Identification Of Neighborhoods Research Articles

Exploring Neighborhood-Level Environmental Justice to Tackle County-Level Disparities in Breast Cancer Outcomes

Breast cancer is a disease in which cells, originating from the breast epithelium, grow out of control and form tumors. The progression of breast cancer can vary, and its severity is often categorized by stages ranging from early, localized forms to more advanced stages involving widespread dissemination. Breast cancer cases can be hereditary or non-hereditary; Hereditary cases are caused by a penetrant gene mutation: one that is pathogenic and has the potential to promote the development of cancer whereas non-hereditary cases can be caused by a variety of day-to-day exposures affected by socioeconomic disparities and health literacy rates. Despite scientific and technological advancements, researchers and health officials still do not know enough about the disease and breast cancer incidence rates in the United States have steadily increased. This literature review aims to synthesize the relationship between breast cancer health outcomes and environmental justice, health literacy, and socioeconomic disparities. This relationship was first examined from a county-level in the state of Florida. We first examined three Florida counties with varying socioeconomic profiles (Broward, Miami-Dade, and Palm Beach) and then examined neighborhoods within the county of Miami-Dade (selected because it has the highest breast cancer rates out of all Florida counties). This examination revealed higher incidences of breast cancer in Latinx and African American communities with high environmental exposures and lower socioeconomic statuses. The identification of specific neighborhoods primarily affected by environmental exposures and low socioeconomic statuses emphasizes the need for neighborhood level modeling to inform future breast cancer research and intervention strategies.

Abstract C065: PlastiNet: Understanding the Epithelial - Mesenchymal Transition Through Graphical Attention in Spatial Transcriptomics

Abstract Plasticity in tumor cells represents the ability of cancer cells to transition between different phenotypic states, including epithelial and mesenchymal states, known as the epithelial-mesenchymal transition (EMT). This transition is critical for processes such as invasion, metastasis, and resistance to therapy. Understanding the dynamics of plasticity and the extrinsic factors influencing state transitions may be crucial for improving therapeutic strategies and patient outcomes. However, deciphering the complex interactions and factors driving these state changes remains challenging. We introduce PlastiNet, a graphical attention-based network (GAT) designed to create spatially aware embeddings that enable the identification of cellular neighborhoods and the plasticity spectrum, despite gene panel limitations. PlastiNet incorporates both self-attention and neighbor-attention mechanisms in its GAT. This approach allows us to account for the influence of surrounding cells in determining the state of a target cell. We validated our model by constructing differentiation paths in healthy colon data, starting from stem cell signatures. The attention mechanisms highlighted known interactions, such as those between fibroblasts and epithelial cells. When applied to pancreatic ductal adenocarcinoma (PDAC) tumors, PlastiNet identified the SPP1-CD44 axis as a potential mediator in the classical-to-basal transition of tumor cells. To further explore the impact of CD44, particularly the isoform switch that occurs during EMT, we studied a patient-derived a PDAC cell line using single-cell RNA sequencing (scRNAseq) and Multiplexed Arrays Isoform Sequencing (Mas-Iso-Seq) to analyze cells spanning a range of the plasticity spectrum. Looping this analysis back to spatial transcriptomics data, we observed that the signature associated with CD44v8-10 or CD44s isoforms is co-localized with the SPP1 ligand, suggesting a spatially confined interaction. This finding underscores the potential of PlastiNet to generate hypotheses directly from patient-derived samples, offering new insights into the cellular mechanisms driving tumor plasticity. PlastiNet effectively constructs spatially aware embeddings that capture the dynamics of tumor cell plasticity. By identifying key interactions and extrinsic factors, this method opens new avenues for understanding and potentially targeting the mechanisms underlying tumor progression and treatment resistance. Citation Format: Izabella L Zamora, Milan Parikh, Samuel Wright, Lynn Bi, Bidish Patel, Dana Pe’er, David Ting, Nir Hacohen, Arnav Mehta. PlastiNet: Understanding the Epithelial - Mesenchymal Transition Through Graphical Attention in Spatial Transcriptomics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C065.

Polycentricity and TOD-ness synergy: A novel composite index for integrated development of employment centers and residential cores in transit-oriented neighborhoods

Proximity of transit-oriented development (TOD) goals to polycentric urban development in the field of controlling negative effects of urban sprawl reinforces polycentricity and TOD-ness spatial synergy. Furthermore, identification of transit-oriented neighborhoods that have high potential to become secondary employment centers (ECs) is one of the most important challenges facing urban transport planners and land use policy makers. This research explained integrated development of ECs and residential cores (RCs) in transit-oriented neighborhoods using a novel composite index. The index is designed based on TOD-ness and polycentricity measures using spatial statistical methods and helps urban planners and policy makers to better identify polycentric TODs as transit-oriented residential neighborhoods with high potential to become secondary employment centers. Research findings confirmed higher accessibility of polycentric TODs to public services and public and private transportation that have better integration in the development of urban facilities and transportation systems. Furthermore, the findings confirmed polycentricity and TOD-ness spatial synergies in polycentric TODs.

CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images.

Recent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution tissue imaging data. In this work, we develop CNTools, a computational toolbox for end-to-end cellular neighborhood analysis on annotated cell images, comprising both the identification and analysis steps. It includes state-of-the-art cellular neighborhood identification methods and post-identification smoothing techniques, with our newly proposed Cellular Neighbor Embedding (CNE) method and Naive Smoothing technique, as well as several established downstream analysis approaches. We applied CNTools on three real-world CODEX datasets and evaluated identification methods with smoothing techniques quantitatively and qualitatively. It shows that CNE with Naive Smoothing overall outperformed other methods and revealed more convincing biological insights. We also provided suggestions on how to choose proper identification methods and smoothing techniques according to input data.

Abstract 1203: Mushroom: a tool for identification of 3D cellular neighborhoods in multi-modal spatial datasets

Abstract A comprehensive understanding of the spatial localization of cellular processes is necessary to fully describe tissue biology. Numerous techniques exist to identify cellular neighborhoods in spatial datasets, however, these techniques are limited in that they either only capture two-dimensional phenomena - and can't describe the full, 3-dimensional tumor volume, or they are restricted to one spatial technology (i.e. IF, 10X Visium, Xenium, MERFISH, etc.). To address this, we developed Mushroom, a tool for identification of three-dimensional cellular neighborhoods in serial-sectioned multi-modal datasets. Mushroom’s methodology consists of two main steps: 1) registration of multi-modal technologies taken in serial sections (such as multiplex imaging, H&E, or spatial transcriptomics), and 2) identification of 3-dimensional cellular neighborhoods comprising the tissue volume that has been serially sectioned. Keypoint registration is performed with BigWarp, after which the resulting dense displacement field (DDF) is used to register neighboring serial sections - including those from different data modalities. We then implement a neural network to identify three dimensional cellular neighborhoods. The network is a variational autoencoder (VAE) with a vision transformer (VIT) backbone that has been adapted to work with multiple spatial data modalities. Features extracted from the VIT backbone are then clustered to identify cellular neighborhoods unique to each input data type. With 3D neighborhoods in hand, biological questions not answerable in traditional 2D approaches can be investigated, such as z-dimensional changes in morphology and cellular interactions. Further, seamless characterization of cellular neighborhoods by simultaneous technologies at multiple levels of spatial and genomic resolution becomes possible. Citation Format: Erik Storrs, Siqi Chen, Chia-Kuei Mo, Andrew Houston, Andrew Shinkle, Austin Southard-Smith, Faria Simin, Andre Targino, Xiang Li, Ateih Abedin, Reyka Jayasinghe, Alla Karpova, Jingxian Liu, John Herndon, David Fenyo, Feng Chen, Ryan Fields, Tao Ju, Ben Raphael, Li Ding. Mushroom: a tool for identification of 3D cellular neighborhoods in multi-modal spatial datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1203.

Abstract 5496: Multi-omic spatial analysis of the tumor microenvironment in gliomas

Abstract Introduction: The diverse tumor environment of high-grade glioma, which remains refractory to treatment, demands new innovative, multi-omic approaches to characterize tumor heterogeneity and expression profiles associated with progression and therapeutic response. Approach: We have applied an integrated, multi-omic approach using paired spatial in situ sequencing (Xenium) and protein profiling (PhenoCycler® Fusion) of glioma tissue to identify distinct glioma cellular phenotypes, activation states and metabolic pathways at true single cell resolution. Our multi-omic approach used hundreds of phenotypic RNA target probes and >50 antibodies in an unbiased single-cell analysis of primary, recurrent, and IDH1 mutant/wild type tumors with a range of heterogeneity. Summary: We were able to distinguish differentially regulated genes and pathways between aggregated primary and recurrent GBM, including cell cycle pathways being upregulated in primary vs recurrent GBM glial cell clusters and a down regulation of ERBB4 signaling in vascular cell clusters in primary GBMs. Our data also suggests that SOX11, known to be involved in tumorigenesis, is >2-fold upregulated in annotated tumor cells in primary compared to recurrent tumors. We were also able to quantitatively localize the expression of Epidermal Growth Factor Receptor, variant III (EGFRvIII) to specific tumor-cell sub-types, which is important given its association with therapeutic resistance. Further, we have been able to establish the cellular neighborhoods and cell-cell and receptor-ligand relationships within the tumors. Our spatial protein analysis identified distinct tumor and immune phenotypes with varying abundance of myeloid and lymphoid populations in IDH1 wt and mt tumors. Moreover, spatial proximity analyses and cellular neighborhood analyses revealed differences in higher order organizational landscapes that may contribute to the differential outcomes across wt and mt tumor subtypes. Conclusion: Multiomic spatial analysis enables deeper characterization of the glioma cellular and functional landscape to broaden our understanding of the key TME features that contribute to disease pathogenesis and prognoses. Our study provides an analytical framework to combine RNA and protein-based spatial data for a holistic investigation into a variety of glioma subtypes and aid in the identification of novel biomarkers, spatial neighborhoods, and functional states that drive glioma progression. Citation Format: Dmytro Klymyshyn, Vaibhav Jain, Lauren Whaley, Emily Hocke, Bassem Ben Cheikh, Alan Smith, Karen Abramson, Nadine Nelson, Diane Satterfield, Elizabeth Thomas, Giselle Lopez, Seetha Hariharan, Michael Brown, Niyati Jhaveri, Roger McLendon, David Ashley, Matthew Waitkus, Simon Gregory. Multi-omic spatial analysis of the tumor microenvironment in gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5496.

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.

Connecting to Community: A Social Identity Approach to Neighborhood Mental Health

Academic Integrative theorizing is needed to advance our understanding of the relationship between where a person lives and their mental health. To this end, we introduce a social identity model that provides an integrated explanation of the ways in which social-psychological processes mediate and moderate the links between neighborhood and mental health. In developing this model, we first review existing models that are derived primarily from a resource-availability perspective informed by research in social epidemiology, health geography, and urban sociology. Building on these, the social identity model implicates neighborhood identification in four key pathways between residents’ local environment and their mental health. We review a wealth of recent research that supports this model and which speaks to its capacity to integrate and extend insights from established models. We also explore the implications of the social identity approach for policy and intervention. Public We need to understand the connection between where people live and their mental health better than we do. This article helps us do this by presenting an integrated model of the way that social and psychological factors affect the relationship between someone’s neighborhood and their mental health. This model builds on insights from social epidemiology, health geography, and urban sociology. Its distinct and novel contribution is to point to the importance of four pathways through which neighborhood identification shapes residents’ mental health. A large body of recent research supports this model and highlights its potential to integrate and expand upon existing theories. We also discuss how our model can inform policies and interventions that seek to improve mental health outcomes in communities.

The more diverse the better: Identifying with a diverse neighbourhood mother community predicts greater intergroup contact

AbstractAlthough it has been shown that identification with a neighbourhood community can support intergroup relations within the community by providing resources to cope with intergroup contact, previous research has not investigated how the perceived diversity of the neighbourhood identity influences this process. This study extends research on neighbourhood identification by focusing on a specific group (mothers of small children), examining how and when identification with other mothers living in the same neighbourhood is related to intergroup contact with local outgroup mothers. We examined this in a survey study, for which we collected data from both Finnish majority and immigrant mothers at public maternal and child healthcare centres (N = 733). Our study showed that identification with the neighbourhood mother community was positively associated with intergroup contact, partly via perceived social support from other neighbourhood mothers for both Finnish majority and immigrant mothers. Our study additionally revealed that the perceived proportion of outgroup mothers in the local mother community moderated the relationship between identification and intergroup contact for both groups. Our findings highlight the significance of considering perceived diversity for determining when identification will support intergroup contact. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

A cyberGIS approach to exploring neighborhood‐level social vulnerability for disaster risk management

AbstractTimely identification of disaster‐prone neighborhoods and examination of disparity in disaster exposure are critical for policymakers to plan efficient disaster management strategies. Many studies have investigated racial, ethnic, and geographic disparities and populations most vulnerable to disasters. However, little attention has been paid to the development of easily accessible and reusable tools to enable: (1) the prompt identification of vulnerable neighborhoods; and (2) the examination of social disparity in disaster impact. In this research, we have developed a visual analytics tool that allows users to: (1) delineate neighborhoods based on their selection of variables; and (2) explore which neighborhoods are susceptible to the impacts of disasters based on specific socioeconomic and demographic characteristics. Through an exploration of COVID‐19 data in the case study, we revealed that the tool can provide new insights into the identification of vulnerable neighborhoods that need immediate attention for disaster control, management, and relief.

Community on the Margins: The Social Consolidation of the HaTikvah Neighborhood in the Late Mandate Period and during Early Israeli Statehood

The definition and analysis of disadvantaged neighborhoods have been rethought in recent years, with the goal of trying to surpass the monolithic identification of all marginal neighborhoods and move towards an analysis of the social, urban, and national circumstances that create marginal neighborhoods in a particular country under specific historical circumstances. This paper offers a micro-historical case study that allows us to examine the social consolidation and civic engagement of a marginal urban community residing in the HaTikvah neighborhood, next to the city of Tel Aviv, during the period between the mid-1930s and the early 1950s. It argues that the residents’ identifications and actions stemmed from an intersectional marginality that was composed of their low socio-economic status, their ethnic origin as Oriental Jews, the geographic location of the neighborhood, and its lack of municipal status. Taking into consideration the circumstances of British Mandatory rule and the processes of the consolidation of the Jewish national society in Palestine as a European society, this paper unveils the struggles of the community vis à vis various institutions for the purposes of recognition, the improvement of living conditions, and, subsequently, the preservation of the fabric of life in the neighborhood.

The damages of stigma, the benefits of prestige: Examining the consequences of perceived residential reputations on neighbourhood attachment

This study examines how perceived residential reputations – that is, how people think non-residents assess the reputation of their neighbourhood – affect neighbourhood attachment, including residents’ sense of belonging, local civic membership, social relationships and compliance with social rules and norms in the neighbourhood. We focus on Santiago, the capital city of Chile: a highly segregated context. We use data from the Chilean Longitudinal Social Survey (ELSOC, 2016–2019) and information on neighbourhood characteristics. Results show that perceived residential reputations affect neighbourhood attachment, even after adjusting for time-invariant individual heterogeneity and lagged dependent variables. Specifically, perceived stigma reduces residents’ neighbourhood identification, physical rootedness, trust and sociability with neighbours, while positive perceived reputations improve these components of neighbourhood attachment, although to a lesser extent. However, perceived residential reputations do not affect the formation of strong ties between neighbours or local participation, suggesting that residential reputations mainly influence affective components of neighbourhood attachment. We conclude that perceived residential reputations reinforce the influence of individual characteristics and objective neighbourhood conditions in producing diverging patterns of neighbourhood attachment, with broader implications for social inequality in the city.

Perimeter fencing in urban landscapes: A perceptual exploration in a traditional Yoruba settler community

ABSTRACT The prevalent transition from ancient city walls to individualized forms of gated communities and fences elicited this study. This paper examined residents` perceptions and the characteristics of perimeter fencing in a traditional Yoruba settlement. It obtained primary data through the administration of questionnaires among residents of neighbourhoods within the study area. Physical observation aided in the identification of all residential neighbourhoods, which were stratified into three distinct and homogenous zones. One out of every four neighbourhoods in each homogenous zone was purposively selected. A total of 2055 buildings were identified within the selected neighbourhoods. Using systematic sampling, a total of 206 residents in separate dwelling units were selected for the survey. Residents perceived perimeter fencing as a feature for land boundary delineation, crime prevention, ownership and control, as well as privacy. Residential areas with a predominant rate of crime, a feeling of insecurity and disputes on land ownership were likely to witness increased use of perimeter fencing. The study recommends that local planning authorities should develop standards and regulations for perimeter fencing with consideration for the unique socio-cultural context. Improved methods of land allocation and subdivision should be implemented within traditional settlements

Abstract 2059: Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets

Abstract Spatial omics technologies are producing an unprecedented amount of ultra-high plex in situ data that are promising to revolutionize cancer prognoses and treatments. Analytical solutions to integrate big spatial data are, however, lagging relative to the rapid technology development and this hinders discoveries into the pathological processes underlying cancer initiation and progression. Here we present STimage, a machine learning approach to flexibly combine transcriptome-wide spatial sequencing data with single-cell protein-based spatial phenotyping (Phenocycler-Fusion), generated from same tissue samples. As a ground-truth for cell typing, Akoya’s single-cell spatial phenotyping technology enabled us to precisely define cell types and cell states that were then used to evaluate and deploy the data integration pipeline. With these data, STimage first maps cells onto tissue sections with reference an H&E image. This way multiple layers of molecular data are transferred into one common framework, which can then be analyzed together. Traditional pathology annotations on the H&E images are also integrated to add human understanding of morphological patterns in a cancer tissue. The integrated analysis improves cell neighborhood identification, which allows cell-cell interaction analysis based on spatial co-localization between cell types (using single-cell resolution protein data) and locally co-expressing ligand-receptor pairs (using transcriptome-wide spatial data). We applied STimage to head and neck and skin cancer samples, in order to demonstrate the broad applicability of this analysis pipeline for various cancer types. We demonstrate applications for both, diagnoses and prognoses. For diagnosis, ST image identified and cross-validated cell types whilst assessing the expression of markers for drug targets. We also present an in-depth case study for an oropharyngeal squamous cell carcinoma patient not responding to Nivolumab treatment (anti-PD-1). Based on Visium and PhenoCycler-Fusion data, we discovered a spatial signature for non-responsiveness. Furthermore, we used the predicted ligand-receptor interactions to rank the patient’s response potential to currently available drugs, with the top target being TF-TFRC. STimage is thus able to integrate multiple layers of spatial omics data to improve and solidify prognostic biomarkers for cancer treatments. This study highlights the power of machine learning integration to combine multiple spatial multi-omics data, in particular PhenoCycler-Fusion and Visium, for improving diagnosis, prognosis and treatment of diverse cancers. Citation Format: Xiao Tan, Andrew Causer, Jazmina Gonzales-Cruz, Ning Ma, Bassem Ben Cheikh, Oliver Braubach, Quan Nguyen. Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2059.

A community-led intervention to build neighbourhood identification predicts better wellbeing following prolonged COVID-19 lockdowns.

A growing body of research supports the importance of social cohesion for population wellbeing. However, the majority of this research has been correlational, and rarely have interventions been evaluated. We conducted a two-timepoint study investigating the role of Neighbour Day, a grass-roots, community-led intervention that seeks to build social cohesion across the population. Among a sample of 843, 125 were Neighbour Day participants while the remainder were not. We found that, compared to non-participants, Neighbour Day participants had significantly higher neighbourhood identification, experienced greater social cohesion, and had larger neighbourhood social networks. Between timepoints, the majority of the sample experienced prolonged lockdowns to prevent COVID-19 transmission, and so unsurprisingly, wellbeing declined and psychological distress increased. However, Neighbour Day participants were protected against these negative mental health effects of lockdown. These benefits of Neighbour Day participation were mediated via neighbourhood identification. Overall, the findings speak to the promise of large-scale interventions to build social identity, particularly due to their capacity to build resilience and protect people's wellbeing during times of collective change or crisis.

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale.

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method-combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups-to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.

Measuring and mapping neighborhood opportunity: A comparison of opportunity indices in California

Backed by decades of empirical research, there has been increasing acknowledgment in policy, practice and research of the importance of neighborhood opportunity in shaping well-being. This has led to the proliferation of opportunity maps in cities throughout the United States with the purpose of identifying low opportunity neighborhoods in need of investment and intervention and high opportunity neighborhoods that can offer access to resources and amenities to disadvantaged population groups. By explicitly linking investment to the identification of neighborhoods that are high or low in opportunity, opportunity indices have the potential to help transform local and regional landscapes of spatial inequality. Despite this common goal, indices rely on varying theoretical conceptualizations, data, variables, and statistical approaches. How much these opportunity definitions overlap has yet to be fully examined. In this study, we compared five approaches to measuring neighborhood opportunity in California. We found low to moderate overlap across the indices, with disagreement higher for low opportunity designations. As with any quantitative analysis, opportunity mapping is not a purely technical exercise and requires a series of subjective decisions. The only way to validate these decisions is for opportunity measures to be constructed transparently and vetted by the research community. This study is a first step in this process.

Quantification of green and walkable neighbourhoods across the distribution of social and material deprivation in Metro Vancouver, Canada

BACKGROUND & AIM: Along with other environmental exposures, both neighbourhood walkability, and greenspace exposure and access have been linked to many diverse health benefits. Previous work has examined the spatial relationship of built and natural environments according to levels of neighbourhood deprivation; however, less is known about how these relationships may vary when different greenspace metrics are used. This work examines how normalized differences vegetation index (NDVI), tree canopy cover, green land cover, and park count metrics relate to walkability, and how these neighbourhood characteristics associate with both social and material deprivation. METHODS: Greenspace exposure was measured using NDVI, tree canopy cover, and green land cover, while park access was quantified by the number of designated public parks within 1000m and 400m network buffers for each six-digit postal-code centroid in Metro Vancouver, Canada. Local area deprivation was measured using the 2016 Material and Social Deprivation Index (MSDI). Pearson’s correlation coefficients were calculated to compare these neighbourhood characteristics. RESULTS: Walkability was positively associated with social deprivation (1000m r = 0.48; 400m r = 0.48, p < .01), while walkability has a weak inverse relationship with material deprivation (1000m r = -0.21, p < .01). Tree canopy was negatively related to both social (1000m r = -0.23, 400m r = -0.23, p < .01) and material deprivation (1000m r = -0.24, 400m r = -0.23, p < .01). In contrast, the relationship between park count and both deprivation measures was weak. CONCLUSION: In this study, areas with greater social and material deprivation tend to have less greenspace, but not necessarily less park access. The identification of neighbourhoods with higher material deprivation, low walkability, and low greenspace, may be prioritized by urban planners and decision makers as possible locations for additional greenspace allocation. KEYWORDS: greenspace, walkability, social deprivation, material deprivation

Defying Expectations: Can We Identify Neighbourhoods with “Other Than Expected” Developmental Outcomes?

ObjectivesTo contribute to the evidence on the association between neighbourhood-level child development in Kindergarten and neighbourhood SES, our objective was to quantify the sociodemographic and child development characteristics of the neighbourhoods that “defy expectations”: high SES neighbourhoods with much-worse-than-expected child outcomes, and low SES neighbourhoods with much-better-than-expected child outcomes. ApproachUsing exploratory and model-based Latent Profile Analysis (LPA), we identified homogenous profile groups of 2038 customized Canadian neighbourhoods using ten SES indicators. We identified the most parsimonious number of profile groups and validated and characterized the derived groups of neighbourhoods using neighbourhood and aggregated child characteristics. Next, as our outcome, we created quartile groups for developmental vulnerability risk, measured with the Early Development Instrument (EDI), to match the number of derived neighbourhood profile groups. Last, we used contingency table analysis to identify neighbourhoods that defy expectations, and then characterized these neighbourhoods using descriptive statistics and correlational analysis. ResultsThe LPA identified four neighbourhood SES groups which we labelled “Low” (31.6%), “Low-moderate” (12.7%), “High-moderate” (38.4%) and “High” (17.4%). These four SES groups were cross-tabulated with quartile groups of EDI vulnerability risk. Inspection of the resulting 4-by-4 contingency table showed that within the “Low” SES profile group 57 (8.9%) neighbourhoods had much-better-than-expected developmental vulnerability risk. Conversely, within the “High” SES profile group, 12 (3.4%) neighbourhood had much-worse-than-expected developmental vulnerability risk. Additionally, these analyses identified large provincial differences in the proportion of neighbourhoods that defy expectation. In 12 provinces and territories in the study, the proportion of neighbourhoods that defied expectations within each province ranged from zero to 50%. ConclusionThe identification of neighbourhoods that defy expectations contributes to our understanding of neighbourhood factors influencing child development. Using mixed-methods approaches, these neighbourhoods can be compared to nearby neighbourhoods from the same SES profile group that do not defy expectations, in an effort to identify contextual factors that differentiate them.

Neighbourhood prevalence-to-notification ratios for adult bacteriologically-confirmed tuberculosis reveals hotspots of underdiagnosis in Blantyre, Malawi.

Local information is needed to guide targeted interventions for respiratory infections such as tuberculosis (TB). Case notification rates (CNRs) are readily available, but systematically underestimate true disease burden in neighbourhoods with high diagnostic access barriers. We explored a novel approach, adjusting CNRs for under-notification (P:N ratio) using neighbourhood-level predictors of TB prevalence-to-notification ratios. We analysed data from 1) a citywide routine TB surveillance system including geolocation, confirmatory mycobacteriology, and clinical and demographic characteristics of all registering TB patients in Blantyre, Malawi during 2015-19, and 2) an adult TB prevalence survey done in 2019. In the prevalence survey, consenting adults from randomly selected households in 72 neighbourhoods had symptom-plus-chest X-ray screening, confirmed with sputum smear microscopy, Xpert MTB/Rif and culture. Bayesian multilevel models were used to estimate adjusted neighbourhood prevalence-to-notification ratios, based on summarised posterior draws from fitted adult bacteriologically-confirmed TB CNRs and prevalence. From 2015-19, adult bacteriologically-confirmed CNRs were 131 (479/371,834), 134 (539/415,226), 114 (519/463,707), 56 (283/517,860) and 46 (258/578,377) per 100,000 adults per annum, and 2019 bacteriologically-confirmed prevalence was 215 (29/13,490) per 100,000 adults. Lower educational achievement by household head and neighbourhood distance to TB clinic was negatively associated with CNRs. The mean neighbourhood P:N ratio was 4.49 (95% credible interval [CrI]: 0.98-11.91), consistent with underdiagnosis of TB, and was most pronounced in informal peri-urban neighbourhoods. Here we have demonstrated a method for the identification of neighbourhoods with high levels of under-diagnosis of TB without the requirement for a prevalence survey; this is important since prevalence surveys are expensive and logistically challenging. If confirmed, this approach may support more efficient and effective targeting of intensified TB and HIV case-finding interventions aiming to accelerate elimination of urban TB.