Neighborhood Level Research Articles

Supporting Older Adults' Social Inclusion and Well‐Being in Neighbourhoods: The Social Hub Model

In recent years, many Finnish cities and municipalities have aspired to develop services that support older adults’ well‐being and social inclusion. This study focuses on the Social Hub model, a local social innovation developed in the city of Tampere. Social hubs operate on a neighbourhood level, providing free‐of‐charge service coordination and counselling, group activities, and meeting places for social gatherings. This study aims to look at whether this kind of local innovation can support older adults’ well‐being and social inclusion. The sociomaterial perspective and multidimensional model of well‐being (the having–doing–loving–being approach) provided theoretical and analytical guidelines to examine older adults’ experiences and perceptions of social hubs. The qualitative interview data was collected among people living in service housing, senior housing, or ordinary housing in the proximity of the social hubs studied. Face‐to‐face and “go‐along” interviews with 19 older adults aged between 57 and 96 were analysed with theory‐driven content analysis. The results showed that the hubs are a valuable local resource for older adults, providing free services, accessible and appealing shared spaces, and activities that promote social well‐being, physical activity, creativity, and autonomy. The hubs serve as important gathering points for older adults in the neighbourhood, fostering community‐building among citizens residing in different types of housing. The results highlight the importance of acknowledging well‐being as a multidimensional phenomenon. The Social Hub model provides one practical tool to support older adults’ well‐being and social inclusion by offering various kinds of resources and social and cultural activities.

Municipal and Urban Renewal Development Index System: A Data-Driven Digital Analysis Framework

Urban renewal planning and development are vital for enhancing the living quality of city residents. However, such improvement activities are often expensive, time-consuming, and in need of standardization. The convergence of remote sensing technologies, social big data, and artificial intelligence solutions has created unprecedented opportunities for comprehensive digital planning and analysis in urban renewal development and management. However, fast interdisciplinary development imposes some challenges because the data collected and the solutions built are defined piece by piece and require further fusion and integration of knowledge, evaluation standards, systematic analyses, and new methodologies. To address these challenges, we propose a municipal and urban renewal development index (MUDI) system with data modeling and mathematical analysis models. The MUDI system is applied and studied in three circumstances: (1) at regional level, 337 cities are selected in China to demonstrate the MUDI system’s comparable analysis capabilities on a large scale across cities; (2) at city level, 285 residential communities are selected in Xiamen to demonstrate the use of remote sensing data as key MUDIs for a temporal urban land change analysis; and (3) at the level of residential neighborhoods’ urban renewal practices, Xiamen’s Yingping District is selected to demonstrate the MUDI system’s project management capabilities. We find that the MUDI system is highly effective in municipal and urban data model building through the abstraction and summation of grid-based satellite and social big data. Secondly, the MUDI system enables comprehension of the high dimensionality and complexity of multisource datasets for municipal and urban renewal development. Thirdly, the system is applied to enable the use of the newly developed UMAP algorithm, a model based on Riemannian geometry and algebraic topology, and the carrying out of a principal component analysis for the key dimensions and an index correlation analysis. Fourthly, various artificial intelligence-driven algorithms can be developed for urban renewal analyses based on the MUDIs. The MUDI system is a new and effective method for urban renewal planning and management that can be flexibly extended and applied to various cities and urban districts.

Crime Risk and Depression Differentially Relate to Aspects of Sleep in Patients with Major Depression or Social Anxiety.

Individuals with internalizing conditions such as depression or anxiety are at risk of sleep difficulties. Social-ecological models of sleep health propose factors at the individual (e.g., mental health) and neighborhood (e.g., crime risk) levels that contribute to sleep difficulties. However, these relationships have been under-researched in terms of internalizing conditions. Therefore, the current study comprised participants diagnosed with major depression (n = 24) or social anxiety (n = 35). Sleep measures included actigraphic variables (i.e., total sleep time, waking after sleep onset, sleep onset latency) and subjective sleep quality. Geocoding was used to assess nationally-normed crime risk exposure at the person level (e.g., murder, assault) and property level (e.g., robbery, burglary). Analyses consisted of independent t-tests to evaluate potential differences between diagnostic groups. To examine relationships, multiple regressions were used with internalizing symptoms, crime risk, and age as independent variables and sleep measures as the dependent variable. The t-test results revealed that groups differed in symptoms and age but not sleep or neighborhood crime. Regression results revealed crime risk positively corresponded with sleep onset latency but no other sleep measures. Also, only depression positively corresponded with total sleep time. Preliminary findings suggest exposure to crime and depression relate differentially to facets of sleep in individuals with internalizing conditions.

Forest fires in southwest Western Siberia: the impact of climate and economic transitions over 9000 years

ABSTRACTHere we compare the long‐term dynamics of fires in the southern taiga of Western Siberia with changes in the environment and ancient economies. Utilizing charcoal particles extracted from peat sediments, we assess charcoal accumulation rates to identify the neighborhood level of fires. Comparison of changes in vegetation, climate and land‐use history with fire dynamics reveals that wildfires were climate‐dependent but inconsequential during the first half of the Holocene (9.0–4.1k cal a bp) in the hunter‐gatherer period. Critical changes and a notable increase in fires were observed in the Late Holocene when pyrogenic events correlate poorly with changes in vegetation cover and climate. Nevertheless, after 4.1k cal a bp, a direct relationship between fire frequency and economic features emerged, primarily linked to the introduction of animal husbandry and metallurgy, along with an increase in the number of settlements. Subsequently, fire activity increased, remaining higher even during periods of cooling and increased humidity, and this appears to have been related more closely to the economic strategies and periods of depopulation. Thus, even in Siberia, where agriculture had not been practised until the last few centuries, the transition to a productive economy in the Bronze Age brought decisive changes in the dynamics of forest fires.

Using remote sensing data to derive built-form indexes to analyze the geography of residential burglary and street thefts

ABSTRACT By deploying remotely sensed data together with spatial statistical modeling, we use regression modeling to investigate the relationship between the density of the built environment and two types of crime. We show how the Global Human Settlement Layer (GHSL) data set, which is a measure of building density generated from Sentinel 2A satellite imagery, can be used to create different indexes to describe the built environment for the purpose of analyzing crime patterns for indoor crimes (residential burglary) and open space crimes (street theft). Analysis is at neighborhood level for Stockholm, Sweden. Modeling is then extended to incorporate six planning areas which represent different neighborhood types within the city. Modeling is further extended by adding selected social, economic, demographic and land use variables that have been found to be significant in explaining spatial variation in the two crime categories in Stockholm. Significant associations between the GHSL-based indexes and the two crime rates are observed but results indicate that allowance for differences in neighborhood type should be recognized. Average income and transport hubs were also significant variables in the investigated crime categories. The article provides a practical demonstration and assessment of the use of high-resolution satellite data to examine the association between urban density and two common types of crime and offers reflections about the use of satellite image data in crime analysis.

ANALISA PERSEPSI TERHADAP SELF CARE SKILLS PADA PASIEN DM TIPE II PADA MASYARAKAT PERKOTAAN

Assessment of the impacts of urban change at the neighborhood level provides a unique opportunity to generate insights into the extent and nature of positive and negative impacts on health-related outcomes. Several specific components and mechanisms that cause urban change can influence behavior, perception and self-care, thereby impacting health. The high prevalence of non-communicable diseases, one of which is Type 2 Diabetes Mellitus. Type 2 Diabetes Mellitus occurs when the body stops producing sufficient amounts of insulin, or when the body does not use insulin properly (something known as insulin resistance). in 2021 in the Jember urban area it will reach 1,700, one of which is due to lifestyle factors in the urban environment. One strategy for dealing with type 2 diabetes mellitus is to optimize the prevention of type 2 diabetes and associated comorbidities with a therapeutic approach, one of which is optimizing self-care which can reduce complications, reduce cardiovascular and kidney risks, and improve health outcomes. The aim of this research was to determine the relationship between disease perception and the self-care skills of type II diabetes mellitus patients. This research used a cross sectional method with a total of 100 respondents, data collection used a disease perception questionnaire and a quality of life questionnaire which were given to respondents directly. The results of the chi square test research obtained a value of p = 0.030 or < (0.05), so Ha was accepted, meaning that there was a relationship between disease perception and the quality of life of type II diabetes mellitus patients. The conclusion obtained from this research is that there is a relationship between perception of illness and self-care skills. The increase in good self-care skills is due to good perception.. Keywords: Perception, Self Care Skills, Urban Society, Diabetes Mellitus

Spatio-temporal patterns and accessibility of green spaces in Kumasi, Ghana

Urban green spaces (UGS) are a central part of cities and offer multiple ecological and social benefits necessary for a higher quality of life of inhabitants. However, rapid unguided urban growth together with planning policies on rezoning can lead to transformation of UGS to commercial, residential, and other uses. These processes of land use change result in inequities in the distribution of UGS in the city. Yet, data and metrics that capture changes in the geography of UGS is scarce within African cities. This paper addresses this need by examining the spatio-temporal geography of UGS at the neighborhood-scale in Kumasi, the next biggest city in Ghana. Five (5) UGS distribution indicators were computed from remote sensing data for 2013 and 2020 and compared across neighborhoods. Further, a GIS network analysis method was deployed to analyse access to parks in Kumasi. The results showed that between 2013 and 2020, green spaces in Kumasi became fragmented and disconnected despite a slight increase in quantity. The level of UGS fragmentation was found to significantly vary among the neighborhoods. Fragmentation and depletion in UGS were profound at the outskirts of Kumasi while majority of the population cannot access the nearest park under 30-min’ walk. These findings contribute to a complete and holistic understanding of the distribution and accessibility of UGS at the neighborhood level in cities and disparities in UGS exposure in the Global South. The findings provide important insights for urban policies while the data and methods serve as blueprints for local authorities, particularly in Ghana to track their progress towards achieving Sustainable Development Goal 11, target 7.

Probing the impact of exposure to diversity on infants' social categorization.

Humans learn about the world through inductive reasoning, generalizing information about an individual to others in the category. Indeed, by infancy, monolingual children expect people who speak the same language (but not people who speak different languages) to be similar in their food preferences (Liberman et al., 2016). Here, we ask whether infants who are exposed to linguistic diversity are more willing to generalize information even across language-group lines. To test this, we ran an inductive inference task and collected data on exposure to linguistic diversity at the interpersonal and neighborhood levels. Infants with more linguistically diverse social networks were more likely to generalize a food preference across speakers of different languages. However, this relationship was not seen for neighborhood diversity. We discuss implications of this work on understanding the development of bias and its malleability based on early social experiences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models

BackgroundIdentifying co-occurring mental disorders and elevated risk is vital for optimization of healthcare processes. In this study, we will use DeepBiomarker2, an updated version of our deep learning model to predict the adverse events among patients with comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD), a high-risk population. MethodsWe analyzed electronic medical records of 5565 patients from University of Pittsburgh Medical Center to predict adverse events (opioid use disorder, suicide related events, depression, and death) within 3 months at any encounter after the diagnosis of PTSD+AUD by using DeepBiomarker2. We integrated multimodal information including: lab tests, medications, co-morbidities, individual and neighborhood level social determinants of health (SDoH), psychotherapy and veteran data. ResultsDeepBiomarker2 achieved an area under the receiver operator curve (AUROC) of 0.94 on the prediction of adverse events among those PTSD+AUD patients. Medications such as vilazodone, dronabinol, tenofovir, suvorexant, modafinil, and lamivudine showed potential for risk reduction. SDoH parameters such as cognitive behavioral therapy and trauma focused psychotherapy lowered risk while active veteran status, income segregation, limited access to parks and greenery, low Gini index, limited English-speaking capacity, and younger patients increased risk. ConclusionsOur improved version of DeepBiomarker2 demonstrated its capability of predicting multiple adverse event risk with high accuracy and identifying potential risk and beneficial factors.

Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer's disease classification.

Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification. Our method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level. Our functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations. Strategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.

Modifying the walk score based on daily social activities: residential neighborhood in Egypt as a case study

Walkability has been linked to quality of life in many ways. The Walk Score is one of several methodologies aimed at assisting people in evaluating walkability. Despite the validation of this method, the amenity schedule around the day and the occupancy percentage in each amenity are not included in the calculation of the Walk Score, resulting in reduced accuracy. The research aims to improve the Walk Score methodology by inserting the amenity schedule around the day and the occupancy percentage in each amenity in the calculating method of the walk score, as the amenities do not have the same occupancy throughout the day. The research proposes a new method to calculate the Walk Score according to the time and occupancy for each amenity at the residential neighborhood level in Egypt. The research uses Salam New City as a case study to apply the new method to a residential neighborhood using the Urban Modelling Interference (Umi) plugin. Finally, the research proposes the Walk Score Model of Salam New City to calculate the Walk Score around the day. The new Walk Score model helps urban planners improve urban space design and the distribution of amenities.

Urban forest biotope mapping: A new approach for sustainable forest management planning in Mexico City

Environmental demands for greener, more sustainable urban areas are increasing worldwide and are particularly magnified in megacities such as Mexico City, the largest city in North America. Strategic land use planning initiatives aimed at integrated and sustainable urban green infrastructure across broader city landscapes require an understanding of urban environments, their urban forest, and their spatial distribution. However, this approach has not been widely used in megacities. Here, we use the urban biotope approach to address the knowledge gap and help inform land-use planning to support greater urban forest sustainability in Mexico City. Two levels of biotope classes, broader-level (3 classes defined by canopy cover) and finer-level (7 classes describing urban forest characteristics), were modeled and mapped across the city. Statistical modeling was done using a random forest algorithm, a set of uncorrelated predictor variables, and field observations to predict biotope presence. Models of the broader-level biotopes yielded better prediction results (AUC > 0.90) than the finer-level biotopes (AUC values from 0.61 to 0.88). Classification errors ranged from 6.5 to 100 %, indicating different levels of predictive confidence. Of all mapping units of the broader-level biotopes 87 % were predicted while of the finer-level biotopes only 12.7 %. As the finer-level predictions often overlap, more field data and better predictors are required to accurately capture biotope classes. The modeled biotopes can inform urban planning across spatial scales, from the neighborhood and borough level to the city-wide scale, and across the residential-commercial land use, which requires more greening efforts. Results suggest that more field and spatial data, and a smaller grain size, can improve biotope models and maps to better represent the variation in urban forest characteristics across Mexico City. Biotope maps are a practical tool to guide spatial planning, identify priority areas for canopy protection and tree planting, and set tree diversification goals.

Impacts of sociodemographic factors, identities and neighbourhood safety on the relationship between urban green space and adolescent mental well-being: Findings from Tāmaki Makaurau Auckland, Aotearoa New Zealand

This study explored the relationship between green space accessibility (GSA) in residential area and adolescents’ mental well-being, and whether the relationship was moderated by sociodemographic factors (sex, ethnicity, neighbourhood deprivation), identities (gender and sexuality minority, disability) and perceived neighbourhood safety simultaneously. Data from 3813 adolescents who lived in Tāmaki Makaurau Auckland, Aotearoa New Zealand were obtained from the Youth19 Rangatahi Smart Survey. A Gaussian-based two-step floating catchment area method was employed to measure the spatial accessibility to green space at the neighbourhood level. The World Health Organization-5 Well-being Index was used to assess emotional well-being (EW), and the Reynolds Adolescent Depression Scale-short form was employed to measure depressive symptoms (DS). Through moderation analyses, results showed that perceived neighbourhood safety plays a vital role in the GSA – mental well-being association, with a negative trend in adolescents who reported being less safe in neighbourhoods. Adverse associations of GSA were found in gender and sexuality minority, disabled, Asian and Pacific adolescents, under the condition of not feeling safe in neighbourhoods all the time. The results showed marginalised adolescents tended to feel less safe in neighbourhoods, have lower EW and a higher level of DS. Additionally, the results from bivariate correlations showed there were inequalities in GSA for adolescents who lived in most deprived neighbourhoods and adolescents of Māori ethnicity. This study provides novel evidence of the importance of safe and inclusive green space for effectively promoting mental health and mitigating health inequalities of adolescents in urban areas.

Effect of neighborhood and individual-level socioeconomic factors on breast cancer screening adherence in a multi-ethnic study

BackgroundAlthough mammography can significantly reduce breast cancer mortality, many women do not receive their annual breast cancer screening. Differences in screening adherence exist by race/ethnicity, socioeconomic status (SES), and insurance status. However, more detailed investigations into the impact of neighborhood disadvantage and access to resources on screening adherence are lacking.MethodsWe comprehensively examined the effect of individual social, economic, and demographic factors (n = 34 variables), as well as neighborhood level SES (nSES) indicators (n = 10 variables) on breast cancer screening adherence across a multi-ethnic population (n = 472). In this cross-sectional study, participants were surveyed from 2017 to 2018. The data was analyzed using univariate regression and LASSO for variable reduction. Significant predictors were carried forward into final multivariable mixed-effect logistic regression models where odds ratios (OR), 95% confidence intervals and p-values were reported.ResultsNineteen percent of participants were non-adherent to breast screening guidelines. Race/ethnicity was not associated with adherence; however, increasing age (OR = 0.97, 95%CI = 0.95–0.99, p = 0.01), renting a home (OR = 0.53, 95%CI = 0.30–0.94, p = 0.04), food insecurity (OR 0.46, 95%CI = 0.22–0.94, p = 0.01), and overcrowding (OR = 0.58, 95% CI = 0.32–0.94, p = 0.01) were significantly associated with lower breast cancer screening adherence.ConclusionSocioeconomic indicators at the individual and neighborhood levels impact low breast cancer screening adherence and may help to inform future screening interventions.

Premature Deaths Due To Heat Exposure: The Potential Effects of Neighborhood-Level Versus City-Level Acclimatization Within US Cities.

For the population of a given US city, the risk of premature death associated with heat exposure increases as temperatures rise, but risks in hotter cities are generally lower than in cooler cities at equivalent temperatures due to factors such as acclimatization. Those living in especially hot neighborhoods within cities might therefore suffer much more than average if such adaptation is only at the city-wide level, whereas they might not experience greatly increased risk if adjustment is at the neighborhood level. To compare these possibilities, we use high spatial resolution temperature data to evaluated heat-related deaths assuming either adjustment at the city-wide or at the neighborhood scale in 10 large US cities. On average, we find that if inhabitants are adjusted to their local conditions, a neighborhood that was 10°C hotter than a cooler one would experience only about 1.0-1.5 excess heat deaths per year per 100,000 persons. By contrast, if inhabitants are acclimatized to city-wide temperatures, the hotter neighborhood would experience about 15 excess deaths per year per 100,000 persons. Using idealized analyses, we demonstrate that current city-wide epidemiological data do not differentiate between these differing adjustments. Given the very large effects of assumptions about neighborhood-level acclimatization found here, as well as the fact that current literature is conflicting on the spatial scale of acclimatization, more neighborhood-level epidemiological data are urgently needed to determine the health impacts of variations in heat exposure within urban areas, better constrain projected changes, and inform mitigation efforts.

Measuring concordance and discordance between selected individual characteristics and corresponding neighborhood-level social determinants of health.

Individuals reside within communities influenced by various social determinants impacting health, which may harmonize or conflict at individual and neighborhood levels. While some experience concordant circumstances, discordance is prevalent, yet poorly understood due to the lack of a universally accepted method for quantifying it. This paper proposes a methodology to address this gap. We propose a systematic approach to operationalize concordance and discordance between individual and neighborhood social determinants, using household income (HHI) (continuous) and race/ethnicity (categorical) as examples for individual social determinants. We demonstrated our method with a small dataset that combines self-reported individual data with geocoded neighborhood level. We anticipate that the risk profiles created by either self-reported individual data or neighborhood-level data alone will differ from patterns demonstrated by typologies based on concordance and discordance. In our cohort, it was revealed that 20% of patients experienced discordance between their HHIs and neighborhood characteristics. Additionally, 38% reside in racially/ethnically concordant neighborhoods, 23% in discordant ones, and 39% in neutral ones. Our study introduces an innovative approach to defining and quantifying the notions of concordance and discordance in individual attributes concerning neighborhood-level social determinants. It equips researchers with a valuable tool to conduct more comprehensive investigations into the intricate interplay between individuals and their environments. Ultimately, this methodology facilitates a more accurate modeling of the true impacts of social determinants on health, contributing to a deeper understanding of this complex relationship.

Income Extraction via the Criminal Legal System: A Community-Level Perspective

Legal financial obligations (LFOs) are financial penalties imposed by U.S. criminal courts that generate disproportionate negative effects for poor and minoritized individuals and communities. In this visualization, the authors use court administrative data for all criminal cases in Washington and Minnesota from 2010 to 2015 to measure community-level income extraction via LFOs in Seattle and Minneapolis-St. Paul. Unlike previous measures of LFOs at the community level, the authors calculate the proportion of income in a given census tract that is extracted in the form of LFOs. This operationalization makes it possible to ask, given a tract-year’s existing income per capita, what proportion of that income went toward LFOs from cases sentenced. The authors’ maps demonstrate that disadvantaged communities pay a higher share of their income in LFOs compared with more advantaged neighborhoods, perpetuating social control and poverty at the neighborhood level.

Exploring an Architectural Paradigm for the 15-minute city planning model: Towards reforming gaps of services in Nablus

The existing literature on the 15-minute city planning model mainly covers conceptual strategies, responsive urban transformations, and innovative urban development projects at the neighborhood level. However, little attention has been given to the actual architecture of the 15-minute city. This planning model emphasizes the relationship between time and space of services in neighborhoods, which can be used as a scientific method to fill the gaps in services in Nablus neighborhoods. Local feedback on the distribution of services, proximity, and mobility in Nablus neighborhoods has highlighted the need to reimagine the comprehensiveness, density, and accessibility of services. Additionally, the prioritization of services has changed during COVID-19, which emphasizes the importance of architecture that considers flexibility in services. To address these challenges, this paper proposes the use of prefabrication construction technology in the placement of urban service units as a sustainable architectural model to realize the 15-minute city planning model. The paper presents the integration of structural system analysis, environmental considerations, and sterilization criteria into the model and proposes guidelines for the feasibility of this model. The resulting urban service framework of the architectural paradigm responds to the pointed-out needs of the population, and the temporary introduction of the model makes the application of the model responsive to the local shortage of vacant lands.

Exposure to mass media chronic health campaign messages and the uptake of non-communicable disease screening in Ghana.

The main goal of this study was to examine the relationship between exposure to mass media health campaign massages and the uptake of non-communicable diseases (NCDs) screening services in Ghana and whether this relationship differs by place of residence. Available evidence suggests a general low uptake of NCDs screening in developing country settings. Unfortunately, many NCDs evolve very slowly and are consequently difficult to detect early especially in situations where people do not screen regularly and in settings where awareness is low. In this study, we contribute to understanding the potential role of the media in scaling up NCDs screening in developing countries. We fitted multivariate logistic regression models to a sample of 1337 individual surveys which were collected at the neighborhood level in three Ghanaian cities. Overall, the results show that exposure to mass media chronic NCD health campaign messages was significantly associated with increased likelihood of screening for NCDs. The results further highlight neighborhood-level disparities in the uptake of NCDs screening services as residents of low-income and deprived neighborhoods were significantly less likely to report being screened for NCDs. Other factors including social capital, knowledge about the causes of NCDs and self-rated health predicted the likelihood of chronic NCDs screening. The results demonstrate mass media can be an important tool for scaling up NCDs screening services in Ghana and similar contexts where awareness might be low. However, place-based disparities need to be addressed.

A new approach to a wicked problem: development of a cross-sector community-centered learning network to tackle childhood food inequity.

Food insecurity is a complex societal problem that disproportionately impacts households with children and those led by minoritized populations, with negative impacts on health across the life course. System to Achieve Food Equity adapted the learning systems model, used to address similarly complex problems, to tackle food insecurity at a neighborhood level. SAFE, born out of the COVID-19 pandemic, leverages a family-centered, community-based, cross-sector network fundamentally aimed at changing the food system so that all children in Cincinnati have the food they need to thrive. Through the following principles, Community-Led Network, Co-Production with Community, Equitable Sustainability, Learning to Learn Together, Distributing Leadership and Power, and Shared Data and Governance, SAFE has grown to over 300 individuals and 100 organizations, funded 9 novel interventions, distributed over 270,000 meals, and created a collaborative of motivated like-minded stakeholders. Future work includes improved data collection and sharing, support for increased stakeholder engagement and greater distribution of leadership and power, advocacy for policy change, refining measurement tools of network maturity for community settings, and collaboration with other efforts that contribute to food security indirectly.