Symptom clusters in childhood cancer survivors in Korea: A latent class analysis

2020 ◽  
Vol 29 (6) ◽  
Author(s):  
Hye Chong Hong ◽  
Young Man Kim ◽  
Ari Min
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Childhood Cancer ◽  
Latent Class ◽  
Childhood Cancer Survivors ◽  
Symptom Clusters ◽  
Class Analysis

scholarly journals Acculturation, Mental Health, and Quality of Life am ong Hispanic Childhood Cancer Survivors: A Latent Class Analysis

2018 ◽  
Vol 28 (1) ◽  
pp. 55 ◽  
Author(s):  
Jessica Tobin ◽  
Kimberly A. Miller ◽  
Lourdes Baezconde-Garbanati ◽  
Jennifer B. Unger ◽  
Ann S. Hamilton ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Depressive Symptoms ◽  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Childhood Cancer ◽  
High Probability ◽  
Latent Class ◽  
Childhood Cancer Survivors ◽  
Class Analysis

<p class="Pa7"><strong>Objective: </strong>Acculturation appears to be an important aspect of the association between ethnicity and disease, but it has not been explored in depth among childhood cancer survivors (CCS). The purpose of our study was to identify distinct acculturative profiles among Hispanic CCS and to assess dif­ferences in quality of life and depressive symptoms.</p><p class="Pa7"><strong>Design: </strong>Latent class analysis was used to identify distinct acculturative profiles using 9 indicator items reflecting Hispanic and An­glo cultural orientation. Multinomial logistic regression was performed to explore differ­ences in depressive symptoms and quality of life between acculturation classes.</p><p class="Pa7"><strong>Setting and Participants: </strong>Participants were diagnosed in Los Angeles County, Califor­nia, USA between 2000-2007 and were recruited for the study in 2009.</p><p class="Pa7"><strong>Main Outcome Measures: </strong>Center for Epi­demiologic Studies depression scale and the PedsQL 4.0 quality of life scale.</p><p class="Pa7"><strong>Results: </strong>Three distinct acculturation classes emerged. All classes displayed a high prob­ability of endorsing all Anglo orientation items. One class additionally demonstrated a high probability of endorsing all Hispanic orientation items and was labeled bicultural (40%); another demonstrated low probabil­ity of endorsing the Hispanic items so was labeled assimilated (32%); and the last dem­onstrated a high probability of endorsing only the Hispanic items related to language use and was labeled linguistically Hispanic/ culturally Anglo (LH) (28%).</p><p class="Pa8"><strong>Conclusions: </strong>The assimilated group had significantly more depressive symptoms and lower quality of life than the other two groups. This may indicate that loss of the Hispanic culture may be associated with poorer psychosocial health among CCS.</p><p class="Pa8"><em>Ethn Dis. </em>2018;28(1):55-60; doi:10.18865/ ed.28.1.55.</p>

scholarly journals Patterns of comorbidities in hospitalised cancer survivors for palliative care and associated in-hospital mortality risk: A latent class analysis of a statewide all-inclusive inpatient data

2019 ◽  
Vol 33 (10) ◽  
pp. 1272-1281 ◽  
Author(s):  
Lan Luo ◽  
Wei Du ◽  
Shanley Chong ◽  
Huibo Ji ◽  
Nicholas Glasgow
Keyword(s):  
Risk Factors ◽  
Palliative Care ◽  
Hospital Mortality ◽  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Cancer Patients ◽  
Mortality Risk ◽  
Latent Class ◽  
Class Analysis ◽  
Hospital Deaths

Background: At the end of life, cancer survivors often experience exacerbations of complex comorbidities requiring acute hospital care. Few studies consider comorbidity patterns in cancer survivors receiving palliative care. Aim: To identify patterns of comorbidities in cancer patients receiving palliative care and factors associated with in-hospital mortality risk. Design, Setting/Participants: New South Wales Admitted Patient Data Collection data were used for this retrospective cohort study with 47,265 cancer patients receiving palliative care during the period financial year 2001–2013. A latent class analysis was used to identify complex comorbidity patterns. A regression mixture model was used to identify risk factors in relation to in-hospital mortality in different latent classes. Results: Five comorbidity patterns were identified: ‘multiple comorbidities and symptoms’ (comprising 9.1% of the study population), ‘more symptoms’ (27.1%), ‘few comorbidities’ (39.4%), ‘genitourinary and infection’ (8.7%), and ‘circulatory and endocrine’ (15.6%). In-hospital mortality was the highest for ‘few comorbidities’ group and the lowest for ‘more symptoms’ group. Severe comorbidities were associated with elevated mortality in patients from ‘multiple comorbidities and symptoms’, ‘more symptoms’, and ‘genitourinary and infection’ groups. Intensive care was associated with a 37% increased risk of in-hospital deaths in those presenting with more ‘multiple comorbidities and symptoms’, but with a 22% risk reduction in those presenting with ‘more symptoms’. Conclusion: Identification of comorbidity patterns and risk factors for in-hospital deaths in cancer patients provides an avenue to further develop appropriate palliative care strategies aimed at improving outcomes in cancer survivors.

scholarly journals Heterogeneity in quality of life of long‐term colon cancer survivors: a latent class analysis of the population‐based PROFILES registry

2020 ◽  
Author(s):  
Felix J. Clouth ◽  
Arturo Moncada‐Torres ◽  
Gijs Geleijnse ◽  
Floortje Mols ◽  
Felice N. Erning ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Colon Cancer ◽  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Population Based ◽  
Class Analysis

scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study (Preprint)

2020 ◽  
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

BACKGROUND eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. OBJECTIVE The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. METHODS Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. RESULTS Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. CONCLUSIONS Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

Determinants of symptom clusters and associations with health outcomes in childhood cancer survivors: A report from the St. Jude Lifetime Cohort (SJLIFE).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10046-10046
Author(s):  
Hyewon Shin ◽  
William N. Dudley ◽  
Robin Bartlett ◽  
Yutaka Yasui ◽  
Deokumar Srivastava ◽  
...  
Keyword(s):  
High School ◽  
Health Outcomes ◽  
Cancer Survivors ◽  
Childhood Cancer ◽  
Symptom Burden ◽  
Childhood Cancer Survivors ◽  
School Education ◽  
Symptom Clusters ◽  
High School Education ◽  
Sensory Motor

10046 Background: Childhood cancer survivors experience concurrent symptoms, but associations with health outcomes are unknown. We characterize symptom clusters among adult survivors of childhood cancer in SJLIFE and tests associations with health-related quality of life (HRQL) and clinically assessed physical and neurocognitive performance. Methods: This cross-sectional study includes survivors diagnosed when <18 years of age, ≥10 years off-therapy, and ≥18 years of age at evaluation. Survivors rated 37 symptoms over 10 domains (cardiac, pulmonary, sensory, motor, nausea, pain, fatigue, memory, anxiety, depression), representing 3 broader symptom groups (physical, somatic, psychological). They also underwent a rating of HRQL (SF-36 PCS/MCS) and testing of physical performance (quantitative sensory, motor, endurance, mobility) and neurocognition (processing speed, executive function, attention, memory problems). Latent class analysis determined survivors with distinct symptom burden. Polytomous logistic regression identified risk factors of symptom clusters; multivariable regression tested associations of symptom clusters with health outcomes. Results: Among 3,085 survivors, mean [SD] age at evaluation was 31.9 [8.3] years, time from diagnosis was 28.1 [9.1] years, 49.7% were female, 37.1% were treated for leukemia and 33.0% for solid tumors. Four groups of survivors with distinct symptom burden were found: Cluster 1 (52%, low prevalence in all 3 symptom groups); Cluster 2 (16%, low in physical, moderate in somatic, high in psychological); Cluster 3 (18%; high in physical, moderate in somatic, low in physiological); and Cluster 4 (14%, high in all 3 symptom groups). Compared to the lowest symptom burden (Cluster 1), survivors with highest burden (Cluster 4) were significantly more likely to be female (OR 2.5; 95%CI 1.9, 3.4), have below a high school education (OR 7.7; 95%CI 4.5, 13.3), no insurance (OR 1.5; 95%CI 1.1, 2.3) and previous exposure to corticosteroids (OR 1.8; 95%CI 1.0, 3.0). High physical, moderate somatic and low psychological symptom burden (Cluster 3) was associated with below high school education (OR 2.7; 95%CI 1.4, 5.0), exposure to platinum agents (OR 2.2; 95%CI 1.4, 3.7) and brain radiation ≥30Gy (OR 4.0; 95%CI 2.3, 6.9) in contrast to Cluster 1. Survivors in Cluster 4 had the poorest PCS, MCS, physical and neurocognitive outcomes vs in Clusters 2 or 3, whereas those in Cluster 1 had the best outcomes (F-values for 4 clusters: 291.4 [PCS], 269.2 [MCS], 61.5 [physical], 36.9 [neurocognitive], p-values <0.001; effect sizes for Clusters 4 vs 1: 0.4-2.0 [4 outcomes]). Conclusions: Nearly 50% of survivors belong to symptom clusters with ≥1 moderate/high burden groups, associated with the socio-demographic and treatment exposures. Survivors in the highest symptom burden cluster had the poorest HRQL and functional outcomes.

scholarly journals Empirical foundations for the diagnosis of somatization: implications for DSM-5

2010 ◽  
Vol 41 (6) ◽  
pp. 1133-1142 ◽  
Author(s):  
J. G. M. Rosmalen ◽  
L. M. Tak ◽  
P. de Jonge
Keyword(s):  
Latent Class Analysis ◽  
Predictive Value ◽  
Latent Class ◽  
Large Population ◽  
Symptom Clusters ◽  
Class Analysis ◽  
Functional Symptoms ◽  
Organ Systems ◽  
Dsm Iv ◽  
Representative Cohort

BackgroundThe aim of this study was to develop empirically validated criteria for the diagnoses of clinically relevant somatization.MethodThis study was performed in a population-representative cohort consisting of 461 males (47.8%) and 503 females (52.2%), with an average age of 55.8 years (s.d.=11.1). Somatization, anxiety and depression were derived from the Composite International Diagnostic Interview. Mplus was used to perform confirmative factor analyses on the current DSM-IV symptom groups; on alternative symptom clusters previously suggested; and to perform latent class analysis in order to define an empirically derived cut-off for somatization.ResultsThe existence of symptom groups as described in DSM-IV was not supported by our data, whereas a differentiation between cardiopulmonary, musculoskeletal, gastrointestinal and general somatic symptoms did fit our data. Latent class analysis revealed two classes characterized by few (n=859) and many (n=105) symptoms. The class of subjects could be approached by a simple cut-off of four functional symptoms (sensitivity 79%, specificity 98%, positive predictive value 82%, negative predictive value 97%) regardless of the number of organ systems involved.ConclusionsThis study in a large population-representative cohort suggests that a simple symptom count can be used as a dimensional diagnosis of somatization. In those instances in which a categorical diagnosis is preferred, a simple cut-off of four out of 43 functional symptoms best fitted our data. We did not find any added value for incorporating the number of symptom clusters into the diagnostic criteria.

scholarly journals Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis

2016 ◽  
Vol 39 (12) ◽  
pp. 1639-1653 ◽  
Author(s):  
Samantha Conley
Keyword(s):  
Latent Class Analysis ◽  
Latent Variable ◽  
Latent Class ◽  
Symptom Clusters ◽  
Symptom Cluster ◽  
Latent Variable Model ◽  
Full Potential ◽  
Class Analysis ◽  
Variable Model

The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study

JMIR Cancer ◽  
10.2196/24137 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e24137
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

Background eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. Objective The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. Methods Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. Results Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. Conclusions Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

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