E-cigarettes on Instagram: Exploring vape content via an Australian vaping influencer.

INTRODUCTION: Mounting evidence suggests that electronic cigarettes (e-cigarettes) are extensively promoted and marketed using social media, including through user-generated content and social media influencers. This study explores how e-cigarettes are being promoted on Instagram, using a case-study approach, and the extent to which Meta's Restricted Goods and Services Policy (Meta's policy) is being applied and enforced. METHODS: We identified the accounts followed by an Australian Instagram influencer who primarily posts e-cigarette-related content. The main foci of these 855 accounts were coded and 369 vaping-focused accounts were identified. These vaping-focused accounts were then further coded by two trained coders. RESULTS: All (n=369; 100.0%) of the vape content posted by these accounts was positive in sentiment. One-third of the vape accounts (n=127; 34.4%) had a shared focus, indicating that vape content may permeate into other online communities through shared interests. A total of 64 accounts (17.3%) potentially violated Meta's policy by attempting to purchase, sell, raffle or gift e-cigarette products. CONCLUSIONS: The findings of this study suggest that pro-vaping information is available and accessible on Instagram. Much of the content identified in this study promoted the purchase or gifting of e-cigarette products and potentially violates Meta's policy. Greater regulation and/or stronger enforcement of e-cigarette content on social media platforms such as Instagram is necessary to prevent the ongoing promotion of these harmful products.

Protocol for a systematic review of reviews on training primary care providers in dermoscopy to detect skin cancers.

INTRODUCTION: Globally, incidence, prevalence and mortality rates of skin cancers are escalating. Earlier detection by well-trained primary care providers in techniques such as dermoscopy could reduce unnecessary referrals and improve longer term outcomes. A review of reviews is planned to compare and contrast the conduct, quality, findings and conclusions of multiple systematic and scoping reviews addressing the effectiveness of training primary care providers in dermoscopy, which will provide a critique and synthesis of the current body of review evidence. METHODS AND ANALYSIS: Four databases (Cochrane, CINAHL, EMBASE and MEDLINE Complete) will be comprehensively searched from database inception to identify published, peer-reviewed English-language articles describing scoping and systematic reviews of the effectiveness of training primary care providers in the use of dermoscopy to detect skin cancers. Two researchers will independently conduct the searches and screen the results for potentially eligible studies using 'Research Screener' (a semi-automated machine learning tool). Backwards and forwards citation tracing will be conducted to supplement the search. A narrative summary of included reviews will be conducted. Study characteristics, for example, population; type of educational programme, including content, delivery method, duration and assessment; and outcomes for dermoscopy will be extracted into a standardised table. Data extraction will be checked by the second reviewer. Methodological quality will be evaluated by two reviewers independently using the Critical Appraisal Tool for Health Promotion and Prevention Reviews. Results of the assessments will be considered by the two reviewers and any discrepancies will be resolved by team consensus. ETHICS AND DISSEMINATION: Ethics approval is not required to conduct the planned systematic review of peer-reviewed, published articles because the research does not involve human participants. Findings will be published in a peer-reviewed journal, presented at leading public health, cancer and primary care conferences, and disseminated via website postings and social media channels. PROSPERO REGISTRATION NUMBER: CRD42023396276.

"Currently flying blind" Stakeholders' perceptions of implementing statewide population-based cancer staging at diagnosis into the Western Australian Cancer Registry: a rapid qualitative process evaluation of the WA Cancer Staging Project.

BACKGROUND: Cancer stage at diagnosis is essential for understanding cancer outcomes, guiding cancer control activities and healthcare services, and enabling benchmarking nationally and internationally. Yet, most cancer registries in Australia do not routinely collect this data. This study explored key stakeholders' perceptions of implementing cancer staging utilising Natural Language Processing and Machine Learning algorithms within the Western Australian Cancer Registry. METHODS: Perceptions of key breast and colorectal cancer stakeholders, including registry staff, clinicians, consumers, data scientists, biostatisticians, data management, healthcare staff, and health researchers, were collected. Prospective and retrospective qualitative proformas at two-time points of the Western Australian Cancer Staging Project were employed. The Consolidated Framework for Implementation Research was used to guide data collection, analysis and interpretation embedded in a Participatory Action Research approach. Data analysis also incorporated Framework Analysis and an adapted version of grading qualitative data using a visual traffic light labelling system to highlight the levels of positivity, negativity, and implementation concern. RESULTS: Twenty-nine pre-proformas and 18 post-proformas were completed online via REDCap. The grading and visual presentation of barriers and enablers aided interpretation and reviewing predicted intervention outcomes. Of the selected constructs, complexity (the perceived difficulty of the intervention) was the strongest barrier and tension for change (the situation needing change) was the strongest enabler. Implementing cancer staging into the Western Australian Cancer Registry was considered vital. Benefits included improved knowledge and understanding of various outcomes (e.g., treatment received as per Optimum Care Pathways) and benchmarking. Barriers included compatibility issues with current systems/workflows, departmental/higher managerial support, and future sustainment. CONCLUSIONS: The findings aid further review of data gaps, additional cancer streams, standardising cancer staging and future improvements. The study offers an adapted version of a rapid qualitative data collection and analytic approach for establishing barriers and enablers. The findings may also assist other population-based cancer registries considering collecting cancer stage at diagnosis.

Promotion of E-Cigarettes on TikTok and Regulatory Considerations.

E-cigarettes are promoted extensively on TikTok and other social media platforms. Platform policies to restrict e-cigarette promotion seem insufficient and are poorly enforced. This paper aims to understand how e-cigarettes are being promoted on TikTok and provide insights into the effectiveness of current TikTok policies. Seven popular hashtag-based keywords were used to identify TikTok accounts and associated videos related to e-cigarettes. Posts were independently coded by two trained coders. Collectively, the 264 videos received 2,470,373 views, 166,462 likes and 3426 comments. The overwhelming majority of videos (97.7%) portrayed e-cigarettes positively, and these posts received 98.7% of the total views and 98.2% of the total likes. A total of 69 posts (26.1%) clearly violated TikTok's own content policy. The findings of the current study suggest that a variety of predominantly pro-vaping content is available on TikTok. Current policies and moderation processes appear to be insufficient in restricting the spread of pro-e-cigarette content on TikTok, putting predominantly young users at potential risk of e-cigarette use.

A machine learning approach to predicting 30-day mortality following paediatric cardiac surgery: findings from the Australia New Zealand Congenital Outcomes Registry for Surgery (ANZCORS).

OBJECTIVES: We aim to develop the first risk prediction model for 30-day mortality for the Australian and New Zealand patient populations and examine whether machine learning (ML) algorithms outperform traditional statistical approaches. METHODS: Data from the Australia New Zealand Congenital Outcomes Registry for Surgery, which contains information on every paediatric cardiac surgical encounter in Australian and New Zealand for patients aged <18 years between January 2013 and December 2021, were analysed (n = 14 343). The outcome was mortality within the 30-day period following a surgical encounter, with ∼30% of the observations randomly selected to be used for validation of the final model. Three different ML methods were used, all of which employed five-fold cross-validation to prevent overfitting, with model performance judged primarily by the area under the receiver operating curve (AUC). RESULTS: Among the 14 343 30-day periods, there were 188 deaths (1.3%). In the validation data, the gradient-boosted tree obtained the best performance [AUC = 0.87, 95% confidence interval = (0.82, 0.92); calibration = 0.97, 95% confidence interval = (0.72, 1.27)], outperforming penalized logistic regression and artificial neural networks (AUC of 0.82 and 0.81, respectively). The strongest predictors of mortality in the gradient boosting trees were patient weight, STAT score, age and gender. CONCLUSIONS: Our risk prediction model outperformed logistic regression and achieved a level of discrimination comparable to the PRAiS2 and Society of Thoracic Surgery Congenital Heart Surgery Database mortality risk models (both which obtained AUC = 0.86). Non-linear ML methods can be used to construct accurate clinical risk prediction tools.

Determining cancer stage at diagnosis in population-based cancer registries: A rapid scoping review.

Introduction: Population-based cancer registries are the main source of data for population-level analysis of cancer stage at diagnosis. This data enables analysis of cancer burden by stage, evaluation of screening programs and provides insight into differences in cancer outcomes. The lack of standardised collection of cancer staging in Australia is well recognised and is not routinely collected within the Western Australia Cancer Registry. This review aimed to explore how cancer stage at diagnosis is determined in population-based cancer registries. Methods: This review was guided by the Joanna-Briggs Institute methodology. A systematic search of peer-reviewed research studies and grey literature from 2000 to 2021 was conducted in December 2021. Literature was included if peer-reviewed articles or grey literature sources used population-based cancer stage at diagnosis, and were published in English between 2000 and 2021. Literature was excluded if they were reviews or only the abstract was available. Database results were screened by title and abstract using Research Screener. Full-texts were screened using Rayyan. Included literature were analysed using thematic analysis and managed through NVivo. Results: The findings of the 23 included articles published between 2002 and 2021 consisted of two themes. (1) "Data sources and collection processes" outlines the data sources used, as well as the processes and timing of data collection utilised by population-based cancer registries. (2) "Staging classification systems" reveals the staging classification systems employed or developed for population-based cancer staging, including the American Joint Committee on Cancer's Tumour Node Metastasis and related systems; simplified systems classified into localised, regional, and distant categories; and miscellaneous systems. Conclusions: Differences in approaches used to determine population-based cancer stage at diagnosis challenge attempts to make interjurisdictional and international comparisons. Barriers to collecting population-based stage at diagnosis include resource availability, infrastructure differences, methodological complexity, interest variations, and differences in population-based roles and emphases. Even within countries, disparate funding sources and funder interests can challenge the uniformity of population-based cancer registry staging practices. International guidelines to guide cancer registries in collecting population-based cancer stage is needed. A tiered framework of standardising collection is recommended. The results will inform integrating population-based cancer staging into the Western Australian Cancer Registry.

Development and validation of a machine learning-based tool to predict autism among children.

Autism is a lifelong condition for which intervention must occur as early as possible to improve social functioning. Thus, there is great interest in improving our ability to diagnose autism as early as possible. We take a novel approach to this challenge by combining machine learning with maternal and infant health administrative data to construct a prediction model capable of predicting autism disorder (defined as ICD10 84.0) in the general population. The sample included all mother-offspring pairs from the Australian state of New South Wales (NSW) between January 2003 and December 2005 (n = 262,650 offspring), linked across three health administrative data sets including the NSW perinatal data collection (PDC); the NSW admitted patient data collection (APDC) and the NSW mental health ambulatory data collection (MHADC). Our most successful model was able to predict autism disorder with an area under the receiver operating curve of 0.73, with the strongest risk factors for diagnoses found to include offspring gender, maternal age at birth, delivery analgesia, maternal prenatal tobacco disorders, and low 5-min APGAR score. Our findings indicate that the combination of machine learning and routinely collected admin data, with further refinement and increased accuracy than achieved by us, may play a role in the early detection of autism disorders.

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews.

BACKGROUND: Systematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature is associated with significant time and economic demands. The screening of titles and abstracts is the most time consuming part of the review process with analysts required review thousands of articles manually, taking on average 33 days. New technologies aimed at streamlining the screening process have provided initial promising findings, yet there are limitations with current approaches and barriers to the widespread use of these tools. In this paper, we introduce and report initial evidence on the utility of Research Screener, a semi-automated machine learning tool to facilitate abstract screening. METHODS: Three sets of analyses (simulation, interactive and sensitivity) were conducted to provide evidence of the utility of the tool through both simulated and real-world examples. RESULTS: Research Screener delivered a workload saving of between 60 and 96% across nine systematic reviews and two scoping reviews. Findings from the real-world interactive analysis demonstrated a time saving of 12.53 days compared to the manual screening, which equates to a financial saving of USD 2444. Conservatively, our results suggest that analysts who scan 50% of the total pool of articles identified via a systematic search are highly likely to have identified 100% of eligible papers. CONCLUSIONS: In light of these findings, Research Screener is able to reduce the burden for researchers wishing to conduct a comprehensive systematic review without reducing the scientific rigour for which they strive to achieve.

Using statistical text classification to identify health information technology incidents.

OBJECTIVE: To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. DESIGN: We used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. Text classifiers using regularized logistic regression were evaluated with both 'balanced' (50% HIT) and 'stratified' (0.297% HIT) datasets for training, validation, and testing. Dataset preparation, feature extraction, feature selection, cross-validation, classification, performance evaluation, and error analysis were performed iteratively to further improve the classifiers. Feature-selection techniques such as removing short words and stop words, stemming, lemmatization, and principal component analysis were examined. MEASUREMENTS: κ statistic, F1 score, precision and recall. RESULTS: Classification performance was similar on both the stratified (0.954 F1 score) and balanced (0.995 F1 score) datasets. Stemming was the most effective technique, reducing the feature set size to 79% while maintaining comparable performance. Training with balanced datasets improved recall (0.989) but reduced precision (0.165). CONCLUSIONS: Statistical text classification appears to be a feasible method for identifying HIT reports within large databases of incidents. Automated identification should enable more HIT problems to be detected, analyzed, and addressed in a timely manner. Semi-supervised learning may be necessary when applying machine learning to big data analysis of patient safety incidents and requires further investigation.