Environmental risk factors associated with malignancy of pediatric neuroblastic tumours are not well-known and few studies have examined the relationship between industrial emissions and neuroblastic tumour diagnosis. A retrospective case series of 310 patients was evaluated at a tertiary hospital in Toronto, Canada between January 2008, and December 2018. Data from the National Pollutant Release Inventory (NPRI) were used to estimate exposure for a dozen chemicals with known or suspected carcinogenicity or embryotoxicity. Comparative analysis and predictive logistic regression models for malignant versus benign neuroblastic tumours included variables for residential proximity, number, and type of industries, mean total emissions within 2 km, and inverse distance weighted (IDW) quantity of chemical-specific industrial emissions estimated within 10 and 50 km of cases. No significant difference was seen between malignant and benign cases with respect to the mean nearest residential distance to industry, the number or type of industry, or the mean total quantity of industrial emissions within a 2 km radius of residential location of cases. However, there were statistically significant differences in the interpolated IDW emissions of dioxins and furans released between 1993 and 2019 within 10 km. Concentrations were significantly higher in malignant neuroblastic tumours at 1.65 grams (g) toxic equivalent (TEQ) (SD 2.01 g TEQ) compared to benign neuroblastic tumours at 1.13 g TEQ (SD 0.84 g TEQ) (p = 0.05). Within 50 km 3 years prior to diagnosis, malignant cases were exposed to higher levels of aluminum, benzene, and nitrogen dioxide (p = 0.02, p = 0.04, and p = 0.02 respectively). Regression analysis of the IDW emissions within a 50 km radius revealed higher odds of exposure to benzene for malignant neuroblastic tumours (OR = 1.03, CI: 1.01-1.05, p = 0.01). These preliminary findings suggest a potential role of industrial emissions in the development of malignant pediatric neuroblastic tumours and underscore the need for further research to investigate these associations.

UNASSIGNED: Childhood exposures to discarded needles pose a direct risk for infection with blood-borne pathogens and psychological trauma for caregivers and children. Little is known about environmental predictors of discarded needles relative to areas where children are frequent, such as schools.
UNASSIGNED: We investigated spatiotemporal trends in discarded needle reports and the density near schools in Boston, Massachusetts, between 2016 and 2019.
UNASSIGNED: We used the kernel density estimation (KDE) and a relative risk function (RRF) to explore their spatial distribution and temporal changes of discarded needles reported through the 311 service request system in Boston. The density of needle pick-up requests around schools was investigated by using Thiessen polygons.
UNASSIGNED: Between January 2016 and December 2019, 18,272 discarded needle reports were made. Publicly reported discarded needles in Boston sharply increased over the 4 years and the highest density of needles was found in 2 central neighborhoods. The density of reports of discarded needles near schools increased among the majority of schools. About 30% of schools demonstrated an increase of 100% or more in reports of discarded needles.
UNASSIGNED: This analysis provides insight into potential risk of exposure to needle stick injuries for children based on utilizing publicly available crowd-sourced data. Monitoring the density of discarded needles near schools may be a novel approach to improve public health efforts to distribute safe needle disposal locations and reduce injection drug use in public.

Nontuberculous mycobacteria (NTM) are ubiquitous environmental bacteria that may cause chronic lung disease and are one of the most difficult-to-treat infections among persons with cystic fibrosis (pwCF). Environmental factors likely contribute to increased NTM densities, with higher potential for exposure and infection.
To identify water-quality constituents that influence odds of NTM infection among pwCF in Colorado.
We conducted a population-based nested case-control study using patient data from the Colorado CF Center NTM database. We associated data from pwCF and water-quality data extracted from the Water Quality Portal to estimate odds of NTM infection. Using Bayesian generalized linear models with binomial-distributed discrete responses, we modeled three separate outcomes; any NTM infection, infections due to Mycobacterium avium complex species, and infections due to M. abscessus group species.
We observed a consistent association with molybdenum in the source water and M. abscessus group species infection among pwCF in all models. For every 1-unit increase in the log concentration of molybdenum in surface water, the odds of infection for those with M. abscessus group species compared to those who were NTM culture-negative increased by 79%. The odds of M. abscessus group infection varied by county; the counties with the highest probability of infection are located along the major rivers.
We have identified molybdenum in the source water as the most predictive factor of M. abscessus group infection among pwCF in Colorado. This finding will help inform patients at risk for NTM of their relative risks in residing within specific regions.

BACKGROUND: Disasters such as the COVID-19 pandemic pose an overwhelming demand on resources that cannot always be met by official organisations. Limited resources and human response to crises can lead members of local communities to turn to one another to fulfil immediate needs. This spontaneous citizen-led response can be crucial to a community\'s ability to cope in a crisis. It is thus essential to understand the scope of such initiatives so that support can be provided where it is most needed. Nevertheless, quickly developing situations and varying definitions can make the community response challenging to measure.
OBJECTIVE: To create an accessible interactive map of the citizen-led community response to need during the COVID-19 pandemic in Wales, UK that combines information gathered from multiple data providers to reflect different interpretations of need and support.
METHODS: We gathered data from a combination of official data providers and community-generated sources to create 14 variables representative of need and support. These variables are derived by a reproducible data pipeline that enables flexible integration of new data. The interactive tool is available online (www.covidresponsemap.wales) and can map available data at two geographic resolutions. Users choose their variables of interest, and interpretation of the map is aided by a linked bee-swarm plot.
CONCLUSIONS: The novel approach we developed enables people at all levels of community response to explore and analyse the distribution of need and support across Wales. While there can be limitations to the accuracy of community-generated data, we demonstrate that they can be effectively used alongside traditional data sources to maximise the understanding of community action. This adds to our overall aim to measure community response and resilience, as well as to make complex population health data accessible to a range of audiences. Future developments include the integration of other factors such as well-being.

UNASSIGNED: Given the significant health effects, we assessed geospatial patterns of adverse events (AEs), defined as physical or sexual abuse and accidents or poisonings at home, among children in a mixed rural-urban community.
UNASSIGNED: We conducted a population-based cohort study of children (<18 years) living in Olmsted County, Minnesota, to assess geographic patterns of AEs between April 2004 and March 2009 using International Classification of Diseases, Ninth Revision codes. We identified hotspots by calculating the relative difference between observed and expected case densities accounting for population characteristics (; hotspot ≥ 0.33) using kernel density methods. A Bayesian geospatial logistic regression model was used to test for association of subject characteristics (including residential features) with AEs, adjusting for age, sex, and socioeconomic status (SES).
UNASSIGNED: Of the 30,227 eligible children (<18 years), 974 (3.2%) experienced at least one AE. Of the nine total hotspots identified, five were mobile home communities (MHCs). Among non-Hispanic White children (85% of total children), those living in MHCs had higher AE prevalence compared to those outside MHCs, independent of SES (mean posterior odds ratio: 1.80; 95% credible interval: 1.22-2.54). MHC residency in minority children was not associated with higher prevalence of AEs. Of addresses requiring manual correction, 85.5% belonged to mobile homes.
UNASSIGNED: MHC residence is a significant unrecognized risk factor for AEs among non-Hispanic, White children in a mixed rural-urban community. Given plausible outreach difficulty due to address discrepancies, MHC residents might be a geographically underserved population for clinical care and research.

BACKGROUND: In Swaziland, as in many high HIV/TB burden settings, there is not information available regarding the household location of TB cases for identifying areas of increased TB incidence, limiting the development of targeted interventions. Data from \"Butimba\", a TB REACH active case finding project, was re-analyzed to provide insight into the location of TB cases surrounding Mbabane, Swaziland.
OBJECTIVE: The project aimed to identify geographical areas with high TB burdens to inform active case finding efforts.
METHODS: Butimba implemented household contact tracing; obtaining landmark based, informal directions, to index case homes, defined here as relative locations. The relative locations were matched to census enumeration areas (known location reference areas) using the Microsoft Excel Fuzzy Lookup function. Of 403 relative locations, an enumeration area reference was detected in 388 (96%). TB cases in each census enumeration area and the active case finders in each Tinkhundla, a local governmental region, were mapped using the geographic information system, QGIS 2.16.
RESULTS: Urban Tinkhundla predictably accounted for most cases; however, after adjusting for population, the highest density of cases was found in rural Tinkhundla. There was no correlation between the number of active case finders currently assigned to the 7 Tinkhundla surrounding Mbabane and the total number of TB cases (Spearman rho = -0.57, p = 0.17) or the population adjusted TB cases (Spearman rho = 0.14, p = 0.75) per Tinkhundla.
CONCLUSIONS: Reducing TB incidence in high-burden settings demands novel analytic approaches to study TB case locations. We demonstrated the feasibility of linking relative locations to more precise geographical areas, enabling data-driven guidance for National Tuberculosis Programs\' resource allocation. In collaboration with the Swazi National Tuberculosis Control Program, this analysis highlighted opportunities to better align the active case finding national strategy with the TB disease burden.