Inequalities in elevated depressive symptoms in middle-aged and older adults by rural childhood residence: The important role of education

Living with technological challenges: does socioeconomic status affect people's health?

BACKGROUND

Technological challenges in accessing medical care services may cause individuals to feel isolated from the medical care system. This study posits that individual's subjective socioeconomic status (SES) contribute to differing levels of technological challenges when seeking medical care services, subsequently impacting their health conditions.

METHODS

A questionnaire survey was administered to 1,932 residents in China (1,037 men, 891 women, and 4 missing; Mage: 64.28 ± 11.30 years, range: 45-99 years). Participants included 792 urban (40.99%) and 1,140 rural (59.01%) residents. We measured SES, technological challenges perceptions, health conditions, and other control variables.

RESULTS

Analysis of 10,000 bootstrapped samples revealed that technological challenges partially mediate the association between SES and health conditions. Moreover, rural people with low SES had poorer health because of technological challenges. This effect was not significant for urban people after controlling for sex, age, education level, marital status, and experience accessing medical care services.

CONCLUSIONS

SES significantly and negatively impacted individuals' health conditions, especially for rural residents, owing to their technological challenges. This study provides evidence and insights into the nexus of policy formulation, modern technology, and public perceptions regarding shortcomings and risks in public health policies.

Socioeconomic status, loneliness, and depression among older adults: a cross-sectional study in Spain

BACKGROUND

The association between socioeconomic status and depression is weaker in older adults than in younger populations. Loneliness may play a significant role in this relationship, explaining (at least partially) the attenuation of the social gradient in depression. The current study examined the relationship between socioeconomic status and depression and whether the association was affected by loneliness.

METHODS

A cross-sectional design involving dwelling and nursing homes residents was used. A total of 887 Spanish residents aged over 64 years took part in the study. Measures of Depression (GDS-5 Scale), Loneliness (De Jong-Gierveld Loneliness Scale), Socioeconomic Status (Education and Economic Hardship), and sociodemographic parameters were used. The study employed bivariate association tests (chi-square and Pearson's r) and logistic regression analyses.

RESULTS

The percentage of participants at risk of suffering depression was significantly higher among those who had not completed primary education (45.5%) and significantly lower among those with university qualifications (16.4%) (X2 = 40.25;p <.001), and respondents who could not make ends meet in financial terms faced a higher risk of depression (X2 = 23.62;p <.001). In terms of the respondents who experienced loneliness, 57.5% were at risk of depression, compared to 19% of those who did not report loneliness (X2 = 120.04;p <.001). The logistic regression analyses showed that having university qualifications meant a 47% reduction in the risk of depression. This risk was 86% higher among respondents experiencing financial difficulties. However, when scores for the loneliness measure were incorporated, the coefficients relating to education and economic hardships ceased to be significant or were significantly reduced.

CONCLUSION

Loneliness can contribute to explaining the role played by socioeconomic inequalities in depression among older adults.

Comparison of Imputation Strategies for Incomplete Longitudinal Data in Life-Course Epidemiology

Incomplete longitudinal data are common in life-course epidemiology and may induce bias leading to incorrect inference. Multiple imputation (MI) is increasingly preferred for handling missing data, but few studies explore MI-method performance and feasibility in real-data settings. We compared 3 MI methods using real data under 9 missing-data scenarios, representing combinations of 10%, 20%, and 30% missingness and missing completely at random, at random, and not at random. Using data from Health and Retirement Study (HRS) participants, we introduced record-level missingness to a sample of participants with complete data on depressive symptoms (1998-2008), mortality (2008-2018), and relevant covariates. We then imputed missing data using 3 MI methods (normal linear regression, predictive mean matching, variable-tailored specification), and fitted Cox proportional hazards models to estimate effects of 4 operationalizations of longitudinal depressive symptoms on mortality. We compared bias in hazard ratios, root mean square error, and computation time for each method. Bias was similar across MI methods, and results were consistent across operationalizations of the longitudinal exposure variable. However, our results suggest that predictive mean matching may be an appealing strategy for imputing life-course exposure data, given consistently low root mean square error, competitive computation times, and few implementation challenges.

The charm of country life? Impact of rural childhood residence on physical and mental health in later life

BACKGROUND

Most studies of geographic health disparities are focused on adult rural residence. However, previous studies have shown that the residential area in which one grows up during childhood has lasting impacts on adult health. In one of the only studies to date to examine the impact of rural childhood residence on mental health in middle-aged and older adults, Murchland and colleagues (2019) evaluated inequalities by childhood residence and noted elevated depressive symptoms were more common among those living in rural areas compared to those living in non-rural areas.

AIMS

The current study expands the model proposed by Murchland and colleagues to include further antecedents related to rural childhood residence, and to include multiple outcomes of physical and mental health among middle-aged and older adults.

METHOD

Participants included 4614 individuals aged 40 or older recruited as part of the Midlife in the United States (MIDUS) study.

RESULTS

Consistent with Murchland's model, childhood rurality played an important part in middle-aged and older adult's health, despite not having a direct influence. Rurality status was impacted by parental education level and SES during childhood, and was associated with the level of education obtained by the participants (and thus their occupation), which played a direct role in their current health status. Mental and physical health had differential predictors.

LIMITATIONS

The study was limited by its non-diverse sample and self-reported measures.

CONCLUSION

Further research into the impact of childhood rurality on health is needed, utilizing comprehensive self-reported and observed outcome measures.

Food insecurity associated with higher COVID-19 infection in households with older adults

OBJECTIVES

As a well-documented social determinant of health, food insecurity may be associated with COVID-19 infection in households with older adults. We examined whether older adults were vulnerable to COVID-19 infection during the early pandemic if they were food insecure versus food secure.

STUDY DESIGN

A cross-sectional study using a nationally representative population-based survey of US older adults.

METHODS

We used a random subsample of Health and Retirement Study (HRS) drawn in June 2020 (N = 3212). We compared the odds of reporting COVID-19 infection in a household, COVID-19 infection and mortality among acquaintances, and respondent's perceived fair or poor health across household food insecurity status resulted from financial or non-financial barriers. Baseline health and socioeconomic circumstances were adjusted in the models.

RESULTS

Results showed a higher COVID-19 infection rate among food-insecure households than among their food-secure counterparts during the pandemic. Food insecurity due to non-financial obstacles was associated with greater likelihood of COVID-19 infection both within respondents' households (adjusted odds ratio [aOR] = 1.73, 95% confidence interval [CI]: 1.03-2.90) and among their acquaintances (aOR = 1.32, 95% CI: 1.05-1.65). Food insecurity caused by both non-financial and financial constraints was associated with twice the likelihood of knowing someone who died from COVID-19 than their food-secure counterparts (aOR = 2.14, 95% CI: 1.27-3.61).

CONCLUSIONS

Food insecurity driven by non-financial constraints played an important role in the ongoing pandemic among US older adults. Policies addressing COVID-19 need to recognize the vulnerability of food-insecure older adults beyond lack of monetary resources.

Does digital technology reduce health disparity? Investigating difference of depression stemming from socioeconomic status among Chinese older adults

Background

Prior studies on health disparity have shown that socioeconomic status is critical to inequality of health outcomes such as depression. However, two questions await further investigation: whether disparity in depression correlated with socioeconomic status will become larger when depression becomes severer, and whether digital technology will reduce the disparity in depression correlated with socioeconomic status. Our study aims to answer the above two questions.

Methods

By using the dataset from China Health and Retirement Longitudinal Study 2015, we use quantile regression models to examine the association between socioeconomic status and depression across different quantiles, and test the moderating effect of digital technology.

Results

Our study obtains four key findings. First, the negative effects of socioeconomic status on depression present an increasing trend at high quantiles. Second, Internet usage exacerbates the disparity in depression associated with education level on average, but reduces this disparity associated with education level at high quantiles. Third, Internet usage reduces the disparity in depression associated with income on average and at high quantiles. Fourth, mobile phone ownership has almost no moderating effect on the relationship between socioeconomic status and depression.

Conclusions

Our findings suggest the potential use of digital technology in reducing disparity in depression correlated with socioeconomic status among middle-aged and aged individuals in developing countries.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02175-0.

Urban and Rural Differences in Trajectories of Depressive Symptoms in Later Life in the United States

OBJECTIVE

This research has two primary goals: to examine the relationship between urban residence and trajectories of depressive symptoms and to investigate whether this relationship differs by social isolation and loneliness.

METHOD

Data are from 2006, 2008, 2010, 2012, 2014, and 2016 waves of the Health and Retirement Study (HRS), a nationally representative sample of U.S. adults aged 51+ (n = 3,346 females and 2,441 males). We conduct latent growth curve analysis to predict both baseline and trajectories of depression based on urban or rural residency.

RESULTS

Residing in urban or rural areas is neither significantly associated with baseline nor the development of late-life depressive symptoms. For females, the relationship between urban residence and baseline depressive symptoms is explained by socioeconomic factors.

DISCUSSION

Findings of this study serve to better understand how social and geographic contexts shape long-term well-being of older adults.