Addressing Black Infant Mortality Through Harnessing the Power of Community

In Hamilton County, Ohio, the infant mortality rate is above the national average and the Black infant mortality rate is more than 3 times the white infant mortality rate. These racial disparities in infant mortality cannot be explained through other socio-economic characteristics like education, income, housing, or medical insurance. Research has shown that racism, not race itself, is the driving force behind the high disparity in infant mortality rates in Hamilton County and the nation as well. The World Health Organization (WHO) and the Institute of Medicine have long cited institutional/structural racism and cultural racism as a key factor in health disparity. A paradigm shift needed to occur to address the consequences of racism within the lives of Black women, namely disempowerment and agency. The Commission on Social Determinants of Health (CSDH) model was which engaged Black women and positioned them as an asset, to share in the process of strategizing, creating, and implementing a plan. Queens Village was founded to implement the CSDH model and address the upstream determinants of infant mortality through cultivating a sense of community.

Incorporating Low-Stress Bicycling Connectivity into Expanded Transit Service Coverage

The speed advantage in bicycling over walking is believed to ease first-and-last mile (F&LM) travel and expand transit service coverage. To quantitatively investigate the potential effect of using bicycle as a F&LM connector, the paper measures and compares the impacts of walking and bicycling F&LM access on transit service coverage. In the estimation of transit service coverage, F&LM travel decay functions representing the attractiveness of public transit that declines with increasing walking/biking time to access transit facilities and the spatial boundaries of transit catchment areas are developed using GPS trajectory data collected from the latest Cincinnati Household Travel Survey in Hamilton County, Ohio. Level of traffic stress is used to evaluate the bicycle suitability of streets and bike network connectivity. Based on the F&LM distance decay functions and low-stress bike network connectivity, the transit service coverage area as well as the transit-served population and employment in Hamilton County, Ohio, are estimated. Results show that more population can reach transit services and therefore employment by bicycling than walking. Meanwhile, disadvantaged groups, that is, low-income and zero-car population, can be better served by transit if using bicycle as the F&LM connector. In addition, low-stress bicycling connectivity is a significant factor determining the bicycle-transit service coverage, and a well-connected low-stress bike network with quality bikeways is crucial to guaranteeing that. These findings can be used as references to assist planners in their decision-making process to achieve better mobility and accessibility.

Membership Committee: ALSC Member Profiles

Alia Jones, formerly Sr. Library Services Assistant at Cincinnati and Hamilton County (OH) Public Library, 2020 Caldecott CommitteeDenise Dávila, PhD, Assistant Professor of Children’s Literature and Literacy Education, Language and Literacy Studies, University of TexasAnn Crewdson, Children’s Specialist, King County (WA) Library System

Explorative Visualization for Traffic Safety using Adaptive Study Areas

The pressing need to improve traffic safety has become a societal concern in many cities around the world. Many traffic accidents are not occurring as stand-alone events but as consequences of other road incidents and hazards. To capture the traffic safety indications from a holistic aspect, this paper presents a suite of visualization techniques to explore large traffic safety datasets collected from different sources using adaptive study areas which include the whole region (Hamilton County, Ohio, U.S.) as well as smaller sub-areas. In the present study, these data source include (1) Hamilton County’s 911 emergency response data, which includes traffic incidents as well as other types of incidents throughout the county, and (2) Tennessee crash data, which contains only vehicle crashes with more detail on the circumstances of each crash. Both abstract and spatial visualization techniques are used to derive a better understanding of traffic safety patterns for different traffic participants in various urban environments. In addition to the entire region of Hamilton County, safety is examined on the highways, in the downtown area, and in a shopping district east of the city center. It is possible to characterize incidents in the different areas, gain a better understanding of common incident patterns, and identify outliers in the data. Finally, a textured tile calendar is presented to compare spatiotemporal patterns.

Using Generalized Linear Mixed Models to Predict the Number of Roadway Accidents: A Case Study in Hamilton County, Tennessee

Introduction: A method for identifying significant predictors of roadway accident counts has been presented. This process is applied to real-world accident data collected from roadways in Hamilton County, TN. Methods: In preprocessing, an aggregation procedure based on segmenting roadways into fixed lengths has been introduced, and then accident counts within each segment have been observed according to predefined weather conditions. Based on the physical roadway characteristics associated with each individual accident record, a collection of roadway features is assigned to each segment. A mixed-effects Negative Binomial regression form is assumed to approximate the relationship between accident counts and several explanatory variables including roadway characteristics, weather conditions, and several interactions between them. Standard diagnostics and a validation procedure show that our model form is properly specified and suitably fits the data. Results: Interpreting interaction terms leads to the follow findings: 1) rural roads with cloudy conditions are associated with relative increases in accident frequency; 2) lower/moderate AADT and rainy weather are associated with relative decreases in accident frequency, while high AADT and rain are associated with relative increases in accident frequency; 3) higher AADT and wider pavements are associated with relative increases in accident frequency; and 4) higher speed limits in residential areas are associated with relative increases in accident frequency. Conclusion: Results illustrate the complicated relationship between accident frequency and both roadway features and weather. Therefore, it is not sufficient to observe the effects of weather and roadway features independently as these variables interact with one another.

Characterization of Community-Based Socioeconomic Factors, Utilization, and Adherence in Children with Sickle Cell Disease

Introduction Sickle cell disease (SCD) affects over 100,000 people in the US, the majority of whom are African American. Socioeconomic challenges have a significant impact on both access and adherence to appropriate treatments which, given a history of racial segregation and discrimination, disproportionately burden under-represented minorities. The distribution of socioeconomic factors, like poverty, educational attainment, and housing quality, can now be assessed routinely at the population level, yet the distribution and impact of such contextual risks in the pediatric sickle cell population have not been sufficiently described. Here, we sought to characterize the burden of neighborhood-level socioeconomic challenges and barriers among children with SCD in one large, urban county. We also sought to determine whether these area-level indicators were associated with hospitalizations and markers of adherence to SCD medications. Methods We pursued a retrospective review of electronic health record data from 2011-2017 for children with HbSS disease in the active Cincinnati Children's Hospital Medical Center's SCD registry which includes all children receiving care within the past two years in the Comprehensive Sickle Cell Center and is representative of nearly all children with SCD in Hamilton County, Ohio. The analysis was performed under an IRB-approved study investigating socioeconomic factors for children in Hamilton County. Children within the SCD registry were excluded from this analysis if they had a non-HbSS genotype or an address outside of Hamilton County. Addresses were geocoded and linked to a specific census tract which approximates local neighborhood boundaries. Once linked to a census tract, that address was connected to a pre-determined list of variables present within the 2013-2017 US Census' American Community Survey. Variables included the census tract poverty rate, educational attainment rate (percentage of adults with less than a high school education), and the percentage of vacant housing. A validated census tract-level deprivation index, assembled from 6 such census variables, was also included. Outcomes of interest included number of hospitalizations and ED visits during the study period and %HbF for the subset on hydroxyurea treatment. Descriptive statistics were used to illustrate ecological socioeconomic characteristics among included patients. Associations between area-based socioeconomic deprivation and outcomes of interest were tested using the Kruskal-Wallis Test. Results There were 141 patients with HbSS included in the analysis (53% Male, 82% publicly insured). Mean age at the end of the analysis period was 9.6±6.3 years. Consistent with the aggressive treatment strategy at our center, most (97%) were on disease modifying treatment with either hydroxyurea (81%) or chronic transfusion therapy (16%). Compared to the county as a whole, children in the registry mapped to areas with relatively high rates of poverty (median 26%; IQR 15%-42%), low rates of education attainment (median with high school degree 86%; IQR 78%-91%), and high rates of vacant housing (median 13%; IQR 8%-19%). The deprivation index is scaled between 0 and 1 with higher values indicative of more socioeconomic deprivation. In our population, the deprivation index median was 0.45 (IQR 0.36-0.61). When the sample was categorized into three deprivation groups (low < 25th percentile, medium between 25th and 75th, and high >75th percentile), we found trends toward associations with utilization and adherence measures (Table 1). Conclusion A majority of our SCD patients live in neighborhoods with stark socioeconomic challenges and barriers which have been shown to negatively affect health outcomes. There appears to be a significant trend towards increased utilization among those living in more deprived neighborhoods, although, the link with adherence was less clear. The latter finding, indicative of similar HbF levels across deprivation groupings, may be the result of efforts made by our multidisciplinary comprehensive care team to optimize care for all patients regardless of socioeconomic challenges. The data presented here are novel and likely representative of socioeconomic challenges of most SCD patients living in the US. Future, larger, multi-center studies should focus on identifying and addressing social determinants of health within this population. Disclosures No relevant conflicts of interest to declare.