Morbidities and perceived health status among elderly in Eastern India: some observations from NSSO data
Abstract Background Since the number of elderly people in Odisha, Eastern India, is predicted to rise faster than the national average and surpass that of developed states like Karnataka, wherein the majority of them live in rural areas with low per capita income, study on their illness prevalence a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Discover Social Science and Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44155-025-00229-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850231381931589632 |
|---|---|
| author | Amit Kumar Sahoo Susanta Nag Kirtti Ranjan Paltasingh Kedarsen Sahoo |
| author_facet | Amit Kumar Sahoo Susanta Nag Kirtti Ranjan Paltasingh Kedarsen Sahoo |
| author_sort | Amit Kumar Sahoo |
| collection | DOAJ |
| description | Abstract Background Since the number of elderly people in Odisha, Eastern India, is predicted to rise faster than the national average and surpass that of developed states like Karnataka, wherein the majority of them live in rural areas with low per capita income, study on their illness prevalence and morbidity patterns and health status is crucial for developing welfare programs and improving healthcare facilities to meet the demands of curative and preventative health services. Therefore, this paper examines the prevalence rate and pattern of morbidities among elderly people in Odisha and subsequently finds the determinants of the prevalence of chronic morbidity and self-reported health status (SRHS). Data source and methodology A unit-level nationally representative sample survey data (75th round of the NSS data) has been used for our analysis. The survey was conducted across all states and UTs, consisting of 1,13,823 households and 555,114 people. From which Odisha’s sample (19,083 individuals) has been selected, of which 1914 individuals were 60 years and above. The outcome variables used in the analysis were chronic illness and self-reported health status. Bivariate and multivariate logit regressions have been used to study the prevalence of morbidity and health status of the elderly. Findings The result shows that hypertension/ heart diseases (57 per thousand), diabetes (38 per thousand), and musculoskeletal (61 per thousand) are the major diseases the elderly mainly suffer from, irrespective of their residence and gender. There is little difference in the prevalence of diseases between males and females in rural areas. However, a gender divide in the prevalence of diseases is noticed in urban areas where females mainly suffer from musculoskeletal health issues, but hypertension/ heart diseases and diabetes are two major diseases affecting male elderly. From logit regression results, it is found that factors like age, caste, place of residence, and MPCE quintile significantly affect the likelihood of prevalence of chronic diseases. It also finds that marital status, education, caste, gender, chronic illness, living arrangements, and economic independence are the major factors influencing their perceived health status (SRHS). Conclusion and policy implication In order to achieve health equity, the study’s findings can be used to support the implementation of various health initiatives, guide intervention efforts, and provide fresh ideas for government policymaking on the health of the elderly. |
| format | Article |
| id | doaj-art-c53e8c2c65504f0ca631e628204e4eb4 |
| institution | OA Journals |
| issn | 2731-0469 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Social Science and Health |
| spelling | doaj-art-c53e8c2c65504f0ca631e628204e4eb42025-08-20T02:03:32ZengSpringerDiscover Social Science and Health2731-04692025-05-015111510.1007/s44155-025-00229-xMorbidities and perceived health status among elderly in Eastern India: some observations from NSSO dataAmit Kumar Sahoo0Susanta Nag1Kirtti Ranjan Paltasingh2Kedarsen Sahoo3Department of Economics, Ravenshaw UniversityDepartment of Economics, Central University of JammuDepartment of Economics, Ravenshaw UniversityDepartment of A& A Economics, Utkal UniversityAbstract Background Since the number of elderly people in Odisha, Eastern India, is predicted to rise faster than the national average and surpass that of developed states like Karnataka, wherein the majority of them live in rural areas with low per capita income, study on their illness prevalence and morbidity patterns and health status is crucial for developing welfare programs and improving healthcare facilities to meet the demands of curative and preventative health services. Therefore, this paper examines the prevalence rate and pattern of morbidities among elderly people in Odisha and subsequently finds the determinants of the prevalence of chronic morbidity and self-reported health status (SRHS). Data source and methodology A unit-level nationally representative sample survey data (75th round of the NSS data) has been used for our analysis. The survey was conducted across all states and UTs, consisting of 1,13,823 households and 555,114 people. From which Odisha’s sample (19,083 individuals) has been selected, of which 1914 individuals were 60 years and above. The outcome variables used in the analysis were chronic illness and self-reported health status. Bivariate and multivariate logit regressions have been used to study the prevalence of morbidity and health status of the elderly. Findings The result shows that hypertension/ heart diseases (57 per thousand), diabetes (38 per thousand), and musculoskeletal (61 per thousand) are the major diseases the elderly mainly suffer from, irrespective of their residence and gender. There is little difference in the prevalence of diseases between males and females in rural areas. However, a gender divide in the prevalence of diseases is noticed in urban areas where females mainly suffer from musculoskeletal health issues, but hypertension/ heart diseases and diabetes are two major diseases affecting male elderly. From logit regression results, it is found that factors like age, caste, place of residence, and MPCE quintile significantly affect the likelihood of prevalence of chronic diseases. It also finds that marital status, education, caste, gender, chronic illness, living arrangements, and economic independence are the major factors influencing their perceived health status (SRHS). Conclusion and policy implication In order to achieve health equity, the study’s findings can be used to support the implementation of various health initiatives, guide intervention efforts, and provide fresh ideas for government policymaking on the health of the elderly.https://doi.org/10.1007/s44155-025-00229-xMorbiditiesHealth statusElderlyOdishaLogit modelNSSO data |
| spellingShingle | Amit Kumar Sahoo Susanta Nag Kirtti Ranjan Paltasingh Kedarsen Sahoo Morbidities and perceived health status among elderly in Eastern India: some observations from NSSO data Discover Social Science and Health Morbidities Health status Elderly Odisha Logit model NSSO data |
| title | Morbidities and perceived health status among elderly in Eastern India: some observations from NSSO data |
| title_full | Morbidities and perceived health status among elderly in Eastern India: some observations from NSSO data |
| title_fullStr | Morbidities and perceived health status among elderly in Eastern India: some observations from NSSO data |
| title_full_unstemmed | Morbidities and perceived health status among elderly in Eastern India: some observations from NSSO data |
| title_short | Morbidities and perceived health status among elderly in Eastern India: some observations from NSSO data |
| title_sort | morbidities and perceived health status among elderly in eastern india some observations from nsso data |
| topic | Morbidities Health status Elderly Odisha Logit model NSSO data |
| url | https://doi.org/10.1007/s44155-025-00229-x |
| work_keys_str_mv | AT amitkumarsahoo morbiditiesandperceivedhealthstatusamongelderlyineasternindiasomeobservationsfromnssodata AT susantanag morbiditiesandperceivedhealthstatusamongelderlyineasternindiasomeobservationsfromnssodata AT kirttiranjanpaltasingh morbiditiesandperceivedhealthstatusamongelderlyineasternindiasomeobservationsfromnssodata AT kedarsensahoo morbiditiesandperceivedhealthstatusamongelderlyineasternindiasomeobservationsfromnssodata |