Health-Related Quality of Life in a National Sample of Caregivers: Findings from the Behavioral Risk Factor Surveillance System

Abstract Purpose Recent national public health agendas, such as Healthy People 2010, call for improved public health surveillance and health promotion programs for people with disabilities and their caregivers. The goal of this study was to understand the public health impact of caregiving on health-related quality of life (HRQoL) using population-level data. Design & Methods A cross-sectional study design was used. 184,450 adults surveyed during the 2000 national Behavioral Risk Factor Surveillance System survey formed the sample. Binary logistic regression models ascertained differences between caregivers and noncaregivers in reporting reduced (''fair'' or ''poor'') health. Ordinary least squares regression (OLS) and multinomial logistic regression models examined the influence of caregiving status on HRQoL, measured as categories of healthy days reported in the last 30 days and the number of days reported as physical and mental health not good in the last 30 days. Results Sixteen percent (16%) of the survey respondents were caregivers. There was an interaction effect between caregiving status and age of the caregiver. In the fully adjusted models, caregivers \55 years old had a 35% increased risk of having fair or poor health (odds ratio to non-caregivers of the same age. In the adjusted models that examined the association of caregiving and healthy days, younger caregivers similarly showed larger deficits in both mental and physical HRQoL compared to older caregivers. For example combining mental and physical days, caregivers \55 had 1.44 fewer healthy days (b = -1.44, standard error (SE) = 0.07), while caregivers 55+ had 0.55 fewer days *b = -0.55, standard error (SE) = 0.13 (compared to non-caregivers in their respective age groups). Implications With increasing population age and the projected increase in caregivers, it is important that we understand the social and public health burden of caregiving and begin to identify interventions to sustain the HRQoL of caregivers. We found that caregivers have a slight to modest decline in HRQoL compared to non-caregivers, and that caregiving affects the HRQoL of younger adults more than older adults. Further research at the population level as to the type and level of burden of caregiving is needed.

Physician Opinions about EHR Use by EHR Experience and by Whether the Practice had optimized its EHR Use

Optimization and experience with using EHRs may improve physician experiences. Physician opinions about EHR-related impacts, and the extent to which these impacts differ by self-reported optimized EHR use and length of experience are examined through nationally representative physician data of EHR users from the National Electronic Health Records Survey extended survey (n=1,471). Logistic regression models first estimated how physicians' length of times using an EHR were associated with each EHR-related impact. Additionally, a similar set of models estimated the association of self-reported optimized EHR use with each EHR impact. At least 70% of physicians using EHRs continue to attribute their administrative burdens to their EHR use. Physicians with 4 or more years of EHR experience accounted for 58% of those using EHRs. About 71% of EHR users self-reported using an optimized EHR. Physicians with more EHR experience and those in practices that optimized EHR use had positive opinions about the impacts of using EHRs, compared to their counterparts. These findings suggest that longer experience with EHRs improves perceptions about EHR use; and that perceived EHR use optimization is crucial to identifying EHR-related benefits. Finding ways to reduce EHR-related administrative burden has yet to be addressed

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions