Individual-Level Predictors for Becoming Homeless and Exiting Homelessness: a Systematic Review and Meta-analysis Abstract Homelessness remains a societal problem. Compiled evidence of predictors for becoming homeless and exiting homelessness might be used to inform policy-makers and practitioners in their work to reduce homeless-related problems. We examined individual-level predictors for becoming homeless and exiting homelessness by searching PubMed, EMBASE, PsycINFO, and Web of Science up to January 2018. Becoming homeless and exiting homelessness were the outcomes. Observational studies with comparison groups from high-income countries were included. The Newcastle Ottawa Quality Assessment Scale was used for bias assessment. Random effects models were used to calculate pooled odds ratios (ORs) with 95% confidence intervals (CIs). We included 116 independent studies of risk factors for becoming homeless and 18 for exiting homelessness. We found evidence of adverse life events as risk factors for homelessness, e.g., physical abuse (OR 2.9, 95% CI 1.8–4.4) and foster care experiences (3.7, 1.9–7.3). History of incarceration (3.6, 1.3–10.4), suicide attempt (3.6, 2.1–6.3), and psychiatric problems, especially drug use problems (2.9, 1.5–5.1), were associated with increased risk of homelessness. The heterogeneity was substantial in most analyses (I2 > 90%). Female sex (1.5, 1.1–1.9; I2 = 69%) and having a partner (1.7, 1.3–2.1; I2 = 40%) predicted higher chances whereas relationship problems (0.6, 0.5–0.8), psychotic disorders (0.4, 0.2–0.8; I2 = 0%), and drug use problems (0.7, 0.6–0.9; I2 = 0%) reduced the chances for exiting homelessness. In conclusion, sociodemographic factors, adverse life events, criminal behaviour, and psychiatric problems were individual-level predictors for becoming homeless and/or exiting homelessness. Focus on individual-level vulnerabilities and early intervention is needed. PROSPERO registration number: CRD42014013119.

Urban Green Space Is Spatially Associated with Cardiovascular Disease Occurrence in Women of Mashhad: a Spatial Analysis of Influential Factors on their Presence in Urban Green Spaces Abstract Chronic diseases have spread around the world. Cardiovascular diseases (CVD), the most important of the chronic diseases and the leading cause of death in women of Mashhad, are impacted by environmental factors. Urban green spaces (UGSs) are important environmental factors playing a critical role in the prevention and control of CVD. Spatial analysis is useful in understanding the application of UGSs in CVD prevention. To identify the spatial distribution of CVD in Mashhad, Moran’s index was used and 7539 home addresses of female patients with CVD were imported into ArcMap. Moran’s coefficient was estimated to be 0.34, revealing a clustered distribution of CVD. The spatial autocorrelation between CVD and UGSs was analyzed using Moran’s I. Moran’s I index value was calculated to be − 0.15, and four types of clusters were identified in eight sub-districts of Mashhad municipality. To find the factors influencing the presence in UGSs among women affected by CVD, 607 female patients living in the selected sub-districts were asked to take part in a telephone survey. Data were analyzed using ordinary least squares (OLS) and geographically weighted regression (GWR) at block level (343 statistical blocks in total). Accordingly, the spatial diversity and effects of three variables of income, level of education, and access to UGSs among female patients with CVD were measured. According to OLS results and the standard residual, two clusters were removed. Finally, vulnerable blocks were identified that could be helpful in the development of prevention policies and place-based interventions.

Urban Health Education: Global Challenges and Opportunities

Xiamen Call for Action: Building the Brain of the City—Universal Principles of Urban Health

Morbidity Forecast in Cities: A Study of Urban Air Pollution and Respiratory Diseases in the Metropolitan Region of Curitiba, Brazil Abstract In the last two decades, urbanization has intensified, and in Brazil, about 90% of the population now lives in urban centers. Atmospheric patterns have changed owing to the high growth rate of cities, with negative consequences for public health. This research aims to elucidate the spatial patterns of air pollution and respiratory diseases. A data-based model to aid local urban management to improve public health policies concerning air pollution is described. An example of data preparation and multivariate analysis with inventories from different cities in the Metropolitan Region of Curitiba was studied. A predictive model with outstanding accuracy in prediction of outbreaks was developed. Preliminary results describe relevant relations among morbidity scales, air pollution levels, and atmospheric seasonal patterns. The knowledge gathered here contributes to the debate on social issues and public policies. Moreover, the results of this smaller scale study can be extended to megacities.

Violence Victimization Predicts Body Mass Index One Decade Later among an Urban Sample of African American Young Adults: Sex as a Moderator and Dehydroepiandrosterone as a Mediator Abstract Psychological stressors such as violence victimization are known contributors to obesity. However, moderators and mediators of this association have not been studied, although they might offer pathways for intervention or prevention. Using a sample of African American young adults, this study tested: (1) the moderating effect of sex on the effect of violence victimization on trajectories of body mass index (BMI), and (2) the mediating effect of dehydroepiandrosterone (DHEA) on this association. This 13-year longitudinal study followed 73 male and 80 female African American young adults who lived in an urban area from 1999 to 2012 when the youth were 20–32 years old. The independent variable was violence victimization measured in 1999 and 2000. The dependent variable was BMI measured in 2002 and 2012. The mediator was DHEA measured in 2001 and 2002. Multilevel path analysis was used to test if males and females differed in violence victimization predicting change in BMI (Model I) and the mediating effect of DHEA change on the above association (Model II). The results of Model I suggested that the change in violence victimization from 1999 to 2000 predicted change in BMI from 2002 to 2012 for females, but not males. Based on Model II, the DHEA change from 2000 to 2001 for females fully mediated the association between violence victimization from 1999 to 2000 and increases in BMI from 2002 to 2012. Our findings suggest that violence victimization in urban areas contributes to the development of obesity among African American female young adults and change in DHEA mediates this link. Violence prevention may have important implications for obesity prevention of African American young women who live in unsafe urban areas. This study also suggests that DHEA may be involved in the violence victimization–obesity link for African American women.

Downward Neighborhood Poverty Mobility during Childhood Is Associated with Child Asthma: Evidence from the Geographic Research on Wellbeing (GROW) Survey Abstract Causal evidence regarding neighborhood effects on health remains tenuous. Given that children have little agency in deciding where they live and spend proportionally more of their lives in neighborhoods than adults, their exposure to neighborhood conditions could make their health particularly sensitive to neighborhood effects. In this paper, we examine the relationship between exposure to poor neighborhoods from birth to ages 4–10 and childhood asthma. We used data from the 2003–2007 California Maternal Infant and Health Assessment (MIHA) and the 2012–2013 Geographic Research on Wellbeing (GROW) survey (N = 2619 mother/child dyads) to fit relative risks of asthma for children who experience different types of neighborhood poverty mobility using Poisson regression controlling for individual-level demographic and socioeconomic characteristics, and neighborhood satisfaction. Our results demonstrate that [1] living in a poor neighborhood at baseline and follow-up and [2] moving into a poor neighborhood were each associated with higher risk of asthma, compared with children not living in a poor neighborhood at either time. Exposure to impoverished neighborhoods and downward neighborhood poverty mobility matters for children’s health, particularly for asthma. Public health practitioners and policymakers need to address downward neighborhood economic mobility, in addition to downward family economic mobility, in order to improve children’s health.

The Effect of Violence and Intersecting Structural Inequities on High Rates of Food Insecurity among Marginalized Sex Workers in a Canadian Setting Abstract Food security is both a basic human right and a public health necessity. Despite known gendered contexts of food insecurity, there is a dearth of research on prevalence and factors driving increased food insecurity for sex workers in a criminalized setting. The current study longitudinally examines the prevalence and structural and individual factors associated with increased odds of food insecurity among street and off-street sex workers in a Canadian urban setting. Prospective analyses drew on data from a community-based longitudinal cohort of cis and trans women in street and off-street sex work in An Evaluation of Sex Workers Health Access (2010–2014). The primary outcome was a time-updated measure of food insecurity, using the Radimer-Cornell scale. We used bivariable and multivariable logistic regression using generalized estimating equations to prospectively model correlates of food insecurity over a five-year period. Of 761 cis and trans women sex workers, 72.4% (n = 551) were food insecure over the study period. Over a third (35.2%, n = 268) identified as Indigenous and a quarter, 25.6% (n = 195) were of a gender/sexual minority. Within the 11.0% (n = 84) of women living with HIV, 96.4% (n = 81) were food insecure over the follow-up period. In multivariable analysis, Indigenous ancestry (AOR = 1.58 [95% CI 1.18, 2.10]), unstable housing (AOR = 1.27 [95% CI 1.03, 1.57]), stimulant use (AOR = 1.97 [95% CI 1.57, 2.45]), heroin use (AOR = 1.72 [95% CI 1.36, 2.19]), mental health diagnosis (AOR = 2.38 [95% CI 1.85, 3.05]), recent violence (AOR = 1.54 [95% CI 1.24, 1.91]), means of food access: reliant on food services only vs. self-sufficient (AOR = 1.78 [95% CI 1.38, 2.29]), and means of food access: both vs. self-sufficient (AOR = 2.29 [95% CI 1.84, 2.86]) were associated with food insecurity. In separate multivariable models, both recent and lifetime physical and/or sexual violence remained independently associated with food insecurity (AOR 1.54 [95% CI 1.24, 1.91]; AOR 4.62 [95% CI 2.99, 7.14], respectively). Almost all study participants living with HIV reported being food insecure. These intersecting risks demonstrate the negative impacts associated with living with HIV, experiencing food insecurity and/or physical or sexual violence. This study also highlights the potential for interventions that address structural inequities (e.g., decriminalizing sex work) to have crosscutting impacts to reduce barriers to accessing necessities (including food) or health and social services (e.g., methadone; primary care).

Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs Abstract Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data—ideally to be made free and publicly available—and offer lay descriptions of some of the difficulties in generating such data products.

A Local View of Informal Urban Environments: a Mobile Phone-Based Neighborhood Audit of Street-Level Factors in a Brazilian Informal Community Abstract Street-level environment characteristics influence the health behaviors and safety of urban residents, and may particularly threaten health within informal communities. However, available data on how such characteristics vary within and among informal communities is limited. We sought to adapt street audit strategies designed to characterize the physical environment for use in a large informal community, Rio das Pedras (RdP) located in Rio de Janeiro, Brazil. A smartphone-based systematic observation protocol was used to gather street-level information for a high-density convenience sample of street segments (N = 630, estimated as 86% of all street segments in the community). We adapted items related to physical disorder and physical deterioration. Measures selected to illustrate the approach include the presence of the following: (1) low-hanging or tangled wires, (2) litter, (3) structural evidence of sinking, and (4) an unpleasant odor. Intercept-only spatial generalized additive models (GAM) were used to evaluate and visualize spatial variation within the RdP community. We also examined how our estimates and conclusions about spatial variation might have been affected by lower-density sampling from random subsets street observations. Random subsets were selected to determine the robustness of study results in scenarios with sparser street sampling. Selected characteristics were estimated to be present for between 18% (unpleasant odor) to 59% (low-hanging or tangled wires) of the street segments in RdP; estimates remain similar (± 6%) when relying on a random subset created to simulate lower-density spatial sampling. Spatial patterns of variation based on predicted probabilities across RdP differed by indicator. Structural sinking and low-hanging or tangled wires demonstrated relatively consistent spatial distribution patterns across full and random subset sample sizes. Smartphone-based systematic observations represent an efficient and potentially feasible approach to systematically studying neighborhood environments within informal communities. Future deployment of such tools will benefit from incorporating data collection across multiple time points to explore reliability and quantify neighborhood change. These tools can prove useful means to assess street-level exposures that can be modifiable health determinants across a wide range of informal urban settings. Findings can contribute to improved urban planning and provide useful information for identifying potential locations for neighborhood-scaled interventions that can improve living conditions for residents in Rio das Pedras.