The Impact of Job Conditions on Health-Related Quality of Life among Working Japanese Older Adults: A Five-Year Longitudinal Study Using J-MICC Okazaki Study Data

ObjectivesThe number of older adults who continue working after retirement is increasing in Japan. Little is known about how job conditions affect older adults’ health. We examined the association between job conditions and health-related quality of life (HRQOL) during a five-year follow-up study.MethodsThis study included participants aged 65 years or older from the Japan Multi-Institutional Collaborative Cohort Study in the Okazaki area recruited at baseline between 2007 and 2011 and followed up five years later. Participants completed a self-reported questionnaire on the physical and mental health aspects of HRQOL (SF-8™), employment status, and job conditions (job satisfaction, skill use, and job suitability).ResultsData of 1,146 men and 522 women were analyzed (mean age: 69.1 and 68.6 years, respectively). Generalized mixed linear regression analysis revealed that, compared to the not-working group, skill use was positively associated with mental health aspects among men (skill use × time: β = 0.16, SE = 0.08, p < 0.05), while poor job satisfaction and job suitability were negatively associated with mental health aspects among women (job satisfaction, not satisfied × time: β = -0.93, SE = 0.47, p < 0.05; job suitability, not suitable × time: β = -1.06, SE = 0.50, p < 0.05).ConclusionsRegarding job conditions among older adults, skill use in men was marginally associated with mental health, and poor job satisfaction and suitability in women were negatively associated with mental health. Considering the job conditions of older workers is necessary to protect their mental health.     

Impact of using concomitant conventional DMARDs on adherence to biologic DMARD treatment in rheumatoid arthritis: Multi-centre,population-based cohort study

PurposeTo evaluate the impact of concomitant use of conventional synthetic DMARDs (csCMARD) on adherence, switching and dose of biologic disease modifying antirheumatic drugs (bDMARD) in rheumatoid arthritis (RA) patients treated with bDMARDs.Patients and methodsThis was a population-based cohort study conducted in five provinces of Canada (Alberta, Manitoba, Ontario, Quebec, and Saskatchewan), and one American database (IBM® MarketScan® Databases). Adult RA patients entered the study after a 3-month initiation period of bDMARDs between 1 January 2007, and 30 March 2014. Concomitant csDMARD exposure was compared to non-csDMARD exposure on the following outcomes: discontinuation of bDMARD therapy, switching of bDMARDs, and percent change in dose of bDMARD compared to initial dose. The effect of the time-varying changes in csDMARD exposure was analyzed using marginal structural models. Dose change was analyzed using linear regression. Results from each participating site were combined using likelihood ratio meta-analysis.ResultsThe study population comprised 20,221 new users of bDMARDs: adalimumab (7609), etanercept (9809), abatacept (1024), infliximab (1779). Concomitant use of csDMARD therapy was not significantly associated with reduced discontinuation of bDMARD treatment (hazard ratio 0.90, 95% intrinsic confidence interval 0.79 to 1.02) or reduced switching of bDMARDs (hazard ratio 0.95, 95% intrinsic confidence interval 0.80 to 1.11), but was associated with a small increase in bDMARD dose compared to the mean dose over the first three months of treatment (mean percentage change in dose +0.56% mg/day, 95% intrinsic confidence interval +0.14% to +0.97%).ConclusionIn this large study of RA patients using bDMARDs in Canada and the United States, we found no clear evidence that patients who received concomitant csDMARD therapy were less likely to discontinue, switch or increase their dose of bDMARD.     

Promoting Healthy Childhood Behaviors With Financial Incentives: A Narrative Review of Key Considerations and Design Features for Future Research

In the last decade, there has been a robust increase in research using financial incentives to promote healthy behaviors as behavioral economics and new monitoring technologies have been applied to health behaviors. Most studies of financial incentives on health behaviors have focused on adults, yet many unhealthy adult behaviors have roots in childhood and adolescence. The use of financial incentives is an attractive but controversial strategy in childhood. In this review, we first propose 5 general considerations in designing and applying incentive interventions to children. These include: 1) the potential impact of incentives on intrinsic motivation, 2) ethical concerns about incentives promoting undue influence, 3) the importance of child neurodevelopmental stage, 4) how incentive interventions may influence health disparities, and 5) how to finance effective programs. We then highlight empirical findings from randomized trials investigating key design features of financial incentive interventions, including framing (loss vs gain), timing (immediate vs delayed), and magnitude (incentive size) effects on a range of childhood behaviors from healthy eating to adherence to glycemic control in type 1 diabetes. Though the current research base on these subjects in children is limited, we found no evidence suggesting that loss-framed incentives perform better than gain-framed incentives in children and isolated studies from healthy food choice experiments support the use of immediate, small incentives versus delayed, larger incentives. Future research on childhood incentives should compare the effectiveness of gain versus loss-framing and focus on which intervention characteristics lead to sustained behavior change and habit formation.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission

ObjectiveTo identify and contrast risk factors for six-month pediatric asthma readmissions using traditional models (Cox proportional-hazards and logistic regression) and artificial neural-network modeling.MethodsThis retrospective cohort study of the 2013 Nationwide Readmissions Database included children 5 to 18 years old with a primary diagnosis of asthma. The primary outcome was time to asthma readmission in the Cox model, and readmission within 180 days in logistic regression. A basic neural network construction with 2 hidden layers and multiple replications considered all dataset variables and potential variable interactions to predict 180-day readmissions. Logistic regression and neural-network models were compared on area-under-the receiver-operating curve.ResultsOf 18,489 pediatric asthma hospitalizations, 1858 were readmitted within 180 days. In Cox and logistic models, longer index length of stay, public insurance, and nonwinter index admission seasons were associated with readmission risk, whereas micropolitan county was protective. In neural-network modeling, 9 factors were significantly associated with readmissions. Four overlapped with the Cox model (nonwinter-month admission, long length of stay, public insurance, and micropolitan hospitals), whereas 5 were unique (age, hospital bed number, teaching-hospital status, weekend index admission, and complex chronic conditions). The area under the curve was 0.592 for logistic regression and 0.637 for the neural network.ConclusionsDifferent methods can produce different readmission models. Relying on traditional modeling alone overlooks key readmission risk factors and complex factor interactions identified by neural networks.     

Photodynamic therapy-a promising treatment of oral mucosal infections

The treatment of oral mucosal infections is increasingly challenging owing to antibiotic resistance. Therefore, alternative antimicrobial strategies are urgently required. Photodynamic therapy (PDT) has attracted attention for the treatment of oral mucosal infections because of its ability to effectively inactivate drug-resistant bacteria, completely heal clinical infectious lesions and usually offers only mild adverse reactions. This review briefly summarizes relevant scientific data and published papers and discusses the potential mechanism and application of PDT in the treatment of oral mucosal infections.