Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19.

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population. Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay. Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity. Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes.

Immediate and Ongoing Impact of COVID-19 on Chlamydia Treatment in Australia.

BACKGROUND: The COVID-19 pandemic has impacted the sexual health and well-being of individuals, directly through risk of contracting COVID-19, and indirectly through government lockdowns. Government restrictions were especially strict and long-lasting in Australia, they also varied by state, offering an interesting opportunity to study the impacts of varying restrictions. This study compares the impact of the COVID-19 pandemic and resulting restrictions on chlamydia treatment prescriptions during 2020, through to July 2021 between different states and demographic groups in Australia. METHODS: The rate of prescriptions per 100,000 population filled each month from January 2017 to July 2021 from Australia's Pharmaceutical Benefits Scheme for Azithromycin with a restricted indication to treat Chlamydia trachomatis was used to measure chlamydia treatment. The impact of COVID-19 lockdowns was modeled using an interrupted time-series Poisson regression model. RESULTS: The data included 520,025 prescriptions to treat chlamydia, averaging 37.5 prescriptions per month per 100,000 population. Prescriptions declined 26% in April to May 2020 when initial COVID-19 lockdowns began in Australia; prescriptions increased in the following months but remained on average 21% below prepandemic (2017-2019) levels through to July 2021. Prescriptions declined the most in 1 Australian state, Victoria, both in the initial lockdown and the following period; generally, states with more COVID-19 cases saw bigger reductions in prescriptions. CONCLUSIONS: This is the first study to examine how treatment for chlamydia in Australia was impacted by the COVID-19 pandemic and restrictions not only in the immediate-term, but also ongoing up to July 2021, providing important information for planning for sexual health services in future pandemics.

Publicly subsidised smoking cessation medicines in times of COVID-19 in Australia: An interrupted time series analysis.

INTRODUCTION: In Australia, the available published literature demonstrated a spike in dispensed prescription medicines after the onset of the COVID-19 pandemic that subsequently returned to expected levels. Smoking cessation medicines may not follow this pattern because quit attempts are influenced by a range of factors. Knowledge of whether dispensing of these medicines has changed since the pandemic is lacking. We explored the change in dispensing of publicly subsidised smoking cessation medicines since the pandemic. METHODS: Australia's universal health-care system provides access to government-subsidised medicines via the Pharmaceutical Benefits Scheme and records of dispensed medicines are publicly available on a nationally aggregated level. We retrieved Pharmaceutical Benefits Scheme data from January 2016 to January 2021. We used interrupted time series modelling to quantify the impact of COVID-19 on dispensing of nicotine replacement therapy (NRT) patches, varenicline and all smoking cessation treatments combined separately. RESULTS: After an initial spike in medicines at the onset of the pandemic, the monthly rate of prescriptions dispensed for varenicline was predominantly within predicted ranges, while that of NRT patches was predominantly below predicted ranges. DISCUSSION AND CONCLUSIONS: There has been a differential change in the number of subsidised smoking cessation medicines supplied in Australia since the COVID-19 pandemic, with varenicline prescriptions largely within, and NRT patches largely lower than, expected ranges. The reasons for the apparent change in dispensing of subsidised smoking cessation medicines are unclear.

A Comparison of Leximancer Semi-automated Content Analysis to Manual Content Analysis: A Healthcare Exemplar Using Emotive Transcripts of COVID-19 Hospital Staff Interactive Webcasts

Effective consumer centred healthcare incorporates consumer and clinician perspectives into decision making, in addition to traditional quantitative measures. This information is usually captured in qualitative data that requires manual analysis. Healthcare systems often lack resources to systematically incorporate qualitative feedback into decision making. Semi-automated content analysis tools, such as Leximancer, provide an efficient and objective alternative to time consuming manual content analysis (MCA). Literature on the validity of Leximancer in healthcare is sparse. This study seeks to validate Leximancer against MCA on a broad emotive conversational dataset gathered in a healthcare setting. At the outset of the COVID-19 pandemic, a large Australian hospital and health service conducted interactive webcasts with staff to provide updates and answer questions. A manual thematic analysis and a Leximancer content analysis were conducted independently on 20 webcast transcripts. The findings were compared, along with the time required to the complete each analysis. The Leximancer analysis identified nine concepts, while the manual analysis identified 12 concepts. The Leximancer concepts mapped to five of the concepts identified in the manual analysis, which accounted for 74% of mentions tagged in the text through the manual analysis. Leximancer missed concepts which required an emotional or contextual interpretation. The Leximancer analysis took 21 hours (excluding time to learn the program), compared to 73 hours for the manual analysis. Semi-automated content analysis provides an efficient alternative to manual qualitative data analysis, shifting it from a small-scale research activity to a more routine operational activity, albeit with some limitations. This is critical to be able to utilise at scale the rich narratives from consumers and clinicians in healthcare decision making.

Machine learning models for diabetes management in acute care using electronic medical records: A systematic review.

BACKGROUND: Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to predict and potentially prevent adverse patient outcomes. There is increasing interest in the application of these models in digital hospitals to improve clinical decision-making and chronic disease management, particularly for patients with diabetes. The potential of ML models using electronic medical records (EMR) to improve the clinical care of hospitalised patients with diabetes is currently unknown. OBJECTIVE: The aim was to systematically identify and critically review the published literature examining the development and validation of ML models using EMR data for improving the care of hospitalised adult patients with diabetes. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were followed. Four databases were searched (Embase, PubMed, IEEE and Web of Science) for studies published between January 2010 to January 2022. The reference lists of the eligible articles were manually searched. Articles that examined adults and both developed and validated ML models using EMR data were included. Studies conducted in primary care and community care settings were excluded. Studies were independently screened and data was extracted using Covidence® systematic review software. For data extraction and critical appraisal, the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was followed. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Quality of reporting was assessed by adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. The IJMEDI checklist was followed to assess quality of ML models and the reproducibility of their outcomes. The external validation methodology of the studies was appraised. RESULTS: Of the 1317 studies screened, twelve met inclusion criteria. Eight studies developed ML models to predict disglycaemic episodes for hospitalized patients with diabetes, one study developed a ML model to predict total insulin dosage, two studies predicted risk of readmission, and one study improved the prediction of hospital readmission for inpatients with diabetes. All included studies were heterogeneous with regard to ML types, cohort, input predictors, sample size, performance and validation metrics and clinical outcomes. Two studies adhered to the TRIPOD guideline. The methodological reporting of all the studies was evaluated to be at high risk of bias. The quality of ML models in all studies was assessed as poor. Robust external validation was not performed on any of the studies. No models were implemented or evaluated in routine clinical care. CONCLUSIONS: This review identified a limited number of ML models which were developed to improve inpatient management of diabetes. No ML models were implemented in real hospital settings. Future research needs to enhance the development, reporting and validation steps to enable ML models for integration into routine clinical care.

Toilet Paper, Minced Meat and Diabetes Medicines: Australian Panic Buying Induced by COVID-19.

The COVID-19 pandemic has impacted the management of non-communicable diseases in health systems around the world. This study aimed to understand the impact of COVID-19 on diabetes medicines dispensed in Australia. Publicly available data from Australia's government subsidised medicines program (Pharmaceutical Benefits Scheme), detailing prescriptions by month dispensed to patients, drug item code and patient category, was obtained from January 2016 to November 2020. This study focused on medicines used in diabetes care (Anatomical Therapeutical Chemical code level 2 = A10). Number of prescriptions dispensed were plotted by month at a total level, by insulins and non-insulins, and by patient category (general, concessional). Total number of prescriptions dispensed between January and November of each year were compared. A peak in prescriptions dispensed in March 2020 was identified, an increase of 35% on March 2019, compared to average growth of 7.2% in previous years. Prescriptions dispensed subsequently fell in April and May 2020 to levels below the corresponding months in 2019. These trends were observed across insulins, non-insulins, general and concessional patient categories. The peak and subsequent dip in demand have resulted in a small unexpected overall increase for the period January to November 2020, compared to declining growth for the same months in prior years. The observed change in consumer behaviour prompted by COVID-19 and the resulting public health measures is important to understand in order to improve management of medicines supply during potential future waves of COVID-19 and other pandemics.