Study area
The present study was conducted in Pancharul Gram Panchayat (local level unit for the governance of rural areas) under Udaynarayanpur Block, located in the district of Howrah of the State of West Bengal in India. Howrah is the nearest town to Pancharul, which is approximately 51km away. The nearest railway station of Pancharul is about 23 km. away (Amta Railway Station). The total geographical area of Pancharul is 410.02 hectares, and the total population of this area is 6,678 persons. Out of the total population, 4,714 people in Pancharul Village are literate. There is only one community health centre, but exclusive geriatric outpatient services are not available in this centre (MHA, 2011).
This study is the part of a large project that examines the effect of a rural geriatric welfare programme on geriatric quality of life. This particular community was purposely selected because a geriatric welfare programme is ongoing in this block, and it also possesses multi-ethnic, single language-speaking (Bengali, the predominant local language of West Bengal) agricultural group, which is a general feature of rural West Bengal.
Sampling
This study follows a population-based cross-sectional design. No statistical sampling procedures have been adopted for the selection of study participants. Household listing was made from the panchayat electoral rolls (Voter list). Based on this selection frame all the eligible individuals (aged 60 years and above) of Pancharul gram panchayat were approached for participation. The sampling frame enumerated a total of 623 elderly individuals inhabiting a cluster of villages of Pancharul gram panchayat. Out of 623 elderly individuals, 500 participants (226 males and 274 females) completed the face-to-face interview by a single researcher and were included in this study between October 2018 and January 2020. All the questionnaires were administered in Bengali, the mother tongue of study participants. The inclusion criteria for the participants were willing elderly individuals providing consent to participate, and living in the selected villages for more than ten years. Those aged below 60 years, on cross-checking of age, and elderly people with disabilities, both physical and mental, were excluded.
Instruments
Socio-demographic characteristics: First of all, Socio-demographic data were collected on age, sex, marital status, living arrangement, occupational status, educational status and socio-economic status. Marital status was categorized as unmarried, married (living with spouse) and widowed. Living arrangement was grouped into three categories, living alone, nuclear family (a social unit of two parents and their unmarried dependent children) and joint family (a family living together with all family members irrespective of marital status up to 2nd generation). The occupational status was categorized as working and non-working. Educational status was grouped into two categories, less than primary education (school education up to class four), and primary education and above. Udai Pareek scale (revised) was used to collect socio-economic status information (Wani, 2019). This scale is well used to understand the socio-economic status of a family in rural India. In the present study, the reliability of the socio-economic status scale was α = 0.78.
Physical frailty assessment: Physical frailty was evaluated using the modified Fried Physical Frailty Scale (Fried et al., 2001). This screening tool consists of five physiological deficits.
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Low weight: Score based on the lowest quintile of Body Mass Index [defined as the body weight (kg.) divided by the square of the height (m.)] for both sexes. Standing height was measured using Anthropometer rod (with the least count of 0.1 cm) and body weight using digital weighing machine (OMRON Model HBF-514).
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Slow walking speed: Score in the bottom quintile of observed values for the 4-meters times walk at a usual walking pace [adjusted for sex and height (median)].
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Reduced muscle strength: Score in the bottom quintile of observed values for handgrip strength [adjusted for sex and Body Mass Index]. Handgrip strength in kilograms was measured three times, using a Jamar Hand Dynamometer. The measurement was taken on the dominant hand.
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Exhaustion: Individuals were asked whether they have had enough energy for their daily activities. Those who responded 'a little' or 'not at all', were denoted as exhausted.
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Low energy expenditure: Individuals' total physical activity levels were calculated by the Global Physical Activity Questionnaire (GPAQ) (Mumu et al., 2017). Score based on the low level of physical activity according to the cut-off of GPAQ. This scale considers an individual as ‘frail’ if they display any three or more out of these five deficits, an individual as ‘pre-frail’ if they display any one or two out of these five deficits, and others are categorized as ‘non-frail’.
Psycho-social characteristics
Depression, anxiety and stress was measured based on DASS-21 (Lovibond & Lovibond, 1995). It is a set of three self-report subscales, seven items per subscale, to access the emotional states of depression, anxiety and stress level.In the present study, the DASS reliability was α = 0.86 for depression, α = 0.75 for anxiety and α = 0.77 for stress. The UCLA Loneliness Scale (Russell et al., 1980) was used to measure one's subjective feelings of loneliness. The total UCLA-loneliness score is 20 items, which exhibited high internal consistency in the present sample (α = 0.92). The Bangla Adaptation of the Mini-mental State Examination (BAMSE) (Kabir et al., 2020) scale (30-point questionnaire) was used to assess the cognitive function. Cronbach’s α value in the present study for cognitive function (BAMSE) was 0.70. Higher scores of the DASS-21 and the UCLA Loneliness scale indicate a greater degree of depression, anxiety, stress and loneliness. Lower scores of the BAMSE scale indicate a lower degree of cognitive function.
Heath-related Quality of life: The Bengali Short Form-36 (SF-36) health survey questionnaire was used to measure Health-Related Quality of Life (HRQoL) (Feroz et al., 2012). This scale consists of eight subscales: physical functioning, role limitations due to physical health, role limitations due to emotional problems, energy/fatigue, emotional well-being, social functioning, body pain and general health. Aggregate scores are compiled as a percentage of the total point yielding overall score. The higher the overall score, the less disability, i.e., a score of zero is equivalent to a maximum disability, and a score of 100 is equivalent to no disability. Cronbach’s α of 0.97 was obtained in the present study.
Data analysis
Descriptive analysis was performed to report the proportions for the categorical variables of socio-demographic characteristics. Multiple linear regressions were performed to find out the association of HRQoL with socio-demographic characteristics. Analysis of Variance (ANOVA) was used to compare the difference in psycho-social characteristics among the frail, pre-frail and non-frail status groups. Cluster analysis was used to identify homogeneous groups of psycho-social characteristics. The hierarchical cluster analysis with Ward's method, applying squared Euclidian distance followed by k-means clustering was conducted to make clusters of psycho-social health based on attaining the extent of the selected psycho-social characteristics instrument scores. One-way ANOVA was also conducted to understand the significant differences across the means of the cluster groups of psycho-social health. Finally, adjusting baseline covariates, a two-way Analysis of Covariance (ANCOVA) was used to identify the association of HRQoL with physical frailty and psycho-social health, respectively. It also highlights the interaction effects of physical frailty and psycho-social health in determining the health-related quality of life. A p value of < 0.05 was considered statistically significant. Statistical analysis was performed using SPSS (Windows version 20; IBM Corp, Armonk [NY], US).