Relating Changes in Life Expectancy to Changes in Mortality

I address the problem of what can be said of changes in mortality rates, if one knows how life expectancies change. I note a general formula relating life expectancies in different ages to mortality and prove that if mortality changes over time following a proportional-hazard model, then there is a one-to-one correspondence between life expectancy at birth and mortality rates. Extensions and an application of these results to the analysis of mortality change are presented.     

Effect of aggregation on the estimation of trend in mortality

"There are three approaches to analyzing and forecasting age-specific mortality: (1) analyze age-specific data directly, (2) analyze each cause-specific mortality series separately and add the results, (3) analyze cause-specific mortality series jointly and add the results. We show that if linear models are used for cause-specific mortality, then the three approaches often give close results even when cause-specific series are correlated. This result holds for cross-correlations arising from random misclassification of deaths by cause, and also for certain patterns of systematic misclassification....The results are illustrated with U.S. age-specific mortality: (1) analyse age-specific mortality data from 1968-1985. In some cases the aggregate forecasts appear to be the more credible ones." This is a revised version of a paper originally presented at the 1990 Annual Meeting of the Population Association of America (see Population Index, Vol. 56, No. 3, Fall 1990, p. 407).     

How to compare interpretatively different models for the conditional variance function

This study considers regression-type models with heteroscedastic Gaussian errors. The conditional variance is assumed to depend on the explanatory variables via a parametric or non-parametric variance function. The variance function has usually been selected on the basis of the log-likelihoods of fitted models. However, log-likelihood is a difficult quantity to interpret – the practical importance of differences in log-likelihoods has been difficult to assess. This study overcomes these difficulties by transforming the difference in log-likelihood to easily interpretative difference in the error of predicted deviation. In addition, methods for testing the statistical significance of the observed difference in test data log-likelihood are proposed.     

Bayes Estimates for the Price and Income Elasticities of Alcoholic Beverages in Finland From 1955 to 1980

The Lindley–Smith theory of Bayes estimates for multiple regression equations with exchangeability between the regression coefficients of the individual equations is extended to the case in which a first-order autoregressive process generates the regression coefficients. The ensuing formulas are applied to study monthly Finnish consumption of alcohol. The point of this application is that exchangeability between the regression coefficients is less than we can assess beforehand when there is a natural ordering, in this case according to chronological time, of the equations. Still, the general task of the Lindley–Smith estimators, to consider the combined data when estimating individual regression coefficients, is a relevant one.     

Health Effects Caused by Primary Fine Particulate Matter (PM2.5) Emitted from Buses in the Helsinki Metropolitan Area, Finland

Fine particle (PM(2.5)) emissions from traffic have been associated with premature mortality. The current work compares PM(2.5)-induced mortality in alternative public bus transportation strategies as being considered by the Helsinki Metropolitan Area Council, Finland. The current bus fleet and transportation volume is compared to four alternative hypothetical bus fleet strategies for the year 2020: (1) the current bus fleet for 2020 traffic volume, (2) modern diesel buses without particle traps, (3) diesel buses with particle traps, and (4) buses using natural gas engines. The average population PM(2.5) exposure level attributable to the bus emissions was determined for the 1996-1997 situation using PM(2.5) exposure measurements including elemental composition from the EXPOLIS-Helsinki study and similar element-based source apportionment of ambient PM(2.5) concentrations observed in the ULTRA study. Average population exposure to particles originating from the bus traffic in the year 2020 is assumed to be proportional to the bus emissions in each strategy. Associated mortality was calculated using dose-response relationships from two large cohort studies on PM(2.5) mortality from the United States. Estimated number of deaths per year (90% confidence intervals in parenthesis) associated with primary PM(2.5) emissions from buses in Helsinki Metropolitan Area in 2020 were 18 (0-55), 9 (0-27), 4 (0-14), and 3 (0-8) for the strategies 1-4, respectively. The relative differences in the associated mortalities for the alternative strategies are substantial, but the number of deaths in the lowest alternative, the gas buses, is only marginally lower than what would be achieved by diesel engines equipped with particle trap technology. The dose-response relationship and the emission factors were identified as the main sources of uncertainty in the model.     

Ageing of a giant: a stochastic population forecast for China, 2006–2060

This paper presents a stochastic population forecast for China with a special emphasis on population ageing. The so-called scaled model for error was used to quantify the uncertainty attached to the population predictions. Data scarcity was a major problem in the specification of the expected error of the population forecast. Therefore, the error structures estimated for European countries were used with some modifications, taking into account the large size and heterogeneity of the Chinese population. The stochastic forecast confirms the expectation of extremely rapid population ageing during the first half of the twenty-first century in China. The old age dependency ratio (OADR) will certainly increase. Simply maintaining the current demographic rates (no international migration) would drive the OADR to 0.42 in 2060, four times the current level. Including expected declines in mortality and net outmigration in the projection would increase the median OADR in 2060 to 0.59, with a 80% prediction interval of [0.47, 0.75]. In particular, the oldest-old population will grow much faster than any other age group. This development has major implications for policy-making in China.

Association between gambling harms and game types: Finnish population study

Understanding of the harmfulness of game types is needed when planning harm minimization and prevention strategies. Adult data (N = 3555) from the Finnish Gambling 2015 survey was used to investigate the associations between different game types and gambling-related harms. A negative binomial regression model was used to analyse the effects of demographics, gambling involvement factors, and engaging in different game types on the number of gambling-related harms, which were evaluated by PGSI and SOGS. Age of 18–24, gambling several times a week, high relative expenditure, online gambling and engaging weekly either in scratch games, betting games or slot machine gambling were associated with a higher number of gambling harms. Chasing losses was the most typical harm among online poker players and fast-paced daily lottery game players, while poor self-control was the most typical harm with other game types, although some of the results were not statistically significant. Multiple factors were associated with gambling harms, including young age and gambling intensity. Games that provide a possibility for high-frequency gambling are more linked to harms. Recommendations should be implemented to incorporate effective harm-minimizing strategies in the regulation of specific game types both at the legislative level (provision and supply) and at the public health level.     

Comparative research in social work: methodological considerations using the ‘diachronic–synchronic’ distinction in linguistics

Although there has been a significant increase in the number of comparative studies in social work, there has, to date, been a deficit in terms of methodological discussion and development in this domain. In this article, a linked pair of methodological concepts (‘diachronic’–‘synchronic’), drawn from linguistics, and often used in comparative research studies in the humanities and social sciences, has been applied to the field of comparative research in social work. The use of this distinction in comparative research of social work contributes to the progress of social work as a research-based scientific discipline. The distinction also offers the potential for the development of research methodology in the field of comparative social work research.     

Keeping up with the Joneses and the welfare effects of monetary policy

This paper examines the implications of “keeping up with the Joneses” preferences (jealousy) for the welfare effects of monetary policy. I develop a New Keynesian model, where households are jealous and the central bank follows the Taylor rule. I show that the welfare effects of monetary policy over time depend significantly on the relative strength of the consumption externality caused by jealousy and the monopolistic distortion. When a first-order approximation of the utility function is used, then the main result is the following: If jealousy (the monopolistic distortion) dominates, then a decrease in the interest rate reduces (increases) welfare in the short run, but increases (reduces) welfare in the medium run. However, the use of a second-order approximation changes the sign of the overall welfare effect of monetary policy if the initial level of employment is at the optimal level or just below it.     

Estimation of Exposure Time Distributions

In many demographic analyses, such as the assessment of environmental cancer risks, one may be interested not only in the age-by-state distribution of the population but also in the distribution of the population by time spent in a given state. States can represent geographic areas, marital statuses, labor force participation, or states of epidemiologic exposure. Recursive formulas for the calculation of the distribution of the population according to exposure time are derived under time-invariant state transition rates. Although populations can have identical growth rates and identical age-by-state distributions, they can have very different distributions by exposure time. An application to the analysis of carcinogenic exposure states is given, using data from Finland. The effect of population heterogeneity on the estimated exposure time distributions is studied.