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

BACKGROUND: This study was carried out to use multiple imputation (MI) in order to correct for the potential nonresponse bias in measurements related to variable fasting blood glucose (FBS) in non-communicable disease risk factors survey conducted in Iran in 2007. METHODS: Five multiple imputation methods as bootstrap expectation maximization, multivariate normal regression, univariate linear regression, MI by chained equation, and predictive mean matching were applied to impute variable fasting blood sugar. To make FBS consistent with normality assumption natural logarithm (Ln) and Box-Cox (BC) transformations were used prior to imputation. Measurements from which we intended to remove nonresponse bias included mean of FBS and percentage of those with high FBS. RESULTS: For mean of FBS results didn’t considerably change after applying MI methods. Regarding the prevalence of high blood sugar all methods on original scale tended to increase the estimates except for predictive mean matching that along with all methods on BC or Ln transformed data didn’t change the results. CONCLUSIONS: FBS-related measurements didn’t change after applying different MI methods. It seems that nonresponse bias was not an important challenge regarding these measurements. However use of MI methods resulted in more efficient estimations. Further studies are encouraged on accuracy of MI methods in these settings.

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

Journal of Affective Disorders ◽

10.1016/j.jad.2018.09.053 ◽

2019 ◽

Vol 243 ◽

pp. 360-365 ◽

Cited By ~ 1

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

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

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.707070 ◽

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.

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

Armed Forces & Society ◽

10.1177/0095327x211046976 ◽

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.

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Prevalence, clustering and sociodemographic distributions of non-communicable disease risk factors in Nepalese adolescents: secondary analysis of a nationwide school survey

BMJ Open ◽

10.1136/bmjopen-2018-028263 ◽

2019 ◽

Vol 9 (5) ◽

pp. e028263 ◽

Cited By ~ 4

Author(s):

Raja Ram Dhungana ◽

Bihungum Bista ◽

Achyut Raj Pandey ◽

Maximilian de Courten

Keyword(s):

Physical Activity ◽

Risk Factors ◽

Alcohol Consumption ◽

Vegetable Intake ◽

Disease Risk ◽

Communicable Disease ◽

Fruit And Vegetable Intake ◽

Fruit And Vegetable ◽

Insufficient Physical Activity ◽

Non Communicable Disease

ObjectivesTo assess the prevalence, clustering and sociodemographic distribution of non-communicable disease (NCD) risk factors in adolescents in Nepal.DesignData originated from Global School Based Student Health Survey, Nepal conducted in 2015–2016.SettingThe study sites were the secondary schools in Nepal; 74 schools were selected based on the probability proportional to school enrolment size throughout Nepal.Participants5795 school-going children aged 13–17 years were included in the study.Primary outcomesNCD risk factors: smoking, alcohol consumption, insufficient fruit and vegetable intake, insufficient physical activity and overweight/obesity were the primary outcomes. Sociodemographic distributions of the combined and individual NCD risk factors were determined by Poisson regression analysis.ResultsFindings revealed the prevalence of smoking (6.04%; CI 4.62 to 7.88), alcohol consumption (5.29%; CI 4.03 to 6.92), insufficient fruit and vegetable intake (95.33%; CI 93.89 to 96.45), insufficiently physical activity (84.77%; CI 81.04 to 87.88) and overweight/obesity (6.66%; CI 4.65 to 9.45). One or more risk factors were present in 99.6%, ≥2 were in 83% and ≥3 were in 11.2%. Risk factors were more likely to cluster in male, 17 years of age and grade 7. Prevalence of smoking (adjusted prevalence ratio (aPR)=2.38; CI 1.6 to 3.51) and alcohol consumption (aPR=1.81; CI 1.29 to 2.53) was significantly high in male, and in 16 and 17 years of age. Prevalence of insufficient physical activity and overweight/obesity was significantly lower in higher grades.ConclusionInsufficient fruit and vegetable intake and insufficient physical activity were highly prevalent in the populations studied. Risk factors were disproportionately distributed and clustered in particular gender, age and grade. The study population requires an age and gender specific preventive public health intervention.

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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

Palliative Medicine ◽

10.1177/0269216319860705 ◽

2019 ◽

Vol 33 (10) ◽

pp. 1272-1281 ◽

Cited By ~ 2

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.

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