Dataset Information

The association of widowhood and living alone with depression among older adults in India

ABSTRACT: Widowhood is a catastrophic event at any stage of life for the surviving partner particularly in old age, with serious repercussions on their physical, economic, and emotional well-being. This study investigates the association of marital status and living arrangement with depression among older adults. Additionally, the study aims to evaluate the effects of factors such as socio-economic conditions and other health problems contributing to the risk of depression among older adults in India. This study utilizes data from the nationally representative Longitudinal Ageing Study in India (LASI-2017–18). The effective sample size was 30,639 older adults aged 60 years and above. Descriptive statistics and bivariate analysis have been performed to determine the prevalence of depression. Further, binary logistic regression analysis was conducted to study the association between marital status and living arrangement on depression among older adults in India. Overall, around nine percent of the older adults suffered from depression. 10.3% of the widowed (currently married: 7.8%) and 13.6% of the older adults who were living alone suffered from depression. Further, 8.4% of the respondents who were co-residing with someone were suffering from depression. Widowed older adults were 34% more likely to be depressed than currently married counterparts [AOR: 1.34, CI 1.2–1.49]. Similarly, respondents who lived alone were 16% more likely to be depressed compared to their counterparts [AOR: 1.16; CI 1.02, 1.40]. Older adults who were widowed and living alone were 56% more likely to suffer from depression [AOR: 1.56; CI 1.28, 1.91] in reference to older adults who were currently married and co-residing. The study shows vulnerability of widowed older adults who are living alone and among those who had lack of socio-economic resources and face poor health status. The study can be used to target outreach programs and service delivery for the older adults who are living alone or widowed and suffering from depression.

SUBMITTER: Srivastava S

PROVIDER: S-EPMC8568934 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups. OBJECTIVE:The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression. METHODS:The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models). RESULTS:On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960). CONCLUSIONS:This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

Project description:BackgroundTo reduce health disparities, prevention of non-communicable diseases (NCD) by performing desirable health behavior in older adults living alone with low socioeconomic status is an essential strategy in public health. Self-perception of personal power and practical skills for daily health are key elements of desirable health behavior. However, methods for measuring these concepts have not been established. This study aimed to develop a health behavior scale for older adults living alone receiving public assistance (HBSO).MethodsThe self-administered mail survey covered 2818 older adults living alone receiving public assistance (OAP) randomly selected from the list of people receiving public assistance (Seikatsu-hogo in Japanese) at all 1250 local social welfare offices across Japan. Construct validity was confirmed using confirmatory factor analysis. Internal consistency was calculated using Cronbach's alpha. The self-efficacy for health promotion scale and Health check-up status were administered to assess the criteria-related validity of the HBSO.ResultsIn total, 1280 participants (response rate: 45.4%) responded, of which 1069 (37.9%) provided valid responses. Confirmatory factor analysis identified 10 items from two factors (self-perception of personal power and practical skills for daily health) with a goodness of fit index of 0.973, adjusted goodness of fit index of 0.953, comparative fit index of 0.954, and root mean square error of approximation of 0.049. Cronbach's alpha was 0.75. The total HBSO score was significantly positively correlated with the self-efficacy for health promotion scale (r = 0.672, p < 0.001) and the group with health check-up had significantly higher HBSO scores than the group without it (p < 0.001).ConclusionsThe HBSO is an easy-to-self-administer instrument that is reliable and valid for OAP. The HBSO could facilitate appropriate assessment of OAP who need to improve their health behavior to prevent NCD, and could be used to determine effective support.

Project description:BackgroundLate-life depression (LLD) is considered as a prodrome to dementia and plays a major role in the development of long-term cognitive disabilities. We aimed to estimate the prevalence and correlates of LLD and cognitive impairment and to explore their associations among older adults in India.MethodsData for this study was derived from the Longitudinal Ageing Study in India (LASI) Wave 1 (2017-18). The total sample included 31,464 (15,098 male and 16,366 female) older individuals aged 60 years and above. Cognitive impairment measured from various domains derived from the cognitive module of the Health and Retirement Study (HRS), and major depression measured by the CIDI-SF (Composite International Diagnostic Interview- Short Form) were the outcome variables. Descriptive, bivariate, and multivariable analyses were performed to fulfill the objectives of the study.ResultsThe overall prevalence of LLD and cognitive impairment for the current sample was 8.7% and 13.7 % respectively. Among older individuals who have rated their health status as poor were 2.59 times more likely to suffer from LLD [OR: 2.59, CI: 2.24-2.99] as compared to their counterparts. The older adults who had difficulty in activities of daily living (ADL) and instrumental activities of daily living (IADL) were 74% and 69 % more likely to suffer from LLD. Similarly, older adults who were depressed had higher odds of cognitive impairment [OR: 1.22, CI: 1.01-1.48] compared to their counterparts. Also, older adults who were depressed and belonged to rural areas were 2.58 times [AOR: 2.58, CI: 1.95-3.41] more likely to be cognitively impaired than those who were not depressed and resided in urban areas.ConclusionsDepression is linked to an increased risk of cognitive decline and dementia; therefore, failing to diagnose and treat LLD in later life may have significant health implications. Moreover, treatment under the care of a cognitive neurologist or geriatric psychiatrist is recommended for people with LLD and cognitive disability due to both the disorders' complex existence.

Project description:Importance:To reduce the rising incidence of clinical impairment due to Alzheimer disease, it is essential to define older adults at highest risk. Widowhood may be an unrecognized factor contributing to accelerated clinical progression along the Alzheimer disease pathway among cognitively unimpaired older adults. Objective:To determine whether widowhood status and level of brain ?-amyloid (ie, the Alzheimer disease pathologic protein) are additively or interactively associated with cognitive decline among cognitively unimpaired older adults. Design, Setting, and Participants:In this cohort study, 257 married, widowed, and unmarried (ie, never married, divorced, or separated) participants from the Harvard Aging Brain Study longitudinal cohort underwent baseline evaluation of neocortical ?-amyloid levels using Pittsburgh compound B positron emission tomography and 4 annual cognitive assessments. Data were collected from September 2010 to February 2017 and analyzed from July 2018 to July 2019. Main Outcomes and Measures:Cognitive performance was measured using the Preclinical Alzheimer Cognitive Composite. Results:Of the 257 participants, 153 (59.5%) were women, and the mean (SD) age was 73.5 (6.1) years; 145 participants (56.4%) were married (66 [45.5%] women), 77 (30.0%) were unmarried (56 [72.7%] women), and 35 (13.6%) were widowed (31 [88.6%] women). Compared with married participants, widowed participants demonstrated worsening cognitive performance after adjusting for age, sex, socioeconomic status, depression, and ?-amyloid levels (??=?-0.11; 95% CI, -0.19 to -0.04; P?=?.002) with no difference observed between married and unmarried participants. Furthermore, widowed participants with higher baseline ?-amyloid levels exhibited steeper cognitive decline (??=?-0.22; 95% CI, -0.42 to -0.03; P?=?.02), indicating both independent and interactive associations of ?-amyloid levels and widowhood with cognition. In a secondary model using dichotomous ?-amyloid-marital status groupings, the rate of cognitive decline among widowed participants with high ?-amyloid was nearly 3 times faster than among married participants with high ?-amyloid (widowed, high ?-amyloid: ?, -0.33; 95% CI, -0.46 to -0.19; P?<?.001; married, high ?-amyloid: ?, -0.12; 95% CI, -0.18 to -0.01; P?<?.001). Conclusions and Relevance:In a sample of cognitively unimpaired older adults, being widowed was associated with accelerated ?-amyloid-related cognitive decline during 3 years. Cognitively unimpaired, widowed older adults were particularly susceptible to Alzheimer disease clinical progression, emphasizing the need for increased research attention and evidenced-based interventions for this high-risk group.

Dataset Information

The association of widowhood and living alone with depression among older adults in India

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets