Ageing is an inevitable biological process which is marked by progressive physiological decline, increased risk of disease, disability and reduced functional capacity.¹ It represents the final stage in the life course and the individual may feel inadequate and dependent. There is no universally accepted definition but, the Indian National Policy on Older Persons defines a senior citizen as anyone aged 60 years or above.^2,3

Population around the world is ageing at an unprecedented pace. The world is expected to have 2 billion elderly people by 2025, who will comprise of over 22% of the total world population.⁴ The demographic transition in India has significantly increased the number of elderly citizens. Data from the census and national estimates has shown that the proportion of elderly increased from 7.4% in 2001 to 8.6% in 2011 and is expected to reach 13.1% by 2031.^5,6 Rajasthan has also shown a similar trend, with the elderly population increasing from 6.7% in 2001 to 7.5% in 2011 and will rise to 11.2% by 2031.⁶

Historical health indicators could not completely capture the overall being as they relied heavily on morbidity and mortality alone. This led to the evolution of Quality of Life (QOL) as a multidimensional measure that considers physical, psychological, social as well as environmental aspects of health.^8,9 The World Health Organization defines QOL as “an individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns”.¹² This holistic perspective is especially relevant in elderly care as it integrates physical morbidities with other relevant determinants of health.

Despite the growing numbers, data on QOL among older adults from semi-urban regions is limited. These areas are often overlooked and subjected to disparities in terms of uneven infrastructure, evolving family structures and transitional socioeconomic conditions which impact elderly well-being in multiple ways.

Bikaner is a rapidly developing district of Rajasthan and aptly represents the semi-urban population. Limited studies have been done to assess the Quality of Life of older adults in this region. Understanding QOL and its determinants is vital for planning appropriate geriatric health services and developing targeted interventions.

The present study aims to assess the Quality of Life of older adults in a semi-urban area of Bikaner and to identify key factors influencing their well-being.

OBJECTIVES

1. To assess the quality of life among older adults residing in a semi-urban area of Bikaner.
2. To identify the sociodemographic and health-related determinants associated with quality of life among the study participants.

Study Design and Setting: A community-based cross-sectional study was conducted in a semi-urban area of Bikaner district, Rajasthan.

Study Population: The study included older adults aged 60 years and above residing in the selected semi-urban area for at least one year.

Sample Size and Sampling Technique: A sample size of 200 older adults was included in the study. Participants were selected using systematic random sampling from the identified study area.

Inclusion and Exclusion Criteria: Individuals aged 60 years and above who provided informed consent were included in the study. Those who were critically ill or unable to communicate at the time of data collection were excluded.

Study Tool and Data Collection: Data were collected using a pretested semi-structured questionnaire comprising two sections. The first section included information on sociodemographic characteristics such as age, sex, marital status, education, socioeconomic status and presence of chronic morbidity. The second section consisted of the World Health Organization Quality of Life–BREF (WHOQOL-BREF) instrument, which assesses quality of life across four domains: physical health, psychological well-being, social relationships and environment.⁵

The WHOQOL-BREF contains 26 items rated on a five-point Likert scale. Domain scores were calculated and transformed to a scale of 0–100 as per WHO guidelines, with higher scores indicating better quality of life.

Statistical Analysis: Data were entered into Microsoft Excel and analysed using statistical software. Categorical variables were expressed as frequencies and percentages. Continuous variables were summarised using mean and standard deviation. The association between quality-of-life scores and selected sociodemographic variables was analysed using appropriate statistical tests. A p-value of less than 0.05 was considered statistically significant.

Ethical Considerations: The study was conducted after obtaining approval from the Institutional Ethics Committee. Written informed consent was obtained from all participants prior to data collection and confidentiality of the information was ensured throughout the study.

A total of 200 older adults participated in the present study. . Table 1 represents the socio-demographic characteristics of the study population included in this study. 63.5% were aged between 60–69 years, 27% were aged 70–79 years and 9.5% were aged ≥80 years. Females constituted more than half (56%) of the study population. Majority of the respondents were married (91.5%) and nearly half of them (46%) were illiterate. 39.5% of the study population was Middle Class according to the Modified BG Prasad classification. A substantial proportion (90.5%) reported the presence of at least one chronic morbidity.

Table 1 represents the socio-demographic characteristics of the study population included in this study.

Table 2 shows that 35% of participants rated their overall quality of life as poor and 53.5% expressed dissatisfaction with their general health.

The mean overall WHOQOL-BREF score of our study population was 58.21 ± 10.94 on the transformed 0–100 scale as seen in Table 3. This suggests a moderate level of quality of life among the study population. Domain-wise analysis, as seen in Figure 1, shows that the psychological domain had the highest mean score followed by physical health. Social relationships and environmental domains scored comparatively lower.

A statistically significant decline in overall quality of life was observed with advancing age (p = 0.042) (Table 3). Participants aged ≥80 years had lower mean scores compared to those aged 60–69 years. Participants aged 60–69 years had higher mean scores in the physical (61.2 ± 15.8), psychological (62.4 ± 9.8), social (57.3 ± 12.1) and environmental (56.4 ± 10.1) domains compared to those aged ≥80 years, who demonstrated lower scores across all domains (physical 54.6 ± 17.3; psychological 56.9 ± 11.1; social 52.4 ± 13.5; environmental 51.8 ± 11.2).

Gender had no significant association with overall QOL (p = 0.137). However, domain-level analysis showed marginally higher scores in males than females. Male participants scored 60.8 ± 15.9 in the physical domain, 62.7 ± 9.5 in psychological, 57.8 ± 11.9 in social and 56.5 ± 10.0 in environmental domains whereas corresponding scores among females were 58.4 ± 16.8, 59.9 ± 10.8, 55.2 ± 13.2 and 54.9 ± 10.9 respectively.

Education emerged as a significant determinant of overall QOL (p = 0.007). Mean scores increased progressively from illiterate individuals to those with secondary education and above as depicted in Table 3. Domain-wise patterns revealed that higher educational attainment was associated with improved scores across all four domains, particularly in psychological and social dimensions. Participants with secondary education and above reported higher mean scores across physical (63.4 ± 14.6), psychological (64.8 ± 8.7), social (60.3 ± 10.9) and environmental (58.9 ± 9.2) domains, whereas illiterate participants had comparatively lower scores (physical 56.9 ± 16.7; psychological 58.7 ± 10.9; social 53.9 ± 13.0; environmental 53.1 ± 10.8).

Socioeconomic status demonstrated the strongest association with overall quality of life (p < 0.001). Mean QOL scores had a constant decline from Class I (63.17 ± 5.89) to Class V (48.95 ± 8.75). Domain-specific analysis also showed uniformly higher scores among upper socioeconomic groups across physical, psychological, social and environmental domains as seen in Figure 2.

The presence of chronic morbidity was significantly associated with lower overall QOL (p = 0.036). Participants without chronic conditions had higher mean scores compared to those with morbidity. Domain-wise findings indicated that participants without chronic morbidity exhibited higher mean scores across domains (physical 64.5 ± 14.8; psychological 66.1 ± 8.9; social 60.8 ± 11.0; environmental 59.4 ± 9.4) compared to those with chronic conditions (physical 58.7 ± 16.4; psychological 60.3 ± 10.5; social 55.6 ± 12.7; environmental 54.7 ± 10.7).

Table 1: Sociodemographic Profile of Study Participants

Table 2: Perceived Quality of Life, Health and Mean WHOQOL-BREF Domain Scores

Figure 1: Domain-wise WHOQOL-BREF Scores among Older Adults (Mean ± SD)

Table 3: Association of Key Determinants with WHOQOL-BREF Domain Scores

*ANOVA for >2 categories; independent t-test for two-category variables.

Figure 2: Quality of Life across Domains of Different Socioeconomic Classes

The present study has demonstrated a moderate overall quality of lime among older adults living in a semi-urban setting. Clear gradients have been observed across age, education, socioeconomic standing and morbidity status. Predominance of younger elderly age group is consistent with findings from Haryana, Karnataka and Tripura which might be due to increased mortality in the advancing age groups. ^{6, 7, 8} The higher proportion of female participants can be attributed to longer survival and increased life expectancy in females. This may also be influenced by differential participation patterns in community-based studies. The high prevalence of chronic morbidity among elderly reflects the demographic and epidemiological transition documented in national and global ageing reports. ^{2, 9, 10}

In terms of subjective perception, the proportion of participants reporting poor overall quality of life and dissatisfaction with general health is comparable to findings from community-based studies in Puducherry, Gujarat and West Bengal. ^{11, 12, 13} The mean WHOQOL-BREF score observed in the present study aligns with findings from Lucknow, Tripura and Puducherry.^{7, 11, 14} Similar findings have also been documented in elderly populations from Iran, Myanmar and the Gaza Strip. ^{15, 16, 17} These findings suggest that moderate QOL levels among elderly populations are not limited to Indian context but reflect broader demographic and social transitions.

Domain-wise analysis revealed relatively higher psychological scores and comparatively lower environmental scores. The relatively higher psychological scores may reflect adaptive coping and emotional resilience among older adults and lower environmental scores may be attributable to infrastructural limitations, financial insecurity and limited access to age-friendly services in semi-urban areas. Variation in domain patterns has been observed across settings. A study conducted in rural area of a backward district as well as an urban study from Ahmedabad have reported highest score in social domains. ^{18, 12} Another study from Myanmar also reported similar results.¹⁶ Contrastingly, a study conducted in a rural block of Haryana reported the environmental domain as the highest scoring domain and social domain as the lowest.⁸ A Brazil study reported higher physical and psychological domain scores.¹⁹ Such variability suggests that sociocultural context and environmental conditions substantially influence domain-specific QOL patterns.

Ageing leads to a multidimensional impact and does not necessarily affecting any single aspect of well-being. Age-related declines in QOL have also been reported in studies from Karnataka and West Bengal, where older age groups demonstrated significantly lower scores across domains.^{6, 13} Evidence from Nepal and Iran also supports the negative association between increasing age and quality of life.^{15, 20} The decline across domains likely reflects progressive functional limitations, increasing dependency with ageing and reduced social engagement with increasing age.

Gender differences in domain scores were modest. While some Indian studies have reported higher QOL among males, ^{12, 21} others have not found significant gender-based differences. ^{6, 14} Similar variability has been observed internationally. ^{16, 17} It probably suggests that gender differences in QOL are influenced by social and economic context rather than biological factors alone.

Educational attainment showed a positive association with quality-of-life domains. Similar associations have been documented in studies from Puducherry, Tamil Nadu and Northern India. ^{11, 22, 23, 24} Studies from Iran and Gaza Strip have also reported education as a significant predictor of better quality of life among older adults. ^{14, 17} Education likely enhances health literacy, adaptive coping and social participation thereby improving multidimensional well-being.

A pronounced socioeconomic gradient was observed, with lower domain scores among lower socioeconomic groups. Comparable socioeconomic gradients have been reported in studies from Haryana, Lucknow and Barabanki (Uttar Pradesh). ^{8, 14, 25} A population-based study conducted in Pakistan also shows similar results. ²⁶ These findings underscore the central role of socioeconomic determinants in shaping ageing outcomes, as financial security influences healthcare access, living conditions, nutrition and opportunities for social engagement.

The presence of chronic morbidity was associated with lower domain scores, particularly in physical and psychological dimensions. Analogous findings have been reported in Indian rural populations and in analyses examining multimorbidity among older adults. ^{1, 27} International evidences also consistently demonstrate the adverse impact of chronic conditions on physical and psychological domains of QOL. ^{15, 17} Chronic conditions impose functional limitations, treatment burden and psychological stress which can further adversely affect the overall well-being.

The results of this study infer that quality of life among older adults in semi-urban setting is shaped by socioeconomic, educational and health-related factors. Psychological well-being appears homogenous but social and environmental dimensions remain comparatively weaker. There is a need for developing comprehensive geriatric policies that address not only medical care but also social protection, financial security and supportive community infrastructure.

This study deduces that quality of life among older adults in a semi-urban area is shaped by many factors which include a combination of demographic, socioeconomic and health-related factors. Advancing age, lower educational attainment, poorer socioeconomic status and the presence of chronic morbidity were significantly associated with diminished well-being. The pattern observed across domains supports the multidimensional nature of quality of life indicating that elderly health cannot be understood solely in terms of disease burden but must incorporate psychological, social and environmental dimensions.

It is important to strengthen geriatric health services within primary care system and also address broader social determinants. Interventions should be aimed at improving educational awareness, economic security and chronic disease management. This may substantially enhance overall well-being among older adults. A comprehensive and integrated approach to ageing is essential to promote healthy, active and dignified ageing in semi-urban communities.

LIMITATIONS

The cross-sectional design cannot establish any causal inference. Reliance on self-reported data may introduce reporting bias. The study was conducted in a single semi-urban setting. So, the results may not be generalisable to rural or metropolitan populations. Further longitudinal and multicentric studies are recommended to explore causal pathways and contextual variations in elderly quality of life.

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