Using Complex, Multi-Sectoral Data in a Needs Assessment to Inform Future Strategies in Childhood Asthma Management

The purpose of this needs assessment was to study the current state of asthma management in high-risk children in Houston, Texas to inform a theory-based approach to improving asthma management. The mixed-method assessment included multi-sectoral survey, quantitative, and geospatial data that address a range of social and community factors in family, community, home, and medical contexts. Houston Emergency Medical Services (EMS) provided ambulance-treated asthma data mapped by geographic area to identify where childhood asthma management was weakest. Texas Children’s Health Plan (TCHP) provided medication compliance rates and counts of children by zip code that TCHP considered high-risk according to claims data. Houston Independent School District (HISD) provided school nurse survey results from schools with high-rates of ambulance-treated asthma attacks regarding local barriers to asthma management. Elementary schools with children at highest risk were identified by overlaying the EMS data, TCHP data, and HISD school zone boundaries. Survey results from the high-rate schools indicate the priority challenges to childhood asthma management, including lack of resources, lack of communication, lack of knowledge of triggers, and inadequate time for quality care from providers. By weaving together EMS, TCHP, and HISD data, the needs assessment informed a socio-ecological view of gaps in high-risk childhood asthma management and control, specifically where and what to target. An assessment approach with multi-sectoral data, geospatial mapping, nurse input, current systems of care, education, and funding helped focus planning on a practical approach to asthma control solutions for high-risk children

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

Background A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. Methods Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. Results About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. Conclusions People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people