Association of multi-morbidity, social participation, functional and mental health with the self-rated health of middle-aged and older adults in India: a study based on LASI wave-1

Abstract

Self-rated health (SRH) is a well-established measure in public health to administer the general health of an individual. It can also be used to assess overall health status’ relationship with the social, physical, and mental health of a person. In this study, we examine the association of SRH and various socio-economic & health-related factors such as multi-morbidity status, mental health, functional health, and social participation. Data used in this paper is collated from the first wave of Longitudinal Ageing Study in India (LASI) 2017-18. A total of 65,562 older adults aged 45 or above are considered in our study. Various indices (multimorbidity, social participation, functional and mental health) have been created to measure factors influencing the SRH of an individual. Overall, in the study population, around 18.4% of people reported poor SRH. Dominance Analysis results show that the contribution of multimorbidity in predicting poor SRH is highest, followed by functional health, mental health, and social participation. In a developing country like India, there is a dire need for policies having a holistic approach regarding the health and well-being of the older population.

Characterizing Multimorbidity Prevalence and Adverse Outcomes in Ethnically and Culturally Diverse Sub-Populations in India: Gaps, Opportunities, and Future Directions

“…Our narrative review was based on 15 articles. To address Objective 1, we summarized the findings from eight articles on the definition, measurement, prevalence, and burden of multimorbidity across sub-populations, presented in Table 1 [ 9 , 10 , 14 , 18 , 19 , 21 , 22 , 23 ] and Table 2 [ 12 , 18 , 24 , 25 , 26 , 27 , 28 , 29 ]. Additionally, in Table 2 , we summarize the findings of the remaining seven articles that reported the associations between multimorbidity and specific adverse health outcomes to address Objective 2.…”

“…Our narrative review was based on 15 articles. To address Objective 1, we summarized the findings from eight articles on the definition, measurement, prevalence, and burden of multimorbidity across sub-populations, presented in Table 1 [9,10,14,18,19,[21][22][23] and Table 2 [12,18,[24][25][26][27][28][29]. Additionally, in Table 2, we summarize the findings of the remaining seven articles that reported the associations between multimorbidity and specific adverse health outcomes to address Objective 2. verbal fluency score, 16 which was the number of named animals within 60 s; the community screening instrument for dementia score 17; and the Raven test, 18 a count of the number of correct answers to a series of images that required the respondent to select the missing piece.…”

“…We identified health disparity populations who are at risk of multimorbidity and described the prevalence and types of adverse outcomes associated with multimorbidity based on 15 key and emerging papers. While previous research has examined multimorbidity in individual studies utilizing LASI data [ 9 , 10 , 12 , 14 , 15 , 16 , 17 , 18 , 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], our narrative review is a focused synthesis of the current state of multimorbidity in India. Our review highlights specific populations that are vulnerable, with a higher multimorbidity prevalence that can be targeted for intervention and policy to ameliorate the consequences of multimorbidity in India.…”

A Holistic Approach to Cognitive Frailty in Community-dwelling Older Adults in India: Evidence from a Nationally Representative Survey

“…Self-Reported Health (SRH) of the respondents was evaluated using the question, “Overall, how is your health in general? Would you say it is very good, good, fair, poor, or very poor?” In our analyses, we utilised a dichotomised version of this variable where ‘poor’ and ‘very poor’ were coded as “poor,” and ‘very good,’ ‘good,’ and ‘fair’ were coded as “good” for poor SRH [45].…”

“…Would you say it is very good, good, fair, poor, or very poor?" In our analyses, we utilised a dichotomised version of this variable where 'poor' and 'very poor' were coded as "poor," and 'very good,' 'good,' and 'fair' were coded as "good" for poor SRH [45].…”