Introduction

India has hit the mark of 1.46 billion people in the year 2025, continuing its position as the most populous nation across the globe (1). By reaching to the size of four trillion dollar, India also has ranked amongst top five economies in the world (2).

The combination of population explosion and socioeconomic growth often brings numerous health challenges along. Multi-morbidity is one such phenomenon. World Health Organization defines multi-morbidity as the co-existence of two or more chronic conditions (3).

Lifestyle disorders were once considered to be the diseases of affluent. But with the increasing life expectancy and transitioning of disease pattern, even the low income and low-to-middle income countries are carrying the burden of non-communicable diseases to a great extent. India has been undergoing a demographic shift and epidemiological transition since past five to sex decades. The average life expectancy has increased significantly, making ‘multi-morbidity’ a common norm rather an exception. Multi-morbidity in India has specifically gained importance in global public health, as the country accounts for approximately one sixth of the world population (4).

While considering multi-morbidity, it cannot be conceptualized as a mere addition of an extra ailment into a pre-existing condition. A person suffering from multi-morbidity experiences two or more health conditions which may or may not be diagnosed; which may or may not interact with each other in their patho-physiology and clinical management. Multi-morbidity has become a major public health concern in recent times. Irrespective of the age group, people experiencing multimorbidity are at the higher risk of all-cause mortality (5-6). It is negatively associated with health related quality of life (HQoL), overall quality of life (7-8) and predicts future functional decline leading to worsening of health outcomes (9). Multi-morbidity impose a significant risk of catastrophic health expenditure and economic burden on health system and societies (10-11). Multimorbidity not only acts at an individual level but the complex health care needs of person suffering from multiple chronic conditions translate into increased health service utilization and health care costs (12-13). From a system’s point of view, the health systems conventionally are designed to tackle single diseases in focus, but multi-morbidity calls for more integrated and person centred approach to care (14). In terms of macroeconomics, multi-morbidity places an upward financial pressure on economies by driving higher health and social care costs, exacerbating inefficiencies in resources and demanding restructuring of service delivery models to ensure affordability and sustainability (15).

Tracking the trends in multimorbidity incidence and prevalence are essential to estimate its future projections and to gear up the health care systems accordingly. Chowdhury et al. (16) conducted a systematic review and meta-analysis on 126 observational studies that were conducted across fifty four countries. The global prevalence of multi-morbidity was found to be 37.2% with regional variations ranging from 45.7% (highest in South America) to 28.2% (lowest in African continent). The same study found out consistent increase in multimorbidity prevalence between 2000 and 2010 attaining a plateau in 2011. Pati et al. (17) published a systematic review of multi-morbidity studies conducted in south Asian countries and reported the prevalence ranging from 4.5% to 83% wherein the number of chronic conditions varied from seven to twenty two. In Indian context, fourth round of National Family and Health Survey (NFHS) and Longitudinal Ageing Survey of India (LASI) are two prominent databases offering the insight of this phenomenon at national level. A study done by Prenissi et al. (18) analysed data of seven lakh individuals from the fourth round of NFHS and found the multi-morbidity prevalence to be around 7.2%, with lowest in Chhattisgarh (3.4%) to highest in Pondicherry (16.9%).

Various research studies at international and regional levels (5, 16, 18-22) show the prominence of multi-morbidity in specific age and gender groups. Interestingly, the relationship between multi-morbidity and socio-economic status (SES) show contrasting results for high, middle and low income countries. In some of the high income countries, multimorbidity is found to be associated with lower socio-economic status (in terms of income and education) and some low income countries report its higher prevalence in wealthiest quintile of population (23). About one-fifth of adults in low-to-middle income countries suffer from multimorbid conditions (24). A few middle-income countries specific studies show that combination of certain diseases are more burdensome, making pattern of multimorbidity more significant in shaping health care demands over the number of morbidities (25).

A pooled prevalence of multi-morbidity in India is around 20% (26). Chronic conditions are estimated to cost the country around $6.2 trillion during 2012-2030. Country is predicted to lose up to $4.8 trillion in its economic output by 2030 due to chronic diseases. Indian government has adopted all the Sustainable Development Goals, wherein the third one is directly linked with reducing premature mortality from non-communicable diseases, and in turn from multi-morbidity. India is the leading society demonstrating the demographic and epidemiologic shift in low and low-to-middle income countries. It is a home to one-sixth of the world population. The impact of multi-morbidity on an average Indian goes beyond the biomedical paradigm affecting his or her productivity potential and economic output. Indian health care system is heavily reliant on single disease models. With a weak foundation of primary health care network and inadequacy of integrated care, the challenges of multi-morbidity for India’s public health system become multi-fold. We also lack a standard definition of multi-morbidity and tools to measure it. We do not have a comprehensive national level database which will capture the diversity in statistics coming along with vulnerable sub-group of populations such as gender minorities, urban poor, migrants etc. These factors provide a strong rationale in conducting a study on the issue of multiple morbidity in India.

Currently, National Sample Survey and National Family and Health Surveys act as good sources in providing health related information. Data generated from NFHS rounds is more robust and rich in terms of socio-economic determinants but it considers limited number of health conditions and is inclined towards specific age and gender groups. Whereas, NSS database takes into account wider age group, greater number of health conditions and overall consumption of health services by assessing out-patient visits, hospitalization and economic burden of health utilization.

Through this secondary analysis, we specifically aim to - a) introduce a newer way of operationally defining multi-morbidity, b) determine its prevalence across national level, c) examine its burden across different demographic and socio-economic sub-groups and, d) find out the co-relation between multi-morbidity and its determinants.

Methods

Data source:

We analysed data from the 71^st round of National Sample Survey to meet our research objectives. This round “Social Consumption: Health” was conducted between June and December 2014. It provides information related to morbidity, out-patient department visit, hospitalization and healthcare expenditure at national and state (provincial) levels. This household survey adopted stratified multistage sampling. In the first stage, units were census villages in rural sectors and urban frame survey blocks in urban sectors. In case of large first stage units, hamlet groups or sub-blocks were selected as intermediate stage. The ultimate stage units were households. The sampling frame for rural and urban areas were made out of 2011 census village list and urban frame survey respectively.

The survey followed all ethical considerations. Informed consent was sought from the participants. Privacy and confidentiality of the data were ensured throughout the research completion.

Ascertaining and defining multi-morbidity:

Morbidity was capture in response to three questions: 1) Were you ill in the past 15 days? 2) Were you hospitalized in the past 365 days? 3) If so, what was the cause? A list of sixty ailments were given along with the questionnaire. The reported morbidity was attributed to the nearest fit of one of these sixty ailments on the basis of medical diagnosis conveyed by the respondent to the study team. With the help of evidence from past study (27), we created eight categories of ailments from the list of sixty – infection, cancers, blood diseases-endocrine-metabolic-nutritional ailments, psychiatric-neurological ailments, cardiovascular ailments, respiratory ailments, gastro-intestinal ailments, musculoskeletal-genitourinary ailments. Individual who has reported multiple episodes of hospitalization due to two or more diseases in a reference period of one year is considered to be suffering from multi-morbidity. Therefore, level of multi-morbidity is determined by identifying persons who availed in-patient care (reported hospitalization) for more than two chronic ailments in a given reference period.

Independent variables:

We examined how the prevalence of multi-morbidity varied across demographic and social variables which are asked in the survey questionnaire: age, sex, marital status, place of residence, geographical zone, level of education, social group, wealth quintile and health insurance coverage.

Statistical analysis:

In the first step of analysis, we assessed the level of multimorbidity by calculating the number of hospitalizations occurred due to two or more diseases in the reference period of past one year.

In the second step, we conducted a bivariate analysis using Pearson’s chi squared test to see whether any significant relationship exists between episodes of morbidity or multimorbidity and the background characteristic. The association between background variable and hospitalization episode and out-patient department visit was tested using this test at 5% significant level.

In next step, two separate binary logistic regression analyses were used for examining the effect of background variables on the odds of outcome variable which is, ‘episodes of hospitalization.’ Our outcome variable had three categories (single hospitalization, multiple hospitalization for single diseases, multiple hospitalizations for multiple diseases). In the first stage, ‘single hospitalization’ was coded as ‘0’ and ‘multiple hospitalizations for single disease’ was coded as ‘1’ after eliminating the third category. In the second stage, ‘single hospitalization’ was coded as ‘0’ and ‘multiple hospitalization for multiple disease’ was coded as ‘1’; here the second category was eliminated from the analysis. The adjusted odds ratio and its 95% confidence interval (CI) were calculated. A p-value of <0.05 was considered significant. The binary response (y, multiple hospitalizations for single (multiple) disease or single hospitalization) was related to a set of categorical predictors, x, and a fixed effect by a logit link function as following:

Logit (π_i) = log [π_i (1-π_i)] = β_0 + β (x)

The probability of multiple hospitalization episodes is π_i. The parameter β0 estimates the log odds of multiple hospitalization episodes for the reference group, and the parameter β estimates with maximum likelihood the differential log odds of multiple hospitalization episodes. These parameters are associated with the predictor x as compared to the reference group and ε represents the error term in the model. Sampling weights were used to account for the complex, multi-stage survey design. Statistical analyses were performed using STATA 14 (StataCorp, College Station, Texas).

Results

Level of multi-morbidity

Figure 1: Proportion of different category of hospitalization depending on the number of diseases

Figure 1 represents distribution of hospitalization episodes into different categories. Two or more than two episodes of in-patient visit (hospital admission) for two or more than two ailments were used as the proxy measure of multi-morbidity.

Seventy first round of NSS database reported 15,427 hospital admissions in the age group of 18 years and above. Out of them, around 83% (12,770) reported cases were of single hospitalization, where the individuals sought in-patient services only once. Nearly 9% (1,356) reported cases took place when the individuals sought in-patient services for same disease multiple times. Nearly 8% (1,301) reported cases took place when the individuals sought in-patient services for multiple times for more than one disease.

Table 1 illustrates the hospitalization episodes took place in the reference period of one year based on the socio-demographic and economic characteristics. A significant association was found between hospitalization episodes and all the socio-demographic characteristics except the level of education.

The age group of 46-60 years had highest number of hospital admissions (4,447). In terms of multi-morbidity, the age group of 80-110 years reported highest number (13.70%) of multiple hospitalization caused by two or more diseases. It was followed by 18-35 years (11.96%) and 61-79 years (11.70%). The phenomenon of multimorbidity was predominant among females (11.49%) over males (7.31%). Individuals who never entered into the institution of marriage reported highest number of multiple hospital admissions attributed to multiple ailments (14.95%), they were followed by widowed individuals (11.26% multiple hospital admissions for multiple ailments). Geographical distribution of multimorbidity suggests greater burden among individuals residing in urban areas than their rural counterpart (10.25% over 9.15%). The southern parts of India reported highest number of multimorbidity cases (12.02%) and the northeastern India has least number of multimorbidity cases (4.94%).

The socio-economic distribution of multimorbidity suggests nearly equal presence among Hindu and Muslim religions (9.24% and 8.81% respectively). The other backward classes reported highest share of multimorbidity followed by schedule castes (9.55% and 9.16% respectively). The wealthiest quintile of population sub-group had the maximum cases of multimorbidity (12.4%) and the lowest quintile of sub-group had the least number (8.03%) of multimorbidity cases. The proportion of people owning a private health insurance was highest (14.98%) reporting multimorbidity and uninsured individuals came up with least number of multimorbidity cases (8.68%).

We did not find any significant association between education of individual and episodes of hospitalization (p=0.37).

Note: ® represents the reference category.

Table 2 illustrates the odds ratio for seeking in-patient services in the form of hospitalization episodes at different instances. Logistic regression test was conducted with hospitalization episodes as an outcome variable to derive these results. After controlling the impact of other background variables, results show that, individuals belonging to above 60 years age group are more likely to seek multiple times in-patient services for single disease as well for multiple disease during last one year from the date of survey as compared to the reference category of 18-35 years of age. If we further categorise 60 years and above age group into two groups as, 61-79 years (OR = 1.230 and CI = 1.012-1.494) and 80-110 years (OR = 1.833 and CI = 1.334-2.517), the odds of having multiple hospitalization for a single disease as well as for multiple diseases increase with the increasing ages. Therefore, odds of having multi-morbidity was increase with an increase in ages.

Women are more likely to undergo multiple hospitalization episodes due to multiple diseases (OR = 2.065 and CI = 1.810-2.357) as compared to men. However, hospitalization due to single disease (OR = 0.833 and CI = 0.735-0.944) are less likely to occur in women as compared to men.

Widowed individuals are less likely to undergo multiple hospital admissions due to multiple diseases (OR=0.460 and CI=0.343-0.618) as compared to the reference category of never married individuals.

As compared to individuals residing in northern zone of the country, the ones living in southern zone of the country are more likely to undergo multiple hospital admissions due to multiple diseases (OR=1.845 and CI=1.542-2.208).

As compared to Hindu people, individuals practicing other religion (apart from Hindu and Muslim) are less likely to undergo multiple hospital admissions due to single disease (OR=0.770 and CI=0.603-0.983). Odds ratio for individuals undergoing multiple hospital episodes due to multiple diseases is found to be insignificant.

Individuals belonging to SC category are more likely to undergo multiple hospitalization episodes due to single diseases (OR=1.331 and CI=1.024-1.731) in relation with the individuals belonging to ST category. Individuals who have attained education till secondary or above level are less likely to undergo multiple hospital admissions due to single disease (OR=0.826 and CI=0.694-0.983) as compared to illiterate individuals. In the case of wealth index. People belonging to highest usual monthly per capita expenditure (rich) are more likely to undergo multiple hospital admissions due to single disease (OR=1.312 and CI=1.133-1.520) as compared to people belonging to poor UMPCE.

In relation with individuals having no insurance coverage, individuals possessing a privately funded health insurance scheme are more likely to undergo multiple hospital admissions due to multiple diseases (OR=1.570 and CI=1.231-2.032).

The odds ratios of multiple hospitalization for multiple diseases in all social groups, people having different education attainment, and amongst population coming from any wealth indices, are found to be statistically insignificant.

Discussion

The objective of this paper is to determine the level of multi-morbidity in different sub-groups of population and understand its demographic, social and economic determinants. For this, we operationalized multimorbidity in terms of ‘number of hospitalization,’ wherein individuals with multiple hospitalizations attributed to multiple (two or more than two) ailments were defined as multi-morbidity cases. The ‘episode of in-patient admission in last 365 days’ was used as a proxy measure of multi-morbidity. The “multiple episodes of hospitalization due to two or more ailments” were considered as a key indicator of multi-morbidity among adults and elderly in India. To the best of our knowledge this is the first time ‘multi-morbidity’ is defined in such a novel manner.

Based on this definition, we found the prevalence of multi-morbidity to be 8.43% among adults and elderly across India. This result aligns with past study done in Indian and South Asian context (17). After analysing the range of seven to twenty two chronic conditions, the prevalence of multimorbidity varied from 4.5% to 83%. Another prospective cohort study conducted in urban settings in India and Pakistan found the prevalence to be 9.4% (28). Evidence from high-income country like Canada provides similar estimate from 7%-35% (29).

We found age, gender, geographic zone and health insurance coverage as the background characteristics, significantly associated with the likelihood of experiencing multi-morbidity related hospitalizations.

Age emerged as a strong predictor of multimorbidity. Individuals aged eighty years and above showed nearly double the odds of multimorbidity compared to those in young adulthood (18-35 years). These finding align with the previous studies, including the large scale multi-country study conducted by Garin et al. (31), which found that the prevalence of multimorbidity gradually increases with age. This observation could possible explained by the fact that ageing is often characterized by increased susceptibility to development of multiple chronic diseases and progressive accumulation of those diseases, which especially get accelerated in old ages (30).

Gender is another demographic factor that consistently demonstrated significant influence on the prevalence of multi-morbidity. Women were found to be more than twice as likely as men to experience multiple hospitalizations due to multiple chronic ailments. Several large scale international research and regional studies reveal the similar finding (24, 32-35). This could be attributed to combination of factors - women’s biological characteristics, socially and economically disadvantaged position which hinders their access to food intake, nutrition, education, resources, health care and other opportunities. Especially, the gender based disparities in seeking health services and diagnostic delays may impose higher disease burden in this gender group. Also, from methodological point of view, women tend to share more information than men in any self-reported health surveys. Greater life expectancy of women make them outlive men in the old ages. Therefore, probability of contracting multiple ailments simultaneously is common in women than in men.

Marital status showed a protective association for multimorbidity related hospitalizations, with currently married and widowed individuals were less likely to be hospitalized multiple times for multiple diseases compared to those who were never married. This finding is strongly supported by longitudinal cohort study that was carried out in multiple countries in different continents. The status of marital relationship is assumed to play a key role in shaping the trajectory of health and well-being across life course (36). The importance of social support in mitigating health risks and promoting health seeking behaviour is also highlighted here. However, the effect of other form of social connect and support on the occurrence of multimorbidity should further be investigated.

We found a pronounced geographic disparity in the distribution of multimorbidity. Individuals residing in Southern states of India exhibits highest odds of multimorbidity, which was followed by the Western states in India. The Northeastern zone reported the lowest level of multimorbidity. These patterns not only reflect the true morbidity distribution but also point at differential health system performances, in terms of improved utilization of services and better case detection rate and a few underlining societal factors like better literacy and awareness among citizens. Similar interpretations were made by Bhise et al. (37) and Dolui et al. (38) in their secondary analysis studies using National Sample Survey and National Family and Health Survey databases respectively.

Finally, having access to health insurance, particularly private health insurance, was significantly associated with higher odds of multimorbidity related hospitalization. This finding is in agreement with the research undertaken by Uejima and colleagues (39), which explains association of voluntary health insurance with the lower risk of multimorbidity. Having a financial protection mechanism facilitates the identification and management of chronic conditions. However, it also highlights the potential diagnosis bias whereby insured individuals are more likely to report and get treated for multimorbidity conditions.

In contrast to our expectations, certain social indicators like education, was not found to be associated with the level of multimorbidity in our research. The evidence from previous systematic review (40) gave mixed results showing proportional, inverse and no association between multimorbidity and educational attainment. We also did not find any significant association between caste group and multimorbidity status. Parts of previous work show no significant association between caste and multimorbidity (41) and some of them show significant association between these same variables (42). We tend to not find any evidence of association between caste and multimorbidity from the international literature (43), because of uniqueness of caste to Indian society. Therefore, it mostly gets excluded from the set of social determinant influencing multimorbidity. Wealth quintile was another such unexpected variable showing no significant association with the multimorbidity. The associations between a few variables and our outcome variable, which is multimorbidity disappeared after controlling for other variables. This discrepancy points to the complex interplay of socio-economic status indicators and health-seeking behaviour. The differential results of the studies can also be attributed to various ways by which multimorbidity is operationalized and the list of health ailments included for the consideration.

With this study, we have tried to introduce a novel way of defining multi-morbidity. To our knowledge, this is a first kind of work carried out in the subject of multiple chronic conditions in our country, where we have extensively studied the phenomenon of multimorbidity across all statues, inclusive of all health conditions. There are papers published on the topic of prevalence of non-communicable diseases and its determinants, but this paper also take into account some of the chronic infectious diseases which have a noticeable stake in the total morbidity burden of the country.

The biggest limitation of this study is the lack of any standard definition of the term ‘multi-morbidity.’ This makes it difficult to compare the results of the study with the findings from other research. While conceptualizing the operational definition of multimorbidity, we restricted ourselves only to in-patient visits and excluded those individuals seeking services from out-patient departments. Thus, our estimates might be lower than the real prevalence of diseases.

Conclusion

Given India’s ongoing demographic and epidemiological transitions, the burden of multimorbidity is poised to rise further. Increasing number of people suffering from two or more conditions will impose another challenge for health system in upcoming years. Insights from this paper call for policy responses that emphasize integrated and person-cantered care, particularly for elderly and female subgroups of population. Strengthening of primary care, enhancing financial protection and addressing regional disparities in health system performance are the essential steps to manage this emerging public health challenge effectively.

Abbreviations

HQoL: Health related Quality of Life

LASI: Longitudinal Ageing Study in India

SES: Socio Economic Status

NSS: National Sample Survey

NFHS: National Family and Health Survey

WHO: World Health Organization

AOR: Adjusted Odds Ratio

OR: Odds ratio

CI: Confidence Interval

SC: Schedule Caste

ST: Schedule Tribe

OBC: Other Backward Class

UMPCE: Usual Monthly Per Capita Consumption Expenditure

Funding

The authors did not receive support from any organization for the submitted work.

Data availability

The present study is based on a large dataset that is conducted by the National Sample Survey Office (NSSO) under the Ministry of Statistics and Programme Implementation, Government of India. The data is accessible from https://microdata.gov.in/NADA/index.php/catalog/135/get-microdata

Conflict of Interest

The authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.

Ethics approval statement

The study requires no ethics approval as the analysis used only de-identified existing unit record data from National Sample Survey.

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