The Effects of Retirement on Physical and Mental Health Outcomes

While numerous studies have examined how health affects retirement behavior, few have analyzed the impact of retirement on subsequent health outcomes. This study estimates the effects of retirement on health status as measured by indicators of physical and functional limitations, illness conditions, and depression. The empirics are based on seven longitudinal waves of the Health and Retirement Study, spanning 1992 through 2005. To account for biases due to unobserved selection and endogeneity, panel data methodologies are used. These are augmented by counterfactual and specification checks to gauge the robustness and plausibility of the estimates. Results indicate that complete retirement leads to a 5-16 percent increase in difficulties associated with mobility and daily activities, a 5-6 percent increase in illness conditions, and 6-9 percent decline in mental health, over an average post-retirement period of six years. Models indicate that the effects tend to operate through lifestyle changes including declines in physical activity and social interactions. The adverse health effects are mitigated if the individual is married and has social support, continues to engage in physical activity post-retirement, or continues to work parttime upon retirement. Some evidence also suggests that the adverse effects of retirement on health may be larger in the event of involuntary retirement. With an aging population choosing to retire at earlier ages, both Social Security and Medicare face considerable shortfalls. Eliminating the embedded incentives in public and private pension plans, which discourage work beyond some point, and enacting policies that prolong the retirement age may be desirable, ceteris paribus. Retiring at a later age may lessen or postpone poor health outcomes for older adults, raise wellbeing, and reduce the utilization of health care services, particularly acute care. Working Paper 07-3

Asset pricing under rational learning about rare disasters : [Version 28 Juli 2011]

This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors’ information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the likelihood of rare disasters drop to a much more pessimistic level once a disaster has occurred. Such a shift in beliefs can trigger massive declines in price-dividend ratios. Pessimistic beliefs persist for some time. Thus, belief dynamics are a source of apparent excess volatility relative to a rational expectations benchmark. Due to the low frequency of disasters, even an infinitely-lived investor will remain uncertain about the exact probability. Our analysis is conducted in continuous time and offers closed-form solutions for asset prices. We distinguish between rational and adaptive Bayesian learning. Rational learners account for the possibility of future changes in beliefs in determining their demand for risky assets, while adaptive learners take beliefs as given. Thus, risky assets tend to be lower-valued and price-dividend ratios vary less under adaptive versus rational learning for identical priors. Keywords: beliefs, Bayesian learning, controlled diffusions and jump processes, learning about jumps, adaptive learning, rational learning. JEL classification: D83, G11, C11, D91, E21, D81, C6

The Effects of Retirement on Physical and Mental Health Outcomes

While numerous studies have examined how health affects retirement behavior, few have analyzed the impact of retirement on subsequent health outcomes. This study estimates the effects of retirement on health status as measured by indicators of physical and functional limitations, illness conditions, and depression. The empirics are based on seven longitudinal waves of the Health and Retirement Study, spanning 1992 through 2005. To account for biases due to unobserved selection and endogeneity, panel data methodologies are used. These are augmented by counterfactual and specification checks to gauge the robustness and plausibility of the estimates. Results indicate that complete retirement leads to a 5-16 percent increase in difficulties associated with mobility and daily activities, a 5-6 percent increase in illness conditions, and 6-9 percent decline in mental health, over an average post-retirement period of six years. Models indicate that the effects tend to operate through lifestyle changes including declines in physical activity and social interactions. The adverse health effects are mitigated if the individual is married and has social support, continues to engage in physical activity post-retirement, or continues to work part-time upon retirement. Some evidence also suggests that the adverse effects of retirement on health may be larger in the event of involuntary retirement. With an aging population choosing to retire at earlier ages, both Social Security and Medicare face considerable shortfalls. Eliminating the embedded incentives in public and private pension plans, which discourage work beyond some point, and enacting policies that prolong the retirement age may be desirable, ceteris paribus. Retiring at a later age may lessen or postpone poor health outcomes for older adults, raise well-being, and reduce the utilization of health care services, particularly acute care.

Affective Bias Through the Lens of Signal Detection Theory

Affective bias – a propensity to focus on negative information at the expense of positive information – is a core feature of many mental health problems. However, it can be caused by wide range of possible underlying cognitive mechanisms. Here we illustrate this by focusing on one particular behavioural signature of affective bias – increased tendency of anxious/depressed individuals to predict lower rewards – in the context of the Signal Detection Theory (SDT) modelling framework. Specifically, we show how to apply this framework to measure affective bias and compare it to the behaviour of an optimal observer. We also show how to extend the framework to make predictions about bias when the individual holds incorrect assumptions about the decision context. Building on this theoretical foundation, we propose five experiments to test five hypothetical sources of this affective bias: beliefs about prior probabilities, beliefs about performance, subjective value of reward, learning differences, and need for accuracy differences. We argue that greater precision about the mechanisms driving affective bias may eventually enable us to better understand the mechanisms underlying mood and anxiety disorders

Examining the Role of Mood Patterns in Predicting Self-reported Depressive Symptoms

Depression is the leading cause of disability worldwide. Initial efforts to detect depression signals from social media posts have shown promising results. Given the high internal validity, results from such analyses are potentially beneficial to clinical judgment. The existing models for automatic detection of depressive symptoms learn proxy diagnostic signals from social media data, such as help-seeking behavior for mental health or medication names. However, in reality, individuals with depression typically experience depressed mood, loss of pleasure nearly in all the activities, feeling of worthlessness or guilt, and diminished ability to think. Therefore, a lot of the proxy signals used in these models lack the theoretical underpinnings for depressive symptoms. It is also reported that social media posts from many patients in the clinical setting do not contain these signals. Based on this research gap, we propose to monitor a type of signal that is well-established as a class of symptoms in affective disorders -- mood. The mood is an experience of feeling that can last for hours, days, or even weeks. In this work, we attempt to enrich current technology for detecting symptoms of potential depression by constructing a 'mood profile' for social media users.Comment: Accepted at The Web Science Conference 202

Early Detection of Depression: Social Network Analysis and Random Forest Techniques

[Abstract] Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span. Methods: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. Results: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.Ministerio de Economía y Competitividad; TIN2015-70648-PXunta de Galicia; ED431G/01 2016-201

Development of a Dynamical Systems Model and Adaptive Intervention Strategy for Stroke Rehabilitation

Each year, approximately 795000 people experience stroke in the United States. After stroke onset, about 80% of patients suffer from hemiparesis, the weakness of face or limb on one side. These people outside clinical setting may develop learned nonuse, which may result in long-term limitation in the outcome of motor recovery. Interventions such as the Constraint Induced Movement Therapy has shown promise in reversing nonuse. However, many chronic individuals do not have access to such training programs. Therefore, some novel tools capable of continuous monitoring patients\u27 health status and furthermore providing appropriate interventions for patients in ambient setting is required to optimize stroke rehabilitation.Dynamical systems modeling combined with wearable technologies may allow to quantitatively describe nonuse evolution. We developed and validated a pendulum-based dynamical model using experimental and simulated motion data. Without direct access to internal torques, we proposed an inverse dynamics-based metric to quantify and compare motor performance between limbs. The primary outcome measure is RMSE between the simulated driving torque for experimental and reference motions. Using RMSEs, we defined a novel within-person comparison factor w participant limb [w], and compared it to the Fugl-Mayer Assessment score. Our dynamic model is capable of mimicking upper-extremity shoulder flexion dynamics. RMSE is sensitive to differences in motor performance between limbs for both groups. Finally, the factor w participant limb [w] is related to post-stroke severity. The arm dynamical model may have great potential for monitoring time-varying motor impairment using noninvasive sensing.Markov decision process (MDP) is a comparatively simple approach of simulation modelling. We implemented MDP to understand the primary factors behind human dynamic decision making on limb choice during rehabilitation. The model showed good performance in understanding the crucial motivators (or barriers) underlying patients\u27 behaviors. We found that a patient with higher motivation, greater perceived benefits of paretic-limb use, and milder motor impairment, would show a better adherence to using paretic limb in physical activity, which suggests that we may provide related interventions in clinical practice to promote a better recovery outcome. MDP modelling may be suggestive in designing cost-effective adaptive intervention for stroke rehabilitation

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Charting the Economic Life Cycle

Understanding the economic lifecycle %u2013 how it varies and why %u2013 is important in its own right, but is also critical to understanding how changes in population age structure influence many features of the macroeconomy. Economic behavior over the life cycle can be summarized by the average levels of consumption and labor earnings at each age, as shaped by biology, culture, institutions and individual choice. Here we present estimates of these in detail for the US and Taiwan, showing the roles played by public and familial transfer systems as well as asset accumulation, and present more basic profiles for selected additional countries drawing on studies from a larger project. Average economic dependency occurs when consumption exceeds labor earnings, typically in childhood and old age. A changing population age distribution alters the relative numbers of weighted consumers and producers, as summarized by the support ratio. The %u201Cdemographic dividend%u201D occurs during a sustained period of improving support ratios during the demographic transition, as can be shown using these profiles. The estimated cross-sectional age profiles of labor income have a broadly similar hump shape. However, there are striking contrasts in the timing of earnings over the life cycle. The consumption profiles reveal even more striking contrasts, with a flat age profile of total adult consumption in Taiwan and a steeply rising one in the U.S. We believe these differences reflect the extended family versus the state as the primary locus of transfers to the elderly. Profiles for private consumption are also quite variable, with Indonesia peaking early around age 25, Taiwan being essentially flat, and the US peaking late at around 55. Private expenditures on education show wide variations, with unusually high expenditures in some Asian countries. Because of possible public-private substitutions, it is questionable to assign causality to either for differences in total consumption, but it is hard to avoid noticing that without public spending on Medicare and institutional Medicaid in the U.S., total consumption would decline after 55, whereas with them, it rises strongly. There is only a short period of life during which production exceeds consumption barely more than 30 years in the US, Taiwan, and Thailand. The brevity of this phase contrasts sharply with high life expectancy, approaching 80 years in many countries.