Continuity of sleep problems from adolescence to young adulthood: results from a longitudinal study

Background: Considering the lack of evidence on incidence and continuity of sleep problems from adolescence to young adulthood, this study explores sleep problems’ incidence and their continuation rates from 14 to 21 years. Methods: Sleep data from the 14-year (n = 4,924) and 21-year (n = 3660) follow-up of the Mater-University of Queensland Study of Pregnancy cohort were used. Sociodemographic, lifestyle, and psychological conditions were explored for their role in sleep problems. Modified Poisson regression with a robust error variance was used to identify predictors. Inverse probability weights were used to account for attrition. Results: Of all subjects, 26.0% of the subjects at 14 years and 28.3% of the subjects at 21 years reported “often” sleep problems, with 41.7% of adolescent sleep problems persisting at 21 years. Perinatal and early-life maternal factors, for example, drug abuse (incidence rate ratio (IRR), 1.32; 95% confidence interval [CI], 1.02-1.71), smoking, depression, and anxiety, were significant predictors of adolescent sleep problems. Female sex (IRR, 2.13; 95% CI, 1.55-2.94), advanced pubertal stages, and smoking were the important predictors of sleep problems at 21 years. Adolescent depression/anxiety supported the continuity of sleep problems (IRR, 1.21; 95% CI, 1.05-1.40), whereas exercise was seen to exert a protective effect. Conclusion: This study indicates high rates of sleep problems in young subjects, with around half of sleep problems originating in adolescence persisting in young adulthood. Therefore, early interventions are needed to manage sleep problems in young subjects and prevent further progression to other life stages. Future studies should explore if sleep problems in young adults also persist in later life stages and identify the factors supporting the continuity of sleep problems

The global distribution of comorbid depression and anxiety in people with diabetes mellitus: risk-adjusted estimates

Background: Previous reports suffer from the problem that they simply pooled data using aggregate means or standard meta-analytic method. The aim of the current study was to re-estimate the point prevalence of comorbid depression and anxiety in people with diabetes. Methods: The estimates were calculated using recently introduced directly standardized effect estimate method, which gives corrected risk-adjusted estimates for the population of interests. Reported are global and regional burden of prevalence, presented as risk-adjusted prevalence estimates with 95% confidence intervals. Results: Globally, the burden of comorbid depression was higher than the burden of anxiety (23.36% vs. 17.58%) symptoms and/or disorder in people with diabetes. There was a higher burden of comorbid depression in people living in developing regions (26.32%), in women (15.41%), and when assessed by self-report scales (SRS) (22.66%). The burden of anxiety was higher in developed regions in people with Type 2 diabetes mellitus (20.15%) and when assessed by SRS (20.75%). No statistically significant differences were observed due to gross heterogeneity across countries. Conclusions: There are wide-ranging differences in studies in developed and developing regions, regarding the burden of comorbid depression and of anxiety among people with diabetes and both conditions affect approximately a fifth of the diabetic population

Hospital effect on infections after four major surgeries: Outlier and volume-outcome analysis using all-inclusive state data

Hospital volume is known to have a direct impact on outcomes of major surgeries. However, it is unclear if the evidence applies specifically to surgical site infections. To determine if there are procedure-specific hospital outliers (with higher surgical site infection rates [SSIR]) for four major surgical procedures, and to examine if hospital volume is associated with SSIR in the context of outlier performance in New South Wales (NSW), Australia. Adults who underwent one of four surgical procedures (colorectal, joint replacement, spinal and cardiac procedures) at a NSW healthcare facility from 2002 through 2013 were included. The hospital volume for each of the four surgical procedures was categorised into tertiles (low, medium and high). Multivariable logistic regression models were built to estimate the expected SSIR for each procedure. The expected SSIR were used to compute an indirect standardised SSIR which was then plotted in funnel plots to identify hospital outliers. One hospital was identified to be an overall outlier (higher SSIR for 3 out of the 4 procedures performed in its facilities); whereas two hospitals were outliers for one specific procedure throughout the entire study period. Low-volume facilities performed the best for colorectal surgery and worst for joint replacement and cardiac surgery. One high-volume facility was an outlier for spinal surgery. Surgical site infections seem to be mostly a procedure-specific as opposed to a hospital-specific phenomenon in NSW. The association between hospital volume and SSIRs differs for different surgical procedures.ACAC is funded by an Australian National Health and Medical Research Council Senior Research Fellowship (#1058878)

Geographical outcome disparities in infection occurrence after colorectal surgery: An analysis of 58,096 colorectal surgical procedures

Background Despite improved surgical practices and in-hospital surveillance systems, surgical site infections remain a major public health problem worldwide and often require readmission to hospital. The aim was to apply an advance and innovative spatial analysis approach to identify spatial pattern and clustering (hotspots) of surgical site infection rate (CSIR), and quantifying disparities across communities. Methods We used the Admitted Patient Data Collection for patients aged 18 years and over who underwent colorectal surgery in a public hospital between 2002 and 2013 in the Australian State of New South Wales (NSW). The colorectal surgical infection rate (CSIR) was computed. We assessed geographical variation and clustering in CSIR patterning to demonstrate spatial pattern and clustering across communities in NSW, Australia. Results There were 58,096 colorectal surgical procedures conducted in NSW from 2002 to 2013. The overall occurrence of CSIR was 9.64% (95%CI 9.40-9.88%). We found significant clusters of both high and low CSIR in outer regional and remote areas of NSW. Conclusion Use of advanced spatial analyses allows identification of hotspots/clusters of adverse events that can help policy makers and clinicians better understand national patterns and initiate research to address disparities/geographical variation, and clustering of adverse events after surgery. 1 2017 IJS Publishing Group LtdScopu

Advances in the meta-analysis of heterogeneous clinical trials I: the inverse variance heterogeneity model

This article examines an improved alternative to the random effects (RE) model for meta-analysis of heterogeneous studies. It is shown that the known issues of underestimation of the statistical error and spuriously overconfident estimates with the RE model can be resolved by the use of an estimator under the fixed effect model assumption with a quasi-likelihood based variance structure — the IVhet model. Extensive simulations confirm that this estimator retains a correct coverage probability and a lower observed variance than the RE model estimator, regardless of heterogeneity. When the proposed IVhet method is applied to the controversial meta-analysis of intravenous magnesium for the prevention of mortality after myocardial infarction, the pooled OR is 1.01 (95% CI 0.71–1.46) which not only favors the larger studies but also indicates more uncertainty around the point estimate. In comparison, under the RE model the pooled OR is 0.71 (95% CI 0.57–0.89) which, given the simulation results, reflects underestimation of the statistical error. Given the compelling evidence generated, we recommend that the IVhet model replace both the FE and RE models. To facilitate this, it has been implemented into free meta-analysis software called MetaXL which can be downloaded from www.epigear.com

Differentiated thyroid cancer: millions spent with no tangible gain?

The incidence of differentiated thyroid cancer (DTC) has rapidly increased worldwide over the last decades. It is unknown if the increase in diagnosis has been mirrored by an increase in thyroidectomy rates with the concomitant economic impact that this would have on the healthcare system. DTC and thyroidectomy incidence as well as DTC specific mortality were modelled using Poisson regression in New South Wales (NSW), Australia per year and by sex. The incidence of 2002 was the point from which the increase in rates were assessed cumulatively over the subsequent decade. The economic burden of potentially avoidable thyroidectomies due to the increase in diagnosis was estimated as the product of the additional thyroidectomy procedures during a decade attributable to rates beyond those reported for 2002 and the national average hospital cost of an uncomplicated thyroidectomy in Australia. We found that the incidence of both DTC and thyroidectomy doubled in NSW between 2003 and 2012, while the DTC specific mortality rate remained unchanged over the same period. Based on the 2002 incidence, the projected increase over 10 years (2003-2012) in thyroidectomy procedures was 2,196. This translates to an extra cost burden of over AUD$ 18,600,000 in surgery-related healthcare expenditure over one decade in NSW. Our findings suggest that, if this rise is solely attributable to overdetection, then the rising expenditure serves no additional purpose. Reducing unnecessary detection and a conservative approach to managing DTC are sensible and would lead to millions of dollars in savings and reduced harms to patients

Physical activity in pregnancy prevents gestational diabetes: A meta-analysis

AimsThe effectiveness of physical activity (PA) programs for prevention of gestational diabetes (GDM) lacks conclusive evidence. The aim of this study was to generate clear evidence regarding the effectiveness of physical activity programs in GDM prevention to guide clinical practice. MethodsPubMed/Medline, ISI Web of Science, Scopus, and EMBASE were searched to identify the randomized trials (RCTs) published until June 2019. Randomised controlled trials enrolling women at high risk before the 20th week of gestation comparing the effect of PA interventions with usual care for prevention of GDM were retrieved. Data obtained were synthesised using a bias-adjusted model of meta-analysis. ResultsA total of 1467 adult women in 11 eligible trials were included. The risk of GDM was significantly lower with PA, but only when it was delivered in the healthcare facility (RR 0.53; 95% CI 0.38–0.74). The number needed to treat with PA in pregnancy (compared to usual care) to prevent one GDM event was 18 (95% CI 14 – 29). The overall effect of PA interventions regardless of location of the intervention was RR 0.69 (95% CI 0.51 – 0.94). ConclusionsThis study provides evidence that in-facility physical activity programs started before the 20th week of gestation can significantly decrease the incidence of GDM among women at high risk

An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)—acquired at hospital admission—were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5–50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.Open access funding provided by the Qatar National Library. This publication was made possible by Qatar University Emergency Response Grant (QUERG-CENG-2020-1) from the Qatar University. The statements made herein are solely the responsibility of the authors

Clustering of venous thrombosis events at the start of tamoxifen therapy in breast cancer: A population-based experience

Introduction: The epidemiology of tamoxifen and venous thromboembolism (VTE) is not well understood, and most data on tamoxifen toxicity are from adjuvant clinical trials. This study examined the relationship between the duration of tamoxifen use in female patients with breast cancer and the risk of VTE in a large population-based setting. Materials and Methods: Retrospective electronic data extraction on tamoxifen utilization was undertaken among a cohort of 3572 women with breast cancer seen at Marshfield Clinic between January 1, 1994 and June 31, 2009. Observational follow-up extended until February, 2010. Results: On initial exposure to tamoxifen, women had a clustering of VTE events. Cox proportional hazards regression, adjusting for multiple clinically-important covariates including age, body mass index, cancer stage, and concurrent diabetes, demonstrated that as use of tamoxifen continued in those without earlier VTE events, risk of subsequent VTE gradually increased, albeit at a lower rate (hazard ratio per year of tamoxifen duration = 1.225, P < 0.0001). Conclusions: In our study population, initiating tamoxifen coincided with an initial clustering of VTE events, with risks due specifically to tamoxifen, increasing during continued exposure. Evidence suggested that the VTE clustering occurred in high risk individuals at initiation of tamoxifen therapy. Careful selection of patients for whom tamoxifen therapy is appropriate based on susceptibility to VTE is thus required prior to initiation of therapy

The Economic Impact of Optimizing a COVID-19 Management Protocol in Pre-Existing Cardiovascular Disease Patients

This study answers the question of whether the health care costs of managing COVID-19 in preexisting cardiovascular diseases (CVD) patients increased or decreased as a consequence of evidence-based efforts to optimize the initial COVID-19 management protocol in a CVD group of patients. A retrospective cohort study was conducted in preexisting CVD patients with COVID-19 in Hamad Medical Corporation, Qatar. From the health care perspective, only direct medical costs were considered, adjusted to their 2021 values. The impact of revising the protocol was a reduction in the overall costs in non-critically ill patients from QAR15,447 (USD 4243) to QAR4337 (USD 1191) per patient, with an economic benefit of QAR11,110 (USD 3051). In the critically ill patients, however, the cost increased from QAR202,094 (USD 55,505) to QAR292,856 (USD 80,433) per patient, with added cost of QAR90,762 (USD 24,928). Overall, regardless of critical care status, the optimization of the initial COVID-19 protocols in patients with preexisting CVD did not reduce overall health care costs, but increased it by QAR80,529 (USD 22,117) per patient