Associations between land bank ownership and stewardship of vacant properties and crime, violence, and youth victimization in Flint, MI.

Land banks across the United States are managing expanding vacant property inventories. By maintaining vacant properties and engaging residents in the process, land banks facilitate processes integral to building safe neighborhoods and may play a role in violence prevention. Using generalized additive mixed model regression, adjusted for spatial and temporal dependencies, we examined whether land bank ownership and stewardship of vacant properties in Flint, Michigan were associated with trends in serious, violent, and firearm-involved crime, between 2015 and 2018. We tested for differences in trends in crime density between properties owned by the Genesee County Land Bank Authority (GCLBA; n = 7151) and comparison properties not owned by the land bank (n = 6,245). In addition, we tested for differences in crime density trends between vacant properties that received different levels of land bank stewardship, including biannual mowing, GCLBA standard stewardship, and GCLBA-sponsored community-engaged stewardship. We found that GCLBA ownership was associated with net declines in densities of all types of crime and violence, over time, relative to properties not owned by the GCLBA. When we distinguished between levels of stewardship, we found that GCLBA stewardship, both with and without community engagement, was associated with net declines in serious and violent crime relative to comparison properties. Only community-engaged GCLBA stewardship was associated with declines in firearm-involved crime and firearm-involved crime with a youth victim over time, relative to comparison properties. Land bank stewardship of vacant properties may be protective against crime, violence, and youth victimization in legacy cities like Flint, MI that experience high rates of vacant properties and violent crime.

Community engagement, greening, and violent crime: A test of the greening hypothesis and Busy Streets.

Researchers have documented that vacant lot greening can reduce community-level crime and violence. Busy Streets Theory (BST) suggests that residents who are involved in the greening process can help to improve physical environments and build social connections that deter crime and violence. Yet few researchers have explored how community engagement in the greening process may affect crime and violence outcomes. We applied BST to test the effects of community-engaged vacant lot greening compared to vacant lots that received either professional mowing or no treatment, on the density of violent crime around study lots. Using mixed effects regression models, we analyzed trends in violent crime density over the summer months from 2016 to 2018 at 2102 street segments in Youngstown, OH. These street segments fell within 150 meters of an intervention parcel that was classified as one of three conditions: community-engaged maintenance, professional mowing, or no treatment (control). We found that street segments in areas receiving community-engaged maintenance or professional mowing experienced greater declines in violent crime density than street segments in areas receiving no treatment, and more decline occurred in the community-engaged condition compared to the professional mow condition. Our findings support BST and suggest that community-engaged greening of vacant lots in postindustrial cities with a concentrated vacancy can reduce crime and violence.

Community Greening, Fear of Crime, and Mental Health Outcomes.

Unmaintained vacant land in urban areas is associated with a number of negative outcomes for residents of urban areas, including mental and physical health, safety, and quality of life. Community programs which promote land parcel maintenance in urban neighborhoods have been found to reverse some of the effects that unmaintained land has on nearby residents. We explored how land parcel maintenance is associated with mental health outcomes using data collected in Flint, MI in 2017-2018. Trained observers assessed the maintenance of approximately 7200 land parcels and surveyed 691 residents (57% Female, 53% Black, M age = 51). We aggregated resident and parcel rating data to 463 street segments and compared three structural equation models (SEM) to estimate the mediating effects of fear of crime on the association of parcel qualities on mental distress for residents. We found that fear of crime mediated the association between parcel maintenance values and mental distress indicating that poor maintenance predicted more fear of crime which was associated with mental distress. Our findings add to our understanding about the mechanism by which vacant lot improvements may operate to enhance psychological well-being of residents who live on streets with vacant and unkept lots.

In Utero Exposure to Opioids: An Observational Study of Mothers Involved in the Child Welfare System.

BACKGROUND: Women are underrepresented in the current substance abuse research; however, women are a particularly vulnerable population when it comes to opioid use and abuse. Pregnant women are even more so, because of the potential that exists for in utero exposure (IUE) to substances. OBJECTIVES: To identify trends in IUE to opioids in order to ensure that resources are allocated effectively to address the current opioid epidemic and to assist the populations most affected by it. METHODS: This study draws on 15 years' worth of clinical assessment data collected from 3598 child welfare-involved mothers to assess for trends in IUE to substances over time. Data from the last 5 year period (N = 852) are then analyzed to identify recent demographic correlates associated with IUE to opioid substances. RESULTS: A substantial increase in the rates of IUE to opioids over the past 15 years is observed among child welfare-involved mothers. Moreover, we find that race is a significant correlate of IUE to opioids. CONCLUSION: Study findings are consistent with other recent research that demonstrates racial differences in the populations that are most affected by the opioid epidemic; however, more research is needed to determine how these racial differences in rates of IUE to opioids affect child welfare outcomes.

Housing and health inequities during COVID-19: findings from the national Household Pulse Survey.

BACKGROUND: COVID-19 has exploited the inequities within the US housing system. Examining the association between housing and health during the pandemic is imperative to reducing health inequities and improving population health. METHODS: We analysed 957 714 responses from the Household Pulse Survey Study, collected between April and July 2020. Using survey-weighted multivariable regression analyses, we assessed the relationships between housing tenure and health, both on average and over time, as well as how these relationships were moderated by COVID-19-related hardships including job loss, food insecurity and inability to afford housing-related costs. We controlled for a variety of potential socioeconomic and demographic confounding factors. RESULTS: We found that housing tenure was significantly associated with both self-rated health and mental distress. Compared with homeowners without mortgage debt, homeowners with mortgage debt reported worse self-rated health (ß=-0.13; 95% CI -0.15 to -0.12, p<0.001) and greater mental distress (ß=0.50; 95% CI 0.44 to 0.55, p<0.001). Renters also reported worse self-rated health (ß=-0.18; 95% CI -0.20 to -0.16, p<0.001) and greater mental distress (ß=0.76; 95% CI 0.69 to 0.83, p<0.001) than homeowners without mortgage debt. Across all tenure groups, self-rated health decreased (ß=-0.007; 95% CI -0.011 to -0.004, p<0.001) and mental distress increased (ß=0.05; 95% CI 0.05 to 0.06, p<0.001) over this period. Additionally, time and COVID-19-related hardships compounded differences in health status between homeowners and renters. CONCLUSIONS: These results add to a limited body of evidence suggesting that, during this period, housing instability and COVID-19-related hardships have contributed to an increase in health inequities in the USA.

Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning.

BACKGROUND: State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considerable resources are required to read and code these text data. Data science and text mining offer potentially efficient and cost-effective strategies for maximizing the value of these data. OBJECTIVE: The current study tests the feasibility of using text mining for extracting information from unstructured text to better understand substance-related problems among families investigated for abuse or neglect. METHOD: A state child welfare agency provided written summaries from investigations of child abuse and neglect. Expert human reviewers coded 2956 investigation summaries based on whether the caseworker observed a substance-related problem. These coded documents were used to develop, train, and validate computer models that could perform the coding on an automated basis. RESULTS: A set of computer models achieved greater than 90% accuracy when judged against expert human reviewers. Fleiss kappa estimates among computer models and expert human reviewers exceeded .80, indicating that expert human reviewer ratings are exchangeable with the computer models. CONCLUSION: These results provide compelling evidence that text mining procedures can be a cost-effective and efficient solution for extracting meaningful insights from unstructured text data. Additional research is necessary to understand how to extract the actionable insights from these under-utilized stores of data in child welfare.