Skip to main content
Download PDF

Multimorbidity, health Literacy, and quality of life among older adults in an urban slum in India: a community-based cross-sectional study

BMC Public Health volume 24, Article number: 1833 (2024) Cite this article

Abstract

Background

India is experiencing a rising burden of chronic disease multimorbidity due to an aging population and epidemiological transition. Older adults residing in urban slums are especially vulnerable due to challenges in managing multimorbidity amid deprived living conditions. This study aimed to assess the prevalence of multimorbidity, associated health literacy, and quality of life impact in this population.

Methods

A community-based cross-sectional study was conducted among 800 adults aged ≥ 65 years in an urban slum in Gujarat, India. Data on sociodemographics, physical and mental health conditions, health literacy (HLS-SF-47), quality of life (Short Form-12 scale), and social determinants of health were collected. Multimorbidity is ≥ 2 physical or mental health conditions in one person.

Results

The prevalence of multimorbidity was 62.5% (500/800). Multimorbidity was significantly associated with lower physical component summary (PCS) and mental component summary (MCS) scores on the SF-12 (p < 0.001). After adjusting for sociodemographic variables, the odds ratio of 0.81 indicates that for every 1 unit increase in the health literacy score, the odds of having multimorbidity decrease by 19%. Older age within the older adult cohort (per year increase) was associated with greater odds of multimorbidity (AOR 1.05, 95% CI 1.02–1.09). Physical inactivity (AOR 1.68, 95% CI 1.027–2.77) and lack of social support (AOR 1.57, 95% CI 1.01–2.45) also increased the likelihood of multimorbidity.

Conclusion

There is a substantial burden of multimorbidity among urban slum dwellers aged ≥ 65 years in India, strongly linked to modifiable risk factors like poor health literacy and social determinants of health. Targeted interventions are essential to alleviate this disproportionate burden among urban slum older adults.

Peer Review reports

Introduction

Multimorbidity, defined as the coexistence of two or more chronic conditions in one person, is increasingly common among older adults globally [1]. The prevalence of multimorbidity increases with age and is greater in low- and middle-income countries than in high-income countries [2]. A multi-country population-based study stretching across low-, middle-, and high-income countries found that the prevalence of multimorbidity increases with age [3]. However, there was heterogeneity in the estimates based on setting, participant age group, and the number and type of chronic conditions included.

In India, the burden of multimorbidity is expected to rise dramatically due to the rapidly aging population coupled with the epidemiological transition from communicable to noncommunicable diseases [4]. Studies from both urban and rural parts of India have shown a high prevalence of multimorbidity among older adults, ranging from 55 to 83% [5, 6]. The most common chronic disase included hypertension, diabetes, heart disease, chronic respiratory conditions, musculoskeletal disorders, and mental health conditions.

With the accumulation of multiple chronic conditions, older adults are at increased risk of adverse health outcomes, including declines in physical and cognitive functioning, poor quality of life, and increased healthcare utilization [7,8,9]. Multimorbidity has been associated with lower health-related quality of life across different populations [10, 11]. An impaired quality of life leads to a loss of independence, social isolation, and greater demands on family members as caregivers [12].

In India, there are wide urban-rural and socioeconomic disparities in access to healthcare and social support systems for older adults with multimorbidity. Those living in urban slums are especially vulnerable due to poverty, substandard housing, lack of infrastructure, and barriers to healthcare access in these informal settlements [13]. The challenges of managing multimorbidity are greater for slum dwellers because of high out-of-pocket expenditures for health services and medications [14].

Despite the growing size of this vulnerable population, there is limited community-based data on the burden and impact of multimorbidity among older adults in urban slums in India. Most related studies have been conducted in community or hospital settings, with an underrepresentation of urban slum populations. Given their deprived living conditions and lack of social protection for healthcare, older slum dwellers likely experience a disproportionately greater burden of multimorbidity and related adverse consequences.

There is a need for representative data on the prevalence of multimorbidity and its relationship with health-related quality of life in this urban slum population. This approach can help identify high-risk groups and modifiable factors to inform targeted interventions and appropriate health services for multimorbidity management.

Health literacy, defined as the degree to which individuals can obtain, process, and understand basic health information needed to make appropriate decisions, is an important factor in the prevention and management of chronic diseases. However, there is limited data on the association between health literacy and multimorbidity burden, especially among vulnerable populations like urban slum dwellers in India.

Therefore, the present study aimed to (i) assess the prevalence of multimorbidity among older adults living in an urban slum, (ii) examine the association of multimorbidity with health literacy and quality of life in this population, (iii) identify high-risk groups based on sociodemographic factors. We hypothesized that multimorbidity would be highly prevalent in this population and associated with poor health literacy and quality of life. This is the first study, to our knowledge, that aims to assess health literacy related to multimorbidity among urban slum dwellers aged ≥ 65 years.

Methodology

Study design and setting

This was a community-based cross-sectional study conducted in an urban slum located in Gujarat between April 2023- Dec2023. This slum has a population of approximately 50,000 residing across 20 municipal wards. The residents belong predominantly to lower socioeconomic status groups and face challenges such as poverty, inadequate housing, poor sanitation, and limited access to healthcare service.

Sample size calculation

Considering a prevalence of 45% based on previous Indian studies [14], with 5% absolute precision and a design effect of 2 for the multistage sampling, the sample size was calculated using the formula:

n = Z2*P(1-P)/d2 *design effect

Where, Z = 1.96 at 95% confidence interval P = 45% = 0.45, d = 5% = 0.05 Design effect = 2

Plugging in the values: n = (1.96)2 × 0.45(1-0.45) / (0.05)2 × 2 n = 768, the final minimum sample size was estimated rounded up to be 800.

The sampling technique employed was a multistage random sampling approach. In the first stage, four out of the approximately 20 municipal wards in the urban slum area were randomly selected using the lottery method. Subsequently, systematic random sampling was applied in each of the four chosen wards to select households. A rough sketch map was used to guide the process, with every 5th household included in the sample. In the third stage, within each selected household, eligible individuals (aged ≥ 65 years) were listed, and one older adult was randomly chosen using the lottery method.

If the initially selected participant was not available at home after two visits, the next older adult from that household was approached. If no eligible individual was available in the selected household, the adjacent household was approached using the lottery method again.

The eligibility criteria included age ≥ 65 years, residence in the selected households, and providing informed consent, while the exclusion criteria included inability to communicate, bedridden, or unwilling to participate.

Data collection

In this research endeavor, a meticulously designed pretested interviewer-administered questionnaire served as the primary tool for data collection. The questionnaire elicited sociodemographic information from participants, providing insights into the studied population. Additionally, participants self-reported chronic conditions, providing information on health issue prevalence. The chronic conditions elicited included hypertension, diabetes, heart disease, stroke, chronic respiratory diseases, musculoskeletal disorders, neurological disorders, mental health conditions, cancer, and others. The questionnaire tool used to collect this data was adapted from validated instruments used in previous Indian and global studies [15] on multimorbidity. It was pretested in a pilot study for comprehensibility, validity, and reliability before use in this study. We have provided the final questionnaire as a supplementary file 1.

The Short Form-12 (SF-12) assessed health-related quality of life [16]. This widely recognized instrument evaluates physical and mental health. Furthermore, the 47-item Short Form Health Literacy Scale (HLS-SF-47) was administered to measure participants’ health literacy across different domains [17, 18]. Anthropometric data such as height, weight, and blood pressure were also recorded. Physical activity level: Assessed using the Global Physical Activity Questionnaire (GPAQ) [19]. Smoking status: Categorized as a non-smoker, current smoker, or former smoker. Alcohol use: Drinking frequency and number of drinks per occasion. Dietary patterns: Assessed using a food frequency questionnaire [20]. Social support/living arrangements: Measured using the Multidimensional Scale of Perceived Social Support (MSPSS) [21]. Healthcare access: Based on the distance to the nearest health facility and reported barriers to healthcare. (Limited healthcare access: Defined based on the distance to the nearest health facility (> 5 km) and self-reported barriers to healthcare access including lack of transportation, inability to pay fees, and lack of social support to attend appointments. Access was categorized as adequate if the nearest facility was within 5 km and no barriers were reported, moderately accessible if the facility was > 5 km but no other barriers, and limited access if the facility was > 5 km and participants reported ≥ 1 barrier).

To ensure high-quality data collection, all investigators and field workers were thoroughly trained in the study objectives, methodology, and use of the data collection tools. The training also emphasized building rapport, confidentiality, and ethical conduct throughout the data-gathering process. Data were checked regularly in the field for completeness and accuracy. Any unclear or missing responses were verified and corrected in the field itself by revisiting the concerned households.

Table 1 Measures used in the study
Full size table

Table 1 summarizes the key-dependent, independent, and covariate variables that were measured in the study, along with the tools used to assess each variable. The table categorizes the variables into dependent (multimorbidity), independent (health literacy, physical activity, social support, quality of life), and covariates including sociodemographic factors, health behaviors, and healthcare access.

Data analysis

The collected data were meticulously processed and analyzed using the Statistical Package for Social Sciences (SPSS version 26). Socioeconomic status was measured using the modified BG Prasad classification based on the consumer price index for the study year. This tool classifies SES into upper, upper middle, middle, lower middle, and lower categories based on the monthly per-capita income [22].

The prevalent disease clusters were identified based on the chronic condition combinations reported by the study participants with multimorbidity. The three most frequently occurring combinations were categorized as the common multimorbidity clusters in this population.

The SF-12 scale was analyzed by calculating the physical and mental component summary scores, which range from 0 to 100 with higher scores indicating better health-related quality of life. The physical and mental component scores were computed using standard scoring algorithms and compared between older adults with and without multimorbidity using the independent samples t-test.

Descriptive statistics were used to summarize the prevalence of multimorbidity and the pattern of chronic disease clusters in the study population. Bivariate analyses using t-tests and chi-square tests were conducted to compare health-related quality of life between older adults with and without multimorbidity.”

Multivariable logistic regression analysis was employed to identify factors associated with multimorbidity after adjusting for sociodemographic covariates. The model is represented by:

Log(p/1 − p) = b0 + b1 × 1 + b2 × 2 + …. + bpXp.

Where p is the probability of having multimorbidity, b0 is the constant, b1 to bp are regression coefficients, and X1 to Xp are explanatory variables including health literacy, physical activity, social support, and sociodemographic factors. The significance level was set at p < 0.05, ensuring a robust statistical threshold.

Ethical consideration

This study started after ethical clearance was obtained from the Institutional Ethics Committee. (REF No: 216/03/23). Written Informed consent was obtained first after the purpose of the study was explained, and participants were not obliged to answer any questions they did not like or were free to terminate the interview at any given time. Assurance was given that confidentiality concerning their information would be strictly maintained.

Results

Table 2 shows the sociodemographic characteristics of the 800 study participants. Frequencies and percentages are presented for the categories of age, sex, religion, marital status, education level, and socioeconomic status (SES).

Table 2 Sociodemographic characteristics of the study participants (n = 800)
Full size table

The prevalence of multimorbidity (defined as ≥ 2 chronic conditions) among the 800 study participants is presented in Table 3. Overall, 500 participants (62.5%) were found to have multimorbidity.

Table 3 Prevalence of multimorbidity (≥ 2 chronic conditions) among study participants (n = 800)
Full size table

Table 4 presents the prevalent disease clusters among older adults with multimorbidity in the urban slum setting. The most common combination was hypertension paired with diabetes, affecting 32% (160 participants) of those with multimorbidity. The second most frequent cluster was hypertension combined with osteoarthritis, observed in 24% (120 participants) of the multimorbid group. Other notable disease combinations included diabetes with heart disease (16%), respiratory disease with heart disease (12%), and depression with osteoarthritis (8%). Less common pairings were diabetes with stroke (4%) and heart disease with cancer (2%). Interestingly, a small proportion (2%) of participants exhibited a triad of conditions: hypertension, diabetes, and osteoarthritis. These findings highlight the complex interplay of chronic conditions in this population, with cardiovascular and metabolic disorders frequently co-occurring. The prevalence of these specific disease clusters underscores the need for integrated care approaches that address multiple chronic conditions simultaneously in older adults residing in urban slums.

Table 4 Prevalent disease clusters among older adults with multimorbidity (n = 500)
Full size table

Table 5 presents the comparison of health-related quality of life, as measured by the SF-12 physical and mental component summary scores, and health literacy, as measured by the HLS-SF-47, between older adults with and without multimorbidity. The mean SF-12 physical component score was significantly lower for those with multimorbidity (39.7 ± 6.5) compared to those without multimorbidity (42.5 ± 5.2), with a p-value < 0.001. Similarly, the mean SF-12 mental component score was significantly lower for the multimorbidity group (45.3 ± 8.9) versus the non-multimorbidity group (49.2 ± 7.1), with p < 0.001. This indicates that the presence of multimorbidity was associated with poorer physical and mental quality of life. For health literacy, the multimorbidity group had a significantly lower mean HLS-SF-47 score (24.7 ± 6.2) than the non-multimorbidity group (32.1 ± 5.8), p < 0.001, showing that increased multimorbidity was correlated with lower health literacy. Additionally, the multimorbidity group had significantly lower social support, as measured by lower mean MSPSS scores (60.3 ± 13.5) compared to the non-multimorbidity group (71.5 ± 11.2), p < 0.001. Overall, Table 5 highlights the significant negative impact of multimorbidity on health-related quality of life across physical, mental, and social health domains in this older adult urban slum population.

Table 5 Comparison of health-related quality of life (SF-12), health literacy (HLS-SF-47), and Social Support (MSPSS scale) between older adults with and without multimorbidity
Full size table

Table 6 shows the Pearson correlation between health literacy (HLS-SF-47) and quality-of-life scores on the SF-12 scale.

Table 6 Correlation between health-related quality of life and health literacy
Full size table

Table 7 shows the Multivariable logistic regression analysis was conducted to identify factors associated with multimorbidity. Older age (per year increase) was associated with greater odds of multimorbidity (AOR 1.05, 95% CI 1.02–1.09). Female gender (AOR 1.86, 95% CI 1.12–3.08), widowed status (AOR 2.05, 95% CI 1.15–3.65), no formal education (AOR 3.12, 95% CI 1.52–6.41), lower socioeconomic status (AOR 2.35, 95% CI 1.22–4.52), being a current smoker (AOR 2.35, 95% CI 1.67–3.46) or former smoker (AOR 2.15, 95% CI 1.59–4.23), physical inactivity (AOR 1.68, 95% CI 1.027–2.77), and lack of social support (AOR 1.57, 95% CI 1.01–2.45) also increased the likelihood of multimorbidity. For every 1 unit increase in the health literacy score, the odds of having multimorbidity decrease by 19% (AOR 0.81, 95% CI 0.78–0.91). Additionally, limited healthcare access was associated with higher odds of multimorbidity (AOR 2.49, 95% CI 1.88–4.27).

Table 7 Factors associated with multimorbidity among study participants according to multivariable logistic regression analysis
Full size table

In summary, the odds of multimorbidity were positively associated with older age, female sex, being widowed, lower levels of education, smoking, physical inactivity, lack of social support, lack of health literacy, and limited access to healthcare. The analysis highlights the impact of sociodemographic disparities on multimorbidity risk in the study population. Targeted interventions to address modifiable risk factors like health literacy and social support may help reduce the burden of multimorbidity among vulnerable older adults.

Discussion

In this community-based cross-sectional study, we found a high prevalence of multimorbidity (≥ 2 chronic conditions) affecting more than 60% of older adults residing in urban slum areas. The most prevalent chronic disase were hypertension, diabetes, musculoskeletal disorders, respiratory diseases, and mental health issues. Multimorbidity was significantly associated with lower quality of life, with older adults reporting poorer physical and mental health on the SF-12 scale.

Our findings on the high burden of multimorbidity align with those of previous studies in India, which reported a prevalence ranging from 55 to 65% among community-dwelling older adults in urban slums [23, 24]. The pattern of common chronic conditions observed in this urban slum population also conforms to the epidemiological transition underway in urban regions [25]. With continuing demographic and lifestyle changes, India is facing escalating burdens of noncommunicable diseases manifesting as multimorbidity among its rapidly growing older adult population.

The two most prevalent clusters were hypertension paired with diabetes (in 80 participants, 32%) and hypertension paired with osteoarthritis (in 60 participants, 24%).

These patterns align with multimorbidity data from previous studies in India that also noted hypertension, diabetes, cardiovascular disease, and musculoskeletal disorders as the predominant co-occurring chronic conditions among older adults [26,27,28].

The high prevalence of certain clusters emphasizes the need to strengthen the integrated screening and management of comorbid conditions such as diabetes and hypertension that tend to coexist and negatively impact outcomes. Tackling common modifiable risk factors and addressing disease combinations through a patient-centered approach can help reduce the burden of multimorbidity as the population ages.

The strong inverse association between multimorbidity and quality of life is consistent with reports across diverse global settings [29, 30]. Managing multiple chronic conditions simultaneously has a detrimental additive effect on physical capacities, psychological well-being, social relationships, and independence in daily living. Multimorbidity also results in complex healthcare needs and polypharmacy, which older adults in resource-constrained slums are ill-equipped to handle. Their poor living conditions, limited access to health services, and lack of social protection exacerbate the challenges of multimorbidity.

A key finding was the high prevalence of inadequate health literacy associated with multimorbidity. After adjusting for sociodemographic variables, the odds ratio of 0.81 indicates that for every 1 unit increase in the health literacy score, the odds of having multimorbidity decrease by 19%. This finding aligns with prior research showing that health literacy is an independent predictor of multimorbidity (31–32). Low health literacy can impede the self-management of chronic diseases, medication adherence, and utilization of preventive services [33]. Enhancing health literacy through community education and capacity building may help reduce the risk and effects of multimorbidity among vulnerable older adult people.

The Multivariable regression analysis showed that inadequate health literacy, lack of physical activity, and lack of social support were significantly associated with a higher likelihood of multimorbidity in this urban slum population. These findings align with prior studies demonstrating the role of health literacy and social determinants in multimorbidity risk. A systematic review found that low health literacy was associated with greater multimorbidity prevalence in several studies [34]. Other research has linked social isolation and poorer social support with an increased number of chronic conditions among older adults [35, 36]. Finally, a cohort study in Brazil concluded that insufficient physical activity was predictive of developing multimorbidity over a 2-year follow-up period [37]. Taken together, these modifiable factors related to health behaviors, capacities, and social environment appear to contribute significantly to the development of multimorbidity, even after accounting for sociodemographic characteristics.

Our study provides representative data on the prevalence of multimorbidity and its impact on quality of life, specifically among older residents of an Indian urban slum - an underserved population often excluded from national health surveys. This highlights the disproportionate multimorbidity burden imposed by socioeconomically marginalized older adult groups dwelling in informal settlements. The much higher prevalence compared to rural counterparts implies urban slum conditions like concentrated poverty, lack of infrastructure, and constrained healthcare access could exacerbate the development of multiple chronic illnesses.

The cumulative out-of-pocket expenditure for healthcare poses catastrophic financial risks to low-income slum households already struggling to meet daily necessities. Older adults with multimorbidity likely face prohibitive barriers in affording treatment and medications over long periods. Many are forced to choose between healthcare costs and other basic needs, which further worsens their disease prognosis and quality of life. Their social vulnerability and lack of financial protection mechanisms make it difficult to effectively manage complex healthcare needs arising from multimorbidity.

Targeted interventions to alleviate the multimorbidity burden among the urban poor should address context-specific social determinants of health-spanning living conditions, access to health services, community awareness, and support systems. Health policies must recognize deprived urban groups and provide tailored financial risk protection alongside prevention and screening initiatives.

Limitations of this study include the cross-sectional design, which restricts causal inference about the association between multimorbidity and quality of life. The reliance on self-reported diagnoses of chronic conditions could result in underreporting. We selected only one slum area, which may limit the generalizability of the findings to other urban slums that differ substantially in their demographic composition and health profiles. Nonetheless, the study provides novel insights into the vulnerability of older slum dwellers to multimorbidity and its adverse effects.

Recommendations

  • Integrated screening and management programs for multimorbidity should be implemented in urban slums targeting older adults.

  • Affordable primary care and geriatric services need to be made accessible within slum settings.

  • Public health policies and interventions must address social determinants such as education, financial security, and living conditions in slums.

  • Family members and caregivers of older adults with multimorbidity require training and support.

  • Community awareness of healthy lifestyles, preventive behaviors, and self-care should be created.

  • Health literacy of older adults in urban slums should be improved through community education programs.

  • Social support systems and financial protection mechanisms are needed for vulnerable older adult groups.

Conclusion

Multimorbidity among older adults in urban slums requires urgent policy attention and action. A multipronged strategy should focus on both preventive and management aspects, spanning health promotion, community-based screening, affordable primary care, geriatric services, and social assistance. Tackling socioeconomic deprivation alongside lifestyle risks and timely disease management can help reduce the multimorbidity burden and improve the quality of life among marginalized older adult people in urban India. The high prevalence of inadequate health literacy associated with multimorbidity suggests low health awareness and self-care capacities among urban older adult slum dwellers. Targeted interventions to improve health literacy through community outreach, patient education, simplified treatment guidelines, and capacity building of family caregivers are essential.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

HLS-SF-47:

Short Form Health Literacy Scale

SF-12:

Short Form-12

References

  1. Fortin M, Stewart M, Poitras M-E, Almirall J, Maddocks H. A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Annals Family Med. 2012;10(2):142–51. https://doi.org/10.1370/afm.1337.

    Article  Google Scholar 

  2. Afshar S, Roderick PJ, Kowal P, Dimitrov BD, Hill AG. Multimorbidity and the inequalities of global aging: a cross-sectional study of 28 countries using the World health surveys. BMC Public Health. 2015;15(1). https://doi.org/10.1186/s12889-015-2008-7.

  3. Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, Lara E, Koskinen S, Tobiasz-Adamczyk B, Ayuso-Mateos JL, Haro JM. Global multimorbidity patterns: a cross-sectional, Population-Based, Multi-country Study. Journals Gerontol Ser A: Biol Sci Med Sci. 2016;71(2):205–14. https://doi.org/10.1093/gerona/glv128.

    Article  Google Scholar 

  4. Barik M, Panda SN, Tripathy SS, Sinha A, Ghosal S, Acharya AS, Kanungo S. Is multimorbidity associated with a higher risk of falls among older adults in India? BMC Geriatr. 2022;22(1). https://doi.org/10.1186/s12877-022-03158-5.

  5. Chauhan S, Srivastava S, Kumar P, Patel R. Decomposing urban-rural differences in multimorbidity among older adults in India: a study based on LASI data. BMC Public Health. 2022;22(1). https://doi.org/10.1186/s12889-022-12878-7.

  6. Patel P, Muhammad T, Sahoo H. The burden of disease-specific multimorbidity among older adults in India and its states: evidence from LASI. BMC Geriatr. 2023;23(1). https://doi.org/10.1186/s12877-023-03728-1.

  7. Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, Meinow B, Fratiglioni L. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev. 2011;10(4):430–9. https://doi.org/10.1016/j.arr.2011.03.003.

    Article  PubMed  Google Scholar 

  8. Ansari S. Multimorbidity and Depression among Older Adults in India: Mediating Role of Functional and Behavioural Health. PLOS ONE, vol. 17, no. 6, 2022, p. e0269646, https://doi.org/10.1371/journal.pone.0269646. Accessed 14 Feb. 2024.

  9. Ansari S, Muhammad T, Dhar M. How does Multi-morbidity relate to feelings of loneliness among older adults? Evidence from a Population-based survey in India. Popul Ageing. 2023;16:45–66. https://doi.org/10.1007/s12062-021-09343-5.

    Article  Google Scholar 

  10. Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004;2(1):51. https://doi.org/10.1186/1477-7525-2-51.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Menotti A, Mulder I, Nissinen A, Giampaoli S, Feskens EJ, Kromhout D. Prevalence of morbidity and multimorbidity in elderly male populations and their impact on 10-year all-cause mortality: the FINE study (Finland, Italy, Netherlands, Elderly). J Clin Epidemiol. 2001;54(7):680–6. https://doi.org/10.1016/s0895-4356(00)00368-1.

    Article  CAS  PubMed  Google Scholar 

  12. Gu J, Chao J, Chen W, Xu H, Zhang R, He T, Deng L. Multimorbidity and health-related quality of life among the community-dwelling elderly: a longitudinal study. Arch Gerontol Geriatr. 2018;74:133–40. https://doi.org/10.1016/j.archger.2017.10.019.

    Article  PubMed  Google Scholar 

  13. Subbaraman R, Nolan L, Shitole T, Sawant K, Shitole S, Sood K, Nanarkar M, Ghannam J, Betancourt TS, Bloom DE, Patil-Deshmukh A. The psychological toll of slum living in Mumbai, India: a mixed methods study. Soc Sci Med. 2014;119:155–69. https://doi.org/10.1016/j.socscimed.2014.08.021.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sinha A, Kerketta S, Ghosal S, Kanungo S, Pati S. Multimorbidity among Urban Poor in India: findings from LASI, Wave-1. Front Public Health. 2022;10. https://doi.org/10.3389/fpubh.2022.881967.

  15. Nair M, Ali MK, Ajay VS, Shivashankar R, Mohan V, Pradeepa R, Deepa M, Khan HM, Kadir MM, Fatmi ZA, Reddy KS, Tandon N, Narayan KM, Prabhakaran D. CARRS Surveillance study: design and methods to assess burdens from multiple perspectives. BMC Public Health. 2012;12:701. https://doi.org/10.1186/1471-2458-12-701.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ware J Jr, Kosinski M, Keller SD. A 12-Item short-form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–33. https://doi.org/10.1097/00005650-199603000-00003.

    Article  PubMed  Google Scholar 

  17. Duong, T. V., Aringazina, А., Kayupova, G., Nurjanah, N., Pham, T. V., Pham, K. M.,… Chang, P. (2019). Development and validation of a new short-form health literacy instrument (HLS-SF12) for the general public in six Asian countries. HLRP: Health Literacy Research and Practice, 3(2). https://doi.org/10.3928/24748307-20190225-01.

  18. Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, Brand H. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health. 2012;12(1):1–13.

    Article  Google Scholar 

  19. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Activity Health. 2009;6(6):790–804.

    Article  Google Scholar 

  20. Cade J, Thompson R, Burley V, Warm D. Development, validation, and utilization of food-frequency questionnaires – a review. Public Health Nutr. 2002;5(4):567–87.

    Article  PubMed  Google Scholar 

  21. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30–41.

    Article  Google Scholar 

  22. Pentapati SS, Kumar, Dhrubajyoti J, Debnath. Updated BG Prasad’s classification for the year 2022. J Family Med Prim care vol. 2023;12(1):189–90. https://doi.org/10.4103/jfmpc.jfmpc_1478_22.

    Article  Google Scholar 

  23. Prenissl J et al. Aug. Patterns of multimorbidity in India: A nationally representative cross-sectional study of individuals aged 15 to 49 years. PLOS global public health vol. 2,8 e0000587. 17 2022, https://doi.org/10.1371/journal.pgph.0000587.

  24. Gummidi B, Gautam V, John O, Ghosh A, Jha V. Patterns of multimorbidity among a community-based cohort in rural India. J Multimorbidity Comorbidity. 2023. https://doi.org/10.1177_26335565221149623.

  25. Price JH et al. Racial/ethnic disparities in chronic diseases of youths and access to health care in the United States. BioMed research international vol. 2013 (2013): 787616. https://doi.org/10.1155/2013/787616.

  26. Puri P, Singh SK, Pati S. Original research: identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach. BMJ Open. 2022;12(7). https://doi.org/10.1136/bmjopen-2021-053981.

  27. Gummidi B, et al. Patterns of multimorbidity among a community-based cohort in rural India. J Multimorbidity Comorbidity vol. Jan. 2023;13 26335565221149623(2). https://doi.org/10.1177/26335565221149623.

  28. Ansari S, Anand A, Hossain B. Exploring multimorbidity clusters in relation to healthcare use and its impact on self-rated health among older people in India. PLOS Global Public Health. 2023;3(12):e0002330. https://doi.org/10.1371/journal.pgph.0002330.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Nóbrega TC, Jaluul O, Machado AN, Paschoal SM, Jacob Filho W. Quality of life and multimorbidity of elderly outpatients. Clin (Sao Paulo Brazil). 2009;64(1):45–50. https://doi.org/10.1590/s1807-59322009000100009.

    Article  Google Scholar 

  30. Valderas JM, Gangannagaripalli J, Nolte E, Boyd CM, Roland M, Sarria-Santamera A, Jones E, Rijken M. Quality of care assessment for people with multimorbidity. J Intern Med. 2019;285(3):289–300. https://doi.org/10.1111/joim.12881.

    Article  CAS  PubMed  Google Scholar 

  31. Yasmin F, Banu B, Zakir SM, Sauerborn R, Ali L, Souares A. The positive influence of short message service and voice call interventions on adherence and health outcomes in case of chronic disease care: a systematic review. BMC Med Inf Decis Mak. 2016;16(1):46. https://doi.org/10.1186/s12911-016-0286-3.

    Article  CAS  Google Scholar 

  32. Geboers B, Reijneveld SA, Jansen C, Winter AF. d. (2016). Health literacy is associated with health behaviors and social factors among older adults: results from the lifelines cohort study. Journal of Health Communication, 21(sup2), 45–53. https://doi.org/10.1080/10810730.2016.1201174.

  33. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97–107. https://doi.org/10.7326/0003-4819-155-2-201107190-00005.

    Article  PubMed  Google Scholar 

  34. Mantwill S, Monestel-Umaña S, Schulz PJ. The relationship between Health Literacy and Health Disparities: a systematic review. PLoS ONE. 2015;10(12):e0145455. https://doi.org/10.1371/journal.pone.0145455.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. LeBlanc RG, Jacelon CS. Self-care among older people living with chronic conditions. Int J Older People Nurs. 2018;13(3):e12191. https://doi.org/10.1111/opn.12191.

    Article  PubMed  Google Scholar 

  36. De Maria M, Tagliabue S, Ausili D, Vellone E, Matarese M. Perceived social support and health-related quality of life in older adults who have multiple chronic conditions and their caregivers: a dyadic analysis. Soc Sci Med. 2020;262:113193. https://doi.org/10.1016/j.socscimed.2020.113193.

    Article  PubMed  Google Scholar 

  37. Carvalho JN, Roncalli ÂG, Cancela MC, Souza DL. Prevalence of multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS ONE. 2017;12(4):e0174322. https://doi.org/10.1371/journal.pone.0174322.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge and are grateful to all the patients who contributed to the collection of data for this study. We are also thankful to Dr. Nandini Desai (Dean and Chairperson of MDRU), Dr. Dipesh Parmar (Professor and Head, the Department of Community Medicine), Shri M P Shah Government Medical College, Jamnagar, India.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Community Medicine, Shri M P Shah Government Medical College, Jamnagar, Gujarat, India

    M. Yogesh, Naresh Makwana, Nidhi Trivedi & Naresh Damor

Authors
  1. M. Yogesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naresh Makwana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nidhi Trivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naresh Damor
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YM contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, NM, ND, and NT contributed to the conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, NM, ND, and NT contributed to the methodology, resources, supervision, validation, and writing (review and editing). YM, NM, ND, and NT contributed to the formal analysis, investigation, writing (original draft), and writing (review and editing). All the authors read and approved the final manuscript.

Corresponding author

Correspondence to M. Yogesh.

Ethics declarations

Ethics approval and consent to participate

Good clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008.

All the participants were given clear instructions about the study before the start of the study.

Written informed consent was obtained from the patients in the vernacular language for study participation. No identifying information or images have been included in the original article, which was submitted for publication in an online open-access publication.

The entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India.

An ethical approval was obtained from the institute (Shri M P Shah Government Medical College, Jamnagar, Gujarat, India) before the start of the study. (REF No: 216/03/23)

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yogesh, M., Makwana, N., Trivedi, N. et al. Multimorbidity, health Literacy, and quality of life among older adults in an urban slum in India: a community-based cross-sectional study. BMC Public Health 24, 1833 (2024). https://doi.org/10.1186/s12889-024-19343-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-024-19343-7

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us