Association Between Multimorbidity and Quality of Life Among Older Adults in Community-Dwelling of Uttar Pradesh, India

Abstract

Background

The rise in life expectancy and the share of older population represents the most significant demographic transformation in the twenty-first century. Increasing longevity with the coexistence of chronic multimorbidity makes the elderly population vulnerable to functional limitation, disability and more frequent hospitalization, resulting deterioration in QoL. The present study aims to investigate the association between non-communicable disease (NCD) multimorbidity and QoL among the older population in Varanasi, India.

Methods

A cross-sectional data of 500 individuals in the Varanasi district aged 50 + years were collected, using a multistage simple random sampling procedure from November 2017 to May 2018. WHOQOL-BREF was utilised to assess the quality of life of the study participants, and the important covariates used in the analysis are; age, sex, marital status, place of residence, health factors, socioeconomic status, and behavioral risk factors. Descriptive analysis was performed to assess the mean QoL score pattern, whereas multivariate linear regression analysis examines the association between multimorbidity and QoL.

Results

The QoL scores decreased with age and was higher among females. Regression results show that demographic and lifestyle risk factors are closely associated with QoL. multimorbidity was significantly associated with reduced quality of life. Older adults with multimorbidity had 5 points lower quality of life than those with no chronic diseases.

Conclusions

Multimorbidity along with demographic and lifestyle factors are significantly associated with QoL. Healthcare programmes need to factor in multimorbidity while promoting a healthy and risk-free lifestyle to control modifiable risk factors. Government assistance is necessary for the most economically dependent older population for their day-to-day needs.

Introduction

The rising share of the older population is one of the significant demographic transformations of the twenty-first century, reflecting the progress in human development. The changing longevity and age-sex structures resulted in a higher dependency ratio [1] and a rising prevalence of chronic diseases [2]. In 2012, on World Health Day, WHO stated as 'Good health adds life to years, highlighting the importance of QoL in later life. The most significant public health challenge of increasing longevity is improving the quality of life (QoL) in later years. In other words, add life to years.

One of the key challenges with ageing is the rising prevalence of chronic non-communicable diseases [2]. Chronic diseases are strongly associated with hospitalization [3], higher healthcare costs [4], and functional limitations and poor mental health [5]. The growing burden of multimorbidity in the particular context of NCD poses several challenges at individual, community, and health system levels [6]. However, the implications are profound at the individual level, including impaired physical and social functioning [7] and poor QoL [3] as well as elder abuse [8]. The coexistence of multimorbidity makes the elderly prey to functional limitation, disability, and frequent hospitalization, resulting in QoL deterioration. This association between QoL and age is well established in many studies, where QoL tends to decline as the individual grows old [9]. Studies show that family structure has changed due to modernization and urbanization, resulting in more psychological problems for the elderly. The combined effect of ageing, disease and social changes is likely to negatively impact the health and wellbeing of the elderly [10, 11]. Concurrent empirical studies suggest the impact of socioeconomic status on the physical environment and, subsequently, health outcomes [5, 12].

A growing body of literature has investigated the QoL of the elderly in the developed countries as the QoL is a subjective phenomenon and cannot be directly measured. Research on QoL remains neglected in developing countries and in low resource setting. Very few studies have been conducted within India in understanding the association between multimorbidity and QoL in the geriatric population [1, 13–16]. However, very few studies have examined the association between multimorbidity and quality of life in the state of Uttar Pradesh. These gaps suggest the need to study QoL and its cofounding factors among the older adult population for effective intervention measures to enhance QoL at the community level. From this background, the objective of the present research is to study the association between NCD multimorbidity and the QoL among the Varanasi district of Uttar Pradesh

Methods

Sampling design

For the present study primary data was collected in Varanasi district of Uttar Pradesh state, India, from December 2017 to May 2018. The data used in the study is cross-sectional, and a multistage simple random sampling procedure was adopted. In the first stage, three blocks were selected out of eight blocks. Three primary sampling units were randomly selected at the second stage from each block, keeping urban and rural composition in view. Further, the third stage adopted a proportional allocation procedure to obtain the desired number of households from every primary sampling unit (PSU). We prepared a list of households with at least one respondent age 50 and above through house listing. Using a systematic simple random sampling method, we selected the targeted number of households, and from every selected household, one older adult age 50 and above was selected for the survey. Respondents with severe cognitive impairment or physical impairment were excluded from the study.

Study area

For the current study, the Varanasi district of Uttar Pradesh state was selected, with   demographic and socioeconomic indicators similar to the state. Varanasi is located in the south-eastern part of Uttar Pradesh, with a population of 3.6 million. The older adult population aged 50 years and above  constituted 14.6%  which is relatively higher than the state (13.9%) in the 2011 census [17]. Varanasi district has  1.84% of the total population of Uttar Pradesh, with a considerable proportion of the urban population  (43%), which is again higher than the state average of 22%. 

Sample size determination

In the absence of morbidity prevalence data for the study area, we took the estimated morbidity prevalence rate of Uttar Pradesh from the WHO-SAGE survey as a proxy for the Varanasi district. The state-level prevalence of multimorbidity was 16.8 % for the older population age 50 and above. Allowing 20% non-response rate,   95 % confidence level, 5 % margin of error with and 1.75% design effect, the estimated sample size was 451. The final sample size of the study was 500.

Outcome variable

Quality of Life

In the present study, the QoL (overall wellbeing) is the outcome variable. The WHOQOL-BREF questionnaire has shown a good, reliable and valid cross-cultural measure for assessing QoL [18]. The WHO-BREF consists of four significant domains, namely- physical, psychological, social relation and environment. The four domain consists of 24 questions. Two additional questions were asked related to overall health and QoL.

For the present analysis, we  used two sets of QoL measures. The first set, including domain-specific QoL, is based on 24 questions and a second index based on 26 items. The WHOQOL-BREF is a combination of both positive and negative items. The positive items include happiness, content, and energy, whereas the negative items include sadness, sexual difficulty, and pain [19]. The scoring method of domain-specific QoL is presented in the supplementary table  at the end of the paper. However, the detailed methodology of WHOQOL-BREF is given elsewhere [20]. The second set outcome variable, which was the overall quality of life, was created from all 26-items. Using the additive method, a raw score was generated, and this raw QoL score rescales on 0-100, where higher values present a higher QoL score.

Predictor variables

Three sets of independent variables were used to assess their effects on QoL, namely demographi, health, socioeconomic factors and lifestyle factors. 

1. Demographic and health factors included are: a) Number of disease (no disease, one disease & two or more diseases), b) Self-rated health (good SRH & poor SRH), c) Age (50-59, 60-69, & 70+), d) Sex (male & female), and e) marital status (currently married & widowed).
2. The socioeconomic factors included are: a) years of schooling (illiterate, 1-5 years, 6-9 years and 10 and above), b) religion (Hindu & Muslim), c) caste (Scheduled Caste (SC)/Scheduled Tribes (ST), Other Backward Class (OBC), & others), d) residence (rural & urban), e) wealth index (poor, middle, & rich), and f) economic independence (totally dependent, partially dependent & independent). 
3. The lifestyle and health factors included are:

a) physical activity based on the WHO recommendation of physical activity which included either 75 minutes of vigorous activity or 150 minutes of moderate physical activity in a week as sufficient physical activity and those failed kept as doing insufficient physical activity [21]

b) sedentary behavior marked  those having more than 4 hours of a day on sitting in leisure time activity (yes & no).

Ethical considerations

The study was approved by the Student Research Ethics Committee (SREC) of International Institute for Population Sciences (IIPS), Mumbai (Sr.No. 15/1843, Date:13/10/2017). All respondents provided informed consent and were informed that they could withdraw from the study at any stage.

Data analysis

Descriptive statistics, mean proportions with standard deviations and Cronbach alpha were used to present the sample characteristics of the study population. Inferential statistics like t-test and variance analysis (ANOVA) were used to test differences in the mean QoL score of multimorbidity and sociodemographic measures. Multivariate linear regression analysis was performed to study the association of chronic multimorbidity and sociodemographic measures with QoL among older adults. All the results were presented with the beta coefficient. A p-value <0.05 was considered as the level of significance. All analysis was performed with STATA  14.2 software [22].

Results

Domain Wise Quality Of Life Among Older Adults

Table 2 presents the mean WHOQOL –BREF domain scores of the study population. The mean physical health score was the highest for the psychological health domain (59.1), followed by physical health domain (56.9) and the environment domain (54.5) with the lowest mean domain score for social relationship (40.6). The Cronbach alpha value of 0.84 among the environment domain showed that the variables were mostly consistent with each other. The lowest Cronbach alpha value was found for the social relationship (α = 0.42) domain. The physical domain's alpha value was 0.77 and 0.82 for the psychological domain, which showed that the variables were consistent.

Domain-specific quality of life score (QoL) by living arrangement with interaction of marital status and sex

Table 3 shows domain-specific QoL score by living arrangement. Older adults living with their spouse and children had a higher mean score for physical, psychological, social and environmental domains, followed by older adults living with spouse only in all domains. For the physical domain, the mean score was 60 among those living with their spouse and children, whereas, among those living alone, the mean score was the lowest of 48.7. Similarly, for the psychological, social and environmental domains, the score was highest among older adults living with their spouse and children, followed by those living with their spouse. These findings show the importance of family and neighbourhood circumstances in determining the quality of life in old age.

Further, older adults without a spouse and living with children and others reported the lowest physical and social domain scores, demonstrating the spouse's role in their social life and the physical domain of quality of life. The lowest mean QoL’s score for the psychological domain was 49.5 among those staying alone. In contrast, the mean score was lowest in the social domain among those living with their children or others. The mean score for the environmental domain was 58.4 among those living with their spouse and children and was the lowest among those living alone (44.1).

Table 4 shows the domain-wise mean score of QoL by gender and marital status. Married males had a higher mean score for all given domains. whereas, widowed males reported a higher mean score for the physical domain than married females. Importantly, widowed women reported the lowest mean score for all four domains as well overall QoL score

Table 5 presents the domain-specific disability score by selected chronic diseases among the study population. The mean score for the physical domain was 56.9, and the lowest for hypertension (49). The mean score for the psychological domain was 59.9, which was higher among hypertensive respondents (56.1) and lower among those with COPD (40.3). Similarly, the social domain mean score was 40.6 with thelowest for those with asthma (32.8), and highest for those with arthritis (41.4). The mean environmental domain score was 54.5, where the lowest environmental domain score wasfor those with COPD (47.3) and the highest for those with arthritis (52.8). However, the differences were small in the overall QoL score by chronic conditions, except thosewith COPD and asthma (63.6) who reported lower quality of life scores.

Discussion

In this study, we used WHO-BREF for assessing the QoL among the study population. The overall mean QoL of 70.1 ± 13.3, is 10 points higher than QoL score reported in a previous study conducted in the Varanasi district's rural area [23]. However, a study conducted in Bangladesh reported a similar level of QoL among the elderly [24]. The inclusion of older adults aged 50 + instead of elderly population aged 60-plus may be part of the reason for higher overall mean QoL .

Our findings showed that chronic morbidity was significantly associated with low QoL scores among the elderly population. This finding is consistent with other studies conducted in India and other countries where the presence of chronic diseases were found significantly associated with lower quality of life [13, 23, 25–27].

The presence of chronic diseases has a significant impact on the individual on economic deterioriation and functional limitation, which is further associated with lower quality of life. Chronic multimorbidity are particularlyassociated with higher hospitalization [28], out-of-pocket expenditure, and other economic consequences that affect the individual's quality of life. Further, the association between multimorbidity and functional health is strongly evident [5] with detoriorating quality of life. In addition, multimorbidity is closely linked with elder abuse [8, 29] and care dependence [30, 31]. As a result, the elderly with multimorbidity are more likely to experience a lower quality of life.

The mean QoL score was higher among males and currently married, consistent with previous findings [23, 32–35]. Also, Barua et al. (2005) showed that currently married elderly had better QoL scores than those not currently married [26]. Living with a partner or spouse is important in protecting QoL old age. Age was found negatively associated with QoL, consistent with the studies [23, 36–38]. However, other studies conducted in higher-income countries showed insignificant association between age and QoL [39, 40].

The highest mean QoL score was observed for the psychological domain (56.9) and the lowest for the social domain. However, other studies showed that the social relationship domain score was higher than other QoL domain scores [23, 25, 26, 35]. A study conducted in Kerala reported a similar mean social domain score [16].

Living arrangements showed a significant role in determining QoL. Consistent with other studies, those living with a spouse had better QoL than others [16, 23, 41]. Married males than females had a higher mean score for all given domains. Interestingly, for the physical domain, widowers reported a higher mean score compared to married females. These gender differences can be traced to the Indian cultural context where women had limited access to finance with their engagement in household work, limiting their bond with other people than men.

In this study, education attainment showed a significant association with quality-of-life scores consistent with previous literature [23, 38, 42]. Similarly, the study results showed that those in the higher wealth quintile reported better QoL than those in lower wealth quintiles [23]. Our finding showed that respondents reporting financially independence had a much higher mean QoL score than those who financially depended on others for their day-to-day needs. Studies based on the NSSO 60th round showed similar results where economic independence was significantly associated with perceived good health [43, 44]. This may be due to economically independent people engaged with their occupation was less bothered about their minor health problem.

The role of physical activity was profound in determining the QoL. Consistent with the previous systematic review reported by Vagetti et al. [45], physical activity was positively and consistently associated with QoL. This can also be understood with the norm that promoting physical activity will impact beyond physical health WHO recommended that older adults should have engaged at least 150 minutes of moderate or 75 minutes of the vigorous level of physical activity for healthy ageing [21].

Limitations And Future Research

This study is cross-sectional and exploratory with self-reported responses on QoL and functional health likely to change over time. However, the least change may be reportedin respondent's morbidity pattern. Although this researcher followed standard study protocols in conducting this research work on a smaller sample, this work can only be generalized for the study area Varanasi of Uttar Pradesh but not for the district or state. However, the study can be extended to the other districts of Uttar Pradesh.

Some methodological consideration is needed to compare the data with other studies. To begin with, the definition of multimorbidity is heterogeneous, and there is diversity in including several chronic conditions, making it difficult to compare between studies [46, 47]. However, the simple definition, the presence of two or more chronic conditions is more common way to define multimorbidity [48]. Another important aspect is the collection of information regarding chronic conditions. Some studies use medical records to identify, whereas most researchers consider information reported by the respondent, also known as self-reported diagnosed [49, 50]. It is noteworthy that self-report data has a potential bias in underestimating prevalence, but it is the most viable method for population-based epidemiologic studies[47].

Conclusion

Prior research around the world has established a significant relationship between health conditions and the overall wellbeing of the population. Chronic multimorbidity (presence of two and more chronic health conditions) is not just a condition that belongs to old age but involves lifestyle and behavioral changes, that pre-emerge in adults' aged 45 in determining the quality of life in old age. This analysis also supports the association of multimorbidity and QoL among older adults. Multimorbidity was more prevalent among females as they live longer but aredependent on their family members for healthcare treatment of their multiple chronic diseases resulting in poor QoL. Improving health and quality life of the growing older population has emerged as a major policy goal with changing morbidity patterns. Policy efforts are needed to offer financial security to elderly females.

Similarly, lifestyle factors were found to have impacted QoL significantly, highlighting need for a campaign for creating awareness to promote physical activity. The Indian traditional exercise method like yoga and meditation should also be actively promoted to maintain both physical and mental health. Apart from this, a healthy diet and avoiding risky behavior such as smoking, alcohol consumption, and the least indulgence in sedentary behavior are importantfor a healthy life. Yoga and physical exercise are more appropriate during the Covid-19 pandemic, where weak immune and unbalanced metabolism play an essential factor in the risk of falling sick.

Abbreviations

Declarations

Ethics approval and consent to participate

The current work is based on the first author's PhD work, and the necessary permission was obtained from the Students Research Ethics Committee (SREC) of International Institute for Population Sciences (IIPS), Mumbai (Sr.No. 15/1843, Date:13/10/2017). All respondents in the study were given complete information about the survey's aim and procedure. The study was carried out in accordance with the guideline of the Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subject. The interview was conducted only after their requisite written or oral consent and were informed that they could withdraw from the study at any stage. The interview was conducted at a time that was convenient for the respondents. All information collected was kept confidential and used only for research purposes.

Consent for publication

Not applicable

Availability of data and material

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

No funding was received for the study.

Authors' contributions

H. contributed to the conceptualization, study design, data collection and analyzed the study data. H and YS contributed to extensive literature review and writing of the manuscript. H, PA and YS contributed to finalising the manuscript, subsequent revisions, and all authors have read and approved the final manuscript for submission. 

Acknowledgements

Not applicable

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