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SUMMARY: Diabetes and depression have a bidirectional relationship, but some antidepressants (such as the tricyclics) may have detrimental effects in diabetes that are exacerbated by behavioural changes associated with depression. This month's Cochrane Review evaluated the efficacy of psychological and pharmacological treatments of comorbid depression in diabetes and found that such interventions have a moderate and clinically significant effect on depression outcomes in people with diabetes. However, conclusions were limited by significant heterogeneity within examined populations and interventions, and significant risk of bias within trials. This commentary critically appraises the review and aims to contextualise its findings. [ABSTRACT FROM AUTHOR]

SUMMARY: Despite their increasing popularity, n -of-1 designs employ data analyses that might not be as complete and powerful as they could be. Borrowing from existing advances in educational and psychological research, this article presents a few techniques and references for rigorous data analytic techniques in n -of-1 research. [ABSTRACT FROM AUTHOR]

Aims and method There are numerous challenges in the recruitment and retention of the medical workforce in psychiatry. This mixed-methods study examined the role of psychiatry clinical attachments for international medical graduates (IMGs) to enhance recruitment and retention. An online survey was launched to capture views and perceptions of IMGs about clinical attachments. The quantitative and qualitative responses were analysed to elicit findings. Results In total, 92 responses were received, with respondents commonly from India, Pakistan and Egypt. Respondents were mostly aged 25–34, with ≥3 years of psychiatry experience. Over 80% expressed strong interest in completing a psychiatry clinical attachment and believed it would support career progression. Qualitative data indicated that IMGs hoped to gain clinical experience and understanding of the National Health Service (NHS). They wished for a clearer, simpler process for clinical attachments. Clinical implications Clinical attachment can be mutually beneficial, providing IMGs with opportunity to confidently start their psychiatry career in the UK and enhance medical recruitment in mental health services across the NHS.

Aims and method: There are numerous challenges in the recruitment and retention of the medical workforce in psychiatry. This mixed-methods study examined the role of psychiatry clinical attachments for international medical graduates (IMGs) to enhance recruitment and retention. An online survey was launched to capture views and perceptions of IMGs about clinical attachments. The quantitative and qualitative responses were analysed to elicit findings. Results: In total, 92 responses were received, with respondents commonly from India, Pakistan and Egypt. Respondents were mostly aged 25–34, with ≥3 years of psychiatry experience. Over 80% expressed strong interest in completing a psychiatry clinical attachment and believed it would support career progression. Qualitative data indicated that IMGs hoped to gain clinical experience and understanding of the National Health Service (NHS). They wished for a clearer, simpler process for clinical attachments. Clinical implications: Clinical attachment can be mutually beneficial, providing IMGs with opportunity to confidently start their psychiatry career in the UK and enhance medical recruitment in mental health services across the NHS. [ABSTRACT FROM AUTHOR]

The increasing rate of tropical cyclone (TC) rainfall has put populations in the Western North Pacific Region at greater risk of TC rainfall‐induced disasters. Statistical methodologies have shown potential in complementing existing prediction approaches. With TC track prediction accuracy significantly improving, statistical predictions have turned to TC tracks as a measure of similarity between TCs. Several studies have utilized Fuzzy C Means (FCM) to this end. However, FCM alone does not provide guidance on how many similar TCs should be used for predicting rainfall through ensemble averaging. While various number of ensemble members have been used to check the average error, such an approach yields only one number, which may not always be the most appropriate. In this study, we proposed a spatial and attribute filter to complement FCM identification of similar TCs. This filter excludes similar TCs with central pressure differences greater than 5% at strategic TC locations near land. The use of the filter yielded better rainfall prediction values than using FCM alone, as demonstrated in this study and validated against previous research findings. Our proposed model offers a reliable means of predicting TC rainfall when used in conjunction with accurately predicted TC tracks, representing a valuable complementary approach to existing prediction methods. [ABSTRACT FROM AUTHOR]

Summary: This Personal View is intended for early-career researchers who are not yet experts in statistics. The Personal View focuses on common but usually avoidable flaws in the context of observational studies. I point out how study design, data collection, and statistical methods impact statistical results and research conclusions. With particular attention to study planning, sample selection, biases, lack of transparency and results misinterpretations.

Abstract Background In early 2021, the Department of Health and Social Care in the UK called for research on the safety and immunogenicity of concomitant administration of COVID-19 and influenza vaccines. Co-administration of these vaccines would facilitate uptake and reduce the number of healthcare visits required. The ComFluCOV trial was designed to deliver the necessary evidence in time to inform the autumn (September–November) 2021 vaccination policy. This paper presents the statistical methodology applied to help successfully deliver the trial results in 6 months. Methods ComFluCOV was a parallel-group multicentre randomised controlled trial managed by the Bristol Trials Centre. Two study statisticians, supported by a senior statistician, worked together on all statistical tasks. Tools were developed to aid the pre-screening process. Automated data monitoring reports of clinic data and electronic diaries were produced daily and reviewed by the trial team and feedback provided to sites. Analyses were performed independently in parallel, and derivations and results of all outcomes were compared. Results Set-up was achieved in less than a month, and 679 participants were recruited over 8 weeks. A total of 537 [at least] daily reports outlining recruitment, protocol adherence, and data quality, and 695 daily reports of participant electronic diaries identifying any missed diary entries and adverse events were produced over a period of 16 weeks. A preliminary primary outcome analysis of validated data was reported to the Department of Health and Social Care in May 2021. The database was locked 6 weeks after the final participant follow-up and final analyses completed 3 weeks later. A pre-print publication was submitted within 14 days of the results being made available. The results were reported 6 months after first discussions about the trial. Conclusion The statistical methodologies implemented in ComFluCOV helped to deliver the study in the timescale set. Working in a new clinical area to tight timescales was challenging. Having two statisticians working together on the study provided a quality assurance process that enabled analyses to be completed efficiently and ensured data were interpreted correctly. Processes developed could be applied to other studies to maximise quality, reduce the risk of errors, and overall provide enhanced validation methods. Trial registration ISRCTN14391248, registered on 30 March 2021

Objectives: Adjusting for potential confounders is crucial for producing valuable evidence in outcome studies. Although numerous studies have been published using the Korea National Health Insurance Claim Database, no study has critically reviewed the methods used to adjust for confounders. This study aimed to review these studies and suggest methods and applications to adjust for confounders. Methods: We conducted a literature search of electronic databases, including PubMed and Embase, from January 1, 2021 to December 31, 2022. In total, 278 studies were retrieved. Eligibility criteria were published in English and outcome studies. A literature search and article screening were independently performed by 2 authors and finally, 173 of 278 studies were included. Results: Thirty-nine studies used matching at the study design stage, and 171 adjusted for confounders using regression analysis or propensity scores at the analysis stage. Of these, 125 conducted regression analyses based on the study questions. Propensity score matching was the most common method involving propensity scores. A total of 171 studies included age and/or sex as confounders. Comorbidities and healthcare utilization, including medications and procedures, were used as confounders in 146 and 82 studies, respectively. Conclusions: This is the first review to address the methods and applications used to adjust for confounders in recently published studies. Our results indicate that all studies adjusted for confounders with appropriate study designs and statistical methodologies; however, a thorough understanding and careful application of confounding variables are required to avoid erroneous results.

Background: In early 2021, the Department of Health and Social Care in the UK called for research on the safety and immunogenicity of concomitant administration of COVID-19 and influenza vaccines. Co-administration of these vaccines would facilitate uptake and reduce the number of healthcare visits required. The ComFluCOV trial was designed to deliver the necessary evidence in time to inform the autumn (September–November) 2021 vaccination policy. This paper presents the statistical methodology applied to help successfully deliver the trial results in 6 months. Methods: ComFluCOV was a parallel-group multicentre randomised controlled trial managed by the Bristol Trials Centre. Two study statisticians, supported by a senior statistician, worked together on all statistical tasks. Tools were developed to aid the pre-screening process. Automated data monitoring reports of clinic data and electronic diaries were produced daily and reviewed by the trial team and feedback provided to sites. Analyses were performed independently in parallel, and derivations and results of all outcomes were compared. Results: Set-up was achieved in less than a month, and 679 participants were recruited over 8 weeks. A total of 537 [at least] daily reports outlining recruitment, protocol adherence, and data quality, and 695 daily reports of participant electronic diaries identifying any missed diary entries and adverse events were produced over a period of 16 weeks. A preliminary primary outcome analysis of validated data was reported to the Department of Health and Social Care in May 2021. The database was locked 6 weeks after the final participant follow-up and final analyses completed 3 weeks later. A pre-print publication was submitted within 14 days of the results being made available. The results were reported 6 months after first discussions about the trial. Conclusion: The statistical methodologies implemented in ComFluCOV helped to deliver the study in the timescale set. Working in a new clinical area to tight timescales was challenging. Having two statisticians working together on the study provided a quality assurance process that enabled analyses to be completed efficiently and ensured data were interpreted correctly. Processes developed could be applied to other studies to maximise quality, reduce the risk of errors, and overall provide enhanced validation methods. Trial registration: ISRCTN14391248, registered on 30 March 2021 [ABSTRACT FROM AUTHOR]

Background A significant rise in mental health disorders was expected during the COVID-19 pandemic. However, referrals to mental health services dropped for several months before rising to pre-pandemic levels. Aims To identify trajectories of incidence and risk factors for common mental disorders among the general population during 14 months of the COVID-19 pandemic, to inform potential mental health service needs. Method A cohort of 33 703 adults in England in the University College London COVID-19 Social Study provided data from March 2020 to May 2021. Growth mixture modelling was used to identify trajectories based on the probability of participants reporting symptoms of depression (Patient Health Questionnaire-9) or anxiety (Generalised Anxiety Disorder-7) in the clinical range, for each month. Sociodemographic and personality-related characteristics associated with each trajectory class were explored. Results Five trajectory classes were identified for depression and anxiety. Participants in the largest class (62%) were very unlikely to report clinically significant symptom levels. Other trajectories represented participants with a high likelihood of clinically significant symptoms throughout, early clinically significant symptoms that reduced over time, clinically significant symptoms that emerged as the pandemic unfolded and a moderate likelihood of clinically significant symptoms throughout. Females, younger adults, carers, those with existing mental health diagnoses, those that socialised frequently pre-pandemic and those with higher neuroticism scores were more likely to experience depression or anxiety. Conclusions Nearly 40% of participants followed trajectories indicating risk of clinically significant symptoms of depression or anxiety. The identified risk factors could inform public health interventions to target individuals at risk in future health emergencies.

In applied sectors, data modeling/analysis is very important for decision-making and future predictions. Data analysis in applied sectors mainly relies on probability distributions. Data arising from numerous sectors such as engineering-related fields have complex structures. For such kinds of data having complex structures, the implementation of classical distributions is not a suitable choice. Therefore, researchers often need to look for more flexible models that might have the capability of capturing a high degree of kurtosis and increasing the fitting power of the classical models. Taking motivation from the above theory, to achieve these goals, we study a new probabilistic model, which we named a new beta power flexible Weibull (NBPF-Weibull) distribution. We derive some of the main distributional properties of the NBPF-Weibull model. The estimators for the parameters of the NBPF-Weibull distribution are derived. The performances of these estimators are judged by incorporating a simulation study for different selected values of the parameters. Three data sets are used to demonstrate the applicability of the NBPF-Weibull model. The first data set is observed from sports. It represents the re-injury rate of various football players. While the other two data sets are observed from the reliability zone. By adopting certain diagnostic criteria, it is proven that the NBPF-Weibull model repeatedly surpasses well-known classical and modified models.

Abstract The increasing rate of tropical cyclone (TC) rainfall has put populations in the Western North Pacific Region at greater risk of TC rainfall‐induced disasters. Statistical methodologies have shown potential in complementing existing prediction approaches. With TC track prediction accuracy significantly improving, statistical predictions have turned to TC tracks as a measure of similarity between TCs. Several studies have utilized Fuzzy C Means (FCM) to this end. However, FCM alone does not provide guidance on how many similar TCs should be used for predicting rainfall through ensemble averaging. While various number of ensemble members have been used to check the average error, such an approach yields only one number, which may not always be the most appropriate. In this study, we proposed a spatial and attribute filter to complement FCM identification of similar TCs. This filter excludes similar TCs with central pressure differences greater than 5% at strategic TC locations near land. The use of the filter yielded better rainfall prediction values than using FCM alone, as demonstrated in this study and validated against previous research findings. Our proposed model offers a reliable means of predicting TC rainfall when used in conjunction with accurately predicted TC tracks, representing a valuable complementary approach to existing prediction methods.

Patients diagnosed with attention-deficit hyperactivity disorder (ADHD) are at an elevated risk for suicide. No prior work has assessed the association between stimulant prescriptions and death by suicide in this population. This retrospective cohort study included Department of Veterans Affairs patients with an active ADHD diagnosis that received stimulant medications between 2016 and 2019. We found that months with active stimulant medication prescription was associated with decreased risk of suicide mortality compared with months without stimulant medication (odds ratio 0.57, 95% CI 0.36–0.88). Our results suggest that prescribing stimulant medications for patients diagnosed with ADHD is associated with decreased risk of suicide mortality.

Background Although the COVID-19 pandemic has affected depression, evidence of the role of pre-pandemic history of depression remains limited. Aims We investigated how long-term trajectories of depressive symptomatology before the COVID-19 pandemic were related to depression during the pandemic, over and above the latest pre-pandemic depression status. Furthermore, we examined whether those experiencing depression closer to the pandemic were at higher risk during the pandemic. Method Employing data from waves 4–9 of the English Longitudinal Study of Ageing (2008–2009 to 2018–2019), we used group-based trajectory modelling on 3925 English older adults aged 50+ years to identify distinctive trajectories of elevated depressive symptoms (EDS). Fully adjusted logistic models were then used to examine the associations between trajectories and depression during the COVID-19 pandemic (June–July and November–December 2020). Results We identified four classes of pre-pandemic trajectories of EDS. About 5% were classed as ‘enduring EDS’, 8% as ‘increasing EDS’, 10% as ‘decreasing EDS’ and 77% as ‘absence of EDS’. Compared with respondents with absence of EDS, those with EDS history were more likely to have depression during the COVID-19 pandemic, particularly those with enduring or increasing EDS in the previous 10 years. Moreover, the frequency of EDS was more crucial in predicting the risks of depression during the pandemic than the timing of the latest episode. Conclusions Trajectories of depressive symptomatology are an important risk factor for older adults’ mental health, particularly in the context of crisis. Older people with enduring or increasing EDS should receive particular attention from policy makers when provisioning post-pandemic well-being support.

Lithium is the primary choice for preventing bipolar disorder relapses, endorsed by guidelines. A recent systematic review by Ulrichsen et al. showed limitations in assessing its specific impact, but data supports lithium's effectiveness in managing symptoms and preventing relapse. Comprehensive guidelines and research are crucial for its continued use.

Depression is a major public health concern. Depressed individuals have received increasing treatment with antidepressants in Western countries. In this study, we examine the relationship among individual symptoms (sadness, worry and unhappiness), human development factors and antidepressant use in 29 OECD countries. We report that increased antidepressant prescribing is not associated with decreased prevalence of sadness, worry or unhappiness. However, income, education and life expectancy (measured using the Human Development Index) are associated with lower prevalence of all these symptoms. This suggests that increasing spending on depression treatment may not be as effective as general public health interventions at reducing depression in communities.

Objectives: Clinical measures of periodontal disease such as attachment loss (CAL) and probing depth (PD) vary considerably between and within individuals with periodontitis and are known to be influenced by person‐level factors (e.g. age and race/ethnicity) as well as intraoral characteristics (e.g. tooth type and location). This study sought to characterize site‐level disease patterns and correlations using both person‐level and intraoral factors through a model‐based approach. Methods: This study used full‐mouth, six sites per tooth, periodontal examination data collected from 2301 Hispanic/Latino adults aged 60–74 years in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The presence of site‐level CAL ≥3 mm and PD ≥4 mm was estimated using generalized estimating equations (GEE), explicitly modelling pairwise periodontal site correlations, while adjusting for number of teeth, sex and Hispanic/Latino background. Subsequently tooth‐ and tooth‐site patterns of intraoral CAL ≥3 mm and PD ≥4 mm were estimated and visualized in the HCHS/SOL population. Results: The findings showed that posterior sites had the highest odds of CAL ≥3 mm and PD ≥4 mm. Sites located in the interproximal space had higher odds of PD ≥4 mm but lower odds of CAL ≥3 mm than non‐interproximal sites. Mexicans had the lowest odds of CAL ≥3 mm among all Hispanic/Latino backgrounds. While Mexicans had lower odds of PD ≥4 mm than Central Americans and Cubans, they had higher odds than Dominicans and Puerto Ricans. Site‐level proportions and pairwise correlations of PD ≥4 mm were generally smaller than those of CAL ≥3 mm. Conclusions: The patterns of site‐level probabilities of clinical measures of periodontal disease can be defined based on tooth, site and individual‐level characteristics. Intraoral correlation patterns, while complex, are quantifiable. The risk factors for site‐level CAL ≥3 mm may differ from those of PD ≥4 mm. Likewise, participant risk factors for site‐level clinical measures of periodontal disease are distinct from those that affect individual‐level periodontitis prevalence. Future epidemiological investigations should consider model‐based approaches when examining site‐level disease probabilities to identify intra‐oral patterns of periodontal disease and make inferences about the larger population. [ABSTRACT FROM AUTHOR]

SUMMARY: This month's Cochrane Corner meta-analysis evaluates the evidence for the use of aripiprazole in 'autism spectrum disorders' – although in fact, outcome measures mainly included subtypes of challenging behaviour and side-effects. Aripiprazole was found to be effective in reducing irritability and hyperactivity, while causing extrapyramidal side-effects and weight gain. Only three trials were included in the review, with two small trials eventually included in the meta-analysis. All trials were conducted in under-18s in the USA, with no requirement for a trial of behavioural management before psychotropic medication, and excluding under-18s with important comorbidities such as medicated attention-deficit hyperactivity disorder. All three studies were sponsored and funded by the manufacturer of aripiprazole. Further, a discontinuation trial showed no evidence of sustained benefit beyond 16 weeks of treatment. [ABSTRACT FROM AUTHOR]

TiO2 nanocomposites on carbonaceous compounds, such as carboxyl-functionalized, multi-walled carbon nanotubes (MWCNT-COOH), and graphene oxide (GO) were synthesized by the sol-gel method. The samples were coded as TiO2 (T), TiO2-MWCNT-COOH (TM), and TiO2-GO (TG). The effect of the addition of carbonaceous compounds on the enhancement of demulsification efficiency of TiO2 nanocomposites ‎in crude oil (W/O) emulsions was then investigated. FT-IR, Raman, and morphological analyses such as FE-SEM, EDXS, XRD, HR-TEM, DRS, BET surface area, and TGA were used to determine the properties and structures of the nanoparticles prepared. In addition, the demulsification efficiency of three nanoparticles was studied under various concentrations, settling times, and temperature conditions by bottle test. According to the screening results, TG was selected as the best sample. The response surface method with a central composite design (CCD) was used to optimize the demulsification activity of TiO2 nanocomposites with graphene oxide (TG). Thus, the impacts of temperature, demulsifier concentration, and time were studied by the RSM-CCD method. Ultimately, the results indicated ~100% demulsification efficiency under optimal conditions at concentration, temperature, and time of 75 ppm, 65oC, and 120 min., respectively.

Background The Autism Spectrum Quotient is a popular autism screening tool recommended for identifying potential cases of autism. However, many women with autism demonstrate a different presentation of traits to those currently captured by screening measures and assessment methods, such as the Autism Spectrum Quotient. Aims Different models of the Autism Spectrum Quotient have been proposed in the literature, utilising different items from the original 50-item scale. Within good-fitting models, the current study aimed to explore whether these items assess autistic traits similarly across men and women. Method Seventeen Autism Spectrum Quotient models were identified from the literature. Using the responses of a large sample of adults from the UK general population (5246 women, 1830 men), confirmatory factor analysis was used to evaluate the fit of each model. Measurement invariance with respect to gender, adjusting for age, was explored in the 11 model frameworks that were found to have satisfactory fit to our data. Results It emerged that only two items were gender invariant (non-biased), whereas for the remaining items, the probability of endorsement was influenced by gender. In particular, women had a higher probability of endorsing items relating to social skills and communication. Conclusions If the items of the Autism Spectrum Quotient indeed reflect autism-related traits, those items should be rephrased to ensure they do not present a gender-related bias. This is vital for ensuring more timely diagnoses and support for all people with autism.

SUMMARY: A 'control' provides a point of clinical comparison for a new intervention, allowing researchers and clinicians to draw more confident conclusions about the effectiveness or potential harm of a given, often novel, therapy. Although this aspect of a trial's design provides the basis from which interventional impact is measured, it is often less closely examined. This commentary appraises a Cochrane Review that compares various controls in common use in modern psychiatric research and aims to characterise their effects on the outcomes of that research. [ABSTRACT FROM AUTHOR]

SUMMARY: Post-traumatic stress disorder is a disabling condition resulting from a range of traumas and affecting many people worldwide. This month's Cochrane Corner review systematically searched and reported findings from 66 randomised controlled trials of pharmacotherapy for PTSD, 54 of which were included in a meta-analysis. Evidence was shown for the benefit of selective serotonin reuptake inhibitors, mirtazapine and amitriptyline in treatment response. This Round the Corner commentary critically appraises the review's findings, concluding that the summative evidence was of poor quality owing to the low number of studies, the high risk of bias and significant heterogeneity. [ABSTRACT FROM AUTHOR]

This study evaluated the consistency of the evaluation for the efficiency of spectrum usage and proposed improvement plans. First, a system for evaluating consistency was devised based on a statistical methodology, and the correlation, discrimination, and adequacy were derived as detailed criteria. Furthermore, a case study is conducted using empirical data to confirm the practical applicability of the proposed system. Moreover, improvement plans, such as the expansion of quantitative variables, a combination of variables reflecting level and change, utilization of complementary variables, control for size effects, consideration of the input-output relationship, weight adequacy test of variables, and classification of business and non-business usage, were devised. The findings of this study can contribute to formulating a detailed spectrum-management policy to promote the efficient use of each frequency band. Moreover, a scientific ap- proach to spectrum management will enhance the reliability and objectivity of policies. [ABSTRACT FROM AUTHOR]

Abstract Purpose Phenylacetylcarbinol (PAC) is an intermediate for the synthesis of several active pharmaceutical ingredients (ephedrine, pseudoephedrine, norephedrine, etc.) used for the production of antiasthematics and decongestants. An efficient biosynthesis of PAC through condensation of benzaldehyde and acetaldehyde catalyzed by a solvent tolerant pyruvate decarboxylase (PDC) is being reported. A process for the biosynthesis of PAC was designed and optimized through response surface methodology (RSM) in the present study. Methods The effects of incubation time (8–18 h), incubation temperature (30–38 °C), medium pH (4–10), and inoculum size (4–10%) on PAC yield, sugar consumption, and PDC activity were determined through submerged fermentation using a newly isolated potent yeast strain of Pichia cecembensis. PAC was quantified spectrophotometerically and through HPLC. PDC produced was exposed to 40 mM benzaldehyde as whole cells, crude extract, and partialy purified preparation to check its stability against the said solvent. Results The highest PDC activity and PAC yield during present study were found to be 56.27 U/ml and 8.44 g/l, respectively. The yield of PAC was increased by 71% (2.22 to 8.44 g/l) after process optimization through RSM with incubation time of 13 h, incubation temperature of 33 °C, and 18% total sugar as significant factors (P-values, 0.902, 0.260, and 0.247, respectively). R-squared value of 0.770 and Adeq Precision value of 4.888 show the goodness of fit of the process design. PDC is used in the form of Pichia cecembensis whole cells revealed higher stability towards benzaldehyde and elevated temperature as compared to partially purified PDC. Whole cells and partially purified PDC showed half-lives of 240 and 72 h at 4 °C, whereas 33 and 28.5 h at 25 °C. PAC was purified though HPLC with a purity level of 76.18%. Conclusion Incubation time, temperature, and sugar concentration were found to be significant factors for the biosynthesis of PAC. A newly isolated Pichia cecembensis produced a highly active, solvent, and temperature-tolerant pyruvate decarboxylase (PDC) which is superior to its counterpart being presently used in the industry. Hence, this novel yeast species is a promising candidate for commercial production of PAC and other related APIs owing to its highly stable PDC.

Objective: Psychotherapy studies have revealed that therapist characteristics are responsible for 5% to 9% of outcome variance. The therapist‐facilitative interpersonal skills (FIS) have been shown to predict both alliance and outcomes, indicating that higher FIS therapists are more effective than lower FIS therapists. The current study focused on the development and validation of the FIS‐client version (FIS‐C) instrument, aimed at collecting the clients' perspectives on relevant therapist characteristics. Method: The clinical outcomes in routine evaluation—outcome measures, the session rating scale, and the FIS questionnaire—client version were filled out by psychotherapy clients. Exploratory, confirmatory factor, and test–retest analysis were conducted. Results: Results indicate robust psychometric characteristics, in terms of validity (factorial, convergent, discriminant, and nomological), reliability, and sensitivity. Conclusion: The validation of the FIS‐C represents an important contribution to clinical research and practice, namely to the field of client feedback and therapist expertise. [ABSTRACT FROM AUTHOR]

Recently, there has been growing interest in artificial intelligence (AI) to improve efficiency and personalisation of mental health services. So far, the progress has been slow, however, advancements in deep learning may change this. This paper discusses the role for AI in psychiatry, in particular (a) diagnosis tools, (b) monitoring of symptoms, and (c) delivering personalised treatment recommendations. Finally, I discuss ethical concerns and technological limitations. [ABSTRACT FROM AUTHOR]

Background A core element of the Strengthening Responses to Dementia in Developing Countries (STRiDE) programme was to generate novel data on the prevalence, cost and impact of dementia in low- and middle-income countries, to build better health policy. Indonesia and South Africa are two middle-income countries in need of such data. Aims To present the STRiDE methodology and generate estimates of dementia prevalence in Indonesia and South Africa. Method We conducted community-based, single-phase, cross-sectional studies in Indonesia and South Africa, randomly sampling participants aged 65 years or older in each country. Dementia prevalence rates for each country were generated by using the 10/66 short schedule and applying its diagnostic algorithm. Weighted estimates were calculated with national sociodemographic data. Results Data were collected between September and December 2021 in 2110 people in Indonesia and 408 people in South Africa. The adjusted weighted dementia prevalence was 27.9% (95% CI 25.2–28.9) in Indonesia and 12.5% (95% CI 9.5–16.0) in South Africa. Our results indicate that there could be >4.2 million people in Indonesia and >450 000 people in South Africa who have dementia. Only five participants (0.2%) in Indonesia and two (0.5%) in South Africa had been previously diagnosed with dementia. Conclusions Despite prevalence estimates being high, formal diagnosis rates of dementia were very low across both countries (

Background Cognitive stimulation therapy (CST) is the only non-pharmacological, treatment for dementia recommended by the UK National Institute for Health and Care Excellence, following multiple international trials demonstrating beneficial cognitive outcomes in people with mild-to-moderate dementia. However, there is limited understanding of whether treatment prognosis is influenced by sociodemographic and clinical variables (such as dementia subtype and gender), information which could inform clinical decision-making. Aim We describe the protocol for a systematic review and individual patient data meta-analysis assessing the prognostic factors related to CST. In publishing this protocol, we hope to increase the transparency of our work, and keep healthcare professionals aware of the latest evidence for effective CST. Method A systematic review will be conducted with searches of the bibliographic databases Medline, EMBASE and PsycINFO, from inception to 7 February 2023. Studies will be included if they are clinical trials of CST, use the Alzheimer's Disease Assessment Scale – Cognitive Subscale (gold-standard measure of cognition in dementia in clinical trials) and include participants with mild-to-moderate dementia. Following harmonisation of the data-set, mixed-effect models will be constructed to explore the relationship between the prognostic indicators and change scores post-treatment. Conclusions This is the first individual patient data meta-analyses on CST, and has the potential to significantly optimise patient care. Previous analyses suggest people with advanced dementia could benefit more from CST treatment. Given that CST is currently used post-diagnosis in people with mild-to-moderate dementia, the implications of confirming this finding, among identifying other prognostic indicators, are profound.

Background In the connected world, although societies are not directly involved in a military conflict, they are exposed to media reports of violence. Aims We assessed the effects of such exposures on mental health in Germany during the military conflict in Ukraine. Method We used the German population-based cohort for digital health research, DigiHero, launching a survey on the eighth day of the Russo-Ukrainian war. Of the 27 509 cohort participants from the general population, 19 444 (70.7%) responded within 17 days. We measured mental health and fear of the impact of war compared with other fears (natural disasters or health-related). Results In a subsample of 4441 participants assessed twice, anxiety in the population (measured by the Generalised Anxiety Disorder-7 screener) was higher in the first weeks of war than during the strongest COVID-19 restrictions. Anxiety was elevated across the whole age spectrum, and the mean was above the cut-off for mild anxiety. Over 95% of participants expressed various degrees of fear of the impact of war, whereas the percentage for other investigated fears was 0.47–0.82. A one-point difference in the fear of the impact of war was associated with a 2.5 point (95% CI 2.42–2.58) increase in anxiety (11.9% of the maximum anxiety score). For emotional distress, the increase was 0.67 points (0.66–0.68) (16.75% of the maximum score). Conclusions The population in Germany reacted to the Russo-Ukrainian war with substantial distress, exceeding reactions during the strongest restrictions in the COVID-19 pandemic. Fear of the impact of war was associated with worse mental health.

The purpose of the paper is to analyze the positioning of Ukraine in the global indices of innovative development and competitiveness, to evaluate the indicators of innovation activity and, based on the outcomes of the research, to determine the place of Ukraine in the global innovation space. The dynamics of innovation activity on an international scale based on the consolidated indicators of the Global Innovation Index are presented. Ukraine's position in it and progress in achieving goals to better understand the processes that stimulate or constrain innovation are determined. Econometric methods to generalize the positioning of Ukraine in the global innovation space and the DEA method to study the relative individual effectiveness of the innovation environment and innovation activities in Europe are used. [ABSTRACT FROM AUTHOR]

The graduate thesis is a fundamental part of creating educational expertise. This study aims to expand the use of the statistical technique in graduate theses. The data collection consists of quantitative and qualitative data from advisers and graduate college students at Mahidol University. The sample includes 650 university students and fifteen key informants, including nine students and six supervisors, divided into three groups; health science, science and technology, and social sciences and the humanities. The results of the study showed that most of the students who collected data worked on a thesis with quantitative research (50.42%), followed by mixed methods research (38.40%) and qualitative research (10.52%). Problems arise when graduate college students lack comprehension and are unable to select the most accurate data for their thesis. This study presents findings regarding the guidelines for the use of statistical techniques to analyze data from theses or dissertations, teaching and learning strategies or additional training in the selection and use of statistics, guidelines for the selection and use of statistics in data analysis, and the use of e-learning to teach statistics. [ABSTRACT FROM AUTHOR]

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Abstract Background Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection. Methods We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence. The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used: model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care. We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England. Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors. Results Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities: C-statistics 0.92–0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled. Conclusions Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.

Background There is cumulative evidence of the importance of exploring the change of dynamics between symptoms over time as reflective of consolidation of psychopathology. Aims To explore the interactions between symptoms of ICD-11 adjustment disorder before and after the second lockdown of the COVID-19 pandemic in Israel and identify the most central symptoms and their concurrent and prospective associations with probable adjustment disorder. Method This is a population-based study drawn from a probability-based internet panel. A representative sample of the adult Israeli population was assessed at two time points (T1, pre-second lockdown, n = 1029, response rate 76.17%; T2, post-second lockdown, n = 764, response rate 74.24%). Symptoms of adjustment disorder were assessed by the International Adjustment Disorder Questionnaire (IADQ). Results Although the overall strength of associations at the two measurement points was similar and two same communities were found, there was a significant change in their structure, with a more consolidated network at T2. The most central item was ‘difficult to relax’ in both networks. Cross-sectionally, all symptoms of failure to adapt significantly predicted adjustment disorder. ‘Worry a lot more’ (preoccupation) and ‘difficult to adapt to life’ (failure to adapt) at T1 significantly predicted this diagnosis at T2. Conclusions Adjustment disorder symptoms consolidated during the second lockdown of the pandemic. In line with the ICD-11 conceptualisation of adjustment disorder, both preoccupation and failure-to-adapt symptoms have prognostic validity. This highlights the importance of identifying and targeting adjustment disorder symptoms during a period of stress such as the COVID-19 pandemic.

Background: The potential for early interventions to reduce the later prevalence of common mental disorders (CMD) first experienced in adolescence is unclear.Aims: To examine the course of CMD and evaluate the extent to which the prevalence of CMD could be reduced by preventing adolescent CMD, or by intervening to change four young adult processes, between the ages of 20 and 29 years, that could be mediating the link between adolescent and adult disorder.Method: This was a prospective cohort study of 1923 Australian participants assessed repeatedly from adolescence (wave 1, mean age 14 years) to adulthood (wave 10, mean age 35 years). Causal mediation analysis was undertaken to evaluate the extent to which the prevalence of CMD at age 35 years in those with adolescent CMD could be reduced by either preventing adolescent CMD, or by intervening on four young adult mediating processes: the occurrence of young adult CMD, frequent cannabis use, parenting a child by age 24 years, and engagement in higher education and employment.Results: At age 35, 19.2% of participants reported CMD; a quarter of these participants experienced CMD during both adolescence and young adulthood. In total, 49% of those with CMD during both adolescence and young adulthood went on to report CMD at age 35 years. Preventing adolescent CMD reduced the population prevalence at age 35 years by 3.9%. Intervening on all four young adult processes among those with adolescent CMD, reduced this prevalence by 1.6%.Conclusions: In this Australian cohort, a large proportion of adolescent CMD resolved by adulthood, and by age 35 years, the largest proportion of CMD emerged among individuals without prior CMD. Time-limited, early intervention in those with earlier adolescent disorder is unlikely to substantially reduce the prevalence of CMD in midlife. [ABSTRACT FROM AUTHOR]

Background: Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence.Aims: To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder.Method: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST).Results: We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility.Conclusions: Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines. [ABSTRACT FROM AUTHOR]

Aims and MethodsRestraint reporting varies, which undermines regulation, obfuscates analyses, and incentivises minimisation. The English Mental Health Units Use of Force Act 2018, “Seni's Law” mandates reporting. This paper analysed open data from all psychiatric and learning disability institutions in England from September 2020 to August 2021. We correlated logarithms of “people restrained per month”, against “bed days” per month and “people under legal mental health detention” per month, per institution. We designated institutions reporting some restraint for at least 11 of 12 months as reporting “completely” and used their trend to infer rates from non-“complete” institutions. Allowance was made for size. Our a priori manual can be shared on request.ResultsLogarithms of people restrained per month and bed-days per month correlated among complete reporters: R2 0.90 (2.s.f). Persons detained per month also correlated with restraint: R2 0.78. “Partial” institutions reported intermittently. “Joiner” institutions reported firstly null, then substantive reporting. “Null” institutions (including the largest) reported no restraint. Precisely-reporting institutions with high inverse variance between months reported similar restraint-rates but less-precise reported lower rates. In institutions reporting no restraint, two independent “true rate” estimations, by bed-days or people detained, correlated across institutions: R2 0.95. Inference from size suggested non-complete reporters restrained 1,774 people in England per month 95% CI (1,449–2,174).Clinical implicationsRestraint remains under-reported. Institutional size explains most restraint variation among complete reporting institutions, 90% of R2. Institutional restraint reports can be compared per-bed per-month. Rates of people detained are a useful independent “checking” comparator in England.

The objective of this study is to identify salt accumulation on the topsoil of arid and semiarid regions. To achieve this goal, thirty-four topsoil samples were analyzed for salt accumulation indicators such as ECe and water leachable ions from the soil. The data were evaluated statistically using Pearson correlation analysis, principal component analysis (PCA), stepwise regression analysis (SWR), and clustering analysis utilizing a K-means technique, which included soil chemical markers as well as spatial salinity behavior. High collinearity (condition number 2160) exists within variables in the obtained data matrix (43 samples; 13 chemical predictors), necessitating a more flexible K-means approach for soil grouping. Pearson analysis indicated that Cl− positively contributed (R2 0.9781) to the total salinity, which was attributed to its ionic conductance and high concentration among leached ions. The PCA analysis indicated the high colinearity among variables, especially those that contribute to salinity like Cl−, Na+, and Ca2+. Based on PCA analysis, the 43 samples were clustered into five groups: three groups have high salinity (15–37 mS/m) but with unequal proportions of high-conductance ions (Cl−, Na+, Ca2+) and two groups of low salinity (1–11 mS/m) but with unequal proportions of low-conductance ions (Fe and SO42−). Stepwise regression analysis was helpful in removing unnecessary chemical variables and predicting the total salinity (mS/cm) from Cl− (0.076), SO42− (0.047), and Na+ (0.022) concentrations (meq/L). The spatial analysis reveals that the salinity effect is heterogeneous and is derived from the original source of the parent material or impacted by the anthropogenic effects. [ABSTRACT FROM AUTHOR]

This article presents a prediction model of (de-)escalation of sub-national violence using gradient boosting. The prediction model builds on updated data from the PRIO-GRID data aggregator, contributing to the ViEWS prediction competition by predicting changes in violence levels, operationalized using monthly fatalities at the 0.5 × 0.5-degree grid (pgm) level. Our model's predictive performance in terms of mean square error (MSE) is marginally worse than the ViEWS baseline model and inferior to most other submissions, including our own supervised random forest model. However, while we knew that the model was comparatively worse than our random forest model in terms of MSE, we propose the gradient boosting model because it performed better where it matters—in predicting when (de-)escalation happens. This choice means that we question the usefulness of using MSE for evaluating model performance and instead propose alternative performance measurements that are needed to understand the usefulness of predictive models. We argue that future endeavors using this outcome should measure their performance using the Concordance Correlation, which takes both the trueness and the precision elements of accuracy into account, and, unlike MSE, seems to be robust to the issues caused by zero inflation. Este artículo presenta un modelo de predicción de la desescalada de la violencia subnacional mediante el uso de la potenciación del gradiente. El modelo de predicción se basa en los datos actualizados que provienen del agregador de datos de PRIO-GRID, contribuye al concurso de predicciones de ViEWS al predecir cambios en los niveles de violencia y es operacionalizado utilizando las muertes mensuales a nivel de cuadrícula de 0.5 × 0.5 grados (pgm). El rendimiento predictivo de nuestro modelo desde el punto de vista del error cuadrático medio (mean square error, MSE) es ligeramente peor que el modelo de referencia del sistema de alerta temprana sobre la violencia (Violence Early Warning System, ViEWS) e inferior en relación con la mayoría de las otras presentaciones, incluido nuestro modelo de bosque aleatorio y supervisado. No obstante, si bien sabíamos que el modelo era comparativamente peor que nuestro modelo de bosque aleatorio en relación con el MSE, proponemos el modelo de potenciación del gradiente porque funcionó mejor en el aspecto que importa: predecir cuándo ocurre la desescalada. Esta elección significa que cuestionamos la utilidad del uso del MSE para evaluar el rendimiento del modelo y, en cambio, proponemos mediciones de rendimiento alternativas que son necesarias para comprender la utilidad de los modelos predictivos. Sostenemos que, en los futuros proyectos en los que se utilice este resultado, se debería medir el rendimiento mediante la correlación de concordancia, la cual tiene en cuenta tanto los elementos de veracidad como los de precisión de la exactitud y, a diferencia del MSE, parece ser resistente a los problemas generados por la inflación cero. Cet article présente un modèle de prédiction de la (dés)escalade de la violence infranationale utilisant un boosting de gradient. Ce modèle de prédiction repose sur des données à jour de l'agrégateur de données de la grille PRIO. Il contribue au concours de prédiction ViEWS (Violence early-warning system, système d'alerte précoce sur la violence) en prédisant les évolutions des niveaux de violence qui sont opérationnalisés sur la base du nombre mensuel de décès au niveau 0.5 × 0.5 degré de la grille (PGM). Les performances prédictives de notre modèle en termes d'erreur quadratique moyenne (EQM) sont légèrement moins bonnes que celles du modèle de référence ViEWS et inférieures à la plupart des autres modèles soumis, y compris à celles de notre propre modèle à forêt aléatoire supervisée. Cependant, bien que nous sachions que ce modèle à boosting de gradient était comparativement moins bon que notre modèle à forêt aléatoire en termes d'EQM, nous l'avons proposé car il était plus efficace dans le domaine qui compte : la prédiction du moment auquel une (dés)escalade interviendrait. Ce choix signifie que nous remettons en question l'utilité de l'utilisation de l'EQM pour évaluer les performances des modèles et nous proposons au lieu de cela des mesures de performances alternatives nécessaires pour comprendre l'utilité des modèles prédictifs. Nous soutenons que les futurs efforts utilisant ce résultat devraient plutôt mesurer leurs performances à l'aide de la Corrélation de concordance, qui prend à la fois en compte les éléments Exactitude et Précision et qui, contrairement à l'EQM, semble être robuste face aux problèmes causés par l'inflation zéro. [ABSTRACT FROM AUTHOR]

This study focused on identifying the best conditions and studying the effects of the operational variables on the integral valorization process of used coffee ground. The valorization approach was defined in two successive steps: oil extraction and using the remaining residue to prepare activated carbon. For this purpose, a set of experiments was designed to test the following four variables in the oil extraction stage: (A) drying time (50 °C/12 h and 105 °C/6 h), (B) relative polarity of hexane and ethanol (0.009 and 0.654 a.u.), (C) solvent ratio (10 and 12 mL/g), and (D) extraction time (30 and 60 min). The best conditions from the corresponding experiments (50 °C/12 h, using ethanol as a solvent in a 12:1 solvent ratio (v/v) with 30 min of extraction time) resulted in the extraction of 0.4 g of oil per gram of spent coffee ground. This value is, to the best of our knowledge, the highest reported thus far and is a result combining of the coffee origin and the significant interactions found for the tested parameters. For the stage of activated carbon preparation, the (E) activating agent ratio (1, 2, and 3 gAA/gS) and the (F) carbonization temperature (450, 550, and 700 °C) were considered independent variables. The corresponding results indicate that 1 gAA/gS and 450 °C are the best conditions, resulting in a sorption capacity of 29 mg/g, which is similar to the value obtained for commercial activated carbon. Physicochemical analysis on the other hand, showed that extracted coffee oil is characterized by a high content of free fatty acids; thus, an additional purification process could be considered to obtain a precursor for biodiesel. Activated carbon was found to have an elemental composition similar to that of vegetal activated carbon. The adsorption kinetics of the activated carbon obtained from the spent coffee residues are characterized by a pseudo-second-order model that suggests chemisorption-controlled phenomena. [ABSTRACT FROM AUTHOR]

Evidence based medicine is the prevailing paradigm of modern healthcare. However, practitioners of traditional Chinese medicine (TCM) vary significantly in their ability to appraise and understand modern research. Part 3 of this series reviews the most important criteria for assessing research papers, so that practitioners of traditional Chinese medicine and other related disciplines are able to identify errors in medical research in order to inform treatment and assist their patients. A summary checklist is included to guide readers when assessing clinical research papers. [ABSTRACT FROM AUTHOR]

Proponemos un procedimiento simple para dar cuenta de la incertidumbre quese produce al tener disponible una multiplicidad de estimadores de indicadores demortalidad adulta en los análisis estadísticos. Consideramos situaciones donde hayestimadores alternativos del mismo parámetro de población, cada uno depende deun conjunto de supuestos que pueden superponerse, y algunos, o todos, podríancaracterizar erróneamente el parámetro objetivo. La incertidumbre surge como resultadode la sensibilidad variable de los estimadores a las violaciones de supuestos o la faltade información sobre las condiciones que generan los datos utilizados para calcularlos estimadores. El procedimiento que aquí se propone permite al investigador utilizartodos los estimadores (plausibles), en lugar de tener que elegir solo uno que ex ante seconsidera “el mejor o el correcto”. Esto se logra asignando a cada estimador un puntajede precisión que depende de: i) errores conocidos debidos a la violación de supuestos en los que se basa el estimador, y ii) probabilidades (estimadas) de que los supuestosse violen en un caso particular. Las subsiguientes inferencias sobre determinanteso tendencias de la mortalidad pueden entonces basarse en todos los estimadores yconducir a pruebas de hipótesis más robustas y conservadoras. Si bien la aplicaciónque utilizamos como ilustración corresponde a la mortalidad, la metodología puedeaplicarse a cualquier tipo de parámetro demográfico.

Behavioral interventions are increasingly based on holistic approaches to health with an understanding that health-related behaviors are linked. A motivating example is provided by the Philani study, an intervention trial conducted to improve the health of South African mothers and their children. Inter-related health problems around maternal alcohol use, malnutrition, and HIV were addressed; multiple endpoints were targeted. The traditional hypothesis testing paradigm that tests significance on one primary outcome did not suffice. Past multiple endpoint studies have utilized a sign test on the number of estimated differences between treatment and control that favor the intervention. However, in order to preserve type 1 error, one must account for correlations among the outcomes. We propose an alternative approach that counts the number of significant treatment-control differences. Monte Carlo simulation is used to adjust for correlation, providing updated critical values and p values. Our method is implemented through an R package and applied to the Philani data to test the intervention's overall effect.

Despite evidence showing that systematic outcome monitoring can prevent treatment failure, the practical conditions that allow for implementation are seldom met in naturalistic psychological services. In the context of limited time and resources, session-by-session evaluation is rare in most clinical settings. This study aimed to validate innovative prediction methods for individual treatment progress and dropout risk based on basic outcome monitoring. Routine data of a naturalistic psychotherapy outpatient sample were analyzed (N = 3902). Patients were treated with cognitive behavioral therapy with up to 95 sessions (M = 39.19, SD = 16.99) and assessment intervals of 5–15 sessions. Treatment progress and dropout risk were predicted in two independent analyses using the nearest neighbor method and least absolute shrinkage and selection operator regression, respectively. The correlation between observed and predicted patient progress was r =.46. Intrinsic treatment motivation, previous inpatient treatment, university-entrance qualification, baseline impairment, diagnosed personality disorder, and diagnosed eating disorder were identified as significant predictors of dropout, explaining 11% of variance. Innovative outcome prediction in naturalistic psychotherapy is not limited to elaborated progress monitoring. This study demonstrates a reasonable approach for tracking patient progress as long as session-by-session assessment is not a valid standard. [ABSTRACT FROM AUTHOR]