Pandemic in Indonesian older people: The implication for sleep deprivation, loss of appetite, and psychosomatic complaints

Background : During the COVID-19 pandemic, many individuals were concerned about being infected. Meanwhile, the elderly felt isolated due to the detrimental effect on their mental and physical health. Therefore, this study aimed to identify the mental health issues suffered by the elderly during the COVID-19 pandemic. The most frequent mental health issues assessed are sleep deprivation, loss of appetite, and psychosomatic complaints.Method : This is a descriptive-analytic study using a cross-sectional approach to find the mental health impact of the COVID-19 pandemic. The population consists of 259 older adults (≥60 years) living in West Java and Jakarta.Results : The average age of study participants is 65.3 years old (±6.8SD range 60–89 years old). The multivariable logistic regression model showed that sleep deprivation is signifantly associated with non-college education background (OR=2.28;95%CI; 1.23-4.61), anxiety (OR=7.09; 95%CI; 3.57-14.08), and the existence of chronic illness (OR=2.75; 95%CI; 1.44 -5.26). Subsequently, the psychosomatic symptom was associated with anxiety (OR=5.27; 95%CI; 2.75 -10.11) and chronic illness (OR=2.80; 95%CI; 1.47 -5.32). Loss appetite was associated with non-college education background (OR=2.50; 95%CI; 1.16-5.41), anxiety (OR=10.41; 95%CI; 5.01-21.63), and the existence of chronic illness (OR=3.60; 95%CI; 1.72-7.55). The analysis showed that none of the COVID-19 related fear is associated with a sleep disorder, loss of appetite, and psychosomatic symptoms.Conclussion : A psychosocial approach is necessary to reduce the mental health issues during the Covid-19 Pandemic, focusing on anxiety management and assisting those with chronic diseases and low education.

Decision Tree Clinical Algorithm for Screening of Mild Cognitive Impairment in the Elderly in Primary Health Care: Development, Test of Accuracy, and Time-Effectiveness Analysis

Mild cognitive impairment (MCI) is predicted to be a common cognitive impairment in primary health care. Early detection and appropriate management of MCI can slow the rate of deterioration in cognitive deficits. The current methods for early detection of MCI have not been satisfactory for some doctors in primary health care. Therefore, an easy, fast, accurate and reliable method for screening of MCI in primary health care is needed. This study intends to develop a decision tree clinical algorithm based on a combination of simple neurological physical examination and brief cognitive assessment for distinguishing elderly with MCI from normal elderly in primary health care. This is a diagnostic study, comparative analysis in elderly with normal cognition and those presenting with MCI. We enrolled 212 elderly people aged 60.04-79.92 years old. Multivariate statistical analysis showed that the existence of subjective memory complaints, history of lack of physical exercise, abnormal verbal semantic fluency, and poor one-leg balance were found to be predictors of MCI diagnosis (p ≤ 0.001; p = 0.036; p ≤ 0.001; p = 0.013). The decision trees clinical algorithm, which is a combination of these variables, has a fairly good accuracy in distinguishing elderly with MCI from normal elderly (accuracy = 89.62%; sensitivity = 71.05%; specificity = 100%; positive predictive value = 100%; negative predictive value = 86.08%; negative likelihood ratio = 0.29; and time effectiveness ratio = 3.03). These results suggest that the decision tree clinical algorithm can be used for screening of MCI in the elderly in primary health care.