Risks in the Elderly Individual - Research Proposal Example

Another study by Hnizdo et al., (2013) conducted a cohort study to evaluate the validity and reliability of the modified Johns Hopkins Fall Risk Assessment Tool (JH-FRAT) among elderly patients receiving home health care visits. Cohort studies are longitudinal studies involving follow up of two or more groups of patients. This is an observational study conducted when RCT is not possible (Aveyard, 2014). The outcome is measured over extended periods of time in the real world (Young and Solomon, 2008).

Cohort studies help discover links between different factors and are often used to find the causes of disease. This study by Hnizdo et al., (2013) conducted the study on 107 patients within the home health population, out of which 33 (30.8%) had one or more falls and seven (6.5%) experienced falls with injury. The study used a monthly fall calendar and a data extraction tool in addition to the JH-FRAT tool. Seven areas of evaluation included in the JH-FRAT were patient age, prior fall history, elimination, medications, use of patient care equipment, mobility, and cognition.

Patients were enrolled in the study from September to December 2011 and were followed till they were discharged from the services. This study found that 100% of the patients who fell and experienced an injury were at high risk. The fall group was older and had a higher frequency of cardio-pulmonary primary medical diagnosis (36.4% vs 17.6%; p ¼ 0.047). The fall group also had had a longer average duration of home health services than the non-fall group. The sample population has informed the details of the study and their consent obtained before data collection.

The researchers conclude that the JH-FRAT may allow focused interventions in a small subset of the population at high fall risk with an injury. However, this study's sample size was small and focused in a community-dwelling setting; it is difficult to predict the outcome if used in a different setting. Another cohort study was conducted by Palumbo et al., to test whether a predictive tool, trained using state-of-the-art statistical learning techniques over an extensive dataset, can outperform current tools for fall risk assessment.