Background: Point prevalence studies identify that pneumonia is the most common healthcare associated infection. However, non-ventilator associated healthcare associated pneumonia (NV-HAP) is both underreported and understudied. Most research conducted to date, focuses on ventilator associated pneumonia. We conducted a systematic review, to provide the latest evidence for strategies to reduce NV-HAP and describe the methodological approaches used. Methods: We performed a systematic search to identify research exploring and evaluating NV-HAP preventive measures in hospitals and aged-care facilities. The electronic database Medline was searched, for peer-reviewed articles published between 1st January 1998 and 31st August 2018. An assessment of the study quality and risk of bias of included articles was conducted using the NewcastleeOttawa Scale. Results: The literature search yielded 1551 articles, with 15 articles meeting the inclusion criteria. The majority of strategies for NV-HAP prevention focussed on oral care (n Z 9). Three studies evaluated a form of physical activity, such as passive movements, two studies used

This original prospective multicentre study highlights stark differences in European MRSA epidemiology compared with the USA, and that the USA300 CA-MRSA clone is not predominant among community-infected patients in Europe.

Accurate and current information has been highlighted across the globe as a critical requirement for the COVID-19 pandemic response. To address this need, many interactive dashboards providing a range of different information about COVID-19 have been developed. A similar tool in Australia containing current information about COVID-19 could assist general practitioners and public health responders in their pandemic response efforts. The COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER) has been developed to provide accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. This system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.

Introduction: People who inject drugs are at risk of hospitalisation with injection-related infections (IRI). We audited the clinical features, microbiology and management of IRI at a tertiary service in Melbourne to describe the burden and identify quality improvement opportunities. Methods: We performed retrospective review of IRI admissions from January 2017 to April 2019. We extracted admissions where ICD-10 codes or triage text suggested injecting drug use, and the diagnosis suggested IRI. We reviewed these for eligibility and extracted data using a standardised form. We performed mixedeffects logistic regression to determine predictors of unplanned discharge. Results: From 574 extracted candidate admissions, 226 were eligible, representing 178 patients. Median age was 41 years (interquartile range 36-47), 66% (117/ 178) male and 49% (111/226) had unstable housing. Over 50% (96/178) had a psychiatric diagnosis and 35% (62/178) were on opioid agonist therapy (OAT) on admission. Skin and soft tissue infection was the most common IRI (119/205, 58%), followed by bacteraemia (36/205, 18%) and endocarditis (26/205, 13%). Management included addictions review (143/226, 63%), blood-borne virus screening (115/226, 51%), surgery (77/226, 34%) and OAT commencement (68/226, 30%).Aggression events (54/226, 15%) and unplanned discharge (69/226, 30%) complicated some admissions. Opioid use without OAT was associated with almost 3-fold increased odds of unplanned discharge compared to no opioid use (odds ratio 2.90, 95% confidence interval 1.23, 6.85, p = 0.015). Discussion and Conclusion: Comorbidities associated with IRI may be amenable to opportunistic intervention during hospitalisation. Further research is needed to develop optimal models of care for this vulnerable patient group.

Introduction. Injection-related infections (IRI) cause morbidity and mortality in people who inject drugs. Hospital administrative datasets can be used to describe hospitalisation trends, but there are no validated algorithms to identify injecting drug use and IRIs. We aimed to validate International Classification of Diseases (ICD) codes to identify admissions with IRIs and use these codes to describe IRIs within our hospital. Methods. We developed a candidate set of ICD codes to identify current injecting drug use and IRI and extracted admissions satisfying both criteria. We then used manual chart review data from 1 January 2017 to 30 April 2019 to evaluate the performance of these codes and refine our algorithm by selecting codes with a high-positive predictive value (PPV). We used the refined algorithm to describe trends and outcomes of people who inject drugs with an IRI at Alfred Hospital, Melbourne from 2008 to 2020. Results. Current injecting drug use was best predicted by opioid-related disorders (F11), 80% (95% confidence interval [CI] 74-85%), and other stimulant-related disorders (F15), 82% (95% CI 70-90%). All PPVs were ≥67% to identify specific IRIs, and ≥84% for identifying any IRI. Using these codes over 12 years, IRIs increased from 138 to 249 per 100 000 admissions, and skin and soft tissues infections (SSTI) were the most common (797/1751, 46%). Discussion and Conclusion. Validated ICD-based algorithms can inform passive surveillance systems. Strategies to reduce hospitalisation with IRIs should be supported by early intervention and prevention, particularly for SSTIs which may represent delayed access to care.