The boundary value problem in spatial statistical analysis

Neighborhood-level Residential Isolation and Neighborhood Composition: Similar but Different

Residential segregation has been considered as a potential cause of racial and/or ethnic disparities in health. Among the five dimensions of residential segregation, the isolation dimension has been conceived to play an essential role in disproportionately shaping the health of racial and ethnic minorities, particularly in urban or metropolitan areas. However, a noticeable amount of research studies has been using informal measures of neighborhood composition (i.e., proportions or percentages), which do not capture any of the five dimensions of residential segregation. Since the inappropriateness of measurement may obstruct a meaningful interpretation and an effective dissemination of research findings, a combination of graphical and non-graphical techniques was used to demonstrate the similarities and differences between formal measures of neighborhood-level residential isolation and informal measures of neighborhood composition. These were intended to provide intuitive and mutual understandings across academic disciples (e.g., city or urban planning, geography, public health, and sociology) and practitioners or professionals in multiple fields (e.g., community development workers, health service providers, policymakers, and social workers). Conceptual and methodological explanations with analytical discussions are also provided to differentiate and/or distinguish the two types of measures. While the concepts, methodologies, and research implications discussed herein are most relevant for research studies in urban or metropolitan areas of the United States, the general framework is also applicable to those of other industrialized counties.

Forward-Solution Noninvasive Computational Arrhythmia Mapping: The VMAP Study

BACKGROUND

The accuracy of noninvasive arrhythmia source localization using a forward-solution computational mapping system has not yet been evaluated in blinded, multicenter analysis. This study tested the hypothesis that a computational mapping system incorporating a comprehensive arrhythmia simulation library would provide accurate localization of the site-of-origin for atrial and ventricular arrhythmias and pacing using 12-lead ECG data when compared with the gold standard of invasive electrophysiology study and ablation.

METHODS

The VMAP study (Vectorcardiographic Mapping of Arrhythmogenic Probability) was a blinded, multicenter evaluation with final data analysis performed by an independent core laboratory. Eligible episodes included atrial and ventricular: tachycardia, fibrillation, pacing, premature atrial and ventricular complexes, and orthodromic atrioventricular reentrant tachycardia. Mapping system results were compared with the gold standard site of successful ablation or pacing during electrophysiology study and ablation. Mapping time was assessed from time-stamped logs. Prespecified performance goals were used for statistical comparisons.

RESULTS

A total of 255 episodes from 225 patients were enrolled from 4 centers. Regional accuracy for ventricular tachycardia and premature ventricular complexes in patients without significant structural heart disease (n=75, primary end point) was 98.7% (95% CI, 96.0%-100%; P<0.001 to reject predefined H0 <0.80). Regional accuracy for all episodes (secondary end point 1) was 96.9% (95% CI, 94.7%-99.0%; P<0.001 to reject predefined H0 <0.75). Accuracy for the exact or neighboring segment for all episodes (secondary end point 2) was 97.3% (95% CI, 95.2%-99.3%; P<0.001 to reject predefined H0 <0.70). Median spatial accuracy was 15 mm (n=255, interquartile range, 7-25 mm). The mapping process was completed in a median of 0.8 minutes (interquartile range, 0.4-1.4 minutes).

CONCLUSIONS

Computational ECG mapping using a forward-solution approach exceeded prespecified accuracy goals for arrhythmia and pacing localization. Spatial accuracy analysis demonstrated clinically actionable results. This rapid, noninvasive mapping technology may facilitate catheter-based and noninvasive targeted arrhythmia therapies.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT04559061.

Healthcare accessibility in preconflict Syria: a comparative spatial analysis

INTRODUCTION

There is scarce information about the allocation of health resources in Syria. Pre-existing inequalities were further intensified after the 2011 conflict which displaced over 50% of the population. This study provides an analysis of health inequalities in Syria focusing on spatial access to public hospitals and employs data from 2010, just prior to the outbreak of conflict. Establishing a preconflict snapshot of the health system could serve as a helpful baseline assessment for future studies to measure the impact of the conflict on the health system. Such information could also offer systematic data to guide postconflict reconstruction efforts.

METHODS

We compared two methods to quantify the inpatient bed access: provider to population ratio (PPR) and two-step floating catchment area (2SFCA) method. We compared PPR calculated at the governorate level with population weighted 2SFCA score calculated at a resolution of 2 km by 2 km. We then aggregated at the governorate level, tested multiple catchment sizes and calculated Gini coefficient for each governorate.

RESULTS

We found high inequality in access to public hospitals across and within governorates, especially in the north and eastern regions, where all governorates ranked in the lowest two quintiles using both PPR and 2SFCA. Relatively small governorates in the west and the south had higher spatial access and less inequality. Testing variability in catchment size showed that even at 125 km catchment, 65% of the country had accessibility below national average.

CONCLUSION

Methodologically, the use of 2SFCA provided more nuanced insights about hospital bed allocation than PPR. 2SFCA was able to account for the cross-boundary effect and road network quality. Realistic representation of health accessibility is possible in data-scarce settings such as Syria and could be adapted to assess health access inequalities in conflict and postconflict settings.

A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Estimating exposure to neighborhood crime by race and ethnicity for public health research

BACKGROUND

Police-reported crime data (hereafter "crime") is routinely used as a psychosocial stressor in public health research, yet few studies have jointly examined (a) differences in crime exposure based on participant race and ethnicity, (b) differences in measures of crime exposure, and (c) considerations for how exposure to police is captured in police-recorded crime data. We estimate neighborhood exposure to crime and discuss the implications of structural differences in exposure to crime and police based on race and ethnicity.

METHODS

Using GPS coordinates from 1188 participants in the Newborn Epigenetics Study, we estimated gestational exposure to crime provided by the Durham, North Carolina, Police Department within (a) 800 m and (b) the Census block group of residence. We controlled for non-overlapping spatial boundaries in crime, Census, residential, and police data to report crime spatial (crime per km2) and population (crime per 1000 people per km2) density.

RESULTS

We demonstrate dramatic disparities in exposure to crime based on participant race and ethnicity and highlight variability in these disparities based on the type of crime and crime measurement method chosen.

CONCLUSIONS

Public health researchers should give thoughtful consideration when using police-reported crime data to measure and model exposure to crime in the United States, as police-reported data encompasses joint exposure to police and crime in the neighborhood setting.

Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru

Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.

Spatial clustering patterns of child weight status in a southeastern US county

Youth obesity is a major public health concern due to associated physical, social, and psychological health consequences. While rates and disparities of youth obesity levels are known, less research has explored spatial clustering patterns, associated correlates of spatial clustering, comparing patterns in urban and rural areas. Therefore, this study 1) examined spatial clustering of youth weight status, 2) investigated sociodemographic correlates of spatial clustering patterns, and 3) explored spatial patterns by level of urbanization. This study occurred in a southeastern US county (pop:474,266) in 2013. Trained physical education teachers collected height and weight for all 3^rd-5th grade youth (n = 13,469) and schools provided youth demographic attributes. BMI z-scores were calculated using standard procedures. Global Moran's Index and Anselin's Local Moran's I (LISA) were used detect global and local spatial clustering, respectively. To examine correlates of spatial clustering, BMI z-score residuals from a series of four linear regression models were spatially analyzed, mapped, and compared. SAS 9.4 and GeoDA were used for analyses; ArcGIS was used for mapping. Significant, positive global clustering (Index = 0.04,p < 0.001) was detected. LISA results showed that about 4.7% (n = 635) and 7.9% (n = 1058) of the sample were identified as high and low obesity localized spatial clusters (p < 0.01), respectively. Individual and neighborhood sociodemographic characteristics accounted for the majority of spatial clustering and differential patterns were observed by level of urbanization. Identifying geographic areas that contain significant spatial clusters is a powerful tool for understanding the location of and exploring contributing factors to youth obesity.

Neighborhood Characteristics Associated with Achievement and Maintenance of HIV Viral Suppression Among Persons Newly Diagnosed with HIV in New York City

We investigated the effect of neighborhood characteristics on achievement and maintenance of HIV viral suppression among New York City (NYC) residents aged 13 years and older diagnosed between 2006 and 2012. Individual records from the NYC HIV surveillance registry (n = 12,547) were linked to U.S. Census and American Community Survey data by census tract of residence. Multivariable proportional hazards regression models indicated the likelihood of achievement and maintenance of suppression by neighborhood characteristics including poverty, accounting for neighborhood clustering and for individual characteristics. In adjusted analyses, no neighborhood factors were associated with achievement of suppression. However, residents of high- or very-high-poverty neighborhoods were less likely than residents of low-poverty neighborhoods to maintain suppression. In conclusion, higher neighborhood poverty was associated with lesser maintenance of suppression. Assistance with post-diagnosis retention in care, antiretroviral therapy prescribing, or adherence targeted to residents of higher-poverty neighborhoods may improve maintenance of viral suppression in NYC.

Predominance of asymptomatic and sub-microscopic infections characterizes the Plasmodium gametocyte reservoir in the Peruvian Amazon

Malaria transmission requires that Anopheles mosquitoes ingest Plasmodium gametocyte stages circulating in the human bloodstream. In the context of malaria elimination, understanding the epidemiology of gametocytes relative to all Plasmodium infections and the contribution of asymptomatic and sub-microscopic parasite carriers to the gametocyte reservoir is necessary, especially in low endemic settings with predominance of P.vivax. A 13-month longitudinal study was conducted in two communities (n = 1935 individuals) of Loreto Department, Peru, with five active screenings for Plasmodium infections and gametocyte stages by quantitative real-time PCR (qPCR) and reverse transcription (RT)-qPCR, respectively. Parasite prevalence by qPCR was 7.2% for P.vivax (n = 520/7235; range by survey 6.0%-8.1%) and 3.2% for P.falciparum (n = 235/7235; range by survey 0.4%-7.7%). Sub-microscopic infections accounted for 73.5% of P.vivax (range by survey 60%-89%) and almost the totality of P.falciparum cases. Gametocytes were found in 28.4% P.vivax infections (range by survey 18.7%-34.1%), with a peak of 61.5% in one community at the start of the transmission season. About 59.8% of all P.vivax gametocyte carriers were asymptomatic and 31.9% were sub-microscopic. Age patterns for gametocyte prevalence paralleled asexual stage infections and peaked among >15–25 year old individuals. Asexual parasite density was found to be the strongest predictor for P.vivax gametocyte presence in longitudinal multivariate analysis (odds ratio 2.33 [95% confidence interval 1.96, 2.78]; P<0.001). Despite significant differences in seasonality patterns and P.vivax prevalence found at the local scale, sub-microscopic and asymptomatic infections predominate and contribute significantly to the gametocyte reservoir in different communities of the Peruvian Amazon. Control and elimination campaigns need sensitive tools to detect all infections that escape routine malaria surveillance, which may contribute to maintain transmission in the region.

Malaria elimination, i.e. the complete interruption of parasite transmission in a region, is in the agenda of health authorities in countries that achieved substantial reduction of the disease burden in the past decade. However, our understanding of transmission epidemiology for low transmission areas where Plasmodium vivax is endemic, like the Amazon basin, is still limited. In this study, we describe the prevalence and risk factors for carrying the parasite stages that are transmitted to the mosquito vectors, named gametocytes, in 1935 individuals from two communities of the Peruvian Amazon that were regularly screened during 1 year. We report that malaria infections with no clinical symptoms and those with parasite levels below microscopy detection threshold, account for two thirds of all P.vivax infections with gametocytes, and that the highest infection rate is found among young adults. In addition, almost the totality of P.falciparum infections detected was sub-microscopic. Because all these infections escape current malaria surveillance systems -based on passive case detection and/or microscopy diagnosis-, new approaches are necessary to target all infections in order to eliminate the malaria transmission reservoir in Peru.

Spatializing Area-Based Measures of Neighborhood Characteristics for Multilevel Regression Analyses: An Areal Median Filtering Approach

Area-based measures of neighborhood characteristics simply derived from enumeration units (e.g., census tracts or block groups) ignore the potential of spatial spillover effects, and thus incorporating such measures into multilevel regression models may underestimate the neighborhood effects on health. To overcome this limitation, we describe the concept and method of areal median filtering to spatialize area-based measures of neighborhood characteristics for multilevel regression analyses. The areal median filtering approach provides a means to specify or formulate "neighborhoods" as meaningful geographic entities by removing enumeration unit boundaries as the absolute barriers and by pooling information from the neighboring enumeration units. This spatializing process takes into account for the potential of spatial spillover effects and also converts aspatial measures of neighborhood characteristics into spatial measures. From a conceptual and methodological standpoint, incorporating the derived spatial measures into multilevel regression analyses allows us to more accurately examine the relationships between neighborhood characteristics and health. To promote and set the stage for informative research in the future, we provide a few important conceptual and methodological remarks, and discuss possible applications, inherent limitations, and practical solutions for using the areal median filtering approach in the study of neighborhood effects on health.

Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru

PURPOSE

To map the geographical distribution and spatial clustering of depressive symptoms cases in an area of Lima, Peru.

METHODS

Presence of depressive symptoms suggesting a major depressive episode was assessed using a short version of the Center for Epidemiologic Studies Depression Scale. Data were obtained from a census conducted in 2010. One participant per selected household (aged 18 years and above, living more than 6 months in the area) was included. Residence latitude, longitude, and elevation were captured using a GPS device. The prevalence of depressive symptoms was estimated, and relative risks (RRs) were calculated to identify areas of significantly higher and lower geographical concentrations of depressive symptoms.

RESULTS

Data from 7946 participants, 28.3% male, mean age 39.4 (SD, 13.9) years, were analyzed. The prevalence of depressive symptoms was 17.0% (95% confidence interval = 16.2%-17.8%). Three clusters with high prevalence of depressive symptoms (primary cluster: RR = 1.82; P = .003 and secondary: RR = 2.83; P = .004 and RR = 5.92; P = .01), and two clusters with significantly low prevalence (primary: RR = 0.23; P = .016 and secondary: RR = 0; P = .035), were identified. Further adjustment by potential confounders confirmed the high prevalence clusters but also identified newer ones.

CONCLUSIONS

Screening strategies for depression, in combination with mapping techniques, may be useful tools to target interventions in resource-limited areas.

Patterns of local segregation: Do they matter for neighborhood crime?

In this paper, we extend recent research on the spatial measurement of segregation and the spatial dynamics of urban crime by conceptualizing, measuring, and describing local segregation by race-ethnicity and economic status, and examining the linkages of these conditions with levels of neighborhood violent and property crime. The analyses are based on all 8895 census tracts within a sample of 86 large U.S. cities. We fit multilevel models of crime that incorporate measures of local segregation. The results reveal that, net of city-level and neighborhood characteristics, White-Black local segregation is associated with lower violent and property crime. In contrast, local segregation of low income from high income households is connected with higher crime, particularly neighborhood violence.

Capturing the two dimensions of residential segregation at the neighborhood level for health research

Two conceptual and methodological foundations of segregation studies are that (i) segregation involves more than one group, and (ii) segregation measures need to quantify how different population groups are distributed across space. Therefore, percentage of population belonging to a group is not an appropriate measure of segregation because it does not describe how populations are spread across different areal units or neighborhoods. In principle, evenness and isolation are the two distinct dimensions of segregation that capture the spatial patterns of population groups. To portray people’s daily environment more accurately, segregation measures need to account for the spatial relationships between areal units and to reflect the situations at the neighborhood scale. For these reasons, the use of local spatial entropy-based diversity index (SH_i) and local spatial isolation index (S_i) to capture the evenness and isolation dimensions of segregation, respectively, are preferable. However, these two local spatial segregation indexes have rarely been incorporated into health research. Rather ineffective and insufficient segregation measures have been used in previous studies. Hence, this paper empirically demonstrates how the two measures can reflect the two distinct dimensions of segregation at the neighborhood level, and argues conceptually and set the stage for their future use to effectively and meaningfully examine the relationships between residential segregation and health.

Modifiable temporal unit problem (MTUP) and its effect on space-time cluster detection

Background

When analytical techniques are used to understand and analyse geographical events, adjustments to the datasets (e.g. aggregation, zoning, segmentation etc.) in both the spatial and temporal dimensions are often carried out for various reasons. The ‘Modifiable Areal Unit Problem’ (MAUP), which is a consequence of adjustments in the spatial dimension, has been widely researched. However, its temporal counterpart is generally ignored, especially in space-time analysis.

Methods

In analogy to MAUP, the Modifiable Temporal Unit Problem (MTUP) is defined as consisting of three temporal effects (aggregation, segmentation and boundary). The effects of MTUP on the detection of space-time clusters of crime datasets of Central London are examined using Space-Time Scan Statistics (STSS).

Results and Conclusion

The case study reveals that MTUP has significant effects on the space-time clusters detected. The attributes of the clusters, i.e. temporal duration, spatial extent (size) and significance value (p-value), vary as the aggregation, segmentation and boundaries of the datasets change. Aggregation could be used to find the significant clusters much more quickly than at lower scales; segmentation could be used to understand the cyclic patterns of crime types. The consistencies of the clusters appearing at different temporal scales could help in identifying strong or ‘true’ clusters.

Spatial heterogeneity of type I error for local cluster detection tests

Background

Just as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs’ type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect.

Methods

A simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff’s spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect.

Results

The predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated.

Conclusions

In routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance.

A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002

BACKGROUND

Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns.

METHODS

A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns.

RESULTS

The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001).

CONCLUSIONS

This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky.

Geographical and temporal distribution of human giardiasis in Ontario, Canada

BACKGROUND: Giardia is the most frequently identified intestinal parasite in North America. Although information on geographical distribution of giardiasis is critical in identifying communities at high risk, little has been done in this area. Therefore, the objective of this study was to investigate the geographical and temporal distribution of human giardiasis in Ontario in order to identify possible high risk areas and seasons. Two spatial scales of analyses and two disease measures were used with a view to identifying the best of each in assessing geographical patterns of giardiasis in Ontario. Global Moran's I and Moran Local Indicators of Spatial Associations were used to test for evidence of global and local spatial clustering, respectively. RESULTS: There were seasonal patterns with summer peaks and a significant (P < 0.001) decreasing temporal trend. Significant (P < 0.05) global spatial clustering of high rates was observed at the Census Sub-division spatial scale but not at the Census Division scale. The Census Sub-division scale was a better scale of analyses but required spatial empirical Bayesian smoothing of the rates. A number of areas with significant local clustering of giardiasis rates were identified. CONCLUSIONS: The study identified spatial and temporal patterns in giardiasis distribution. This information is important in guiding decisions on disease control strategies. The study also showed that there is benefit in performing spatial analyses at more than one spatial scale to assess geographical patterns in disease distribution and that smoothing of disease rates for mapping in small areas enhances visualization of spatial patterns.

Exploratory tools for disease mapping in geographical epidemiology

In the present paper a new shrinkage estimator of relative risk, useful in disease mapping, is compared with the empirical and full Bayes estimators, using death certificate data for lung cancer (males and females) and breast cancer (females) 1982-1988 in the Emilia-Romagna region (Italy). The estimates are obtained averaging the relative risks obtained from recursive partitioning of the set of observed values. The number of partitions is fixed in advance and behaves as a smoothing parameter, the lower the number the higher the degree of shrinkage toward the overall mean of the observed values.

Assessing spatial patterns in disease rates

We describe the empirical performance of three indices of spatial autocorrelation (Moran's I, Geary's c and a rank adjacency statistic D) in the analysis of regional cancer incidence data. Heterogeneity in regional population sizes and age structure leads to variable precision in estimated rates; the usual methods for assessing I, c and D, which ignore such heterogeneity, are shown to be liberally biased, especially for c and D. The power of these indices to detect likely disease patterns is estimated by stimulation; the power is quite variable, depending on the exact pattern assumed, although I tends to have the highest power. The null distributions appear quite robust in small samples, even when several regions have no observed case. Preliminary work on the Ontario cancer registry showed generally unimportant effects on the spatial analysis of variation in case registration rates or missing residence data.

Multi-sectoral urban growth in space and time: an empirical approach

"This paper compares the distribution of growth of several economic sectors in an attempt to trace and analyse their spatiotemporal sequence of decentralization. The paper applies a polynomial regression model capable of dealing with detailed spatiotemporal series. The application of the model to the urban field of Philadelphia [Pennsylvania] shows population to be the farthest spread followed by manufacturing industries. Despite, or probably because of, their extensive spread, both sectors display low levels of growth." In addition to population and manufacturing, the patterns of growth in retail and wholesale trade, services, and the provision of financial, insurance, and real estate services are examined for the period 1960-1980. (summary in FRE, GER)