Incidence and Risk Factors for Hepatitis C Virus Infection among Illicit Drug Users in Italy

So far, only three small outdated studies have investigated hepatitis C virus (HCV) incidence and risk factors among illicit drug users (DUs) in Italy. Thus, during 2007–2010, we conducted a prospective cohort study among DUs attending 17 Italian rehabilitation centers serving urban areas. Two hundred eighty-four HCV-uninfected DUs were prospectively followed by interview and anti-HCV antibody and RNA testing every 6 months. Incidence was calculated using the person-years method. Infection predictors were assessed by time-dependent Cox analysis. Participants were mostly male (83.4%), under opioid substitution therapy (OST) (78.9%), non-injecting DUs (67.9%), and with a mean age of 30.8. Ninety-one of 224 DUs initially under OST interrupted treatment during the follow-up. Overall HCV incidence was 5.83/100 person-years at risk (PYAR) [95% confidence intervals (CI), 3.63–9.38]. The incidence did not significantly differ according the participants’ sociodemographic characteristics or the degree of urbanization of the towns involved in the study. The incidence was higher for DUs under than for those not under OST (6.23 vs 4.50/100 PYAR; p = 0.681). Incidence was also higher for those with than for those without OST interruption (7.17 vs 5.04/100 PYAR; p = 0.55). However, all these differences were non-significant. At last follow-up visit, a significant decrease in frequency of sharing equipment for preparation/using drugs (by injection or not) was observed by analyzing either the whole cohort or DUs under OST only. Anti-HCV seroconversion resulted independently associated with sharing drug preparation/use equipment, backloading, having a HCV-positive sexual partner, or household and (marginally) intravenous injection. In this study, HCV incidence was non-negligible and OST seemed to lack effectiveness in reducing it. In Italy, implementation of combined harm reduction interventions and antiviral treatment of chronically infected DUs would be needed.     

Peripheral blood leukocyte telomere length is associated with age but not renal function: A cross-sectional follow-up study

Objectives

We aimed to evaluate the relationship between baseline renal function and changes in telomere length in Han Chinese.

Methods

The telomere restriction fragment (TRF) length of leukocytes in the peripheral blood was measured in healthy volunteers recruited in 2014. The estimated glomerular filtration rate (eGFR) was calculated based on serum creatinine (Scr) and serum cystatin C (CysC)-eGFRcys and eGFRScr-cys through the Cockcroft-Gault formula (eGFRC-G) or the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI / eGFRCKD-EPI) equation. The correlation between telomere length changes over time and renal function was analyzed.

Results

Leukocyte TRF lengths were negatively correlated to age (r = -0.393, p < 0.001) and serum CysC (r = -0.180, p < 0.01), while positively associated with eGFRCKD-EPI, eGFRC-G, eGFRcys, and eGFRScr-cys (r = 0.182, 0.122, 0.290, and 0.254 respectively, p < 0.01). The 3-year change of telomere length was 46 bp/years. When adjusted for age, the associations between telomere length changes and baseline, subsequent TRF lengths, and serum CysC were no longer present. No association was observed between TRF length changes and renal function.

Conclusion

The rate of telomere length changes was affected by age and baseline telomere length. The telomere length changes might be important markers for aging.

     

Assessment of Physical Activity of Hospitalised Older Adults: A Systematic Review

Background

The assessment of physical activity levels of hospitalised older people requires accurate and reliable measures. Physical activities that older people in hospital commonly engage in include exercises and walking. Measurement of physical activity levels of older inpatients is essential to evaluate the impact of interventions to improve physical activity levels and to determine associations between physical activity in hospital and other health-related outcome measures.

Objective

To determine which measures are used to measure physical activity of older people in hospital, and to describe their properties and applications.

Method

A systematic review of four databases: Medline, Embase, CINAHL and AMED was conducted for papers published from 1996 to 2016. Inclusion criteria were participants aged ≥ 65 years and studies which included measures of physical activity in the acute medical inpatient setting. Studies which specifically assessed the activity levels of surgical patients or patients with neurological conditions such as stroke or brain injury were excluded. All study designs were included in the review.

Results

18 studies were included from 127 articles selected for full review. 15 studies used objective measures to measure the physical activity of older inpatients: 11 studies used accelerometers and four used direct systematic observations. Seven accelerometers were identified including the StepWatch Activity Monitor, activPAL, GENEActiv, Kenz Lifecorder EX, Actiwatch-L, Tractivity and AugmenTech Inc. Pittsburgh accelerometer. Three studies used a subjective measure (interviews with nurses and patients) to classify patients into low, intermediate and high mobility groups. The StepWatch Activity Monitor was reported to be most accurate at step-counting in patients with slow gait speed or altered gait. The activPAL was reported to be highly accurate at classifying postures.

Conclusion

Physical activity levels of older inpatients can be measured using accelerometers. The accuracy of the accelerometers varies between devices and population-specific validation studies are needed to determine their suitability in measuring physical activity levels of hospitalised older people. Subjective measures are less accurate but can be a practical way of measuring physical activity in a larger group of patients.

     

Initial Validation of the Toulouse St. Louis University Mini Falls Assessment in Older Adults

Background/Objectives

Falls are one of the most prevalent health issues facing older adults. This study examines the validity of the Toulouse-St. Louis University Mini Falls Assessment (TSLUMFA). Objectives were to validate the TSLUMFA by testing if it differentiates between prior non fallers (n=80) and fallers (n=23), and predicts future falls as well as or better than the gold standard Tinetti Gait and Balance Instrument (TGBI). Examine if the subset of FRAIL Scale items on the TSLUMFA distinguishes between previous non fallers (n=75) and fallers (n=20), and predicts future falls as well as or better than the TGBI. Identify TSLUMFA cut offs scores for fall risk.

Design

Prospective validation study.

Setting

Participants were ambulatory patients presenting to the SLU Geriatrics Clinic.

Participants

103 ambulatory older adults.

Measurements

Fall risk was assessed using the three assessments. Outcome measures were previous falls and follow up falls.

Results

TSLUMFA, FRAIL, and TGBI differentiated between previous fallers and non fallers. A TSLUMFA score <23 stratified patients as moderate risk (Sensitivity=0.806 Specificity=0.776) and a score <21 stratified patients as high risk (Sensitivity=0.929 Specificity=0.897). 78% of patients (n=80) participated in follow up and 20% (n=16) of these patients fell during follow up. TSLUMFA and TGBI absolute scores were lower among patients who fell during the follow up period versus non fallers but the observed differences were not statistically significant (TSLUMFA P=0.123 and TGBI P=0.074).

Conclusion

This study validated the TSLUMFA and FRAIL. No test predicted falls with statistical significance (most likely due to the low follow up participation) but a positive trend was seen. Clinical recommendations from this study are to use the FRAIL as an initial fall screen and patients scoring > 3 should be analyzed by TSLUMFA. The TSLUMFA’s advantage is that it pinpoints areas that will directly benefit from therapy to reduce falls.

     

DXA-Rectified Appendicular Lean Mass: Development of Ultrasound Prediction Models in Older Adults

Objective

Dual-energy X-ray absorptiometry (DXA)-derived appendicular lean soft tissue mass (aLM) is used to diagnose sarcopenia. However, DXA-derived aLM includes non-skeletal muscle components, such as fat-free component of adipose tissue fat cell. These components, if not accounted for, could falsely inflate the aLM in individuals with a high amount of adipose tissue mass. B-mode ultrasound accurately measures muscle size in older adults. We sought to develop regression-based prediction equations for estimating DXA-rectified appendicular lean tissue mass (i.e. DXA-derived aLM minus appendicular fat-free adipose tissue (aFFAT); abbreviated as aLM minus aFFAT) using B-mode ultrasound.

Design

Cross-sectional study.

Measurements

Three hundred and eighty-nine Japanese older adults (aged 60 to 79 years) volunteered in the study. aLM was measured using a DXA, and muscle thickness (MT) was measured using ultrasound at nine sites. An ordinary least-squares multiple linear regression model was used to predict aLM minus aFFAT from sex, age and varying muscle thicknesses multiplied by height. Based on previous studies, we chose to use 4 MT sites at the upper and lower extremities (4-site MT model) and a single site (1-site MT model) at the upper extremity to develop prediction models.

Results

The linear prediction models (4 site MT model; R² = 0.902, adjusted R² = 0.899, and 1-site MT model; R² = 0.868, adjusted R² = 0.866) were found to be stable and accurate for estimating aLM minus aFFAT. Bootstrapping (n=1000) resulted in optimism values of 0.0062 (4-site MT model) and 0.0036 (1-site MT model).

Conclusion

The results indicated that ultrasound MT combined with height, age and sex can be used to accurately estimate aLM minus aFFAT in older Japanese adults. Newly developed ultrasound prediction equations to estimate aLM minus aFFAT may be a valuable tool in population-based studies to assess age-related rectified lean tissue mass loss.

     

Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

Background

Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards.

Methods

A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]).

Results

Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7).

Conclusions

Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

     

Mimesis and clinical pictures: thinking with Plato and Broekman through the production and meaning of images of disease

This paper contends, following Plato and Broekman, that (1) seeing images as images is crucial to theorizing medicine and that (2) considering clinical pictures as images of images is a much-needed epistemic complement to the domineering view that sees clinical pictures as mirrors of disease. This does not only offer epistemic, but also ethical benefits to individual patients, especially in those cases where patients suffer from chronic, debilitating, and terminal illnesses and where medicine provides no, or limited, answers in terms of treatment, intervention, and meaning. By creating room for a theory of clinical pictures that rightfully emphasizes its pictorial nature, patients and doctors alike may be encouraged to consider under what authorship, and with which epistemic tools, alternative, supplemental images may be produced to get at the existential reality of disease and suffering. Ultimately, this paper argues that the epistemic tools provided by aesthetics may offer such glimpses into the reality of disease and suffering, and I conclude by discussing a few artistic renditions of breast cancer to illustrate my point.     

The Jefferson Scale of Empathy: a nationwide study of measurement properties,underlying components,latent variable structure,and national norms in medical students

The Jefferson Scale of Empathy (JSE) is a broadly used instrument developed to measure empathy in the context of health professions education and patient care. Evidence in support of psychometrics of the JSE has been reported in health professions students and practitioners with the exception of osteopathic medical students. This study was designed to examine measurement properties, underlying components, and latent variable structure of the JSE in a nationwide sample of first-year matriculants at U.S. colleges of osteopathic medicine, and to develop a national norm table for the assessment of JSE scores. A web-based survey was administered at the beginning of the 2017–2018 academic year which included the JSE, a scale to detect “good impression” responses, and demographic/background information. Usable surveys were received from 6009 students enrolled in 41 college campuses (median response rate?=?92%). The JSE mean score and standard deviation for the sample were 116.54 and 10.85, respectively. Item-total score correlations were positive and statistically significant (p?<?0.01), and Cronbach α?=?0.82. Significant gender differences were observed on the JSE scores in favor of women. Also, significant differences were found on item scores between top and bottom third scorers on the JSE. Three factors of Perspective Taking, Compassionate Care, and Walking in Patient’s Shoes emerged in an exploratory factor analysis by using half of the sample. Results of confirmatory factor analysis with another half of the sample confirmed the 3-factor model. We also developed a national norm table which is the first to assess students’ JSE scores against national data.     

Using entropy balancing to strengthen an observational cohort study design: lessons learned from an evaluation of a complex multi-state federal demonstration

We conducted an evaluation of a patient-centered medical home demonstration sponsored by the Centers for Medicare & Medicaid Services. We implemented a quasi-experimental pre-post with a comparison group design. Traditional propensity score weighting failed to achieve balance (exchangeability) between the two groups on several critical characteristics. In response, we incorporated a relatively new alternative known as entropy balancing. Our objective is to share lessons learned from using entropy balancing in a quasi-experimental study design. We document the advantages and challenges with using entropy balancing. We also describe a set of best practices, and we present a series of illustrative analyses that empirically demonstrate the performance of entropy balancing relative to traditional propensity score weighting. We compare alternative approaches based on: (i) covariate balance (e.g., standardized differences); (ii) overlap in conditional treatment probabilities; and (iii) the distribution of weights. Our comparison of overlap is based on a novel approach we developed that uses entropy balancing weights to calculate a pseudo-propensity score. In many situations, entropy balancing provides remarkably superior covariate balance compared to traditional propensity score weighting methods. Entropy balancing is also preferred because it does not require extensive iterative manual searching for an optimal propensity score specification. However, we demonstrate that there are some situations where entropy balancing “fails”. Specifically, there are instances where entropy balancing achieves adequate covariate balance only by using a distribution of weights that dramatically up-weights a small set of observations, giving them a disproportionately large and undesirable influence.