Identifying Patterns of Lifestyle Behaviors among People with Type 2 Diabetes in Tianjin, China: A Latent Class Analysis.
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SUBMITTER: Wang X
PROVIDER: S-EPMC5688992 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMultiple drug and sexual risk behaviors among people who inject drugs (PWID) in intimate relationships increase the risk of HIV and HCV transmission. Using data on PWID in intimate partnerships in Almaty, Kazakhstan, this study performed latent class analysis (LCA) on drug and sexual risk behaviors and estimated associations between dyadic relationship factors and membership in latent classes.MethodsLCA was performed on a sample of 510 PWID (181-females/FWID, 321-males/MWID) to identify levels of drug and sexual risk behaviors. Generalized structural equation modeling with multinomial regressions estimated associations between relationship factors (length risk reduction communication, risk reduction self-efficacy) and class membership after adjusting for substance use severity, overdose, depression, binge drinking, intimate partner violence, structural factors, and sociodemographic characteristics. Models were sex-stratified to include FWID and PWID.ResultsA 3-class model best fit the data and consisted of low, medium, and high-risk classes. GSEM found that greater injection self-efficacy was associated with a lower likelihood of membership in the high-risk class for PWID and FWID. For MWID, greater length of the relationship was associated with a lower likelihood of membership in the medium-risk class. Greater relationship communication was associated with increased risk of membership in the high-risk latent class for MWID.ConclusionsFuture research must investigate if increasing risk reduction and safe sex self-efficacy could reduce drug and sexual risk behaviors and HIV transmission among PWID and their intimate partners. Interventions are needed that reduce power inequities within relationships as a method of increasing self-efficacy, particularly among women.
| S-EPMC6415908 | biostudies-literature
Project description:BackgroundAdolescent health risk behaviors are a public health priority given their prevalence and their associations with chronic diseases and life quality in adulthood. This study examined the heterogeneity of adolescent health risk behaviors and the associations between demographic characteristics and subgroup membership in rural western China.MethodsIn fall 2015, 2805 students from rural middle schools in Sichuan Province were surveyed using the Health-Related Behavior Questionnaire for Adolescents. Latent class analysis (LCA) was used to identify subgroups of adolescents with distinct patterns of health risk behaviors. Differences in class membership related to selected demographic characteristics were examined using multinomial logistic regression analysis.ResultsA four-class model emerged: (1) high-risk group (n = 108, 4.0%), (2) high-physical-inactivity and suicide-risk group (n = 340, 12.1%), (3) moderate-risk group (n = 897, 32.0%), and (4) low-risk group (n = 1460, 52.1%). The multinomial logistic regression analysis revealed that boys and adolescents with poor parental relationships and high allowances (spending money) were significantly more likely to be in the high-risk group than the low-risk group.ConclusionsAdolescents in rural western China are a heterogeneous population requiring different tailored and effective interventions.
| S-EPMC6019255 | biostudies-literature
Project description:ObjectiveIn the absence of adequate nationally-representative empirical evidence on multimorbidity, the existing healthcare delivery system is not adequately oriented to cater to the growing needs of the older adult population. Therefore, the present study identifies frequently occurring multimorbidity patterns among older adults in India. Further, the study examines the linkages between the identified patterns and socioeconomic, demographic, lifestyle and anthropometric correlates.DesignThe present findings rest on a large nationally-representative sample from a cross-sectional study.Setting and participantsThe study used data on 58 975 older adults (45 years and older) from the Longitudinal Ageing Study in India, 2017-2018.Primary and secondary outcome measuresThe study incorporated a list of 16 non-communicable diseases to identify commonly occurring patterns using latent class analysis. The study employed multinomial logistic regression models to assess the association between identified disease patterns with unit-level socioeconomic, demographic, lifestyle and anthropometric characteristics.ResultsThe present study demonstrates that older adults in the country can be segmented into six patterns: 'relatively healthy', 'hypertension', 'gastrointestinal disorders-hypertension-musculoskeletal disorders', 'musculoskeletal disorders-hypertension-asthma', 'metabolic disorders' and 'complex cardiometabolic disorders'. Additionally, socioeconomic, demographic, lifestyle and anthropometric factors are significantly associated with one or more identified disease patterns.ConclusionsThe identified classes 'hypertension', 'metabolic disorders' and 'complex cardiometabolic disorders' reflect three stages of cardiometabolic morbidity with hypertension as the first and 'complex cardiometabolic disorders' as the last stage of disease progression. This underscores the need for effective prevention strategies for high-risk hypertension group. Also, targeted interventions are essential to reduce the burden on the high-risk population and provide equitable health services at the community level.
| S-EPMC9277367 | biostudies-literature
Project description:ObjectivePublic knowledge of early onset symptoms and risk factors (RF) of acute myocardial infarction (AMI) is very important for prevention, recurrence and guide medical seeking behaviours. This study aimed to identify clusters of knowledge on symptoms and RFs of AMI, compare characteristics and the awareness of the need for prompt treatment.DesignMultistage stratified sampling was used in this cross-sectional study. Latent GOLD Statistical Package was used to identify and classify the respondent subtypes of the knowledge on AMI symptoms or modifiable RFs. Multivariable logistic regression was performed to identify factors that predicted high knowledge membership.ParticipantsA structured questionnaire was used to interview 4200 community residents aged over 35 in China. 4122 valid questionnaires were recovered.ResultsFor AMI symptoms and RFs, the knowledge levels were classified into two or three distinct clusters, respectively. 62.7% (Symptom High Knowledge Cluster) and 39.5% (RF High Knowledge Cluster) of the respondents were able to identify most of the symptoms and modifiable RFs. Respondents who were highly educated, had higher monthly household income, were insured, had regular physical examinations, had a disease history of AMI RFs, had AMI history in immediate family member or acquaintance or had received public education on AMI were observed to have higher probability of knowledge on symptoms and RFs. There was significant difference in awareness of the prompt treatment in case of AMI occurs among different clusters. 'Calling an ambulance' was the most popular option in response of seeing others presenting symptoms of AMI.ConclusionsA moderate or relatively low knowledge on AMI symptoms and modifiable RFs was observed in our study. Identification of Knowledge Clusters could be a way to detect specific targeted groups with low knowledge of AMI, which may facilitate health education, further reduce the prehospital delay in China and improve patient outcomes.
| S-EPMC9196158 | biostudies-literature
Project description:ObjectivesThis study aimed to identify latent classes in hypertensive patients based on the clustering of factors including lifestyle risk factors, metabolic risk factors, and obesity in each sex.MethodsThis cross-sectional study included 102,780 male and 103,710 female hypertensive patients who underwent health check-ups at 16 centers in Korea, in 2018. A latent class analysis approach was used to identify subgroups of hypertensive patients. Multinomial logistic regression was performed to examine the association between latent classes and comorbidities of hypertension.ResultsA four-class model provided the best fit for each sex. The following latent classes were identified: Class I (male: 16.9%, female: 1.7%; high risk of lifestyle behaviors [HB] with metabolic disorders and obesity [MO]), Class II (male: 32.4%, female: 47.1%; low risk of lifestyle behaviors [LB] with MO), Class III (male: 15.3%, female: 1.8%; HB with metabolic disorders and normal weight [MNW]), Class IV (male: 35.5%, female: 49.4%; LB with MNW). Lifestyle patterns in the latent classes were classified as high-risk or low-risk according to smoking and high-risk drinking among male, and presented complex patterns including physical inactivity alone or in combination with other factors, among female. Stage 2 hypertensive or diabetic individuals were likely to belong to classes including obesity (HB-MO, LB-MO) in both sexes, and additionally belonged to the HB-MNW class in male.ConclusionsMetabolic disorders were included in all latent classes, with or without lifestyle risk factors and obesity. Hypertensive females need to manage obesity, and hypertensive males need to manage lifestyle risk factors and obesity. Sex-specific lifestyle behaviors are important for controlling hypertension.
| S-EPMC7871153 | biostudies-literature
Project description:Background: Current evidence suggests that the information needs of people with diabetes mellitus differ across patient groups. With a view to being able to provide individualized information, this study aims to identify (i) the diabetes-related information needs of people with diabetes mellitus; (ii) different subgroups of people with specific information needs; and (iii) associated characteristics of the identified subgroups, such as sociodemographic characteristics, diabetes-related comorbidities, and well-being.Methods: This cross-sectional study was based on data from 837 respondents with diabetes mellitus who participated in the population-based KORA (Cooperative Health Research in the Augsburg Region) Health Survey 2016 in Southern Germany (KORA GEFU 4 study) (45.6% female, mean age 71.1?years, 92.8% Type 2 diabetes). Diabetes-related information needs were assessed with a questionnaire asking about patients' information needs concerning 11 diabetes-related topics, e.g. 'long-term complications' and 'treatment/therapy'. Subgroups of people with different information needs and associated characteristics were identified using latent class analysis.Results: We identified the following four classes of people with different information needs: 'high needs on all topics', 'low needs on all topics', 'moderate needs with a focus on complications and diabetes in everyday life', and 'advanced needs with a focus on social and legal aspects and diabetes research'. The classes differed significantly in age, years of education, type of diabetes, diabetes duration, diabetes-related comorbidities, smoking behaviour, diabetes education, current level of information, and time preference.Conclusions: Knowledge about different patient subgroups can be useful for tailored information campaigns or physician-patient interactions. Further research is needed to analyse health care needs in these groups, changes in information needs over the course of the disease, and prospective health outcomes.
| S-EPMC7730786 | biostudies-literature
Project description:Over the past three decades, obesity-related diseases have increased tremendously in China, and are now the leading causes of morbidity and mortality. Patterns of weight change can be used to predict risk of obesity-related diseases, increase understanding of etiology of disease risk, identify groups at particularly high risk, and shape prevention strategies.Latent class trajectory modeling was used to compute weight change trajectories for adults aged 18 to 66 using the China Health and Nutrition Survey (CHNS) data (n = 12,611). Weight change trajectories were computed separately for males and females by age group at baseline due to differential age-related patterns of weight gain in China with urbanization. Generalized linear mixed effects models examined the association between weight change trajectories and baseline characteristics including urbanicity, BMI category, age, and year of study entry.Trajectory classes were identified for each of six age-sex subgroups corresponding to various degrees of weight loss, maintenance and weight gain. Baseline BMI status was a significant predictor of trajectory membership for all age-sex subgroups. Baseline overweight/obesity increased odds of following 'initial loss with maintenance' trajectories. We found no significant association between baseline urbanization and trajectory membership after controlling for other covariates.Trajectory analysis identified patterns of weight change for age by gender groups. Lack of association between baseline urbanization status and trajectory membership suggests that living in a rural environment at baseline was not protective. Analyses identified age-specific nuances in weight change patterns, pointing to the importance of subgroup analyses in future research.
| S-EPMC4336139 | biostudies-literature
Project description:Background:Hong Kong's construction industry currently faces a manpower crisis. Blue-collar workers are a disadvantaged group and suffer higher levels of chronic diseases, for example, cancer, than the wider population. Cancer risk factors are likely to cluster together. We documented prevalence of cancer-associated lifestyle risk behaviors and their correlates among Hong Kong construction workers. Methods:Data were collected from workers at 37 railway-related construction worksites throughout Hong Kong during May 2014. Tobacco use, alcohol consumption, unbalanced nutrition intake, and physical inactivity were included in the analysis. Latent class analysis and multivariable logistic regression were performed to identify the patterns of risk behaviors related to cancer, as well as their impact factors among construction workers in Hong Kong. Results:Overall, 1,443 workers participated. Latent class analysis identified four different behavioral classes in the sample. Fully adjusted multiple logistic regression identified age, gender, years of Hong Kong residency, ethnicity, educational level, and living status differentiated behavioral classes. Conclusion:High levels of lifestyle-related cancer-risk behaviors were found in most of the Hong Kong construction workers studied. The present study contributes to understanding how cancer-related lifestyle risk behaviors cluster among construction workers and relative impact factors of risk behaviors. It is essential to tailor health behavior interventions focused on multiple risk behaviors among different groups for further enlarging the effects on cancer prevention.
| S-EPMC7078528 | biostudies-literature
Project description:IntroductionWithin many sub-Saharan African countries including Malawi, HIV prevalence varies widely between regions. This variability may be related to the distribution of population groups with specific sociobehavioural characteristics that influence the transmission of HIV and the uptake of prevention. In this study, we intended to identify groups of people in Malawi with similar risk profiles.MethodsWe used data from the Demographic and Health Survey in Malawi (2015 to 2016), and stratified the analysis by sex. We considered demographic, socio-behavioural and HIV-related variables. Using Latent Class Analysis (LCA), we identified groups of people sharing common sociobehavioural characteristics. The optimal number of classes (groups) was selected based on the Bayesian information criterion. We compared the proportions of individuals belonging to the different groups across the three regions and 28 districts of Malawi.ResultsWe found nine groups of women and six groups of men. Most women in the groups with highest risk of being HIV positive were living in female-headed households and were formerly married or in a union. Among men, older men had the highest risk of being HIV positive, followed by young (20 to 25) single men. Generally, low HIV testing uptake correlated with lower risk of having HIV. However, rural adolescent girls had a low probability of being tested (48.7%) despite a relatively high HIV prevalence. Urban districts and the Southern region had a higher percentage of high-prevalence and less tested groups of individuals than other areas.ConclusionsLCA is an efficient method to find groups of people sharing common HIV risk profiles, identify particularly vulnerable sub-populations, and plan targeted interventions focusing on these groups. Tailored support, prevention and HIV testing programmes should focus particularly on female household heads, adolescent girls living in rural areas, older married men and young men who have never been married.
| S-EPMC7521110 | biostudies-literature
Project description:AimsThis study sought to determine whether clinical clusters exist in takotsubo cardiomyopathy. Takotsubo cardiomyopathy (TCM) is a heterogeneous disorder with a complex, poorly understood pathogenesis. To better understand the heterogeneity of TCM, we identified different clinical phenotypes in a large sample of TCM patients by using latent class analysis (LCA).Methods and resultsUsing the National Inpatient Sample (NIS) database, we identified 3139 patients admitted to hospitals in 2016-2017 with a primary diagnosis of TCM. We performed LCA based on several patient demographics and comorbidities: age, sex, hypertension, hyperlipidaemia, diabetes mellitus, obesity, current smoking, asthma, chronic obstructive pulmonary disease (COPD), and anxiety and depressive disorders. We then repeated LCA separately with the NIS 2016 and 2017 data sets and performed a robust test to validate our results. We also compared in-hospital outcomes among the different clusters identified by LCA. Four patient clusters were identified. C1 (n = 1228, 39.4%) had the highest prevalence of hyperlipidaemia (93.4%), hypertension (61.6%), and diabetes (34.3%). In C2 (n = 440, 14.0%), all patients had COPD, and many were smokers (45.8%). C3 (n = 376, 11.8%) largely comprised patients with anxiety disorders (98.4%) and depressive disorders (80.1%). C4 (n = 1097, 34.8%) comprised patients with isolated TCM and few comorbidities. Among all clusters, C1 had the lowest in-hospital mortality (1.0%) and the shortest length of stay (3.2 ± 3.1 days), whereas C2 had the highest in-hospital mortality (3.4%).ConclusionsUsing LCA, we identified four clinical phenotypes of TCM. These may reflect different pathophysiological processes in TCM. Our findings may help identify treatment targets and select patients for future clinical trials.
| S-EPMC7835582 | biostudies-literature