Loneliness at Older Ages in Japan: Variation in Lonely Life Expectancy and the Role of Social Isolation

Despite growing media, policy, and research attention to loneliness, it remains an understudied dimension of inequality in demography. Additionally, research on loneliness often fails, both methodologically and conceptually, to distinguish loneliness from social isolation. This is an important limitation given the positive correlation between measures of these two distinct concepts, a relationship that may be particularly relevant in collectivistic societies. This study focuses on Japan, describing the synthetic cohort duration of exposure to loneliness at older ages, with and without adjusting for the correlation between loneliness and social isolation. Combining life tables from the Human Mortality Database with individual data from the National Survey of Japanese Elderly, we calculated isolation-adjusted lonely life expectancies. We also evaluated regional and educational differences in isolation-adjusted lonely life expectancies. Results showed significant differences in lonely life expectancy before and after adjusting for social isolation; however, the attention to social isolation did little to alter our general understandings of trends and differentials in lonely life expectancy. In contrast to public perceptions of growing loneliness, we find that lonely life expectancy is short among older Japanese and has not increased over time. Additionally, we found no clear regional nor educational differences in isolation-adjusted lonely life expectancy.

Smoothing age/time structure of HIV prevalence, for optimal use in synthetic cohort based incidence estimation

Population-based surveys which ascertain HIV status are conducted in heavily affected countries, with the estimation of incidence being a primary goal. Numerous methods exist under the umbrella of ‘synthetic cohort analysis’, by which we mean estimating incidence from the age/time structure of prevalence (given knowledge on mortality). However, not enough attention has been given to how serostatus data is ‘smoothed’ into a time/age-dependent prevalence, so as to optimise the estimation of incidence.To support this and other related investigations, we developed a comprehensive simulation environment in which we simulate age/time structured SI type epidemics and surveys. Scenarios are flexibly defined by demographic rates (fertility, incidence and mortality – dependent, as appropriate, on age, time, and time-since-infection) without any reference to underlying causative processes/parameters. Primarily using 1) a simulated epidemiological scenario inspired by what is seen in the hyper-endemic HIV affected regions, and 2) pairs of cross-sectional surveys, we explored A) options for extracting the age/time structure of prevalence so as to optimise the use of the formal incidence estimation framework of Mahiane et al, and B) aspects of survey design such as the interaction of epidemic details, sample-size/sampling-density and inter-survey interval.Much as in our companion piece which crucially investigated the use of ‘recent infection’ (whereas the present analysis hinges fundamentally on the estimation of the prevalence gradient) we propose a ‘one size fits most’ process for conducting ‘synthetic cohort’ analyses of large population survey data sets, for HIV incidence estimation: fitting a generalised linear model for prevalence, separately for each age/time point where an incidence estimate is desired, using a ‘moving window’ data inclusion rule. Overall, even in very high incidence settings, sampling density requirements are onerous.The general default approach we propose for fitting HIV prevalence to data as a function of age and time appears to be broadly stable over various epidemiological stages. Particular scenarios of interest, and the applicable options for survey design and analysis, can readily be more closely investigated using our approach. We note that it is often unrealistic to expect even large household based surveys to provide meaningful incidence estimates outside of priority groups like young women, where incidence is often particularly high.

Smoothing age/time structure of HIV prevalence, for optimal use in synthetic cohort based incidence estimation

BackgroundPopulation-based surveys which ascertain HIV status are conducted in heavily affected countries, with the estimation of incidence being a primary goal. Numerous methods exist under the umbrella of ‘synthetic cohort analysis’, by which we mean estimating incidence from the age/time structure of prevalence (given knowledge on mortality). However, not enough attention has been given to how serostatus data is ‘smoothed’ into a time/age-dependent prevalence, so as to optimise the estimation of incidence.MethodsTo support this and other related investigations, we developed a comprehensive simulation environment in which we simulate age/time structured SI type epidemics and surveys. Scenarios are flexibly defined by demographic rates (fertility, incidence and mortality – dependent, as appropriate, on age, time, and time-since-infection) without any reference to underlying causative processes/parameters. Primarily using 1) a simulated epidemiological scenario inspired by what is seen in the hyper-endemic HIV affected regions, and 2) pairs of cross-sectional surveys, we explored A) options for extracting the age/time structure of prevalence so as to optimise the use of the formal incidence estimation framework of Mahiane et al, and B) aspects of survey design such as the interaction of epidemic details, sample-size/sampling-density and inter-survey interval.ResultsMuch as in our companion piece which crucially investigated the use of ‘recent infection’ (whereas the present analysis hinges fundamentally on the estimation of the prevalence gradient) we propose a ‘one size fits most’ process for conducting ‘synthetic cohort’ analyses of large population survey data sets, for HIV incidence estimation: fitting a generalised linear model for prevalence, separately for each age/time point where an incidence estimate is desired, using a ‘moving window’ data inclusion rule. Overall, even in very high incidence settings, sampling density requirements are onerous.ConclusionThe general default approach we propose for fitting HIV prevalence to data as a function of age and time appears to be broadly stable over various epidemiological stages. Particular scenarios of interest, and the applicable options for survey design and analysis, can readily be more closely investigated using our approach. We note that it is often unrealistic to expect even large household based surveys to provide meaningful incidence estimates outside of priority groups like young women, where incidence is often particularly high.

Evaluating early life risk factors for late life health outcome using probabilistic matching of early and late life cohorts.

Research on Alzheimer's Disease and Related Dementias (ADRD) is hampered by the absence of studies including prospective follow-up from early life through older ages when ADRD is diagnosed. This is a notable gap in the United States and impedes research on lifecourse determinants of ADRD and ADRD disparities, many of which appear attributable to early life experiences. In this simulation project, we evaluate a matching method to create a synthetic lifecourse cohort by merging early and late life cohorts on a set of harmonized covariates. We evaluate performance under several causal scenarios for the association between our exposure and outcome, and varying characteristics of the matching method. In scenarios when a measure is available along all pathways linking exposure and outcome, the synthetic cohort performs well, with bias approaching null as the number of matching levels increases. This approach may create novel opportunities to rigorously evaluate early- and mid-life determinants of ADRD and ADRD disparities.

Life expectancy: what does it measure?

Life expectancy (LE) is considered a straightforward summary measure of mortality that comes with an implicit age standardisation. Thus, it has become common to present differences in mortality across populations as differences in LE, instead of, say, relative risks. However, most of the time LE does not quite provide what the term promises. LE is based on a synthetic cohort and is therefore not the true LE of anyone. Also, the implicit age standardisation is construed in such a way that it can be questioned whether it standardises age at all. In this paper, we examine LE from the point of view of its applicability to epidemiological and public health research and provide examples on the relation between an LE difference and a relative risk. We argue that the age standardisation in estimations of LE is not straightforward since it is standardised against different age distributions and that the translation of changes in age specific mortality into change in remaining LE will depend on the level and the distribution of mortality in the population. We conclude that LE is not the measure of choice in aetiological research or in research with the aim to identify risk factors of death, but that LE may be a compelling choice in public health contexts. One cannot escape the thought that the mathematical elegance of LE has contributed to its popularity.

Patterns of Anemia in Married Women and Their Children in Cambodia: A Synthetic Cohort Analysis

Aim To explore the prevalence of anemia in three cohorts of women, namely, married yet to be mothers, married and are mothers, and currently pregnant, to ascertain the patterns in anemia in women. Methods We analyzed a sample of 130,965 married women from four Demographic Health Surveys: 2000, 2005, 2009 and 2015. The primary focus for the analysis was married women aged 15 to 49 years. In the absence of a longitudinal data that followed the same women over the periods, a synthetic cohort of the women of that age-group was constructed to get women aged 15 to 64 years over the four surveys. Women who were aged 15 to 19 years in 2000 were the same as those 30 to 34 years in 2015, while those aged 45 to 49 years in 2000 were the same as 60 to 64 years in 2015. Results Logistic regression revealed that young mothers were significantly more infected ( p < .001). Pregnancy affected anemia in the women ( p < .001). Being younger and richer were associated with odds ratios of 0.599 (95% confidence interval, CI: [0.560, 0.640]) and 0.765 (95% CI: [0.726, 0.807]) for anemia, respectively. Being pregnant had odds ratio of 1.642 (95% CI: [1.439, 1.872]) for anemia. Conclusion Public health strategies should target social deprivation at the household level while addressing maternal health issues. An analysis of data on unmarried women and their children is recommended.

The Cumulative Prevalence of Termination of Parental Rights for U.S. Children, 2000–2016

Recent research has used synthetic cohort life tables to show that having a Child Protective Services investigation, experiencing confirmed maltreatment, and being placed in foster care are more common for American children than would be expected based on daily or annual rates for these events. In this article, we extend this literature by using synthetic cohort life tables and data from the Adoption and Foster Care Analysis and Reporting System to generate the first cumulative prevalence estimates of termination of parental rights. The results provide support for four conclusions. First, according to the 2016 estimate, 1 in 100 U.S. children will experience the termination of parental rights by age 18. Second, the risk of experiencing this event is highest in the first few years of life. Third, risks are highest for Native American and African American children. Nearly 3.0% of Native American children and around 1.5% of African American children will ever experience this event. Finally, there is dramatic variation across states in the risk of experiencing this event and in racial/ethnic inequality in this risk. Taken together, these findings suggest that parental rights termination, which involves the permanent loss of access to children for parents, is far more common than often thought.