A multistate model to project elderly disability in case of limited data

Gaag, N.L. van der; Bijwaard, Govert E.; Beer, Joop de; Bonneux, Luc

doi:10.4054/demres.2015.32.3

Cited by 10 publications

(6 citation statements)

References 45 publications

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“…The basic agespecific projections are sometimes augmented by a stage classification to examine the joint age 9 stage dynamics. The flavor of these efforts is shown by studies on, e.g., the interaction of age with citizenship status (S anchez Gassen 2014), disability (Van der Gaag et al 2015), or education level (Loichinger 2015).…”

Section: Explicit and Implicit Age Dependencementioning

confidence: 99%

Age × stage‐classified demographic analysis: a comprehensive approach

Caswell

Vries

Hartemink

et al. 2018

Ecological Monographs

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This paper presents a comprehensive theory for the demographic analysis of populations in which individuals are classified by both age and stage. The earliest demographic models were age classified. Ecologists adopted methods developed by human demographers and used life tables to quantify survivorship and fertility of cohorts and the growth rates and structures of populations. Later, motivated by studies of plants and insects, matrix population models structured by size or stage were developed. The theory of these models has been extended to cover all the aspects of age‐classified demography and more. It is a natural development to consider populations classified by both age and stage. A steady trickle of results has appeared since the 1960s, analyzing one or another aspect of age × stage‐classified populations, in both ecology and human demography. Here, we use the vec‐permutation formulation of multistate matrix population models to incorporate age‐ and stage‐specific vital rates into demographic analysis. We present cohort results for the life table functions (survivorship, mortality, and fertility), the dynamics of intra‐cohort selection, the statistics of longevity, the joint distribution of age and stage at death, and the statistics of life disparity. Combining transitions and fertility yields a complete set of population dynamic results, including population growth rates and structures, net reproductive rate, the statistics of lifetime reproduction, and measures of generation time. We present a complete analysis of a hypothetical model species, inspired by poecilogonous marine invertebrates that produce two kinds of larval offspring. Given the joint effects of age and stage, many familiar demographic results become multidimensional, so calculations of marginal and mixture distributions are an important tool. From an age‐classified point of view, stage structure is a form of unobserved heterogeneity. From a stage‐classified point of view, age structure is unobserved heterogeneity. In an age × stage‐classified model, variance in demographic outcomes can be partitioned into contributions from both sources. Because these models are formulated as matrices, they are amenable to a complete sensitivity analysis. As more detailed and longer longitudinal studies are developed, age × stage‐classified demography will become more common and more important.

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Section: Explicit and Implicit Age Dependencementioning

confidence: 99%

Age × stage‐classified demographic analysis: a comprehensive approach

Caswell

Vries

Hartemink

et al. 2018

Ecological Monographs

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“…One alternative could be to extrapolate only 5 or 10 years in order to harmonise the data and have a more robust comparison across countries. This would be an interesting avenue for further investigation, as while there is a wide array of demographic and statistics methods available to interpolate, graduate and smooth mortality data both at young and older ages, strategies for dealing with age-specifi c prevalence are still limited in health research, often relying on multistate methods (van der Gaag et al 2015). In part, this is due to the fact that contrary to mortality, health prevalence by age may not increase monotonically, as recovery from certain conditions is possible.…”

Section: Figurementioning

confidence: 99%

The Sensitivity of the Healthy Life Years Indicator: Approaches for Dealing with Age-Specific Prevalence Data

Lego¹,

Sauerberg²

2023

CPoS

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The Healthy Life Years (HLY) indicator is the official European Union indicator and a cornerstone of many health policies used in over 15 countries in the EU region to set national health plans and monitor targets. It is also used to investigate trends over time in the proportion of total life years spent in good or poor health, socioeconomic inequalities in health and mortality and the male-female health survival paradox. Based on the Global Activity Limitation Indicator (GALI) included in the European Union Statistics on Income and Living Conditions (EU-SILC), a great amount of effort has been directed at harmonising and making HLY comparable across countries. Nonetheless, the characteristics of the age-specific prevalence distribution are still rarely accounted for, regardless of the fact that patterns of prevalence often fluctuate considerably by age. In addition, the impact of assumptions used at very young ages on HLY estimates are seldom discussed, despite the fact that the majority of policies and initiatives at the EU level use HLY at birth, while data on health is only available after age 16. In this paper, we assess whether smoothing the age-specific prevalence distributions by different methods, extrapolating to older ages and changing assumptions at younger ages affect HLY estimates. Overall, assumptions made before age 15 are the most important and affect women and men differently, thus affecting HLY at birth for some countries. Estimates at age 65 are very slightly impacted. Generalised linear models (GAMs) seem promising for harmonising and extrapolating to older ages, while using polynomials or aggregating into 5-year age groups seem best for younger ages. As most EU policies use HLY at birth and by sex for developing and monitoring health policies, caution is needed when estimating HLY at birth. * This article belongs to a special issue on “Levels and Trends of Health Expectancy: Understanding its Measurement and Estimation Sensitivity”.

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“…Many incidence-based models in the literature, including the CRC model, contain only one-way, irreversible transitions. This may be due to the irreversibility of some health conditions (e.g., dementia), or it may reflect the attraction of the ability to write the entries of the transition matrix P from the intensity matrix Q when the transitions are irreversible (e.g., Touraine, Helmer, and Joly 2016;Van der Gaag et al 2015) .The availability of reliable routines for computing the matrix exponential makes the latter reason less compelling. Nothing in our analysis is restricted to one-way transitions.…”

Section: Notes On the Modelmentioning

confidence: 99%

Healthy longevity from incidence-based models: More kinds of health than stars in the sky

Caswell¹,

Daalen²

2021

DemRes

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BACKGROUNDHealthy longevity (HL) is an important measure of the prospects for quality of life in ageing societies. Incidence-based (cf. prevalence-based) models describe transitions among age classes and health stages. Despite the probabilistic nature of those transitions, analyses of healthy longevity have focused persistently on means ("health expectancy"), neglecting variances and higher moments.

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A multistate model to project elderly disability in case of limited data

Cited by 10 publications

References 45 publications

Age × stage‐classified demographic analysis: a comprehensive approach

Age × stage‐classified demographic analysis: a comprehensive approach

The Sensitivity of the Healthy Life Years Indicator: Approaches for Dealing with Age-Specific Prevalence Data

Healthy longevity from incidence-based models: More kinds of health than stars in the sky

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