US Migration Status of Adult Children and Cognitive Decline Among Older Parents Who Remain in Mexico

A Scoping Review on the Opportunities for Social Engagement and Cognitive Frailty in Older Adults

Cognitive frailty (CF) is defined as the clinical syndrome of the combination of physical frailty and cognitive impairment, without dementia. Numerous risk factors for CF have been previously identified but this scoping review focusses on the critical need for social engagement and the association with cognition. The focus of this scoping review on the opportunity for social engagement rather than on perception or experience of loneliness. Based on the results of 55 studies were synthesised into four social engagement categories, namely participation, household, network, and habitat. Social engagement is associated with maintaining or improving cognition, particularly through active participation in social roles. Habitat (i.e., rural or urban settings) also influences cognition and the challenge is to enable social participation.

The impact of adult children's support on the psychological health of rural older adult people in China

Background

Family old-age care is dominant in Chinese rural society, and children's support is an important force in family old-age care. However, the migration of a large number of young and middle-aged rural laborers has undermined the traditional arrangements for old-age care in rural areas and affected the psychological health of the older adult.

Methods

2014 China Longitudinal Aging Social Survey targets Chinese citizens aged 60 or older and covers 28 provinces in mainland China. In this paper, the database of the CLASS was selected for empirical analysis to explore the impact of children's support on the depression level and loneliness of rural older adults through multiple linear regression, and was divided into two groups according to children's migration to analyze heterogeneity.

Results

Children's financial support facilitates the maintenance of mental health among rural older adults. Children's support promotes mental health among rural older adults, but this association does not exist among older adults without children's migration. Individual characteristics of older people have a greater impact on mental health.

Discussion

Our study firstly compares the differences of children's migration status between children's support and mental health among the older adult in rural China. In order to improve the mental health of the older adult, it is necessary to create a favorable atmosphere of love and respect for the older adult, improve the social security system in rural areas, and give full play to the strengths of the social forces, so as to ensure that the older adult have a sense of worthiness and enjoyment in their old age.

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

PURPOSE

The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments.

METHODS

We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results.

RESULTS

Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE.

CONCLUSIONS

There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Background

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH).

Methods

Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report.

Results

Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness).

Conclusions

While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Invited Commentary: Adult Child Migration and Parental Cognitive Decline-A New Perspective on Loneliness and Social Isolation

There is a growing body of research on the link between social isolation, loneliness, and health outcomes in later life, including cognitive decline and dementia. In the current issue of the Journal, Torres et al. (Am J Epidemiol. 2020;189(8):761-769) present findings from a study of the association between adult child migration status and cognitive performance among parents who stayed behind in Mexico, using longitudinal data (2001-2015) from the Mexican Health and Aging Study. This is an excellent example of the role that emotional and social factors may play in the development of cognitive impairment among older adults. In their study, having an adult child in the United States was associated with a steeper decline in verbal memory scores over time, but only among women. In the light of the increasing levels of immigration worldwide, this work is highly relevant, as it touches upon a less recognized socioemotional risk factor for cognitive decline: family-member migration status. Further research on this topic should be conducted in other countries and should include assessment of the social and emotional needs of those staying behind when a family member migrates. This will increase our understanding of how social isolation and loneliness relate to cognitive decline and may contribute to new directions for interventions.