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Indian J Community Med
Indian J Community Med
IJCM
Indian J Community Med
Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine
0970-0218
1998-3581
Wolters Kluwer - Medknow India

IJCM-49-642
10.4103/ijcm.ijcm_904_22
Short Communication
Survival Analysis of the Geriatric Population having Multiple Diseases in the Jammu District, J and K, India
Shivgotra Vijay K.
Kumar Manjeet
Nanda Himani
Department of Statistics, University of Jammu, Jammu, J and K, India
Address for correspondence: Dr. Himani Nanda, Department of Statistics, University of Jammu, Jammu - 180 006, J and K, India. E-mail: himaniandnanda@gmail.com
Jul-Aug 2024
09 7 2024
49 4 642648
06 11 2022
22 2 2024
Copyright: © 2024 Indian Journal of Community Medicine
2024
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
Aging is a complex, multifactorial, and inevitable process, which begins before birth and continues throughout the life. Multimorbidity prevailing among the geriatric population is an important health challenge for most of the developing countries. To examine the effect of gender and increasing age on the survival of the geriatric population suffering from multimorbidity. A cross-sectional study was conducted among the geriatric population of the Jammu district, J and K, using multistage sampling procedure, and the analysis was conducted using the Kaplan-Meier method and survival analysis using software IBM SPSS version 24.0. Our study included 1150 study subjects, of whom 610 (53%) were males and 540 (47%) were females, respectively. It was indicated that the probability for the survival of the study population suffering from morbidity belonging to 60–64 years was higher than the survival of the geriatric population belonging to other age-groups or we can say that survival probability of the geriatric population suffering from morbidities decreases with the increase in age. Also, it was reported that probability for the survival of the female geriatric population suffering from morbidity was slightly higher than the survival of the male geriatric population. Gender had no significant effect on survival of the geriatric population suffering from morbidities, whereas baseline age had a significant effect on the survival of the geriatric population suffering from morbidities as their survival probability decreases with the increase in age.

Aging
geriatric population
hazard plot
Kaplan-Meier method
morbidity
multimorbidity
survival
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pmcINTRODUCTION

Aging is the phenomenon that is faced by every human being in his/her life span. Aging has been one of the most important developments in this century all over the world and will be one of the major challenges in the next millennium.[1] It is a complex, multifactorial, and inevitable process, which begins before birth and continues throughout the life.[2] The elderly people are the precious asset for any country. With their rich experience and wisdom, they contribute their strength for the substance and progress of the nation.[3]

Elderly refers to ages close to or over the average life span of human beings. This cannot be defined with precision because it varies in all communities. Different countries have defined the age of elderly persons differently as most of the developed countries have accepted the chronological age for elderly people as 65 years but the Government of India adopted “National Policy on Older Persons” in January 1999 which defines “senior citizen” or “elderly” as a person who is of age 60 years and above.[4]

The 2011 census shows that the older population of India reached 100 million. The United Nations statistical projection indicates that the size of India’s population aged 60 years and above is expected to increase to 117 million in 2015, 193 million in 2030, and further to 335 million in 2050. The proportion is likely to reach 13% population in 2030 and 20% in 2050.[5]

The elderly are one of the most vulnerable and high-risk groups in terms of health status in any society. Evaluation of the morbidity profile will have implications for providing health care for the elderly population and its costs.[6] A growing aging population in any country carries great social, economic, and public health implications. The burden of morbidity and mortality in the population will also undergo change from burden profiles dominated by infectious diseases to those affected by chronic non-communicable diseases.[7]

Survival times are data that measure follow-up time from defined starting point to the occurrence of given event. In analyzing survival data, two functions that are dependent on time are of particular interest: survival function and hazard function. The survival function is the probability of surviving (or not experiencing an event) up to specified time point, whereas hazard rate is the rate of occurrence for the event within a given period.[8] The survival function is rewritten as follows:

Here, T is the random lifetime which is taken from the population on which survival analysis has to be performed, and hence, it is non-zero.

Survival analysis techniques were first used for medical studies but have expanded to a variety of other disciplines, including financial services and engineering.[9] This study was undertaken with the objectives to study the effect of gender on survival of the geriatric population suffering from multimorbidity and to study whether survival time of the geriatric population suffering from multimorbidity was independent of their age at baseline examination.

MATERIALS AND METHODS

Ethical consideration

This study was approved by the Department of Statistics, University of Jammu, and the Institutional Ethics Committee of Government Medical College, Jammu.

Study design

A cross-sectional study was conducted in the Jammu district, J and K, during the period November 2017 to January 2020.

Sample size determination

The sample size was determined by single-proportion formula.

By considering a 95% confidence interval (CI), the z-value at 5% level of significance for the two-tailed test is 1.96, and (39.0%) is the proportion of diabetes among the elderly from the study conducted by Reshmi et al.[10] in Kalaburgi, Karnataka, in 2016 with absolute precision margin of error of 2.85% and with usual statistical constant (α =0.05) and (β =0.2) which gave a sample size of 1125.166, which was rounded off to 1125. Finally, the required sample size for geriatric persons of the Jammu district considered in our study was about 1150.

Sampling methods

In our study, we used the multistage sampling technique for selecting a sample of the geriatric population of the Jammu district, J and K. In the first stage, various tehsils were selected from the Jammu district, J and K, and in the second stage, these tehsils were further subdivided into subdivisions, blocks, and villages. Data were collected from the geriatric population residing in these blocks or villages by visiting their homes, district and subdistrict hospitals, primary health centers, old-age homes, etc.

Data collection

A pre-designed and pre-tested questionnaire was used for data collection. Data regarding the socio-demographic profile and various morbidities prevailing among the elderly population were collected. Modified Kuppuswamy scale and Udai Pareek scale were used for measuring socioeconomic status of urban and rural old-aged people, respectively.[1112] Also, the Geriatric Depression scale was used for computing the depression level among the geriatric population.[13]

Data analysis

Data were entered in Excel spreadsheets and then compiled, tabulated, and analyzed using software IBM SPSS (version 26.0 for Windows; SPSS Inc., Chicago, USA). Descriptive statistics were calculated for all the study variables. Data were analyzed using survival analysis. Survival analysis was a timeline analysis that understudies some group of individuals with some prior experience with a view of looking forward to the occurrence of event of interest. Survival analysis provides special techniques that are required to compare the risks for death associated with different treatments or groups, where the risk changes over time.

The analysis of survival data was conducted using the Kaplan-Meier method.[14] The Kaplan-Meier method, also known as product-limit estimator, was a non-parametric method used to estimate survival time from lifetime data. It was used to estimate the curves from observed survival times without assumption of underlying probability distribution.[15] The log-rank test was a popular test closely related to the Chi-square test which tests null hypothesis of no difference in survival time of two or more independent groups.

In our study, we have collected the data regarding socio-demographic and morbidity profile of 1150 study subjects by contacting them directly or from their blood relatives or close relatives or caretakers, respectively. Of the total study subjects, 400 were the cases of dead persons. Of the remaining 750 geriatric persons, it was assumed that 116 geriatric cases who were suffering from three or more morbidities were dead at the end of the study period because the persons suffering from multimorbidity were not able to survive for a longer time. It means that of the total 1150 cases, 516 cases were those in which event (death) had occurred due to the presence of multimorbidity and the remaining 634 cases were censored. The variable “survival time” was computed from the date of onset of morbidity to the end of the study period for persons who were surviving at the end of the study period. Also, the survival time was computed from date of onset of disease to the date of death for persons who were dead at the end of the study period. Here, the dependent variable was “survival time” of the geriatric population suffering from multimorbidity and independent variables included were their age at baseline examination and gender, respectively. The Kaplan-Meier method was used to compare survival times and survival rates of the geriatric population suffering from multimorbidity. Here, the age at baseline examination means the age of the study population at the time of onset of morbidity.

RESULTS

In our study, we enrolled 1150 study subjects, of whom 610 (53%) were males and 540 (47%) were females, respectively. More than half of the study subjects included the following: 749 (65.1%) had normal weight, whereas 360 (31.3%) respondents were overweight and only 41 (3.6%) respondents were obese. Three-fourths of the respondents (74.6%) belonged to the general category, whereas the remaining 292 (25.4%) belonged to other categories including SC/ST, respectively. About two-third (66.4%) of the study subjects were from nuclear families, whereas the remaining one-third (33.6%) respondents were from joint families. Economically, nearly half of the geriatric population, that is, 616 (53.6%), were dependent on others, whereas the remaining 534 (46.4%) study subjects were living independently, respectively. Half of the geriatric population involved in our study, that is, 581 (50.5%), belonged to urban areas, whereas the other 569 (49.5%) study subjects belonged to rural areas. It was also observed that the majority of the geriatric population, that is, 827 (71.9%), belonged to the Hindu religion, followed by 163 (14.2%) respondents belonging to the Muslim religion, whereas the remaining 160 (13.9%) respondents belonged to the Sikh religion, respectively.

From morbidity point of view, chronic morbidities occurred in at least (50%) of the geriatric Indian population. This implies that the aging population was suffering from chronic medical conditions. It was observed that of the total 1150 respondents, 493 (42.9%) respondents were suffering from two morbidities followed by 477 (41.5%) respondents having no morbidity or one morbidity, 152 (13.2%) respondents having three morbidities, whereas the remaining 28 (2.4%) respondents were suffering from more than three morbidities, respectively. Table 1 shows the socio-demographic profile of study subjects, respectively.

Table 1: Socio-demographic profile of the geriatric population of the Jammu district, J and K

Variables	Males n (%)	Females n (%)	Total n (%)	
BMI				
    Normal	447 (73.3)	302 (55.9)	749 (65.1)	
    Overweight	147 (24.1)	213 (39.4)	360 (31.3)	
    Obese	16 (2.6)	25 (4.6)	41 (3.6)	
Social category				
    General	473 (77.5)	385 (71.3)	858 (74.6)	
    Others	137 (22.5)	155 (28.7)	292 (25.4)	
Family type				
    Nuclear	401 (66.4)	363 (67.2)	764 (66.4)	
    Joint	209 (34.3)	177 (32.8)	386 (33.6)	
Marital status				
    Married	454 (74.4)	304 (56.3)	758 (65.9)	
    Widow/widower	156 (25.6)	236 (43.7)	392 (34.1)	
Dependency status				
    Living independent	507(83.1)	27 (5.0)	534 (46.4)	
    Dependent on others	103 (16.9)	513 (95.0)	616 (53.6)	
Living status				
    LSC	563 (92.3)	494 (91.5)	1057 (91.9)	
    Others	47 (7.7)	46 (8.5)	93 (8.1)	
Age (in years)				
    60-64	148 (24.3)	210 (38.9)	358 (31.1)	
    65-69	168 (27.5)	130 (24.1)	298 (25.9)	
    70-74	123 (20.2)	87 (16.1)	210 (18.3)	
    ≥75	171 (28.0)	113 (20.9)	284 (24.7)	
Area				
    Urban	314 (51.5)	267 (49.4)	581 (50.5)	
    Rural	296 (49.5)	273 (50.6)	569 (49.5)	
Religion				
    Hindu	439 (72.0)	388 (71.9)	827 (71.9)	
    Muslim	97 (15.9)	66 (12.2)	163 (14.2)	
    Sikh	74 (12.1)	86 (15.9)	160 (13.9)	
SES				
    Upper lower	104 (17.0)	126 (23.3)	230 (20.0)	
    Lower middle	232 (38.0)	219 (40.6)	451 (39.2)	
    Upper middle	197 (32.3)	160 (29.6)	357 (31.0)	
    Upper	77 (12.6)	35 (6.5)	112 (9.7)	
Number of morbidities				
    No or one morbidity	275 (45.1)	202 (37.4)	477 (41.5)	
    Two morbidities	244 (40.0)	249 (46.1)	493 (42.9)	
    Three morbidities	80 (13.1)	72 (13.3)	152 (13.2)	
    More than three morbidities	11 (1.8)	17 (3.1)	28 (2.4)	
LSC—living with spouse and children; SES—socioeconomic status

The age-wise case processing summary revealed that of the total 1150 cases, 516 (44.9%) were the persons who were dead at the end of study and the remaining 634 (55.1%) were censored, respectively. Of the total 516 dead persons, 121 (33.8%) geriatric persons belonged to age-group of 60–64 years followed by 118 (31.6%) in 65–69 years of age-group, 112 (33.3%) geriatric persons in 70–74 years of age-group, and the remaining 165 (58.1%) persons were aged ≥75 years, respectively.

Table 2 shows the age-wise mean and median survival times of the geriatric population of the Jammu district. It was observed that the estimated mean survival times of the geriatric population suffering from morbidity until death for the age-groups 60–64 years, 65–69 years, 70–74 years, and ≥75 years were 10.3 years, 8.7 years, 8.6 years, and 6.7 years, respectively, whereas the median survival times of the geriatric population suffering from morbidity until death for the age-groups 60–64 years, 65–69 years, 70–74 years, and ≥75 years were 10 years, 7 years, 7 years, and 5 years, respectively. It was also indicated that the probability for the survival of the study population suffering from morbidity belonging to 60–64 years was higher than the probability for the survival of the geriatric population belonging to other age-groups or we can say that the survival probability of the geriatric population suffering from morbidities decreases with the increase in age. Also, it was indicated that age had a significant effect on the survival of the geriatric population suffering from morbidities as the log-rank (Mantel-Cox) statistic was 25.690 (P = 0.001 < 0.05), respectively, which means that there was a significant difference between the age-wise survival times of the geriatric population of the Jammu district, respectively. Figure 1 and Figure 2 display the age-wise survival plots and hazard plots of the geriatric population of the Jammu district, J and K, India. It was indicated that survival probability of the geriatric population suffering from morbidities belonging to age-group 60–64 years was higher than the survival probability of the geriatric population belonging to other age-groups, respectively.

Figure 1 Kaplan-Meier plot for the survival of the geriatric population of the Jammu district, J and K, due to the presence of multimorbidity by age-group

Figure 2 Kaplan-Meier hazard plot for the survival of the geriatric population of the Jammu district, J and K, due to the presence of multimorbidity by age-group

Table 2: Age-wise mean and median for survival time of the geriatric population of the Jammu district, J&K, India

Age-group	Mean for survival time (in years)	Median for survival time (in years)	
	Estimate	Std. error	95% CI	Estimate	Std. error	95% CI	
			LB	UB			LB	UB	
60-64	10.3	0.429	9.414	11.095	10	0.487	9.045	10.955	
65-69	8.7	0.532	7.717	9.801	7	0.588	5.848	8.152	
70-74	8.6	0.558	7.568	9.754	7	0.857	5.319	8.681	
≥ 75	6.7	0.451	5.810	7.579	5	0.353	4.309	5.691	
Overall	8.7	0.250	8.240	9.221	8	0.346	7.321	8.679	
CI—confidence interval; LB—lower bound; UB—upper bound

The gender-wise case processing summary of the geriatric population of the Jammu district, J and K, India, revealed that of the total 1150 cases, 516 were the persons who were dead at the end of study and the remaining 634 were censored, respectively. Of the total 516 dead persons, 248 were males and 268 were females, respectively.

Table 3 shows the gender-wise mean and median survival times of the geriatric population of the Jammu district, J and K, India. It was observed that estimated mean and median survival times for the female geriatric population suffering from morbidity until death were 8.9 years and 8 years, respectively, whereas the mean and median survival times for the male geriatric population suffering from morbidity were 8.5 years and 7.5 years, respectively. It was also reported that probability for the survival of the female geriatric population suffering from morbidity was slightly higher than the survival of the male geriatric population. Also, it was clearly indicated that gender had no significant effect on the survival of the geriatric population suffering from morbidities as the log-rank (Mantel-Cox) statistic was 0.972 (P = 0.324 > 0.05), respectively.

Table 3: Gender-wise mean and median for survival time of the geriatric population of the Jammu district, J&K, India

Gender	Mean for survival time (in years)	Median for survival time (in years)	
	Estimate	Std. error	95% CI	Estimate	Std. error	95% CI	
			LB	UB			LB	UB	
Female	8.9	0.374	8.202	9.668	8	0.537	5.948	8.052	
Male	8.5	0.335	7.884	9.198	7.5	0.426	7.164	8.836	
Overall	8.7	0.250	8.240	9.221	8	0.346	7.321	8.679	
CI—confidence interval; LB—lower bound; UB—upper bound

Figures 3 and 4 display the gender-wise survival plots and hazard plots of the geriatric population of the Jammu district. It was indicated that survival probability of the male geriatric population suffering from morbidities was lower than the survival probability of the female geriatric population, respectively. The vertical axis of Figure 4 denotes the cumulative hazard, which was equal to the negative log of survival probability.

Figure 3 Kaplan-Meier plot for gender-wise survival of the geriatric population of the Jammu district, J and K, due to the presence of multimorbidity

Figure 4 Kaplan-Meier plot for gender-wise hazard of the geriatric population of the Jammu district, J and K, due to the presence of multimorbidity

DISCUSSION

Aging is a normal irreversible enhancing change in all living organisms over a chronological period of time. It was a universal phenomenon which presented both challenges and opportunities for family as well as for the society. The trend of decreasing percentage of the geriatric population with the increment of age was observed in our study. Similar findings were observed in studies conducted by different researchers, such as Verma et al.[16171819] Like the studies conducted by Chaudhary et al., the number of geriatric males was predominant in our study as compared to the number of females.[2021] This was contrary to the studies conducted by Shraddha et al. in which the proportion of female study subjects outnumbered male study subjects.[172223]

In our study, about two-thirds of the study population (65.1%) was having normal weight, followed by overweight (31.3%), whereas the remaining (3.6%) study subjects were obese. Similar pattern was reported by Bhatt et al. in their respective studies.[1624] Our study reported that most of the geriatric persons were from urban areas. Similar findings were reported by Sahu et al.[25] but this was contrary to the study conducted by Verma et al. in Punjab in which the maximum geriatric population was from rural areas, respectively.[26] Our study concluded that most of the study subjects included were Hindus (71.9%) followed by Muslims (14.2%) and Sikhs (13.9%), respectively. Another study conducted by Bardhan et al. observed the same pattern in which about (85.5%) of the total geriatric population included were Hindus.[27]

In our study, it was reported that two-thirds (66.4%) of the geriatric population included were from nuclear families, whereas the remaining (33.6%) of the geriatric population were from joint families. Shraddha et al. also reported the same findings in their study.[17] It was found that the majority of the geriatric persons included in our study belonged to the general caste category (74.6%), which was contrary to the study conducted by Banjare et al.[2223] Verma et al. reported that the majority of the geriatric population were married.[161923] Similar findings were reported in our study in which (65.9%) of geriatric persons were married followed by (34.1%) widows/widowers. These findings were contrary to the study conducted by Sahu et al. in Varanasi in which the majority of geriatric persons involved were widows/widowers, respectively.[25]

Our study reported that according to the modified Kuppuswamy socioeconomic classification, (39.2%) of the total population belonged to the lower-middle class, followed by the upper-middle class (31%), upper-lower class (20%), and upper class (9.7%), respectively. These findings were contrary to studies conducted by Verma et al. and Reshmi et al. who reported that the majority of the population belonged to lower class, respectively.[1016] The morbidity pattern observed in our study depicted that the most common morbidity was vision problem (50.9%) followed by gastrointestinal disorders (42.3%), hypertension (37.7%), arthritis (35.1%), cataract (30%), body aches (28.9%), other problems (including thyroid, stroke, edema, back pain, and urological disorders) (26.3%), diabetes (24.3%), insomnia (22%), asthma (19.1%), cardiovascular diseases (18.1%), hearing impairment (17%), psychological disorders (12.7%), cancer (12.3%), anemia (11.6%), kidney diseases and dementia (11.1%), and so on. Gupta et al. also revealed the same morbidity pattern where eye problems were observed in (68.1%) of subjects followed by hypertension (44%), and so on.[28]

In our study, we computed age-wise mean and median survival times of the geriatric population of the Jammu district. It was observed that the probability of survival of the geriatric population suffering from morbidity belonging to 60–64 years was higher than the probability of survival of the geriatric population belonging to other age-groups or we can say that the survival probability of the geriatric population suffering from morbidities decreases with the increase in age. Also, it was clearly indicated that baseline age had a significant effect on the survival of geriatric persons suffering from morbidities as the log-rank (Mantel-Cox) statistic was 25.690 (P = 0.001), respectively, which means that there was a significant difference between age-wise survival times of the geriatric population of the Jammu district, respectively.

Similarly, we computed the gender-wise mean and median survival times of the geriatric population of the Jammu district. It was observed that the probability of survival of the female geriatric population suffering from morbidity was slightly higher than the survival of the male geriatric population of the Jammu district. Also, it was found that gender had no significant effect on the survival of the geriatric population suffering from morbidities as the log-rank (Mantel-Cox) statistic was 0.972 (P = 0.324 > 0.05), respectively, which means that there was no significant difference between the survival times of male geriatric and female geriatric populations, respectively. As such, there was no study available which used this model to examine the survival times of the geriatric population of India suffering from multimorbidity but the findings of our study were similar to the study conducted by Rizzuto et al. in Stockholm, Sweden, in which there were no gender differences observed in mean and median survival times of the geriatric population suffering from multimorbidity.[29]

CONCLUSION

Old age is regarded as the normal inevitable biological phenomenon faced by all living beings in his/her life span. The findings of our study indicated that the survival time of the geriatric population with multimorbidity was dependent on the age at baseline examination as the survival probability of study subjects suffering from morbidities decreases with the increase in age. Also, it was indicated that gender had no significant effect on survival of the geriatric population suffering from multimorbidity but the probability for the survival of the female geriatric population suffering from morbidity was slightly higher than the survival of the male geriatric population, respectively.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Acknowledgement

We would like to express our sincere and heartfelt thanks to everyone responsible for conduction and completion of study and to everyone who provided information and participated in the study.
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