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This paper studies the role of the Community Reinvestment Act (CRA) in the U.S. housing boom-bust cycle. I find that enhanced CRA enforcement in 1998 increased the growth rate of mortgage lending by CRA-regulated banks to CRA-eligible census tracts. I show that during the boom period house price growth was higher in the eligible census tracts because of the shift in mortgage supply of regulated banks. Consequently, these census tracts experienced a worse housing bust. I find that CRA-induced mortgages were awarded to borrowers with lower FICO scores and were more frequently delinquent. [ABSTRACT FROM AUTHOR]

We evaluate the relationships between immigration and crime at the census tract level. Using multiple measures of immigration considering nuances of the immigrant population and a statistical technique allowing for the identification of long- and short-run effects, we provide further evidence of the negative relationship between immigration and crime. However, we note that these relationships are not monolithic. Similar to the original work on social disorganization theory, we find evidence for census tracts with increases in immigration have increased levels of crime because of restricted access to resources and initial settlement in high crime areas. However, over time, our results suggest that greater integration and the development of social capital (longer term immigration) is negatively associated with crime. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives. To investigate how census tract (CT) estimates of mortality rates and inequities are affected by (1) differential privacy (DP), whereby the public decennial census (DC) data are injected with statistical "noise" to protect individual privacy, and (2) uncertainty arising from the small number of different persons surveyed each year in a given CT for the American Community Survey (ACS). Methods. We compared estimates of the 2008–2012 average annual premature mortality rate (death before age 65 years) in Massachusetts using CT data from the 2010 DC, 2010 DC with DP, and 2008–2012 ACS 5-year estimate data. Results. For these 3 denominator sources, the age-standardized premature mortality rates (per 100 000) for the total population respectively equaled 166.4 (95% confidence interval [CI] = 162.2, 170.6), 166.4 (95% CI = 162.2, 170.6), and 166.3 (95% CI = 162.1, 170.5), and inequities in the range from best to worst quintile for CT racialized economic segregation were from 103.4 to 260.1, 102.9 to 258.7, and 102.8 to 262.4. Similarity of results across CT denominator sources held for analyses stratified by gender and race/ethnicity. Conclusions. Estimates of health inequities at the CT level may not be affected by use of 2020 DP data and uncertainty in the ACS data. [ABSTRACT FROM AUTHOR]

We empirically test for spatial heterogeneity or local effects of multiple immigration measures on various property crime classification across Vancouver census tracts, 2016. Using spatially referenced property crime data and census data, we use geographically weighted regression to investigate the neighbourhood-level effects of immigration on crime. We find that estimated parameters vary across space, but these local immigration effects do not always vary significantly at the local level. Overall, significant spatial variation in the effects of immigration on property crime is present. These are important for policy and theory. The identification of varied spatial patterns of immigration effects on crime may help explain some of the inconsistent/disparate results found in neighbourhood-level studies on immigration and 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.)

Welcome to Census Tract 1186.02! Here, in a small sliver of Cleveland's Glenville neighborhood, tucked between Superior and Hough Avenues, you will uncover a lot. You will discover a rich history of the city's ethnic and cultural roots. You will also find gang violence, underperforming schools, a median household income of $9,526, and a poverty rate of 66.5 percent. Something you will not find in 1186.02 is investment. Private or public, money is not flowing in to 1186.02 and it has not for a long time. The substantial toll of continuous underinvestment on the residents of this neighborhood, one of Cleveland's poorest, is palpable and the need for relief is clear. This relief recently became possible when 1186.02 was designated as a Qualified Opportunity Zone by the U.S. Treasury Department. This designation, made available through the Tax Cuts and Jobs Act of 2017, could be a watershed moment for Glenville. By providing significant federal tax benefits to investors, Congress hopes to encourage a massive inflow of investment into low-income communities, like Glenville, that have long suffered from perennial underinvestment. Unfortunately, the hope of relief for Tract 1186.02 and its residents will likely be short-lived. Because the federal Opportunity Zone provision was written too broadly, it not only permitted too many designations, but it also allowed for the designation of numerous not-so-poor neighborhoods. In other words, the law pits communities that need the investment most--like Glenville--against neighborhoods that are much better-off--like Cleveland's downtown core and trendy Tremont neighborhood. For context, there are tracts in the city's downtown core and Tremont neighborhood that were designated with median household incomes that septuple (i.e. seven times as much) those in 1186.02. The odds that areas like Glenville can outcompete such bettersituated areas for private investment dollars are slim-to-none. As a result, the residents of 1186.02 will be left behind again. But it does not have to be this way. It is not too late for the City of Cleveland and Cuyahoga County to take advantage of the many positive aspects of the Opportunity Zone provision, while ensuring that those benefits are directed towards their most-inneed neighborhoods. By enacting local legislation that targets these underdog tracts for additional services and public investment, this Note argues Cleveland and Cuyahoga County can level the playing field and provide neighborhoods like Glenville and tracts like 1186.02 with the competitive advantages they need to rediscover their potential. [ABSTRACT FROM AUTHOR]

Social and epidemiological aspects of dengue were evaluated in an important metropolitan area in southern Brazil, from August 2012 to September 2014. Demographic, clinical, serological data were collected from patients with acute dengue symptoms treated at public health system units (HSUs). A systematic approach to analyze the spatial and temporal distribution of cases was developed, considering the temporal cross-correlation between dengue and weather, and the spatial correlation between dengue and income over the city's census tracts. From the 878 patients with suggestive symptoms, 249 were diagnosed as positive dengue infection (28%). Considering the most statistically significant census tracts, a negative correlation was found between mean income and dengue (r = −0.65; p = 0.02; 95% CI: −0.03 to −0.91). The occurrence of dengue followed a seasonal distribution, and it was found to be three and four months delayed in relation to precipitation and temperature, respectively. Unexpectedly, the occurrence of symptomatic patients without dengue infection followed the same seasonal distribution, however its spatial distribution did not correlate with income. Through this methodology, we have found evidence that suggests a relation between dengue and poverty, which enriches the debate in the literature and sheds light on an extremely relevant socioeconomic and public health issue. [ABSTRACT FROM AUTHOR]

Using a newly compiled dataset, we measure the effects of participatory budgeting on the allocation of capital funding among areas of different income levels within New York City council districts. A difference‐in‐differences design compares changes in the allocation of funding in adopting districts before and after the adoption of participatory budgeting to changes over the same period among a control group consisting of later adopters. On average, adopting districts increase funding in the next to the lowest income census tracts more than the control group, but participatory budgeting does not redirect funds to the lowest income census tracts. [ABSTRACT FROM AUTHOR]

Abstract Slum identification and mapping are fundamental to support public policies. However, slum data is affected by differences in definition, identification, spatial delimitation, sample and data collection periods. This study compares slum maps in Sao Mateus (a peripheral region of Sao Paulo city) from the 2010 demographic census and the Sao Paulo municipal government. The census and the municipal basemaps and orthophotos were overlaid in a Geographic Information System (GIS) to evaluate their similarities and differences, the reasons for inconsistencies between them. Of the 68 districts with subnormal agglomerates (EDSAs; special enumeration districts for slum areas in the Brazilian demographic census), 59% are in the categories 'high coincidence' or 'partial coincidence' with the perimeters of favelas (the main type of slum in Brazil) delineated by the Sao Paulo municipal government; the names of the residential area (favela/EDSA) are the same in only 51.5% of the subnormal agglomerates, while 26.5% of the EDSAs include residential areas not considered favelas by the municipality. Many Brazilian researchers state that the underestimation of EDSA census data is accentuated by the exclusion of favelas smaller than 51 dwellings from the census. However, at least in Sao Mateus, the exclusion of small favelas (3.6%) was not the only reason for census underestimation of favelas. Highlights • In São Paulo City, two different databases about slums are available, but with the conflicting aspects. • Cartographic differences have shown that federal and municipal institutions do not count the same housing in the favelas. • The coincidence of perimeters and names of the Sao Mateus favelas are lesser them 60% between government agencies. • In Sao Mateus, the exclusion of small favelas (3.6%) was not the only reason for the census' underestimation of favelas. • The perimeters' similarity differences and problems in identification/delimitation had more impact in this underestimation. [ABSTRACT FROM AUTHOR]

The long-range dispersal of pelagic larvae is often assumed to be the dominant force behind connectivity in the marine environment, with little consideration given to benthic movements. We analyzed data from an American lobster (Homarus americanus) tagging study (1980–1996) in the Gulf of St. Lawrence during which 37 579 adults from 14 locations were tagged and 6296 were recaptured after 1–6 years at large. The 10th percentile greatest distance moved by tagged lobsters after 1 year at large contributed to demographic connectivity between neighbouring statistical districts, fishing ports, and (in one location) management areas. Considering the incremental dispersal of lobsters after 2 versus 1 year at large, we estimated that the 10th percentile greatest benthic dispersal distances after 1, 2, and 5 years at large represented, respectively, 7%, 14%, and 35% of larval dispersal in the "downstream" direction and 75%, 111%, and 220% of larval dispersal in the "upstream" direction (similar results obtained based on mean dispersal values). We conclude that more attention should be given to benthic movements in estimating connectivity and stock structure in American lobster. [ABSTRACT FROM AUTHOR]

This study presents a novel geo-based metric to identify neighborhoods with high burdens of prostate cancer, and compares this metric to other methods to prioritize neighborhoods for prostate cancer interventions. We geocoded prostate cancer patient data (n = 10,750) from the Pennsylvania cancer registry from 2005 to 2014 by Philadelphia census tract (CT) to create standardized incidence ratios (SIRs), mortality ratios (SMRs), and mean prostate cancer aggressiveness. We created a prostate cancer composite (PCa composite) variable to describe CTs by mean-centering and standard deviation-scaling the SMR, SIR, and mean aggressiveness variables and summing them. We mapped CTs with the 25 highest PCa composite scores and compared these neighborhoods to CTs with the 25 highest percent African American residents and the 25 lowest median household incomes. The mean PCa composite score among the 25 highest CTs was 4.65. Only seven CTs in Philadelphia had both one of the highest PCa composite scores and the highest percent African American residents. Only five CTs had both the highest PCa composites and the lowest median incomes. Mean PCa composite scores among CTs with the highest percent African American residents and lowest median incomes were 2.08 and 1.19, respectively. The PCa composite score is an accurate metric for prioritizing neighborhoods based on burden. If neighborhoods were prioritized based on percent African American or median income, priority neighborhoods would have been very different and not based on PCa burden. These methods can be utilized by public health decision-makers when tasked to prioritize and select neighborhoods for cancer interventions. [ABSTRACT FROM AUTHOR]

Objective: Bicycling is an affordable way to increase access to employment, schooling, and services and an effective measure against obesity. Bikeshare programs can make bicycling accessible to diverse populations, but little evidence exists on their adoption in low-resource neighborhoods. Our study examined factors associated with bikeshare use in a metropolitan area in the southern United States.Methods: We performed a retrospective cross-sectional analysis of a database of clients (N=815) who rented a bicycle from Zyp Bikeshare in Birmingham, Alabama between October 2015 and November 2016. Individual-level variables included bike use frequency, average speed, total miles traveled, total minutes ridden, bike type (traditional vs electricity-assisted pedelec), membership type, sex, and age. Area-level data aggregated to Census tracts, proxies for neighborhoods, were obtained from the 2010 US Census after geocoding clients' billing addresses. Using exploratory factor analysis, a neighborhood socioeconomic disadvantage index (SDI) was constructed. Bikeshare station presence in a tract was included as a covariate. Multivariate linear regression models, adjusted for clustering on Census tracts, were estimated to determine predictors of bikeshare use.Results: In a multivariate regression model of individual and neighborhood characteristics adjusted for clustering, each decile increase in the SDI was associated with a 9% increase in bikeshare use (P<.001). Bikeshare use was also positively associated with speed (.1, P<.001), total miles (.008, P<.001), and pedelec use (1.02, P<.01).Conclusion: Higher neighborhood socioeconomic disadvantage is associated with higher bikeshare use. Bikeshare is a viable transportation option in low-resource neighborhoods and may be an effective tool to improve the connectivity, livability, and health of urban communities. [ABSTRACT FROM AUTHOR]

Early ethnographic studies suggested that police intervention should be understood within its larger context. Still, the number of studies focused on the ecology of force remains small compared to those of studies on individual and situational factors. Furthermore, Canada remains nearly absent from the academic literature on police use of force. Assuming that force does not occur in a spatially random manner, this article aims to test propositions for the main macrosociological perspectives in the use-of-force literature: social disorganization theory, the minority-threat hypothesis and the theory of police rigour. Another purpose of this study is to investigate whether, at the level of the census tract (CT), visitor inflows are predictive of police action. Negative binomial regression modelling is used to predict the occurrence of 1,411 self-reported uses of force in 506 CTs. The findings show that social disorganization is the most predictive explanation for the frequency of use-of-force situations in an area. The analysis also supports the proposition that the frequency of use-of-force situations is positively related to the level of crime in the area. While the inclusion of visitor inflows significantly improves the analysis of spatial variations of police use of force, it contributes relatively little relative to other explanations. No support was found for the minoritythreat hypothesis, nor for Klinger's theory of police vigour. [ABSTRACT FROM AUTHOR]

Communities across the United States have become increasingly concerned over the presence of sex offenders in their neighborhoods. The purpose of this research is to examine the factors that are associated with the concentration of sex offenders in a large geographic area with few residency restrictions. This research also examines multiple categories of sex offenders subject to varying levels of community notification, allowing for an assessment of what, if any, effect community notification has on the residential patterns of sex offenders. Concentrated disadvantage, concentrated affluence, and housing affordability are all significant factors in explaining the concentration of multiple categories of sex offenders. Concentrated affluence relative to poverty is the most consistent predictor of sex offender concentration, revealing that more affluent communities ward off sex offender residents, regardless of community notification requirements. [ABSTRACT FROM AUTHOR]

Copyright of RGO: Revista Gaúcha de Odontologia is the property of RGO: Revista Gaucha de Odontologia 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.)

In the previous census, Estonia provided the people the opportunity of self-enumeration in an electronic e-census environment. The goal of the new solutions was to ensure a bigger coverage for the Census, a better quality and faster publication of the results. The e-census was a success: 2/3 of the persons enumerated took advantage of the new possibility and participated in the census online. By the way the e-census reduced census costs. The present article describes the experience of Statistics Estonia in preparing and conducting the e-census. [ABSTRACT FROM AUTHOR]

The social interaction potential (SIP) metric measures urban structural constraints on social interaction opportunities of a metropolitan region based on the time geographic concept of joint accessibility. Previous implementations of the metric used an interaction surface based on census tracts and the locations of their centroids. This has been shown to be a shortcoming, as the metric strongly depends on the scale of the zoning system in the region, making it difficult to compare the SIP metric between metropolitan regions. This research explores the role of spatial representation in the SIP metric and identifies a suitable grid-based representation that allows for comparison between regions while retaining cost-effectiveness with respect to computational burden. We also report on findings from an extensive sensitivity analysis investigating the SIP metric's input parameters such as a travel flow congestion factor and the length of the allowable time budget for social activities. The results provide new insights on the role of the modifiable areal unit problem in the computation of time geographic measures of accessibility. [ABSTRACT FROM AUTHOR]

Objectives Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York. Methods We utilize 2.8 million tweets collected between February–August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics. Results For a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths. Conclusions Social media can be leveraged to provide greater understanding of the well-being and health behaviors of communities—information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health. [ABSTRACT FROM AUTHOR]

Background: In Spain, several ecological studies have analyzed trends in socioeconomic inequalities in mortality from all causes in urban areas over time. However, the results of these studies are quite heterogeneous finding, in general, that inequalities decreased, or remained stable. Therefore, the objectives of this study are: (1) to identify trends in geographical inequalities in all-cause mortality in the census tracts of 33 Spanish cities between the two periods 1996-1998 and 2005-2007; (2) to analyse trends in the relationship between these geographical inequalities and socioeconomic deprivation; and (3) to obtain an overall measure which summarises the relationship found in each one of the cities and to analyse its variation over time.Methods: Ecological study of trends with 2 cross-sectional cuts, corresponding to two periods of analysis: 1996-1998 and 2005-2007. Units of analysis were census tracts of the 33 Spanish cities. A deprivation index calculated for each census tracts in all cities was included as a covariate. A Bayesian hierarchical model was used to estimate smoothed Standardized Mortality Ratios (sSMR) by each census tract and period. The geographical distribution of these sSMR was represented using maps of septiles. In addition, two different Bayesian hierarchical models were used to measure the association between all-cause mortality and the deprivation index in each city and period, and by sex: (1) including the association as a fixed effect for each city; (2) including the association as random effects. In both models the data spatial structure can be controlled within each city. The association in each city was measured using relative risks (RR) and their 95 % credible intervals (95 % CI).Results: For most cities and in both sexes, mortality rates decline over time. For women, the mortality and deprivation patterns are similar in the first period, while in the second they are different for most cities. For men, RRs remain stable over time in 29 cities, in 3 diminish and in 1 increase. For women, in 30 cities, a non-significant change over time in RR is observed. However, in 4 cities RR diminishes. In overall terms, inequalities decrease (with a probability of 0.9) in both men (RR = 1.13, 95 % CI = 1.12-1.15 in the 1st period; RR = 1.11, 95 % CI = 1.09-1.13 in the 2nd period) and women (RR = 1.07, 95 % CI = 1.05-1.08 in the 1st period; RR = 1.04, 95 % CI = 1.02-1.06 in the 2nd period).Conclusions: In the future, it is important to conduct further trend studies, allowing to monitoring trends in socioeconomic inequalities in mortality and to identify (among other things) temporal factors that may influence these inequalities. [ABSTRACT FROM AUTHOR]

The use of a single geographical unit to both collect and disseminate census data is common in many countries across the world, especially in developing countries. In South Africa this approach poses some challenges, as the design of small geographical units called enumeration areas to facilitate data collection differs considerably from the design of units that aid data analysis and interpretation. We aimed to create optimised census output areas using the Automated Zone-design Tool (AZTool) program, using the 2001 census enumeration areas as building blocks at various spatial levels, for both rural and urban settings in two South African provinces. The results were consistent and stable. The primary criterion of the confidentiality limit of 500 people was respected at all geographical levels or regions, in both urban and rural settings, for newly created optimised output areas. For the second criterion, lower intra-area correlation values at lower geographical levels for both rural and urban areas showed that higher geographical levels produced more homogeneous output areas than did lower geographical levels or regions. Our obtained intra-area correlation of 0.62 for the two provinces combined indicated that the selected homogeneity variables were good indicators of social homogeneity for creating optimised output areas in South Africa. We conclude that the AZTool software can be used to effectively and objectively create optimised output areas for South African data. Further research on the comparison of the newly created output areas with existing output areas in South Africa should be explored. [ABSTRACT FROM AUTHOR]

A common policy approach to reducing childhood obesity aims to shape the environment in which children spend most of their time: neighborhoods and schools. This paper uses richly detailed data on the body mass index (BMI) of all New York City public school students in grades K-8 to assess the potential for place-based approaches to reduce child obesity. We document variation in the prevalence of obesity across NYC public schools and census tracts, and then estimate the extent to which this variation can be explained by differences in individual-level predictors (such as race and household income). Both unadjusted and adjusted variability across neighborhoods and schools suggest place-based policies have the potential to meaningfully reduce child obesity, but under most realistic scenarios the improvement would be modest. [ABSTRACT FROM AUTHOR]

Background: To date, 72% of variability in survival following out-of-hospital cardiac arrest (OHCA) is explained by the Utstein variables. Whether neighborhood factors further influence a return of spontaneous circulation or survival after OHCA is poorly understood.Methods: We completed a retrospective cohort study of all paramedic-treated OHCA within the City of Toronto's 531 census tracts between 2006 and 2014. Neighborhood variables included the Ontario Marginalization Index - a measure of poverty, ethnicity and instability - crime rate and the density of family physicians. Hierarchical logistic regression analysis explored the association between a patient's census tract (neighborhood) characteristics and survival to hospital discharge and a prehospital return of spontaneous circulation (ROSC). Receiver operator characteristics curves measured how the Utstein variables and neighborhood factors discriminate OHCA outcomes.Results: There were 23,067 OHCA during the study period, with 10,097 cases excluded due to obvious death, 896 from an obvious etiology and 2589 cases for other reasons, leaving 9485 patients for analysis. Of the neighborhood variables, only census tracts with a moderate ethnic concentration had an increased likelihood of survival-to-hospital discharge. The Utstein variables accounted for 89.2% of survival and 39.8% of prehospital ROSC. Adding all neighborhood factors to the Utstein model increased discrimination for survival to discharge to 89.8% (p=0.005) and of a prehospital ROSC to 40.8% (p=0.006).Conclusions: Residential neighborhood factors marginally improve discrimination for outcomes after an OHCA, beyond the Utstein variables. Further research should explore the influence of other currently unmeasured neighborhood factors on OHCA outcomes. [ABSTRACT FROM AUTHOR]

This study examined the census tract characteristics associated with the spatial concentration of registered sex offender (RSO) residences in 1,823 census tracts across 53 counties in upstate New York. The concentration of RSOs for each tract was measured using excess risk scores that essentially measure disproportionate concentrations of RSOs based on the resident population of the county and tract. The tract characteristics examined included structural characteristics from the 2010 Census, such as indicators of social disorganization, housing availability and affordability, and population density, legal characteristics describing the presence of residence restrictions, and controls for spatial autocorrelation and regional differences. Results indicate that registered sex offenders (RSOs) are disproportionately more likely to be found in tracts exhibiting high levels of concentrated disadvantage, available housing, and affordable housing, and disproportionately less likely to be found in tracts with high levels of ethnic heterogeneity. Controlling for spatial autocorrelation (lag) did not change overall results but was significantly and positively associated with excess risk. Implications for future policy and research practices are discussed. [ABSTRACT FROM AUTHOR]

Contextual research on time and place requires a consistent measurement instrument for neighborhood conditions in order to make unbiased inferences about neighborhood change. We develop such a time-invariant measure of neighborhood socio-economic status (NSES) using exploratory and confirmatory factor analyses fit to census data at the tract level from the 1990 and 2000 U.S. Censuses and the 2008-2012 American Community Survey. A single factor model fit the data well at all three time periods, and factor loadings--but not indicator intercepts--could be constrained to equality over time without decrement to fit. After addressing remaining longitudinal measurement bias, we found that NSES increased from 1990 to 2000, and then--consistent with the timing of the "Great Recession"--declined in 2008-2012 to a level approaching that of 1990. Our approach for evaluating and adjusting for time-invariance is not only instructive for studies of NSES but also more generally for longitudinal studies in which the variable of interest is a latent construct. [ABSTRACT FROM AUTHOR]

This article explores the impact of school district consolidation on house values based on house sales in upstate New York State from 2000 to 2012. By combining propensity score matching (PSM) and double-sales data to compare house value changes in consolidating and comparable school districts, we find that, except in one relatively large district, consolidation has a negative impact on house values during the years right after it occurs and that this effect then fades away and is eventually reversed. This pattern suggests that it takes time either for the advantages of consolidation to be apparent or for the people who prefer consolidated districts to move in. Finally, as in previous studies, the long-run impacts of consolidation on house values are positive in census tracts that initially have low incomes, but negative in high-income census tracts, where parents may have a relatively large willingness to retain the nonbudgetary advantages of small districts. [ABSTRACT FROM AUTHOR]

The American Community Survey (ACS) multiyear estimation program has greatly advanced opportunities for studying change in the demographic and socioeconomic characteristics of U.S. communities. Challenges remain, however, for researchers studying years prior to the full implementation of the ACS or areas smaller than the thresholds for ACS annual estimates (i.e., small counties and census tracts). We evaluate intercensal estimates of the demographic and socioeconomic characteristics of U.S. counties and census tracts produced via linear interpolation between the 2000 census and both the 2010 census and 2005-2009 ACS. Discrepancies between interpolated estimates and reference estimates from the Population Estimates Program, the Small Area Income and Poverty Estimates, and ACS are calculated using several measures of error. Findings are discussed in relation to the potential for measurement error to bias longitudinal estimates of linearly interpolated neighborhood change, and alternative intercensal estimation models are discussed, including those that may better capture non-linear trends in economic conditions over the 21st century. [ABSTRACT FROM AUTHOR]

Background: Few studies of dengue have shown group-level associations between demographic, socioeconomic, or geographic characteristics and the spatial distribution of dengue within small urban areas. This study aimed to examine whether specific characteristics of an urban slum community were associated with the risk of dengue disease. Methodology/Principal Findings: From 01/2009 to 12/2010, we conducted enhanced, community-based surveillance in the only public emergency unit in a slum in Salvador, Brazil to identify acute febrile illness (AFI) patients with laboratory evidence of dengue infection. Patient households were geocoded within census tracts (CTs). Demographic, socioeconomic, and geographical data were obtained from the 2010 national census. Associations between CTs characteristics and the spatial risk of both dengue and non-dengue AFI were assessed by Poisson log-normal and conditional auto-regressive models (CAR). We identified 651 (22.0%) dengue cases among 2,962 AFI patients. Estimated risk of symptomatic dengue was 21.3 and 70.2 cases per 10,000 inhabitants in 2009 and 2010, respectively. All the four dengue serotypes were identified, but DENV2 predominated (DENV1: 8.1%; DENV2: 90.7%; DENV3: 0.4%; DENV4: 0.8%). Multivariable CAR regression analysis showed increased dengue risk in CTs with poorer inhabitants (RR: 1.02 for each percent increase in the frequency of families earning ≤1 times the minimum wage; 95% CI: 1.01-1.04), and decreased risk in CTs located farther from the health unit (RR: 0.87 for each 100 meter increase; 95% CI: 0.80-0.94). The same CTs characteristics were also associated with non-dengue AFI risk. Conclusions/Significance: This study highlights the large burden of symptomatic dengue on individuals living in urban slums in Brazil. Lower neighborhood socioeconomic status was independently associated with increased risk of dengue, indicating that within slum communities with high levels of absolute poverty, factors associated with the social gradient influence dengue transmission. In addition, poor geographic access to health services may be a barrier to identifying both dengue and non-dengue AFI cases. Therefore, further spatial studies should account for this potential source of bias. [ABSTRACT FROM AUTHOR]

Background Prior research has shown that high-risk census tracts for out-of-hospital cardiac arrest (OHCA) can be identified. High-risk neighborhoods are defined as having a high incidence of OHCA and a low prevalence of bystander cardiopulmonary resuscitation (CPR). However, there is no consensus regarding the process for identifying high-risk neighborhoods. Objective We propose a novel summary approach to identify high-risk neighborhoods through three separate spatial analysis methods: Empirical Bayes (EB), Local Moran's I (LISA), and Getis Ord Gi* (Gi*) in Denver, Colorado. Methods We conducted a secondary analysis of prospectively collected Emergency Medical Services data of OHCA from January 1, 2009 to December 31, 2011 from the City and County of Denver, Colorado. OHCA incidents were restricted to those of cardiac etiology in adults ≥18 years. The OHCA incident locations were geocoded using Centrus. EB smoothed incidence rates were calculated for OHCA using Geoda and LISA and Gi* calculated using ArcGIS 10. Results A total of 1102 arrests in 142 census tracts occurred during the study period, with 887 arrests included in the final sample. Maps of clusters of high OHCA incidence were overlaid with maps identifying census tracts in the below the Denver County mean for bystander CPR prevalence. Five census tracts identified were designated as Tier 1 high-risk tracts, while an additional 7 census tracts where designated as Tier 2 high-risk tracts. Conclusion This is the first study to use these three spatial cluster analysis methods for the detection of high-risk census tracts. These census tracts are possible sites for targeted community-based interventions to improve both cardiovascular health education and CPR training. [ABSTRACT FROM AUTHOR]

Existing research in small-area demographic forecasting suffers from two important limitations: (1) a paucity of studies that quantify patterns of error in either total or age/sex-specific estimates and (2) limited methodological innovation aimed specifically at improving the accuracy of such forecasts. This paper attempts to fill, in part, these gaps in existing research by presenting a comparative evaluation of the accuracy of standard and spatially-weighted Hamilton-Perry forecasts for urbanized census tracts within incorporated New Mexico municipalities. These comparative forecasts are constructed for a 10-year horizon (base 1 April 2000 and target 1 April 2010), then compared to the results of the 2010 Census in an ex post facto evaluation. Results are presented for the standard Hamilton-Perry forecasts as well as two sets that incorporate two common variants of spatial weights to improve forecast accuracy. Findings are discussed in the context of what is currently known about error in small-area demographic forecasts and with an eye toward continued innovations. [ABSTRACT FROM AUTHOR]