A Regression Model of Mortality, with Application to the Netherlands

Heathcote, C. R.; Higgins, Timothy

doi:10.1007/0-306-47562-6_3

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…Before proceeding to mortality surfaces we give some basic results of the regression modelling approach applied to a single cohort, and summarize the main result of Heathcote and Higgins (2001a). For a cohort of a known number lð0Þ of initial lives introduce the random variables l lðxÞ ¼ number of survivors at age x; x ¼ 1; 2; .…”

Section: A Single Cohortmentioning

confidence: 99%

“…Exploration of the data and the calculation of observed and realizedl lðt; xÞ 80 C. Heathcote and T. Higgins andq qðt; xÞ are discussed in Heathcote and Higgins (2001a, p. 70), and in Higgins (2002). Due to a marked discontinuity in mortality pre-and post-World War II, Heathcote and Higgins (2001a) included an indicator with interactions to capture the surface. Indicators were also included for periods of extreme mortality, such as 1899, 1918, 1943, and 1944, among others. This paper uses the approach of Higgins (2002), whereby separate surfaces were generated for 1890 to 1938 and 1946 to 1991, and an indicator was only included for the Spanish Influenza outbreak of 1918.…”

Section: Selecting and Fitting Models For The Netherlandsmentioning

confidence: 99%

“…The regression approach used here is described in the next section for a single cohort and extended to mortality surfaces in Section 3. An earlier, more restrictive, treatment of these issues was given by us in Heathcote and Higgins (2001a).…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Regression Modelling of Mortality Surfaces and the Deceleration of Mortality

Heathcote¹,

Higgins²

2004

Mathematical Population Studies

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A mortality surface is a measure of mortality indexed by year and age. A central limit theorem for aggregate data is established for the mortality surface defined by the logistic transform of the year and age-specific probability of death and this is used to postulate and estimate a regression model. Extra variance may be the result of heterogeneity within cohorts, and it is shown how the model based on aggregate data could be decomposed to accommodate sub-cohorts by using proportional odds. In the absence of disaggregated data, excess variance is modelled as a function of age and year and estimation is done by maximum likelihood. The parametric surface so estimated is used to examine deceleration of mortality at old ages and trends in deceleration are discussed with reference to selection and heterogeneity. The results are applied to mortality data from the Netherlands for 1890-1991, ages 50-90.aging, deceleration, heterogeneity, mortality, mortality surface, regression,

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Section: A Single Cohortmentioning

confidence: 99%

Section: Selecting and Fitting Models For The Netherlandsmentioning

confidence: 99%

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Regression Modelling of Mortality Surfaces and the Deceleration of Mortality

Heathcote¹,

Higgins²

2004

Mathematical Population Studies

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“…Regression methods have previously been used in the study of ordinary life tables by Heligman and Pollard (1980), Kostaki (1992), Heathcote and McDermid (1994) and Heathcote and Higgins (2001). The Heligman-Pollard approach is to fit a regression by ordinary least squares to the logistic transform of the age-specific probability of death.…”

mentioning

confidence: 99%

“…Forecasting is not attempted. In the terminology of Heathcote and Higgins (2001), attention is confined to descriptive, not predictive, models. However, it is noted that the results of the 1981 survey are not consistent with those for later years.…”

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confidence: 99%

The health expectancies of older Australians

Heathcote

Davis

Puza

et al. 2003

Journal of Population Research

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Health expectancies of the states 'Disability-free' and 'Disabled' are estimated for Australian females and males aged 60 and over, both by cohort from 1980 and current for survey years 1981, 1988, 1993 and 1998. Modifications of recently developed logistic regression techniques are used rather than the standard 1971 method due to Sullivan. Results from the three later surveys are broadly similar and differ in important respects from those of the 1981 survey. Based on the last three surveys our estimates support the view that, depending on age, two-thirds or more of the increase in female life expectancy over the decade 1988-1998 is spent in the Disabled state. The situation is worse for elderly men, for whom all of the increased years of expected life are estimated to be spent in the Disabled state. The findings do not support rectangularization of the survival curve or Disability-free survival curve.Keywords: Disability, health expectancy, logistic regression, multistate life tables, life expectancy, regression analysis, Australia Health expectancies are the future durations that a person expects to spend in different states of health before the inevitable outcome of death and thus are components of life expectancies. It is well known that life expectancies have been increasing, and a proposition that has attracted some support is that the extra years are ones of ill-health. Bebbington (1988:321) provides a succinct summary of alternative views, while a wider discussion can be found in Crimmins, Hayward and Saito (1994) and Verbrugge (1984).Australian health expectancies in 1981, 1988 and 1993 were estimated by Mathers (1996) following an earlier treatment using data for the first two of the three survey years (Mathers 1991). He found that, unlike other countries for which data were then available, Australian expectations of years with disability had increased both per se and relative to life expectancies. The development of new methods and the availability of updated and compatible data enables these findings to be re-examined. This is the main aim of this paper. Our findings are that the situation is complex but, depending on age and sex, two-thirds or more of the increased years of life expectancy are spent in a disabled state.

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Forecasting Mortality, Different Approaches for Different Cause of Deaths? The Cases of Lung Cancer; Influenza, Pneumonia, and Bronchitis; and Motor Vehicle Accidents

Cesare

Murphy²

2009

Br. Actuar. J.

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Most of the methods of mortality forecasting have been assessed using performance on overall mortality, and few studies address the issue of identifying the appropriate forecasting models for specific causes of deaths. This study analyses trends and forecasts mortality rates for three major causes of death ö lung cancer, influenza-pneumonia-bronchitis, and motor vehicle accidents ö using Lee^Carter, Booth^Maindonald^Smith, Age-Period-Cohort, and Bayesian models, to assess how far different causes of death need different forecasting methods. Using data from the Twentieth and Twenty-First Century Mortality databases for England and Wales, results show major differences among the different forecasting techniques. In particular, when linearity is the main driver of past trends, Lee^Carter-based approaches are preferred due to their straightforward assumptions and limited need for subjective judgment. When a clear cohort pattern is detectable, such as with lung cancer, the Age-Period-Cohort model shows the best outcome. When complete and reliable historical trends are available the Bayesian model does not produce better results than the other models.

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A Regression Model of Mortality, with Application to the Netherlands

Cited by 8 publications

References 19 publications

Regression Modelling of Mortality Surfaces and the Deceleration of Mortality

Regression Modelling of Mortality Surfaces and the Deceleration of Mortality

The health expectancies of older Australians

Forecasting Mortality, Different Approaches for Different Cause of Deaths? The Cases of Lung Cancer; Influenza, Pneumonia, and Bronchitis; and Motor Vehicle Accidents

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