Relevant Determinants Research Articles

Motivators and barriers for smoking cessation in people with multiple sclerosis: a qualitative study to inform the design of a tailored intervention

BackgroundTobacco smoking is a relevant determinant of multiple sclerosis (MS) onset, and smokers have increased risk for faster progression of MS compared to non-smokers. While the smoking prevalence is high in Germany, no smoking cessation programs have been developed specifically in MS populations to date, and only little is known about the motivators and barriers influencing smoking cessation in people with MS (pwMS) in Germany. This study aims to identify these factors to inform the design of a tailored smoking cessation intervention.MethodsAs part of a larger program of work, we conducted semi-structured interviews in people with MS (pwMS) to explore their needs, motivators and barriers regarding smoking cessation. We recruited via MS-websites and the email-newsletter of our institution at the University Medical Centre Hamburg-Eppendorf. Participants were eligible if they had a self-reported MS-diagnosis and currently smoked or quit smoking within the last two years but after their MS-diagnosis. Interviews were conducted online and via telephone during May and June 2023. Data were analysed using thematic analysis based on a realistic approach.ResultsEight women and seven men participated in our interviews. Eleven were current, four were former smokers. Median time since diagnosis was 4 years (range: 1–26). Interviews identified MS-diagnosis, concerns about general health, and social factors as relevant motivators to stop smoking. Furthermore, worries about negative consequences when quitting (e.g. fear of missing out on social interactions or weight gain) were identified as a great barrier to smoking cessation. Knowledge about the connection between MS and smoking, and satisfaction with communication with MS clinicians were low. PwMS expressed a need for better conversations with neurologists and expert-led smoking cessation interventions. Additionally, we found that the wish for peer-exchange and the willingness to participate in smoking cessation programs was high.ConclusionOur results confirm findings of previous studies from other countries, identifying lack of knowledge, unsatisfactory communication with MS clinicians, and worries about negative consequences when quitting as barriers, and the MS-diagnosis as a motivator for smoking cessation. In a next step, we will use our findings for the development of an MS-specific online smoking cessation program.

Pregnancy outcomes in adolescent and young adult patients with multiple sclerosis: A case series.

While there is increasing understanding of the effect of pregnancy on the clinical course of multiple sclerosis (MS), there are limited studies focused on younger populations. We report neurologic and obstetric outcomes for 14 pregnancies from 11 adolescent and young adult female patients with pediatric-onset MS who delivered live births prior to age 26, and describe their relevant social determinants of health. Neurologic and obstetric outcomes in this cohort were generally good. Many of the pregnancies in our cohort appeared unplanned, underscoring the importance of discussing family planning with all MS patients of childbearing potential.

Are Historically Black Colleges and Universities an Ecosystem for Black Entrepreneurship? Some Evidence From the Brookings Institution 2022 Black Progress Index Data

Historically, Black Americans have owned businesses at a lower rate than White Americans. While there is a vast literature on the factors that explain this disparity, machine-learning analytics have not been exploited to capture relevant predictive determinants of the formation of Black-owned Businesses. In this paper, we utilize the Brookings Institution 2022 Black Progress Index Data to explore, similar to the approaches in criminology, the attractors and generators of Black-owned businesses across US counties. We augment the Brookings data to account for the presence of a Historically Black College/University (HBCU) in the county, and deploy a rigorous least absolute shrinkage and selection operator—a machine-learning analytical tool—to identify the best predictors of Black-owned businesses in a county, and classify them as attractors and/or generators in regression specifications. Parameter estimates from several mixed effects regressions specifications reveal that HBCUs are an attractor/generator of Black entrepreneurship. Treatment effect matching parameter estimates suggest that the HBCU effects on Black entrepreneurship outcomes are causal. Overall, our findings suggest that HBCUs constitute an ecosystem for Black entrepreneurship as they are an important catalyst and driver of Black-owned businesses, and can serve a substantive role in reducing racial entrepreneurship disparities. Our results suggest that policies that engender private or public investments in HBCU capacity to enhance their Black entrepreneurship ecosystem can be effective in reducing Black-White disparities in entrepreneurship and business ownership.

Exposure assessment of the European adult population to deoxynivalenol – Results from the HBM4EU Aligned Studies

Mycotoxins are natural toxins produced by fungi that may cause adverse health effects thus constituting a public health concern. Deoxynivalenol (DON), a mycotoxin affecting the immune system and causing intestinal disorders, was selected as a priority under the European Human Biomonitoring Initiative (HBM4EU). Urinary total DON levels (tDON) of 1270 participants from six countries were used to characterize the internal exposure of the adult European population and identify the most relevant determinants of exposure. tDON concentrations’ P50 and P95 were in the range of 0.41–10.16 µg/L (0.39–9.05 µg/g crt) and 3.25–46.58 µg/L (2.12–33.50 µg/g crt) respectively. Higher tDON levels were observed for (i) male participants from France and Germany, (ii) samples collected in spring and summer, (iii) participants with a lower educational level, (iv) participants living in rural areas, (v) individuals without a job in France and Luxembourg, while in Portugal higher exposure was observed in working individuals, (vi) individuals with higher consumption of cereals and bread. The proportion of individuals with exposure levels exceeding the HBM-GV of 23 µg/L was 12.3 %, ranging from 0.8 % to 20.7 % in the individual countries. This study on mycotoxins exposure has used post harmonized questionnaire data and validated analytical methodologies for analysis and covered countries representing the four geographical regions of Europe, having produced much needed knowledge on the exposure of the European adult population to deoxynivalenol.

One Concentration Does Not Fit All: It is Time to Personalize the Therapeutic Range of Infliximab in Crohn Disease.

Therapeutic drug monitoring of infliximab is commonly performed based on trough concentration. However, doses and dosing intervals may be adapted to patient outcomes, and this trough concentration target may correspond to a large range of exposures in terms of the area under the concentration-time curve (AUC). The objectives of this study were to assess the real-life exposure to intravenous infliximab in patients with Crohn disease in remission at year 1 and to assess the evolution of exposure in patients who switched to subcutaneous infliximab. The authors conducted a retrospective observational pharmacokinetic study in patients with Crohn disease who had available infliximab concentrations during intravenous and subcutaneous infliximab maintenance therapy as per the standard of care. Infliximab exposure parameters (AUCs and trough concentrations, C0) were compared for different dosing regimens of intravenous infliximab before (intravenous) and after (subcutaneous) the switch. A total of 113 patients had 383 intravenous infliximab concentrations. Dosing intervals ranged from 4 to 12 weeks. The median/range/CV% C0, AUC0-t, and AUC0-8weeks were 5.3 mcg/mL [<LLoQ-49.6]/71.6%, 37,792 mcg.h/mL [4971-116,366]/33.1%, and 41,582 mcg.h/mL [7953-232,048]/43.9%, respectively. Forty-one patients had available paired C0 after both intravenous and subcutaneous administration. A poor correlation was found between preswitch intravenous infliximab C0 and postswitch subcutaneous infliximab C0. In this study, the authors suggested that in patients treated with IV IFX, different targets of C0 should be proposed according to treatment schemes and that AUC0-t might be a relevant determinant of clinical remission. Moreover, exposure did not remain stable throughout the switch from IV to SC IFX in any patient. These variations may depend on the intravenous dosing interval before switching.

Accuracy of an nnUNet neural network for the automatic segmentation of intracranial aneurysms, their parent vessels and major cerebral arteries from magnetic resonance imaging-Time of flight (MRI-TOF).

To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset. In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection. Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds. The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction. IA = Intracranial Aneurysm; MRI-TOF＝ Magnetic Resonance Imaging - Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95th Percentile Hausdorff Distance.

Improving measures of context in process evaluations: development and use of the Context Tracker tool

BackgroundProcess evaluations are increasingly integrated into randomised controlled trials (RCTs) of complex interventions to document their delivery and interactions with local systems and dynamics, helping understand observed health outcomes. Yet process evaluations often struggle to assess relevant contextual determinants, leaving much of the important role of “context” in shaping an intervention’s mechanisms opaque in many studies. A lack of easily adapted data collection methods to help define and operationalise indicators of context likely contributes to this.MethodsWe present a method to help structure measures of context in process evaluations and describe its use in two very different settings. The “Context Tracker” is an innovative tool for use within trials and quasi-experiments to more systematically capture and understand key dimensions of context. It was developed in Zimbabwe as part of a cluster randomised controlled trial and then adapted for a quasi-experimental evaluation in the UK. Both studies provided harm reduction and health services for marginalised and hard-to-reach populations.ResultsWe developed the Context Tracker to be both standardised (i.e. formatted and applied in the same way across study sites) and flexible enough to allow unique features to be explored in greater detail. Drawing on the Context and Implementation of Complex Interventions (CICI) and Risk Environments frameworks, we mapped 5 domains across micro, meso and macro levels in a simple table and used existing evidence and experience to predict factors likely to affect delivery of and participation in intervention components. We tracked these over time across study sites using routine programme statistics, observation and qualitative methods. The Context Tracker enables identification and comparison of facilitators and barriers to implementation, variations in engagement with interventions, and how mechanisms of action are (or are not) triggered in different settings.ConclusionsThe Context Tracker is one example of how evidence-based contextual determinants can be used to guide data collection and analysis within process evaluations. It is relevant in low- and high-income settings and applicable to both qualitative and quantitative analyses. While perhaps most useful to process evaluations of complex interventions targeting marginalised communities, the broader approach would benefit a more general research audience.

Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings

Sparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain–computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms. The goal of this paper is to propose a novel robust implementation of sparse Bayesian learning so that robustness and sparseness can be realized simultaneously. Motivated by the exceptional robustness of maximum correntropy criterion (MCC), we proposed integrating MCC to the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC, and then leveraged it for the likelihood function. Meanwhile, we utilized the automatic relevance determination technique as the sparse prior distribution. To fully evaluate the proposed method, a synthetic example and a real-world muscle activity reconstruction task with two different brain modalities were leveraged. Experimental results showed, our proposed sparse Bayesian correntropy learning framework significantly improves the robustness for the noisy regression tasks. Our proposed algorithm could realize higher correlation coefficients and lower root mean squared errors for the real-world muscle activity reconstruction scenario. Sparse Bayesian correntropy learning provides a powerful approach for brain activity decoding which will promote the development of brain–computer interface technology.

Combining the pieces: identifying key determinants of export diversification in Africa amidst model uncertainty

AbstractExport diversification can be a driver of economic growth. Policies to diversify exports attempt to defy an existing comparative advantage in low-value-added goods to promote transformation. Many structural, general policy, and trade-related variables have been identified as important drivers of diversification. This article reviews the literature on the determinants of export diversification and applies Bayesian Model Averaging (BMA) to empirically tackle model uncertainty stemming from many possible determinants. It assesses the relevance and impact of up to 46 drivers of export diversification for up to 47 African countries and 123 trading partners from 1995 to 2018. It finds that African countries’ structural features are especially influential in determining export diversification, a potential explanation for Africa’s more concentrated export baskets compared to other world regions. Furthermore, trade policies, like tariffs and regional integration, are critical in promoting export diversification. These findings underscore the AfCFTA’s potential to foster export diversification across African economies. A diverse set of non-trade-related policy variables also significantly affect export diversification. Factors such as institutions, education, service sector, resource rents, financial development, FDI and exchange rate stability are relevant determinants in different settings. Moreover, the analysis shows that whom you trade with is also important for diversification, and that context-specific differences, such as specific regional integration contexts or type of commodity-dependence, matter. The results suggest that the goals to diversify exports at the extensive margin for structural change and the intensive margin to tackle volatility, seem often not achievable via the same policies.

An Overview of a Re-Emerging Disease in Italy: Bovine Tuberculosis Outbreaks in Cattle from MTBC-Free Territories.

Bovine tuberculosis (bTB) is a zoonotic disease with consequences for public health as well as the economy. In the EU, compulsory eradication programmes have been applied, and most territories in Italy have been reported as disease-free (FTs). However, outbreaks (OBs), i.e., an officially confirmed occurrence of bTB in one or more animals in an establishment, have continued to be reported. In this study we provide an overview of bTB in terms of OB numbers in cattle from Italian FTs. Legislative sources were collected to find the FTs, the relevant declaration of free status year (FSY), and regional control and surveillance plans. Then, descriptive and statistical analyses were applied to the collected OBs. A total of 12 regions and 19 provinces were declared FTs in the 20 years from 2003 to 2023. Differences in regional plans were observed with respect to the percentages of herds that were annually controlled (control frequency). Overall, 370 OBs were recorded. A non-statistically significant decrease in the OB incidence rate after the FSY was declared. However, a notable increase in OBs detected at slaughterhouses after the FSY suggests that control systems (serological tests) at the herd level are not completely effective. Differences in the herds' control frequencies among FTs seem to not have had a significant influence on the observed OB number. The Tuscany region was the most affected FT based on the OB numbers after the FSY (especially in the last year). Epidemiologically relevant primary determinants seem to be the farming system (semi-extensive and adjacent herds) and the cattle movements from positive incidence areas (trade and animal fairs). The role of wild boars in the disease maintenance cannot be excluded. The results of this study stress the need to revise bTB eradication and surveillance plans based on risk analysis.

Emotional Marketing, Customer Retention, and Brand Loyalty in Vietnam’s Retail Sector: Unveiling the Moderating Impact of Digital Innovativeness

This study investigates the relationship between emotional marketing, brand loyalty, and customer retention in Vietnam’s retail sector, while examining the moderating role of digital innovativeness, comprising personal and brand innovativeness. A conceptual model is developed and empirically tested using a sample of 533 Vietnamese retail consumers. The model incorporates emotional marketing as the main predictor, brand loyalty as the mediator, customer retention as the outcome, and digital innovativeness as the moderator. Relevant determinants of brand loyalty, such as customer satisfaction, perceived value, emotional attachment, and brand identity, are included as control variables. The data is analyzed using structural equation modeling. The results reveal that emotional marketing has a significant positive impact on brand loyalty, which in turn leads to higher customer retention. Digital innovativeness is found to positively moderate the relationship between emotional marketing and brand loyalty. The study highlights the importance of leveraging emotional marketing strategies and fostering digital innovativeness to build strong emotional connections, brand loyalty, and long-term customer retention in the Vietnamese retail market. This research contributes to the understanding of emotional marketing’s impact on brand loyalty and customer retention in the context of Vietnam’s retail sector, while uniquely exploring the moderating effect of digital innovativeness. The findings provide valuable insights for retailers seeking to optimize their marketing strategies and adapt to the evolving needs of Vietnamese consumers in the digital age.

Community-Level Social Vulnerability and Hip and Knee Joint Replacement Surgery Receipt Among Medicare Enrollees With Arthritis.

(1) Explore associations between county minority health social vulnerability index (MH-SVI) and total joint replacement (TJR), and (2) assess associations by individual-level race/ethnicity. An expanded understanding of relevant social determinants of health is essential to inform policies and practices that promote equitable access to hip and knee TJR. Retrospective cohort study of Medicare enrollees. Centers for Medicare and Medicaid Services claims data were linked with MH-SVI. Multivariable logistic regression models were used to evaluate the odds of TJR according to the MH-SVI quartile in which enrollees resided. A total of 10,471,413 traditional Medicare enrollees in 2018 aged 67 years or older with arthritis. The main outcome was enrollee primary TJR during hospitalization. The main exposure was the MH-SVI (composite and 6 themes) for the county of enrollee residence. Results were stratified by enrollee race/ethnicity. Asian American, Native Hawaiian, or Pacific Islander (AANHPI), Black or African American (Black), and Hispanic enrollees comparatively had 26%-41% lower odds of receiving TJR than White enrollees. Residing in counties within the highest quartile of composite and socioeconomic status vulnerability measures were associated with lower TJR overall and by race/ethnicity. Residing in counties with increased medical vulnerability for Black and White enrollees, housing type and transportation vulnerability for AANHPI and Hispanic enrollees, minority status and language theme for AANHPI enrollees, and household composition vulnerability for White enrollees were also associated with lower TJR. Higher levels of social vulnerability were associated with lower TJR. However, the association varied by individual race/ethnicity. Implementing multisectoral strategies is crucial for ensuring equitable access to care.

Forecasting Trends in the Real Estate Market: Analysis of Relevant Determinants

Forecasting Trends in the Real Estate Market: Analysis of Relevant Determinants

Use of implementation mapping to develop a multifaceted implementation strategy for an electronic prospective surveillance model for cancer rehabilitation

BackgroundElectronic Prospective Surveillance Models (ePSMs) remotely monitor the rehabilitation needs of people with cancer via patient-reported outcomes at pre-defined time points during cancer care and deliver support, including links to self-management education and community programs, and recommendations for further clinical screening and rehabilitation referrals. Previous guidance on implementing ePSMs lacks sufficient detail on approaches to select implementation strategies for these systems. The purpose of this article is to describe how we developed an implementation plan for REACH, an ePSM system designed for breast, colorectal, lymphoma, and head and neck cancers.MethodsImplementation Mapping guided the process of developing the implementation plan. We integrated findings from a scoping review and qualitative study our team conducted to identify determinants to implementation, implementation actors and actions, and relevant outcomes. Determinants were categorized using the Consolidated Framework for Implementation Research (CFIR), and the implementation outcomes taxonomy guided the identification of outcomes. Next, determinants were mapped to the Expert Recommendations for Implementing Change (ERIC) taxonomy of strategies using the CFIR-ERIC Matching Tool. The list of strategies produced was refined through discussion amongst our team and feedback from knowledge users considering each strategy’s feasibility and importance rating via the Go-Zone plot, feasibility and applicability to the clinical contexts, and use among other ePSMs reported in our scoping review.ResultsOf the 39 CFIR constructs, 22 were identified as relevant determinants. Clinic managers, information technology teams, and healthcare providers with key roles in patient education were identified as important actors. The CFIR-ERIC Matching Tool resulted in 50 strategies with Level 1 endorsement and 13 strategies with Level 2 endorsement. The final list of strategies included 1) purposefully re-examine the implementation, 2) tailor strategies, 3) change record systems, 4) conduct educational meetings, 5) distribute educational materials, 6) intervene with patients to enhance uptake and adherence, 7) centralize technical assistance, and 8) use advisory boards and workgroups.ConclusionWe present a generalizable method that incorporates steps from Implementation Mapping, engages various knowledge users, and leverages implementation science frameworks to facilitate the development of an implementation strategy. An evaluation of implementation success using the implementation outcomes framework is underway.

What can talent management do about involuntary remote working in the post-COVID-19 era? Spanish IT employees’ organizational commitment

The purpose of this study is to understand the role of professional isolation and work–family balance (WFB) as talent retention strategies, considering organizational commitment (OC) and the mediating role of job satisfaction (JS) among IT employees in the technological industry have been forced by their companies to telework. While previous research has examined the connections between professional isolation (PI), OC, WFB, and JS separately in the context of teleworking, this research proposes an integrative model examining the connections between all these work-related constructs and allowing for mediating and moderating effects. It focuses on IT employees in the technological industry forced to telework, a setting underexamined by previous literature. The final sample is composed of 294 teleworkers forced to work partly at home by their companies. Partial least squares structural equation modeling (PLS-SEM) was applied, and SmartPLS 4.0.8.7 software was used to analyze the data and test the hypotheses. Our results indicate a positive and significant direct effect of WFB on JS and OC, a negative and significant effect of PI on JS and OC, and a positive and significant effect of JS on OC. Our results also indicate that JS mediates the relationship between WFB and OC and between PI and OC and that neither time spent teleworking nor gender moderates the association between PI and OC. Overall, our results suggest that while PI negatively affects OC, JS, and WFB are the most relevant determinants of OC in the context of teleworking. In addition, a complementary IPMA analysis reinforces this view by suggesting that WFB and JS are the most important factors in determining OC performance among IT employees working from home, while PI is not important. The originality of this article is the proposal of an integrative model examining the connections between JS, WFB, PI, and OC and allowing for mediating (JS) and moderating (gender and percentage of time teleworking) effects from a talent management perspective. Moreover, this study focuses on information technology companies and the situation of forced remote working, two settings that have been underexamined. The results could help companies with forced teleworking develop effective strategies to attract, develop, and retain top talent in the information technology industry in the post-pandemic era. It is important for practitioners to consider the interactions between diverse work-related dimensions and the mediating and moderating effects between them to efficiently implement talent management strategies and reinforce OC and, in turn, economic performance.

Evaluation of a hyperspectral image pipeline toward building a generalisation capable crop dry matter content prediction model

Hyperspectral imaging has proven to be a reliable technique for estimating dry matter, a common variable when considering the quality of the fresh produce. However, developing models capable of generalising across different crops is challenging. In this study, several pipelines were explored towards achieving a robust and accurate generic regression model were evaluated and the development of Automatic Relevance Determination (ARD) and Partial Least Squares (PLS) algorithms for fruit and vegetable dry matter estimation. The models were built using a VIS-NIR dataset that includes both fruit and vegetables, namely, apples, broccoli and leek (n = 779). The PLS regression model obtained Root Mean Square on Prediction (RMSEP) = 0.0137, outperforming ARD regression (RMSEP = 0.0140) on a 10x5-fold cross-validation protocol. The evaluated preprocessing techniques affect the two regression algorithms differently, with the best results achieved when the pipeline was used without feature extraction. Overall, the pipeline using either ARD or PLS regression shows strong performance and generalisation for Visible-Near Infrared (VIS-NIR)-based dry matter estimation across diverse fruits and vegetables.

P19-26 Moving towards a framework for in vitro risk assessment and determination of human relevance of rodent liver-mediated thyroid toxicity

P19-26 Moving towards a framework for in vitro risk assessment and determination of human relevance of rodent liver-mediated thyroid toxicity

P5: PREMATURE FUNCTIONAL DECLINE IN WOMEN OVER 40 YEARS OF AGE

Introduction: Early functional changes in women compromise elements such as muscle mass and strength, physical function, and gait speed, leading to a physiological decline that may contribute to frailty in old age.Objectives: This study aimed to relate strength and physical performance to premature functional decline in women aged 40 years and older.Methods: A cross-sectional descriptive observational study was conducted, and two groups of women were contrasted (40 to 60 years old and 60 and over), completing a sample of 31 women. Functional decline, lower limb muscle power, prehensile strength, physical performance, gait speed, phenotype of frailty status, level of physical activity, and other sociodemographic factors were contrasted. The UCT internal ethicscommittee.Results: Preliminary results showed that, among the demographic variables, women over 60 had a lower level of schooling, with 29% having incomplete basic education. Both groups reported a similar number of children: 93% in those under 60 and 100% in those over 60, with an average of 2.3 and 3.06 children, respectively. Statistically significant differences were detected in physical performance, lower limb strength, and gait speed, which were higher in women under 60.Conclusions: The present report detected that lower limb strength, a determinant of great relevance in the aging population, affects gait speed. Likewise, muscle strength leads to a more rapid decline in older people. In both groups, physical performance played a crucial role in characterizing aging and frailty, particularly at ages over 60, when a decline in performance was evident. Notably, there were no variations in grip strength or physical activity levels, possibly because both groups performed poorly, raising the possibility of functional decline in women who have not yet reached old age.

Heart Failure With Reduced Ejection Fraction Polypill Implementation Strategy in India: A Convergent Parallel Mixed Methods Study.

A polypill-based implementation strategy has been proposed to increase rates of guideline-directed medical therapy (GDMT) in patients with heart failure with reduced ejection fraction. This has the potential to improve mortality and morbidity in India and undertreated populations globally. We conducted a convergent parallel mixed methods study integrating quantitative data from stakeholder surveys using modified implementation science outcome measures and qualitative data from key informant in-depth interviews. Our objective was to explore physician, nurse, pharmacist, and patient perspectives on a HFrEF polypill implementation strategy in India from January 2021 to April 2021. Quantitative and qualitative data were integrated to develop an Implementation Research Logic Model. Among 69 respondents to the stakeholder survey, there was moderate acceptability (mean [SD] 3.8 [1.0]), appropriateness (3.6 [1.0]), and feasibility (3.7 [1.0]) of HFrEF polypill implementation strategy. Participants in the key-informant in-depth interviews (n = 20) highlighted numerous relative advantages of the HFrEF polypill innovation including potential to simplify medication regimens and improve patient adherence. Key relative disadvantages elucidated, include concerns about side effects and interruption of multiple GDMT medications due to polypill discontinuation for side effects or hospitalizations. Based on this data, the proposed implementation strategies in the Implementation Research Logic Model include 1) HFrEF polypills, 2) HFrEF polypill initiation, titration, and maintenance protocols, and 3) HFrEF polypill laboratory monitoring protocols for safety which we postulate will lead to desired clinical and implementation outcomes through multiple mechanisms including increased medication adherence to a single pill. This study demonstrates that a HFrEF polypill-based implementation strategy is considered acceptable, feasible, and appropriate among healthcare providers in India. We identified contextually relevant determinants, strategies, mechanism, and outcomes outlined in an Implementation Research Logic Model to inform future research to improve heart failure care in South Asia.

A Gaussian process embedded feature selection method based on automatic relevance determination

In Gaussian Process, feature importance is inversely proportional to the corresponding length scale when applying the Automatic Relevance Determination (ARD) structured kernel function. Features can be selected by ranking them according to their importance. Among the ARD-based feature selection methods, no uniform score exists for quantifying the output variation explained by feature subsets. This study proposes two feature selection approaches using two cumulative feature importance scores, one titled derivative decomposition ratio and the other normalized sensitivity, to determine the optimal feature subset. The performance of the approaches is assessed to test if irrelevant features are accurately identified and if the feature rankings are correct. The approaches are applied to identify relevant dimensionless inputs for a hybrid model estimating liquid entrainment fraction in two-phase flow. The results reveal that the proposed methods can identify the optimal feature subset for the hybrid model without significantly worsening its Root Mean Squared Error.