Neighborhood Demographic Characteristics Research Articles

Associations between a Novel Measure of Census Tract-Level Credit Insecurity and Frequent Mental Distress in US Urban Areas, 2020.

Access to and utilization of consumer credit remains an understudied social determinant of health. We examined associations between a novel, small-area, multidimensional credit insecurity index (CII), and the prevalence of self-reported frequent mental distress across US cities in 2020. The census tract-level CII was developed by the Federal Reserve Bank of New York using Census population information and a nationally representative sample of anonymized Equifax credit report data. The CII was calculated for tracts in 766 cities displayed on the City Health Dashboard at the time of analysis, predominantly representing cities with over 50,000 residents. The CII combined data on tract-level participation in the formal credit economy with information on the percent of individuals without revolving credit, percent with high credit utilization, and percent with deep subprime credit scores. Tracts were classified as credit-assured, credit-likely, mid-tier, at-risk, or credit-insecure. We used linear regression to examine associations between the CII and a modeled tract-level measure of frequent mental distress, obtained from the CDC PLACES project. Regression models were adjusted for neighborhood economic and demographic characteristics. We examined effect modification by US region by including two-way interaction terms in regression models. In adjusted models, credit-insecure tracts had a modestly higher prevalence of frequent mental distress (prevalence difference = 0.38 percentage points; 95% CI = 0.32, 0.44), compared to credit-assured tracts. Associations were most pronounced in the Midwest. Local factors impacting credit access and utilization are often modifiable. The CII, a novel indicator of community financial well-being, may be an independent predictor of neighborhood health in US cities and could illuminate policy targets to improve access to desirable credit products and downstream health outcomes.

Race, Socioeconomic Status, and Food Access in Two Predominantly White Cities: The Case of Lansing, East Lansing, and Surrounding Townships in Michigan

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.

Changes in Ethnic Identity in Middle Childhood: Family and Neighborhood Determinants

The present study examined changes in ethnic and racial identity (ERI) over one year among 353 Black and Latino early adolescents in relation to ethnic-racial socialization (ERS) and neighborhood economic and demographic characteristics. Parent and child reports of ERS were collected, and child ERI was assessed via self-report. Neighborhood measures included economic disadvantage and ethnic diversity. Results indicated parent report of preparation for bias was associated with greater increases in exploration but not among boys living in non-impoverished neighborhoods. In contrast, child report of preparation for bias was associated with decreases in belonging independent of child gender or neighborhood context. These findings highlight the complexities of neighborhood economic disadvantage and ERI development. Implications for future research examining gender differences and children’s interpretation of ERS messages are discussed.

Sense of belonging, neighborhood satisfaction, and children’s independent mobility license: a case study of Qom, Iran

ABSTRACT The physical activity levels among younger people worldwide are significantly low, which can lead to adverse outcomes for their health. In this regard, this study aimed to investigate the effect of parents’ sense of belonging as a social capital on children’s independent mobility license. Moreover, the mediating variable of parents’ neighborhood satisfaction (as the predictor of the living environment’s quality) and demographic characteristics were assessed. The research used path analysis to examine data obtained from parents of 10-12-year children. The results showed that, although parents’ sense of belonging cannot directly affect children’s independent mobility license, it improves children’s mobility through the mediating variable of neighborhood satisfaction. Also, parents’ attributes, including length of residency, age, and sex, were influential in their sense of belonging and neighborhood satisfaction, and children’s attributes, including age and sex, were influential in their license to move independently. Parents’ neighborhood satisfaction was the most significant factor in children’s independent mobility license. In this regard, the sense of belonging can affect the parents’ perspective regarding their neighborhoods, so by considering social issues in city planning, children’s mobility and, thus, their health outcomes can be improved.

Victimization and Mobility Intentions: Accounting for Neighborhood Perceptions in Central American Contexts

ABSTRACT Although the topics of victimization, neighborhood perceptions and mobility intentions are researched in Western contexts, the extant work linking these topics is noticeably absent in the Central American contexts of Guatemala, El Salvador, and Honduras even though past victimization has been found to be a salient predictor of migration to the United States from these countries. This study uses United States Agency for International Development (USAID) and Vanderbilt University’s data from an impact evaluation of USAID’s Community-Based Crime and Violence Prevention Approach in Central America and mixed effect multi-level logistic regression models to examine whether perceptions of neighborhoods are associated with a greater mobility risk, and whether this is also heightened among individuals who had previously been a victim. Results show that victimization predicts mobility intentions, followed by the traditional “push factors” of migration more than neighborhood perceptions in all three country contexts. However, social cohesion seems to be the most relevant predictor of mobility compared to other neighborhood factors. More research should focus on the indirect effects of neighborhood perceptions, victimization, and demographic characteristics on mobility and consider how perceptions are impacted by cultural differences across country contexts.

Predictors of electronic health record (EHR) portal registration and frequency of portal use among patients with cancer prior to engagement in the IMPACT Consortium symptom management trials.

419 Background: The patient portal is part of an electronic health record (EHR) that allows patients to communicate with their healthcare team. The portal also provides a platform for patients to receive and complete symptom surveys that can be directly integrated into the EHR, allowing clinical care teams to monitor symptoms and provide cancer symptom management. The Improving Management of symPtoms during And following Cancer Treatment (IMPACT) consortium, supported by the National Cancer Institute’s Cancer MoonshotSM, aims to improve symptom control for cancer patients through assessment and symptom management interventions deployed via the EHR. This initiative presents an opportunity to examine portal enrollment and variation in use, factors critical to successful implementation, especially among groups that have high cancer symptom burden. To this end, we examine: 1) relationships between portal enrollment prior to the launch of IMPACT interventions and neighborhood broadband access, demographic, and social characteristics; and 2) frequency of pre-intervention portal use for any purpose among enrollees. Methods: Data are derived from two of three IMPACT research centers. Enrollment in and frequency of portal use, mode of accessing the portal (web vs. phone), social, demographic, and cultural factors were extracted from the EHR. Rural Urban Commuting Area (RUCA) codes were used to classify population density and degree of rurality. Broadband access was estimated using 2015-2019 American Community Survey estimates matched to zip codes from enrolled IMPACT patients and classified as a patient’s residence being in a community with high (≥85% of households) or low (< 85% of households) access. Bivariate comparisons and adjusted odds ratios were used to describe all associations. Results: Forty-seven percent of patients (22,596/48,034) were enrolled in the portal prior to the intervention. Patients in zip codes with low broadband access and those who were men, > 65 years old, not White, of Hispanic ethnicity, or disabled or not employed had significantly lower odds of being enrolled in the portal. If enrolled, 21% (n = 4825) used the portal at least once a week. Less variation was found in the average frequency of portal use, but patients younger than 40, and those who were Black, disabled, unemployed, or those who used a mobile device to access the portal had the lowest odds of accessing it at least once a week. Conclusions: Significant disparities in portal enrollment exist across demographic groups and among those with limited broadband access. Among those enrolled, most used the portal less than once a week. Fewer differences in frequency of use were observed by sociodemographic factors. Improving portal enrollment and frequency of use may be critical for symptom management interventions deployed via patient portals.

Neighborhood disparities in COVID-19 outcomes in New York city over the first two waves of the outbreak

PurposeTo assess the association of neighborhood demographic and socioeconomic characteristics with COVID-19 incidence and mortality in New York City (NYC) over the first two waves of outbreak.MethodsThis retrospective study used neighborhood-level data from 177 modified ZIP code tabulation areas in NYC between March 01, 2020 and April 30, 2021.ResultsNeighborhoods that were most severely impacted in wave 1 were also more affected in wave 2. Neighborhoods with a higher percentage of seniors (≥75 years), males, Black and Hispanic population, and large-size households had higher incidence rates of COVID-19 in wave 1 but not in wave 2. Neighborhoods with higher percentage of Black and Hispanic population and lower insurance coverage had higher death rate per capita and case fatality ratio in wave 1, and neighborhoods with higher percentage of Black and Asian population had elevated case fatality ratio in wave 2. Median household income was negatively associated with incidence rate and death rate per capita but not associated with case fatality ratio in both waves. Neighborhoods with more seniors had higher death rate and case fatality ratio in both waves.ConclusionsNeighborhood disparities in COVID-19 incidence and mortality across NYC neighborhoods were dynamic during the first two waves of outbreak.

Profile of Victims of Neighbourhood Crimes in Ghanaian Cities

Generally, urban crime research in Ghana is non-ambiguous on the socio-cultural, political economy and environmental ramifications of neighbourhood crimes in emerging cities, but the personal and neighbourhood level characteristics of the most likely victims of neighbourhood crime in Ghanaian cities remain meagre. This paper fills the gap in knowledge by answering the question ‘who/where is the most likely victim of crime in urban Ghana?’ This study employed a sequential mixed methods approach to collect data through the administration of a household survey, as well as focus group discussion (FDG) sessions. The survey data were analysed using binary logistic regression while the qualitative data were analysed using thematic analysis. The study found that socio-demographic characteristics, which are associated with a higher likelihood of victimisation, include young unemployed persons, residents of a detached or ‘self-contained’ apartment, household of increasing household size, residents of neighbourhoods with less police visibility and residents of unsafe neighbourhoods. Cognisance of the limited capacity of the Ghana Police Service, this study recommends the need for the Ghana Police Service to consider neighbourhood demographic characteristics in their efforts to enhance distributive justice in the provision of internal security.

Neighborhood Disadvantage Measures and COVID-19 Cases in Boston, 2020.

Understanding the pattern of population risk for coronavirus disease 2019 (COVID-19) is critically important for health systems and policy makers. The objective of this study was to describe the association between neighborhood factors and number of COVID-19 cases. We hypothesized an association between disadvantaged neighborhoods and clusters of COVID-19 cases. We analyzed data on patients presenting to a large health care system in Boston during February 5-May 4, 2020. We used a bivariate local join-count procedure to determine colocation between census tracts with high rates of neighborhood demographic characteristics (eg, Hispanic race/ethnicity) and measures of disadvantage (eg, health insurance status) and COVID-19 cases. We used negative binomial models to assess independent associations between neighborhood factors and the incidence of COVID-19. A total of 9898 COVID-19 patients were in the cohort. The overall crude incidence in the study area was 32 cases per 10 000 population, and the adjusted incidence per census tract ranged from 2 to 405 per 10 000 population. We found significant colocation of several neighborhood factors and the top quintile of cases: percentage of population that was Hispanic, non-Hispanic Black, without health insurance, receiving Supplemental Nutrition Assistance Program benefits, and living in poverty. Factors associated with increased incidence of COVID-19 included percentage of population that is Hispanic (incidence rate ratio [IRR] = 1.25; 95% CI, 1.23-1.28) and percentage of households living in poverty (IRR = 1.25; 95% CI, 1.19-1.32). We found a significant association between neighborhoods with high rates of disadvantage and COVID-19. Policy makers need to consider these health inequities when responding to the pandemic and planning for subsequent health needs.

Lead Distribution in Urban Soil in a Medium-Sized City: Household-Scale Analysis.

This study characterizes potential soil lead (Pb) exposure risk at the household scale in Greensboro, North Carolina, using an innovative combination of field sampling, statistical analysis, and machine-learning techniques. Soil samples were collected at the dripline, yard, and street side at 462 households (total sample size = 2310). Samples were analyzed for Pb and then combined with publicly available data on potential historic Pb sources, soil properties, and household and neighborhood demographic characteristics. This curated data set was then analyzed with statistical and machine-learning techniques to identify the drivers of potential soil Pb exposure risks and to build predictive models. Among all samples, 43% exceeded current guidelines for Pb in residential gardens. There were significant racial disparities in potential soil Pb exposure risk; soil Pb at the dripline increased by 19% for every 25% increase in the neighborhood population identifying as Black. A machine-learned Bayesian network model was able to classify residential parcels by risk of exceeding residential gardening standards with excellent reproducibility in cross validation. These findings underscore the need for targeted outreach programs to prevent Pb exposure in residential areas and demonstrate an approach for prioritizing outreach locations.

East Side Story: Disaggregating Gang Homicides in East Los Angeles

This research extends the homicide literature by using latent class analysis methods to examine the neighborhood structural and demographic characteristics of different categories of homicides in the Hollenbeck Community Policing Area of the Los Angeles Police Department (LAPD). The Hollenbeck area itself is a 15 square-mile region with approximately 187,000 residents, the majority of whom are Latino (84 percent). Hollenbeck also has a protracted history of intergenerational Latinx gangs with local neighborhood residents viewing them as a fundamental social problem. Hollenbeck has over 30 active street gangs, each claiming a geographically defined territory, many of which have remained stable during the study period. Over twenty years (1990–2012) of homicide data collected from Hollenbeck’s Homicide Division are utilized to create an empirically rigorous typology of homicide incidents and to test whether or not gang homicides are sufficiently distinct in nature to be a unique category in the latent class analysis.

Frequency of Intimate Partner Violence among an Urban Emergency Department Sample: A Multilevel Analysis.

Intimate partner violence (IPV) is a pervasive public health problem. Within the U.S., urban emergency department (ED) patients have elevated prevalence of IPV, substance use, and other social problems compared to those in the general household population. Using a social-ecological framework, this cross-sectional study analyzes the extent to which individual, household, and neighborhood factors are associated with the frequency of IPV among a socially disadvantaged sample of urban ED patients. Confidential survey interviews were conducted with 1037 married/partnered study participants (46% male; 50% Hispanic; 29% African American) at a public safety-net hospital. Gender-stratified multilevel Tobit regression models were estimated for frequency of past-year physical IPV (perpetration and victimization) and frequency of severe IPV. Approximately 23% of participants reported IPV. Among men and women, impulsivity, adverse childhood experiences, substance use, and their spouse/partner’s hazardous drinking were associated with IPV frequency. Additionally, household food insufficiency, being fired or laid off from their job, perceived neighborhood disorder, and neighborhood demographic characteristics were associated with IPV frequency among women. Similar patterns were observed in models of severe IPV frequency. IPV prevention strategies implemented in urban ED settings should address the individual, household, and neighborhood risk factors that are linked with partner aggression among socially disadvantaged couples.

Comparing Neighborhoods of Children With Autism Spectrum Disorder in a Medicaid Waiver Program and a State Population, 2007-2015.

This study investigated equity in enrollment in a Medicaid waiver program for early intensive behavioral intervention for children with autism spectrum disorder (ASD). State administrative, Medicaid, and U.S. Census data for children enrolled in the waiver program between 2007 and 2015 (N=2,111) were integrated. Multivariate and bivariate analyses were used to compare enrollees' neighborhood demographic characteristics with those of the state's general population, with controls for enrollees' age, sex, and race-ethnicity. Findings indicate that in general, enrollment was equitable. During the years in which there were inequities, children who lived in neighborhoods of privilege were favored. These neighborhoods had higher median incomes, lower poverty levels, and fewer female-headed households and were located in urban areas. As states work to provide equitable treatment to children with ASD and their families, it is important to track potential inequities between children who do and do not enroll in services and to use this information to inform outreach efforts. States may turn to South Carolina for insight on how to ensure equity.

The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach

Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services.

Urban outdoor water use and response to drought assessed through mobile energy balance and vegetation greenness measurements

Urban vegetation provides many highly valued ecosystem services but also requires extensive urban water resources. Increasingly, cities are experiencing water limitations and managing outdoor urban water use is an important concern. Quantifying the water lost via evapotranspiration (ET) is critical for urban water management and conservation, especially in arid or semi-arid regions. In this study, we deployed a mobile energy balance platform to measure evaporative fraction throughout Riverside, California, a warm, semi-arid, city. We observed the relationship between evaporative fraction and satellite derived vegetation index across 29 sites, which was then used to map whole-city ET for a representative mid-summer period. Resulting ET distributions were strongly associated with both neighborhood population density and income. By comparing 2014 and 2015 summer-period water uses, our results show 7.8% reductions in evapotranspiration, which were also correlated with neighborhood demographic characteristics. Our findings suggest a mobile energy balance measurement platform coupled with satellite imagery could serve as an effective tool in assessing the outdoor water use at neighborhood to whole city scales.

Point-of-Sale E-cigarette Advertising Among Tobacco Stores.

The marketing expenditure and sale of e-cigarettes increased sharply in the United States in recent years. However, little is known about neighborhood characteristics of point-of-sale (POS) e-cigarette advertising among tobacco stores. The purpose of this study was to examine socio-demographic characteristics of POS e-cigarette advertising among tobacco stores in the Omaha metropolitan area of Nebraska, USA. Between April and June 2014, trained fieldworkers completed marketing audits of all stores that sell tobacco (n = 463) in the Omaha metropolitan area and collected comprehensive e-cigarette advertising data of these stores. Based on the auditing information, we categorized tobacco stores based on e-cigarette advertising status. Logistic regression was used to examine the association between neighborhood socio-demographic factors and e-cigarette advertising among tobacco stores. 251 (54.2%) of the 463 tobacco stores had e-cigarette advertisements. We found that neighborhoods of stores with POS e-cigarette advertising had higher per capita income (p < 0.05), higher percentage of non-hispanic whites (p < 0.005), and higher percentage of individuals with high school education (p < 0.005) than neighborhoods of stores without POS e-cigarette advertising. There were negative associations between e-cigarette advertising and number of adolescents or number of middle/high school students. After adjusting for covariates, only percentage of non-Hispanic Whites remained a significant factor for e-cigarette advertising. POS e-cigarette advertising among tobacco stores is related with neighborhood socioeconomic and demographic characteristics. Future studies are needed to understand how these characteristics are related with e-cigarette purchasing and e-cigarette prevalence among social groups.

The Resilience of the Premium for Homes in New Urbanist Neighborhoods

In this study, I analyze longitudinal differences in single-family home prices in two new urbanist neighborhoods versus surrounding conventional neighborhoods. Using data on 78,513 single-family home sales transactions in Montgomery County, Maryland, I adopt a novel multilevel methodology to assess the effects of neighborhood demographic, economic, and locational characteristics as well as the effects of year on home prices. Results support empirical evidence on the premium that buyers willingly pay for homes in new urbanist neighborhoods. Year effects indicate that the premium withstood the Great Recession quite well.

Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity.

BackgroundStudies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research.ObjectiveThe aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors.MethodsWe utilized Twitter’s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics.ResultsMachine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic.ConclusionsMachine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors.

Point-of-Sale Tobacco Marketing to Youth in New York State

PurposeTo assess youth exposure to menthol versus nonmenthol cigarette advertising, we examined whether menthol cigarette promotions are more likely in neighborhoods with relatively high youth populations. MethodsWe linked 2011 New York State Retail Advertising Tobacco Survey observational data with U.S. Census and American Community Survey demographic data. Multivariable models assessed the relationship between neighborhood youth population and point-of-sale cigarette promotions for three brands of cigarettes, adjusting for neighborhood demographic characteristics including race/ethnicity and poverty. ResultsMenthol cigarette point-of-sale marketing was more likely in neighborhoods with higher proportions of youth, adjusting for presence of nonmenthol brand marketing, neighborhood race/ethnicity, neighborhood poverty, and urban geography. ConclusionsData from the 2011 Retail Advertising Tobacco Study linked to block level census data clearly indicate that price reduction promotions for menthol cigarettes are disproportionately targeted to youth markets in New York State.

Mapping the Profit Motive: A Comparative Analysis of For-­Profit and Non-­Profit Charter Schools

For-profit charter schools represent a controversial new market-based education reform (Garcia, Barber, &amp; Molnar, 2009; Conn, 2002). This essay explores how schools operated by for-profit corporations differ from those operated by non-profit organizations. Specifically, do for-profit charter schools locate in demographically distinct areas and serve different student populations than similar schools operated by non-profit providers? Research suggests that charter school operators in some districts appear to strategically locate their schools in an effort to exclude certain student populations (Lubienski, Gulosino, &amp; Weitzel, 2009). If charter schools seek to exclude certain students, as these schools proliferate in a district or region there may be an intensifying stratification effect detrimental to the most disadvantaged students (Bifulco, Ladd, &amp; Ross, 2009). This essay undertakes a broad comparative analysis of the neighborhood demographic and student population characteristics of charter schools operated by for-profit corporations and nonprofit organizations, using multilevel modeling and geographic information systems, to compare the two school types across dissimilar state contexts. In so doing, the study seeks to isolate the impact of a school being operated by a for-profit corporation and illuminate what impact the proliferation of for-profit schools might have on the distribution of student populations across regions.