Falls in older adults are multifactorial in origin with various internal and external factors leading to them. In India, there is no instrument for validated assessment that can assess the risk of falls in the elderly in a simple and fast manner and consequently can be used in primary health care by professionals from different areas.

In India, with increasing urbanization, the urban and rural distinction is widening at a faster pace. The cultural levels, living habits, physical activities, and environments of older adults in urban and rural areas are very different. Hence the study aims to carry out research in two Phases. Phase I we would recruit 1250 subjects. In this we would focus on identifying the prospective fall risk among older adults in urban and rural areas of Maharashtra using clinical evaluative methods and understanding the various risk factors (e.g., age, gender, anthropometric measurements, exercise/ physical activities, co-morbidities, depression, socio-economic status, use of slippers/ walking aids, consumption of tobacco/ alcohol, use of medications, joint pains, ADL scores, joint pains etc.) contributing to the same. The subjects would be then categorized into high risk and low risk of falls based on their scores on Quickscreen Fall Risk Assessment Scale. In phase II of the study, we would follow up 775 subjects from Phase I and aim to establish the diagnostic test properties of the Quickscreen Clinical Fall Risk Assessment Scale in order to establish the best cut-off scores among older adults. We also aim to study the incidence of falls by a 12-month follow-up study in order to see that those at risk of falls actually have a fall or not in a 12-month time span. We would also compare the intrinsic neurological factors (balance, mobility, autonomic dysfunction, reaction time etc.) of falls with older adults at risk and not at risk of falls.

We will estimate the mean and standard deviation (SD), or median and interquartile range (IQR) for continuous variables. We will estimate proportions for categorical variables. The normality of the data will be assessed using the Shapiro-Wilk test. The means will be compared using t-test for two groups or Analysis of Variance (ANOVA) for more than two groups. The distribution for non-parametric data will be compared using the Mann Whitney Wilcoxon test for two groups or the Kruskal Wallis test for more than two groups. The proportions will be compared using the chi square test or Fisher’s exact test for low expected cell counts.

For Phase II we will estimate the sensitivity, specificity, positive predictive value, and negative predictive value for prediction of falls in this population. We will also use receiver operating characteristics (ROC) curve for identifying the optimal cut-off. A p value of < 0.05 will be considered statistically significant. Data will be analyzed using Stata Version 17 (© StataCorp, College Station, Texas, USA).