The effect of work on mental health: Does occupation Matter?

mental health panel data model labour market status occupation

Adult body height as a mediator between early-life conditions and socio-economic status:The case of the Dutch Potato Famine, 1846–1847

Adult body height appears to be a relatively accurate summary variable of early-life exposures’ influence on health, and may be a useful indicator of health in populations where more traditional health-related indicators are lacking. In particular, previous studies have shown a strong, positive relationship between environmental conditions in early life (particularly nutritional availability and the disease environment)and adult height. Research has also demonstrated positive associations between height and socioeconomic status. We therefore hypothesize that height mediates the relationship between early-life conditions and later-life socio-economic outcomes. We also hypothesize that the period of exposure in early life matters, and that conditions during pregnancy or the first years of life and/or the years during puberty have the largest effects on height and socio-economic status. To test these relationships, we use a sample of 1817 Dutch military conscripts who were exposed during early life to the Dutch Potato Famine (1846–1847). We conduct mediation analyses using structural equation modelling, and test seven different time periods in early-life. We use potato prices and real wages to proxy early-life environmental conditions, and occupational status (using the HISCAM scale)to proxy socioeconomic status. We find no evidence of mediation, partial or full, in any models. However, there are significant relationships between potato prices in adolescence, height and socio-economic status. To determine causality in these relationships, further research is needed

The Role of Early-Life Conditions in the Cognitive Decline due to Adverse Events Later in Life

Cognitive functioning of elderly individuals may be affected by events such as the loss of a (grand)child or partner or the onset of a serious chronic condition, and by negative economic shocks such as job loss or the reduction of pension benefits. It is conceivable that the impact of such events is stronger if conditions early in life were adverse. In this paper we address this using a Dutch longitudinal database that follows elderly individuals for more than 15 years and contains information on demographics, socio-economic conditions, life events, health, and cognitive functioning. We exploit exogenous variation in early-life conditions as generated by the business cycle. We also examine to what extent the cumulative effect of consecutive shocks later in life exceeds the sum of the separate effects, and whether economic and health shocks later in life reinforce each other in their effect on cognitive functioning.cognitive functioning, business cycle, bereavement, developmental origins, retirement, health, long-run effects, dementia

An econometric analysis of the mental-health effects of major events in the life of older individuals

Major events in the life of an older individual, such as retirement, a significant decrease in income, death of the spouse, disability, and a move to a nursing home, may affect the mental health status of the individual. For example, the individual may enter a prolonged depression. We investigate this using unique longitudinal panel data that track labor market behavior, health status, and major life events, over time. To deal with endogenous aspects of these events we apply fixed effects estimation methods. We find some strinkingly large effects of certain events on the occurence of depression. We relate the results to the health care and labor market policy towards older individuals.Death; retirement; income loss; disease; depression; health indicators; widowhood; care; panel data; endogeneity; fixed effects

Attributing a monetary value to patients’ time: A contingent valuation approach

It is hard to ignore the importance of patient time investment in the production of health since the influential paper by Grossman (1972). Patient time includes time to admission, travel time, waiting time, and treatment time and can be substantial. Time to admission is the time between the first referral and the moment that the treatment actually starts. Travel time is the time that a patient needs to travel between the place where the patient lives and the medical care centre where the patient is treated. Waiting time is the time that the patient waits at the medical care centre before treatment. Treatment time is the time spent getting active treatment for example by a doctor or a nurse. Patient time is, however, often ignored in economic analyses. This may lead to biased results and inappropriate policy recommendations, which may eventually influence patients’ health, wellbeing and welfare. How to value patient time is not straightforward. It is even less straightforward for patients who are not participating in the labour market. Although there is some emerging literature on the monetary valuation of patient time, an important challenge remains to develop an approach that can be used to monetarily value time of patients not participating in the labour market. We aim to contribute to the health economics literature by describing and empirically illustrating how to monetarily value patients’ time comprehensively, using the contingent valuation method. Comprehensively means including various types of patient time (time to admission, travel time, waiting time, and treatment time) as the previous literature focused mainly on valuing a particular type of patient time, for instance waiting time. This paper describes the development of the contingent valuation survey. The survey is added as an appendix to this paper. This paper also presents the first empirical results of applying our survey approach in a sample of patients in the Netherlands not participating in the labour market. These results show that the monetary value of waiting time was the highest (€30.10/£34.76 per hour) and travel and treatment time were equally valued (respectively €13.20/£11.43 and €13.32/£11.54 per hour)

Utilization of acute and long-term care in the last year of life: comparison with survivors in a population-based study

Background. It is well-known that the use of care services is most intensive in the last phase of life. However, so far only a few determinants of end-of-life care utilization are known. The aims of this study were to describe the utilization of acute and long-term care among older adults in their last year of life as compared to those not in their last year of life, and to examine which of a broad range of determinants can account for observed differences in care utilization. Methods. Data were used from the Longitudinal Aging Study Amsterdam (LASA). In a random, age and sex stratified population-based cohort of 3107 persons aged 55 ? 85 years at baseline and representative of the Netherlands, follow-up cycles took place at 3, 6 and 9 years. Those who died within one year directly after a cycle were defined as the "end-of-life group" (n = 262), and those who survived at least three years after a cycle were defined as the "survivors". Utilization of acute and long-term care services, including professional and informal care, were recorded at each cycle, as well as a broad range of health-related and psychosocial variables. Results. The end-of-life group used more care than the survivors. In the younger-old this difference was most pronounced for acute care, and in the older-old, for long-term care. Use of both acute and long-term home care in the last year of life was fully accounted for by health problems. Use of institutional care at the end of life was partly accounted for by health problems, but was not fully explained by the determinants included. Conclusion. This study shows that severity of health problems are decisive in the explanation of the increase in use of care services towards the end-of-life. This information is essential for an appropriate allocation of professional health care to the benefit of older persons themselves and their informal caregivers. © 2009 Pot et al; licensee BioMed Central Ltd