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1.
Health Econ ; 29(12): 1728-1743, 2020 12.
Article in English | MEDLINE | ID: mdl-32969122

ABSTRACT

This study analyzes the complexity of female longevity improvements. As socioeconomic status is found to influence health and mortality, we partition all individuals, at each age in every year, into five socioeconomic groups based on an affluence measure that combine an individual's income and wealth. We identify the particular socioeconomic groups that have been driving the standstill for Danish females at older ages. Within each socioeconomic group, we further analyze the cause of death patterns. The decline in life expectancy for Danish females is present for four out of five subgroups, however, with particular large decreases for the low-middle and middle-affluence groups. Cancers, smoking-related lung and bronchus causes, and other diseases particularly contribute to the stagnation. For four of the five socioeconomic groups only small cardiovascular improvement are experienced during the period of stagnating life expectancy compared to an equally long and subsequent period.


Subject(s)
Income , Life Expectancy , Adult , Aged , Cause of Death , Female , Humans , Longevity , Middle Aged , Mortality , Social Class , Socioeconomic Factors
2.
Health Aff (Millwood) ; 36(7): 1211-1217, 2017 07 01.
Article in English | MEDLINE | ID: mdl-28679807

ABSTRACT

Although end-of-life medical spending is often viewed as a major component of aggregate medical expenditure, accurate measures of this type of medical spending are scarce. We used detailed health care data for the period 2009-11 from Denmark, England, France, Germany, Japan, the Netherlands, Taiwan, the United States, and the Canadian province of Quebec to measure the composition and magnitude of medical spending in the three years before death. In all nine countries, medical spending at the end of life was high relative to spending at other ages. Spending during the last twelve months of life made up a modest share of aggregate spending, ranging from 8.5 percent in the United States to 11.2 percent in Taiwan, but spending in the last three calendar years of life reached 24.5 percent in Taiwan. This suggests that high aggregate medical spending is due not to last-ditch efforts to save lives but to spending on people with chronic conditions, which are associated with shorter life expectancies.


Subject(s)
Financing, Government/statistics & numerical data , Health Expenditures/statistics & numerical data , Terminal Care/economics , Europe , Global Health , Humans , Japan , North America
3.
Health Econ ; 21 Suppl 1: 56-100, 2012 Jun.
Article in English | MEDLINE | ID: mdl-22556002

ABSTRACT

The justification bias in the estimated impact of health shocks on retirement is mitigated by using objective health measures from a large, register-based longitudinal data set including medical diagnosis codes, along with labor market status, financial, and socio-economic variables. The duration until retirement is modeled using single and competing risk specifications, observed and unobserved heterogeneity, and flexible baseline hazards. Wealth is used as a proxy for elapsed duration to mitigate the potential selection bias stemming from conditioning on initial participation. The competing risk specification distinguishes complete multiperiod routes to retirement, such as unemployment followed by early retirement. A result on comparison of coefficients across all states is offered. The empirical results indicate a strong impact of health changes on retirement and hence a large potential for public policy measures intended to retain older workers longer in the labor force. Disability responds more to health shocks than early retirement, especially to diseases of the circulatory, respiratory, and musculoskeletal systems, as well as mental and behavioral disorders. Some unemployment spells followed by early retirement appear voluntary and spurred by life style diseases.


Subject(s)
Clinical Coding , Health Status , Retirement/economics , Retirement/statistics & numerical data , Sick Leave/economics , Sick Leave/statistics & numerical data , Age Factors , Aged , Data Interpretation, Statistical , Denmark , Female , Humans , International Classification of Diseases , Male , Middle Aged , Models, Econometric , Old Age Assistance/statistics & numerical data , Sex Factors , Socioeconomic Factors
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