Use of saliva to monitor meningococcal vaccine responses: proposing a threshold in saliva as surrogate of protection

Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies

BACKGROUND

In binary classification for clinical studies, an imbalanced distribution of cases to classes and an extreme association level between the binary dependent variable and a subset of independent variables can create significant classification problems. These crucial issues, namely class imbalance and complete separation, lead to classification inaccuracy and biased results in clinical studies.

METHOD

To deal with class imbalance and complete separation problems, we propose using a fuzzy logistic regression framework for binary classification. Fuzzy logistic regression incorporates combinations of triangular fuzzy numbers for the coefficients, inputs, and outputs and produces crisp classification results. The fuzzy logistic regression framework shows strong classification performance due to fuzzy logic's better handling of imbalance and separation issues. Hence, classification accuracy is improved, mitigating the risk of misclassified conditions and biased insights for clinical study patients.

RESULTS

The performance of the fuzzy logistic regression model is assessed on twelve binary classification problems with clinical datasets. The model has consistently high sensitivity, specificity, F1, precision, and Mathew's correlation coefficient scores across all clinical datasets. There is no evidence of impact from the imbalance or separation that exists in the datasets. Furthermore, we compare the fuzzy logistic regression classification performance against two versions of classical logistic regression and six different benchmark sources in the literature. These six sources provide a total of ten different proposed methodologies, and the comparison occurs by calculating the same set of classification performance scores for each method. Either imbalance or separation impacts seven out of ten methodologies. The remaining three produce better classification performance in their respective clinical studies. However, these are all outperformed by the fuzzy logistic regression framework.

CONCLUSION

Fuzzy logistic regression showcases strong performance against imbalance and separation, providing accurate predictions and, hence, informative insights for classifying patients in clinical studies.

Adaptation of the technology readiness levels for impact assessment in implementation sciences: The TRL-IS checklist

Background

Intervention development is a critical process in implementation research. There are key stages involved in the process to design, pilot, demonstrate and release a technology or an intervention. The Technology Readiness Level (TRL) is a globally accepted instrument for assessing the maturity of research development. However, the original levels do not fit all, and some adjustments are required for its applicability in implementation sciences.

Aims

This study aimed to gather the prior knowledge base on TRL in public and population health research; to develop a standard definition of readiness, and to adapt and validate the TRL to an implementation science context (TRL-IS).

Materials and methods

A Mixed methods approach has been followed in this study. A scoping review using the PRISMA extension (PRISMA-ScR) informed a nominal expert panel for developing a standard definition of readiness and to modify the TRL following an ontoterminology approach. Then the maturity of six practical case study examples were rated by ten researchers using the modified TRL to estimate inter-rater reliability, and a group of experts provided final content and face validity and feasibility.This mixed methods study included 1) a scoping review to examine the current literature and develop a knowledge base, identify knowledge gaps and to clarify concepts; 2) the development of a standard definition of 'Readiness' and related terms; and 3) adaptation of the TRL to implementation science and development of a checklist to rate the maturity of applications.A standard definition of readiness and related terms was produced by the core team, and an international nominal group (n = 30) was conducted to discuss and validate the definition and terms, and the location of 'Readiness' in the initiation and early development phases of implementation.Following feedback from the nominal group, the development of the TRL-IS was finalised and a TRL-IS rating checklist was developed to rate the maturity of applications. The TRL-IS checklist was tested using six cases based on real world studies on implementation research.The inter-rater reliability of the TRL-IS was evaluated by ten raters and finally six raters evaluated the content and face validity, and feasibility, of the TRL-IS checklist using the System Usability Scale (SUS).

Results

Few papers (n = 11) utilised the TRL to evaluate the readiness of readiness of health and social science implementation research. The main changes in the adaption of the TRL-IS included the removal of laboratory testing, limiting the use of "operational" environment and a clearer distinction between level 6 (pilot in a relevant environment) and 7 (demonstration in the real world prior to release). The adapted version was considered relevant by the expert panel. The TRL-IS checklist showed evidence of good inter-rater reliability (ICC = 0.90 with 95 % confident interval = 0.74-0.98, p < .001) and provides a consistent metric.

Conclusions

In spite of recommendations made by national and international research funding agencies, few health and social science implementation studies include the TRL as part of their evaluation protocol. The TRL-IS offers a high degree of conceptual clarity between scientific maturity phases or readiness levels, and good reliability among raters of varying experience. This study highlights that adoption of the TRL-IS framework in implementation sciences will bolster the scientific robustness and comparability of research maturity in this domain.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care using routinely collected electronic health data: a landmarking approach

OBJECTIVES

Falls pose a significant challenge in residential aged care facilities (RACFs). Existing falls prediction tools perform poorly and fail to capture evolving risk factors. We aimed to develop and internally validate dynamic fall risk prediction models and create point-based scoring systems for residents with and without dementia.

MATERIALS AND METHODS

A longitudinal cohort study using electronic data from 27 RACFs in Sydney, Australia. The study included 5492 permanent residents, with a 70%-30% split for training and validation. The outcome measure was the incidence of falls. We tracked residents for 60 months, using monthly landmarks with 1-month prediction windows. We employed landmarking dynamic prediction for model development, a time-dependent area under receiver operating characteristics curve (AUROCC) for model evaluations, and a regression coefficient approach to create point-based scoring systems.

RESULTS

The model identified 15 independent predictors of falls in dementia and 12 in nondementia cohorts. Falls history was the key predictor of subsequent falls in both dementia (HR 4.75, 95% CI, 4.45-5.06) and nondementia cohorts (HR 4.20, 95% CI, 3.87-4.57). The AUROCC across landmarks ranged from 0.67 to 0.87 for dementia and from 0.66 to 0.86 for nondementia cohorts but generally remained between 0.75 and 0.85 in both cohorts. The total point risk score ranged from -2 to 57 for dementia and 0 to 52 for nondementia cohorts.

DISCUSSION

Our novel risk prediction models and scoring systems provide timely person-centered information for continuous monitoring of fall risk in RACFs.

CONCLUSION

Embedding these tools within electronic health records could facilitate the implementation of targeted proactive interventions to prevent falls.

Developing an intelligent prediction system for successful aging based on artificial neural networks

Background

Due to the growing number of disabilities in elderly, Attention to this period of life is essential to be considered. Few studies focused on the physical, mental, disabilities, and disorders affecting the quality of life in elderly people. SA1 is related to various factors influencing the elderly's life. So, the objective of the current study is to build an intelligent system for SA prediction through ANN2 algorithms to investigate better all factors affecting the elderly life and promote them.

Methods

This study was performed on 1156 SA and non-SA cases. We applied statistical feature reduction method to obtain the best factors predicting the SA. Two models of ANNs with 5, 10, 15, and 20 neurons in hidden layers were used for model construction. Finally, the best ANN configuration was obtained for predicting the SA using sensitivity, specificity, accuracy, and cross-entropy loss function.

Results

The study showed that 25 factors correlated with SA at the statistical level of P < 0.05. Assessing all ANN structures resulted in FF-BP3 algorithm having the configuration of 25-15-1 with accuracy-train of 0.92, accuracy-test of 0.86, and accuracy-validation of 0.87 gaining the best performance over other ANN algorithms.

Conclusions

Developing the CDSS for predicting SA has crucial role to effectively inform geriatrics and health care policymakers decision making.

A qualitative meta-synthesis of studies of patients' experience of exercise interventions in advanced cancer

Background

People with advanced cancer often experience reduced functional capacity and quality of life. Research evaluating the potential benefit of exercise programmes for limiting such decline is accumulating. However, an appraisal of the evidence that considers the patient experience of exercise programmes, what mattered to them and what motivated and encouraged them to engage in exercise, has not been published. The aim of this meta-synthesis was to identify, appraise and bring together evidence from qualitative research in this area.

Methods

Four databases were searched from 2nd January to 8th January 2023 for relevant studies. Qualitative studies investigating the experience of exercise as an intervention for adults with advanced cancer were included. Major findings and study characteristics were extracted. Findings were summarised, compared, and synthesised using meta-synthesis.

Results

Eight studies were eligible and generated seven sub themes which informed the construction of three key themes: (1) Impact of Delivery Method; (2) Emerging Motivation; and (3) Physical Impact.

Conclusion

The analysis revealed that exercise has the potential to positively influence all four dimensions of well-being: physical, psychological, social, and spiritual, for people with advanced cancer. Future research is required to consider the differential impact that the type, volume, and duration of exercise may have on the exercise experience for this patient group.

Connecting the Dots of Care: A pilot study linking Aboriginal and/or Torres Strait Islander peoples with diabetes care in hospital, using hospital pharmacists

Background

Diabetes is common among Aboriginal and/or Torres Strait Islander peoples, yet often undetected in hospital.

Objective

To identify how urban hospital pharmacists can detect if Aboriginal and/or Torres Strait Islander patients have diabetes or a higher chance of getting diabetes.

Methods

A multi-methods study used data from patients, and researcher field notes. Aboriginal and/or Torres Strait Islander peoples admitted to hospital over 12-weeks (July-October 2021) were prospectively identified from admissions lists. A hospital pharmacist-researcher visited eligible patients. Consenting participants had their blood glucose and HbA1c checked. Participants with HbA1c > 6.5% (no known diabetes) or 7% (known diabetes) were referred for endocrinology review during their stay. Test results and resultant diabetes plan were shared with their general practitioner. Two days after discharge, participants were called to gauge views on their hospital-based diabetes care. Barcode technology recorded pharmacist time. Voice-recorded field notes were thematically analysed. Ethics approval was obtained.

Results

Seventy-two patients were eligible for inclusion, 67/72 (93%) consented to take part. Sixty-one (91%) patients returned a HbA1c < 6.5, of which, 4/61 (6.5%) returned a HbA1c, 6-6.4. They were contacted to yarn about diabetes prevention. Six of the 67 (9%) qualified for endocrine review, 5 had known diabetes, one newly diagnosed. None were known to endocrinology. All participants telephoned were satisfied with their hospital-based diabetes care. Pharmacist time for initial introductory yarn, consenting process, organisation of HbA1c and results discussion was 20 min or 40 min if referred for endocrine review. Field notes guided understanding of service implementation.

Conclusion

This novel pharmacist-led diabetes screening service for Aboriginal and/or Torres Strait Islander peoples appeared to provide a unique opportunity for screening and referral links in a holistic way. Future research is required to test this model by upscaling to include more pharmacists and other chronic disease screening and referral pathways.

Evaluation of Spin in the Abstracts of Systematic Reviews and Meta-Analyses of Ulnar Collateral Ligament Reconstruction

Purpose

To identify the quantity and types of spin present in systematic reviews and meta-analyses of ulnar collateral ligament reconstruction (UCLR) outcomes and to characterize the studies with spin to determine if any patterns exist.

Methods

This study was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In August 2022, PubMed, Scopus, and SportDiscus databases were searched using the terms "ulnar collateral ligament reconstruction" AND "systematic review" OR "meta-analysis." Each abstract was assessed for the presence of the 15 most common types of spin derived from a previously established methodology. General data that were extracted included study title, authors, publication year, journal, level of evidence, study design, funding source, reported adherence to PRISMA guidelines, preregistration of the study protocol, and methodologic quality per A Measurement Tool to Assess Systematic Reviews Version 2 (AMSTAR 2).

Results

In total, 122 studies were identified during the preliminary search, of which 19 met the inclusion criteria. Each study had at least 1 form of spin. The most common type of spin identified was type 5 ("The conclusion claims the beneficial effect of the experimental treatment despite a high risk of bias in primary studies") (7/19, 36.8%). AMSTAR type 9 ("Did the review authors use a satisfactory technique for assessing the RoB [risk of bias] in individual studies that were included in the review?") was associated with both a lower Clarivate Impact Factor (P = .001) and a lower Scopus CiteScore (P = .015). Studies receiving external funding were associated with the failure to satisfy AMSTAR type 3 ("Did the review authors explain their selection of the study designs for inclusion in the review?") (P = .047).

Conclusions

Spin is highly prevalent in the abstracts of systematic reviews and meta-analyses that investigate the outcomes of UCLR.

Clinical Relevance

Spin has been identified in peer-reviewed articles published on various topics, including many in orthopaedics. Systematic reviews and meta-analyses contain the most comprehensive evidence regarding a clinical question, so it is important to identify spin that may be included in these reports. Greater efforts are needed to ensure that the abstracts of papers accurately represent the results in the full text.

Managing With Atrial Fibrillation: An Exploratory Model-Based Cluster Analysis of Clinical and Personal Patient Characteristics

Background

Examining characteristics of patients with atrial fibrillation (AF) has the potential to help in identifying groups of patients who might benefit from different management approaches.

Methods

Secondary analysis of online survey data was combined with clinic referral data abstraction from 196 patients with AF attending an AF specialty clinic. Cluster analyses were performed to identify distinct, homogeneous clusters of AF patients defined by 11 relevant variables: CHA2DS2-VASc score, age, AF symptoms, overall health, mental health, AF knowledge, perceived stress, household and recreation activity, overall AF quality of life, and AF symptom treatment satisfaction. Follow-up analyses examined differences between the cluster groups in additional clinical variables.

Results

Evidence emerged for both 2- and 4-cluster solutions. The 2-cluster solution involved a contrast between patients who were doing well on all variables (n = 129; 66%) vs those doing less well (n = 67; 34%). The 4-cluster solution provided a closer-up view of the data, showing that the group doing less well was split into 3 meaningfully different subgroups of patients who were managing in different ways. The final 4 clusters produced were as follows: (i) doing well; (ii) stressed and discontented; (iii) struggling and dissatisfied; and (iv) satisfied and complacent.

Conclusions

Patients with AF can be accurately classified into distinct, natural groupings that vary in clinically important ways. Among the patients who were not managing well with AF, we found 3 distinct subgroups of patients who may benefit from tailored approaches to AF management and support. The tailoring of treatment approaches to specific personal and/or behavioural patterns, alongside clinical patterns, holds potential to improve patient outcomes (eg, treatment satisfaction).

Antibodies against Neisseria meningitidis serogroups A, C, W and Y in serum and saliva of Norwegian adolescents

INTRODUCTION

The incidence of invasive meningococcal disease (IMD) among Norwegian 16-19-year-olds was 1-7/100,000 in the decade before the COVID-19 pandemic, with serogroup Y (MenY) dominance. In contrast to many other European countries, meningococcal vaccines are not part of the national immunisation program (NIP) in Norway. This cross-sectional study aimed to measure the degree of natural immunity against Neisseria meningitidis among adolescents in Norway to evaluate the need for introducing tetravalent meningococcal conjugate vaccine (MCV4) in the NIP.

MATERIALS AND METHODS

Serum and saliva samples were collected from students in upper and lower secondary schools in Norway in 2018. Samples were analysed for meningococcal capsular polysaccharide (PS)-specific antibodies using a bead-based multiplex immunoassay. PS-specific antibody levels were linked to data on meningococcal carriage, vaccination status and risk factors for carriage (assessed with questionnaire) and analysed by linear regression of log transformed concentrations. A subset of samples from unvaccinated individuals was analysed for serum bactericidal antibodies (SBA).

RESULTS

A total of 1344 participants, median age 16 years (range 12-24), were included in the study. Overall, 60.9% of the participants were female and 1137 (84.6%) were not vaccinated with MCV4. PS-specific antibody concentrations in serum and saliva were low among unvaccinated individuals for all serogroups and only 6.7-20.0% of the subpopulation with high PS-specific antibodies assessed with SBA had protective levels. Unvaccinated MenY carriers had higher levels of MenY anti-PS IgG in serum and IgA in saliva than those not carrying MenY. Use of Swedish snus was associated with lower anti-PS IgG levels in serum and waterpipe use with lower anti-PS IgG levels in saliva.

CONCLUSION

Unvaccinated adolescents in Norway have a low degree of natural immunity against the serogroups of N. meningitidis predominating among cases of IMD in this age group. Therefore, introduction of MCV4 for adolescents in the NIP is recommended.

The potential of saliva as an accessible and sensitive sample type for the detection of respiratory pathogens and host immunity

Despite its prominence in early scientific records, the usefulness of saliva as a respiratory specimen has been de-emphasised over the past century. However, due to its low cost and reliance on specific supply chains and the non-invasive nature of its collection, its benefits over swab-based specimens are again becoming increasingly recognised. These benefits were highlighted over the course of the COVID-19 pandemic, where saliva emerged as a more practical, clinically non-inferior sample type for the detection of SARS-CoV-2 and saw numerous saliva-based diagnostic tests approved for clinical use. Looking forward, as saliva uniquely contains both respiratory secretions and immunological components, it has potentially wide applications, ranging from clinical diagnostics to post-vaccine disease burden and immunity surveillance. This Personal View seeks to summarise the existing evidence for the use of saliva in detecting respiratory pathogens, beyond SARS-CoV-2, as well as detailing methodological factors that can influence sample quality and thus, clinical utility.

"We don't really do doctors." messages from people diagnosed with occupational leptospirosis for medical professionals on infection, hospitalisation, and long-term effects

Leptospirosis is largely an occupational disease for people working with livestock in Aotearoa New Zealand. Introduction of livestock vaccination and use of personal protective equipment has been associated with a reduction in the incidence. However, the incidence of occupational leptospirosis remains high, with significant burdens for affected families and healthcare system. For this article, a subset of thirteen participants from a nationwide leptospirosis case-control study (2019-2021) who were diagnosed with leptospirosis and worked with livestock at the time of illness were invited and agreed to a semi-structured interview. Interviewees reflected on their experiences as messages for medical professionals. The analysis of transcripts reveals widely shared experiences with infection, hospitalisation, and treatment, as well as long-term effects and recovery. Conclusions for medical professionals include that ill workers continue to have their diagnosis of leptospirosis delayed. This delay may contribute to more than half the people ill with leptospirosis hospitalised. Further, medical professionals' communication and relationship with ill people strongly colours the latter's experience, for good or for bad. Moreover, most interviewees experienced a recovery process that took several months of feeling tired, which undermined professional performance and emotional wellbeing.

The COVID-19 green certificate's effect on vaccine uptake in French and Italian regions

The COVID-19 green certificates were introduced in many countries in 2021 to encourage vaccine uptake against the COVID-19 virus in order to reduce the spread of severe infection among the population, ensure the safety of cross-border movements, and facilitate the resumption of social life and economic activities. This study uses a single-group interrupted time series approach to examine the effect of the green certificate announcement on the first doses of the COVID-19 vaccine in 20 Italian and 18 French regions during the summer of 2021. The estimation results show that the green certificate announcement mitigated regional disparities in vaccine uptake. It persuaded undecided people to have their first doses of the COVID-19 vaccine, especially in regions lagging in the mass vaccination campaign. It was less effective in those regions where there was already a high level of vaccine protection. The announcement also proved to be an effective political strategy with which to increase the first-dose rates immediately, but not in the long term.

"Healthy immigrant effect" among individuals experiencing homelessness in Spain?: Foreign-born individuals had higher average age at death in 15-year retrospective cohort study

BACKGROUND

Individuals experiencing homelessness (IEHs) suffer from severe health inequities. Place of origin is linked to health and mortality of IEHs. In the general population the "healthy immigrant effect" provides a health advantage to foreign-born people. This phenomenon has not been sufficiently studied among the IEH population. The objectives are to study morbidity, mortality, and age at death among IEHs in Spain, paying special attention to their origin (Spanish-born or foreign-born) and to examine correlates and predictors of age at death.

METHODS

Retrospective cohort study (observational study) of a 15-year period (2006-2020). We included 391 IEHs who had been attended at one of the city's public mental health, substance use disorder, primary health, or specialized social services. Subsequently, we noted which subjects died during the study period and analyzed the variables related to their age at death. We compared the results based on origin (Spanish-born vs. foreign-born) and fitted a multiple linear regression model to the data to establish predictors of an earlier age at death.

RESULTS

The mean age at death was 52.38 years. Spanish-born IEHs died on average almost nine years younger. The leading causes of death overall were suicide and drug-related disorders (cirrhosis, overdose, and chronic obstructive pulmonary disease [COPD]). The results of the linear regression showed that earlier death was linked to COPD (b = - 0.348), being Spanish-born (b = 0.324), substance use disorder [cocaine (b =-0.169), opiates (b =.-243), and alcohol (b =-0.199)], cardiovascular diseases (b = - 0.223), tuberculosis (b = - 0.163), high blood pressure (b =-0.203), criminal record (b =-0.167), and hepatitis C (b =-0.129). When we separated the causes of death for Spanish-born and foreign-born subjects, we found that the main predictors of death among Spanish-born IEHs were opiate use disorder (b =-0.675), COPD (b =-0.479), cocaine use disorder (b =-0.208), high blood pressure (b =-0.358), multiple drug use disorder (b =-0.365), cardiovascular disease (b =-0.306), dual pathology (b =-0.286), female gender (b =-0.181), personality disorder (b =-0.201), obesity (b =-0.123), tuberculosis (b =-0.120) and having a criminal record (b =-0.153). In contrast, the predictors of death among foreign-born IEHs were psychotic disorder (b =-0.134), tuberculosis (b =-0.132), and opiate (b =-0.119) or alcohol use disorder (b =-0.098).

CONCLUSIONS

IEHs die younger than the general population, often due to suicide and drug use. The healthy immigrant effect seems to hold in IEHs as well as in the general population.

Affective state estimation based on Russell's model and physiological measurements

Affective states are psycho-physiological constructs connecting mental and physiological processes. They can be represented in terms of arousal and valence according to the Russel's model and can be extracted from physiological changes in human body. However, a well-established optimal feature set and a classification method effective in terms of accuracy and estimation time are not present in the literature. This paper aims at defining a reliable and efficient approach for real-time affective state estimation. To obtain this, the optimal physiological feature set and the most effective machine learning algorithm, to cope with binary as well as multi-class classification problems, were identified. ReliefF feature selection algorithm was implemented to define a reduced optimal feature set. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis, were implemented to compare their effectiveness in affective state estimation. The developed approach was tested on physiological signals acquired on 20 healthy volunteers during the administration of images, belonging to the International Affective Picture System, conceived for inducing different affective states. ReliefF algorithm reduced the number of physiological features from 23 to 13. The performances of machine learning algorithms were compared and the experimental results showed that both accuracy and estimation time benefited from the optimal feature set use. Furthermore, the KNN algorithm resulted to be the most suitable for affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation.

Assessment of the Psychometric Properties of the Danish VISA-P

Purpose

The objective of the current study was to conduct a rigorous assessment of the psychometric properties of the Victorian Institute of Sports Assessment-patellar tendinopathy (VISA-P).

Methods

Rasch analysis, confirmatory factor analysis (CFA), and multivariable linear regression were used to assess the psychometric properties of the VISA-P questionnaire in 184 Danish patients with patellar tendinopathy who had symptoms ranging from under 3 months to over 1 year. A group of 100 healthy Danish persons was included as a reference for known-group validation.

Results

The analyses revealed that the 8-item VISA-P did not fit a unidimensional model, yielded at best a 3-factor model, and exhibited differential item functioning (DIF) across healthy subjects versus people with patellar tendinopathy.

Conclusion

VISA-P in its present form does not satisfy a measurement model and is not a robust scale for measuring patellar tendinopathy. A new PROM for patellar tendinopathy should be developed and appropriately validated, and meanwhile, simple pain scoring (e.g., numeric rating scales) and functional tests are suggested as more appropriate outcome measures for studies of patellar tendinopathy.

Can oral sex be performed safely among men who have sex with men (MSM) and transgender women in Bangladesh? Challenges, complexities and the way forward

Introduction

There is currently ample research and intervention initiatives addressing anal sex and sexually transmitted infections (STIs) among gender and sexually diverse people (i.e., men who have sex with men (MSM) and transgender women). However, oral sex and oral STIs are not prioritized to the same extent, despite their concerning implications. This article aimed to delineate the underlying contexts of unprotected oral sex and the management challenges of oral STIs.

Methodology

This qualitative study constituted 30 in-depth interviews, 14 focus group discussions, and 10 key-informant interviews with gender and sexually diverse people, service providers of HIV interventions and sexuality researchers. Thematic analysis conventions were applied.

Results

Findings revealed various contexts of unprotected oral sex. In particular, we found a discordance between pleasure and protected sex where participants believed the two phenomena were mutually exclusive, therefore they did not want to compromise their pleasure by using condoms. Moreover, their low awareness, risk perception, and unchallenged misconceptions about the harms of unprotected oral sex fueled their hesitancy to use condoms during oral sex. Compared to anal sex and STIs, oral sex and STIs were less prioritized in the current HIV intervention modalities, where healthcare providers lacked adequate knowledge and training about oral sex and oral STIs, as well as their complexities.

Conclusions

In Bangladesh and several other settings, oral sex is considered a taboo, thus imbuing silence about this issue and its complexities. In this context, it is integral to eradicate the taboos and silence surrounding oral sex and oral STIs in order to strengthen the overall STI management strategy. Therefore, HIV/STI prevention programs and mainstream healthcare facilities need to underscore oral STI interventions, otherwise this issue would remain under-prioritized.

Effectiveness of measures to preserve labour and childbirth companionship at the times of COVID-19 outbreak

INTRODUCTION

Although childbirth services were accessible after COVID-19 outbreak, the measures taken by the Italian Government for contagion containment required some restrictions on the presence of trusted persons for mothers, forcing them to isolation during hospitalization. To preserve companionship, the Regional Health Authority of Tuscany issued a resolution providing partners with the possibility to be present during labour and childbirth for non-asymptomatic women.

OBJECTIVES

In this study, we: 1) analyse the impact of pandemic on companionship in terms of significant reduction of the possibility for women to be accompanied by a trusted person during labour and childbirth; and 2) ascertain if the regional resolution issued was effective in containing the reduction of companionship.

METHODS

We performed an interrupted time series analysis to measure the variation of the possibility for women to be accompanied by a trusted person during labour and childbirth, in response to formalization of lock-down due to COVID-19 outbreak and the introduction of the regional resolution aimed at contrasting negative effects on companionship.

RESULTS AND CONCLUSIONS

The ITS analysis showed that there was a significant decrease in the women-reported experience of companionship in the month of the formalization of lock-down, namely March 2020, followed by a slight increase in the upcoming months. A trend reversal was observed after May 2020, when the regional resolution was fully operational.

The prevalence and socio-demographic associations of household food insecurity in seven slum sites across Nigeria, Kenya, Pakistan, and Bangladesh. A cross-sectional study

Although the proportion of people living in slums is increasing in low- and middle-income countries and food insecurity is considered a severe hazard for health, there is little research on this topic. This study investigated and compared the prevalence and socio-demographic associations of household food insecurity in seven slum settings across Nigeria, Kenya, Pakistan, and Bangladesh. Data were taken from a cross-sectional, household-based, spatially referenced survey conducted between December 2018 and June 2020. Household characteristics and the extent and distribution of food insecurity across sites was established using descriptive statistics. Multivariable logistic regression of data in a pooled model including all slums (adjusting for slum site) and site-specific analyses were conducted. In total, a sample of 6,111 households were included. Forty-one per cent (2,671) of all households reported food insecurity, with varying levels between the different slums (9-69%). Household head working status and national wealth quintiles were consistently found to be associated with household food security in the pooled analysis (OR: 0·82; CI: 0·69-0·98 & OR: 0·65; CI: 0·57-0·75) and in the individual sites. Households which owned agricultural land (OR: 0·80; CI: 0·69-0·94) were less likely to report food insecurity. The association of the household head's migration status with food insecurity varied considerably between sites. We found a high prevalence of household food insecurity which varied across slum sites and household characteristics. Food security in slum settings needs context-specific interventions and further causal clarification.

An exploratory study on the application of screening of activity limitation and safety awareness (SALSA) scale for evaluation of activity limitation among elderly

BACKGROUND

Aging poses a significant risk for activity and functional limitations. Problems among aged populations are interrelated, and these problems can lead to other disorders and finally leave a negative impact on the quality of life (QoL) This study used the screening of activity limitation and safety awareness (SALSA) scale to screen and measure activity limitations among the elderly.

MATERIALS AND METHODS

A community-based descriptive cross-sectional study was conducted among 928 elderly from the three districts of Tamil Nadu, India. The SALSA scale was administered via a door-to-door interview, and data analysis was carried out. Simple random sampling was used to obtain the study subjects in the selected study area. Chi-square tests and binary logistic regression were performed to find the association between selected risk factors.

RESULTS

Among the 928 elderly, 53.7% of them were males, 84.9% were between the age of 60 and 69 years, and 31% were 70 years and above. The study found that 66.5% of the elderly have mild activity limitations, 15.7% of them had moderate limitations, 4.8% of them had severe limitations, around 3% of them had extreme limitations, and 10.3% of them had no significant limitations. The binary logistic regression found people of age 65 and above (OR = 3.80), female gender (OR = 2.90), with hypertension (OR = 1.95), vision problem (OR = 1.92), decreased sensation of the foot (OR = 3.41), as the significant independent risk factors for activity limitation among the elderly.

CONCLUSION

The study found self-reported activity limitations among the elderly to be nearly 90%. The SALSA scale may be used as a handy tool to identify the activity limitation in community-based geriatric checkups.

The role of machine learning in HIV risk prediction

Despite advances in reducing HIV-related mortality, persistently high HIV incidence rates are undermining global efforts to end the epidemic by 2030. The UNAIDS Fast-track targets as well as other preventative strategies, such as pre-exposure prophylaxis, have been identified as priority areas to reduce the ongoing transmission threatening to undermine recent progress. Accurate and granular risk prediction is critical for these campaigns but is often lacking in regions where the burden is highest. Owing to their ability to capture complex interactions between data, machine learning and artificial intelligence algorithms have proven effective at predicting the risk of HIV infection in both high resource and low resource settings. However, interpretability of these algorithms presents a challenge to the understanding and adoption of these algorithms. In this perspectives article, we provide an introduction to machine learning and discuss some of the important considerations when choosing the variables used in model development and when evaluating the performance of different machine learning algorithms, as well as the role emerging tools such as Shapely Additive Explanations may play in helping understand and decompose these models in the context of HIV. Finally, we discuss some of the potential public health and clinical use cases for such decomposed risk assessment models in directing testing and preventative interventions including pre-exposure prophylaxis, as well as highlight the potential integration synergies with algorithms that predict the risk of sexually transmitted infections and tuberculosis.

The effects of spinal stabilization exercises in patients with myasthenia gravis: a randomized crossover study

PURPOSE

The effects of spinal stabilization exercises (SSE) on fatigue, muscle strength, respiratory functions, functional capacity, and quality of life (QoL) in myasthenia gravis (MG) patients were investigated.

MATERIALS AND METHODS

This study which was designed a single-blinded, randomized crossover trial were included mild to moderate 10 MG patients, aged between 18 and 65. SSE and home program were applied to the patients for six weeks, four weeks between each exercise program. Fatigue assessed with Fatigue Severity Scale (FSS) and Visual Analog Fatigue Scale (VAFS). Muscle strengths were assessed with dynamometric measures. Respiratory Function Test, 6 Minute Walk Test (6MWT), Quantitative Myasthenia Gravis Score (QMGS), Myasthenia Gravis Quality of Life Scale-15 (MGQOL-15) were used.

RESULTS

All parameters were improved in SSE group, but not neck muscles strength, QMGS, and respiratory functions in home program group. FSS, VAFS, muscle strength, 6MWT, MGQOL-15 values were found significantly different in SSE group (p = .005 in all parameters).

CONCLUSIONS

SSE are safe and effective on fatigue, muscle strength, respiratory functions, functional capacity, and QoL for MG patients, so it may be suggested to include SSE in rehabilitation programs of MG patients.Implications For RehabilitationSpinal stabilization exercises are safe and effective for Myasthenia Gravis patients.Home exercise program which included breathing exercises and callisthenic exercises tailored to the patients' needs is safe and effective for Myasthenia Gravis patients.Rehabilitation programs of Myasthenia Gravis patients should include spinal stabilization exercises.

Urban-rural disparities in the healthy ageing trajectory in China: a population-based study

Purpose

The aim of this study is to measure the trajectory of healthy ageing among Chinese middle-aged and older population, and explore the disparity of the trajectory, as well as contributing factors, between urban and rural areas in China.

Methods

A total of 9402 respondents aged 45 years and older interviewed in four waves (2011, 2013, 2015 and 2018) were selected from the China Health and Retirement Longitudinal Study. Healthy ageing score was calculated through item response theory. A latent growth mixture model (LGMM) was applied to distinguish the trajectory of healthy aging. A multinomial logistics regression model (MLRM) was used to explore the relationship between urban-rural areas and healthy aging trajectories, and further to explore associated factors in rural and urban areas separately.

Results

The healthy ageing score was lower in rural areas than urban areas in each survey wave. Five classes (“continuing-low”, “continuing-middle”, “continuing-middle-to-high”, “significantly-declining”, “continuing-high”) were grouped through LGMM. The MLRM results showed that urban living was significantly associated with a higher likelihood of being healthy (for [continuing-low/continuing-high]: β = − 1.17, RRR = 0.31, P < 0.001, 95% CI = 0.18–0.53; and for [continuing-middle/continuing-high]: β = − 0.53, RRR = 0.59, P < 0.001, 95% CI = 0.49–0.71).

Conclusion

Healthy ageing is a prominent objective in the development of a country, and rural-urban disparities are an essential obstacle to overcome, with the rural population more likely to develop a low level of healthy ageing trajectory. Prevention and standardized management of chronic diseases should be enhanced, and social participation should be encouraged to promote healthy ageing. The policy inclination and resource investment should be enhanced to reduce disparity in healthy ageing between urban and rural areas in China.

Do older adults construct more emotionally gratifying social environments than younger adults? Evidence from a social network decision task

Do older adults construct more emotionally gratifying social environments than younger adults? According to socioemotional selectivity theory (SST), older adults actively construct their social environments to satisfy emotional goals, drawing closer to gratifying close others and pruning less rewarding persons. Yet, there is a scarcity of direct evidence showing that older adults indeed construct more gratifying social environments by pruning negative persons and by drawing closer to positive persons. We employed a novel social network decision task to study age-related differences in the emotional composition of social environments that people construct and associations with emotional experience. In three studies, participants spanning the adult age range constructed hypothetical social environments, choosing among players according to their performance on word search puzzles and valenced feedback provided by the players about the participants’ own performance. Positive valence players always provided positive feedback, whereas negative valence players always provided negative feedback. Our findings partially support SST but draw a theoretically important distinction between pruning existing social environments and constructing new social environments. When pruning an existing social environment, older adults maintained more positive as well as more negative valence players. Conversely, when required to include players to construct their social environment, older adults produced more positive social environments by excluding negative valence players. The social environment participants constructed was associated with their emotional experience, but our findings suggest that emotion drives gratifying social choices rather than vice versa. Implications for lifespan theory of motivation are discussed.

When do people believe, check, and share health rumors on social media? Effects of evidence type, health literacy, and health knowledge

Vaccine rumors on social media endanger public health. This study examined how evidence types influenced perceived persuasiveness and relevance and engagement intentions of vaccine rumors. We conducted a 2 (evidence type: anecdotes vs. anecdotal statistics) × 2 (stance: pro-vaccine rumor vs. anti-vaccine rumor) online experiment (N = 551) and surveyed participants' health literacy and vaccine knowledge. Anecdotal statistics were perceived as more relevant than anecdotes and indirectly influenced perceived persuasiveness and behavior intentions. This finding was confirmed when vaccine rumors were pro-attitudinal. Health literacy positively predicted perceived persuasiveness; health knowledge negatively predicted relevance and behavior intentions. Practical implications and future research directions are discussed.

Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support

Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targeted therapies for high-risk patients. Methods: We identified adult patients in the United Network for Organ Sharing database undergoing isolated heart transplantation between 01/2009 and 12/2017 who were supported with tMCS at the time of transplant. We constructed a machine learning model using extreme gradient boosting (XGBoost) with a 70:30 train:test split to predict 1-year post-operative mortality. All pre-transplant variables available in the UNOS database were included to train the model. Shapley Additive Explanations was used to identify and interpret the most important features for XGBoost predictions. Results: A total of 1584 patients were included, with a median age of 56 (interquartile range: 46-62) and 74% male. Actual 1-year mortality was 12.1%. Out of 498 available variables, 43 were selected for the final model. The area under the receiver operator characteristics curve (AUC) for the XGBoost model was 0.71 (95% CI: 0.62-0.78). The most important variables predictive of 1-year mortality included recipient functional status, age, pulmonary capillary wedge pressure (PCWP), cardiac output, ECMO usage, and serum creatinine. Conclusions: An interpretable machine learning model trained on a large clinical database demonstrated good performance in predicting 1-year mortality for patients bridged to heart transplantation with tMCS. Machine learning may be used to enhance clinician judgement in the care of markedly high-risk transplant recipients.

Survival prediction models: an introduction to discrete-time modeling

Background

Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.

Methods

Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.

Results

Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.

Conclusions

We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-022-01679-6).

A Day-to-Day Approach for Automating the Hospital Course Section of the Discharge Summary

Optimal solutions for abstractive summarization of electronic health record content have yet to be discovered. Although studies have applied state-of-the-art transformers in the clinical domain to radiology reports and information extraction, little is known of transformers' performance with the hospital course section of the discharge summary. This paper compares two summarization approaches for automating the hospital course section within the discharge summary: (1) a truncation approach that uses all clinical notes and (2) a day-to-day approach that segments the notes per clinical day. We pair both approaches with different transformer encoder-decoder based-models - BART, BERT2GPT2, ClinicalBERT2GPT2, and ClinicalBERT2ClinicalBERT and evaluate the transformers that work best for each approach using ROUGE metrics. The results demonstrate that the day-to-day approach can overcome the limitations of longform document summarization for the patient clinical record.

Is disgust proneness prospectively associated with influenza vaccine hesitancy and uptake?

Although various demographic and psychosocial factors have been identified as correlates of influenza vaccine hesitancy, factors that promote infectious disease avoidance, such as disgust proneness, have been rarely examined. In two large national U.S. samples (Ns = 475 and 1007), we investigated whether disgust proneness was associated with retrospective accounts of influenza vaccine uptake, influenza vaccine hesitancy, and eventual influenza vaccine uptake, while accounting for demographics and personality. Across both studies, greater age, higher education, working in healthcare, and greater disgust proneness were significantly related to greater likelihood of previously receiving an influenza vaccine. In Study 2, which was a year-long longitudinal project, disgust proneness prospectively predicted influenza vaccine hesitancy and eventual vaccine uptake during the 2020–2021 influenza season. Findings from this project expand our understanding of individual-level factors associated with influenza vaccine hesitancy and uptake, highlighting a psychological factor to be targeted in vaccine hesitancy interventions.

Stepped Wedge Cluster Randomized Trials: A Methodological Overview

BACKGROUND

Stepped wedge cluster randomized trials enable rigorous evaluations of health intervention programs in pragmatic settings. In the present study, we aimed to update neurosurgeon scientists on the design of stepped wedge randomized trials.

METHODS

We have presented an overview of recent methodological developments for stepped wedge designs and included an update on the newer associated methodological tools to aid with future study designs.

RESULTS

We defined the stepped wedge trial design and reviewed the indications for the design in depth. In addition, key considerations, including mainstream methods of analysis and sample size determination, were discussed.

CONCLUSIONS

Stepped wedge designs can be attractive for study intervention programs aiming to improve the delivery of patient care, especially when examining a small number of heterogeneous clusters.

Mind the gap: covariate constrained randomisation can protect against substantial power loss in parallel cluster randomised trials

Background

Cluster randomised trials often randomise a small number of units, putting them at risk of poor balance of covariates across treatment arms. Covariate constrained randomisation aims to reduce this risk by removing the worst balanced allocations from consideration. This is known to provide only a small gain in power over that averaged under simple randomisation and is likely influenced by the number and prognostic effect of the covariates.

We investigated the performance of covariate constrained randomisation in comparison to the worst balanced allocations, and considered the impact on the power of the prognostic effect and number of covariates adjusted for in the analysis.

Methods

Using simulation, we examined the Monte Carlo type I error rate and power of cross-sectional, two-arm parallel cluster-randomised trials with a continuous outcome and four binary cluster-level covariates, using either simple or covariate constrained randomisation. Data were analysed using a small sample corrected linear mixed-effects model, adjusted for some or all of the binary covariates. We varied the number of clusters, intra-cluster correlation, number and prognostic effect of covariates balanced in the randomisation and adjusted in the analysis, and the size of the candidate set from which the allocation was selected. For each scenario, 20,000 simulations were conducted.

Results

When compared to the worst balanced allocations, covariate constrained randomisation with an adjusted analysis provided gains in power of up to 20 percentage points. Even with analysis-based adjustment for those covariates balanced in the randomisation, the type I error rate was not maintained when the intracluster correlation is very small (0.001). Generally, greater power was achieved when more prognostic covariates are restricted in the randomisation and as the size of the candidate set decreases. However, adjustment for weakly prognostic covariates lead to a loss in power of up to 20 percentage points.

Conclusions

When compared to the worst balanced allocations, covariate constrained randomisation provides moderate to substantial improvements in power. However, the prognostic effect of the covariates should be carefully considered when selecting them for inclusion in the randomisation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01588-8.

Study protocol to assess de-implementation of the initial provider encounter for diagnosis and treatment of obstructive sleep apnea: the DREAM (Direct Referral for Apnea Monitoring) Project

Background

Obstructive sleep apnea (OSA) is a very common and serious health condition which is highly prevalent among U.S. military Veterans. Because the demand for sleep medicine services often overwhelms the availability of such services, it is necessary to streamline diagnosis and treatment protocols. The goals of this study are to, (1) assess the efficacy of de-implementing the initial provider encounter for diagnosis and treatment of OSA; (2) determine the negative predictive value (NPV) of home sleep apnea testing (HSAT); (3) develop HSAT usage recommendations for various at-risk patient populations.

Methods

This is a large, pragmatic study that will take place in 3 VA sleep medicine programs: San Francisco, CA; Portland, OR; and Pittsburgh, PA. All Veterans referred for new sleep apnea evaluations at these sites will be included in this four-year study. Outcomes will include time from referral for OSA to sleep testing and treatment; positive airway pressure (PAP) treatment adherence measures; patient-reported clinical outcomes and measures of satisfaction; determination of the NPV of HSAT; HSAT usage recommendations for at-risk patient populations.

Discussion

The DREAM (Direct Referral for Apnea Monitoring) Project will inform sleep medicine providers and clinical organizations regarding strategies to streamline diagnosis and treatment protocols for OSA. Results of this study should have significant impact on clinical practices and professional guidelines.

Trial registration The majority of this project is an observational study of clinical procedures. Therefore, clinical trial registration is not required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01899-y.

Evaluation proposal of a national community-based obesity prevention programme: a novel approach considering the complexity perspective

Community-based obesity prevention programmes are considered an important strategy to curb the obesity epidemic. The JOGG (Youth At a Healthy Weight) approach is a large-scale community-based programme for childhood obesity prevention in the Netherlands that has been implemented over the past ten years. Practice-based development of the programme, both at the national and local level, increasingly poses challenges for its evaluation. One considerable challenge is the increasing acknowledgement of the complexity in the JOGG-approach, characterized by (a) objectives that vary locally, (b) adaptions to the programme over time in response to a community's shifting needs, challenges and opportunities, and (c) emergent outcomes and non-linear causality.We propose an evaluation framework that highlights elements of the complex local practice, including the local programme theory, implementation, adaption, the influence of context and feedback loops and intended as well as emergent and unintended outcomes. By studying each of these elements in practice, we hope to learn about principles that guide effective obesity prevention across contexts. The results of the proposed evaluation will inform both practice and research.Considering complexity in evaluation is a relatively new challenge in public health and therefore an emergent research area. The proposed framework for complex evaluations allows to retrospectively evaluate a programme that was implemented and developed in practice, and enables us to learn from practice-based experiences. Following the ISBNPA Dare2Share initiative, we kindly invite other researchers in the field to share their ideas and experiences regarding integration of complexity in evaluation.

Near-roadway air pollution, immune cells and adipokines among obese young adults

BACKGROUND

Air pollution has been associated with metabolic disease and obesity. Adipokines are potential mediators of these effects, but studies of air pollution-adipokine relationships are inconclusive. Macrophage and T cells in adipose tissue (AT) and blood modulate inflammation; however, the role of immune cells in air pollution-induced dysregulation of adipokines has not been studied. We examined the association between air pollution exposure and circulating and AT adipokine concentrations, and whether these relationships were modified by macrophage and T cell numbers in the blood and AT.

METHODS

Fasting blood and abdominal subcutaneous AT biopsies were collected from 30 overweight/obese 18-26 year-old volunteers. Flow cytometry was used to quantify T effector (Teff, inflammatory) and regulatory (Treg, anti-inflammatory) lymphocytes and M1 [inflammatory] and M2 [anti-inflammatory]) macrophage cell number. Serum and AT leptin and adiponectin were measured using enzyme-linked immunosorbent assay (ELISA). Exposure to near-roadway air pollution (NRAP) from freeway and non-freeway vehicular sources and to regional particulate matter, nitrogen dioxide and ozone were estimated for the year prior to biopsy, based on participants' residential addresses. Linear regression models were used to examine the association between air pollution exposures and adipokines and to evaluate effect modification by immune cell counts.

RESULTS

An interquartile increase in non-freeway NRAP exposure during 1 year prior to biopsy was associated with higher leptin levels in both serum [31.7% (95% CI: 10.4, 52.9%)] and AT [19.4% (2.2, 36.6%)]. Non-freeway NRAP exposure effect estimates were greater among participants with greater than median Teff/Treg ratio and M1/M2 ratio in blood, and with greater M1 counts in AT. No adipokine associations with regional air pollutants were found.

DISCUSSION

Our results suggest that NRAP may increase serum leptin levels in obese young adults, and this association may be promoted in a pro-inflammatory immune cell environment in blood and AT.

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

This systematic review aims to study and classify machine learning models that predict pandemics' evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region's criticality and optimize hospitals' approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models' ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], I 2 =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.

Experiences of Siblings of Children With Neurodevelopmental Disorders: Comparing Qualitative Analysis and Machine Learning to Study Narratives

INTRODUCTION

Relatively few studies have focused on the wellbeing, experiences and needs of the siblings of children with a psychiatric diagnosis. However, the studies that have been conducted suggest that the impact of such circumstances on these siblings is significant. Studying narratives of diagnosed children or relatives has proven to be a successful approach to gain insights that could help improve care. Only a few attempts have been made to study narratives in psychiatry utilizing a machine learning approach.

METHOD

In this current study, 13 narratives of the experiences of siblings of children with a neurodevelopmental disorders were collected through largely unstructured interviews. The interviews were analyzed using the traditional qualitative, hermeneutic phenomenology method as well as latent Dirichlet allocation (LDA), an unsupervised machine learning method clustering words from documents into topics. One aim of this study was to evaluate the experiences of the siblings in order to find leads to improve care and support for these siblings. Furthermore, the outcomes of both analyses were compared to evaluate the role of machine learning in analyzing narratives.

RESULTS

Qualitative analysis of the interviews led to the formulation of nine main themes: confrontation with conflicts, coping strategies siblings, need for rest and time for myself, need for support and attention from personal circle, wish for normality, influence on personal choices and possibilities for development, doing things together, recommendations and advices, ambivalence and loyalty. Using unsupervised machine learning (LDA) 24 topics were formed that mostly overlapped with the qualitative themes found. Both the qualitative analysis and the LDA analysis detected themes that were unique to the respective analysis.

CONCLUSION

The present study found that studying narratives of siblings of children with a neurodevelopmental disorder contributes to a better understanding of the subjects' experiences. Siblings cope with ambivalent feelings toward their brother or sister and this emotional conflict often leads to adapted behavior. Several coping strategies are developed to deal with the behavior of their brother or sister like seeking support or ignoring. Devoted support, time and attention from close relatives, especially parents, is needed. The LDA analysis didn't appear useful to distract meaning and context from the narratives, but it was proposed that machine learning could be a valuable and quick addition to the traditional qualitative methods by finding overlooked topics and giving a rudimental overview of topics in narratives.

On the feasibility of pediatric dose-finding trials in small samples with information from a preceding trial in adults

We consider the case of pediatric dose-finding trials with extremely limited sample size. The operating characteristics of the standard design, the Continual Reassessment Method (CRM), are only well described for sample sizes of about 20 patients or more. In this simulation study, we assume the situation of a pediatric trial with only 10 patients and a preceding dose-finding trial in adults. Based on the adult data, we reduce the set of pediatric doses and formulate (partially) informative prior distributions for the pediatric trial. Our simulations show that such small pediatric dose-finding trials with robustified priors may provide sufficient operating characteristics.

The new normal? Redaction bias in biomedical science

A concerning amount of biomedical research is not reproducible. Unreliable results impede empirical progress in medical science, ultimately putting patients at risk. Many proximal causes of this irreproducibility have been identified, a major one being inappropriate statistical methods and analytical choices by investigators. Within this, we formally quantify the impact of inappropriate redaction beyond a threshold value in biomedical science. This is effectively truncation of a dataset by removing extreme data points, and we elucidate its potential to accidentally or deliberately engineer a spurious result in significance testing. We demonstrate that the removal of a surprisingly small number of data points can be used to dramatically alter a result. It is unknown how often redaction bias occurs in the broader literature, but given the risk of distortion to the literature involved, we suggest that it must be studiously avoided, and mitigated with approaches to counteract any potential malign effects to the research quality of medical science.

Bayesian Hodges-Lehmann tests for statistical equivalence in the two-sample setting: Power analysis, type I error rates and equivalence boundary selection in biomedical research

BACKGROUND

Null hypothesis significance testing (NHST) is among the most frequently employed methods in the biomedical sciences. However, the problems of NHST and p-values have been discussed widely and various Bayesian alternatives have been proposed. Some proposals focus on equivalence testing, which aims at testing an interval hypothesis instead of a precise hypothesis. An interval hypothesis includes a small range of parameter values instead of a single null value and the idea goes back to Hodges and Lehmann. As researchers can always expect to observe some (although often negligibly small) effect size, interval hypotheses are more realistic for biomedical research. However, the selection of an equivalence region (the interval boundaries) often seems arbitrary and several Bayesian approaches to equivalence testing coexist.

METHODS

A new proposal is made how to determine the equivalence region for Bayesian equivalence tests based on objective criteria like type I error rate and power. Existing approaches to Bayesian equivalence testing in the two-sample setting are discussed with a focus on the Bayes factor and the region of practical equivalence (ROPE). A simulation study derives the necessary results to make use of the new method in the two-sample setting, which is among the most frequently carried out procedures in biomedical research.

RESULTS

Bayesian Hodges-Lehmann tests for statistical equivalence differ in their sensitivity to the prior modeling, power, and the associated type I error rates. The relationship between type I error rates, power and sample sizes for existing Bayesian equivalence tests is identified in the two-sample setting. Results allow to determine the equivalence region based on the new method by incorporating such objective criteria. Importantly, results show that not only can prior selection influence the type I error rate and power, but the relationship is even reverse for the Bayes factor and ROPE based equivalence tests.

CONCLUSION

Based on the results, researchers can select between the existing Bayesian Hodges-Lehmann tests for statistical equivalence and determine the equivalence region based on objective criteria, thus improving the reproducibility of biomedical research.

Fomites and the environment did not have an important role in COVID-19 transmission in a Brazilian mid-sized city

It is not clear if COVID-19 can be indirectly transmitted. It is not possible to conclude the role of the environment in transmission of SARS-CoV-2 without studying areas in which people transit in great numbers. In this work we aimed to better understand the role of environment in the spread of COVID-19. We investigated the presence of SARS-CoV-2 in fomites as well as in the air and in the sewage using RT-qPCR. We studied both, a reference market area and a COVID-19 reference hospital at Barreiras city, Brazil. We collected and analyzed a total of 418 samples from mask fronts, cell phones, paper money, card machines, sewage, air and bedding during the ascendant phase of the epidemiological curve of COVID-19 in Barreiras. As a result, we detected the human RNAse P gene in most of samples, which indicates the presence of human cells or their fragments in specimens. However, we did not detect any trace of SARS-CoV-2 in all samples analyzed. We conclude that, so far, the environment and inanimate materials did not have an important role in COVID-19 transmission in Barreiras city. Therefore, similar results can probably be found in other cities, mainly those with COVID-19 epidemiological scenarios similar to that of Barreiras city. Our study is a small piece indicating the possibility that fomites and the environment do not have an important role in COVID-19 transmission. However, further studies are necessary to better understand the world scenario.

The first experiences of implementation of the Swedish tonsil register in Denmark

Background: Tonsil surgery is a daily procedure at Ear, Nose and Throat (ENT) departments as well as ENT practices. Despite national tonsil guidelines in Denmark, no national survey has been introduced.Objectives: To display the process of implementing a Danish version of the Swedish tonsil register including a presentation and validation of the first year's results.Methods: The Swedish tonsil register was translated into Danish. Minor modifications were executed and the register was implemented at the ENT department, Region Hospital West Jutland, Denmark. All patients undergoing surgery due to benign tonsil and/or adenoid diseases were eligible. Written informed consent was obtained. Indication for surgery, comorbidity, surgical and haemostasis techniques, and complications were registered by the surgeon.Results: Several obstacles were encountered due to legal procedures, daily work, IT organisation, and discontinuous information. 58% of eligible patients were registered. The incidence of post-tonsillectomy haemorrhage requiring secondary surgery was 6.4%. The potential underlying risk factors were easily retrieved from the register.Conclusions and significance: The Danish version of the national Swedish tonsil database is a useful, easily accessible, and informative tool for monitoring and validation of tonsil/adenoid surgery. We recommend a nationwide register with a central steering committee.