CONCLUSION

Our study examined the lag pattern and spatial heterogeneity of the lag-response relationship between temperature and HFMD. Those findings gave us new insights into the complex association and the related mechanisms between weather and HFMD and important information for weather-based disease early warning systems.

Mental Health and Recreation Opportunities

The environment has direct and indirect effects on mental health. Previous studies acknowledge that the poor design of communities and social environments leads to increased psychological distress, but methodological issues make it difficult to draw clear conclusions. Recent public health, leisure and recreation studies have tried to determine the relationship between recreation opportunities and mental health. However, previous studies have heavily focused on individual contexts rather than national or regional levels; this is a major limitation. It is difficult to reflect the characteristics of community environments effectively with such limited studies, because social environments and infrastructure should be analyzed using a spatial perspective that goes beyond an individual’s behavioral patterns. Other limitations include lack of socioeconomic context and appropriate data to represent the characteristics of a local community and its environment. To date, very few studies have tested the spatial relationships between mental health and recreation opportunities on a national level, while controlling for a variety of competing explanations (e.g., the social determinants of mental health). To address these gaps, this study used multi-level spatial data combined with various sources to: (1) identify variables that contribute to spatial disparities of mental health; (2) examine how selected variables influence spatial mental health disparities using a generalized linear model (GLM); (3) specify the spatial variation of the relationships between recreation opportunities and mental health in the continental U.S. using geographically weighted regression (GWR). The findings suggest that multiple factors associated with poor mental health days, particularly walkable access to local parks, showed the strongest explanatory power in both the GLM and GWR models. In addition, negative relationships were found with educational attainment, racial/ethnic dynamics, and lower levels of urbanization, while positive relationships were found with poverty rate and unemployment in the GLM. Finally, the GWR model detected differences in the strength and direction of associations for 3109 counties. These results may address the gaps in previous studies that focused on individual-level scales and did not include a spatial context.

Subsidized Households and Wildfire Hazards in California

As deadly and destructive wildfires become increasingly common in the western United States due to climate change, low-income households face particular difficulties recovering from these disasters. Despite this threat, surprisingly little empirical evidence exists about the exposure and vulnerability to wildfire hazards of residents of subsidized housing. This study focuses on the subsidized housing population for several reasons: residents generally have less adaptive capacity to respond to wildfires; the locations of subsidized housing units reflect relatively stable locations of low-income households for decades; and policymakers can intervene to retrofit existing housing as well as shape future housing siting and design. The dataset created for this study includes all Census tracts in California with housing units by type, wildland-urban interface (WUI) coverage, and an index of social vulnerability. Using a combination of descriptive statistics and spatial regression models, the analysis focuses on the intersection of subsidized housing and wildfire hazards. Results show that subsidized housing is disproportionately located outside the WUI in California's metropolitan and nonmetropolitan areas. However, policy interventions are necessary because many vulnerable households-including those residing in the 140,000 subsidized units in the WUI-live in harm's way.

An alternative comprehensive index to quantify the interactive effect of temperature and relative humidity on hand, foot and mouth disease: A two-stage time series study including 143 cities in mainland China

BACKGROUND

Comprehensive indices have been used to quantify the interactive effect of temperature and humidity on hand, foot and mouth disease (HFMD). The majority of them reflect how weather feels to humans. In this study, we propose an alternative index aiming to reflect the impacts of weather on HFMD and compare its performance with that of previous indices.

METHODS

We proposed an index defined as the product of temperature and a weight parameter raised to the rescaled relative humidity, denoted by THIa. We then compared its model fit and heterogeneity with those of previous indices (including the humidex, heat index and temperature) by a multicity two-stage time series analysis. We first built a common distributed lag nonlinear model to estimate the associations between different indices and HFMD for each city separately. We then pooled the city-specific estimates and compared the average model fit (measured by the QAIC) and heterogeneity (measured by I2) among the different indices.

RESULTS

We included the time series of HFMD and meteorological variables from 143 cities in mainland China from 2009 to 2014. By varying the weight parameter of THIa, the results suggested that 100% relative humidity can amplify the effects of temperature on HFMD 1.6-fold compared to 50% relative humidity. By comparing different candidate indices, THIa performed the best in terms of the average of the model fits (QAIC = 9449.37), followed by humidex, heat index and temperature. In addition, the estimated exposure-response curves between THIa and HFMD were consistent across climate regions with minimum heterogeneity (I2 = 65.90), whereas the others varied across climate regions.

CONCLUSIONS

This study proposed an alternative comprehensive index to characterize the interactive effects of temperature and humidity on HFMD. In addition, the results also imply that previous human-based indices might not be sufficient to reflect the complicated associations between weather and HFMD.

Short-term effects of rainfall on childhood hand, foot and mouth disease and related spatial heterogeneity: evidence from 143 cities in mainland China

BACKGROUND

Numerous studies have demonstrated the potential association between rainfall and hand, foot and mouth disease (HFMD), but the results are inconsistent. This study aimed to quantify the relationship between rainfall and HFMD based on a multicity study and explore the potential sources of spatial heterogeneity.

METHODS

We retrieved the daily counts of childhood HFMD and the meteorological variables of the 143 cities in mainland China between 2009 and 2014. A common time series regression model was applied to quantify the association between rainfall and HFMD for each of the 143 cities. Then, we adopted the meta-regression model to pool the city-specific estimates and explore the sources of heterogeneity by incorporating city-specific characteristics.

RESULTS

The overall pooled estimation suggested a nonlinear exposure-response relationship between rainfall and HFMD. Once rainfall exceeded 15 mm, the HFMD risk stopped increasing linearly and began to plateau with the excessive risk ratio (ERR) peaking at 21 mm of rainfall (ERR = 3.46, 95% CI: 2.05, 4.88). We also found significant heterogeneity in the rainfall-HFMD relationships (I² = 52.75%, P < 0.001). By incorporating the city-specific characteristics into the meta-regression model, temperature and student density can explain a substantial proportion of spatial heterogeneity with I² statistics that decreased by 5.29 and 6.80% at most, respectively.

CONCLUSIONS

Our findings verified the nonlinear association between rainfall and HFMD. The rainfall-HFMD relationship also varies depending on locations. Therefore, the estimation of the rain-HFMD relationship of one location should not be generalized to another location.

Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing

The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socio-economic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

The social context of nearest neighbors shapes educational attainment regardless of class origin

We study the association between sociospatial neighborhood conditions throughout childhood and educational attainment in adulthood. Using unique longitudinal microdata for a medium-sized Swedish town, we geocode its population at the address level, 1939 to 1967, and link individuals to national registers, 1968 to 2015. Thus, we adopt a long-term perspective on the importance of nearby neighbors during a period when higher education expanded. Applying a method for estimating individual neighborhoods at the address level, we analyze the association between the geographically weighted social class of the nearest 6 to 100 childhood neighbors (ages 2 to 17), and the likelihood of obtaining a university degree by age 40, controlling for both family social class and school districts. We show that even when growing up in a town with relatively low economic inequality, the social class of the nearest same-age neighbors in childhood was associated with educational attainment, and that the associations were similar regardless of class origin. Growing up in low-class neighborhoods lowered educational attainment; growing up in high-class neighborhoods increased attainment. Social class and neighborhoods reinforced each other, implying that high-class children clustered with each other had much higher odds of obtaining a university degree than low-class children from low-class neighborhoods. Thus, even if all groups benefited from the great expansion of free higher education in Sweden (1960s to 1970s), the large inequalities between the classes and neighborhoods remained unchanged throughout the period. These findings show the importance of an advantageous background, both regarding the immediate family and the networks of nearby people of the same age.

Decoupling of Blacklegged Tick Abundance and Lyme Disease Incidence in Southern Maine, USA

Lyme disease is caused by the bacterial spirochete Borrelia burgdorferi Johnson, Schmid, Hyde, Steigerwalt, and Brenner (Spirocheatales: Spirochaetaceae) which is transmitted through the bite of an infected blacklegged tick Ixodes scapularis Say (Ixodida: Ixodidae). Maine, USA, is a high Lyme disease incidence state, with rising incidence of Lyme disease and other tick-borne illnesses associated with increasing I. scapularis abundance and northward range expansion. Members of the public submitted ticks to a tick identification program (1990-2013). From these passive surveillance data, we characterized temporal trends in I. scapularis submission rate (an index of abundance), comparing Maine's northern tier (seven counties) versus southern tier (nine counties). In the northern tier, the I. scapularis submission rate increased throughout the duration of the time series, suggesting I. scapularis was emergent but not established. By contrast, in the southern tier, submission rate increased initially but leveled off after 10-14 yr, suggesting I. scapularis was established by the mid-2000s. Active (field) surveillance data from a site in the southern tier-bird tick burdens and questing adult tick collections-corroborated this leveling pattern. Lyme disease incidence and I. scapularis submission rate were temporally correlated in the northern but not southern tier. This suggested a decoupling of reported disease incidence and entomological risk.

Spatiotemporal patterns and risk factors concerning hepatitis B virus infections in the Beijing-Tianjin-Hebei area of China

Beijing–Tianjin–Hebei is the largest urban agglomeration in northern China, but the spatiotemporal patterns and risk factors concerning hepatitis B virus (HBV) incidence in this area have been unclear. The present study aimed to reveal the spatiotemporal epidemiological features of HBV infection and quantify the association between HBV infection and socio-economic risk factors. The data on HBV cases in Beijing–Tianjin–Hebei from 2007 to 2012 was collected for each county. The Bayesian space–time hierarchy model and the GeoDetector method were used to reveal spatiotemporal patterns and detect risk factors. High-risk regions were mainly distributed in the underdeveloped rural areas in the north and mid-south of the study region, while low-risk regions were mainly distributed in the urban and western areas. The HBV annual incidence rate decreased substantially over the 6-year period, dropping from 7.34/10⁵ to 5.51/10⁵. Compared with this overall trend, 38.5% of high-risk counties showed a faster decrease, and 35.9% of high-risk counties exhibited a slower decrease. Meanwhile, 29.7% of low-risk counties had a faster decrease, and 44.6% of low-risk counties exhibited a slower decrease. Socio-economic factors were strongly associated with the spatiotemporal patterns and variation. The population density and gross domestic product per capita were negatively associated with HBV transmission, with determinant powers of 0.17 and 0.12, respectively. The proportion of primary industry and the number of healthcare workers were positively associated with the disease incidence, with determinant powers of 0.11 and 0.8, respectively. The interactive effect between population density and the other factors exerted a greater influence on HBV transmission than that of these factors measured independently.

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson's r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson's r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products.

The Use of Geographic Information Systems for Spatial Ecological Risk Assessments: An Example from the Athabasca Oil Sands Area in Canada

There is an acknowledged need in ecotoxicology for methods that integrate spatial analyses in risk assessment. This has resulted in the emergence of landscape ecotoxicology, a subdiscipline of ecotoxicology. However, landscape ecotoxicology has yet to become common practice in risk assessment due to the underdevelopment of techniques and a lack of standardized methods. In the present study, we demonstrate how geographic information systems (GISs) can serve as a standardized platform to integrate data, assess spatial patterns of ecotoxicological data for multiple species, and assess relationships between chemical mixture exposures and effects on biota for landscape ecotoxicological risks assessment. We use data collected under the Joint Oil Sands Monitoring Program in the Athabasca Oil Sands Region in Alberta, Canada. This dataset is composed of concentrations of contaminants including metals and polycyclic aromatic compounds, and health endpoints measured in 1100 biological samples, including tree swallows, amphibians, gull and tern eggs, plants, and mammals. We present 3 examples using a GIS as a platform and geospatial analysis to: 1) integrate data and assess spatial patterns of contaminant exposure in the region, 2) assess spatial patterns of exposures to complex mixtures, and 3) examine patterns of exposures and responses across the landscape. We summarize the methods used in the present study into a workflow for ease of use. The GIS methods allow researchers to identify hot spots of contamination, use georeferenced monitoring data to derive quantitative exposure-response relationships, and assess complex exposures with more realism. Environ Toxicol Chem 2019;38:2797-2810. © 2019 SETAC.

Geographic Variance in Maryland's Potentially Preventable Emergency Visits: Comparison of Explanatory Models

The use of emergency departments (EDs) for potentially preventable visits is costly and inefficient. In Maryland, about 20%-30% of such visits are ambulatory care sensitive and thus potentially preventable. The uninsured are often perceived to account for a disproportionate share of such visits. This analysis aimed to (a) compare and explain the geographic variance in Maryland's potentially preventable ED visit (PPV) rates for the total and uninsured populations and (b) test the predictive value of regression models developed. Geographic hot spots of increased PPV rates were highly correlated for uninsured and total populations, but uninsured rates were more clustered in urban areas. Poisson and geographically weighted regression (GWR) models best fit the data and predicted 40%-52% and 46% of the variance in 2009 total and uninsured rates, respectively. Significant predictors of increased PPV rates were social determinants of health: lower per capita income and education levels, and higher percentage of female-headed households.

How Do Neighbourhood Definitions Influence the Associations between Built Environment and Physical Activity?

Researchers investigating relationships between the neighbourhood environment and health first need to decide on the spatial extent of the neighbourhood they are interested in. This decision is an important and ongoing methodological challenge since different methods of defining and delineating neighbourhood boundaries can produce different results. This paper explores this issue in the context of a New Zealand-based study of the relationship between the built environment and multiple measures of physical activity. Geographic information systems were used to measure three built environment attributes-dwelling density, street connectivity, and neighbourhood destination accessibility-using seven different neighbourhood definitions (three administrative unit boundaries, and 500, 800, 1000- and 1500-m road network buffers). The associations between the three built environment measures and five measures of physical activity (mean accelerometer counts per hour, percentage time in moderate-vigorous physical activity, self-reported walking for transport, self-reported walking for recreation and self-reported walking for all purposes) were modelled for each neighbourhood definition. The combination of the choice of neighbourhood definition, built environment measure, and physical activity measure determined whether evidence of an association was detected or not. Results demonstrated that, while there was no single ideal neighbourhood definition, the built environment was most consistently associated with a range of physical activity measures when the 800-m and 1000-m road network buffers were used. For the street connectivity and destination accessibility measures, associations with physical activity were less likely to be detected at smaller scales (less than 800 m). In line with some previous research, this study demonstrated that the choice of neighbourhood definition can influence whether or not an association between the built environment and adults' physical activity is detected or not. This study additionally highlighted the importance of the choice of built environment attribute and physical activity measures. While we identified the 800-m and 1000-m road network buffers as the neighbourhood definitions most consistently associated with a range of physical activity measures, it is important that researchers carefully consider the most appropriate type of neighbourhood definition and scale for the particular aim and participants, especially at smaller scales.

Changing contribution of area-level deprivation to total variance in age at death: a population-based decomposition analysis

OBJECTIVES

Two processes generate total variance in age at death: heterogeneity (between-group variance) and individual stochasticity (within-group variance). Limited research has evaluated how these two components have changed over time. We quantify the degree to which area-level deprivation contributed to total variance in age at death in Scotland between 1981 and 2011.

DESIGN

Full population and mortality data for Scotland were obtained and matched with the Carstairs score, a standardised z-score calculated for each part-postcode sector that measures relative area-level deprivation. A z-score above zero indicates that the part-postcode sector experienced higher deprivation than the national average. A z-score below zero indicates lower deprivation. From the aggregated data we constructed 40 lifetables, one for each deprivation quintile in 1981, 1991, 2001 and 2011 stratified by sex.

PRIMARY OUTCOME MEASURES

Total variance in age at death and the proportion explained by area-level deprivation heterogeneity (between-group variance).

RESULTS

The most deprived areas experienced stagnating or slightly increasing variance in age at death. The least deprived areas experienced decreasing variance. For males, the most deprived quintile life expectancy was between 7% and 11% lower and the SD is between 6% and 25% higher than the least deprived. This suggests that the effect of deprivation on the SD of longevity is comparable to its effect on life expectancy. Decomposition analysis revealed that contributions from between-group variance doubled between 1981 and 2011 but at most only explained 4% of total variance.

CONCLUSIONS

This study adds to the emerging body of literature demonstrating that socio-economic groups have experienced diverging trends in variance in age at death. The contribution from area-level deprivation to total variance in age at death, which we were able to capture, has doubled since 1981. Area-level deprivation may play an increasingly important role in mortality inequalities.

When multi-functional landscape meets Critical Zone science: advancing multi-disciplinary research for sustainable human well-being

Environmental degradation has become one of the major obstacles to sustainable development and human well-being internationally. Scientific efforts are being made to understand the mechanism of environmental degradation and sustainability. Critical Zone (CZ) science and research on the multi-functional landscape are emerging fields in Earth science that can contribute to such scientific efforts. This paper reviews the progress, similarities and current status of these two scientific research fields, and identifies a number of opportunities for their synergistic integration through functional and multi-functional approaches, process-based monitoring, mechanistic analyses and dynamic modeling, global long-term and networked monitoring and systematic modeling supported by scaling and deep coupling. These approaches proposed in this paper have the potential to support sustainable human well-being by strengthening a functional orientation that consolidates multi-functional landscape research and CZ science. This is a key challenge for sustainable development and human well-being in the twenty-first century.

Individual and area-level determinants associated with C-reactive protein as a marker of cardiometabolic risk among adults: Results from the German National Health Interview and Examination Survey 2008-2011

BACKGROUND

High-sensitivity C-reactive protein (hsCRP) is a sensitive biomarker of systemic inflammation and is related to the development and progression of cardiometabolic diseases. Beyond individual-level determinants, characteristics of the residential physical and social environment are increasingly recognized as contextual determinants of systemic inflammation and cardiometabolic risks. Based on a large nationwide sample of adults in Germany, we analyzed the cross-sectional association of hsCRP with residential environment characteristics. We specifically asked whether these associations are observed independent of determinants at the individual level.

METHODS

Data on serum hsCRP levels and individual sociodemographic, behavioral, and anthropometric characteristics were available from the German Health Interview and Examination Survey for Adults (2008-2011). Area-level variables included, firstly, the predefined German Index of Socioeconomic Deprivation (GISD) derived from the INKAR (indicators and maps on spatial and urban development in Germany and Europe) database and, secondly, population-weighted annual average concentration of particulate matter (PM10) in ambient air provided by the German Environment Agency. Associations with log-transformed hsCRP levels were analyzed using random-intercept multi-level linear regression models including 6,768 participants aged 18-79 years nested in 162 municipalities.

RESULTS

No statistically significant association of PM10 exposure with hsCRP was observed. However, adults residing in municipalities with high compared to those with low social deprivation showed significantly elevated hsCRP levels (change in geometric mean 13.5%, 95%CI 3.2%-24.7%) after adjusting for age and sex. The observed relationship was independent of individual-level educational status. Further adjustment for smoking, sports activity, and abdominal obesity appeared to markedly reduce the association between area-level social deprivation and hsCRP, whereas all individual-level variables contributed significantly to the model.

CONCLUSIONS

Area-level social deprivation is associated with higher systemic inflammation and the potentially mediating role of modifiable risk factors needs further elucidation. Identifying and assessing the source-specific harmful components of ambient air pollution in population-based studies remains challenging.

Spillover effects in epidemiology: parameters, study designs and methodological considerations

Many public health interventions provide benefits that extend beyond their direct recipients and impact people in close physical or social proximity who did not directly receive the intervention themselves. A classic example of this phenomenon is the herd protection provided by many vaccines. If these 'spillover effects' (i.e. 'herd effects') are present in the same direction as the effects on the intended recipients, studies that only estimate direct effects on recipients will likely underestimate the full public health benefits of the intervention. Causal inference assumptions for spillover parameters have been articulated in the vaccine literature, but many studies measuring spillovers of other types of public health interventions have not drawn upon that literature. In conjunction with a systematic review we conducted of spillovers of public health interventions delivered in low- and middle-income countries, we classified the most widely used spillover parameters reported in the empirical literature into a standard notation. General classes of spillover parameters include: cluster-level spillovers; spillovers conditional on treatment or outcome density, distance or the number of treated social network links; and vaccine efficacy parameters related to spillovers. We draw on high quality empirical examples to illustrate each of these parameters. We describe study designs to estimate spillovers and assumptions required to make causal inferences about spillovers. We aim to advance and encourage methods for spillover estimation and reporting by standardizing spillover parameter nomenclature and articulating the causal inference assumptions required to estimate spillovers.