Frailty is a multidimensional geriatric syndrome characterized by reduced physiological reserves and increased vulnerability to adverse outcomes such as falls, disability, hospitalization, and mortality (1). It can affect multiple domains of functioning such as physical, cognitive, and psychological, leading to the need for assistance from caregivers or healthcare professionals, and may also result in social isolation and depression (2).

Globally, the prevalence of frailty ranges from 4% to 17%, while studies in India report higher estimates, up to 40% among community-dwelling elderly, reflecting socio-economic and regional disparities in health, access to care, and social support (3,4). The 2011 Census of India reported that there were approximately 103 million elderly individuals, representing 8.6% of the total population, with the state of Punjab having a higher proportion at 10.3% (5). India’s aging population is projected to reach 20% by 2050, posing significant healthcare challenges, particularly in urban areas undergoing rapid demographic and lifestyle transitions (6).

Faridkot, located in Punjab, India, offers a unique setting for examining frailty among urban older adults, an area with limited prior research. The city’s socio-economic diversity, urbanisation, and changing family structures contribute to risks such as social isolation and poor health outcomes (7,8). Most existing studies on frailty in India have focused on metropolitan or rural populations, leaving a gap in evidence from smaller urban settings (9). Identifying these determinants is essential for tailoring interventions under India’s National Programme for Health Care of the Elderly (NPHCE). Evidence from cities like Faridkot can inform public health strategies that promote healthy aging, independence, and dignity in similar urban environments (10).

The present study aims to estimate the prevalence of frailty and identify its determinants among the elderly population residing in urban Faridkot, Punjab, India. By examining socio-demographic, health, and behavioral factors, the findings are expected to guide strategies to strengthen geriatric care.

Study Design and Setting

A community-based cross-sectional study was conducted in the urban field practice area of the Department of Community Medicine, Guru Gobind Singh Medical College & Hospital, Faridkot, Punjab, India, from June 2023 to December 2024.

Study Population

The study population comprised elderly individuals aged ≥60 years residing in urban Faridkot. Those who refused consent or had critical illness preventing comprehension of study questions were excluded.

Sample Size and Sampling Method

The sample size was calculated using the single-proportion formula: n =  (11), where Z = 1.96 (95% confidence level), p = 12.3% (frailty prevalence from Zheng et al., 2016) (12), and d = 5% (margin of error). The minimum sample size obtained was 166, which was increased to 184 to account for a 10% non-response rate.

A line list of households with at least one elderly resident was obtained from the Auxiliary Nurse Midwife. Households were assigned unique identification numbers, and 184 elderly were selected via simple random sampling using computer-generated random numbers. All eligible elderly individuals in selected households were included. If an elderly in the household was unavailable or consent was not obtained, the next household on the list was approached.

Data Collection

Data were collected through face-to-face interviews using a pretested, structured proforma capturing socio-demographic, socio-economic status (Modified Kuppuswamy Scale 2022) (13), health profile and lifestyle characteristics. Frailty was assessed using the Tilburg Frailty Indicator (TFI), a validated self-reported tool evaluating physical (8 items), psychological (4 items), and social (3 items) domains. TFI scores range from 0 to 15, with a score ≥5 indicating frailty (14).

Data Analysis

Data were entered into Microsoft Excel 2019 and analyzed using Jamovi version 2.6. Descriptive statistics summarized participant characteristics and frailty prevalence. Chi-square tests assessed associations between frailty and categorical variables, with Fisher’s Exact Test applied when expected cell frequencies were <5. Binary logistic regression identified significant determinants of frailty, adjusting for potential confounders. A p-value <0.05 was considered statistically significant.

A total of 184 elderly participants were included in the study. The mean age was 67.6±7 years. Of these, 55.4% were female and 44.6% male. Most were Sikh (62.5%), married (60.9%), literate (57.6%), unemployed (73.9%), and lived in joint families (71.7%). Socio-economic assessment showed 43.5% belonged to the upper lower class, while only 4.3% were in the upper class (Table 1).

Table 1: Sociodemographic Characteristics of Study Participants (N=184)

Health-related characteristics are presented in Table 2. Multimorbidity (≥2 chronic conditions) was present in 34.2% of participants, and 21.7% reported polypharmacy (≥5 medications). Most participants (51.6%) had a normal BMI.

Table 2: Health and Behavioral Characteristics of Study Participants (N=184)

Frailty was present in 38.6% of participants, while 61.4% were non-frail (Figure 1).

Figure 1: Distribution of Frailty Status among Study Participants (N=184)

Table 3: Association of Sociodemographic Characteristics with Frailty Status among Study Participants (N=184)

Table 3 shows that frailty prevalence increased significantly with advancing age: 29.7% among young-old (60–69 years), 47.2% in middle-old (70–79 years), and 84.6% among very-old (≥80 years) (p < 0.001). Female participants (46.1%) were more likely to be frail compared to males (29.3%) (p = 0.020). Widowhood was associated with higher frailty (51.4%) compared to married elderly (30.4%) (p = 0.004). Education showed a significant relationship, with illiterate participants (48.7%) being more frail than literate ones (31.1%) (p = 0.015). Employment appeared protective: only 18.8% of employed elderly were frail compared to 45.6% of unemployed (p = 0.001). Socio-economic status (SES) was strongly associated with frailty (p = 0.001), with the prevalence highest in the lower class (64%) and lowest in the upper class (12.5%). No significant associations were observed for religion (p = 0.324) or family type (p = 0.172).

Table 4: Association of Health and Behavioral Characteristics with Frailty Status among Study Participants (N=184)

As shown in Table 4, alcohol use was associated with lower frailty prevalence (20.7% vs. 41.9%, p = 0.031). Tobacco use did not show a significant association (p = 0.075). Frailty was significantly more common among participants with multimorbidity (63.5% vs. 25.6%, p < 0.001), and polypharmacy (77.5% vs. 27.8%, p < 0.001). BMI was not significantly associated with frailty (p = 0.388).

Table 5: Multivariable Logistic Regression Analysis of Determinants of Frailty among Study Participants (N=184)

In logistic regression analysis (Table 5), age, SES, and polypharmacy emerged as independent predictors of frailty. Compared to the young-old, the odds of frailty were almost three times higher in the middle-old (AOR = 2.998, 95% CI: 1.131–7.949, p = 0.027) and more than eleven times higher among the very-old (AOR = 11.392, 95% CI: 1.536–84.509, p = 0.017). Socio-economic disadvantage was a strong determinant, with participants in the lower and upper-lower SES classes showing markedly higher odds of frailty (AOR = 42.928, p = 0.021; AOR = 37.414, p = 0.018, respectively) compared to the upper class. Polypharmacy was also strongly associated, with nearly tenfold increased odds of frailty (AOR = 9.841, 95% CI: 2.683–36.097, p < 0.001). Gender, marital status, education, occupation, alcohol use, and multimorbidity were not significant in adjusted analysis.

This study estimated frailty prevalence and identified its determinants among 184 elderly individuals (aged ≥60 years) in urban Faridkot, Punjab, revealing a frailty prevalence of 38.6% using the TFI. This aligns with Indian studies reporting similar rates, such as Dasgupta et al. (2019) (38.8% in rural West Bengal) and Panda et al. (2021) (38% in peri-urban Delhi), but is higher than global estimates, such as Zhang et al. (2018) (8.8% in females and 5.4% in males, USA) and O’Caoimh et al. (2021) (12–31% globally) (15)(16)(17)(18). The higher prevalence in Faridkot may reflect socio-economic disparities, multimorbidity burden, and limited healthcare access in smaller urban Indian settings compared to high-income countries.

Bivariate analysis showed significant associations with age, gender, marital status, education, employment, socio-economic status, alcohol use, multimorbidity, and polypharmacy. However, in multivariable binary logistic regression only age category, socio-economic status and polypharmacy remained significant determinants, while other variables lost significance, suggesting confounding effects. Age showed a strong graded association with frailty, with both the middle-old (70–79 years) and very-old (≥80 years) having significantly higher odds of being frail compared to the young-old (60–69 years). Upper socio-economic status was protective (OR 0.22, 95% CI 0.08–0.62, p=0.004), consistent with Srivastava and Muhammad (2022), who linked economic vulnerability to higher frailty (AOR 1.14, CI: 1.06-1.24) (19). Polypharmacy (≥5 medications) increased frailty odds (OR 5.20, CI: 1.40–19.27, p=0.014), aligning with Zheng et al. (2016), who reported higher frailty with multiple medications (12).

Advancing age itself emerged as a strong predictor of frailty, as older individuals exhibited greater biological decline, reduced physiological reserves, and increased vulnerability. The protective effect of higher SES suggests that improving economic conditions and healthcare access could reduce frailty. Polypharmacy’s strong association highlights the importance of medication reviews to minimize frailty risk, as recommended by Cesari et al. (2015) (20). Compared to metropolitan Indian studies (e.g., Kendhapedi and Devasenapathy, 2019, 28–63% frailty), Faridkot’s prevalence reflects unique urban challenges, such as social isolation and changing family structures (21).

The cross-sectional design limits causal inference, and the small sample of very-old participants may reduce precision for this subgroup. The TFI’s self-reported nature may introduce recall bias, though it is validated for community settings. The study’s focus on urban Faridkot limits generalizability to rural or other urban Indian populations.

Longitudinal studies are needed to explore frailty progression and causality in urban Punjab. Interventions targeting polypharmacy, socio-economic disparities, and early functional decline (e.g., exercise programs, nutritional support) could reduce frailty burden. Integrating frailty screening into NPHCE primary care initiatives would enhance early detection and management.

Frailty prevalence among the elderly in urban Faridkot was high (38.6%), with significant determinants being advancing age, low socio-economic status and polypharmacy. These findings highlight frailty as an urgent public health concern requiring region-specific strategies. Strengthening geriatric services, incorporating frailty screening into routine care, and addressing socio-economic vulnerabilities are essential to promote healthy and dignified aging in India.

None

Ethical approval was obtained from the Institutional Ethics Committee of GGSMCH, Faridkot, Punjab. Written informed consent was obtained from all the participants. Confidentiality was maintained, and all data were securely stored with access limited to authorized personnel.

1. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in Elderly People. Lancet. 2013;381(9868):752-762.
2. Morley JE, Vellas B, van Kan GA, Anker SD, Bauer JM, Bernabei R, et al. Frailty Consensus: A Call to Action. J Am Med Dir Assoc. 2013;14(6):392-397.
3. Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of Frailty in Community-dwelling Older Persons: A Systematic Review. J Am Geriatr Soc. 2012;60(8):1487-1492.
4. Pilania M, Yadav V, Bairwa M, Behera P, Gupta SD, Khurana H, et al. Prevalence and Predictors of Frailty among Elderly (≥ 60 Years) in India: A Systematic Review and Meta-analysis. Ageing Res Rev. 2017;29:34-41.
5. Alam M, Yadav P. The Status of Elderly in Punjab, 2011 2013. p 1–6. Available from: http://www.isec.ac.in/BKPAI_Punjab_State%20report.pdf
6. United Nations Population Fund. ‘Caring for Our Elders: Early Responses’ - India Ageing Report – 2017. New Delhi, India; 2017.
7. Ministry of Statistics and Programme Implementation, Government of India. Elderly in India 2021. New Delhi: National Statistical Office, Social Statistics Division; 2021. Available from: https://mospi.gov.in/sites/default/files/publication_reports/Elderly%20in%20India%202021.pdf
8. Office of the Registrar General & Census Commissioner, India. District Census Handbook: Faridkot, Series-04 Part XII-A & B. Punjab: Directorate of Census Operations; 2011. Available from: https://punjab.census.gov.in/PDF/DCHB/DH_2011_0311_PART_A_DCHB_FARIDKOT.pdf
9. Biritwum RB, Minicuci N, Yawson AE, Theou O, Mensah GP, Naidoo N, et al. Prevalence of and factors associated with frailty and disability in older adults from China, Ghana, India, Mexico, Russia, and South Africa. Maturitas. 2016;91:8-18.
10. Cesari M, Prince M, Thiyagarajan JA, De Carvalho IA, Bernabei R, Chan P, et al. Frailty: An Emerging Public Health Priority. J Am Med Dir Assoc. 2016;17(3):188-192.
11. Lwanga SK, Lemeshow S. Sample size determination in health studies: a practical manual. Geneva: World Health Organization; 1991.
12. Zheng Z, Guan S, Ding H, Wang Z, Zhang J, Zhao J, et al. Prevalence and incidence of frailty in community-dwelling older people: Beijing Longitudinal Study of Aging II. J Am Geriatr Soc. 2016;64(6):1281-1286.
13. Kumar G, Dash P, Patnaik J, Pany G. Socioeconomic Status Scale-Modified Kuppuswamy Scale For The Year 2022. Int J Comm Dent. 2022;10(1):1–6.
14. Gobbens RJ, Uchmanowicz I. Assessing frailty with the Tilburg Frailty Indicator (TFI): A review of reliability and validity. Clin Interv Aging. 2021;16:863-875.
15. Dasgupta A, Bandyopadhyay S, Bandyopadhyay L, Roy S, Paul B, Mandal S. How frail are our elderly? An assessment with Tilburg frailty indicator (TFI) in a rural elderly population of West Bengal. J Family Med Prim Care. 2019;8(7):2242.
16. Panda M, Islam F, Roy S, Pathak R, Kashyap V. Assessment of frailty and outcome of an ethnogeriatric population in periurban slums of Delhi, India – An interventional strategy in a primary health care setting. J Fam Med Prim Care. 2021;10(6):2272-2278.
17. Zhang Q, Guo H, Gu H, Zhao X. Gender-associated factors for frailty and their impact on hospitalization and mortality among community-dwelling older adults: a cross-sectional population-based study. PeerJ. 2018;6.
18. O’Caoimh R, Sezgin D, O’Donovan MR, Molloy DW, Clegg A, Rockwood K, et al. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing. 2021;50(1):96-104.
19. Srivastava S, Muhammad T. Socioeconomic vulnerability and frailty among community-dwelling older adults: cross-sectional findings from longitudinal aging study in India, 2017–18. BMC Geriatr. 2022;22(1):201.
20. Cesari M, Vellas B, Hsu FC, Newman AB, Doss H, King AC, et al. A physical activity intervention to treat frailty in older persons—results from the LIFE-P study. J Gerontol A Biol Sci Med Sci. 2015;70(2):216–222.
21. Kendhapedi KK, Devasenapathy N. Prevalence and factors associated with frailty among community-dwelling older people in rural Thanjavur district of South India: a cross-sectional study. BMJ Open. 2019;9(10):1-12.