Complex home care: challenges arising from the blurring of boundaries between family and professional care

Over the last 50 years, increasingly complex care (such as tracheostomy management, dialysis or enteral feeding) has shifted from hospital to home, with a concomitant rise in patient self-management and care given by family members. Recognition of the importance of the contribution of family care to the health system is also growing. This article reports the findings of a New Zealand study which explored the experiences of family carers who manage technical health procedures at home. It then draws attention to some broader issues raised by shifting complex care from professional management in hospital settings to family care at home, namely the ways in which complex home care blurs the boundaries between professional and family care (creating the ambiguous position of the expert carer) and questions of safety and responsibility in family care. We also discuss the implications for policy around family caregiving in New Zealand. Given the potential physical and mental health impacts of caring, it is time for renewed consideration of what family carers should be expected or allowed to manage and how the health system can support them in their important role.     

Multivariate Return Decomposition: Theory and Implications

In this paper, we propose a model based on multivariate decomposition of multiplicative – absolute values and signs – components of asset returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting bond returns illustrates the usefulness of the proposed method.     

Effect of cigarette smoking on sexual functions,psychological factors,and disease activity in male patients with ankylosing spondylitis

Objective: This study aims to investigate the effect of smoking on sexual functions in AS patients.

Patients and methods: A total of 67 male AS patients with a median age of 34 years (range: 18–57) reporting sexual activity at least for the past 4 weeks period were included. Patients were divided into smokers (Group 1, n?=?47) and non-smokers (Group 2, n?=?20). Fagerström test for nicotine dependence, smoking history, exhaled carbon monoxide test were recorded for smoking AS patients. Visual analogue scale (VAS), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Metrology Index (BASMI), Bath Ankylosing Spondylitis Functional Index (BASFI), Ankylosing Spondylitis Quality of Life (ASQoL), International Index of Erectile Function (IIEF), Beck Depression Inventory (BDI) were filled for both groups.

Results: There was no significant difference between smokers and non-smokers in all evaluation parameters. BASMI scores were significantly lower in the mild dependency subgroup as compared to those with moderate or severe dependency (p?=?.005 and p?=?.007, respectively). Total IIEF score and IIEF categories correlated significantly with BASDAI, BASFI, BASMI, ASQoL, pain, fatigue, and cumulative smoking. BDI showed an inverse correlation with the IIEF score and IIEF category (p?r?=?–0.520, p?r?=?–0.508, respectively).

Conclusions: Sexual function in AS patients is associated with the pain, fatigue, disease activity, functional status, quality of life, depression as well as the cumulative exposure to smoking, and that sexual functions tend to decline with increasing degree of cigarette dependency.     

Does the Role of Student Factors with Adolescent Substance Use Vary by Geography?

This study assesses the association of 12th-grade student factors (e.g., grades, classroom misbehaviors) with substance use (i.e., binge drinking, cigarette smoking, marijuana) across large, medium-sized, and nonmetropolitan areas. Based on a sample of 2,189 students from the 2013 Monitoring the Future data set, logistic regression analyses showed that poor student academic and behavioral factors were associated with higher substance use after controlling for gender, race, and socioeconomic status. Interaction analyses further showed no significant differences in the influence of student factors on substance use across locales. Results suggest that prevention efforts against substance use should target student factors regardless of area of residency.     

Elementary renewal theorems for widely dependent random variables with applications to precise large deviations

Abstract

On the basis of Wang and Cheng (J. Math. Anal. Appl. 384 (2011) 597–606), this paper further investigates elementary renewal theorems for counting processes generated by random walks with widely orthant dependent increments. The obtained results improve the corresponding ones of the above-mentioned paper mainly in the sense of weakening the moment conditions on the positive parts of the increments. Meanwhile, a revised version of strong law of large numbers for random walks with widely orthant dependent increments is established, which improves Theorem 1.4 of Wang and Cheng (2011 Wang, Y., and D. Cheng. 2011. Basic renewal theorems for a random walk with widely dependent increments and their applications. Journal of Mathematical Analysis and Applications 384 (2):597–606. doi:10.1016/j.jmaa.2011.06.010.[Crossref], [Web of Science ®] , [Google Scholar]) by enlarging the regions of dominating coefficients. Finally, by using the above results, some precise large deviation results for a nonstandard renewal risk model are established, in which the innovations are widely orthant dependent random variables with common heavy tails, and the inter-arrival times are also widely orthant dependent.     

Testing for Serial Independence: Beyond the Portmanteau Approach

Portmanteau tests are typically used to test serial independence even if, by construction, they are generally powerful only in presence of pairwise dependence between lagged variables. In this article, we present a simple statistic defining a new serial independence test, which is able to detect more general forms of dependence. In particular, differently from the Portmanteau tests, the resulting test is powerful also under a dependent process characterized by pairwise independence. A diagram, based on p-values from the proposed test, is introduced to investigate serial dependence. Finally, the effectiveness of the proposal is evaluated in a simulation study and with an application on financial data. Both show that the new test, used in synergy with the existing ones, helps in the identification of the true data-generating process. Supplementary materials for this article are available online.     

Prediction intervals for farima processes by bootstrap methods

In this paper we introduce a procedure to compute prediction intervals for FARIMA (p d q) processes, taking into account the variability due to model identification and parameter estimation. To this aim, a particular bootstrap technique is developed. The performance of the prediction intervals is then assessed and compared to that of stand­ard bootstrap percentile intervals. The methods are applied to the time series of Nile River annual minima.     

Modeling binary familial data using Gaussian copula

Modeling binary familial data has been a challenging task due to the dependence among family members and the constraints imposed on the joint probability distribution of the binary responses. This paper investigates some useful familial dependence structures and proposes analyzing binary familial data using Gaussian copula model. Advantages of this approach are discussed as well as some computational details. An numerical example is also presented with an aim to show the capability of Gaussian copula model in more sophisticated data analysis.     

Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.