scholarly journals Clusters of the Risk Markers and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Leila Jahangiry ◽  
Mahdieh Abbasalizad Farhangi ◽  
Mahdi Najafi ◽  
Parvin Sarbakhsh
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Latent Class Analysis ◽  
High Probability ◽  
Latent Class ◽  
Class Analysis ◽  
Risk Markers ◽  
History Of ◽  
Opium Addiction ◽  
The Mean

Background: Coronary heart disease (CHD) is the major cause of mortality in the world with a significant impact on the younger population. The aim of this study was to identify prematurity among patients with coronary artery bypass graft surgery (CABG) based on the clustering of CHD risk factors.Methods: Patients were recruited from an existing cohort of candidates for CABG surgery named Tehran Heart Center Coronary Outcome Measurement (THC-COM). A latent class analysis (LCA) model was formed using 11 potential risk factors as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol use, opium addiction, hypertension, history of stroke, history of myocardial infarction (MI), peripheral vascular disease (PVD), and hyperlipidemia (HLP). We analyzed our data to figure out how the patients are going to be clustered based on their risk factors.Results: For 566 patients who were studied, the mean age (SD) and BMI of patients were 59.1 (8.9) and 27.3 (4.1), respectively. The LCA model fit with two latent classes was statistically significant (G2 = 824.87, df = 21, p < 0.0001). The mean (SD) age of patients for Class I and Class II was 55.66 (8.55) and 60.87 (8.66), respectively. Class I (premature) was characterized by a high probability of smoking, alcohol consumption, opium addiction, and a history of MI (P < 0.05), and class II by a high probability of obesity, diabetes, and hypertension.Conclusion: Latent class analysis calculated two groups of severe CHD with distinct risk markers. The younger group, which is characterized by smoking, addiction, and the history of MI, can be regarded as representative of premature CHD.

scholarly journals Identifying Risk Profiles of Malignant Prostate Cancer Surgical Delay Using a Person-Centered Approach to Understand Prostate Cancer Disparities: The Constellation of Health Determinants Using Latent Class Analysis on Cancer Registry Data

2020 ◽  
Vol 14 (6) ◽  
pp. 155798832098428
Author(s):  
Francisco A. Montiel Ishino ◽  
Claire Rowan ◽  
Rina Das ◽  
Janani Thapa ◽  
Ewan Cobran ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Health Insurance ◽  
Latent Class Analysis ◽  
Cancer Registry ◽  
High Probability ◽  
Private Health Insurance ◽  
Latent Class ◽  
Class Analysis ◽  
Risk Profiles ◽  
Surgical Delay

Surgical prostate cancer (PCa) treatment delay (TD) may increase the likelihood of recurrence of disease, and influence quality of life as well as survival disparities between Black and White men. We used latent class analysis (LCA) to identify risk profiles in localized, malignant PCa surgical treatment delays while assessing co-occurring social determinants of health. Profiles were identified by age, marital status, race, county of residence (non-Appalachian or Appalachian), and health insurance type (none/self-pay, public, or private) reported in the Tennessee Department of Health cancer registry from 2005 to 2015 for adults ≥18 years ( N = 18,088). We identified three risk profiles. The highest surgical delay profile (11% of the sample) with a 30% likelihood of delaying surgery >90 days were young Black men, <55 years old, living in a non-Appalachian county, and single/never married, with a high probability of having private health insurance. The medium surgical delay profile (46% of the sample) with a 21% likelihood of delay were 55–69 years old, White, married, and having private health insurance. The lowest surgical delay profile (42% of the sample) with a 14% likelihood of delay were ≥70 years with public health insurance as well as had a high probability of being White and married. We identified that even with health insurance coverage, Blacks living in non-Appalachian counties had the highest surgical delay, which was almost double that of Whites in the lowest delay profile. These disparities in PCa surgical delay may explain differences in health outcomes in Blacks who are most at-risk.

Identifying individuals with multiple non-communicable disease risk factors in Kenya: a latent class analysis

Public Health ◽  
2021 ◽  
Vol 198 ◽  
pp. 180-186
Author(s):  
R.S. Mkuu ◽  
T.D. Gilreath ◽  
A.E. Barry ◽  
F.M. Nafukho ◽  
J. Rahman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Disease Risk ◽  
Communicable Disease ◽  
Class Analysis ◽  
Non Communicable Disease

Prevalence, risk factors and multi-group latent class analysis of lifetime anxiety disorders comorbid depressive symptoms

2019 ◽  
Vol 243 ◽  
pp. 360-365 ◽  
Author(s):  
Hongguang Chen ◽  
Xiao Wang ◽  
Yueqin Huang ◽  
Guohua Li ◽  
Zhaorui Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Depressive Symptoms ◽  
Anxiety Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis

Risk Factors Explaining Military Deaths From Suicide, 2008–2017: A Latent Class Analysis

2021 ◽  
pp. 0095327X2110469
Author(s):  
Scott D. Landes ◽  
Janet M. Wilmoth ◽  
Andrew S. London ◽  
Ann T. Landes
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Latent Class Analysis ◽  
Military Service ◽  
Latent Class ◽  
Service Providers ◽  
Service Members ◽  
Latent Classes ◽  
Class Analysis ◽  
Military Service Members

Military suicide prevention efforts would benefit from population-based research documenting patterns in risk factors among service members who die from suicide. We use latent class analysis to analyze patterns in identified risk factors among the population of 2660 active-duty military service members that the Department of Defense Suicide Event Report (DoDSER) system indicates died by suicide between 2008 and 2017. The largest of five empirically derived latent classes was primarily characterized by the dissolution of an intimate relationship in the past year. Relationship dissolution was common in the other four latent classes, but those classes were also characterized by job, administrative, or legal problems, or mental health factors. Distinct demographic and military-status differences were apparent across the latent classes. Results point to the need to increase awareness among mental health service providers and others that suicide among military service members often involves a constellation of potentially interrelated risk factors.

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 Work Disability among Employees with Diabetes: Latent Class Analysis of Risk Factors in Three Prospective Cohort Studies

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143184 ◽  
Author(s):  
Marianna Virtanen ◽  
Jussi Vahtera ◽  
Jenny Head ◽  
Rosemary Dray-Spira ◽  
Annaleena Okuloff ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Studies ◽  
Latent Class Analysis ◽  
Prospective Cohort ◽  
Work Disability ◽  
Latent Class ◽  
Class Analysis ◽  
Prospective Cohort Studies

scholarly journals Hyperhomocysteinemia as an Independent Risk Factor for Coronary Heart Disease. Comparison with Conventional Risk Factors

2023 ◽  
Vol 83 ◽  
Author(s):  
R. Muzaffar ◽  
M. A. Khan ◽  
M. H. Mushtaq ◽  
M. Nasir ◽  
A. Khan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Logistic Regression ◽  
Risk Factor ◽  
Heart Disease ◽  
Independent Risk Factor ◽  
Plasma Homocysteine ◽  
History Of ◽  
The Mean ◽  
Conventional Risk Factors

Abstract The present study was designed to evaluate the strength of association of raised plasma homocysteine concentration as a risk factor for coronary heart disease independent of conventional risk factor. It was a case control study conducted at Punjab Institute of Cardiology Lahore. A total of 210 subjects aged 25 to 60 years comprising of 105 newly admitted patients of CHD as cases and 105 age and sex matched healthy individuals with no history of CHD as control were recruited for the study. Fasting blood samples were obtained from cases and controls. Plasma homocysteine was analyzed by fluorescence polarization immunoassay (FPIA) method on automated immunoassay analyzer (Abbott IMX). Total cholesterol, triglyceride and HDL cholesterol were analyzed using calorimetric kit methods. The concentration of LDL cholesterol was calculated using Friedewald formula. The patients were also assessed for traditional risk factors such as age, sex, family history of CVD, hypertension, smoking and physical activity, and were compared with control subjects. The collected data was entered in SPSS version 24 for analysis and interpretation.The mean age in controls and experimental groups were 43.00± 8.42 years and 44.72± 8.59 years with statistically same distribution (p- value= 0.144). The mean plasma homocysteine for cases was 22.33± 9.22 µmol/L where as it was 12.59±3.73 µmol/L in control group. Highly significant difference was seen between the mean plasma level of homocysteine in cases and controls (p˂0.001).Simple logistic regression indicates a strong association of coronary heart disease with hyperhomocysteinemia (OR 7.45), which remained significantly associated with coronary heart disease by multivariate logistic regression (OR 7.10, 95%C1 3.12-12.83, p=0.000). The present study concludes that elevated levels of Plasma homocysteine is an independent risk factor for coronary heart disease independent of conventional risk factors and can be used as an indicator for predicting the future possibility for the onset of CVD.

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