Original article| Volume 15, 101025, May 01, 2022

# Prevalence of multi-morbidities among older adults in India: Evidence from national Sample Survey organization, 2017-18

Open AccessPublished:March 29, 2022

## Abstract

This article describes the prevalence of multi-morbidity and its association with socioeconomic and demographic factors using National Sample Survey 2017-18 data, on 42756 older adults aged 60+. The prevalence of multi-morbidity is assessed as the count of self-reported morbidity among the older adults. Bivariate and logistic regression is estimated to examine the covariates of multi-morbidity. Overall, our study found that there was 277 older adults per 1000 population who reported their morbidity, co-morbidity and multi-morbidity. The prevalence of multi-morbidity was higher at a higher level of education, income and in urban areas. While a higher prevalence in multi-morbidity is observed among the other backward community compared to the forward caste. The multivariate analysis suggests that compared to individuals aged 70 years and above were more likely to report multi-morbidity as compared to individuals aged 60–64 years [OR:2.17; CI: 1.602–2.939]. In the economic groups, individuals belonging top 20% of the income groups has higher odds of reporting multi-morbidity as compared to those individuals belonging to the bottom 20% of the population [OR; 10.63, CL; 5.223–21.657]. Further, hypertension along with diabetes was the most common reported multi-morbidity. Policy initiatives aiming to reduce the burden of these multi-morbidities must focus on reorienting programme through increasing public investment in health. These investments must increase by providing preventive care facilities for the early prevention of diseases and improving the provision of geriatric curative services both in rural and urban areas as needed by the community in India.

## 1. Introduction

The pace of population ageing around the world is rapidly increasing. Estimation shows that by 2050, the world's population aged 60 years and older is expected to increase to a total of 2 billion, up from 900 million in 2015 which is proportionally doubled from 12% to 22%.
• WHO
World Report and Ageing on Health.
Further, by 2050, nearly 80% of older people would be living in low-and-middle-income countries (LMIC). The rapidly increasing the ageing population in these LMICs is much faster in the current decade than in the past including India. As per the 2011 census, nearly about 104 million persons were aged 60 years and above of which 53 million were females and 51 million were male. In 2011 the older adults' population was 8% of the total population and is expected to increase by 12.4% by 2026. The changing age structure of the Indian population, on the one hand, is having a substantial effect on health through an epidemiological transition where chronic illness has replaced infectious diseases-existence of multiple chronic illnesses and age-related health issues have a severe burden on healthcare.
However, the empirical pieces of evidence suggest that in most developing countries, older adults continued to have barriers in seeking healthcare services.
• WHO
World Report and Ageing on Health.
• Castelli R.
• Schiavon R.
• Lambertenghi G.
The impact of anaemia , transfusion dependency , comorbidities and polypharmacy in elderly patients with low - risk myelodysplastic syndromes.
• Marmot M.
• Allen J.J.
Social determinants of health equity.
• Rajesh Kumar R.A.I.
• Kumar C.
• Singh P.K.
• Singh L.
• Barik A.
• Chowdhury A.
Incidence of prehypertension and hypertension in rural India, 2012-2018: a sex-stratified population based prospective cohort study.
Thus due to poor health care access leading to under-utilization leading to services may further severely affect their health vulnerability.
• Marmot M.
• Allen J.J.
Social determinants of health equity.
,
• Sathya T.
• Nagarajan R.
• Selvamani Y.
Multimorbidity as a risk factor of elder abuse/mistreatment in India : a cross-sectional study.
,
• Selvamani Y.
• Singh P.
Socioeconomic patterns of underweight and its association with self-rated health , cognition and quality of life among older adults in India.
In this way, the high prevalence of disease patterns among older adults put them at high risks and severely affected their health status.
• Anushree K.N.
Inequalities in health outcomes: evidence from NSS data.
• Arokiasamy P.
• Uttamacharya
• Jain K.
Multi-morbidity, functional limitations, and self-rated health among older adults in India: cross-sectional analysis of LASI pilot survey, 2010.
• Pati S.
• Swain S.
• Hussain M.A.
• Salisbury C.
Prevalence, correlates, and outcomes of multimorbidity among patients attending primary care in Odisha, India.
For instance, A study by
• Arokiasamy P.
• Uttamacharya
• Jain K.
Multi-morbidity, functional limitations, and self-rated health among older adults in India: cross-sectional analysis of LASI pilot survey, 2010.
results suggest a larger prevalence of multi-morbidity, limitations in ADL and poor self-rated health with higher state variations. Further, the study also found that the educational status of older adults was negatively associated with the prevalence of ADL limitations and poor self-rated health. While the prevalence of multimorbidity was higher at a higher level of education, wealth, and caste. Similarly,
• Pati S.
• Swain S.
• Hussain M.A.
• Salisbury C.
Prevalence, correlates, and outcomes of multimorbidity among patients attending primary care in Odisha, India.
in their study found that higher females, individuals from higher income groups had higher odds of reporting multimorbidity as compared to their counterparts. At the same time, socio-economic inequality was also found to be more significant with violence against older adults
• Sinha D.
• Mishra P.S.
• Srivastava S.
• Kumar P.
Socio-economic inequality in the prevalence of violence against older adults–findings from India.
The results of the primary study conducted by
• Jeemon P.
• Rohini C.
Prevalence and patterns of multi-morbidity in the productive age group of 30-69 years: a cross-sectional study in Pathanamthitta District, Kerala.
in Pathanamthitta District of Kerala suggests that multimorbidity was observed in one of two participants in the age group 30–69 years and diabetes with hypertension was the most common multi-morbidity observed among the study population. Apart from these non-communicable diseases the older adutls also suffer from age-related health issues such as includes hearing loss, cataracts and refractive errors, back and neck pain, etc. Thus, the existing evidence suggests a higher prevalence of multimorbidity among the older adults in India. However, these studies are mostly small area studies lacking generalization for the entire population of the country. Thus, keeping in view the existing gap the current study examines levels of multi-morbidity prevalence among older adults using representative data.

## 2. Data and methods

### 2.1 Data

The present study uses the unit-level data collected by the National Sample Survey Organization conducted in 2017–18. The 75th round-Social Consumption health round is a nationally representative large-scale survey comprising 1,13,823 households and 5,55352 individuals. However, the current study focuses only on the older (60 years and above) adults. Thus, this article analysis information on about 42,756 older adults with the main focus on multi-morbidity prevalence (Fig. 1). The survey has collected information on socioeconomic and demographic characteristics of the household, reproductive and child healthcare utilization, fertility, mortality, family planning methods, self-reported morbidity profiles of the individuals, health care use and related healthcare expenditure, death-related information and lastly on conditions and problems of the aged. Two stages stratified sampling technique was used to collect samples with census villages and urban blocks as the First Stage Units (FSUs) in rural and urban areas respectively and households as the Second Stage Units. A detailed description of the sampling design and survey procedure is stated in the NSS 75th Round on Social Consumption- Health report.

## 3. Methods

Overall morbidity prevalence and multi-morbidity prevalence was calculated per 1000 population. Formula (i) was used to calculate overall morbidity prevalence; while formula (ii) was used to calculate multimorbidity prevalence.
$Equation 1.$
(1)

where.
• $Ai$ = Number of ailing persons/Number of persons reporting multi-morbidity
• $Pi$ = Total number of persons alive in the sample households.
Further, to understand the determinants of multi-morbidity binary logistic regression analysis were performed, since the dependent variable is a dichotomous variable (yes/no).
The logit model of multi-morbidity is defined as follows
$Equation 2.$
(2)

where p is the probability of the event and α is the intercept, βs are the regression coefficients, Xis are the predictors and $∈$ is the error term. The descriptive statistics of the variable used in the analysis are presented in (Table 1). The current study uses STATA 13 for analysis.
Table 1General frequency distribution of older adults India, 2017-18.
Source: Author's Calculation from 75th Round NSS Unit Level Data.
Total Number of CasesFrequency Distribution in Percentage
Age
60–64 years1512335.4
65–70 years1687639.5
71 years and above1075725.2
Gender
Male2190151.2
Female2085548.8
Education
Illiterate2489558.2
Primary835419.5
Secondary and above950722.2
Caste
Scheduled Tribe39139.2
Scheduled Caste613014.3
Other Backward Community1651638.6
Others (Forward Caste)1619737.9
Place of Residence
Rural2359355.2
Urban1916344.8
Income (Monthly Per Capita Consumption Expenditure)
Poorest654515.3
Poor697016.3
Middle894720.9
Rich962322.5
Richest1067125.0
Total42,756

## 4. Results

### 4.1 Prevalence of overall self-reported morbidity in India

The prevalence of overall self-reported morbidity (single morbidity + multi-morbidity) at all-India levels among the older adults was 277 per 1000 population. Gender differences in overall self-reported morbidity were observed with female older adults reporting marginally higher levels of overall morbidity compared to male counterparts (279 per 1000 population v/s 275 per 1000 population). Likewise, rural-urban differences in the prevalence of morbidity were observed with older adults persons from urban areas reporting higher levels of overall morbidity (340 per 1000 population v/s 246 per 1000 population) compared to older adults persons from rural areas (Fig. 2).

### 4.2 Prevalence of self-reported single and self-reported multi-morbidity in India

Further, at all Indian levels the prevalence of single self-reported morbidity and self–reported multi-morbidity among the older adults was 249 per 1000 population and 29 per 1000 population respectively. Spatial differences in reporting of single and multi-morbidity were observed in rural and urban areas with older adults from urban areas reporting 1.3 times and 1.9 times higher levels of single morbidity and multimorbidity compared to the rural counterparts respectively (Fig. 3). Statistically significant gender differences in reporting of single and multi-morbidity were observed (Table 2). Further, both single reported morbidity and multimorbidity prevalence was higher among those belonging to the Other caste (single reported morbidity 292 per 1000 population; 40 per 1000 population) as compared to older adults belonging to Scheduled Tribe (single reported morbidity 171 per 1000 population; 7 per 1000 population). Reporting of single reported morbidity and multi-morbidity among older adults increased with increasing level of education. The self–reported single morbidity prevalence rate was 1.9 times higher (337 per 1000 population v/s 172 per 1000 population) among the top 20% of the population as compared to the bottom 20% of the population. While, the self–reported multi-morbidity prevalence rate was 11.0 times higher (337 per 1000 population v/s 172 per 1000 population) among the top 20% of the population as compared to the bottom 20% of the population (Table 3) (see Fig. 4).
Table 2Single Self-Reported Morbidity Prevalence Rate and Self-Reported Multi Morbidity Prevalence Rate (per 1000 population) by Gender in India, 2017-18.
Source: Author's Calculation from 75th Round NSS Unit Level Data.
GenderReported Single MorbidityReported Multi Morbidityχ2
Male247286.804**
Female25029
Total24929
Table 3Single Self-Reported Morbidity Prevalence Rate and Self-Reported Multi Morbidity Prevalence Rate (per 1000 population) by Socio-economic and Demographic Characteristics in India, 2017-18.
Source: Author's Calculation from 75th Round NSS Unit Level Data.
FactorsSingle Self-Reported Morbidity PrevalenceSelf-Reported Multi Morbidity Prevalence
Age
60–64 years22022
65–70 years23824
71 years and above31148
Caste
Scheduled Tribe1717
Scheduled Caste23710
Other Backward Community23030
Others (Forward Caste)29240
Income (Monthly Per Capita Consumption Expenditure)
Poorest1726
Poor21513
Middle23020
Rich28938
Richest33766
Education
Illiterate23318
Primary27951
Secondary and Above27545

### 4.3 The pattern of self-reported single and multi-morbidity diseases across states in India

Variations in the prevalence of self-reported single and self-reported multi-morbidity were observed across Indian states and Union Territories (UTs). States and UTs were classified as high and low morbid states. States/UTs with morbidity prevalence higher than the national average were classified as a high morbid state while states with morbidity prevalence lower than the national average were classified as low morbid states. High morbid states and UTS (self-reported single morbidity) includes states such as West Bengal, Lakshadweep Andhra Pradesh, Kerala, Jammu and Kashmir, Andaman & Nicobar Islands; while low morbid states and UTS (self-reported single morbidity) includes states such as Karnataka, Tamil Nadu, all BIMARU states, Puducherry (Map1). On the other hand, in the states of West Bengal, Lakshadweep Andhra Pradesh, Kerala, and Himachal Pradesh the self-reported multi-morbidity prevalence was higher than the national average; while, in the rest of the states and UTs the self-reported multi-morbidity prevalence was lower than the national average (Map 2).

### 4.4 Emerging self-reported single morbidity disease pattern

Prevalence of higher levels of non-communicable disease (146 per 1000 population) was observed among those who reported single morbidity followed by other diseases (59 per 1000 population), and infections (42 per 1000 population). Differences in the prevalence of disease were observed between rural and urban areas with higher prevalence of infectious disease (46 per 1000 population v/s 35 per 1000 population) and other diseases (62 per 1000 population v/s 52 per 1000 population) in rural areas as compared to urban areas. On contrary, the prevalence of non-communicable diseases was higher among persons residing in urban areas (212 per 1000 population) as compared to those residing in rural areas (113 per 1000 population) (Table 4).
Table 4Different Types of Single Self-Reported Prevalence Rate (per 1000 population) by Gender and Place of Residence in India Among Older Adults, 2017-18.
Source: Author's Calculation from 75th Round NSS Unit Level Data.
FactorsInfectionsNCDInjuryOthers
Gender
Male42149253
Female43142264
Place of Residence
Rural46113262
Urban35212152

## 5. Emerging self-reported multi morbidity disease pattern

Table 5 presents common patterns of multi-morbidity. Hypertension and diabetes and were the most frequently reported co-existing conditions (1.163%). The second most common pair of illnesses reported among older adults was Hypertension - Muscular-Skeletal (0.17%) followed by Hypertension - Heart Disease (0.11%).
Table 5Common pairs of self-reported multi morbidity (%)Among older adults in India, 2017-18.
Source: Author's Calculation from 75th Round NSS Unit Level Data.
Common Self-Reported Multi MorbidityN = 42,762
Hypertension -Diabetes1.163 (551)
Hypertension - heart disease0.118 (65)
Hypertension - Muscular-Skeletal0.178 (59)
Diabetes – heart disease0.060 (48)
Diabetes – Muscular-Skeletal0.057 (35)
Heart Disease- Muscular-Skeletal0.007 (10)
Hypertension –Diabetes – heart disease0.061 (15)

### 5.1 Results of the regression analysis

Table 6 shows the probability of reporting multi morbidity by older adults with different predictor variables. The multivariate analysis suggests that compared to individuals aged 70 years and above were more likely to report multi morbidity as compared to individuals aged 60–64 years [OR:2.17; CI: 1.602–2.939]. In the case of the economic groups individuals belonging top 20% of the income, groups have higher odds of reporting multi morbidity as compared to those individuals belonging to the bottom 20% of the population [OR; 10.636, CL; 5.223–21.657]. Among social groups, individuals belonging to Other Backward Community groups were more likely to report multi-morbidity compared to persons belonging to Scheduled Tribe [OR; 2.738, CL; 0.891–8.415]. Individuals with the primary and secondary and above level of educational attainment had higher odds of reporting multi-morbidity as compared to those with no education (illiterate) respectively [(OR:2.221, CL; 1.635–3.016); (OR:1.462, CL; 1.096–1.950)].
Table 6Determinants of self-reported multi morbidity prevalence among older adults in India, 2017-18.
Source: Author's Calculation from 75th Round NSS Unit Level Data
65–70 years1.093(0.799–1.493)1.182(0.858–1.629)
71 years and above2.248***(1.670–3.027)2.170***(1.602–2.939)
Female1.009*(0.794–1.281)1.137(0.881–1.468)
Primary2.920***(2.171–3.928)2.221***(1.635–3.016)
Secondary and above2.543***(1.919–3.370)1.462**(1.096–1.950)
Scheduled Caste1.446(0.461–4.527)1.154(0.363–3.668)
Other Backward Community4.367**(1.452–13.135)2.738*(0.891–8.415)
Others (Forward Caste)5.903**(1.964–17.735)2.411(0.783–7.425)
Rural0.544***(0.429–0.689)1.567***(1.195–2.056)
Poor2.191***(0.967–4.963)1.986(0.867–4.551)
Middle3.455***(1.693–7.048)3.055***(1.472–6.339)
Rich6.631***(3.352–13.116)5.784***(2.859–11.701)
Richest11.756***(6.080–22.733)10.636***(5.223–21.657)
Constant0.005***(0.003–0.011)0.001***(0.000–0.004)
N42,756
Note: Reference category: 60–64 years, Male, illiterate, Scheduled Tribe, Urban, Poorest; ***if p-value<0.01; **if p-value<0.05; *if p-value<0.10.

## 6. Discussion

The present paper utilizes the nationally representative data to explore the prevailing patterns of morbidity, co-morbidity and multi-morbidity among the older adults in India. Unlike the previous studies which have focused on understanding the morbidity and co-morbidity patterns among the older adults in India using primary-based small scale surveys, however, this paper is aimed to understand the prevailing levels and patterns of morbidity, co-morbidity and multi-morbidity among older adults in India and its states by using the spatial-regional distribution approach. Further, we documented the risk factors that influenced the high prevalence of co-morbidity and multi-morbidity among older adults in India and also it is well understood through our analysis that where the high concentration of co-morbidity and multi-morbidity laid down across socio-economic groups and regions.
The increasing morbidity, co-morbidity and multi-morbidity among the older adults is really a devastating phenomenon that has been found witnessing in many low and middle-income countries including immensely in India. Overall, our study found that there was 277 older adults per 1000 population who reported their morbidity, co-morbidity and multi-morbidity. Further, our study also found that there is a slight difference between male and female older adults in reporting the overall morbidity (279 per 1000 population for male v/s 275 per 1000 population for female). Similarly, the rural-urban difference in the high prevalence of morbidity and co-morbidity and multi-morbidity is seen as 340 per 1000 population v/s 246 per 1000 population respectively.
Furthermore, our study also found that there is a huge disparity in reporting of self-reported morbidity and self-reported multi-morbidity. At all Indian levels, the prevalence of single self-reported morbidity among the older adults was 249 per 1000 population compared to 29 per 1000 population self-reported multi–morbidity among the older adults. Spatial differences in reporting of single and multi-morbidity were observed across rural-urban areas. In urban areas, the reporting of single and multi-morbidity were 1.3 times and 1.9 times higher than in rural areas. Similarly, the previous studies made a huge difference between geographical and socio-economic groups in multi-morbidity
• Parmar M.C.
• Saikia N.
Chronic morbidity and reported disability among older persons from the India Human Development Survey.
,
• Sathya T.
• Nagarajan R.
• Selvamani Y.
Multimorbidity as a risk factor of elder abuse/mistreatment in India : a cross-sectional study.
,
• WHO
World Report and Ageing on Health.
), this study has also supported the findings with earlier ones.
The World Health Organization member states signed the Global Action Plan for the prevention and control of NCDs by 25% between 2010 and 2025 that is a part of Sustainable Development Goal 3 (WHO, 2013). In the case of India, which is currently going through a rapid epidemiological transition. The increased ageing population face multiple burdens in communication and non-communicable diseases.
While comparing with other studies with regard to data availability and quality, it was necessary to make some methodological considerations. The first one is how we have defined the multi-morbidity and co-morbidity across a different set of morbidities. The second one is to categories the heterogeneous morbidities that are really is much variable, which was provided in the methodological section and appendix. Though there is a lacuna in data in India while reporting the co-morbidity and multi-morbidity with segregating it on several disease patterns. This huge under-reporting of self-reporting morbidities can be possible but the most feasible method used in the study is population-based. There are primary-based studies that found a huge prevalent of co-morbidity and multi-morbidity in India as a whole and particularly states. The state-wise variations clearly showed that there is a divide in the high prevalence of co-morbidity and multi-morbidity in India. The states like Kerala, Tamil Nadu, Karnataka Telangana, and Andhra in the South region; Himachal Pradesh, Punjab, Haryana in the North region followed by the Eastern region's state like West Bengal and Odisha were witnessed with high co-morbidity and multi-morbidity. Although, there are many policies and programs introduced by the government of India in order to deal with the coping strategies and further reducing them. However, the effectiveness and efficiency of these programs are not well implemented and introduced till the ground levels.
The increasing morbidity, co-morbidity and multi-morbidity are also due to untreated diseases or morbidity that older people are having (Srivastava & Gill, 2020). Further, a huge socio-economic differential was found in the prevalent of single-morbidity and multi-morbidity among the older adults in India. Chronic morbidity is also associated with several socio-economic causes that led to the older adults in critical conditions. A study by Parmar & Saikia found that socio-economic vulnerability plays a vital role in the prevalence of chronic morbidity and co-morbidity that needs to be timely reduced in order to minimize the enormous associated disability associated with the older adults.
• Parmar M.C.
• Saikia N.
Chronic morbidity and reported disability among older persons from the India Human Development Survey.
Similarly, a study conducted in western countries found that large variability in the prevalence of multi-morbidity was closely associated with socio-economic, physical characteristics and self-rated health and quality of care (Bezerra de Souza DL et al., 2021).
The high geographical and socio-economic variations in co-morbidity and multi-morbidity among the older adults made worsen in terms of quality of life such as HALY (healthy adjusted life years) and DALY (disability-adjusted life years) and further its impact on health and healthcare costs have led to household into the poverty trap. The high out-of-pocket expenditure on health care has emerged as a key and crucial factor that hindrances to older adults for not utilizing proper and adequate services.
• WHO
World Report and Ageing on Health.

### 6.1 Limitation

The current study is not free from limitation and there are as follows. First self-reported morbidity suffers from both under-reporting and over-reporting among the population subgroups. The under-reporting and over-reporting of morbidity are also further fueled by the existing level of healthcare and its utilization in the respective state. Thus, the absence of any objective measure of health in NSS surveys makes it difficult to detach the real increase in the disease burden and enhanced subjective perception of illness from increasing levels of morbidity prevalence. Second, lack of information on factors like lifestyle conditions, and occupational status may have a significant bearing on health which is not captured in the present study groups.

## 7. Conclusion

In conclusion, this study provides evidence of a relatively higher prevalence of both single and multi-morbidity self-reported prevalence in India. Policy initiatives aiming to reduce the burden of these multi-morbidities must focus on reorienting programme through increasing public investment in health. These investments must increase by providing preventive care facilities for the early prevention of diseases and improving the provision of geriatric curative services both in rural and urban areas as needed by the community in India.

## Ethics approval and consent to participate

The data is freely available in the public domain and survey agencies that conducted the field survey for the data collection have collected prior consent from the respondent. There is no formal ethics approval was required to carry out research from this data source.

## Consent for publication

Not applicable.

## Availability of data and materials

The study utilizes a secondary source of data that is freely available in the public domain. And, it can be available by a request through http://mospi.nic.in/NSSOa.

## Competing interests

The authors declare that they have no competing interests.

## Funding

Authors did not receive any funding to carry out this research.

## Acknowledgments

Not applicable.

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