Causes of death among Australians with type 1 or type 2 diabetes, 2002-2019.

AIMS: This population-based study sought to explore in detail the conditions driving the diversification in causes of death among people with diabetes. METHODS: We linked Australians with type 1 or type 2 diabetes of all ages on the National Diabetes Services Scheme to the National Death Index for 2002-2019. We investigated the proportional contributions of different causes of death to total deaths over time across eight categories of causes of death, stratified by sex and diabetes type. The underlying causes of death were classified according to the International Classification of Diseases, Tenth Revision codes. RESULTS: Between 2002 and 2019, there was a shift in the causes of death among Australians with diabetes away from cardiovascular disease. The proportion of deaths attributed to cardiovascular disease declined in both sexes (ptrend <0.001), most substantially among women with type 2 diabetes from 48.2% in 2002 to 30.7% in 2019. Among men with type 2 diabetes, cancer replaced cardiovascular disease as the leading cause of death. The proportion of deaths due to dementia increased overall, from 2% in 2002 to over 7% in 2019, and across all age groups, notably from 1% to 4% in those aged 70-79. The proportion of deaths due to falls and Parkinson's disease also increased. CONCLUSIONS: There has been a shift of causes of death among those with diabetes away from cardiovascular disease. The proportion of deaths due to conditions such as dementia and falls is increasing among those with diabetes, which will require consideration when planning future resource allocation.

Persistent effects of COVID-19 in patients hospitalized during the first wave of the pandemic: The impact of persistent fatigue on quality of life in a cross-sectional study.

COVID-19 can affect physical and mental health long after acute infection. In this descriptive study, 48 individuals hospitalized for COVID-19 between April and May 2020 were interviewed regarding their experience with COVID-19 after hospitalization. The mean age of participants was 51.1 (±11.91) years (range 25-65 years) and 26 (54.2%) were men. Individuals had a mean of 1.2 (±0.94) comorbidities associated with more severe COVID-19, with hypertension (37.5%) being most common. Nineteen (39.6%) individuals required treatment in the intensive care unit. Participants were interviewed a median time of 553 days (IQR, 405.5-589.0) after discharge from the hospital. Thirty-seven (77.1%) individuals had 5 or more persistent symptoms at time of interview with only 3 (6.3%) experiencing none. The most reported persistent symptoms were fatigue (79.2%), difficulty breathing (68.8%), and muscle weakness (60.4%). Poor quality of life was experienced by 39 (81.3%) participants and 8 (16.7%) had a posttraumatic stress disorder (PTSD) score within the clinical range for diagnosis. For multivariable analyses, persistent fatigue was significantly predicted by number of symptoms during acute COVID-19 (t = 4.4, p < 0.001). Number of symptoms during acute COVID-19 was also significantly associated with persistent dyspnea (t = 3.4, p = 0.002). Higher scores on the Chalder fatigue scale after COVID-19 was significantly associated with poor quality of life (t = 2.6, p = 0.01) and PTSD symptomatology (t = 2.9, p = 0.008). More research is needed to highlight the wide range of resources those suffering from Long COVID require long after discharge.

Analysis of Multiple Causes of Death: A Review of Methods and Practices.

BACKGROUND: Research and reporting of mortality indicators typically focus on a single underlying cause of death selected from multiple causes recorded on a death certificate. The need to incorporate the multiple causes in mortality statistics-reflecting increasing multimorbidity and complex causation patterns-is recognized internationally. This review aims to identify and appraise relevant analytical methods and practices related to multiple causes. METHODS: We searched Medline, PubMed, Scopus, and Web of Science from their incept ion to December 2020 without language restrictions, supplemented by consultation with international experts. Eligible articles analyzed multiple causes of death from death certificates. The process identified 4,080 items of which we reviewed 434 full-text articles. RESULTS: Most articles we reviewed (76%, n = 332) were published since 2001. The majority of articles examined mortality by "any- mention" of the cause of death (87%, n = 377) and assessed pairwise combinations of causes (57%, n = 245). Since 2001, applications of methods emerged to group deaths based on common cause patterns using, for example, cluster analysis (2%, n = 9), and application of multiple-cause weights to re-evaluate mortality burden (1%, n = 5). We describe multiple-cause methods applied to specific research objectives for approaches emerging recently. CONCLUSION: This review confirms rapidly increasing international interest in the analysis of multiple causes of death and provides the most comprehensive overview, to our knowledge, of methods and practices to date. Available multiple-cause methods are diverse but suit a range of research objectives. With greater availability of data and technology, these could be further developed and applied across a range of settings.

A new data driven method for summarising multiple cause of death data.

BACKGROUND: National mortality statistics are based on a single underlying cause of death. This practice does not adequately represent the impact of the range of conditions experienced in an ageing population in which multimorbidity is common. METHODS: We propose a new method for weighting the percentages of deaths attributed to different causes that takes account of the patterns of associations among underlying and contributing causes of death. It is driven by the data and unlike previously proposed methods does not rely on arbitrary choices of weights which can over-emphasise the contribution of some causes of death. The method is illustrated using Australian mortality data for people aged 60 years or more. RESULTS: Compared to the usual method based only on the underlying cause of death the new method attributes higher percentages of deaths to conditions like diabetes and dementia that are frequently mentioned as contributing causes of death, rather than underlying causes, and lower percentages to conditions to which they are closely related such as ischaemic heart disease and cerebrovascular disease. For some causes, notably cancers, which are usually recorded as underlying causes with few if any contributing causes the new method produces similar percentages to the usual method. These different patterns among groups of related conditions are not apparent if arbitrary weights are used. CONCLUSION: The new method could be used by national statistical agencies to produce additional mortality tables to complement the current tables based only on underlying causes of death.

Psychosis with Methylphenidate or Amphetamine in Patients with ADHD.

BACKGROUND: The prescription use of the stimulants methylphenidate and amphetamine for the treatment of attention deficit-hyperactivity disorder (ADHD) has been increasing. In 2007, the Food and Drug Administration mandated changes to drug labels for stimulants on the basis of findings of new-onset psychosis. Whether the risk of psychosis in adolescents and young adults with ADHD differs among various stimulants has not been extensively studied. METHODS: We used data from two commercial insurance claims databases to assess patients 13 to 25 years of age who had received a diagnosis of ADHD and who started taking methylphenidate or amphetamine between January 1, 2004, and September 30, 2015. The outcome was a new diagnosis of psychosis for which an antipsychotic medication was prescribed during the first 60 days after the date of the onset of psychosis. To estimate hazard ratios for psychosis, we used propensity scores to match patients who received methylphenidate with patients who received amphetamine in each database, compared the incidence of psychosis between the two stimulant groups, and then pooled the results across the two databases. RESULTS: We assessed 337,919 adolescents and young adults who received a prescription for a stimulant for ADHD. The study population consisted of 221,846 patients with 143,286 person-years of follow up; 110,923 patients taking methylphenidate were matched with 110,923 patients taking amphetamines. There were 343 episodes of psychosis (with an episode defined as a new diagnosis code for psychosis and a prescription for an antipsychotic medication) in the matched populations (2.4 per 1000 person-years): 106 episodes (0.10%) in the methylphenidate group and 237 episodes (0.21%) in the amphetamine group (hazard ratio with amphetamine use, 1.65; 95% confidence interval, 1.31 to 2.09). CONCLUSIONS: Among adolescents and young adults with ADHD who were receiving prescription stimulants, new-onset psychosis occurred in approximately 1 in 660 patients. Amphetamine use was associated with a greater risk of psychosis than methylphenidate. (Funded by the National Institute of Mental Health and others.).

Nicotine Increases Activation to Anticipatory Valence Cues in Anterior Insula and Striatum.

Introduction: Smoking is associated with significant morbidity and mortality. Understanding the neurobiology of the rewarding effects of nicotine promises to aid treatment development for nicotine dependence. Through its actions on mesolimbic dopaminergic systems, nicotine engenders enhanced responses to drug-related cues signaling rewards, a mechanism hypothesized to underlie the development and maintenance of nicotine addiction. Methods: We evaluated the effects of acute nicotine on neural responses to anticipatory cues signaling (nondrug) monetary reward or loss among 11 nonsmokers who had no prior history of tobacco smoking. In a double-blind, crossover design, participants completed study procedures while wearing nicotine or placebo patches at least 1 week apart. In each drug condition, participants underwent functional magnetic resonance imaging while performing the monetary incentive delay task and performed a probabilistic monetary reward task, probing reward responsiveness as measured by response bias toward a more frequently rewarded stimulus. Results: Nicotine administration was associated with enhanced activation, compared with placebo, of right fronto-anterior insular cortex and striatal regions in response to cues predicting possible rewards or losses and to dorsal anterior cingulate for rewards. Response bias toward rewarded stimuli correlated positively with insular activation to anticipatory cues. Conclusion: Nicotinic enhancement of monetary reward-related brain activation in the insula and striatum in nonsmokers dissociated acute effects of nicotine from effects on reward processing due to chronic smoking. Reward responsiveness predicted a greater nicotinic effect on insular activation to salient stimuli. Implications: Previous research demonstrates that nicotine enhances anticipatory responses to rewards in regions targeted by midbrain dopaminergic systems. The current study provides evidence that nicotine also enhances responses to rewards and losses in the anterior insula. A previous study found enhanced insular activation to rewards and losses in smokers and ex-smokers, a finding that could be due to nicotine sensitization or factors related to current or past smoking. Our finding of enhanced anterior insula response after acute administration of nicotine in nonsmokers provides support for nicotine-induced sensitization of insular response to rewards and losses.

Identifying Psychosis Episodes in Psychiatric Admission Notes via Rule-based Methods, Machine Learning, and Pre-Trained Language Models.

Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admission notes for the diagnosis of psychosis, providing a detailed evaluation of rule-based algorithms, machine learning models, and pre-trained language models. Additionally, the study investigates the effectiveness of employing keywords to streamline extensive note data before training and evaluating the models. Analyzing 4,617 initial psychiatric admission notes (1,196 cases of psychosis versus 3,433 controls) from 2005 to 2019, we discovered that the XGBoost classifier employing Term Frequency-Inverse Document Frequency (TF-IDF) features derived from notes pre-selected by expert-curated keywords, attained the highest performance with an F1 score of 0.8881 (AUROC [95% CI]: 0.9725 [0.9717, 0.9733]). BlueBERT demonstrated comparable efficacy an F1 score of 0.8841 (AUROC [95% CI]: 0.97 [0.9580,0.9820]) on the same set of notes. Both models markedly outperformed traditional International Classification of Diseases (ICD) code-based detection methods from discharge summaries, which had an F1 score of 0.7608, thus improving the margin by 0.12. Furthermore, our findings indicate that keyword pre-selection markedly enhances the performance of both machine learning and pre-trained language models. This study illustrates the potential of NLP techniques to improve psychosis detection within admission notes and aims to serve as a foundational reference for future research on applying NLP for psychosis identification in EHR notes.

Welcome to the Metaverse: Virtual Reality in Nursing Professional Development.

Virtual reality (VR) is an innovative teaching strategy for professional development using computer-generated, three-dimensional images in an interactive virtual environment. Self-reported survey responses of nurses who used VR in orientation and the nurse residency program demonstrated improved knowledge, skills, and confidence. VR provides an innovative and engaging educational medium for learning that may have implications for future clinical practice and research.

Identifying Psychosis Episodes in Psychiatric Admission Notes via Rule-based Methods, Machine Learning, and Pre-Trained Language Models.

Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admission notes for the diagnosis of psychosis, providing a detailed evaluation of rule-based algorithms, machine learning models, and pre-trained language models. Additionally, the study investigates the effectiveness of employing keywords to streamline extensive note data before training and evaluating the models. Analyzing 4,617 initial psychiatric admission notes (1,196 cases of psychosis versus 3,433 controls) from 2005 to 2019, we discovered that the XGBoost classifier employing Term Frequency-Inverse Document Frequency (TF-IDF) features derived from notes pre-selected by expert-curated keywords, attained the highest performance with an F1 score of 0.8881 (AUROC [95% CI]: 0.9725 [0.9717, 0.9733]). BlueBERT demonstrated comparable efficacy an F1 score of 0.8841 (AUROC [95% CI]: 0.97 [0.9580, 0.9820]) on the same set of notes. Both models markedly outperformed traditional International Classification of Diseases (ICD) code-based detection methods from discharge summaries, which had an F1 score of 0.7608, thus improving the margin by 0.12. Furthermore, our findings indicate that keyword pre-selection markedly enhances the performance of both machine learning and pre-trained language models. This study illustrates the potential of NLP techniques to improve psychosis detection within admission notes and aims to serve as a foundational reference for future research on applying NLP for psychosis identification in EHR notes.