Severity of frailty using modified Thai frailty index, social factors, and prediction of mortality among community-dwelling older adults in a middle-income country - Morkphrom, Ekkaphop; Srinonprasert, Varalak; Sura-amonrattana, Unchana; Siriussawakul, Arunotai; Sainimnuan, Supawadee; Preedachitkun, Rinrada; Aekplakorn, Wichai (Frontiers in Medicine, 2022)
Background Frailty has been increasingly recognized as a public health problem for aging populations with significant social impact, particularly in low- and middle-income countries. We aimed to develop a modified version of the Thai Frailty Index (TFI) and explore the association between different frailty statuses, socioeconomic factors, and mortality in community-dwelling older people from a middle-income country. Methods The data from participants aged ≥60 years in the Fourth Thai National Health Examination Survey were used to construct the 30-item TFI. Cutoff points were created based on stratum-specific likelihood ratio. TFI ≤ 0.10 was categorized as fit, 0.10–0.25 as pre-frail, 0.25–0.45 as mildly frail, and >0.45 as severely frail. The association of frailty status with mortality was examined using Cox proportional hazard models. Findings Among 8,195 older adults with a mean age of 69.2 years, 1,284 died during the 7-year follow-up. The prevalence of frailty was 16.6%. The adjusted hazard ratio (aHR) for mortality in pre-frail was 1.76 (95% CI = 1.50–2.07), mildly frail 2.79 (95% CI = 2.33–3.35), and severely frail 6.34 (95% CI = 4.60–8.73). Having a caretaker in the same household alleviated mortality risk for severely frail participants with an aHR of 2.93 (95% CI = 1.92–4.46) compared with an aHR of 6.89 (95% CI = 3.87–12.26) among those living without a caretaker. Interpretation The severity of frailty classified by the modified TFI can predict long-term mortality risk for community-dwelling older adults. Identification of severely frail older people to provide appropriate care might alleviate mortality risk. Our findings can inform policymakers to appropriately allocate services in a resource-limited setting.