Spatiotemporal trends in discarded needle reports near schools in Boston, Massachusetts, between 2016-2019

Development of a systematic social observation tool for monitoring use of harm reduction supplies

BACKGROUND

Harm reduction services such as safer injection supply distribution are essential to reducing morbidity and mortality among people who use drugs (PWUD); however, local use of harm reduction supplies (e.g., tourniquets, saline solution) is difficult to routinely and systematically monitor. The purpose of this study was to develop and validate a systematic social observation tool designed to assess use of harm reduction supplies at the street block level.

METHODS

Data collection took place on a random sample of 150 blocks located throughout the Kensington neighborhood of North Philadelphia from November 2021 to January 2022. We measured inter-rater reliability by two-way mixed-effects intra-class correlation coefficients (ICC) with the consistency agreement definition and internal consistency reliability using Cronbach's alpha and McDonald's omega. Exploratory factor analysis with principal component extraction and promax rotation assessed internal consistency. We validated scales against locations of public syringe disposal boxes, a proxy measure for areas of concentrated drug use, using logistic regression.

RESULTS

Naloxone canisters, syringe caps, saline and sterile water solution bottles showed the highest reliability (ICC≥0.7). Items also showed high internal consistency (alpha, omega>0.7). Exploratory factor analysis identified one, three-item scale with high internal consistency: syringe caps, vials, and baggies (alpha = 0.85; omega = 0.85)-all supplies used concurrently with drug injection but not discarded in syringe disposal boxes. Drug use (OR = 1.78, 95 % CI = (1.48, 2.23)), harm reduction (OR = 3.53, 95 % CI = (2.20, 6.12)), and EFA scales (OR = 1.85, 95 %CI = (1.51, 2.34)) were significantly and positively associated with being within walking distance (≤0.25 miles or 0.4 km) of a syringe disposal box.

CONCLUSION

This study provides an efficient tool with high reliability and validity metrics to assess community uptake of harm reduction supplies designed for use by community organizations, policy makers, or other groups providing resources to PWUD.

Exploring the spatio-temporal clusters of closed restaurants after the COVID-19 outbreak in Seoul using relative risk surfaces

This study explores the clusters of closed restaurants in Seoul in response to the COVID-19 pandemic using the relative risk surface (RRS). The RRS developed based on kernel density estimation provides alternative perspectives for finding the cluster by combining different control and case events. Specifically, the varying impacts on diverse types of restaurants are examined by comparing the densities of closed casual restaurants and cafes. The clusters of closed businesses following the COVID-19 outbreak are subsequently explored through a comparison of the densities of the closed businesses preceding the outbreak. Furthermore, this analysis estimates the clusters of declined commercial areas after the pandemic outbreak based on the comparison between the densities of opened and closed restaurants. Finally, the specific time and region of the clusters are explored using space-time RRS. The analysis results effectively demonstrate various aspects of the closed restaurant clusters. For example, in the central business areas, the densities of closed cafes have decreased after the pandemic outbreak, and the density of closed cafes is significantly higher than that of opened cafes. This study would contribute to the literature on spatial data analysis and urban policy support in response to future epidemics.

Evaluation of Therapeutic Effects of Computed Tomography Imaging Classification Algorithm-Based Transcatheter Arterial Chemoembolization on Primary Hepatocellular Carcinoma

To investigate the evaluation of therapeutic effects of computerized tomography (CT) imaging machine learning classification algorithm-based transcatheter arterial chemoembolization (TACE) on primary hepatocellular carcinoma (PHC), machine learning algorithm was optimized to propose the feature extraction of soft margin, analyze CT images, and acquire relevant texture features to assess if it can predict the multistage features of PHC for the application of the therapeutic effects of TACE on PHC. Besides, PHC patients receiving surgical excision were retrospectively collected, and then 483 patients with hepatocellular carcinoma (HCC) were determined from cases. After that, a total of 162 cases meeting the standards were selected. Besides, the features of images were classified and analyzed by machine learning algorithm, and volume of interest (VOI) images of patients in each group were acquired by image segmentation layer by layer. In addition, the texture features of images were extracted. The results showed that 5 CT image-based texture features, including 2 histogram features and 3 matrix-based features, all described the specificity and heterogeneity of tumors. The analysis of the diagnostic effectiveness of the evaluation of response group by each texture parameter demonstrated that its sensitivity, specificity, and area under curve (AUC) were 83.63%, 90.91%, and 0.08%, respectively. Based on CT prediction, machine learning algorithm was fused to realize excellent classification effects on multistage and multiphase features and offer imaging support to the clinical selection of reasonable therapeutic plans. In addition, multiphase and multifeature-based medical tumor classification method was put forward.