ABSTRACT
The COVID-19 pandemic has caused widespread illness with varying clinical manifestations. One less-commonly-reported presentation of COVID-19 infection is chilblain-like lesions. We conducted an ecological analysis of chilblain presentations in comparison with confirmed and suspected COVID-19 infections in a primary care setting to establish that a relationship exists between the two. Our study collated data from three Primary Health Networks across Victoria, Australia, from 2017–2021, to understand patterns of chilblain presentations prior to and throughout the pandemic. Using a zero-inflated negative binomial regression analysis, we estimated the relationship between local minimum temperature, COVID-19 infections and the frequency of chilblain presentations. We found a 5.72 risk ratio of chilblain incidence in relation to COVID-19 infections and a 3.23 risk ratio associated with suspected COVID-19 infections. COVID-19 infections were also more strongly associated with chilblain presentations in 0-16-year-olds throughout the pandemic in Victoria. Our study statistically suggests that chilblains are significantly associated with COVID-19 infections in a primary care setting. This has major implications for clinicians aiming to diagnose COVID-19 infections or determine the cause of a presentation of chilblains. Additionally, we demonstrate the utility of large-scale primary care data in identifying an uncommon manifestation of COVID-19 infections, which will be significantly beneficial to treating physicians.
ABSTRACT
The COVID-19 pandemic has caused widespread illness with varying clinical manifestations. One less-commonly-reported presentation of COVID-19 infection is chilblain-like lesions. We conducted an ecological analysis of chilblain presentations in comparison with confirmed and suspected COVID-19 infections in a primary care setting to establish that a relationship exists between the two. Our study collated data from three Primary Health Networks across Victoria, Australia, from 2017-2021, to understand patterns of chilblain presentations prior to and throughout the pandemic. Using a zero-inflated negative binomial regression analysis, we estimated the relationship between local minimum temperature, COVID-19 infections and the frequency of chilblain presentations. We found a 5.72 risk ratio of chilblain incidence in relation to COVID-19 infections and a 3.23 risk ratio associated with suspected COVID-19 infections. COVID-19 infections were also more strongly associated with chilblain presentations in 0-16-year-olds throughout the pandemic in Victoria. Our study statistically suggests that chilblains are significantly associated with COVID-19 infections in a primary care setting. This has major implications for clinicians aiming to diagnose COVID-19 infections or determine the cause of a presentation of chilblains. Additionally, we demonstrate the utility of large-scale primary care data in identifying an uncommon manifestation of COVID-19 infections, which will be significantly beneficial to treating physicians.
ABSTRACT
INTRODUCTION: As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies. METHODS: The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures. RESULTS: Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement. CONCLUSIONS: The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response.