Heather Booth

Applying Lee-Carter under conditions of variable mortality decline.

The Lee-Carter method of mortality forecasting assumes an invariant age component and most applications have adopted a linear time component. The use of the method with Australian data is compromised by significant departures from linearity in the time component and changes over time in the age component. We modify the method to adjust the time component to reproduce the age distribution of deaths, rather than total deaths, and to determine the optimal fitting period in order to address non-linearity in the time component. In the Australian case the modification has the added advantage that the assumption of invariance is better met. For Australian data, the modifications result in higher forecast life expectancy than the original Lee-Carter method and official projections, and a 50 per cent reduction in forecast error. The model is also expanded to take account of age-time interactions by incorporating additional terms, but these are not readily incorporated into forecasts.

Mortality modelling and forecasting : a review of methods

ABSTRACT Continuing increases in life expectancy beyond previously-held limits have brought to the fore the critical importance of mortality forecasting. Significant developments in mortality forecasting since 1980 are reviewed under three broad approaches: expectation, extrapolation and explanation. Expectation is not generally a good basis for mortality forecasting, as it is subjective; expert expectations are invariably conservative. Explanation is restricted to certain causes of death with known determinants. Decomposition by cause of death poses problems associated with the lack of independence among causes and data difficulties. Most developments have been in extrapolative forecasting, and make use of statistical methods rather than models developed primarily for age-specific graduation. Methods using two-factor models (age-period or age-cohort) have been most successful. The two-factor Lee–Carter method, and, in particular, its variants, have been successful in terms of accuracy, while recent advances have improved the estimation of forecast uncertainty. Regression-based (GLM) methods have been less successful, due to nonlinearities in time. Three-factor methods are more recent; the Lee–Carter age-period-cohort model appears promising. Specialised software has been developed and made available. Research needs include further comparative evaluations of methods in terms of the accuracy of the point forecast and its uncertainty, encompassing a wide range of mortality situations.

Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models

When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary time series models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations.

Cohort profile: The Dynamic Analyses to Optimize Ageing (DYNOPTA) project

National Health and Medical Research Council (410215); NHMRC Fellowships (#366756 to K.J.A. and #316970 to P.B.)

The Future Aged: New Projections of Australia's Elderly Population

Objectives: To use new methodology to forecast mortality for use in projections of the elderly population of Australia and to compare them with official projections.

Transforming gompertz's function for fertility analysis: The development of a standard for the relational gompertz function

Summary The relational Gompertz function improves upon the Gompertz for fertility analysis by achieving a better fit in the tails of the distribution. This is obtained by a transformation of the age scale corresponding to an empirical standard. This standard is developed from Coale and Trussells model and is appropriate for use with populations of high fertility. The model is tested on two sets of data and is shown to produce good estimates of completed fertility even for data truncated at quite early ages. Good results are also obtained for declining fertility.

The Evolution of Epidemic Suicide on Guam: Context and Contagion

Thirty years of suicide rates for Guam were analyzed by age, sex, period, and cohort. Youth suicide increased rapidly in the 1990s; certain cohorts have higher rates. Four explanatory factors are discussed, including ecological factors and migration from the Federated States of Micronesia. Direct and indirect suicide contagion followed the death by suicide of a respected politician, strongly influencing period and cohort patterns. Suicide pacts inflated suicide among young people. These factors acted in combination to produce epidemic levels of suicide in the 1990s.

Sociodemographic determinants of energy, fat and dietary fibre intake in Australian adults

OBJECTIVE To examine the relationship between sociodemographic factors (sex, age, education, occupation and region of birth) and absolute levels of energy, fat and fibre intake in adults at the national level. DESIGN, SETTING AND SUBJECTS The 1983 National Dietary Survey of Adults (NDSA), covering six Australian capital cities, collected food and nutrient intake data using the 24-hour recall method, from subjects aged 25-64 years (n=6255). RESULTS Interactions of variables occurred, especially for males. The greatest effect on male intake of all three dietary components was a combination of age and education. For females, the main explanatory variable for fat and energy intake was age, but that for fibre was a combination of region of birth and education. Both education (alone or in combination) and region of birth (alone or in combination) had a greater effect than occupation (alone or in combination). CONCLUSIONS Energy, fat and fibre intakes vary considerably between sociodemographic groups. Such variability must be taken into account in formulating policy and planning decisions and in assessing temporal change.

Structural and functional social network attributes moderate the association of self-rated health with mental health in midlife and older adults

BACKGROUND Social relationships are multifaceted, and different social network components can operate via different processes to influence well-being. This study examined associations of social network structure and relationship quality (positive and negative social exchanges) with mental health in midlife and older adults. The focus was on both direct associations of network structure and relationship quality with mental health, and whether these social network attributes moderated the association of self-rated health (SRH) with mental health. METHODS Analyses were based on survey data provided by 2001 (Mean age = 65, SD = 8.07) midlife and older adults. We used Latent Class Analysis (LCA) to classify participants into network types based on network structure (partner status, network size, contact frequency, and activity engagement), and used continuous measures of positive and negative social exchanges to operationalize relationship quality. Regression analysis was used to test moderation. RESULTS LCA revealed network types generally consistent with those reported in previous studies. Participants in more diverse networks reported better mental health than those categorized into a restricted network type after adjustment for age, sex, education, and employment status. Analysis of moderation indicated that those with poorer SRH were less likely to report poorer mental health if they were classified into more diverse networks. A similar moderation effect was also evident for positive exchanges. CONCLUSIONS The findings suggest that both quantity and quality of social relationships can play a role in buffering against the negative implications of physical health decline for mental health.

Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model

Abstract Background models projecting future disease burden have focussed on one or two diseases. Little is known on how risk factors of younger cohorts will play out in the future burden of multi-morbidity (two or more concurrent long-term conditions). Design a dynamic microsimulation model, the Population Ageing and Care Simulation (PACSim) model, simulates the characteristics (sociodemographic factors, health behaviours, chronic diseases and geriatric conditions) of individuals over the period 2014–2040. Population about 303,589 individuals aged 35 years and over (a 1% random sample of the 2014 England population) created from Understanding Society, the English Longitudinal Study of Ageing, and the Cognitive Function and Ageing Study II. Main outcome measures the prevalence of, numbers with, and years lived with, chronic diseases, geriatric conditions and multi-morbidity. Results between 2015 and 2035, multi-morbidity prevalence is estimated to increase, the proportion with 4+ diseases almost doubling (2015:9.8%; 2035:17.0%) and two-thirds of those with 4+ diseases will have mental ill-health (dementia, depression, cognitive impairment no dementia). Multi-morbidity prevalence in incoming cohorts aged 65–74 years will rise (2015:45.7%; 2035:52.8%). Life expectancy gains (men 3.6 years, women: 2.9 years) will be spent mostly with 4+ diseases (men: 2.4 years, 65.9%; women: 2.5 years, 85.2%), resulting from increased prevalence of rather than longer survival with multi-morbidity. Conclusions our findings indicate that over the next 20 years there will be an expansion of morbidity, particularly complex multi-morbidity (4+ diseases). We advocate for a new focus on prevention of, and appropriate and efficient service provision for those with, complex multi-morbidity.