|Year : 2022 | Volume
| Issue : 2 | Page : 152-158
Analysing the burden of morbidity, associated expenditure, and coping strategies among india's elderly population: Evidence from national sample survey 75th round
Sujata Sujata, Bhed Ram, Ramna Thakur
PhD. Scholar, School of Humanities and Social Sciences, Indian Institute of Technology Mandi, Kamand Campus, Himachal Pradesh, India
|Date of Submission||26-Aug-2021|
|Date of Decision||06-Jan-2022|
|Date of Acceptance||07-Jan-2022|
|Date of Web Publication||12-Jul-2022|
School of Humanities and Social Sciences, Indian Institute of Technology, Mandi, Kamand Campus - 175 075, Himachal Pradesh
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: An increase in average life expectancy has raised a concern about whether these extra added years are characterized by good health and independence or health problems and dependency on others for care. The current study aimed to analyze the morbidity burden, associated expenditure, and coping strategies among India's elderly population. Data and Methods: The study uses cross-sectional data of the National Sample Survey 75th round. Multivariable logistic regression has been used to examine morbidity and associated expenditure differentials among the elderly population in different socioeconomic variables in India. Results: Findings show that cardiovascular diseases (CVDs) are the leading cause of morbidity and economic burden among the elderly population in India in the case of inpatient care. However, in outpatient care, CVDs are the leading cause of morbidity, while cancer is the main cause of economic burden (measured only through OOPE). Although CVDs are the leading cause of morbidity and economic burden, psychological and neurological, injuries, cancer, and gastrointestinal ailments force the elderly population to borrow for inpatient care. Further, it is the oldest, minority (Muslims) and richest section of the elderly population who are most likely to report health issues. Gender differential is also clear from the results as females are more likely to report for ailments in outpatient care, whereas the reverse is the incident in inpatients. Conclusion: The study concluded that there is a need to increase government spending on social security such as old age pensions like Indira Gandhi National Old Age Pension Scheme, keeping in view the changing needs of the elderly population.
Keywords: Elderly, expenditure, healthcare, morbidity, mortality, population
|How to cite this article:|
Sujata S, Ram B, Thakur R. Analysing the burden of morbidity, associated expenditure, and coping strategies among india's elderly population: Evidence from national sample survey 75th round. Indian J Public Health 2022;66:152-8
|How to cite this URL:|
Sujata S, Ram B, Thakur R. Analysing the burden of morbidity, associated expenditure, and coping strategies among india's elderly population: Evidence from national sample survey 75th round. Indian J Public Health [serial online] 2022 [cited 2023 Mar 26];66:152-8. Available from: https://www.ijph.in/text.asp?2022/66/2/152/350653
| Introduction|| |
World Health Organization defines the elderly as the lifelong process of getting older at the cellular, organ, or whole-body level throughout the lifetime. In most developed countries, the “elderly population” is defined as above 65 years, whereas the United Nations proposed 60 + years to refer to the older population. The process of “demographic transition,” which is defined as the shift in fertility and mortality rates from high and fluctuating to low and relatively stable, started in many developed countries (European countries and some parts of America) more than a century ago. This shift in fertility and mortality rates has led to an increase in the elderly population. The proportion of the elderly population is increasing throughout the world. In low and middle-income countries (LMIC) like India, the process of the aging population has started recently with the demographic transition and is increasing at an increasing rate. As individuals age, their health status generally becomes poor, leading to increased health-care utilization. The increasing elderly population is often seen as a success of the public health-care system. Still, consequences are different in high-income countries (HICs), and LMICs as the social security measures are not adequate in the latter case. Because of the improved living environment, medical advancements, and better control over infectious ailments, the mortality rate among elderly populations has decreased over the period in HICs and LMICs. The life expectancy has increased from 32 years to 69 years in LMICs, raising a concern whether these extra added years are characterized by good health and independence or health problems and dependency on others for care.,
A strong correlation between age and health-care costs has also been observed in both HICs and LMICs. Studies from different countries over the 30 years have shown high health-care consumption in the last years of life. The prevalence of health problems and related medical costs, social services, and long-term care increase with age. According to a report by United Nations Population Fund, globally, 11.5% of the total population belongs to the age group of 60 and above, and by 2050, this percentage is expected to rise to 22%. In India, it is expected that the proportion of elderly people will increase from 8% to 19% by 2050. However, in a developing country like India, in the absence of better health care facilities, the risk of growth in age-related ailments increases with aging progression.
Some studies have focused on the pattern of diseases, access and utilization of health care, and out-of-pocket expenditure on health care among the elderly population in India.,, Other studies have investigated into the socioeconomic profile of the elderly, their living arrangements, economic dependency, health perception, factors affecting health-care utilization, financial protection during hospitalization, and other related health challenges., Few other studies have focused on depression among older adults and its association with living conditions as well as on the prevalence of multi-morbidity and its association with factors such as obesity and physical inactiveness.,
It is clear from the above discussion that there are some attempts toward capturing the morbidity burden, associated economic cost, health-care utilization, and coping strategies, but these studies are either old or have looked at these aspects in isolation. Few recent studies have tried to capture the living arrangement and economic dependency among the elderly population in India but failed to measure the morbidity level and financial burden. Therefore, by considering this gap in the literature, this study aims to analyse the burden of morbidity, associated expenditure, and coping strategies in inpatient and outpatient care separately among the elderly population in India.
| Data and Methods|| |
The analysis is based on the elderly population (60 and above) of the cross-sectional data of the 75th round of the National Sample Survey (NSS). NSS has collected information for 60 ailments divided into 11 broader categories in this study. The survey period for the 75th round was from July 2017 to June 2018, consisting of 1, 13, 823 households consisting of 42,762 elderly population. Information on the incidence of various ailments and sources of financing has been provided in the survey. These financial sources include household savings, borrowings, sale of assets, the contribution from friends and relatives, and other sources. The survey has provided information on both inpatient and outpatient care separately.
This study's dependent and independent variables are as below:
In our multivariable logistic regression model, the dependent variable is a binary variable which shows whether a person is suffering from an ailment or not.
For analysis, different socioeconomic and demographic variables, namely place of residence, religion, social category, Monthly Per capita Consumption Expenditure (MPCE) quintile, household size, gender, education, and marital status, have been considered independent variables. Place of residence includes whether the households reside in rural or urban areas. Social categories include general, scheduled castes (SCs), scheduled tribes (STs) and Other Backward Classes (OBCs). The MPCE quintile variable categorizes households based on whether they belong to the poorest, poor, middle class, rich or richest section of the society. Further, religion includes Hinduism, Islam, and other religions (Christianity, Sikhism, Buddhism, Jainism, Zoroastrianism and other religions). We have also included a household size consisting of fewer family members than five or >5 in a family. Based on educational qualification, individuals have been categorized as illiterate, literate up to primary level, literate up to higher secondary level, and bachelor and above. Based on age, individuals have been classified as to whether they fall into 60–70 years, 70–80 years, or above. Further, we have also included marital status as an independent variable, i.e., never married, currently married, widowed, or divorced/separated.
This study uses data from the 75th round of NSSO, which is publicly available. Hence, no ethical approval was needed.
As in the data set, information on inpatient care (hospitalization cases only) has been given on the recall period of 365 days and outpatient care (nonhospitalization cases) on the recall period of 15 days. Hence, this study has converted all the information into monthly figures.
We have calculated prevalence rates of different categories of ailments as follows:
where n = total number of persons suffering from a particular ailment; N = total population.
After that, we calculated the average out-of-pocket expenditure (OOPE) as:
OOPE is calculated as the difference between total expenditure on health and the amount reimbursed from insurance schemes. The total expenditure on health includes medical expenses like doctor's or surgeon's fee, expenditure on medicines, diagnostic tests, bed charges and other medical expenses (attendant charges, physiotherapy, personal medical appliances, blood, oxygen, etc.) as well as nonmedical expenses like expenditure on transport for the patient and other nonmedical expenses incurred by the household (registration fee, food, transportation for others, expenditure on the attendant, lodging charges, etc.).
The mean positive gap (MPG) has been calculated as:
Further, we have applied multivariable logistic regression to estimate the probability of occurrence of an ailment, i.e., whether an ailment is occurring or not. The multivariable logistic regression model is noted as:
P is the probability of suffering from an ailment, and 1-P is the probability of not suffering from an ailment. Xi′s are the different socioeconomic and demographic variables, ui is random error term and B1,B2,…B9 are the parameters to be estimated.
The association between different socioeconomic and demographic variables and the probability of suffering from an ailment is estimated through the odds ratio.
| Results|| |
Results of [Table 1] show that cardiovascular diseases (CVDs) are the leading cause of morbidity burden with 1.24% and 9.77% prevalence rates in inpatient and outpatient care, respectively.
|Table 1: Prevalence rate of various ailments in both inpatient and outpatient care|
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In inpatient and outpatient care, the prevalence rate is lowest in cancer (0.16%; 0.15%). Results also reveal that infectious ailments (1.22%) are the second most prevalent ailments in inpatient care. However, in the case of outpatient care, it is the endocrine, metabolic and nutritional ailments that are second most prevalent with a prevalence rate of 7%.
[Table 2] reveals that in the case of inpatient care, the average OOPE is highest for CVDs (INR 50), followed by injuries (INR 26) and psychological and neurological (INR 18) ailments. Whereas MPG is highest for cancer (INR 8583), followed by CVDs (INR 4067) and psychological and neurological (INR 3563) ailments. Results further reveal that in outpatient care, it is CVDs followed by endocrine, metabolic and nutritional ailments on which average OOPE is maximum. At the same time, MPG is highest for cancer (INR 7912), followed by injuries (3645) and other (INR 2839) ailments.
|Table 2: Out-of-pocket expenditure on various ailments in both inpatient and outpatient care|
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[Table 3] shows the different sources of financing on which the elderly population is dependent. In the case of inpatient care, the elderly population preferred to finance their treatment of endocrine, metabolic and nutritional (84%) ailments followed by infections (85.59%) and eyes (83.94%) through savings, whereas psychological and neurological, injuries, cancer and gastrointestinal are the ailments which force the elderly population to borrow. In the case of outpatient care, the elderly population spends the highest percentage of their savings on cancer (93.10%), followed by infections (88%) and injuries (87.69%). Whereas, gastrointestinal followed by respiratory and infectious ailments forces them to borrow more. Results also reveal that injuries cause the elderly population to sell their physical assets or get monetary contributions from their friends and relatives for treatment in case of inpatient. In contrast, in outpatient care, psychological, and neurological ailments followed by eyes treatment force them to do so.
|Table 3: Utilisation of various sources of finance as a coping mechanism in both inpatient and outpatient care|
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[Table 4] reveals that in outpatient care, the elderly population is less likely to report ailments in urban areas than in the rural areas' elderly. Elderly population belonging to a household size <5 is less likely to report ailments than households with greater than five members. Results also show that the elderly population belonging to Muslims and the general category are more likely to report ailments. The elderly population belonging to socially disadvantaged sections (SCs, OBCs and STs) is less likely to report ailments in outpatient and inpatient care. Further, the results show that the richest group's elderly population is more likely to report ailments than their counterparts in outpatient and inpatient care. The elderly population of 80 and above and widowed, divorced or separated are more likely to report ailments than their respective reference categories. By taking illiterate as the reference variable, we found that the elderly population having education up to primary level are more likely to report ailments. However, the result for inpatient care is nonsignificant.
| Discussion|| |
The findings of this study show that in the case of inpatient care, CVDs are the leading cause of morbidity and economic burden (measured only through OOPE) among the elderly population in India. Whereas in outpatient care, CVDs followed by endocrine, metabolic, and nutritional ailments are the leading cause of morbidity, while cancer is the main cause of economic burden. The high prevalence of CVDs is because of the increase in urbanization as people in urban areas have more intake of energy-dense foods and less physical activities. A high level of psychological stress is also one of the prominent reasons for the prevalence of CVDs. Further, Musco-skeletal ailments increase with age as musculoskeletal tissues show increased bone fragility, loss in cartilage resilience, reduction in ligament elasticity and muscular strength. These negative changes decrease the tissues' ability to carry out their normal function. It is interesting to see that although CVDs are the leading cause of morbidity and economic burden, psychological and neurological ailments followed by injuries and cancer force the elderly population to borrow in case of inpatient care. Whereas, in the case of outpatient care, borrowing is maximum for gastrointestinal and respiratory ailments.
The primary reason for high OOPE in India is inadequacies in the public health-care system and the lack of a universal health-insurance mechanism. In India, only 1.17% of the GDP is spent on healthcare. Public health investment is only 0.9%, which is significantly less to fulfill the poor's requirements. People borrow and sell their assets sometimes because of the lack of savings, mainly because of limited income sources. The current pattern of health expenditure in India shows that about 69.3% of the total health expenditure accounts for OOPE by households. Results also reveal that the oldest section of the elderly population (80 and above) is most likely to suffer in inpatient and outpatient care because of their multiple chronic ailments and hearing, vision, and walking issues. In LMICs, the number of older people is increasing because of modern technology and foreign aid enhancement in primary health programs. It has reduced mortality, but health levels have not improved. Gender differential is also clear from the results as females are more likely to suffer from ailments in outpatient care, whereas the reverse is the trend in inpatient care. It means that the proportion admitted to hospitals for health care is slightly lower among females than males. It should not be surprising because males are more open to their ailments than females. Furthermore, in India, elderly females think of themselves as a burden to their family and don't want to spend their family's money on their healthcare. The minority (Muslims) sections' elderly population are most likely to suffer from the ailments. Furthermore, socially disadvantaged sections' elderly population are less likely to suffer from the ailments, and the reason could be their less reporting of the ailments. Further, we have observed that the elderly population from the richest and rural households is more likely to report ailments in inpatient and outpatient care. The reason could be older people do not report illness or discontinue their treatment because of financial issues and their inability to walk to the hospitals.
India has tried to reduce the financial burden of health through different health insurance schemes. Since 1952, India has had two health insurance schemes-the Employees' State Insurance Scheme and Central Government Health Scheme. Though these schemes covered a wide range of services in both inpatients as well as outpatient care, the major drawback lies in the fact that they covered only organized sector households and left the informal sector of the economy, which constitutes a significant part of the Indian economy. Keeping this in view, in 2008, India launched Rashtriya Swasthya Bima Yojana (RSBY), aiming to cover BPL families with a cap of five members per family. Further, it provided only an annual cover of INR 30,000 per household for inpatient care which is very low. Hence, in September 2018, the Government of India launched the Ayushman Bharat scheme to achieve universal health coverage and address the unfinished agenda of RSBY. PM-JAY under the Ayushman Bharat scheme aims to cover almost 107.4 million households. Unlike its predecessor, RSBY, PM-JAY does not put exclusion criteria based on family size, age, and gender. PM-JAY covers poor and vulnerable households as per the Socio-Economic Caste Census 2011 (SECC 2011) and those covered under RSBY but do not fall into the SECC database. This scheme provides coverage of up to 5 lakhs per family per year for secondary and tertiary health-care utilization either in public or private hospitals. The coverage includes expenses incurred for 3 days prehospitalization and 15 days posthospitalization.
Various states are providing their health insurance schemes along with PM-JAY, still, these schemes cover either certain sections of the society like migrant laborers, BPL families, rural families, farmers and peasants, lower-middle-class families, or consider annual earning of the families. These schemes focus primarily on tertiary health-care expenditures. The majority of them do not cover outpatient care expenses, which contributed about 67.1% of the total OOPE in 2011–12 and 60% in 2016.,,
The earlier evaluation of the success of PM-JAY shows that its utilization is mainly in the wealthiest states such as Gujarat, Sikkim, and Nagaland. In contrast, poorer states such as Bihar, Uttar Pradesh, and Madhya Pradesh have shown low utilization. There is no doubt that PM-JAY is an improvement over RSBY, but reports suggest it has not improved access to care and reduced financial burden.
In addition to these schemes, there exist several health insurance and pension schemes with a special focus on the elderly population such as Indira Gandhi National Old Age Pension Scheme (IGNOAPS), National Program for Health Care of Elderly, Rashtriya Vayoshri Yojana, Varishtha Pension Bima Yojana, Pradhan Mantri Vaya Vandana Scheme, but the assistance provided is not enough to significantly cover or reduce the financial burden of morbidity.
| Conclusion|| |
Based on findings, we suggest increasing awareness about CVDs, endocrine, metabolic and nutritional ailments, and infectious ailments. We conclude that the exposure of the elderly population to ailments' higher economic burden is due to the low public spending on health care and lack of health insurance coverage. Although the government is spending on several growth-oriented policies, we still observe widening economic, regional, and gender disparities in health care, posing challenges for the health sector. Therefore, the current need is to balance the “biomedical model” and “sociocultural model” in health necessary to bridge the gaps and improve the quality of people's lives. Health policy in developing countries, especially in a diverse country like India, needs to be formulated by considering the vulnerabilities of the elderly population in health care. A focused National Health Policy addressing the prevailing conditions and promoting a long-term perspective plan for the elderly population is imperative. Health insurance schemes need to consider the high cost of OOPE and cover these costs, especially for the elderly population. This study also advocates for increased government spending on social security measures such as old age pensions like IGNOAPS, keeping in view the changing needs of the country's elderly population.
Financial support and sponsorship
The authors acknowledge the financial support of the Ministry of Human Resource Development (MHRD), Government of India, under the Scheme for Promotion of Academic and Research Collaboration (SPARC) (Project code: P1173).
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]