ABSTRACT
If the virus spreads further throughout our communities, and authorities ask people to limit social contact and self-isolate, cooperation with these necessary measures will play a crucial role in minimising COVID-19's spread and protecting the most vulnerable among us. Teams of infectious disease, public health and primary care experts are advising the Government on the best way to deal with this threat.
ABSTRACT
Within 30 years, the global number of deaths from AMR-associated infections is predicted to increase from ~700,000 to ~10 million people annually, if we do not act now.1 The Aotearoa New Zealand (NZ) response to the current COVID-19 pandemic has been lauded internationally-found-ed in science, responsive to expert advice, implemented with clear leadership and communication, and subject to ongoing critical evaluation and improvement. AMR-associated infections and related care (eg, time off work or school to travel to hospital for treatment) will disproportionately impact the most socioeconomically disadvantaged among us, those living in rural or remote settings, and Maori and Pacific populations who shoulder a greater infection and AMR burden and have increased reliance on antimicrobial therapy.6'7 One of the biggest drivers for AMR is antimicrobial use, which is high in human health in NZ compared with many developed countries.8'9 Most of our antimicrobial use (95%) is in the community9 and up to 50% may be inappropriate.2 The NZ community antibacterial consumption rate increased 49% between 2006 and 2014;in 2013, it exceeded that of 22 out of 29 European countries.8 A subsequent modest 14% decrease occurred across 2015 to 2018, mainly due to reductions in under 5 year olds,10 which is pleasing as antimicrobial use in childhood may create reservoirs of resistant pathogens impacting communities cross-generationally. In 2013, the Health Quality and Safety Commission (HQSC) published a scoping report that offered insight into what was needed to progress AMS in NZ.14 Key recommendations were to establish: * National leadership and coordination of AMS activities * National antimicrobial prescribing guidelines * Quality improvement tools and measures In the near decade that has followed this report, none of these recommendations have been achieved. The NCAMS should provide access to (and support use of) quality improvement tools (eg, auditing systems for between facility benchmarking), develop initiatives to improve antimicrobial use (including those involving consumers), monitor performance against quality markers, and establish clinical care standards with the oversight of NAMSEG.
ABSTRACT
AIM: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.