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Purpose: Telehealth care is emerging as a viable intervention model to treat complex chronic conditions, such as heart failure (HF) and chronic obstructive pulmonary disease (COPD), and to engage older adults in self-care disease management. Design and Methods: We report on a randomized controlled trial examining the impact of a multifaceted telehealth intervention on health, mental health, and service utilization outcomes among homebound medically ill older adults diagnosed with HF or COPD. Random effects regression modeling was used, and we hypothesized that older adults in the telehealth intervention ( n = 51) would receive signifi cantly better quality of care resulting in improved scores in healthrelated quality of life, mental health, and satisfaction with care at 3 months follow-up as compared with controls ( n = 51) and service utilization outcomes at 12 months follow-up. Results: At follow-up, the telehealth intervention group reported greater increases in general health and social functioning, and improved in depression symptom scores as compared with usual care plus education group. The control group had signifi cantly more visits to the emergency department than the telehealth group. There was an observed trend toward fewer hospital days for telehealth participants, but it did not reach signifi cance at 12 months. Implications: Telehealth may be an effi cient and effective method of systematically delivering integrated care in the home health sector. The use of telehealth technology may benefi t homebound older adults who have diffi culty accessing care due to disability, transportation, or isolation.

Greening of vacant urban land may affect health and safety. The authors conducted a decade-long difference-indifferences analysis of the impact of a vacant lot greening program in Philadelphia, Pennsylvania, on health and safety outcomes. ‘‘Before’’ and ‘‘after’’ outcome differences among treated vacant lots were compared with matched groups of control vacant lots that were eligible but did not receive treatment. Control lots from 2 eligibility pools were randomly selected and matched to treated lots at a 3:1 ratio by city section. Random-effects regression models were fitted, along with alternative models and robustness checks. Across 4 sections of Philadelphia, 4,436 vacant lots totaling over 7.8 million square feet (about 725,000 m 2 ) were greened from 1999 to 2008. Regressionadjusted estimates showed that vacant lot greening was associated with consistent reductions in gun assaults across all 4 sections of the city (P < 0.001) and consistent reductions in vandalism in 1 section of the city (P < 0.001). Regression-adjusted estimates also showed that vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the city (P < 0.01). Once greened, vacant lots may reduce certain crimes and promote some aspects of health. Limitations of the current study are discussed. Community-based trials are warranted to further test these findings. city planning; crime; geography; urban health; urban renewal; wounds and injuries

SUMMARY The compliance score in randomized trials is a measure of the effect of randomization on treatment received. It is in principle a group-level pretreatment variable and so can be used where individual-level measures of treatment received can produce misleading inferences. The interpretation of models with the compliance score as a regressor of interest depends on the link function. Using the identity link can lead to valid inference about the effects of treatment received even in the presence of nonrandom noncompliance; such inference is more problematic for nonlinear links. We illustrate these points with data from two randomized trials.

The analyses of observational longitudinal studies involving concurrent changes in treatment and medical conditions present difficulties because of the multitude of directions of potential relationships: past medication influences current symptoms; past symptoms influence current medication; and current medication is associated with current symptoms. In the context of a long-term study of non-randomized pharmacological treatment of schizophrenic relapse, we present an analysis of bivariate discrete-time transitional data with binary responses in an attempt to understand the transitional and concurrent relationships between schizophrenia relapse and medication use. A naive analysis does not show any association between previous medication and current relapse. However, we provide evidence suggesting that current treatment may impact current relapse for those who have previously taken medication, but not for those who haven't taken medication in the past. When univariate models are specified to assess these associations, the bivariate nature of the problem requires a choice of which response, relapse or medication, should be the dependent variable. In this case, the choice of relapse or medication as a dependent variable does matter. Hence, our results derive from models where both relapse and medication are treated as dependent variables. Specifically, we specify a bivariate log odds ratio for current relapse and current medication use and a separate univariate logit component for each of these outcomes. Each of these components contains transitional associations with previous relapse and medication. Such models represent extensions of univariate transitional association models (e.g. Diggle et al. (1994)) and correspond to bivariate transitional models (e.g. Zeger and Liang (1991)). We incorporate changes in transitional associations into the full-data parametric model for final inference, and investigate if these temporal changes are due to learning effects or the impact of drop-out. We also perform residual analyses and sensitivity analyses in the context of missing data patterns.

Summary The elderly population in the USA is expected to double in size by the year 2025, making longitudinal health studies of this population of increasing importance. The degree of loss to follow-up in studies of the elderly, which is often because elderly people cannot remain in the study, enter a nursing home or die, make longitudinal studies of this population problematic. We propose a latent class model for analysing multiple longitudinal binary health outcomes with multiple-cause non-response when the data are missing at random and a non-likelihood-based analysis is performed. We extend the estimating equations approach of Robins and co-workers to latent class models by reweighting the multiple binary longitudinal outcomes by the inverse probability of being observed. This results in consistent parameter estimates when the probability of non-response depends on observed outcomes and covariates (missing at random) assuming that the model for non-response is correctly specified. We extend the non-response model so that institutionalization, death and missingness due to failure to locate, refusal or incomplete data each have their own set of non-response probabilities. Robust variance estimates are derived which account for the use of a possibly misspecified covariance matrix, estimation of missing data weights and estimation of latent class measurement parameters. This approach is then applied to a study of lower body function among a subsample of the elderly participating in the 6-year Longitudinal Study of Aging.

This research investigated the relationship of an affective-cognitive schema, valuation of life (VOL), to older people's responses to a set of health utility (years of desired life) questions. Six hundred healthy and chronically ill elders aged 70 and older were interviewed to measure quality of life (QOL), mental health, and VOL. Valuation of life was significantly correlated with longer Years of Desired Life under 8 of 10 health conditions when background, health, QOL, and mental health states were controlled. We concluded that VOL is an internal representation of the many positive and negative features of the person and her everyday life that is necessary to comprehend how people may cling to life or welcome its end.