1 Introduction

World Health Organization (WHO) defines Quality of Life (QoL) as individual perceptions of how one feels about one’s goals, expectations, and fears in the cultural and value system in which one has grown up [1, 2]. It is a multidimensional concept that involves perceptions of well-being as well as physical, psychological, social, and spiritual components [1, 2]. With advancements in medicine, better living standards, and the subsequent increase in life expectancy, the concept of QoL has become a crucial aspect of population health as medical interventions, at times, extend the length of life but at the expense of QoL [3, 4]. While individuals today enjoy longer lifespans compared to previous centuries, the extension of life does not necessarily equate to an improvement in QoL [3]. Therefore, merely measuring death rates is no longer sufficient to determine the changes in population health, and assessment of QoL is becoming increasingly important [3, 4]. Assessing QoL enables us to evaluate outcomes beyond morbidity and biological functioning [3, 5].

As a result, many healthcare and research workers have given major attention to exploring QoL in terms of individuals’ own perception of their physical and mental health, independence, and ability to adapt to their environment as well as their well-being within their culture and value systems [5, 6]. QOL serves as a measure reflecting the well-being of individuals within a nation or locality and is being increasingly recognized as a valid conceptual framework for assessing societal living standards across various communities [4].

QoL involves both objective and subjective approaches [3, 7, 8]. The objective approach relies on observations made by external observers, assessing variables such as disease, symptom remission, side effects, functional capacity changes, and participation in daily and social activities to evaluate the quality of the physical and social environment [3, 7, 8]. In contrast, the subjective approach provides opportunities for individuals to express their thoughts, knowledge, attitudes, moods, and feelings, prioritizing inner experiences that are influenced by their personal values and past experiences, and it is highly individualistic [3, 6,7,8]. In an attempt to go beyond objective measurements of QoL and include the subjective aspect of it, WHO developed a generic and subjective QoL instrument, namely the WHOQOL-BREF version [9]. It is a cross-culturally relevant and valid instrument, available in multiple languages [9].

As stated earlier, subjective QoL assessment is crucial for the appropriate evolution of population health or healthcare intervention [6, 10]. A considerable body of literature exists exploring the impact of health conditions and medical interventions on individuals’ subjective quality of life [6, 11,12,13,14]. Besides, studies have also attempted to understand the QoL within specific populations, such as older individuals, homeless people, refugees, etc. [15,16,17,18,19] as well as general populations worldwide [4].

However, limited research has explored QoL in the Indian general population, especially in rural areas [4, 20]. Also, the role of factors like factors such as age, sex, marital status, education, family structure, etc. in affecting QoL in Indian populations remains understudied. Considering the lack of studies on QoL and associated risk factors in rural Indian populations, the present study aimed to explore the QoL and the sociodemographic factors affecting it among community-dwelling adults in rural Punjab.

2 Methods

2.1 Study area and participants

The present cross-sectional study was conducted on a total of 931 apparently healthy adult individuals aged 20 to 75 years of either sex (59.6% female) residing in rural areas of Mansa district of Punjab, India. The state of Punjab is located in northwest India and shares borders with Pakistan, Jammu and Kashmir, Himachal Pradesh, Haryana, and Rajasthan (Fig. 1) [21]. The state covers an area of 50,362 square kilometres, with a diverse landscape that ranges from plains to foothills, and according to the 2011 Census of India, has a population of approximately 27.7 million [21]. Punjabi is the official language, and Chandigarh, a Union Territory, serves as the capital [21]. The state’s economy is mainly agricultural, and it contributes significantly to the country's food security, specifically in wheat production [21]. Punjab has experienced significant economic growth after Independence, particularly with initiatives such as the Green Revolution, and despite having a small population share, Punjabis have a high per capita income, making them one of India’s most prosperous communities [21].

Fig. 1
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Map of India showing the state of Punjab (in red colour) (created with www.mapchart.net)

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All the participants were recruited randomly using house-to-house survey method. Data were collected using the interview method. The inclusion criteria for the present study were to recruit participants with no self-reported chronic mental illness, cancer, or severe infectious diseases. Pregnant and lactating women were excluded. The present study was approved by the departmental ethics committee, Department of Anthropology, University of Delhi (Ref. No./Anth./2018/2890/28-12-18). In collecting data from the participants, several ethical considerations were carefully addressed. These included maintaining the confidentiality and anonymity of participants, obtaining informed consent, ensuring voluntary participation, minimizing discomfort, and adhering to the regulations specific to the field of study. Written informed consent (in Punjabi) was obtained prior to data collection from each participant.

2.2 Data collection tools and techniques

Data pertaining to socio-demographic variables like age, sex, education status, marital status, education status, occupation status, and family income were collected using a pretested and modified interview schedule.

2.3 Assessment of Quality of Life

The WHQOL-BREF Hindi version was used for assessing the QoL of the participants [2, 9]. It consists of 26 items, and these items are categorized into four health domains, namely physical (7 items), psychological (6 items), social relationship (3 items), and environment (8 items) [2, 9]. There are three ways of calculating the WHOQOL-BREF scores: raw scores, transformed scores ranging from 4 to 20, and transformed scores ranging from 0 to 100, where higher scores represent better QoL [2, 9]. For the present study, the raw scores of each domain have been scaled to 100. Further, to estimate the overall well-being, average scores of all four domains have been used. Since there is no specific cutoff for defining good and poor QoL, medians of overall and domain-specific scores have been used as cut-off points to define overall and domain-specific good and poor QoL [22]. Those scoring equal to or above median levels were defined as having good QoL, while those below the median level were defined as having poor QoL.

2.4 Statistical analysis

Data analysis for this study was performed using SPSS version 22. The Kolmogorov–Smirnov test was used to determine whether or not the continuous variables were normally distributed. Continuous variables are expressed as mean value and standard deviation (SD). Categorical variables are described as frequencies and percentages. Differences in means of two and more than two groups were established using t-test and ANOVA, respectively. Logistic regression analysis was performed to calculate the odds ratio. Logistic regression models were adjusted for confounders like age, sex, education status, and occupation status. A value of p-value < 0.05 was considered as the level of significance for all the statistical tests used in the present study.

3 Results

3.1 Overall and gender-wise mean QoL scores

The general characteristic of participants is presented in Table 1. The overall mean QoL score of the sample was found to be 67.44 + 14.7 (Table 2). In gender-stratified analysis, females were found to have lower overall and domain-specific QoL scores than males. The observed differences were statistically significant for the psychological, social relationship, and environment domains as well as overall QoL (Table 2).

Table 1 General characteristics of the study population
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Table 2 Overall and gender-wise mean quality of life (QoL) scores
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3.2 Sociodemographic determinants of QoL

Significant differences in the mean QoL scores with observed respect to differences in age groups, sex, marital status, educational status, and employment status, where lower mean QoL scores were observed for those in older age groups than those in younger age groups, females than males, widowed than married, non-literates than literates and unemployed than employed (Table 3). Differences in mean QoL were not significant with respect to religion, caste (except for the environment domain), family size, and average annual income (except for the social relationship domain).

Table 3 Mean quality of life (QoL) scores with respect to socio-demographic variable
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3.3 Odds ratio analysis

Adjusted logistic regression analysis was performed to understand the association of studied sociodemographic variables with QoL (Table 4). This analysis revealed that older participants (aged 60 years and above) were at 3.27 to 4.24 times significantly increased risk for poor QoL in the physical domain than those in the 20–29 age group. Further, females were at 1.7 to 2.1-fold increased risk for poor QoL in the psychological, social-relationship, and environment domains as well in the overall assessment. Similarly, unmarried participants were at a 2.3-fold higher risk of poor QoL in the social relationship domain than married participants. Compared to literates, illiterates were found to be at 1.5 to 1.7 folds increased risk of poor QoL in physical and environment domains as well as overall poor QoL. Nevertheless, those with a family size of 6–10 members were at a reduced risk of poor QoL in the physical domain and overall assessment than those with five or fewer family members. Surprisingly, illiterate participants, compared to literates, were at a reduced risk of poor QoL in the psychological domain (Table 4).

Table 4 Adjusted odds ratio analysis for overall and domain-wise quality of life (QoL)
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4 Discussion

The present study was undertaken to assess the QoL of rural communities in Punjab, India, and also to explore the socio-demographic determinants of poor QoL. In the present study, the overall mean QoL score was found to be 67.44 ± 14.7. In a meta-analysis examining the QoL across various populations globally, categorized by their Human Development Index (HDI), it was found that very high HDI countries had an overall mean QoL score of 74.26, while high to low HDI countries ranged from 65.57 to 64.10 [4]. The mean QoL score of 67.44 observed in the present study suggests that the QoL in the study sample surpasses that of countries with low, medium, and high HDI, but remains below the QoL observed in countries with very high HDI [4].

Most previous studies from India have been conducted among the elderly population or individuals with health conditions and have reported lower QoL than the present study. For instance, the mean QoL scores were reported as 38.9 among the elderly in Kerala [23]. 48.86 among the elderly in Haryana [22], 49.74 among the elderly in Urban Puducherry [24], 55.10 among diabetics in Andhra Pradesh [25], 58.05 among diabetics in Tamil Nadu, 61.49 among people with epilepsy in Tamil Nadu, and 63.8 among people with epilepsy in Punjab [26]. Only a few studies from India have assessed the QoL in the general population and have reported either comparable or lower mean QoL scores than the present study. For instance, mean QoL scores were reported as 63.5 among adults in Puducherry [27] and 67.6 among rural adults from Haryana [20]. However, higher mean QoL scores, for instance, 86.6 among adults in Delhi, have also been reported [28]. When compared to studies from other countries, the mean QoL in the present study is comparable to that reported in some of the previous studies [10, 29]; such as those by Lodhi et al. among a Pakistani population, Chen et al. among a Chinese population, Wong et al. among Hong Kong population [29,30,31]. Based on these observations, the present study indicates an overall good QoL in the study sample. However, the higher mean QoL in very high HDI countries as well as some studies from India [4, 28], suggest the scope for improvement.

Further, it is pertinent to compare the findings of the present studies with those of the World Happiness Report 2024 [32]. Although the tools used in both studies differ, they enable a subjective assessment. In contrast to the observations of the World Happiness Report 2024, which ranks India 126th in terms of life evaluations for the entire population [32], the study sample seems to exhibit a notably higher quality of life. This is particularly evident when considering that the mean QoL in the present study is relatively higher than that reported in many middle and high-income countries, several of which were ranked above India in the World Happiness Report 2024 [4, 32].

Coming to the domain-wise analysis, in the present study, the QoL was found to be the most affected in the physical health domain, followed by the psychological and social relationship domains. The least affected domain was environmental health. A similar pattern of physical followed by psychological health being most affected domains has been reported by Malibary et al. among medical students of Saudi Arabia, Gholami et al. among patients with cataract in Iran, and Mohammed et al. among elderlies of the Gaza Strip [33,34,35]. However, other studies on general populations have reported environmental and psychological domains to be the most affected domains [4, 29]. For instance, low QoL score in environmental health was reported among adults in Pakistan [29]. Lower well-being in psychological health has been reported by Zang et al. among Chinese medical students [36], and Ghazanfar et al. among medical students in Punjab [37].

The variation in the patterns of most affected domains may be attributed to variations in study settings and associated factors. The available literature on QoL among general populations largely agrees on environmental and psychological domains being the most affected domains [4]. However, the present study found the physical health domain to be the most affected among the participants. This is a worrying trend, given that the participants are from younger age groups as well. Long hours of engagement in farming and related work, as well as a high prevalence of hypertension and diabetes in the study area [38] could be some of the factors behind this observation. This indicates that there is a need to improve healthcare facilities at the community level as well as implement early intervention programs. Further, since a sizable proportion of participants are farmers, ergonomic farming practices must also be promoted.

Regarding socio-demographic determinants of QoL, while age, sex, marital status, education, and family size, were found to be associated with poor QoL, other factors like religion, caste, and annual income, were not found to be associated in the present study. The adjusted regression model revealed higher age to be associated with poor QoL. Other studies have also reported similar findings [29, 39]. However, a contrary observation was reported by Cruz et al. among Brazil’s population, where the age group 30–40 years was found to have most affected QoL in all the domains [40]. Aging is associated with both physical and mental changes, which, in turn, is thought to affect the QoL [29]. Literature suggests that, with age emotional sensitivity also increases due to factors like social loneliness, the burden of age-related disease, and the loss of close ones leading to decreased well-being among elderlies [41].

Though studies among elderlies from India have mostly found the physical domain to be severely affected [24], studies from developed nations like Japan have found no association between advanced age and poor physical QoL, indicating that increasing age may not necessarily reduce the QoL [42]. While old age increases one’s vulnerability to disorders and disabilities which, at times, may not be preventable, a healthy lifestyle, better healthcare facilities, including family physicians at the level of the primary healthcare system, and easy access to tertiary health care facilities when needed, may help reduce the burden of physical health conditions among elderlies. Further, social and emotional support programs should also be promoted in rural areas to improve psychological, social, and environmental well-being.

In sex-wise analysis, mean QoL scores of females in all domains (except physical health) as well as in the overall assessment, were found to be significantly lower than that of their male counterparts. Moreover, the adjusted regression model revealed females to be at a higher risk of poor QoL in the social, and psychological domain and also in the overall assessment compared to males. These observations are in concordance with previous studies [35, 43]. However, a contrasting finding has been reported from Japan, where women were found to have better QoL than men [44]. Poorer QoL among females than males could be due to different societal factors and cultural norms, such as the high prevalence of illiteracy among females in low- and middle-income countries including India, financial dependence, familial stress, lack of time for leisure activity and heavy work pressure, as women, in general, are expected to take up multiple responsibilities including household chores and farming work [43]. The recently published World Happiness Report 2024 also enables us to reflect on the findings of the present study. According to the report, among Indians, older age was associated with higher life satisfaction, and older women had higher life satisfaction than their male counterparts after adjusting for covariates. This suggests that age and gender alone may not be associated with reduced quality of life; however, other contributing factors may also play a role [45].

Further, marital status was found to be significantly associated with poor QoL in the social-relationship domain, where widow, widower, divorced, or unmarried participants were found to be having poorer QoL as compared to married participants. Similar findings have been reported in some of the previous studies [44, 46], where married individuals had better QoL than widowed/divorced/unmarried individuals. Being married, or having a companion has repeatedly been associated with better well-being [22]. Companionship helps in building dependable emotional and social networks, which is crucial for well-being and QoL [22].

Expectedly, illiterate participants in the present study were at a higher risk of poor QoL in physical health, psychological health, and environmental health domains than their literate counterparts. A similar observation, of better QoL with improved educational status, has been reported in previous studies [47], Good education, apart from being positively associated with better socioeconomic status, has been found to be independently associated with better health outcomes and well-being [48]. It is apt to discuss a closely related factor, employment status, at this point. Though unemployed participants were found to have significantly lower mean QoL scores in all the domains than employed participants, adjusted regression analysis revealed no significant association between employment status and QoL in the present study. These findings are largely in contradiction to other studies [29, 49]. This can be due to the fact that the majority of the participants in the present study are farmers, who may be seasonally unemployed but may have other sources of income. Together these observations suggest that more than employment status, education may play a role in ensuring better well-being in farming communities.

This proposition is further strengthened by another finding of the present study. Rather surprisingly, average annual income was also not found to be associated with QoL. Again, this observation is also in contradiction to a previous report [50], yet underscores the point that indicators of QoL in a rural farming community are likely to be different from urban and non-agricultural communities. In this study population, the majority of participants had attained formal education only up to the secondary school level, which may not have substantial employment benefits, yet even this level of education appears to play a protective role against poor QoL. Overall, this discussion highlights that even though education may not translate into non-farming or high-paid jobs, it is essential for better QoL.

Lastly, the present study revealed that participants from a larger family (of 6–10 members) had a significantly reduced risk of poor QoL than those from a smaller family (of five or fewer members). This result indicates that family plays a crucial role in modulating psychological and social well-being. The role of family size in QoL in rural communities should be further investigated.

When interpreting the result, it is important to take into consideration the study’s limitations. This is a cross-sectional study therefore it is limited to assessing the association, rather than causality between QoL and sociodemographic factors.

5 Conclusions

In the present study, the most affected QoL was in the physical domain, followed by psychological, social-relationship, and environmental domains. Adjusted regression analysis revealed female sex, advanced age (age group ≥ 60 years), illiteracy, and unmarried status to be positively associated and family size of 6–10 members to be negatively associated with poor QoL. The present study indicates that occupation and family income may not be directly associated with QoL; however, education appears to play an important role. This suggests that even though education may not be translating into non-farming jobs or monetary advances, it remains a significant contributor to better QoL.