Latent Class Analysis Approach Research Articles

Profiles of Basic Psychological Needs Satisfaction and Sociodemographic and School-Associated Factors

The fulfillment of basic psychological needs (competency, autonomy, relatedness) enhances intrinsic motivation, school engagement, and academic achievement. Establishing profiles of psychological needs satisfaction using a person-centered approach may help in identifying students at-risk of low academic success. This study investigated how students vary in their basic psychological needs satisfaction and their associated sociodemographic and school-related factors. Using a latent class analysis approach, five profiles of students were identified at the end of elementary school (n = 1,362). A multinomial regression analysis revealed that academic achievement and the quality of relationship with peers and teachers distinguished most profiles of basic psychological needs satisfaction. In particular, having conflict with teachers increased the likelihood of belonging to profiles with low psychological needs satisfaction. Closeness with the teacher and friendship decreased the likelihood of having average to low levels of psychological needs satisfaction. Students with higher grades in French and Math were less likely to belong to profiles with low competency, autonomy, and/or relatedness satisfaction. Sociodemographic factors, having negative relationship and joint negative behaviors with peers did not distinguish profiles of basic psychological needs satisfaction.

Examining Childhood Behavior Problems as Predictors of Alcohol Dependency in Emerging Adulthood: A Latent Class Analysis Approach

There is limited research on within-person clustering of early-life behavioral issues and risk for alcohol use issues. This study examined the relevance of childhood behavior problem clusters for predicting alcohol problems in emerging adulthood. This study examined full behavioral profiles as predictors of alcohol-related problems, rather than individual behaviors. The Pathways to Desistance dataset comprised of 1354 justice-involved youth followed across seven years post-adjudication collected from Maricopa County, Arizona and Philadelphia Pennsylvania were analyzed. Latent class analysis was used to identify clusters of childhood behavioral problems (cheating, disturbing class, getting drunk/stoned, stealing, fighting). Regression modeling was used to examine various alcohol use outcomes. Findings indicated that a three-class model best fit the data (Low; FDC; High). Assignment to the High class predicted greater social consequences of alcohol use, but this effect was only marginally significant. No significant effects were observed for either alcohol dependence measure.

Identifying secondary science teachers’ adoption styles of technology-based assessments and examining the patterns of teachers’ beliefs, attitudes, and intention: A latent class analysis approach

Identifying secondary science teachers’ adoption styles of technology-based assessments and examining the patterns of teachers’ beliefs, attitudes, and intention: A latent class analysis approach

Exploring unobserved heterogeneity in ICT usage and travel pattern changes as the pandemic subsides: A quasi-longitudinal analysis in Florida

This paper presents a study that explored the behavioral heterogeneity in changes in people's ICT usage and travel patterns at the end of the pandemic. A quasi-longitudinal approach was employed to collect data from Florida residents, capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021. Utilizing the latent class analysis (LCA) approach to identify subgroups based on the online activity durations and trip frequencies, four distinct classes were identified. A little more than one third (35%) of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies. About 33% of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities. About 16% of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities. Another 16% of the respondents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities. These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities. The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.

P062 Trust performance in managing inflammatory arthritis over time in England and Wales: a latent class analysis approach

Abstract Background/Aims To evaluate trust-level performance in time to initiation of disease-modifying antirheumatic drug (DMARD) therapy in patients with early inflammatory arthritis (EIA), with identification of change in performance trajectories over time and investigation of trust characteristics associated with this change. Methods We included 130 trusts from the UK contributing to the National Early Inflammatory Arthritis Audit (NEIAA) from 2018 to 2020. The primary outcome was days from referral to initiation of DMARD therapy in patients with EIA. Latent class growth mixture models were applied to identify distinct groups of trusts with similar trajectories of performance change over time. We used mixed-effects linear and multinomial logistic regression models to evaluate the association between delay in treatment and trust-level characteristics. Results The mean (SD) time to DMARD initiation was 53 (18) days, with an average 0.3-day decrease with each month over time. Four latent trajectories were identified in our cohort, with over 77% of individual trusts showing ongoing improvements in decreasing treatment waiting times. Prior to separating by latent class, time to DMARD initiation was shorter in trusts with higher rheumatology staffing, a local EIA treatment pathway and those with access to musculoskeletal ultrasound. Trusts with more nurses in the rheumatology department were less likely to be in the worse performance group (OR = 0.69 [95%CI 0.49, 0.93]). Conclusion In this cohort study, we observed a reduction in treatment waiting time over time. Trusts with better staffed and improved EIA clinical structure are likely to initiate definitive treatment earlier in patients with EIA. Disclosure Z. Yang: None. N. Arumalla: None. M.D. Russell: Honoraria; Lilly, Menarini, Galapagos. Grants/research support; NIHR. Other; Pfizer, Janssen, Biogen, and UCB. K. Bechman: Honoraria; Menarini, Galapagos. Grants/research support; NIHR,Versus Arthritis and Pfizer Global Medical Grants for Quality Improvement in Rheumatology Practice. Other; UCB. E. Alveyn: Other; UCB. S. Gallagher: None. E. Price: None. S. Norton: None. J. Galloway: Honoraria; AbbVie, Biovitrum, BMS, Celgene, Chugai, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Sanofi, Sobi and UCB.

Understanding risky driving behaviors among young novice drivers in Nigeria: A latent class analysis coupled with association rule mining approach

Traffic crashes are significant public health concern in Nigeria, particularly among young drivers. The study aims to explore the underlying pattern of risky driving behaviors and the associations with demographic factors among young drivers in Nigeria. A combined approach of Latent Class Analysis (LCA) and Association Rule Mining is applied to the dataset comprising responses from 684 young drivers who complete the “Behavior of Young Novice Drivers Scale” (BYND) questionnaires. The LCA identifies four distinct classes of drivers based on the risky behavior profiles: Reckless-Speedsters, Cautious Drivers, Distracted Multitaskers, and Emotion-impacted Drivers. Association rule mining further connects these driver classes to demographic and driving history variables, uncovering intriguing insights. Reckless-Speedsters predominantly consist of young males who engage in riskier driving behaviors, including exceeding speed limits and disregarding traffic rules. Conversely, Cautious Drivers, also predominantly young males, exhibit a safer driving profile marked by rule adherence and a notably lower crash rate. Distracted Multitaskers, sharing a demographic profile with Cautious Drivers, diverge significantly due to their higher crash involvement, hinting at a propensity for distracted driving practices. Lastly, Emotion-Impacted Drivers, primarily comprising young employed males, display behaviors influenced by emotions, shorter driving distances, and prior unsupervised driving experience. Most of the behaviors are attributed to inadequate traffic control, absence of traffic signs in most of the roads, preferential treatment, and lack of strict law enforcement in the country. The findings hold substantial implications for road safety interventions in Nigeria, urging targeted approaches to address the unique challenges presented by each driver class. With acknowledging the study limitations and advocating for future research in objective measures and emotion-behavior interactions, the comprehensive approach provides a robust foundation for enhancing road safety in the Nigerian context.

Detection of differential item functioning with latent class analysis: PISA 2018 mathematical literacy test

The purpose of this research is to determine whether PISA 2018 mathematical literacy test items show a differential item functioning across countries. For this purpose, only the items in booklet number three were examined using the MIMIC method with Latent Class Analysis (LCA) approach. PISA 2018 tests are mostly developed in English. Therefore, in DIF analyses, the reference group is the UK, while the focal groups consist of the other countries examined in the research (Türkiye, Finland, Japan, and the USA). According to the results, of the 23 test items, statistically significant DIF was observed in eight items in the UK-Türkiye sample, in seven items in the UK-Finland sample, in eleven items in the UK-Japan sample, and in three items in the UK-USA sample. It is seen that the effect and size of DIF in non-homogeneous groups differ between groups and these effects can be examined in more detail with the LCA method.

Classifying climate vulnerability and inequalities in India, Mexico, and Nigeria: a latent class analysis approach

The climate crisis exacerbates social, economic, and health disparities. This study employs innovative methods to identify distinct groups affected by recent climate events. A mobile phone-based survey was conducted in April 2022 with individuals residing in multiple climate-affected states across three countries: India (n = 1020), Mexico (n = 1020), and Nigeria (n = 1021). Latent class analysis and classification and regression tree analysis were used to identify the groups most exposed to climate events, the effects and responses taken, and then to identify the characteristics associated with group membership. Effects included housing damage or lost work, while responses included actions such as borrowing money or dropping out of school. Findings revealed four distinct groups: Group 1 reported low exposure, no effects, or responses (49% of respondents in India, 43% in Mexico, and 27% in Nigeria); Group 2 experienced multiple hazards with moderate effects and some responses; Group 3 was characterized by drought exposure with more effects and responses taken; Group 4 was affected by heavy flooding and rainfall with varied effects. Notably, India had the largest proportion of respondents in Group 3 (17%), in Mexico over a quarter (29%) were in group 4, while over half of Nigerian respondents were in Group 2 (52%). Characteristics associated with membership in each group varied by country. Overall, men from rural areas with lower incomes and reliant on agriculture experienced the highest levels of exposure and vulnerability, while urban women from higher-income households were the least affected. This study underscores the importance of considering the intersectionality of risk and vulnerability when formulating policies and programs to address the impacts of climate change. Results emphasize the need for multi-sectoral policies that target the needs of different groups, to reduce inequalities and tailor to the context-specific needs of the most vulnerable people and households.

Examining changes in sexual lifestyles in Britain between 1990–2010: a latent class analysis approach

BackgroundUnderstanding sexual lifestyles and how they change over time is important for determining the likelihood of sexual health outcomes. Standard descriptive and regression methods are limited in their ability to capture multidimensional concepts such as sexual lifestyles. Latent Class Analysis (LCA) is a mixture modelling method that generates a categorical latent variable to derive homogenous groups from a heterogeneous population. Our study investigates (1) the potential of LCA to assess change over time in sexual lifestyles and (2) how quantifying this change using LCA compares to previous findings using standard approaches.MethodsProbability-sampled data from three rounds of the National Survey of Sexual Attitudes and Lifestyle (Natsal) were used, restricted to sexually active participants (i.e., those reporting sexual partners in the past year) aged 16–44 years (N1990 = 11,738; N2000 = 9,690; N2010 = 8,397). An LCA model was built from four variables: number of sexual partners (past year), number of partners without a condom (past year), age at first sex and self-perceived HIV risk. Covariates included age, ethnicity, educational attainment, same-sex attraction, and marital status. Multinomial regression analyses and Chi-Squared tests were used to investigate change over time in the size of each class.ResultsWe successfully used a LCA approach to examine change in sexual lifestyle over time. We observed a statistically significant increase between 1990 and 2010 in the proportion of men (χ2 = 739.49, p < 0.01) and women (χ2 = 1270.43, p < 0.01) in a latent class associated with reporting 2 or more partners in the last year, relatively high probabilities of reporting condomless sex partners, greater self-perceived HIV risk, and a high probability of first sex before age 16 years, increasing from 19.5% to 31.1% (men) and 9.9% to 22.1% (women).ConclusionOur results indicate the viability of LCA models to assess change over time for complex behavioural phenomena. They align with previous findings, namely changing sexual lifestyles in Britain in recent decades, partnership number driving class assignment, and significant sex differences in sexual lifestyles. This approach can be used to extend previous LCA models (e.g., to investigate the impact of COVID-19 on sexual lifestyles) and to support empirical evidence of change over time, facilitating more nuanced public health policy.

Understanding key contributing factors on the severity of traffic violations by elderly drivers: a hybrid approach of latent class analysis and XGBoost based SHAP

Traffic violation is one of the leading causes of traffic crashes. In the context of global aging, it is important to study traffic violations by elderly drivers for improving traffic safety in preparation for a worldwide aging population. In this study, a hybrid approach of Latent Class Analysis (LCA) and XGBoost based SHAP is proposed to identify hidden clusters and to understand the key contributing factors on the severity of traffic violations by elderly drivers, based on the police-reported traffic violation dataset of Beijing (China). First, LCA is applied to segment the dataset into several latent homogeneous clusters, then XGBoost based SHAP is established on each cluster to identify feature contributions and the interaction effects of the key contributing factors on the severity of traffic violations by elderly drivers. Two comparison groups were set up to analyze factors, which are responsible for the different severities of traffic violations. The results show that elderly drivers can be classified into four groups by age, urban or not, license, and season; factors such as less annual number of traffic violations, national & provincial highway, night and winter are key contributing factors for higher severity of traffic violations, which are consistent with common cognition; key contributing factors for all clusters are similar but not identical, for example, more annual number of traffic violations contribute to more severe violation for all clusters except for Cluster 2; some factors which are not key contributing factors may affect the severity of traffic violations when they are combined with other factors, for example, the combination of lower annual number of traffic violations and county & township highway contributes to more severe violation for Cluster 1. These findings can help government to formulate targeted countermeasures to decrease the severity of traffic violations by specific elderly groups and improve road service for the driving population.

Subjective well-being patterns in older men and women without someone to confide in: a latent class analysis approach.

This study aimed to identify the latent subtypes of subjective well-being (SWB) and associated factors in older adults without a confidant in China. The data came from the most recent (seventh) wave (2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). This cross-sectional study included 350 older adults who lacked a close confidant. We utilized latent class analysis and multiple logistic regression models to examine the latent SWB subtypes and associated factors. Three distinct patterns of SWB were identified: the very low SWB class (32%), the medium-low SWB class (46%), and the low evaluative and high affective SWB class (22%). The results indicated that compared to the low evaluative and high affective SWB class, respondents who self-rated their health as not good, currently drank alcohol and rated their financial status as poor/very poor were more likely to be in the very low SWB class, while those who participated in social activities were less likely to be in the very low SWB class. Respondents who had limitations in instrumental activity of daily living (IADL) and rated their financial status as poor/very poor were more likely to be in the medium-low SWB class. However, gender did not affect SWB patterns. Our findings highlight awareness of the heterogeneity of SWB in older adults without close confidants and provide valuable information for the development of tailored intervention programs to improve their well-being.

Exploring trauma exposure and post-traumatic stress in university students of different identity statuses in Lithuania and Japan.

Emerging adulthood is the time when identity questions are addressed. It is also a time of excessive stress and risk for mental health problems. Different identity statuses relate to different mental health outcomes. Yet, little research has addressed how identity status is interlinked with trauma exposure and post-traumatic stress reactions, especially in multicultural contexts. The current study aimed to explore whether different traumatic experiences are related to the current identity status of university students aged between 18 and 29 years and investigate to what extent trauma-exposed emerging adults of different identity statuses report symptoms of post-traumatic stress disorder (PTSD) and complex PTSD (CPTSD). In total, 2237 university students from Lithuania (n = 791) and Japan (n = 1345) participated in the current study. Identity profiles were revealed by using the Latent Class Analysis approach. Lithuania and Japan were comparable in terms of identity profiles and structure of PTSD/CPTSD. Trauma-exposed emerging adults reported a higher probability of being in troubled diffusion identity status; students in achievement identity status had a lower probability of CPTSD and lower rates of symptoms of disturbances in self-organization. The diffused identity of emerging adults from Lithuania and Japan is associated with trauma exposure, and positive identity is linked with fewer CPTSD reactions.

Trust performance in managing inflammatory arthritis over time in England and Wales: a latent class analysis approach.

To evaluate trust-level performance in time to initiation of DMARD therapy in patients with early inflammatory arthritis (EIA), with identification of the change in performance trajectories over time and investigation of trust characteristics associated with this change. We included 130 trusts from the UK contributing to the National Early Inflammatory Arthritis Audit (NEIAA) from 2018 to 2020. The primary outcome was days from referral to initiation of DMARD therapy in patients with EIA. Latent class growth mixture models were applied to identify distinct groups of trusts with similar trajectories of performance change over time. We used mixed effects linear and multinomial logistic regression models to evaluate the association between delay in treatment and trust-level characteristics. The mean time to DMARD initiation was 53days (s.d. 18), with an average 0.3-day decrease with each month over time. Four latent trajectories were identified in our cohort, with >77% of individual trusts showing ongoing improvements in decreasing treatment waiting times. Prior to separating by latent class, time to DMARD initiation was shorter in trusts with higher rheumatology staffing, a local EIA treatment pathway and those with access to musculoskeletal ultrasound. Trusts with more nurses in the rheumatology department were less likely to be in the worst performance group [odds ratio 0.69 (95% CI 0.49, 0.93)]. In this cohort study, we observed a reduction in treatment waiting time over time. Trusts with better staffed and improved EIA clinical structure are likely to initiate definitive treatment earlier in patients with EIA.

Mental health impact of multiple sexually minoritized and gender expansive stressors among LGBTQ+ young adults: a latent classanalysis.

In the United States, lesbian, gay, bisexual, transgender, queer, intersex, asexual and other sexually minoritized and gender expansive (LGBTQ+) young adults are at increased risk for experiencing mental health inequities, including anxiety, depression and psychological distress-related challenges associated with their sexual and gender identities. LGBTQ+ young adults may have unique experiences of sexual and gender minority-related vulnerability because of LGBTQ+-related minority stress and stressors, such as heterosexism, family rejection, identity concealment and internalized homophobia. Identifying and understanding specific LGBTQ+-related minority stress experiences and their complex roles in contributing to mental health burden among LGBTQ+ young adults could inform public health efforts to eliminate mental health inequities experienced by LGBTQ+ young adults. Therefore, this study sought to form empirically based risk profiles (i.e., latent classes) of LGBTQ+ young adults based on their experiences with familial heterosexist experiences, LGBTQ+-related family rejection, internalized LGBTQ+-phobia and LGBTQ+ identity concealment, and then identify associations of derived classes with psychological distress. We recruited and enrolled participants using nonprobability, cross-sectional online survey data collected between May and August 2020 (N=482). We used a three-step latent class analysis (LCA) approach to identify unique classes of response patterns to LGBTQ+-related minority stressor subscale items (i.e., familial heterosexist experiences, LGBTQ+-related family rejection, internalized LGBTQ+-phobia and LGBTQ+ identity concealment), and multinomial logistic regression to characterize the associations between the derived classes and psychological distress. Five distinct latent classes emerged from the LCA: (1) low minority stress, (2) LGBTQ+ identity concealment, (3) family rejection, (4) moderate minority stress and (5) high minority stress. Participants who were classified in the high and moderate minority stress classes were more likely to suffer from moderate and severe psychological distress compared to those classified in the low minority stress class. Additionally, relative to those in the low minority stress class, participants who were classified in the LGBTQ+ identity concealment group were more likely to suffer from severe psychological distress. Familial heterosexist experiences, LGBTQ+-related family rejection, internalized LGBTQ+-phobia and LGBTQ+ identity concealment are four constructs that have been extensively examined as predictors for mental health outcomes among LGBTQ+ persons, and our study is among the first to reveal nuanced gradients of these stressors. Additionally, we found that more severe endorsement of minority stress was associated with greater psychological distress. Given our study results and the previously established negative mental health impacts of minority stressors among LGBTQ+ young adults, findings from our study can inform research, practice, and policy reform and development that could prevent and reduce mental health inequities among LGBTQ+ young adults.

The effect of multimorbidity patterns on physical and cognitive function in diabetes patients: a longitudinal cohort of middle-aged and older adults in China.

The prevalence of diabetes has increased rapidly, and comorbid chronic conditions are common among diabetes patients. However, little is known about the pattern of multimorbidity in diabetes patients and the effect on physical and cognitive function. This study aimed to assess the disease clusters and patterns of multimorbidity in diabetes patients using a novel latent class analysis (LCA) approach in middle-aged and older adults and explore the association between different clusters of multimorbidity in diabetes and the effect on physical and cognitive function. This national observational study included 1,985 diabetes patients from the four waves of the China Health and Retirement Longitudinal Study (CHARLS) in 2011 to 2018. Thirteen chronic diseases were used in latent class analysis to identify the patterns of multimorbidity in diabetes, which span the cardiovascular, physical, psychological, and metabolic systems. Cognitive function is assessed via a structured questionnaire in three domains: memory, executive function, and orientation. We combined activities of daily living (ADL) with instrumental activities of daily living (IADL) to measure physical function. Linear mixed models and negative binomial regression models were used to analyze the association between patterns of multimorbidity in diabetes and the effect on cognitive function and disability, respectively. A sample of 1,985 diabetic patients was identified, of which 1,889 (95.2%) had multimorbidity; their average age was 60.6 years (standard deviation (SD) = 9.5), and 53.1% were women. Three clusters were identified: "cardio-metabolic" (n = 972, 51.5%), "mental-dyslipidemia-arthritis" (n = 584, 30.9%), and "multisystem morbidity" (n = 333, 17.6%). Compared with diabetes alone, the "multisystem morbidity" class had an increased association with global cognitive decline. All patterns of multimorbidity were associated with an increased risk of memory decline and disability; however, the "multisystem morbidity" group also had the strongest association and presented a higher ADL-IADL disability (ratio = 4.22, 95% CI = 2.52, 7.08) and decline in memory Z scores (β = -0.322, 95% CI = -0.550, -0.095, p = 0.0058). Significant longitudinal associations between different patterns of multimorbidity in diabetes patients and memory decline and disability were observed in this study. Future studies are needed to understand the underlying mechanisms and common risk factors for multimorbidity in diabetes patients and to propose treatments that are more effective.

An Illustration of a Latent Class Analysis for Interrater Agreement: Identifying Subpopulations with Different Agreement Levels

This study proposes a latent class analysis (LCA) approach to investigate interrater agreement based on rating patterns. LCA identifies which subjects are rated similarly or differently by raters, providing a new perspective for investigating agreement. Using an empirical dataset of parents and teachers evaluating pupils, the study found two latent classes of respondents, one belonging to a moderate agreement pattern and one belonging to low agreement pattern. We calculated raw agreement coefficient (RAC) per behaviour in the whole sample and each latent class. When RAC was calculated in the whole sample, many behaviour had low/moderate RAC values. However, LCA showed that these items had higher RAC values in the high agreement and lower RAC values in the low agreement class.

Parent Physical and Psychological Aggression and Youth Dating Violence: A Latent Class Analysis Approach.

Adolescent dating violence is a national public health issue and research suggests that aggressive parenting may predict the likelihood that a child will subsequently experience abuse. The purpose of this study is to examine the effect of parent physical and psychological aggression on adolescent dating violence perpetration and victimization. Data derived from the Future of Families and Child Wellbeing Study resulted in a racially and ethnically diverse sample of adolescents in dating relationships at the age of 15 years (N = 952). Utilizing both parent and adolescent data which assessed parenting practices at ages 3, 5, 9, and 15, and adolescent dating violence victimization and perpetration at age 15, we analyzed the data using a latest class analysis. Youth were typologized into three classes: the non-physically aggressive parenting, nonaggressive dating class (Class 1; 16% of youth), the aggressive parenting, nonaggressive dating class (Class 2; 76% of youth), and the aggressive parenting, aggressive dating class (Class 3; 8% of youth). Parents across all three classes utilized high levels of psychologically aggressive parenting. An important finding from this study is that parents' use of both physically and psychologically aggressive parenting only predicted subsequent dating violence victimization and perpetration among a small portion of adolescents. Findings suggest that additional risk factors, including household income and adolescent impulsivity, may help to elucidate pathways to adolescent dating violence. There is also a need to further explore the resiliency factors at play for youth who, despite having experienced both psychologically and physically aggressive parenting across the lifespan, did not experience dating violence victimization or perpetration.

The three Ps of supply chain resilience stages: a latent class analysis approach

ABSTRACT This study aims to extend the theory around SC resilience by empirically building a taxonomy of SC resilience capabilities. This study uses a survey approach with a sample of 196 manufacturing companies. Data analysis follows the latent class analysis approach and Welch ANOVA. Findings reveal that firms can be categorised into three clusters – the three Ps – primitive, precautious, and progressive – based on their level of adoption of SC resilience capabilities. Furthermore, the transition of firms from the primitive to the precautious stage is more challenging than moving up to the third stage. The 3Ps of SC resilience will aid business managers in determining their firms’ current resilience stage and eventually build an improvement agenda. Future researchers may combine ‘vulnerabilities’ to enhance this taxonomy.

The effect of a mobile health technology integrated care across multimorbidity patterns in atrial fibrillation patients: a latent-class ancillary analysis from the mAFA-II randomised clinical trial

Abstract Background Multimorbidity confers an increasing health problem, especially with the progressive aging of the population. Hypertension, diabetes mellitus (DM), chronic heart failure (CHF) are highly prevalent and ultimately entails multimorbidity in patients with atrial fibrillation (AF), with detrimental effects on prognosis. The Mobile Health Technology for Improved Screening and Optimized Integrated Care in AF (mAFA-II) cluster randomised trial showed the efficacy of a mobile health implemented ‘Atrial fibrillation Better Care’ (ABC) pathway (mAFA intervention) for an integrated care management of patients with AF. Purpose Evaluate the effect of mAFA intervention according to the patterns of multimorbidity as identified through a latent-class analysis (LCA) approach. Methods The mAFA-II trial is a cluster randomised trial that enrolled adults AF patients between June 2018 and August 2019 in 40 centres in China. Clusters were randomised to mAFA intervention or usual care. We performed a LCA according to several variables (including age over 75, coronary artery disease (CAD), CHF, chronic kidney disease (CKD), DM, hypertension, peripheral artery disease and prior ischemic stroke), and modal posterior probability was used to define class membership of patients. The interaction between the effect of mAFA intervention and class allocation on the risk of the primary composite outcome of stroke, thromboembolism, all-cause death, and rehospitalizations was assessed through multivariable Cox-regression models, and results were expressed as adjusted Hazard Ratio (aHR) and 95% Confidence Intervals (95%CI). Results 3324 AF patients (mean age 68.5±13.9; 38.0% females) were enrolled in the trial and included in the analysis. We identified 3 comorbidity patterns, which differed according to prevalence of conditions and risk factors: (i) low morbidity pattern (n=1234, 37.1%), (ii) hypertensive/CAD pattern (n=1534, 46.2%), and (iii) mixed morbidity pattern (n=556, 16.7%). Overall, subjects in the mixed morbidity patterns were older, had a higher burden of diseases, and showed the highest prevalence of CKD, diabetes and history of stroke (Figure 1). Multivariable Cox-regression analysis showed a significant interaction between the effect of mAFA intervention and comorbidity patterns on the risk of the primary composite outcome (pint= 0.004), with the effect of mAFA intervention being greatest in the low morbidity pattern (aHR:0.08, 95%CI:0.02-0.32) and somewhat diluted in the mixed morbidity pattern group with a trend towards lower risk (aHR:0.69, 95%CI:0.37-1.28, Figure 2). Conclusions Multimorbidity is common and heterogeneous in AF patients. The effect of a mHealth-technology implemented ABC pathway appeared lower in magnitude in those patients with mixed morbidity patterns, suggesting that these patients require further interventions, aiming at addressing non-cardiovascular comorbidities and overall clinical complexity, to improve their outcomes.Clusters distributionCox-regression models for interaction

Measuring Gradations of Culture-Specific Support for Native American College Students: A Latent Class Analysis Approach

Measuring Gradations of Culture-Specific Support for Native American College Students: A Latent Class Analysis Approach