Health Forecasting and Models of Aging

Mantón, Kenneth G.

doi:10.1007/978-1-4613-9332-0_4

Cited by 2 publications

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“…Entirely new areas of applications for parameterisation have emerged, such as modelling disease processes (e.g. Manton andStallard, 1984, 1988; also Van den Berg Jeths et al, Chapter 2 in this volume), modelling heterogeneity, stochasticity and homeostasis in survival (e.g. Yashin, Chapter 11), and expanding the structure of age-period-cohort models (e.g.…”

Section: Parameterisation Functionsmentioning

confidence: 99%

“…Vaupel et al, 1998). Manton (1993) also finds that approximately 95 per cent of the age dependence represented by the Gompertz function in human populations could be explained by measures of chronic disability. Despite the long tradition of biologically motivated models of the age trajectory of human mortality in actuarial science, current forecasting procedures are often based on extrapolation and do not directly reflect physiological processes at the individual level or in a cohort.…”

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confidence: 94%

“…A constant was added to this relation by Makeham (1860) to reflect mortality caused by chance factors. Manton (1993) notes that it was shown empirically that Gompertz tends to overpredict mortality at advanced ages (see also Boleslawski and Tabeau, Chapter 6). The theoretical curve more closely approaches the slower rate of increase in mortality at advanced ages in the model proposed by Perks (1932).…”

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confidence: 96%

“…Manton and Stallard, 1984;Manton, 1993). They suggest that efforts aimed at modelling and forecasting mortality can be classified into empirical or extrapolation procedures and procedures based on theories of human mortality and ageing.…”

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confidence: 98%

“…They suggest that efforts aimed at modelling and forecasting mortality can be classified into empirical or extrapolation procedures and procedures based on theories of human mortality and ageing. Projecting mortality from biological theories of ageing has a long actuarial tradition which began with the Gompertz law of mortality (1825; Manton, 1993). The rationale behind this law is the proportional loss with age of man's ability to withstand 'destruction'.…”

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A Review of Demographic Forecasting Models for Mortality

Tabeau¹

2001

European Studies of Population

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The goal of Chapter 1 is to describe and comment on the methods and approaches that have been in use or have emerged in recent years. Section 1.1 introduces the most common classifications of forecasting models for mortality. Section 1.2 is devoted to a brief historical review of parameterisation functions. In this context, attention is paid to prediction based on parameterised age schedules, in particular by using time series models. Section 1.3 focuses on the (statistical association) models of Lee and Carter and Section 1.4 characterises the (log-linear) age-period-cohort models. In Section 1.5 the reader can find a review of the methods used in international statistical practice and in Section 1.6 the importance of uncertainty in forecasting is addressed. Section 1.7 outlines the prospects for modelling and forecasting mortality as seen from the perspective of this chapter. | Most Common Classifications of Forecasting Models for MortalityA positive feature of forecasting mortality in developed countries is that adequate historical information is usually available, at least for aggregate measurements. In this case the two salient questions facing a forecaster are finding an accurate description of the past, and secondly, taking on judgemental factors in order to produce plausible forecasts. The first can be viewed as a technical problem essentially concerned with modelling and 1 E. Tabeau et al. (eds.), Forecasting Mortality in Developed Countries, 1-32.

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Section: Parameterisation Functionsmentioning

confidence: 99%

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confidence: 94%

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confidence: 96%

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confidence: 98%

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confidence: 99%

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