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This paper provides an analytical framework for the study of spatial income dynamics using the classical measures of σ -convergence (reduction in dispersion) and β -convergence (poor areas grow more quickly than rich ones) linked by a re-ranking metric and reinterpreted based on the 'leave no one behind' principle. Our approach allows identifying the contribution of each territorial unit to each of the three facets of distributional change (σ -convergence, β -convergence, and re-ranking), as well as gauging the part of each component of distributional change that corresponds to geographically neighbouring and non-neighbouring units. We illustrate our proposal by examining convergence across the census tracts of Malaga – the sixth most populated city of Spain – before and after the COVID-19 pandemic. We find that while income convergence across Malaga's census tracts tended to improve over the period 2015–2019, this process was interrupted during the first year of the pandemic, affecting some specific census tracts. We examine our outcomes by grouping the results into deciles and districts made up of specific census tracts. Finally, we analyse the impact of neighbours on regional convergence and each of its components. This spatial decomposition highlights the crucial role of the spatial component in the convergence process. [ABSTRACT FROM AUTHOR]

Dam removal in the United States has grown exponentially, yet we do not know whether the pattern of such removals comports with principles of environmental justice. This exploratory study investigates the spatial pattern of dam removals across the United States to ascertain whether there were any geographic areas where the probability of removal was correlated with the racial or ethnic composition of the environs. We analyze dam removals since 2010 using national data on existing dams, removed dams, and demographics. We estimate multivariate probability models of dam removal stratified by census region and dam ownership to pinpoint contexts where significant spatial-racial patterns occur that cannot be attributed to dam characteristics. Our exploration reveals only a few such contexts. After controlling for dam purpose, construction type, age, and height, the probability of a dam being removed since 2010 is positively associated with the proportion of nearby White residents for dams owned by local or state governments in the South. The probability of removal is negatively associated with the proportion of nearby White residents for dams owned privately or by state or local governments in the West. Future case studies should probe these contexts of clear spatial–racial patterns from an environmental justice perspective. [ABSTRACT FROM AUTHOR]

Studies of neighborhood change rely on interpolated data to cope with inconsistent boundaries of geographic units over time. The standard approach introduces error by assuming, counterfactually, that all kinds of people are distributed in the same manner within tracts as the whole population. This study evaluates estimates of 2,000 neighborhood characteristics using 2010 boundaries in the Longitudinal Tract Data Base (LTDB) that uses the standard approach, and an alternative trait-based (TB) method that uses additional small area data to account for spatial heterogeneity. Both are compared to the true (but confidential) original census data. For variables that are available from full-count census data at the block level (including race, age, and some housing characteristics), the TB estimates are much better than the LTDB estimates. The same general approach is ineffective, however, when the small area data are subject to sampling variability and published with less spatial granularity. [ABSTRACT FROM AUTHOR]

BackgroundSpatial variability of COVID-19 cases may suggest geographic disparities of social determinants of health. Spatial analyses of population-level data may provide insight on factors that may contribute to COVID-19 transmission, hospitalization, and death.MethodsGeneralized additive models were used to map COVID-19 risk from March 2020 to February 2021 in Orange County (OC), California. We geocoded and analyzed 221,843 cases to OC census tracts within a Poisson framework while smoothing over census tract centroids. Location was randomly permuted 1000 times to test for randomness. We also separated the analyses temporally to observe if risk changed over time. COVID-19 cases, hospitalizations, and deaths were mapped across OC while adjusting for population-level demographic data in crude and adjusted models.ResultsRisk for COVID-19 cases, hospitalizations, and deaths were statistically significant in northern OC. Adjustment for demographic data substantially decreased spatial risk, but areas remained statistically significant. Inclusion of location within our models considerably decreased the magnitude of risk compared to univariate models. However, percent minority (adjusted RR: 1.06, 95%CI: 1.06, 1.07), average household size (aRR: 1.06, 95%CI: 1.05, 1.07), and percent service industry (aRR: 1.05, 95%CI: 1.04, 1.06) remained significantly associated with COVID-19 risk in adjusted spatial models. In addition, areas of risk did not change between surges and risk ratios were similar for hospitalizations and deaths.ConclusionSignificant risk factors and areas of increased risk were identified in OC in our adjusted models and suggests that social and environmental factors contribute to the spread of COVID-19 within communities. Areas in north OC remained significant despite adjustment, but risk substantially decreased. Additional investigation of risk factors may provide insight on how to protect vulnerable populations in future infectious disease outbreaks.

Objective: To define neighborhood-level disparities in the receipt of complex cancer surgery. Background: Little is known about the geographic variation of receipt of surgery among patients with complex gastrointestinal (GI) cancers, especially at a small geographic scale. Methods: This study included individuals diagnosed with 5 invasive, nonmetastatic, complex GI cancers (esophagus, stomach, pancreas, bile ducts, liver) from the Ohio Cancer Incidence Surveillance System during 2009 and 2018. To preserve patient privacy, we combined US census tracts into the smallest geographic areas that included a minimum number of surgery cases (n=11) using the Max-p-regions method and called these new areas "MaxTracts." Age-adjusted surgery rates were calculated for MaxTracts, and the Hot Spot analysis identified clusters of high and low surgery rates. US Census and CDC PLACES were used to compare neighborhood characteristics between the high- and low-surgery clusters. Results: This study included 33,091 individuals with complex GI cancers located in 1006 MaxTracts throughout Ohio. The proportion in each MaxTract receiving surgery ranged from 20.7% to 92.3% with a median (interquartile range) of 48.9% (42.4--56.3). Low-surgery clusters were mostly in urban cores and the Appalachian region, whereas high-surgery clusters were mostly in suburbs. Low-surgery clusters differed from high-surgery clusters in several ways, including higher rates of poverty (23% vs. 12%), fewer married households (40% vs. 50%), and more tobacco use (25% vs. 19%; all P<0.01). Conclusions: This improved understanding of neighborhood-level variation in receipt of potentially curative surgery will guide future outreach and community-based interventions to reduce treatment disparities. Similar methods can be used to target other treatment phases and other cancers. [ABSTRACT FROM AUTHOR]

BackgroundAir pollution is the greatest environmental threat to human health in the world today and is responsible for an estimated 7–9 million deaths annually. One of the most damaging air pollutants is PM2.5 pollution, fine airborne particulate matter under 2.5 microns in diameter. Exposure to PM2.5 pollution can cause premature death, heart disease, lung cancer, stroke, diabetes, asthma, low birthweight, and IQ loss. To avoid these adverse health effects, the WHO recommends that PM2.5 levels not exceed 5 μg/m3.MethodsThis study estimates the negative health impacts of PM2.5 pollution in Texas in 2016. Local exposure estimates were calculated at the census tract level using the EPA’s BenMAP-CE software. In BenMAP, a variety of exposure-response functions combine air pollution exposure data with population data and county-level disease and death data to estimate the number of health effects attributable to PM2.5 pollution for each census tract. The health effects investigated were mortality, low birthweight, stroke, new onset asthma, new onset Alzheimer’s, and non-fatal lung cancer.FindingsThis study found that approximately 26.7 million (98.9%) of the 27.0 million people living in Texas in 2016 resided in areas where PM2.5 concentrations were above the WHO recommendation of 5 μg/m3, and that 2.6 million people (9.8%) lived in areas where the average PM2.5 concentration exceeded 10 μg/m3. This study estimates that there were 8,405 (confidence interval [CI], 5,674–11,033) premature deaths due to PM2.5 pollution in Texas in 2016, comprising 4.3% of all deaths. Statewide increases in air-pollution-related morbidity and mortality were seen for stroke (2,209 – CI: [576, 3,776]), low birthweight (2,841 – CI: [1,696, 3,925]), non-fatal lung cancers (636 – CI: [219, 980]), new onset Alzheimer’s disease (24,575 – CI: [20,800, 27,540]), and new onset asthma (7,823 – CI: [7,557, 8,079]).ConclusionThis study found that air pollution poses significant risks to the health of Texans, despite the fact that pollution levels across most of the state comply with the EPA standard for PM2.5 pollution of 12 μg/m3. Improving air quality in Texas could save thousands of lives from disease, disability, and premature death.

Access to fresh, healthy, affordable foods is a pressing concern in cities worldwide. American cities are no exception. Although many scholars study food access in large cities, small and mid-sized American cities can provide valuable information about inequities in the food system. This paper focuses on two adjoining, racially mixed Mid-Michigan cities—Lansing and East Lansing. It examines the extent to which different food outlets exist in the cities and surrounding townships. It probes the following questions: (1) How are food outlets distributed throughout the cities and suburbs? (2) What is the relationship between neighborhood demographic characteristics and the distribution of food outlet types? We collected data on food outlets from September 2020 to June 2022 using Data Axle as our primary source of information. We used ArcGIS 10.8.1 for the spatial mapping and SPSS 28 for statistical analyses. We conducted regression analyses to identify the difference in the likelihood of finding food retailers in census tracts where 0–20% of the residents were People of Color (VL-POC), 20.01–40% of the inhabitants were People of Color (L-POC), 40.1–60% of the residents were People of Color (H-POC), and more than 60% of residents were People of Color (VH-POC). There were 1647 food outlets in the study area: 579 were in Lansing, 220 were in East Lansing, and the remaining 848 were in the surrounding townships. Restaurants dominated the food landscape, while small groceries and convenience stores were the grocery sector's most common food outlet types. Supermarkets and large grocery stores comprised only 5.6% of the study area's food outlets. The study finds a nonlinear relationship between the racial composition of census tracts and the prevalence of food outlets. The VH-POC census tracts had very few food outlets. For instance, the tracts had no supermarkets, mass merchandisers or supercenters, small grocery or convenience stores, pharmacies or drug stores, or farmers' markets. The findings illustrate the diversity and complexity of the Lansing–East Lansing metropolitan area's food landscape. [ABSTRACT FROM AUTHOR]

Theoretical frameworks in environmental inequality suggest that affluent, white, and educated communities have a greater ability to control local environmental change. With a focus on neighborhood-level land development, the authors evaluate this proposition considering the spatial shifts that are reshaping metropolitan areas across the United States at the beginning of the twenty-first century. With coverage for 52,473 metropolitan census tracts, the authors integrate sociodemographic variables from governmental sources with longitudinal data on developed land area from the National Land Cover Database, 2001–2011. Controlling for a host of other factors, results from spatial regression models with fixed effects show that new land development is negatively associated with affluence and educational attainment. Situating the notion of environmental privilege in a historical context, we propose that, with the “back to the city” movement, these groups are moving back into the urban core, which is already relatively built-out and thus has a lower rate of new land development.

COVID-19 had serious repercussions on public transportation throughout the USA. The aftermath of the peak of the crisis marked the path towards a slow and gradual recovery characterizing the shift to a new normal. Given the limited information on the recovery trends of public transportation, this paper compares the actual ridership and bus supply data for the years of 2019 and 2020 to study the timeline impacts of the pandemic on the bus system of the mid-sized city of Syracuse, NY. A data-driven analysis is presented across the city's bus routes, university bus routes, and categorical bus stops. Various census tract socio-demographic data are also correlated with passenger activity changes and mapped using ArcGIS. The findings show that overall bus ridership in 2020 fell by 70%, on average, during the three months that followed the onset of the pandemic. Since the lifting of the initial restrictions, concerns about using public transportation had partially been alleviated; however, passengers remained reluctant with ridership decline stabilizing at approximately 55% during the last four months of the year. While bus lines serving the university area, which houses a high percentage of youth, were severely affected by the pandemic, passenger activity near hospital stops were less affected and those near major supermarkets/ hypermarkets seemed unaffected, showing a surge especially in the two months that followed the onset of the pandemic. Passenger activity at census tracts having low poverty levels mostly located on the outskirts of the city of Syracuse were the least affected tracts in the last six months of 2020. It is anticipated that the insights presented will help service planners in preparing for similar future events by better understanding what stops and routes are deemed essential during a public health crisis and how the socio-demographics impacted the recovery after restrictions were removed. [ABSTRACT FROM AUTHOR]

Abstract Background Spatial variability of COVID-19 cases may suggest geographic disparities of social determinants of health. Spatial analyses of population-level data may provide insight on factors that may contribute to COVID-19 transmission, hospitalization, and death. Methods Generalized additive models were used to map COVID-19 risk from March 2020 to February 2021 in Orange County (OC), California. We geocoded and analyzed 221,843 cases to OC census tracts within a Poisson framework while smoothing over census tract centroids. Location was randomly permuted 1000 times to test for randomness. We also separated the analyses temporally to observe if risk changed over time. COVID-19 cases, hospitalizations, and deaths were mapped across OC while adjusting for population-level demographic data in crude and adjusted models. Results Risk for COVID-19 cases, hospitalizations, and deaths were statistically significant in northern OC. Adjustment for demographic data substantially decreased spatial risk, but areas remained statistically significant. Inclusion of location within our models considerably decreased the magnitude of risk compared to univariate models. However, percent minority (adjusted RR: 1.06, 95%CI: 1.06, 1.07), average household size (aRR: 1.06, 95%CI: 1.05, 1.07), and percent service industry (aRR: 1.05, 95%CI: 1.04, 1.06) remained significantly associated with COVID-19 risk in adjusted spatial models. In addition, areas of risk did not change between surges and risk ratios were similar for hospitalizations and deaths. Conclusion Significant risk factors and areas of increased risk were identified in OC in our adjusted models and suggests that social and environmental factors contribute to the spread of COVID-19 within communities. Areas in north OC remained significant despite adjustment, but risk substantially decreased. Additional investigation of risk factors may provide insight on how to protect vulnerable populations in future infectious disease outbreaks.

The connection between urban greenspace and mental health is a robust but unsettled area of research in the public health and urban planning literatures. Inconsistent findings in prior studies are mostly due to differences in greenspace measurements and interrelations with socio-demographic factors. This study examines the relationships of mental health prevalence (MHP) with health prevention, socioeconomic and race-ethnicity factors, and proximity to greenspace at the census-tract level in the City of San Diego, California, using data from the CDC 500 Cities Project and US Census Bureau. We considered three greenspace proximity measures: distances to specified vegetation types, parks, and tree cover. Spear-man’s rank correlation showed that MHP was significantly correlated to distances to greenspace (rho = 0.480), parks (rho = 0.234), and tree cover (rho = 0.342), and greenspace proximity plus crime occurrence explained 37.8% of the variance in MHP in regression analysis. Further analysis revealed that socioeconomic status, race-ethnicity, and health prevention explained more than 93% of the variance in MHP, while greenspace proximity did not enter the regression model with statistical significance. We discovered that certain socioeconomic and race-ethnicity variables, such as proportion of Hispanic population, poverty, and regular checkup, may fully represent the effects of greenspace on MHP in the City of San Diego. Regression analysis for three subsections of the city suggested that different predictors of MHP should be considered in formulating intervention measures. Our results indicate the need to improve mental health conditions through a range of interventions that address the disparities experienced by racial-ethnic minorities and those in lower-socioeconomic classes.

Among patients of an urban primary care network in Philadelphia with a universal hepatitis C virus (HCV) screening policy for patients born during 1945–1965, we examined whether being unscreened and HCV positivity were associated with attributes of the census tracts where patients resided, which we considered as proxies for social health determinants. For patients with at least one clinic visit between 2014 and mid-2017, we linked demographic and HCV screening information from electronic health records with metrics that described the census tracts where patients resided. We used generalized estimating equations to estimate adjusted relative risk ratios (aRRs) for being unscreened and HCV positive. Overall, 28% of 6,906 patients were unscreened. Black race, male gender, and residence in census tracts with relatively high levels of violent crime, low levels of educational attainment and household incomes, and evidence of residential segregation by Hispanic ethnicity were associated with lower aRRs for being unscreened. Among screened patients, 9% were HCV positive. Factors associated with lower risks of being unscreened were, in general, associated with higher HCV positivity. Attributes of census tracts where patients reside are probably less apparent to clinicians than patients’ gender or race but might reflect unmeasured patient characteristics that affected screening practices, along with preconceptions regarding the likelihood of HCV infection based on prior screening observations or implicit biases. Approaching complete detection of HCV-infected people would be hastened by focusing on residents of census tracts with attributes associated with higher infection levels or, if known, higher infection levels directly.

El estudio de la distribución de la población en el interior de los municipios rurales presenta importantes dificultades, derivadas de los reducidos volúmenes de población que muchos de ellos presentan, y que, a efectos prácticos, dejan al Padrón Municipal de Habitantes como la única fuente estadística a partir de la cual llevar a cabo este tipo de investigaciones. Desde la vertiente territorial estas dificultades son patentes en lo referente al empleo del seccionado censal, desde el momento en que, debido al escaso número de empadronados, la totalidad del término municipal queda embutida en una única sección, haciendo imposible, por tanto, conocer cómo se distribuye la población en su interior. Sin embargo, el empleo del Nomenclátor de Entidades y Núcleos de Población permite soslayar este escollo, por lo que su empleo se muestra como más conveniente en estos casos. Pero no sólo eso, desde el momento en que el Nomenclátor presenta la misma amplitud temática que el Padrón, también es posible no sólo indagar acerca de cómo se distribuye la población en el interior de los municipios, sino también dividirla en grupos a partir de alguna variable relevante, e indagar acerca de la distribución espacial intramunicipal de dichos grupos de población; tareas que no pueden ser llevadas a cabo a partir del seccionado, cuando éste tiene una única sección, y que, incluso siendo posible, hay ocasiones en las que la interpretación de la realidad queda desvirtuada por el trazado de los límites de las secciones. A partir de ejemplos de municipios rurales de la provincia de Málaga, en la presente investigación mostramos alguna de las principales ventajas que el Nomenclátor tiene sobre el seccionado censal. Para ello hemos empleado, por un lado, el volumen de población total, y su distribución entre nacidos en España y nacidos en el extranjero, datos procedentes de la Estadística del Padrón Continuo correspondientes a 2018; por otro, el seccionado censal de ese año, y la cartografía del Nomenclátor, también referida a 2018. The study of the spatial distribution of population within rural municipalities faces important difficulties, due to the small volumes of population that many of them present, leaving, at practical purposes, the Padron Municipal de Habitantes as the only statistical source to carry out this type of research. Taking into account the territorial aspect, these difficulties are evident when using census tracts, because when the population number is low, the entire municipal area is embedded in a single one, thus making impossible to know how it is distributed inside the municipality. However, the employment of the Nomenclator de Entidades y Núcleos de Población allows us to avoid this, so it is more convenient. Moreover, since the Nomenclator contains the same number of variables as the Padron, it is also possible not only to inquire about how the population is distributed within the municipalities, but also to divide it into groups using some relevant variable, and describe their intra-municipal spatial distribution. These tasks cannot be carried out using census tracts when only one is delimitated inside the municipality. Moreover, under some circunstances, the interpretation of reality is distorted by the layout of their boundaries. Based on examples of rural municipalities in the province of Malaga, in this paper we show some of the main advantages that the Nomenclator has over the census tract. To fullfill this objective, we have used both the total population volume, and its distribution among those born in Spain and born abroad, referred to 2018, and the census tracts and Nomenclator cartography, also referred to 2018.

We present evidence on the long-term relationship between the breadth (the proportion of households) and depth (the amount per household) of public assistance and the prevalence of self-employment in US neighbourhoods. The analysis of decennial data of 71,437 census tracts over four decades (1970 to 2000) shows that the poverty ratio lowers self-employment, and that breadth (but not depth) of public assistance mitigates the negative relationship between the poverty ratio and self-employment. The results are robust to alternate model specifications and are informative about the distributional effects of welfare spendings. [ABSTRACT FROM AUTHOR]

Objective: People who inject drugs are among the groups most vulnerable to HIV infection. The objective of this study was to describe differences in the geographic distribution of HIV diagnoses and social determinants of health (SDH) among people who inject drugs (PWID) who received an HIV diagnosis in 2017. Methods: We used data from the National HIV Surveillance System (NHSS) to determine the counts and percentages of PWID aged ≥18 with HIV diagnosed in 2017. We combined these data with data from the US Census Bureau's American Community Survey at the census tract level to examine regional, racial/ethnic, and population-area-of-residence differences in poverty status, education level, income level, employment status, and health insurance coverage. Results: We observed patterns of disparity in HIV diagnosis counts and SDH among the 2666 PWID with a residential address linked to a census tract, such that counts of HIV diagnosis increased as SDH outcomes became worse. The greatest proportion of PWID lived in census tracts where ≥19% of the residents lived below the federal poverty level, ≥18% of the residents had

Abstract Background Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Methods Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Results Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Conclusions Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

The three-generational household was a focal point of concern for school and community the Coronavirus Disease 2019 (COVID-19) transmission. The current study, using small area data and household variables, reported an approach to neighborhood-level COVID-19 mitigation for school reopening and communities returning to normalcy. The study started with an age-stratified Poisson regression to examine the association between the proportion of three-generational households and COVID-19 infection rates based on data from 74 census tracts in Lancaster County, Nebraska, U.S. from March 5, 2020 to August 22, 2020, followed by mapping the model-based risk score by census tract in the study area. We explored the feasibility of using COVID-19 infection rates and vaccination rates to inform decision-making on school opening from March 5, 2020 to February 3, 2021. The overall infection rate increased by 3% for every unit increased in the percentage of three-generational households after controlling for other covariates in the model. The census tracts were classified into low-, medium-, and high-priority neighborhoods for potential community-based interventions, such as targeted messages for household hygiene and isolation strategies.

Objective: To compare predominantly-Black and predominantly-White Maryland areas with similar socioeconomic status to examine the role of both race and socioeconomic status on tobacco outlet availability and tobacco outlet access. Design: Maryland tobacco outlet addresses were geocoded with 2011–2015 American Community Survey sociodemographic data. Two-sample t-tests were conducted comparing the mean values of sociodemographic variables and tobacco outlet density per Census Tract, and spatial lag based regression models were conducted to analyze the direct association between covariables and tobacco outlet density while accounting for spatial dependence between and within jurisdictions. Results: Predominantly-White jurisdictions had lower tobacco outlet availability and access than predominantly-Black jurisdictions, despite similar socioeconomic status. Spatial lag model results showed that median household income and vacant houses had consistent associations with tobacco outlet density across most of the jurisdictions analyzed, and place-based spatial lag models showed direct associations between predominantly-Black jurisdictions and tobacco outlet availability and access. Conclusion: Predominantly-White areas have lower levels of tobacco outlet density than predominantly-Black areas, despite both areas having similar socioeconomic statuses. [ABSTRACT FROM AUTHOR]

Copyright of EMPIRIA: Revista de Metodología de Ciencias Sociales is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona's arid climate. The state of Arizona is vulnerable to dust storms, especially in the monsoon season because of the anomalies in wind direction and magnitude. In this study, a high-resolution Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona. Subsequently, Ordinary Least Square (OLS), spatial lag, spatial error, and Geographically Weighted Regression (GWR) techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms. Census tracts were adopted to create local spatial averages for the chosen variables. Terrain height, temperature, wind speed, and vegetation fraction were designated as the most significant variables, whereas base state and perturbation pressures, planetary boundary layer height and soil moisture were adopted as supplementary variables. The determination coefficient for OLS, spatial lag, spatial error, and GWR models peaked at 0.92, 0.93, 0.96, and 0.97, respectively. These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends. [ABSTRACT FROM AUTHOR]

Background: Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Methods: Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Results: Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p <.0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Conclusions: Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes. [ABSTRACT FROM AUTHOR]

We empirically evaluate the distribution of spatial patterns at the census tract (CT) level for various immigration and property crime measures in Vancouver, British Columbia, 2003 and 2016, using a spatial point pattern test that identifies significant similarities, or otherwise, in the spatial patterns of (a) multiple measures of immigration, (b) various property crime classifications, and (c) immigration and crime patterns together. Results show local-level variations in the spatial concentration of immigration in Vancouver CTs. The use of multiple measures of immigration showed substantive variations of immigrant settlement at the local level. Moreover, results reveal that while immigrant concentration patterns are stable over time and, thus, demonstrate ecological stability, property crime patterns shift from year to year. The spatial analytic approach utilized in this study provides support for the use of local-level spatial models and the multidimensional operationalization of the immigration variable even when their correlations are high. There is heterogeneity among immigrant groups, an important yet often overlooked aspect in assessments of immigration effects on crime. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Studies in Population is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Numerous studies have shown significant neighbourhood level variation in out-of-hospital cardiac arrest (OHCA) incidence rates, however, few have provided an explanation for these disparities beyond traditional socioeconomic measures.Methods: This was a retrospective study using data from a large population-based OHCA database (Rescu Epistry). We included adults ≥20 years who experienced a non-traumatic OHCA and were treated by emergency medical services within Toronto, Canada between 2006-2012. The residential address of each OHCA patient was spatially mapped to 1 of 517 Toronto census tracts (CTs). Patient and CT level characteristics were included in multivariate regression models to assess their association with OHCA incidence per 100,000 persons.Results: Of the 7775 OHCAs occurring in the study area, 7692 (98.9%) were eligible for inclusion. OHCA incidence rates varied widely across CT quintiles, with rates differing almost 4-fold (109.1 per 100,000 yearly Q5 most deprived vs. 30.0 per 100,000 yearly Q1 least deprived p < 0.0001). Numerous areas of high incidence adjacent to areas of low incidence were observed. After adjustment, all variables except the Activity Friendly Index showed highly significant linear trends, with increasing age, sex ratio, diabetes prevalence, material deprivation and ethnic concentration being independently associated with increasing OHCA incidence. In contrast, we did not observe a linear relationship between high OHCA incidence and median household income.Conclusions: This study showed almost 4-fold OHCA incidence variability across a large metropolitan area. This variability was partially correlated with population and health data, but not typical socioeconomic predictors, such as median household income. [ABSTRACT FROM AUTHOR]

The purpose of this research was to investigate rural-urban differences in participation rates in three modes of active commuting (AC) and their built environmental correlates. The 2010 Census supplemented with other datasets were used to analyze AC rates in percent of workers age 16+ walking, biking, or taking public transportation to work in 70,172 Census tracts, including 12,844 rural and 57,328 urban. Random-intercept factional logit regressions were used to account for zero-inflated data and for clustering of tracts within counties. We found that the average AC rates were 3.44% rural and 2.77% urban (p

Performing longitudinal analysis of socio‐economic change in small‐area spatial units such as census tracts presents several methodological complications and requires significant data preparation. Unit boundaries are revised each census year because of changes in population and delineation methodologies. This limits cross‐year comparison since data are not representative of the same spatial units. To address these problems, we have developed an innovative procedure to reduce error when comparing tract‐level data across census years by apportioning data to the same areal units. This paper describes the methods used to create the Canadian Longitudinal Tract Database. Our procedure is a combination of map‐matching techniques, dasymetric overlays, and population‐weighted areal interpolation. The output is a set of tables with apportionment weights pertaining to pairs of unique boundary identifiers across census years, which can be linked with census data or other data with census identifiers that require longitudinal comparison. Key Messages: Neighbourhood change research is challenged by census boundaries being revised each census year.This paper describes the creation of a longitudinal spatial database of census tracts in Canada, bridging tract‐level data for the 1971–2016 quinquennial censuses to a common set of boundaries.Methodology includes map‐matching, dasymetric overlays, and population‐weighted areal interpolation in order to minimize error when boundaries change over time. [ABSTRACT FROM AUTHOR]

Objectives: To disaggregate the crime impact of visitor inflows. There is increasing evidence that visitors can make a major contribution to levels of crime in a given neighbourhood: crimes by visiting offenders may add to those committed by local offenders, while visitors (and their property) may provide local offenders with additional opportunities for crime.Methods: Using police-recorded crime data for a large Eastern Canadian city we determined whether individuals charged or chargeable for property and violent crimes were visitors or residents of census tracts (CT) where crimes had been committed. This information was combined with data from a large transportation survey, allowing us to estimate daily population flows into each CT for four purposes (work, shopping, recreation, and education). Negative binomial regression models including spatial lags were used.Results: An increase in visitor inflow not only increases the number of visitors charged with crimes but also the number of local residents charged. These effects vary significantly by visit purpose: more infractions are committed in tracts where visits are for recreation and, to a lesser extent, for shopping. Findings for work and education are mixed.Conclusions: One important implication of our results is that, because most studies of aggregate crime counts or rates fail to account for whether crimes have been committed by visitors or residents, previous research may have provided hasty, partial, or even erroneous explanations for crime concentrations. [ABSTRACT FROM AUTHOR]

Introduction Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Method Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) are developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposes a method to compare the modeling performance of the three types of geographic units at different spatial configurations through a grid based framework. Specifically, the study region is partitioned to grids of various sizes and the model prediction accuracy of the various macro models is considered within these grids of various sizes. Results These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperform the ones that do not consider it. Conclusions Based on the modeling results and motivation for developing the different zonal systems, it is recommended using CTs for socio-demographic data collection, employing TAZs for transportation demand forecasting, and adopting TADs for transportation safety planning. Practical Applications The findings from this study can help practitioners select appropriate zonal systems for traffic crash modeling, which leads to develop more efficient policies to enhance transportation safety. [ABSTRACT FROM AUTHOR]

Social scientists regularly rely on population estimates when studying change in small areas over time. Census tract data in the United States are a prime example, as there are substantial shifts in tract boundaries from decade to decade. This study compares alternative estimates of the 2000 population living within 2010 tract boundaries to the Census Bureau's own retabulation. All methods of estimation are subject to error; this is the first study to directly quantify the error in alternative interpolation methods for U.S. census tracts. A simple areal weighting method closely approximates the estimates provided by one standard source (the Neighborhood Change Data Base), with some improvement provided by considering only area not covered by water. More information is used by the Longitudinal Tract Data Base (LTDB), which relies on a combination of areal and population interpolation as well as ancillary data about water-covered areas. Another set of estimates provided by the National Historical Geographic Information Systems (NHGIS) uses data about land cover in 2001 and the current road network and distribution of population and housing units at the block level. Areal weighting alone results in a large error in a substantial share of tracts that were divided in complex ways. The LTDB and NHGIS perform much better in all situations but are subject to some error when boundaries of both tracts and their component blocks are redrawn. Users of harmonized tract data should be watchful for potential problems in either of these data sources. [ABSTRACT FROM AUTHOR]

Objectives: Studies have shown that communities with higher concentrations of low-income racial and ethnic minorities correlate with a greater presence of tobacco outlets. Community-level income has consistently been among the strongest predictors of tobacco outlet density. This study analyzes two Maryland geopolitical areas with similar racial concentrations yet differing income levels in an attempt to disentangle the race--income relationship with tobacco outlet density. Study design: In this cross-sectional examination of tobacco outlet and census tract-level sociodemographic data, Baltimore City, Maryland, and Prince George's County, Maryland, were geocoded to determine tobacco outlet density. Methods: Tobacco outlet density was defined as the mean number of tobacco outlets per 1000 persons per census tract. Comparisons of tobacco outlet density and sociodemographic variables were analysed via two-sample t-tests, and the direct effect of sociodemographic variables on tobacco outlet density for each area was analysed via spatial lag regressions. Results: Prince George's County, the area with the higher income level ($77,190 vs $43,571), has a significantly lower tobacco outlet density than Baltimore City (P < 0.001). Prince George's County has a 67.5% Black population and an average of 3.94 tobacco outlets per 1000 persons per tract. By contrast, Baltimore City has a 65.3% Black population and an average of 7.95 tobacco outlets per 1000 persons per tract. Spatial lag regression model results indicate an inverse relationship between income and tobacco outlet density in Baltimore City and Prince George's County (β = 0.03, P < 0.01 & β = 0.01, P = 0.02, respectively), and a significant interaction term indicating a greater magnitude in the relationship between income and tobacco outlet density in Baltimore City (β = 0.05, P < 0.01). Conclusion: Results suggest that higher socio-economic status, even in primarily underrepresented racial and ethnic geopolitical areas, is linked to lower tobacco outlet density. [ABSTRACT FROM AUTHOR]

Research that attempts to characterize urban form is confronted with two key issues: criticism of the use of aggregate units of analysis, such as census tracts, and a general lack of consideration of variables related to elements other than the built environment, such as residents’ behavior. This methodological study explores the impact of travel behavior variables in the quantitative characterization of urban form at the census tract level for the Montreal region. Two separate factor-cluster analyses are performed: the first includes built-environment variables commonly used to typify areas within a region, and a second includes additional travel behavior variables. The results of both models are compared to satellite images to determine which analysis more accurately represents the reality on the ground. The results provide empirical evidence that travel behavior variables, in addition to built form, provide a more accurate representation of urban form at the census tract level. These variables refine the model output by moderating the effect of features that generally led to misleading results. This effect is particularly evident in areas represented by large census tracts. These results suggest that considering both built environment and behavioral characteristics in an analysis of urban form yields more precise results at the (aggregate) census tract level. The findings from this study could be helpful for engineers and planners when conducting property value studies, urban investment analysis, and policy intervention prioritization and when expanding the well-known land use classification of urban and rural categories.

Individual-level data from the Florida Cancer Data System (1981–2007) were analyzed to explore temporal trends of prostate cancer late-stage diagnosis, and how they vary based on race, income and age. Annual census-tract rates were computed for two races (white and black) and two age categories (40–65, >65) before being aggregated according to census tract median household incomes. Joinpoint regression and a new disparity statistic were applied to model temporal trends and detect potential racial and socio-economic differences. Multi-dimensional scaling was used as an innovative way to visualize similarities among temporal trends in a 2-D space. Analysis of time-series indicated that late-stage diagnosis was generally more prevalent among blacks, for age category 40–64 compared to older patients covered by Medicare, and among classes of lower socio-economic status. Joinpoint regression also showed that the rate of decline in late-stage diagnosis was similar among older patients. For younger patients, the decline occurred at a faster pace for blacks with rates becoming similar to whites in the late 1990s, in particular for higher incomes. Both races displayed distinct spatial patterns with higher rates of late-stage diagnosis in the Florida Panhandle for whites whereas high rates clustered in South-eastern Florida for blacks. [ABSTRACT FROM AUTHOR]

Background: In New Haven County, CT (NHC), influenza hospitalization rates have been shown to increase with census tract poverty in multiple influenza seasons. Though multiple factors have been hypothesized to cause these inequalities, including population structure, differential vaccine uptake, and differential access to healthcare, the impact of each in generating observed inequalities remains unknown. We can design interventions targeting factors with the greatest explanatory power if we quantify the proportion of observed inequalities that hypothesized factors are able to generate. Here, we ask if population structure is sufficient to generate the observed area-level inequalities in NHC. To our knowledge, this is the first use of simulation models to examine the causes of differential poverty-related influenza rates.Methods: Using agent-based models with a census-informed, realistic representation of household size, age-structure, population density in NHC census tracts, and contact rates in workplaces, schools, households, and neighborhoods, we measured poverty-related differential influenza attack rates over the course of an epidemic with a 23 % overall clinical attack rate. We examined the role of asthma prevalence rates as well as individual contact rates and infection susceptibility in generating observed area-level influenza inequalities.Results: Simulated attack rates (AR) among adults increased with census tract poverty level (F = 30.5; P < 0.001) in an epidemic caused by a virus similar to A (H1N1) pdm09. We detected a steeper, earlier influenza rate increase in high-poverty census tracts-a finding that we corroborate with a temporal analysis of NHC surveillance data during the 2009 H1N1 pandemic. The ratio of the simulated adult AR in the highest- to lowest-poverty tracts was 33 % of the ratio observed in surveillance data. Increasing individual contact rates in the neighborhood did not increase simulated area-level inequalities. When we modified individual susceptibility such that it was inversely proportional to household income, inequalities in AR between high- and low-poverty census tracts were comparable to those observed in reality.Discussion: To our knowledge, this is the first study to use simulations to probe the causes of observed inequalities in influenza disease patterns. Knowledge of the causes and their relative explanatory power will allow us to design interventions that have the greatest impact on reducing inequalities.Conclusion: Differential exposure due to population structure in our realistic simulation model explains a third of the observed inequality. Differential susceptibility to disease due to prevailing chronic conditions, vaccine uptake, and smoking should be considered in future models in order to quantify the role of additional factors in generating influenza inequalities. [ABSTRACT FROM AUTHOR]

Urban form—the structure of the built environment—can influence physical activity, yet little is known about how walkable design differs according to neighborhood sociodemographic composition. We studied how walkable urban form varies by neighborhood sociodemographic composition, region, and urbanicity across the United States. Using linear regression models and 2000–2001 US Census data, we investigated the relationship between 5 neighborhood census characteristics (income, education, racial/ethnic composition, age distribution, and sex) and 5 walkability indicators in almost 65,000 census tracts in 48 states and the District of Columbia. Data on the built environment were obtained from the RAND Corporation's (Santa Monica, California) Center for Population Health and Health Disparities (median block length, street segment, and node density) and the US Geological Survey's National Land Cover Database (proportion open space and proportion highly developed). Disadvantaged neighborhoods and those with more educated residents were more walkable (i.e., shorter block length, greater street node density, more developed land use, and higher density of street segments). However, tracts with a higher proportion of children and older adults were less walkable (fewer street nodes and lower density of street segments), after adjustment for region and level of urbanicity. Research and policy on the walkability-health link should give nuanced attention to the gap between persons living in walkable areas and those for whom walkability has the most to offer. [ABSTRACT FROM PUBLISHER]

In France, reducing social health inequalities has become an explicit goal of health policies over the past few years, one of its objectives is specifically the reduction of the perinatal mortality rate. This study investigates the association between infant mortality and social deprivation categories at a small area level in the Lille metropolitan area, in the north of France, to identify census blocks where public authorities should prioritize appropriate preventive actions. We used census data to establish a neighbourhood deprivation index whose multiple dimensions encompass socioeconomic characteristics. Infant mortality data were obtained from the Lille metropolitan area municipalities to estimate a death rate at the census tract level. We used Bayesian hierarchical models in order to reduce the extra variability when computing relative risks (RR) and to assess the associations between infant mortality and deprivation. Between 2000 and 2009, 668 cases of infant death occurred in the Lille metropolitan area (4.2 per 1,000 live births). The socioeconomic status is associated with infant mortality, with a clear gradient of risk from the most privileged census blocks to the most deprived ones (RR = 2.62, 95 % confidence interval [1.87; 3.70]). The latter have 24.6 % of families who were single parents and 29.9 % of unemployed people in the labor force versus 8.5 % and 7.7 % in the former. Our study reveals socio-spatial disparities in infant mortality in the Lille metropolitan area and highlights the census blocks most affected by the inequalities. Fine spatial analysis may help inform the design of preventive policies tailored to the characteristics of the local communities. [ABSTRACT FROM AUTHOR]