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How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach
被引:3
|作者:
Kaakai, Sarah
[1
]
Hardy, Heloise Labit
[2
]
Arnold , Severine
[3
]
El Karoui, Nicole
[4
]
机构:
[1] Le Mans Univ, Risk & Insurance Inst, Lab Manceau Math, F-72085 Le Mans, France
[2] UNSW Australia, ARC Ctr Excellence Populat Ageing Res CEPAR, Sydney, NSW 2052, Australia
[3] Univ Lausanne, Fac HEC, DSA, Extranef, CH-1015 Lausanne, Switzerland
[4] Sorbonne Univ, LPSM, 4 Pl Jussieu, F-75005 Paris, France
关键词:
Population dynamics;
Deprivation;
Heterogeneity;
Cause-of-death mortality;
Cohort effect;
MORTALITY;
IMPROVEMENT;
HEALTH;
DIFFERENTIALS;
INEQUALITIES;
DEPRIVATION;
ENGLAND;
IMPACT;
D O I:
10.1016/j.insmatheco.2019.07.005
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
In the context of widening socioeconomic inequalities in mortality, it has become crucially important to understand the impact of population heterogeneity and its evolution on mortality. In particular, recent developments in multi-population mortality have raised a number of questions, among which is the issue of evaluating cause-of-death reduction targets set by national and international institutions in the presence of heterogeneity. The aim of this paper is to show how the population dynamics framework contributes to addressing these issues, relying on English population data and cause-specific number of deaths by socioeconomic circumstances, over the period 1981-2015. The analysis of the data first highlights the complexity of recent demographic developments, characterized by significant compositional changes, with considerable variations according to the age class or cohort, along with a widening of socioeconomic inequalities. We then introduce a dynamic framework for studying the impact of composition changes on the mortality of the global population. In particular, we show how a cause-of-death reduction could be compensated for in the presence of heterogeneity, which could lead to misinterpretations when assessing public policies impacts and/or for the forecasting of future trends. (C) 2019 Elsevier B.V. All rights reserved.
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页码:16 / 37
页数:22
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