Widowhood and Alcohol, Tobacco, and Other Drug Use Among Older Adults in India

DeepBiomarker2: Prediction of alcohol and substance use disorder risk in post-traumatic stress disorder patients using electronic medical records and multiple social determinants of health

Introduction

Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. In our previous study, we developed a deep learning-based model, DeepBiomarker by utilizing electronic medical records (EMR) to predict the outcomes of patients with suicide-related events in post-traumatic stress disorder (PTSD) patients.

Methods

We improved our deep learning model to develop DeepBiomarker2 through data integration of multimodal information: lab tests, medication use, diagnosis, and social determinants of health (SDoH) parameters (both individual and neighborhood level) from EMR data for outcome prediction. We further refined our contribution analysis for identifying key factors. We applied DeepBiomarker2 to analyze EMR data of 38,807 patients from University of Pittsburgh Medical Center diagnosed with PTSD to determine their risk of developing alcohol and substance use disorder (ASUD).

Results

DeepBiomarker2 predicted whether a PTSD patient will have a diagnosis of ASUD within the following 3 months with a c-statistic (receiver operating characteristic AUC) of 0·93. We used contribution analysis technology to identify key lab tests, medication use and diagnosis for ASUD prediction. These identified factors imply that the regulation of the energy metabolism, blood circulation, inflammation, and microbiome is involved in shaping the pathophysiological pathways promoting ASUD risks in PTSD patients. Our study found protective medications such as oxybutynin, magnesium oxide, clindamycin, cetirizine, montelukast and venlafaxine all have a potential to reduce risk of ASUDs.

Discussion

DeepBiomarker2 can predict ASUD risk with high accuracy and can further identify potential risk factors along with medications with beneficial effects. We believe that our approach will help in personalized interventions of PTSD for a variety of clinical scenarios.

Marital Disruption and Disparity in Tobacco Use in Reproductive-Aged Women: Evidence from India

Marital disruption defined as widowhood, divorce, or separation, has adverse consequences for women’s health and wellbeing. Extant evidence, however, is primarily available for older women or in developed country settings. Consequences of marital disruption for younger women in the developing countries is relatively less visited. The aim of this cross-sectional study is to assess whether maritally disrupted women of reproductive age (18–49 years) had differential risk of tobacco-use compared to their married counterparts. Using nationally representative data from India, we estimated multivariable logistic regressions to obtain the odds in favor of tobacco-use for maritally disrupted women. We found that compared to women remained in marriage, maritally disrupted women were 1.5 times (95% CI: 1.4–1.6) more likely to consume tobacco. The higher risk of tobacco-use of maritally disrupted women was evident in both younger (age 18–34) and older (age 35–49) cohorts. The results were robust across urban and rural areas, high- and low- education groups, and poor- and non-poor households. The higher odds of tobacco-use among maritally disrupted women persisted even after accounting for household fixed effects. The study findings thus, have implications for strengthening targeted tobacco control policies and health promotion among maritally disrupted women in low-and-middle income countries.

Family influences on older adults' problem drinking: A representative nationwide study of China

Aim

It is reported that problem drinking is severe among the elderly. The family environment has been regarded as a significant effecting factor in alcohol consumption of the drinker. With the increasing number of older people, paying more attention to this vulnerable group's drinking status and its' influencing factors is substantial for improving older adults' health and the quality of health services.

Methods

This study used data from the Chinese Longitudinal Healthy and Longevity Study (CLHLS), which was a representative survey covering 23 provinces in mainland China. Cross-sectional analyses were conducted with 15,142 older individuals (aged ≥65 years). Three self-reported questions about drinking behavior were examined to calculate alcohol consumption and categorize problem drinkers. Three multi-level models were utilized while adjusting for numerous socio-demographic and self-reported health factors to analyze the effect of family factors associated with problem drinking among the elderly.

Results

A total of 1,800 problem drinkers (12%) were identified in the sample. Key factors for the problem drinker were assessed such as Hukou (governmental household registration system), current marital status, years of schooling, primary caregivers, and financial sources of living were associated with problem drinking. The older population who live in rural areas (OR = 1.702, CI = 1.453, 1.994), with advanced years of education (OR = 1.496, CI = 1.284, 1.744), and making life by themselves (OR = 1.330, CI = 1.139, 1.552) were more likely to engage in problem drinking while those participants who are widowed (OR = 0.678, CI = 0.574, 0.801), cared for by children or other relatives (OR = 0.748, CI = 0.642, 0.871), adult care giver (OR = 0.348, CI = 0.209, 0.578) or by no one (OR = 0.539, CI = 0.348, 0.835), provided with financial support from their children (OR = 0.698, CI = 0.605, 0.806), other relatives (OR = 0.442, CI = 0.332, 0.587), or the government/community (OR = 0.771, CI = 0.650, 0.915), with insufficient financial support (OR = 0.728, CI = 0.608, 0.872) were at lower risk of problem drinking.

Conclusions

This study provides a strong correlation of various family factors that were associated with problem drinking among the elderly. The findings underscore the effort to promote healthy behaviors, including the importance of positive family factors and appropriate levels of alcohol consumption.

Effect of Spousal Loss on Depression in Older Adults: Impacts of Time Passing, Living Arrangement, and Spouse's Health Status before Death

In addition to increasing the mortality among older adults, spousal death (SD) increases their risk of depression. This study explored the factors affecting depression among widowed older adults to provide health care strategies for successful aging. A total of 710 adults older than 60 years completed a questionnaire before and after their spouses’ deaths. The survey data included age, sex, ethnic group, education level, financial station socioeconomic status, SD (including time point), smoking status, alcohol consumption, self-rated health status, Center for Epidemiologic Studies Depression Scale score, mobility, and degree of support from relatives and friends. The proportion of participants with depression after SD was 1.7 times that of before SD (p < 0.0001). Worsened mobility (odds ratio [OR] = 1.3, p < 0.01), low self-rated health status (OR = 0.5, p < 0.01), and a high degree of support from relatives and friends (OR = 1.5, p < 0.01) had a significant positive correlation with depression after SD. The proportion of depression that occurred within 6 months after SD was 6.0 times higher than that of depression before SD. Participants who lived alone after losing their spouses who were healthy before their deaths exhibited a significantly increased proportion of depression after their spouses’ deaths. Male sex, spouse’s health, and the period of 6 months after SD are risk factors for depression in older adults. The maintenance of mobility, positive self-rated health status, and a shorter period of depression after a spouse’s death result in more favorable adaptability among women. Social workers or family members should focus on older adults whose spouses died unexpectedly or within the last 6 months. Living with family members after SD can alleviate depression in older adults.

Tobacco Use and Secondhand Smoke Exposure Among Older Adults in India

Objectives: Current tobacco use (CTU) and secondhand smoke (SHS) exposure among older adults in India (≥60 years) are prevalent in India and indicate the importance of addressing associated factors. Methods: Pooled Global Adult Tobacco Survey India 2009-2010 and 2016-2017 data (n = 17,299) for older adults examined prevalence of CTU and SHS exposure at home and/or in public places and associated socioeconomic and demographic correlates. Results: CTU among older adults in India was 44.6%, and SHS exposure at home and public places were 20.0% and 30.0%, respectively. Men, younger age-group, rural, lower education, lower wealth index, and lower knowledge were independently associated with CTU. Men, rural, lower education, lower wealth index, CTU, and lower knowledge were independently associated with SHS exposure at home. Men, younger age, and rural residence were associated with SHS exposure in public places. Conclusion: CTU and SHS exposure among older adults in India suggest targeted interventions to address associated social and demographic factors.

Relationship between chewing tobacco, smoking, consuming alcohol and cognitive impairment among older adults in India: a cross-sectional study

BACKGROUND

Physical aging increases the sensitivity to the effects of substance use, elevating the risk for cognitive impairment among older adults. Since studies on the association of substance use with cognitive ability in later years are scant in India, we aimed to explore the factors associated with cognitive impairment especially, alcohol consumption, smoking, and chewing tobacco later in life.

METHODS

The present research used nationally representative data from Building a Knowledge Base on Population Aging in India (BKPAI) that was conducted in 2011, across seven states of India (N=9,453). Sample distribution along with percentage distribution was calculated for cognitive impairment over explanatory variables. For finding the association between cognitive impairment over explanatory variables, binary logistic regression models were estimated.

RESULTS

About 16.5 percent of older adults in rural areas consumed smoked tobacco compared to 11.7 percent in urban areas. Nearly, 23.7 percent of rural older adults consumed smokeless tobacco in comparison to 16 percent in urban areas. Alcohol consumption was high among rural residents (7.9%) than urban counterparts (6.7%). The prevalence of cognitive impairment was 62.8% and 58% among older adults from rural and urban areas respectively. Older adults who smoked tobacco had a 24 percent significantly higher likelihood to have cognitive impairment with reference to older adults who did not smoke [OR: 1.24, CI: 1.02-1.49]. Moreover, older adults who consumed alcohol had a 30 percent significantly higher likelihood to have cognitive impairment [OR: 1.02, 1.65]. It was also found that older adults who had smoked along with consuming alcohol were at risk of worse cognitive outcomes than those who neither smoke nor drink alcohol [OR: 1.56, CI: 1.21-2.00] or consumed either of them unlike consuming smokeless tobacco only.

CONCLUSION

The encouragement of older people to stop smoking and smokeless tobacco use could be considered as part of a strategy to reduce the incidence of cognitive impairment. Further, appropriate measures should be taken for the detection of early stages of cognitive decline in older individuals and efforts should be made to improve the availability and quality of care for dementing older adults.

Widowhood Status as a Risk Factor for Cognitive Decline among Older Adults

OBJECTIVES

This study investigated whether widowhood status has an effect on cognitive decline among older adults in the United States.

DESIGN

Longitudinal analysis of existing secondary data.

SETTING

The 1996-2012 waves of the Health and Retirement Study.

PARTICIPANTS

A total of 6,766 individuals (28,420 observations) aged 50 years and older who responded to all questions.

MEASUREMENTS

Widow/widower status, cognitive functioning score, and various covariates.

RESULTS

Growth-curve models show that after controlling for covariates, widowhood status was related to cognitive decline (95% CI: -0.8090, -0.4674). We also found a linear relationship between time since spousal loss and cognitive decline. Conditional upon spousal bereavement status, higher education and having at least one living sibling were found to be protective factors against cognitive decline.

CONCLUSIONS

Widowhood status accelerated cognitive decline over time among widowed older adults. Findings suggest that extra support is needed to monitor cognitive functioning for those experiencing widowhood.