Voluntary and involuntary driving cessation in later life

This study explores the decision-making process of driving cessation in later life, with a focus on voluntariness. The sample included 83 former drivers from the Baltimore Epidemiologic Catchment Area Study. A majority of participants (83%) reportedly stopped driving by their own decision. However, many voluntary driving retirees reported external factors such as financial difficulty, anxiety about driving, or lack of access to a car as main reasons for driving cessation. These findings imply that distinction between voluntary and involuntary driving cessation is ambiguous and that factors beyond health status, including financial strain, play a role in the transition to non-driving.     

A class of semiparametric transformation models for survival data with a cured proportion

We propose a new class of semiparametric regression models based on a multiplicative frailty assumption with a discrete frailty, which may account for cured subgroup in population. The cure model framework is then recast as a problem with a transformation model. The proposed models can explain a broad range of nonproportional hazards structures along with a cured proportion. An efficient and simple algorithm based on the martingale process is developed to locate the nonparametric maximum likelihood estimator. Unlike existing expectation-maximization based methods, our approach directly maximizes a nonparametric likelihood function, and the calculation of consistent variance estimates is immediate. The proposed method is useful for resolving identifiability features embedded in semiparametric cure models. Simulation studies are presented to demonstrate the finite sample properties of the proposed method. A case study of stage III soft-tissue sarcoma is given as an illustration.     

Supply Networks as a Complex Adaptive System: Toward Simulation‐Based Theory Building on Evolutionary Decision Making

In this article, we examine how the firms embedded in supply networks engage in decision making over time. The supply networks as a complex adaptive system are simulated using cellular automata (CA) through a dynamic evolution of cooperation (i.e., “voice” decision) and defection (i.e., “exit” decision) among supply network agents (i.e., firms). Simple local rules of interaction among firms generate complex patterns of cooperation and defection decisions in the supply network. The incentive schemes underlying decision making are derived through different configurations of the payoff‐matrix based on the game theory argument. The prisoner's dilemma game allows capturing the localized decision‐making process by rational agents, and the CA model allows the self‐organizing outcome to emerge. By observing the evolution of decision making by cooperating and defecting agents, we offer testable propositions regarding relationship development and distributed nature of governance mechanisms for managing supply networks.     

A modified generalized lasso algorithm to detect local spatial clusters for count data

Detecting local spatial clusters for count data is an important task in spatial epidemiology. Two broad approaches—moving window and disease mapping methods—have been suggested in some of the literature to find clusters. However, the existing methods employ somewhat arbitrarily chosen tuning parameters, and the local clustering results are sensitive to the choices. In this paper, we propose a penalized likelihood method to overcome the limitations of existing local spatial clustering approaches for count data. We start with a Poisson regression model to accommodate any type of covariates, and formulate the clustering problem as a penalized likelihood estimation problem to find change points of intercepts in two-dimensional space. The cost of developing a new algorithm is minimized by modifying an existing least absolute shrinkage and selection operator algorithm. The computational details on the modifications are shown, and the proposed method is illustrated with Seoul tuberculosis data.     

Potential consequences of raising the Social Security eligibility age on low-income older workers

To examine the potential consequences of raising the Social Security retirement age on future cohorts of low-income elders, this study, based on data from the Health and Retirement Study, 1992-1994, identifies factors that may hinder or facilitate continuous employment among older workers born between 1931 and 1941. Specifically, following the analysis of labor-force participation rates and self-reported reasons for non-work, multivariate logistic regression models tested the relationship between individual strengths and constraints, social-structural opportunities and constraints, and economic need variables and the likelihood of work. The findings show that for both men and women, having disabilities was the most significant predictor of non-work. Racial differences, especially in men's labor-force participation rates, appeared to be due in large part to significant racial differences in disability rates. A higher proportion of blacks and Hispanics than whites also reported that they were unemployed. Based on the findings, raising the Social Security eligibility age is likely to result in increased numbers of Disability Insurance (DI) claimants, and the fiscal impact of such an increase needs to be examined. The need to assist unemployed older persons is also discussed.     

Implementation and results of the statewide ONHRSS

In response to increased consumer demand, state governments are publishing more detailed information about the quality of nursing homes. In 2002, Ohio legislation mandated the publication of a Long-Term Care Consumer Guide that included information on nursing home resident and family satisfaction surveys. Using the 2002 Ohio Nursing Home Resident Satisfaction Survey as an example, this article addresses two research questions: (1) how can statewide resident face-to-face satisfaction interviews be implemented to maximize participation of residents and facilities and produce reliable and valid results, and (2) how satisfied are residents with their nursing home experiences, overall and by level of cognitive function.     

Mental Health Conditions Among North Korean Female Refugee Victims of Sexual Violence

The study aims to examine the negative effects of sexual violence on North Korean (NK) female refugees in South Korea. Although the prevalence of sexual violence victimization is extremely high and mental health problems are serious among these refugees, little to no research has been done on the relationship between sexual violence and mental health among these women. The mental health conditions of two groups of women (sexual violence victims and those who have not experienced sexual violence) were compared using ANCOVA analyses. The results show that suicidal ideation and alcohol use are significantly more prevalent in the sexual violence group than in the non‐sexual violence group. The women who had experienced sexual violence in particular should be provided with more professional and sustained treatment and management services. The government must improve the effectiveness of existing policies related to suicide and the drinking culture in South Korea.     

A hierarchical Bayesian model for binary data incorporating selection bias

We consider a Bayesian nonignorable model to accommodate a nonignorable selection mechanism for predicting small area proportions. Our main objective is to extend a model on selection bias in a previously published paper, coauthored by four authors, to accommodate small areas. These authors assume that the survey weights (or their reciprocals that we also call selection probabilities) are available, but there is no simple relation between the binary responses and the selection probabilities. To capture the nonignorable selection bias within each area, they assume that the binary responses and the selection probabilities are correlated. To accommodate the small areas, we extend their model to a hierarchical Bayesian nonignorable model and we use Markov chain Monte Carlo methods to fit it. We illustrate our methodology using a numerical example obtained from data on activity limitation in the U.S. National Health Interview Survey. We also perform a simulation study to assess the effect of the correlation between the binary responses and the selection probabilities.     

Regularized boxplot via convex clustering

A boxplot is a simple and effective exploratory data analysis tool for graphically summarizing a distribution of data. However, in cases where the quartiles in a boxplot are inaccurately estimated, these estimates can affect subsequent analyses. In this paper, we consider the problem of constructing boxplots in a bivariate setting with a categorical covariate with multiple subgroups, and assume that some of these boxplots can be clustered. We propose to use this grouping property to improve the estimation of the quartiles. We demonstrate that the proposed method more accurately estimates the quartiles compared to the usual boxplot. It is also shown that the proposed method identifies outliers effectively as a consequence of accurate quartiles, and possesses a clustering effect due to the group property. We then apply the proposed method to annual maximum precipitation data in South Korea and present its clustering results.