Lee Studies the Health of Older Adults in Three New Research Articles
Posted: 11/9/2023 (CSDE Research)
CSDE Affiliate Chiyoung Lee (Nursing) recently released three new article with co-authors on the health of older adults. The first article is published in the Journal of Aging and Health, titled “Racial Differences in Older Adult’s Mental Health and Cognitive Symptomatology: Identifying Subgroups Using Multiple-Group Latent Class Analysis“. Little is known on the potential racial differences in latent subgroup membership based on mental health and cognitive symptomatology among older adults. Authors performed a secondary data analysis of Wave 2 data from the National Social Life, Health, and Aging Project (N = 1819). Symptoms were depression, anxiety, loneliness, happiness, and cognition. Multiple-group latent class analysis was conducted to identify latent subgroups based on mental health and cognitive symptoms and to compare these differences between race. They found that Class 1: “Severe Cognition & Mild-Moderate Mood Impaired,” Class 2: “Moderate Cognition & Mood Impaired,” and Class 3: “Mild Cognition Impaired & Healthy Mood” were identified. Black older adults were more likely to be in Class 1 while White older adults were more likely to be in Class 2 and Class 3. Findings highlight that clinicians need to provide culturally-sensitive care when assessing and treating symptoms across different racial groups.
The second article was published in Geroscience, “A network-based approach to explore comorbidity patterns among community-dwelling older adults living alone“. The detailed comorbidity patterns of community-dwelling older adults have not yet been explored. This study employed a network-based approach to investigate the comorbidity patterns of community-dwelling older adults living alone. The sample comprised a cross-sectional cohort of adults 65 or older living alone in a Korean city (n = 1041; mean age = 77.7 years, 77.6% women). A comorbidity network analysis that estimates networks aggregated from measures of significant co-occurrence between pairs of diseases was employed to investigate comorbid associations between 31 chronic conditions. A cluster detection algorithm was employed to identify specific clusters of comorbidities. The association strength was expressed as the observed-to-expected ratio (OER). As a result, fifteen diseases were interconnected within the network (OER > 1, p-value < .05). While hypertension had a high prevalence, osteoporosis was the most central disease, co-occurring with numerous other diseases. The strongest associations among comorbidities were found between thyroid disease and urinary incontinence, chronic otitis media and osteoporosis, gastric duodenal ulcer/gastritis and anemia, and depression and gastric duodenal ulcer/gastritis (OER > 1.85). Three distinct clusters were identified as follows: (a) cataracts, osteoporosis, chronic otitis media, osteoarthritis/rheumatism, low back pain/sciatica, urinary incontinence, post-accident sequelae, and thyroid diseases; (b) hyperlipidemia, diabetes mellitus, and hypertension; and (c) depression, skin disease, gastric duodenal ulcer/gastritis, and anemia. The results may prove valuable in guiding the early diagnosis, management, and treatment of comorbidities in older adults living alone.
The third article is published in Clinical Nursing Research, titled “Comorbidity Patterns in Older Patients Undergoing Hip Fracture Surgery: A Comorbidity Network Analysis Study“. Comorbidity network analysis (CNA) is a technique in which mathematical graphs encode correlations (edges) among diseases (nodes) inferred from the disease co-occurrence data of a patient group. The present study applied this network-based approach to identifying comorbidity patterns in older patients undergoing hip fracture surgery. This was a retrospective observational cohort study using electronic health records (EHR). EHR data were extracted from the one University Health System in the southeast United States. The cohort included patients aged 65 and above who had a first-time low-energy traumatic hip fracture treated surgically between October 1, 2015 and December 31, 2018 (n = 1,171). Comorbidity includes 17 diagnoses classified by the Charlson Comorbidity Index. The CNA investigated the comorbid associations among 17 diagnoses. The association strength was quantified using the observed-to-expected ratio (OER). Several network centrality measures were used to examine the importance of nodes, namely degree, strength, closeness, and betweenness centrality. A cluster detection algorithm was employed to determine specific clusters of comorbidities. Twelve diseases were significantly interconnected in the network (OER > 1, p-value < .05). The most robust associations were between metastatic carcinoma and mild liver disease, myocardial infarction and congestive heart failure, and hemi/paraplegia and cerebrovascular disease (OER > 2.5). Cerebrovascular disease, congestive heart failure, and myocardial infarction were identified as the central diseases that co-occurred with numerous other diseases. Two distinct clusters were noted, and the largest cluster comprised 10 diseases, primarily encompassing cardiometabolic and cognitive disorders. The results highlight specific patient comorbidities that could be used to guide clinical assessment, management, and targeted interventions that improve hip fracture outcomes in this patient group.