Dataset Information

Expanding Tools for Investigating Neighborhood Indicators of Drug Use and Violence: Validation of the NIfETy for Virtual Street Observation.

ABSTRACT: A growing body of evidence suggests that characteristics of the neighborhood environment in urban areas significantly impact risk for drug use behavior and exposure to violent crime. Identifying areas of community need, prioritizing planning projects, and developing strategies for community improvement require inexpensive, easy to use, evidence-based tools to assess neighborhood disorder that can be used for a variety of research, urban planning, and community needs with an environmental justice frame. This study describes validation of the Neighborhood Inventory for Environmental Typology (NIfETy), a neighborhood environmental observational assessment tool designed to assess characteristics of the neighborhood environment related to violence, alcohol, and other drugs, for use with Google Street View (GSV). GSV data collection took place on a random sample of 350 blocks located throughout Baltimore City, Maryland, which had previously been assessed through in-person data collection. Inter-rater reliability metrics were strong for the majority of items (ICC???0.7), and items were highly correlated with in-person observations (r???0.6). Exploratory factor analysis and constrained factor analysis resulted in one, 14-item disorder scale with high internal consistency (alpha?=?0.825) and acceptable fit indices (CFI?=?0.982; RMSEA?=?0.051). We further validated this disorder scale against locations of violent crimes, and we found that disorder score was significantly and positively associated with neighborhood crime (IRR?=?1.221, 95% CI?=?(1.157, 1.288), p?

SUBMITTER: Nesoff ED

PROVIDER: S-EPMC6992509 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Expanding Tools for Investigating Neighborhood Indicators of Drug Use and Violence: Validation of the NIfETy for Virtual Street Observation.

Nesoff Elizabeth D ED Milam Adam J AJ Barajas Clara B CB Furr-Holden C Debra M CDM

Prevention science : the official journal of the Society for Prevention Research 20200201 2

A growing body of evidence suggests that characteristics of the neighborhood environment in urban areas significantly impact risk for drug use behavior and exposure to violent crime. Identifying areas of community need, prioritizing planning projects, and developing strategies for community improvement require inexpensive, easy to use, evidence-based tools to assess neighborhood disorder that can be used for a variety of research, urban planning, and community needs with an environmental justice ...[more]

PMID: 31637579

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Project description:ImportanceRacist policies (such as redlining) create inequities in the built environment, producing racially and ethnically segregated communities, poor housing conditions, unwalkable neighborhoods, and general disadvantage. Studies on built environment disparities are usually limited to measures and data that are available from existing sources or can be manually collected.ObjectiveTo use built environment indicators generated from online street-level images to investigate the association among neighborhood racial and ethnic composition, the built environment, and health outcomes across urban areas in the US.Design, setting, and participantsThis cross-sectional study was conducted using built environment indicators derived from 164 million Google Street View images collected from November 1 to 30, 2019. Race, ethnicity, and socioeconomic data were obtained from the 2019 American Community Survey (ACS) 5-year estimates; health outcomes were obtained from the Centers for Disease Control and Prevention 2020 Population Level Analysis and Community Estimates (PLACES) data set. Multilevel modeling and mediation analysis were applied. A total of 59 231 urban census tracts in the US were included. The online images and the ACS data included all census tracts. The PLACES data comprised survey respondents 18 years or older. Data were analyzed from May 23 to November 16, 2022.Main outcomes and measuresModel-estimated association between image-derived built environment indicators and census tract (neighborhood) racial and ethnic composition, and the association of the built environment with neighborhood racial composition and health.ResultsThe racial and ethnic composition in the 59 231 urban census tracts was 1 160 595 (0.4%) American Indian and Alaska Native, 53 321 345 (19.5%) Hispanic, 462 259 (0.2%) Native Hawaiian and other Pacific Islander, 17 166 370 (6.3%) non-Hispanic Asian, 35 985 480 (13.2%) non-Hispanic Black, and 158 043 260 (57.7%) non-Hispanic White residents. Compared with other neighborhoods, predominantly White neighborhoods had fewer dilapidated buildings and more green space indicators, usually associated with good health, and fewer crosswalks (eg, neighborhoods with predominantly minoritized racial or ethnic groups other than Black residents had 6% more dilapidated buildings than neighborhoods with predominantly White residents). Moreover, the built environment indicators partially mediated the association between neighborhood racial and ethnic composition and health outcomes, including diabetes, asthma, and sleeping problems. The most significant mediator was non-single family homes (a measure associated with homeownership), which mediated the association between neighborhoods with predominantly minority racial or ethnic groups other than Black residents and sleeping problems by 12.8% and the association between unclassified neighborhoods and asthma by 24.2%.Conclusions and relevanceThe findings in this cross-sectional study suggest that large geographically representative data sets, if used appropriately, may provide novel insights on racial and ethnic health inequities. Quantifying the impact of structural racism on social determinants of health is one step toward developing policies and interventions to create equitable built environment resources.

Project description:ObjectiveTo determine whether neighborhood socioeconomic disadvantage, as determined by the Area Deprivation Index, increases 30-day hospital re-observation risk.Participants and methodsThis retrospective study of 20% Medicare fee-for-service beneficiary observation stays from January 1, 2014, to November 30, 2014, included 319,980 stays among 273,308 beneficiaries. We evaluated risk for a 30-day re-observation following an index observation stay for those living in the 15% most disadvantaged compared with the 85% least disadvantaged neighborhoods.ResultsOverall, 4.5% (270,600 of 6,080,664) of beneficiaries had index observation stays, which varied by disadvantage (4.3% [232,568 of 5,398,311] in the least disadvantaged 85% compared with 5.6% [38,032 of 682,353] in the most disadvantaged 15%). Patients in the most disadvantaged neighborhoods had a higher 30-day re-observation rate (2857 of 41,975; 6.8%) compared with least disadvantaged neighborhoods (13,543 of 278,005; 4.9%); a 43% increased risk (unadjusted odds ratio [OR], 1.43; 95% CI, 1.31 to 1.55). After adjustment, this risk remained (adjusted OR, 1.13; 95% CI, 1.04 to 1.22). Discharge to a skilled nursing facility reduced 30-day re-observation risk (OR, 0.63; 95% CI, 0.57 to 0.69), whereas index observation length of stay of 4 or more days (3 midnights) conferred increased risk (OR, 1.29; 95% CI, 1.09 to 1.52); those living in disadvantaged neighborhoods were less likely to discharge to skilled nursing facilities and more likely to have long index stays. Beneficiaries with more than one 30-day re-observation (chronic re-observation) had progressively greater disadvantage by number of stays (adjusted incident rate ratio, 1.08; 95% CI, 1.02 to 1.14). Observation prevalence varied nationally.ConclusionThirty-day re-observation, especially chronic re-observation, is highly associated with socioeconomic neighborhood disadvantage, even after accounting for factors such as race, disability, and Medicaid eligibility. Beneficiaries least able to pay are potentially most vulnerable to costs from serial re-observations and challenges of Medicare observation policy, which may discourage patients from seeking necessary care.

Project description:Objectives Breastfeeding has short- and long-term health benefits for children and mothers, but US breastfeeding rates are suboptimal. Exposure to violence may contribute to these low rates, which vary by race/ethnicity. We studied: (1) whether patterns of violence exposure differ by race/ethnicity and (2) whether these patterns are associated with breastfeeding outcomes. Methods We conducted a secondary analysis of data drawn from self-report surveys completed by a convenience sample of low-income postpartum women (n?=?760) in upstate New York. Latent class analysis was used to identify groups of women with similar responses to seven violence measures, including childhood physical and/or sexual violence, experience of partner violence during or just after pregnancy (physical, emotional, verbal), and neighborhood violence (perceived or by ZIP code). Logistic regression and survival analysis were utilized to determine if classes were associated with breastfeeding initiation, duration, and exclusivity, controlling for demographics. Results Exposure to at least one form of violence was high in this sample (87%). We identified 4 classes defined by violence exposure (combining current and historical exposures). Violence exposure patterns differed between racial/ethnic groups, but patterns were inconsistently associated with breastfeeding plans or outcomes. For White women, history of violence exposure increased the likelihood of earlier breastfeeding cessation. By contrast, among Black women, history of violence exposure increased the likelihood of having a breastfeeding plan and initiating breastfeeding. Conclusions for Practice Some differences between violence exposure classes are likely due to the correlation between race/ethnicity and socioeconomic status in the community studied. Additional studies are warranted to better understand how exposure to violence is related to breastfeeding and how best to support women making decisions about intention, initiation, and duration of breastfeeding.

Project description:Researchers have linked neighborhood food availability to the overall frequency of using food outlets without noting if those outlets were within or outside of participants' neighborhoods. We aimed to examine the association of neighborhood restaurant and food store availability with frequency of use of neighborhood food outlets, and whether such an association was modified by neighborhood street connectivity using a large and diverse population-based cohort of middle-aged U.S. adults. We used self-reported frequency of use of fast food restaurants, sit-down restaurants, and grocery stores in respondents' home neighborhoods using data from the Coronary Artery Risk Development in Young Adults study Year 20 exam in 2005-2006 (n = 2860; Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA) and geographically matched GIS-measured neighborhood-level food resource, street, and U.S. Census data. We used mixed-effects logistic regression to examine the associations of the GIS-measured count of neighborhood fast food restaurants, sit-down restaurants, and grocery stores with self-reported frequency of using neighborhood restaurants and food stores and whether such associations differed by GIS-measured neighborhood street connectivity among those who perceived at least one such food outlet. In multivariate analyses, we observed a positive association between the GIS-measured count of neighborhood sit-down restaurants (OR = 1.02, 95% CI 1.00-1.04) and the self-reported frequency of using neighborhood sit-down restaurants. We observed no statistically significant association between GIS-measured count of neighborhood fast food restaurants and self-reported frequency of using neighborhood fast food restaurants, nor did we observe a statistically significant association between GIS-measured count of neighborhood grocery stores and self-reported frequency of using neighborhood grocery stores. We observed inverse associations between GIS-measured neighborhood street connectivity and the self-reported frequencies of using neighborhood fast food restaurants (OR = 0.42, 95% CI 0.26-0.68) and grocery stores (OR = - 2.26, 95% CI - 4.52 to - 0.01). Neighborhood street connectivity did not modify the association between GIS-measured neighborhood restaurant and food store count and the self-reported frequency of using neighborhood restaurants and food stores. Our findings suggest that, for those who perceived at least one sit-down restaurant in their neighborhood, individuals who have more GIS-measured sit-down restaurants in their neighborhoods reported more frequent use of sit-down restaurants than those whose neighborhoods contain fewer such restaurants. Our results also suggest that, for those who perceived at least one fast food restaurant in their neighborhood, individuals who live in neighborhoods with greater GIS-measured street connectivity reported less use of neighborhood fast food restaurants than those who live in neighborhoods with less street connectivity. The count of neighborhood sit-down restaurants and the connectivity of neighborhood street networks appear important in understanding the use of neighborhood food resources.

Dataset Information

Expanding Tools for Investigating Neighborhood Indicators of Drug Use and Violence: Validation of the NIfETy for Virtual Street Observation.

Publications

Expanding Tools for Investigating Neighborhood Indicators of Drug Use and Violence: Validation of the NIfETy for Virtual Street Observation.

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