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The purpose of this study is to determine how consumer perceptions of brand ethical behavior can affect their commitment to brands during turbulent times. A study of the effects of perceived ethical behavior of brands in Colombia during the initial months of the COVID-19 outbreak was conducted in May 2020 in Bogota, Colombia, to ascertain customer perception of brand ethical actions during the first 2 months of the COVID-19 outbreak. A Bayesian model was developed to assess the impact of brands' ethical behavior on brand recognition benefits, brand image, and brand commitment. The selection of the initial months of the outbreak for this project was important because the COVID-19 pandemic had the potential to augment consumer perception of brands' ethical actions at a pivotal moment in consumers' lives. Our findings demonstrate that customers expressed a positive perception of brands' ethical actions during the early months of the pandemic, which resulted in high commitment intention to these brands in the model examined. The findings highlight the critical nature of the perception of ethical behavior in the eyes of customers during a major global health crisis. More than ever, organizations must commit their brands to fully live out their publicly expressed ethical principles and continuously monitor consumer perceptions of brand attributes and behaviors associated with ethical actions regardless of market conditions. Commitment to living the brand's stated ethical principles can be demonstrated via branding activities in ways that can be especially welcomed by customers during turbulent times. The insights mentioned in this article are crucial for brands already present in South America or exploring expansion into these regions. The findings provide compelling evidence of the impact of ethical actions on consumer commitment to brands, suggesting that brands must now, more than ever, stay in touch with their customers and truthfully live the ethical values they transmit to customers.

Affirming and accessible health care may improve health outcomes for trans individuals in the U.S. We explored strategies to improve affirming care for trans individuals from the perspectives of providers and transgender and non-binary community members. Forty members of a collective group of gender-affirming providers (GAP) in the southern U.S. were recruited to participate in a brief online survey. A graphical LASSO undirected network analysis approach visualized associations across outcomes and explanatory variables. Multinomial ordered (or logistic, for binary outcomes) models explored associations between a common set of explanatory variables and outcomes. Strong partial correlations (network) and statistically significant explanatory variables (ordinal and logistic models) were identified. Additionally, we conducted three focus groups (FGs) audio-recorded over Zoom with 11 community members. Four study team members analyzed the transcripts using content analyses. Survey results indicated that higher frequency of attendance at monthly provider meetings, additional training, and provision of training and consultation were associated with greater perceived competence among GAP members. To improve services, FG participants suggested treating patients as experts, increasing diversity and representation among providers, and expanding the GAP group. These results highlight the critical need to design and implement community-identified interventions to improve gender-affirming services and enhance provider training.

Risk-adjustment models are widely used methodological approaches within the healthcare industry to measure hospital performance and quality of care. However, the Centers for Medicare and Medicaid Services (CMS) do not fully adjust for socioeconomic status (SES) in acute myocardial infarction (AMI) models. A review and evidence synthesis was conducted to identify associations of SES factors with hospital readmission and mortality in AMI patients.Multiple electronic databases were queried to identify studies assessing risk for AMI-related mortality or hospital readmissions and SES factors. Identified studies were screened by title and abstract. Full-text reviews followed for articles meeting the inclusion criteria, including quality assessments. Data were extracted from all included studies, and evidence synthesis was performed to identify associations between SES factors and outcome variables.Ten studies were included in the review. One study showed that Black patients had higher AMI-related readmission rates compared to White patients (mean difference 4.3% [SD 1.4%], p 0.001). Another study showed that income inequality was associated with increased risk of AMI-related readmissions (RR 1.18 [95% CI], 1.13-1.23). One study found that unemployed individuals experienced significantly greater rates of AMI-related mortality than those working full-time (HR 2.08, 1.51-2.87). According to another study, lack of health insurance was associated with worse rates for in-hospital AMI-related mortality (OR 1.77, 1.72-1.82). Based on one study, AMI-related mortality was higher in those with8 years of education compared to those with16 years (17.5% vs. 3.5%, p 0.0001). Five of six studies found a significant association between ZIP code/neighborhood/location and AMI-related readmission or mortality.Race, ZIP code/neighborhood/location, insurance status, income/poverty, and education comprise SES factors found to be associated with AMI-related mortality and/or readmission outcomes. Including these SES factors in future updates of CMS's risk-adjusted models has the potential to provide more appropriate compensation mechanisms to hospitals.

Introduction: In recent years, practice friendly models of suicidal behavior have emerged in the form of “ideation-to-action” frameworks. These frameworks focus on processes influencing both the formation of suicidal ideation and the translation of that ideation to suicide attempt and death. In this paper, we proffer an emerging model of suicide, Preferences in Information Processing (PIP), to augment contemporary suicide theories. First, we provide a primer on dual-process models of information processing theory and research as the foundation for the PIP. Next, drawing on a number of initial cross-sectional studies, we outline a rationale and examples of how preferences in motivated affect (i.e., Need for Affect) and cognition (i.e., Need for Cognition) may be integrated into existing ideation-to-action frameworks. Methods: We conducted secondary analysis of our pooled community sample PIP data. Results: We present new findings suggesting Need for Affect avoidance and Need for Cognition may be clinically relevant for persons at escalated risk for suicide. Discussion: The PIP offers new testable propositions within ideation-to-action suicide frameworks. We end with recommendations for a research agenda to further investigate the potential validation and utility of a PIP approach to suicidology.

In a polarized society, it is a university’s responsibility to offer courses that explore highly controversial issues. Traditional forms of debate may create barriers to knowledge and entrenchment ...

Background Childhood obesity is a major global health concern. Weight-management camps involving delivery of a program of physical activity, health education, and healthy eating are an effective treatment, although post-intervention weight-management is less well understood. Our objective was to assess the effectiveness of a weight-management camp followed by a community intervention in supporting weight-management for overweight children and children with obesity. Methods Participants were overweight Qatari schoolchildren or schoolchildren with obesity, ages 8–14 years, (n = 300) recruited over a three-year period across 14 randomly selected schools in the Doha area. They attended a two-week weight management camp, then a 10-week program of weekly lifestyle education and physical activity sessions, which also included behavior change techniques. The programme was cognitive behavioural therapy (CBT)-focused with a strong element of behavioural economics blended in. Results Participants saw a significant BMI SDS reduction as a result of the entire intervention (camp + education and activity sessions) both at the individual (p p = 0.0002) levels, and weight loss occurred during each intervention stage separately for the camp (p p p = 0.0220 at the individual and cluster/school levels, respectively). Conclusions Weekly lifestyle education and activity sessions which include behavior change techniques may be useful in promoting continued weight management in the period following intensive, immersive childhood obesity interventions. Trial registration ClinicalTrials.gov NCT02972164, November 23, 2016.

Aim To examine the impact of attainment of primary care diabetes clinical indicators on progression to sight‐threatening diabetic retinopathy (STDR) among those with mild non‐proliferative diabetic retinopathy (NPDR). Materials and Methods An historical cohort study of 18,978 adults (43.63% female) diagnosed with type 2 diabetes before 1 April 2010 and mild NPDR before 1 April 2011 was conducted. The data were obtained from the UK Clinical Practice Research Datalink during 2010‐2017, provided by 330 primary care practices in England. Exposures included attainment of the Quality and Outcomes Framework HbA1c (≤59 mmol/mol [≤7.5%]), blood pressure (≤140/80 mmHg) and cholesterol (≤5 mmol/L) indicators in the financial year 2010‐2011, as well as the number of National Diabetes Audit processes completed in 2010‐2011. The outcome was time to incident STDR. Nearest neighbour propensity score matching was undertaken, and univariable and multivariable Cox proportional hazards models were then fitted using the matched samples. Concordance statistics were calculated for each model. Results A total of 1037 (5.5%) STDR diagnoses were observed over a mean follow‐up of 3.6 (SD 2.0) years. HbA1c, blood pressure and cholesterol indicator attainment were associated with lower rates of STDR (adjusted hazard ratios [95% CI] 0.64 [0.55‐0.74; p

More robust attribution methods are necessary to understand physician-level variation in quality of care across risk-adjusted inpatient measures. We address a gap in the literature involving attribution of physicians to inpatient stays using administrative claims data, in which rule-based methods often inadequately attribute physicians.Methodology comparison study using a cross-section of inpatient stays.A novel approach is proposed in which physicians' relative degrees of responsibility for inpatient stays are expressed through physician-specific attribution ratios informed by existing patient characteristics and comorbidities. Attribution results are compared with the rule-based benchmark method for 7 CMS-defined clinical cohorts, including a COVID-19 cohort.Using 6,835,460 unique patient encounters during 2020 (n = 136,339 in out-of-sample cohort), the proposed approach favored specialists generally considered responsible for primary clinical conditions when compared with the benchmark. The most salient shift within the acute myocardial infarction (+17.0%), heart failure (+20.2%), and coronary artery bypass graft (+4.0%) cohorts was toward the cardiovascular diseases specialty, and the chronic obstructive pulmonary disease (+24.0%) and pneumonia (+16.2%) cohorts resulted in a shift toward the pulmonary diseases specialty. The COVID-19 cohort resulted in considerable shifts toward infectious diseases and pulmonary diseases specialties (+17.4% and +14.1%, respectively). The stroke cohort experienced a considerable shift toward the neurology specialty (+42.2%).We provide a robust method to attribute physicians to patients, which is a necessary tool to understand physician-level variation in quality of care within the inpatient acute care setting. The proposed method provides consistency across facilities and eliminates unattributed patients resulting from unsatisfied business rules.

We report a study designed to determine the most efficient means of pursuing sonic seasoning in international marketing. For the first time, music chosen to trigger specific emotional responses was directly and cross-culturally compared with music chosen as crossmodally congruent with specific taste/flavors (the latter usually referred to as ‘sonic seasoning’). The effects triggered by ‘emotional’ music were more prominent than those triggered by ‘crossmodally-corresponding’ music. Specifically, chocolate was liked more, rated as sweeter, and the purchase intent was higher, when tasted while listening to music that conveyed positive, as compared to negative, emotion. By contrast, the same chocolate was mostly rated as tasting more bitter with the negative music, as compared to the positive music. Companies looking to use sonic seasoning in marketing strategies, should therefore principally aim at intelligently classifying music based on the likely emotions that they can trigger in their customers (at least when thinking globally).

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory1, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes2. This was first demonstrated on biological samples a decade ago on the giant mimivirus3. Since then a large collaboration4has been pushing the limit of the smallest sample that can be imaged5,6. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that ofEscherichia coliGroEL which at 14 nm in diameter7is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale.

Nature’s vital, but notoriously inefficient, CO2-fixing enzyme Rubisco often limits the growth of photosynthetic organisms including crop species. Form I Rubiscos comprise eight catalytic large subunits and eight auxiliary small subunits and can be classified into two distinct lineages—‘red’ and ‘green’. While red-type Rubiscos (Form IC and ID) are found in rhodophytes, their secondary symbionts, and certain proteobacteria, green-type Rubiscos (Form IA and IB) exist in terrestrial plants, chlorophytes, cyanobacteria, and other proteobacteria. Eukaryotic red-type Rubiscos exhibit desirable kinetic properties, namely high specificity and high catalytic efficiency, with certain isoforms outperforming green-type Rubiscos. However, it is not yet possible to functionally express a high-performing red-type Rubisco in chloroplasts to boost photosynthetic carbon assimilation in green plants. Understanding the molecular and evolutionary basis for divergence between red- and green-type Rubiscos could help us to harness the superior CO2-fixing power of red-type Rubiscos. Here we review our current understanding about red-type Rubisco distribution, biogenesis, and sequence–structure, and present opportunities and challenges for utilizing red-type Rubisco kinetics towards crop improvements.

The SAFEHEART tool has shown good discrimination in predicting cardiovascular events in a bespoke genotyped cohort with familial hypercholesterolaemia (FH). We assessed whether the tool could aid clinical decision making in an English routine care cohort with FH.A historical (2000-2017) open cohort of 3643 participants aged 18-79 years and ≥6-months since FH diagnosis was derived from the Clinical Practice Research Datalink. Individual 10-year cardiovascular risks were predicted using the SAFEHEART model, with multiple imputation used to manage missing data. Outcomes were the first occurrence of myocardial infarction, coronary revascularisation, ischaemic stroke, carotid revascularisation, peripheral arterial revascularisation, non-traumatic lower limb amputation, or cardiovascular death. Model performance was assessed using standard measures of calibration and discrimination, and decision curve analysis.147 outcome events were observed over a median 3.73 (IQR 1.59-6.48) years follow-up. While the model had some discriminatory value (Harrell's c-statistic 0.67 (95% CI 0.61-0.72)), observed outcome risks departed substantially from predicted risks. Calibration slopes for men and women by age decile were 10.09 (95% CI 7.40-12.77) and 2.85 (1.25-4.45), respectively. Recalibration-in-the-large led to closer alignment of observed and predicted risks (recalibration slopes 3.48 (2.55-4.41) and 1.14 (0.50-1.79), respectively). Decision curve analysis suggested the recalibrated model had net benefit at predicted risks of 10-30%.The original SAFEHEART model has limited generalisability to the routinely identifiable English primary care FH population. With recalibration it appears to have moderate utility at 10-30% predicted risk. It may have greater validity in more bespoke genetically defined FH populations.

INTRODUCTION: Medical schools are shifting to a holistic approach for admissions. As non-cognitive abilities become more valued in the medical field and admissions become more competitive, multiple mini-interviews (MMI) are becoming increasingly common in selection criteria. The purpose of this article is to examine the relationship among admissions criteria to create a predictive model of acceptance to medical school. This article also aims to examine, among matriculated students, the relationship between MMI and traditional academic success, and the relationship between MMI scores and clinical competence and academic accolades. METHODS: This longitudinal study followed a cohort of students from the MMI process through the first two pre-clinical years at a medical school in the southeastern USA. Multiple logistic regression with backward elimination variable selection was used to examine the relationship between admission criteria and acceptance. A multivariate multinomial ordered probit model was used to assess the relationship between MMI and traditional academic success among matriculated students. Simple linear regression models were used to assess relationships between MMI and Objective Structured Clinical Examination (OSCE) scores and honors nomination. RESULTS: MMI are among eight significant predictors of acceptance. Among matriculated students, there were weak negative associations between MMI and traditional academic success; however, all but one relationship was statistically non-significant. There was not a significant association between MMI and OSCE scores or academic accolades. CONCLUSIONS: While MMI can identify students with non-cognitive skills needed to be a good physician, continued assessment needs to be incorporated in their education. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40670-020-01013-z) contains supplementary material, which is available to authorized users.

The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to be associated with disparate health outcomes. Associations between county-level SES factors and CMS’s risk-adjusted 30-day acute myocardial infarction (AMI) mortality rates are explored for n = 2462 hospitals using a variety of sources for county-level SES information. Upon performing multiple imputation, a stepwise backward elimination model selection approach using Akaike’s information criteria was used to identify the optimal model. The resulting model, comprised of 14 predictors mostly at the county level, provides an additional 8% explanatory power to capture the variability in 30-day risk-standardized AMI mortality rates, which already account for patient-level clinical differences. SES factors may be an important feature for inclusion in future risk-adjustment models, which will have system and policy implications for distributing resources to hospitals, such as reimbursements. It also serves as a stepping stone to identify and address long-standing SES-related inequities.

The cost of healthcare in the United States has increased over time. However, patient health outcomes have not trended with spending. There is a need to better comprehend the association between healthcare costs in the United States and hospital quality outcomes. Medicare spending per beneficiary (MSPB), a homogeneous metric across providers, can be used to evaluate the association between episodic Medicare spending and quality of care. Fifteen inpatient outcome measures were selected from Hospital Compare data among all (n = 4758) facilities and transformed to quintiles to ensure comparability across measures and to reduce the influence of outliers on the analysis. Both univariate and multiresponse multinomial ordered probit regression models were utilized across outcome domains to quantify associations between outcomes and spending. We found that MSPB was not associated with quality of care in most cases, adding evidence of a lack of outcome accountability among Medicare-funded facilities. Furthermore, worse outcomes were found to be associated with increased spending for some metrics. Policies are needed to align quality of care outcomes with the increasing costs of U.S. healthcare.

OBJECTIVES To examine potential biases in standardized infection ratio (SIR) metrics due to static U.S. Centers for Disease Control and Prevention (CDC) parameters and non-linearity of infection outcomes with volume. Correspondingly, to enhance the CDC predictions by incorporating additional information from volume metrics and explore an alternative approach to more fairly rank hospitals to address the SIR=0 problem. METHODS This population-based study uses publicly available 2019 healthcare-associated infections (HAI) data from 3096 acute care U.S. hospitals. HAI-specific Poisson generalized additive models illustrate the recalibration of CDC predictions, using volume-based spline functions to adjust for biases. Implied cumulative distribution functions (CDF) were derived, and HAI-facility-specific probabilities were calculated. Hospital rankings implied from these HAI-stratified probabilities were calculated. RESULTS Calibration plots demonstrate existing biases associated with CDC infection over-predictions. Volume-based spline functions were significant for all HAIs (P

Objectives England has invested considerably in diabetes care over recent years through programmes such as the Quality and Outcomes Framework and National Diabetes Audit. However, associations between specific programme indicators and key clinical endpoints, such as emergency hospital admissions, remain unclear. We aimed to examine whether attainment of Quality and Outcomes Framework and National Diabetes Audit primary care diabetes indicators is associated with diabetes-related, cardiovascular, and all-cause emergency hospital admissions. Design Historical cohort study. Setting A total of 330 English primary care practices, 2010–2017, using UK Clinical Practice Research Datalink. Participants A total of 84,441 adults with type 2 diabetes. Main Outcome Measures The primary outcome was emergency hospital admission for any cause. Secondary outcomes were (1) diabetes-related and (2) cardiovascular-related emergency admission. Results There were 130,709 all-cause emergency admissions, 115,425 diabetes-related admissions and 105,191 cardiovascular admissions, corresponding to unplanned admission rates of 402, 355 and 323 per 1000 patient-years, respectively. All-cause hospital admission rates were lower among those who met HbA1c and cholesterol indicators (incidence rate ratio = 0.91; 95% CI 0.89–0.92; p Conclusions Attaining Quality and Outcomes Framework-defined diabetes intermediate outcome thresholds, and comprehensive completion of care processes, may translate into considerable reductions in emergency hospital admissions. Out-of-hospital diabetes care optimisation is needed to improve implementation of core interventions and reduce unplanned admissions.

The U.S. Centers for Medicare and Medicaid Services (CMS) assigns quality star ratings to hospitals upon assessing their performance across 57 measures. Ratings can be used by healthcare consumers for hospital selection and hospitals for quality improvement. We provide a simpler, more intuitive modeling approach, aligned with recent criticism by stakeholders. An ordered logistic regression approach is proposed to assess associations between performance measures and ratings across eligible (n = 4519) U.S. hospitals. Covariate selection reduces the double counting of information from highly correlated measures. Multiple imputation allows for inference of star ratings when information on all measures is not available. Twenty performance measures were found to contain all the relevant information to formulate star rating predictions upon accounting for performance measure correlation. Hospitals can focus their efforts on a subset of model-identified measures, while healthcare consumers can predict quality star ratings for hospitals ineligible under CMS criteria.

Background The associations between England’s incentivised primary care-based diabetes prevention activities and hard clinical endpoints remain unclear. We aimed to examine the associations between attainment of primary care indicators and incident diabetic retinopathy (DR) among people with type 2 diabetes. Methods A historical cohort (n = 60,094) of people aged ≥ 18 years with type 2 diabetes and no DR at baseline was obtained from the UK Clinical Practice Research Datalink (CPRD). Exposures included attainment of the Quality and Outcomes Framework (QOF) HbA1c (≤ 7.5% or 59 mmol/mol), blood pressure (≤ 140/80 mmHg), and cholesterol (≤ 5 mmol/L) indicators, and number of National Diabetes Audit (NDA) care processes completed (categorised as 0–3, 4–6, or 7–9), in 2010–2011. Outcomes were time to development of DR and sight-threatening diabetic retinopathy (STDR). Nearest neighbour propensity score matching was undertaken and Cox proportional hazards models then fitted using the matched samples. Concordance statistics were calculated for each model. Results 8263 DR and 832 STDR diagnoses were observed over mean follow-up periods of 3.5 (SD 2.1) and 3.8 (SD 2.0) years, respectively. HbA1c and blood pressure (BP) indicator attainment were associated with lower rates of DR (adjusted hazard ratios (aHRs) 0.94 (95% CI 0.89–0.99) and 0.87 (0.83–0.92), respectively), whereas cholesterol indicator attainment was not (aHR 1.03 (0.97–1.10)). All QOF indicators were associated with lower rates of STDR (aHRs 0.74 (0.62–0.87) for HbA1c, 0.78 (0.67–0.91) for BP, and 0.82 (0.67–0.99) for cholesterol). Completion of 7–9 vs. 0–3 NDA processes was associated with fewer STDR diagnoses (aHR 0.72 (0.55–0.94)). Conclusions Attainment of key primary care indicators is associated with lower incidence of DR and STDR among patients with type 2 diabetes in England.

AIMS: To describe associations between incentivised primary care clinical and process indicators and mortality, among patients with type 2 diabetes in England. METHODS: A historical 2010-2017 cohort (n = 84,441 adults) was derived from the UK CPRD. Exposures included English Quality and Outcomes Framework glycated haemoglobin (HbA1c; 7.5%, 59 mmol/mol), blood pressure (140/80 mmHg), and cholesterol (5 mmol/L) indicator attainment; and number of National Diabetes Audit care processes completed, in 2010-11. The primary outcome was all-cause mortality. RESULTS: Over median 3.9 (SD 2.0) years follow-up, 10,711 deaths occurred. Adjusted hazard ratios (aHR) indicated 12% (95% CI 8-16%; p

AimsReliably quantifying event rates in secondary prevention could aid clinical decision-making, including quantifying potential risk reductions of novel, and sometimes expensive, add-on therapies. We aimed to assess whether the SMART risk prediction model performs well in a real-world setting.Methods and resultsWe conducted a historical open cohort study using UK primary care data from the Clinical Practice Research Datalink (2000–2017) diagnosed with coronary, cerebrovascular, peripheral, and/or aortic atherosclerotic cardiovascular disease (ASCVD). Analyses were undertaken separately for cohorts with established (≥6 months) vs. newly diagnosed ASCVD. The outcome was first post-cohort entry occurrence of myocardial infarction, stroke, or cardiovascular death. Among the cohort with established ASCVD [n = 244 578, 62.1% male, median age 67.3 years, interquartile range (IQR) 59.2–74.0], the calibration and discrimination achieved by the SMART model was not dissimilar to performance at internal validation [Harrell’s c-statistic = 0.639, 95% confidence interval (CI) 0.636–0.642, compared with 0.675, 0.642–0.708]. Decision curve analysis indicated that the model outperformed treat all and treat none strategies in the clinically relevant 20–60% predicted risk range. Consistent findings were observed in sensitivity analyses, including complete case analysis (n = 182 482; c = 0.624, 95% CI 0.620–0.627). Among the cohort with newly diagnosed ASCVD (n = 136 445; 61.0% male; median age 66.0 years, IQR 57.7–73.2), model performance was weaker with more exaggerated risk under-prediction and a c-statistic of 0.559, 95% CI 0.556–0.562.ConclusionsThe performance of the SMART model in this validation cohort demonstrates its potential utility in routine healthcare settings in guiding both population and individual-level decision-making for secondary prevention patients.

IntroductionEngland has invested considerably in diabetes care through such programs as the Quality and Outcomes Framework (QOF) and National Diabetes Audit (NDA). Associations between program indicators and clinical endpoints, such as amputation, remain unclear. We examined associations between primary care indicators and incident lower limb amputation.Research design and methodsThis population-based retrospective cohort study, spanning 2010–2017, was comprised of adults in England with type 2 diabetes and no history of lower limb amputation. Exposures at baseline (2010–2011) were attainment of QOF glycated hemoglobin (HbA1c), blood pressure and total cholesterol indicators, and number of NDA processes completed. Propensity score matching was performed and multivariable Cox proportional hazards models, adjusting for disease-related, comorbidity, lifestyle, and sociodemographic factors, were fitted using matched samples for each exposure.Results83 688 individuals from 330 English primary care practices were included. Mean follow-up was 3.9 (SD 2.0) years, and 521 (0.6%) minor or major amputations were observed (1.62 per 1000 person-years). HbA1c and cholesterol indicator attainment were associated with considerably lower risks of minor or major amputation (adjusted HRs; 95% CIs) 0.61 (0.49 to 0.74; pConclusionsComprehensive primary care-based secondary prevention may offer considerable protection against diabetes-related amputation. This has important implications for diabetes management and medical decision-making for patients, as well as type 2 diabetes quality improvement programs.

Sonic seasoning refers to the way in which music can influence multisensory tasting experiences. To date, the majority of the research on sonic seasoning has been conducted in Europe or the USA, typically in a within-participants experimental context. In the present study, we assessed the applicability of sonic seasoning in a large-scale between-participants setting in Asia. A sample of 1611 participants tasted one sample of chocolate while listening to a song that evoked a specific combination of cross-modal and emotional consequences. The results revealed that the music&rsquo, s emotional character had a more prominent effect than its cross-modally corresponding attributes on the multisensory tasting experience. Participants expressed a higher buying intention for the chocolate and rated it as having a softer texture when listening to mainly positive (as compared to mainly negative) music. The chocolates were rated as having a more intense flavor amongst those participants listening to &lsquo, softer&rsquo, as compared to &lsquo, harder&rsquo, music. Therefore, the present study demonstrates that music is capable of triggering a combination of specific cross-modal and emotional effects in the multisensory tasting experience of a chocolate.

Plant photosynthesis and growth are often limited by the activity of the CO(2)-fixing enzyme Rubisco. The broad kinetic diversity of Rubisco in nature is accompanied by differences in the composition and compatibility of the ancillary proteins needed for its folding, assembly, and metabolic regulation. Variations in the protein folding needs of catalytically efficient red algae Rubisco prevent their production in plants. Here, we show this impediment does not extend to Rubisco from Rhodobacter sphaeroides (RsRubisco)—a red-type Rubisco able to assemble in plant chloroplasts. In transplastomic tobRsLS lines expressing a codon optimized Rs-rbcLS operon, the messenger RNA (mRNA) abundance was ∼25% of rbcL transcript and RsRubisco ∼40% the Rubisco content in WT tobacco. To mitigate the low activation status of RsRubisco in tobRsLS (∼23% sites active under ambient CO(2)), the metabolic repair protein RsRca (Rs-activase) was introduced via nuclear transformation. RsRca production in the tobRsLS::X progeny matched endogenous tobacco Rca levels (∼1 µmol protomer·m(2)) and enhanced RsRubisco activation to 75% under elevated CO(2) (1%, vol/vol) growth. Accordingly, the rate of photosynthesis and growth in the tobRsLS::X lines were improved >twofold relative to tobRsLS. Other tobacco lines producing RsRubisco containing alternate diatom and red algae S-subunits were nonviable as CO(2)-fixation rates (k(cat)(c)) were reduced >95% and CO(2)/O(2) specificity impaired 30–50%. We show differences in hybrid and WT RsRubisco biogenesis in tobacco correlated with assembly in Escherichia coli advocating use of this bacterium to preevaluate the kinetic and chloroplast compatibility of engineered RsRubisco, an isoform amenable to directed evolution.

Introduction Research productivity has been linked to a country's intellectual and economic wealth. Further analysis is needed to assess the association between the distribution of research across disciplines and the economic status of countries. Methods By using 55 years of data, spanning 1962 to 2017, of Elsevier publications across a large set of research disciplines and countries globally, this manuscript explores the relationship and evolution of relative research productivity across different disciplines through a network analysis. It also explores the associations of those with economic productivity categories, as measured by the World Bank economic classification. Additional analysis of discipline similarities is possible by exploring the cross-country evolution of those disciplines. Results Results show similarities in the relative importance of research disciplines among most high-income countries, with larger idiosyncrasies appearing among the remaining countries. This group of high-income countries shows similarities in the dynamics of the relative distribution of research productivity over time, forming a stable research productivity cluster. Lower income countries form smaller, more independent and evolving clusters, and differ significantly from each other and from higher income countries in the relative importance of their research emphases. Country-based similarities in research productivity profiles also appear to be influenced by geographical proximity. Conclusions This new form of analyses of research productivity, and its relation to economic status, reveals novel insights to the dynamics of the economic and research structure of countries. This allows for a deeper understanding of the role a country's research structure may play in shaping its economy, and also identification of benchmark resource allocations across disciplines for developing countries.

In this paper, we propose maximum entropy in the mean methods for propensity score matching classification problems. We provide a new methodological approach and estimation algorithms to ha...

Resource utilization measures are typically modeled by relying on clinical characteristics. However, in some settings, those clinical markers are not available, and hospitals are unable to explore potential inefficiencies or resource misutilization. We propose a novel approach to exploring misutilization that solely relies on administrative data in the form of patient characteristics and competing resource utilization, with the latter being a novel addition. We demonstrate this approach in a 2019 patient cohort diagnosed with prostate cancer (n = 51,111) across 1056 U.S. healthcare facilities using Premier, Inc.’s (Charlotte, NC, USA) all payor databases. A multivariate logistic regression model was fitted using administrative information and competing resources utilization. A decision curve analysis informed by industry average standards of utilization allows for a definition of misutilization with regards to these industry standards. Odds ratios were extracted at the patient level to demonstrate differences in misutilization by patient characteristics, such as race; Black individuals experienced higher under-utilization compared to White individuals (p < 0.0001). Volume-adjusted Poisson rate regression models allow for the identification and ranking of facilities with large departures in utilization. The proposed approach is scalable and easily generalizable to other diseases and resources and can be complemented with clinical information from electronic health record information, when available.

High sodium consumption is one of the four major risk factors contributing to non-communicable diseases around the world. Thailand has one of the highest rates of sodium consumption, with fish sauce being one of the main sources. The aim of this study was to examine whether changes in the micro-environment factors can affect fish sauce consumption behavior in a university setting in Thailand. We implemented four interventions (with one control) in five canteens across a Thai university. The study design was a Latin square, where the five canteens were randomized over five weeks to implement four interventions plus a control. Our interventions included behavior-oriented, cognitive-oriented, and affective-oriented nudges aimed to reduce the amount of fish sauce people add to their noodles during lunchtime at the university canteens. Results indicate that a simple change in how fish sauce was served can reduce fish sauce consumption. Serving fish sauce in a bowl with a spoon reduced the amount of fish sauce used per noodle bowl by 0.25 grams, compared to the normal condition where fish sauce is served in a bottle. Using a specially-designed spoon with a hole induced a larger reduction of 0.58 grams of fish sauce used per bowl. The other two interventions, cognitive- and affective- oriented nudges, also showed reductions of fish sauce usage, but the differences were not statistically significant. The findings can be used for policy implementation to advocate the use of a smaller sized spoon and a bowl to serve fish sauce instead of a bottle to reduce sodium consumption among Thai people.

Background Social prescribing programmes expand the range of options available to primary care health professionals to address patients’ psychosocial needs, impacting on their health and well-being. The objective of this study was to assess the change in the mental well-being of service users after participation in the Luton social prescribing programme. Methods Skew-normal (SN) regression was applied to analyse the change in mental well-being post-intervention (N = 63). The short Warwick–Edinburgh mental well-being scale was used as the outcome measure. Results The SN regression found a statistically significant change (P Conclusion Findings of this study indicate that social prescribing may have the potential to improve the mental well-being of service users. The study findings contribute to the sparse evidence base on social prescribing outcomes by socio-demographic characteristics of participants and highlight the importance of considering subgroup analysis in future research.

ObjectivesThe objective of this study was to assess the change in energy expenditure levels of service users after participation in the Luton social prescribing programme.DesignUncontrolled before-and-after study.SettingThis study was set in the East of England (Luton).ParticipantsService users with complete covariate information and baseline measurements (n=146) were included in the analysis.InterventionSocial prescribing, which is an initiative that aims to link patients in primary care with sources of support within the community sector to improve their health, well-being and care experience. Service users were referred to 12 sessions (free of charge), usually provided by third sector organisations.Primary outcome measureEnergy expenditure measured as metabolic equivalent (MET) minutes per week.ResultsUsing a Bayesian zero-inflated negative binomial model to account for a large number of observed zeros in the data, 95% posterior intervals show that energy expenditure from all levels of physical activities increased post intervention (walking 41.7% (40.31%, 43.11%); moderate 5.0% (2.94%, 7.09%); vigorous 107.3% (98.19%, 116.20%) and total 56.3% (54.77%, 57.69%)). The probability of engaging in physical activity post intervention increased, in three of four MET physical activity levels, for those individuals who were inactive at the start of the programme. Age has a negative effect on energy expenditure from any physical activity level. Similarly, working status has a negative effect on energy expenditure in all but one MET physical activity level. No consistent pattern was observed across physical activity levels in the association between gender and energy expenditure.ConclusionThis study shows that social prescribing may have the potential to increase the physical activity levels of service users and promote the uptake of physical activity in inactive patient groups. Results of this study can inform future research in the field, which could be of use for commissioners and policy makers.