Latent Class Analysis Model Research Articles

Contribution of County Characteristics to Disparities in Rural Mortality After Cancer Diagnosis

IntroductionRural disparities in cancer outcomes have been widely evaluated, but limited evidence is available to describe what characteristics of rural environments contribute to the increased risk of poor outcomes. Therefore, this manuscript sought to assess the mediating effects of county characteristics on the relationship between urban/rural status and mortality among patients with cancer, characterize county profiles, and determine at-risk county profiles alongside rural settings. MethodsPatients diagnosed with cancer between 2000 and 2016 were assessed using Surveillance, Epidemiology and End Results (SEER) data linked to the 2010 Rural-Urban Commuting Codes and 2010 County Health Rankings (CHR). There were 757,655 patients representing 596 counties (of 3143 in the US) and 12 states. Mediation analyses, conducted in 2023, estimated the direct contribution of rurality to five-year all-cause survival and the contribution of the rural effect indirectly through CHR domains. Latent class analysis and survival models identified county groupings and estimated the hazard of mortality associated with class membership. ResultsRankings for premature death, clinical care, and physical environment resulted in rural patients having 17.9%-20.2% less survival time compared to urban patients. Of this, 4.1%-12.6% of the total excess risk was mediated by these characteristics. Patients living in rural and high-risk county classes saw higher all-cause mortality than those in urban-lower risk counties (HR, 1.04, 95%CI 1.01-1.08 and 1.07, 95%CI, 1.03-1.11). ConclusionsCounties with poorer health rankings had increased mortality risks regardless of rurality; however, the poor rankings, notably health behaviors and social & economic factors, elevated the risk for rural counties.

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.

Exploring the change in the heterogeneity of public preferences for air quality improvement policies before and after the COVID-19 pandemic: comparative results from latent class model analysis

Exploring the change in the heterogeneity of public preferences for air quality improvement policies before and after the COVID-19 pandemic: comparative results from latent class model analysis

Exploring the unifying concept of spondyloarthritis: a latent class analysis of the REGISPONSER registry.

The aim of our study was to identify the potential distinct phenotypes within a broad SpA population. We conducted a cross-sectional study using the REGISPONSER registry, which has data from 31 specialist centres in Spain, including patients with SpA who have fulfilled the ESSG criteria. A latent class analysis (LCA) was performed to identify the latent classes underlying SpA according to a set of predefined clinical and radiographic features, independently of expert opinion. In a population of 2319 SpA patients, a five-classes LCA model yielded the best fit. Classes named 'Axial with spine involvement' and 'Axial with isolated SI joint involvement' showed a primarily axial SpA phenotype defined by inflammatory back pain and high HLA-B27 prevalence. Patients in class 'Axial + peripheral' showed a similar distribution of manifest variables to previous classes but also had a higher likelihood of peripheral involvement (peripheral arthritis/dactylitis) and enthesitis, therefore representing a mixed (axial and peripheral) subtype. Classes 'Peripheral + psoriasis' and 'Axial + peripheral + psoriasis' were indicative of peripheral SpA (and/or PsA) with high likelihood of psoriasis, peripheral involvement, dactylitis, nail disease, and low HLA-B27 prevalence, while class 'Axial + peripheral + psoriasis' also exhibited increased probability of axial involvement both clinically and radiologically. The identification of five latent classes in the REGISPONSER registry with significant overlap between axial and peripheral phenotypes is concordant with a unifying concept of SpA. Psoriasis and related features (nail disease and dactylitis) influenced the phenotype of both axial and peripheral manifestations.

Accuracy of an AI-based automated plate reading mobile application for the identification of clinical mastitis-causing pathogens in chromogenic culture media

Using on-farm microbiological culture (OFC), based on chromogenic culture media, enables the identification of mastitis causing pathogens in about 24 h, allows rapid decision making on selective treatment and control management measures of cows with clinical mastitis (CM). However, accurate interpretation of OFC results requires trained and experienced operators, which could be a limitation for the use of OFC in dairy farms. Our hypothesis was that AI-based automated plate reading mobile application can analyze images of microorganisms’ colonies in chromogenic culture media with similar diagnostic performance as a trained specialist evaluator. Therefore, the aim of the present study was to evaluate the diagnostic accuracy of an AI-based application (Rumi; OnFarm, Piracicaba, São Paulo, Brazil) for interpreting images of mastitis causing microorganism colonies grown in chromogenic culture media. For this study two trials were organized to compare the results obtained using an AI-based application Rumi with the interpretation of: (1) a trained specialist, using MALDI-TOF MS as the gold standard; (2) farm personnel users (FPU). In trial 1, a total of 476 CM milk samples, from 11 farms located in São Paulo (n = 7) and Minas Gerais (n = 4), southeast Brazil, were inoculated in chromogenic culture media plates (Smartcolor 2, OnFarm, Piracicaba, São Paulo, Brazil) by specialists under lab conditions, and digital images were recorded 24 h after incubation at 37 °C. After that, all the 476 digital images were analyzed by the Rumi and by another specialist (who only had access to the digital images) and the diagnostic accuracy indicators sensitivity (Se) and specificity (Sp) were calculated using MALDI-TOF MS microbiological identification of the isolates as the reference. In Trial 2, a total of 208 CM milk samples, from 150 farms from Brazil, were inoculated in chromogenic culture media plates by FPU, and the results of microbiological growth were visually interpreted by FPU under on-farm conditions. After visual interpretation, results were recorded using an OnFarmApp application (herd manage application for mastitis by OnFarm, Piracicaba, São Paulo, Brazil), and the images of the chromogenic culture plates were captured by the OnFarmApp to be evaluated by Rumi and Bayesian Latent Class Models were performed to compare Rumi and the FPU. In Trial 1, Rumi presented high and intermediate accuracy results, with the only exception of the low Enterococcus spp.’s Se. In comparison with the specialist, Rumi performed similarly in Se and Sp for most groups of pathogens, with the only exception of non-aureus staphylococci where Se results were lower. Both Rumi and the specialist achieved Sp results > 0.96. In Trial 2, Rumi had similar results as the FPU in the Bayesian Latent Class Model analysis. In conclusion, the use of the AI-based automated plate reading mobile application can be an alternative for visual interpretation of OFC results, simplifying the procedures for selective treatment decisions for CM based on OFC.

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.

Computer user profiles in early adolescence and digitally assessed mathematics: A latent class analysis

This study aimed at exploring the pattern of computer usage among early adolescents in Italy, about one year before the spread of the COVID-19 pandemic. The study also aimed at developing and testing models on the associations between socio-demographic characteristics, computer-user profiles, and mathematics achievement. A three-step estimation of Latent Class Analysis models and auxiliary variable models was applied to data collected on a sample of 1155 students attending grade 8 in Italy in the school year 2018–19. Four subpopulations were uncovered, which differ both with regards to their activities with computers (desktop, laptop, and tablet computers) and the likelihood of being daily users. Students’ socioeconomic and cultural background and gender predicted their pattern of computer usage, which in turn predicted digitally assessed mathematics. The main results suggested that excessive and almost exclusive use of computers for entertainment and communication purposes may depict an “at-risk" profile in young adolescents, with possible negative consequences for their learning achievements. This profile was more likely to be found among students from more disadvantageous backgrounds and males, at least before the COVID-19 pandemic started.

Distinct risk groups with different healthcare barriers and acute care use exist in the U.S. population with chronic liver disease.

The relationship between community-based healthcare barriers and risk of recurrent hospital-based care among persons with chronic liver disease (CLD) is understudied. We aimed to uncover distinct groups among adults in the United States with CLD based on healthcare barriers and risk-stratify recurrent acute care use by group. Using National Health Interview Survey (2011 to 2017) data, we performed latent class analysis (LCA) to uncover groups experiencing distinct sets of healthcare barriers. We assessed sociodemographic and health characteristics and probabilities of recurrent acute care use by group. The sample included 5,062 (estimated 4.7 million) adults with CLD (median [range] age 55 [18-85]). LCA modeling differentiated 4 groups: minimal barriers (group 1) (n = 3,953; 78.1%), unaffordability (group 2) (n = 540; 10.7%), care delays (group 3) (n = 328; 6.5%), and inability to establish care (group 4) (n = 240; 4.8%). Group 2 had the most uninsured persons (n = 210; 38.9%), whereas group 3 was mostly insured (n = 305; 93.1%). Group 4 included the most adults under 65 years old (n = 220; 91.7%), females (n = 156; 65.1%), and persons with unemployment (n = 169; 70.6%) and poverty (n = 85; 35.3%). Compared to group 1, the likelihood of recurrent acute care use was highest for group 4 (aOR, 1.85; 95% CI, 1.23-2.79 followed by group 3 (aOR, 1.50; 95% CI, 1.07-2.11) and group 2 (aOR, 1.48; 95% CI, 1.11-1.97). US adults with CLD can be categorized into 4 distinct groups based on healthcare barriers, which are associated with different probabilities of recurrent acute care use. Findings from this study are important for future interventions to reduce potentially avoidable hospital-based care among the highest-risk persons with CLD.

Latent Class Groups of Concurrent Substance Use Among Adolescents in an Urban Community: Correlates With Mental Health, Access to Drugs and Alcohol, and Risk Perception.

Concurrent substance use among adolescents has been associated with an increase in physical and mental health problems. These outcomes tend to be exacerbated among adolescents of color in underserved urban settings. The purpose of this study was to understand alcohol and concurrent drug use patterns among adolescents in an underserved urban community to provide targeted prevention and treatment recommendations. This study examined data among adolescents in an underserved urban community (N = 1789; 56.90% female; 70.86% Hispanic/Latino/a; meanage = 15.96 ± 1.56). Using latent class analysis (LCA) and multinomial logistic regression modeling, analyses identified independent correlates of latent class membership. Five latent classes (LC) were identified including LC group 1: Predominant alcohol use and limited to no concurrent-drug use (n = 213; 11.9%); LC group 2: Concurrent drug and alcohol use including methamphetamine, marijuana and synthetic marijuana use, and alcohol use (n = 74; 4.2%); LC group 3: Concurrent drug and alcohol use, with no marijuana use (n = 204; 11.39%); LC group 4: High Concurrent drug use and alcohol use (n = 204; 11.40%); and LC group 5: Concurrent drug use without alcohol use (n = 1101; 61.52%). Significant between group differences were noted between latent class groups and sociodemographic characteristics. Multinomial logistic regression models identified the associations between sociodemographic characteristics and corollary clinical features of substance use on latent class groupings of alcohol and concurrent drug use. Understanding concurrent substance use LC groups among adolescents is essential to providing targeted interventions and treatment programs, as well as early intervention programs that may help reduce substance use during adolescence.

Bayesian diagnostic test evaluation and true prevalence estimation of malnutrition in gastric cancer patients

Malnutrition is prevalent among gastric cancer (GC) patients, necessitating early assessment of nutritional status to guide monitoring and interventions for improved outcomes. We aim to evaluate the accuracy and prognostic capability of three nutritional tools in GC patients, providing insights for clinical implementation. The present study is an analysis of data from 1308 adult GC patients recruited in a multicenter from July 2013 to July 2018. Nutritional status was assessed using Nutritional Risk Screening 2002 (NRS-2002), Patient-Generated Subjective Global Assessment (PG-SGA) and Global Leadership Initiative on Malnutrition (GLIM) criteria. Bayesian latent class model (LCM) estimated the malnutrition prevalence of GC patients, sensitivity and specificity of nutritional tools. Cox regression model analyzed the relationship between nutritional status and overall survival (OS) in GC patients. Among 1308 GC patients, NRS-2002, PG-SGA, and GLIM identified 50.46%, 76.76%, and 68.81% as positive, respectively. Bayesian LCM analysis revealed that PG-SGA had the highest sensitivity (0.96) for malnutrition assessment, followed by GLIM criteria (0.78) and NRS-2002 (0.65). Malnutrition or being at risk of malnutrition were identified as independent prognostic factors for OS. Use any of these tools improved survival prediction in TNM staging system. PG-SGA is the most reliable tool for diagnosing malnutrition in GC patients, whereas NRS-2002 is suitable for nutritional screening in busy clinical practice. Given the lower sensitivity of NRS-2002, direct utilization of GLIM for nutritional assessment may be necessary. Each nutritional tool should be associated with a specific course of action, although further research is needed.

Well‐supervised, highly motivated, and healthy? Using latent class analysis and structural equation modelling to study doctoral candidates' health satisfaction

AbstractMore and more empirical studies address doctoral candidates' health. Yet, the mechanisms linking supervision and doctoral candidates' health often remain unclear. We start to fill this research gap with classifications of supervisors produced by latent class analysis, which were introduced into structural equation models with motivation towards the dissertation research as a mediator to predict doctoral candidates' health satisfaction. We used data from more than 200 doctoral candidates from a German university. Three types of supervisor support were extracted (poor support: 18.4%; good support: 26.4%; very good support: 55.2%). Poor support was significantly negatively associated with doctoral candidates' levels of motivation and health satisfaction. The relationship between poor support and health was partly mediated by motivation. By means of the advanced statistical models, mechanisms linking supervision and doctoral candidates' health could be identified and research on the dimensions of (very) good supervisor support could be expanded.

Prosocial Behaviour and Antibiotic Resistance: Evidence from a Discrete Choice Experiment.

The health of a community depends on the health of its individuals; therefore, individual health behaviour can implicitly affect the health of the entire community. This is particularly evident in the case of infectious diseases. Because the level of prosociality in a community might determine the effectiveness of health programmes, prosocial behaviour may be a crucial disease-control resource. This study aimed to extend the literature on prosociality and investigate the role of altruism in antibiotic decision making. A discrete choice experiment was conducted to assess the influence of altruism on the general public's preferences regarding antibiotic treatment options. The survey was completed by 378 Swedes. Latent class analysis models were used to estimate antibiotic treatment characteristics and preference heterogeneity. A three-class model resulted in the best model fit, and altruism significantly impacted preference heterogeneity. Our findings suggest that people with higher altruism levels had more pronounced preferences for treatment options with lower contributions to antibiotic resistance and a lower likelihood of treatment failure. Furthermore, altruism was statistically significantly associated with sex, education, and health literacy. Antibiotic awareness, trust in healthcare systems, and non-discriminatory priority setting appear to be structural elements conducive to judicious and prosocial antibiotic behaviour. This study suggests that prosocial messages could help to decrease the demand for antibiotic treatments.

Patterns of health care interactions of individuals with alcohol use disorder: A latent class analysis

IntroductionGiven the high rates at which individuals with alcohol use disorder (AUD) utilize health care for co-existing conditions, health systems are promising venues for interventions that will facilitate access to AUD treatment. However, how individuals with AUD interact with such systems and, thus, how systems should intervene is unclear. In this study, we seek to identify patterns in how individuals diagnosed with AUD within an academic health system interacted with the system prior to diagnosis. MethodsWe use electronic health records from a single academic health system in a major US metropolitan area to create a deidentified retrospective cohort including all individuals age 18+ diagnosed with AUD 2010–2019 (n = 26,899). Latent class analysis (LCA) identified subgroups defined by aspects of previous system interaction and health status, including having an in-system primary care provider, previous utilization of primary and specialty care, diagnosis setting, payer, and presence of other chronic conditions. We then assessed subgroup differences in demographics and associations with in-system AUD treatment receipt in the year following diagnosis, adjusting for demographics. ResultsThe population was on average 38.6 years old (standard deviation = 15.4) and predominantly male (66.1 %), White (64.5 %), and not of Hispanic/Latino ethnicity (87.8 %). Only 4.7 % received in-system treatment following diagnosis. We deemed the four-class model the optimal LCA model. This model identified subgroups that can be described as 1) average utilization (20.7 % of population), 2) low utilization (54.5 %), 3) high health burden and low utilization (14.2 %), and 4) high health burden and high utilization (10.6 %). Predicted membership in the high health burden and high utilization subgroup and low utilization subgroup were associated with higher and lower odds of treatment receipt, respectively, compared with predicted membership in the average utilization subgroup (odds ratio (OR) for high/high subgroup = 1.21, 95 % confidence interval (CI) = 1.01, 1.27; OR for low subgroup = 0.29 95 % CI = 0.24, 0.34). ConclusionIndividuals diagnosed with AUD within a health system interact with that system in markedly different ways and are unlikely to benefit uniformly from system-based interventions to facilitate treatment. Group-tailored interventions are more likely to have impact and provide returns on investments for systems.

Clinical Manifestations.

Despite the well-known deleterious health effects of childhood and adulthood trauma and the aging of the global population, little is known about trauma experiences in older adults. Moreover, childhood trauma may beget adulthood trauma and alter life-course trajectories of trauma exposure, and thus exhibit certain patterns in older adults. Using a national sample, this study aimed to (1) determine patterns of childhood and adulthood trauma, (2) examine their association with cognition and depression, and (3) investigate whether the association between trauma and depression is moderated by cognition. Data were from participants aged 60+ in the Health and Retirement Study in 2008 and 2012. Latent class analysis (LCA) was conducted to identify childhood and adulthood trauma patterns in 2008 (N = 6545). Depressive symptoms were measured by Center for Epidemiological Studies-Depression scale (CESD) and cognition was assessed by total cognition summary score (range 0-35 and cognitive impairment defined as 8 or less) in both 2008 and 2012. The final sample size is 4684. Multiple regressions were conducted to examine cross-sectional and longitudinal associations between trauma patterns, cognition, and depression, while moderation analyses were conducted to examine the moderating role of cognition, adjusting for age, gender, race, education, marital status, and body mass index. Participants had a mean age of 71.5(7.53) years, 58.4% female, and 85.7% White. A 3-class LCA model was determined to be the best fit for the data with three patterns: (1) low trauma exposure (61.04%), (2) family life-threatening events (20.07%) and (3) moderate trauma exposure (i.e., moderate probability of having experienced every type of trauma) (18.89%). Older adults experiencing family life-threatening events or moderate trauma exposure have significantly worse cognition and higher depression. Furthermore, cognition moderated the association between trauma exposure and depression, especially for older adults who experienced family life-threatening events. Findings indicated significant associations between three distinct trauma patterns and depression, and the buffering role of cognition. Educating health professionals about trauma patterns, rather than only considering accumulation, could optimize targeted, integrated public health and clinical strategies to improve cognitive performance and decrease depression in vulnerable older adults who have experienced trauma.

Percepção de cuidadores sobre a qualidade de serviços de Fisioterapia Neuropediátrica em Centros de reabilitação no estado da Paraíba, Brasil

This study evaluates the perception of caregivers of the quality of neuropediatric physiotherapy services offered at specialized rehabilitation centers (SRC) in Brazil. This is a cross-sectional, quantitative, evaluative study whose target population was caregivers of children in rehabilitation. The AQSF-Neuroped instrument was used to evaluate the quality of three SRC based on the perception of 320 caregivers. The one-proportion hypothesis test was used based on each AQSF-Neuroped item and dimension. The Weight of Evidence and Latent Class Analysis models were used to evaluate the influence of variables on the perception of quality. A mean overall quality score of 133±14 points indicated a positive perception of quality; however, there were aspects related to physical structure (Interpersonal Dimension) and access (Organizational Dimension II) that needed to be improved (p-values

Continuing medical education in China: evidence from primary health workers’ preferences for continuing traditional Chinese medicine education

BackgroundContinuing Medical Education (CME) is an important part of the training process for health workers worldwide. In China, training in Traditional Chinese Medicine (TCM) not only improves the expertise of medical workers, but also supports the Chinese Government’s policy of promoting TCM as an equal treatment to western medicine. CME, including learning Traditional Chinese Medicine Technologies (TCMTs), perform poorly and research into the motivation of health workers to engage in CME is urgently required. Using a discrete choice experiment, this study assessed the CME learning preferences of primary health workers, using TCMT as a case study of CME programs.MethodsWe conducted a discrete choice experiment among health workers in Shandong Province, Guizhou Province, and Henan provinces from July 1, 2021 to October 1, 2022 on the TCMT learning preferences of primary health workers. The mixed logit model and latent class analysis model were used to analyze primary health workers’ TCMT learning preferences.ResultsA total of 1,063 respondents participated in this study, of which 1,001 (94.2%) passed the consistency test and formed the final sample. Our key finding was that there were three distinct classes of TCMT learners. Overall, the relative importance of the seven attributes impacting the learning of TCMTs were: learning expenses, expected TCMT efficacy, TCMT learning difficulty, TCMT mode of learning, TCMT type, time required to learn, and expected frequency of TCMT use. However, these attributes differed significantly across the three distinct classes of TCMT learners. Infrequent users (class 1) were concerned with learning expenses and learning difficulty; workaholics (class 2) focused on the mode of learning; and pragmatists (class 3) paid more attention to the expected TCMT efficacy and the expected frequency of TCMT use. We recommend targeted strategies to motivate TCMT learning suited to the requirements of each class of TCMT learners.ConclusionRather than a single TCMT medical education program for primary health workers, CME programs should be targeted at different classes of TCMT learners.

Early-onset restrictive food intake disorders in children: a latent class analysis.

The two most frequent early-onset restrictive food intake disorders are early-onset anorexia nervosa (EOAN) and avoidant/restrictive food intake disorders (ARFID). Although the core symptoms of EOAN (i.e., fear of gaining weight and disturbed body image) are not present in ARFID, these symptoms are difficult to assess during the initial phase of hospitalisation.Ouraim was to identify restrictive food intake disorder subtypes in children using latent class analysis (LCA) based on the information available at admission to hospital, and to determine the agreement between the subtypes identified using LCA and the final diagnosis: EOAN or ARFID. We retrospectively included 97 children under 13years old with severe eating disorders (DSM-5) at their first hospitalisation in a specialised French paediatric unit. LCA was based on clinical information, growth chart analyses and socio-demographic parameters available at admission. We then compared the probabilities of latent class membership with the diagnosis (EOAN or ARFID) made at the end of the hospitalisation. The most parsimonious LCA model was a 2-class solution. Children diagnosed with EOAN at the end of hospitalisation had a 100% probability of belonging to class 1 while children diagnosed with ARFID had an 8% probability of belonging to class 1 based on parameters available at admission. Our results indicate that clinical and socio-demographic characteristics other than the core symptoms of EOAN may be discriminating for a differential diagnosis.

Agreement and disagreement between child-caregiver symptom and function reports in pediatric oncology.

The use of patient-reported outcomes (PRO) is increasing in pediatric clinical practice and research, including in clinical trials. Treatment teams are now able to discern differences in symptom and function reports between children with cancer and their family caregivers. Few studies, however, have quantified such differences. Children and caregivers were matched to create a dyad sample, and child/caregiver agreement of responses to the Patient-Reported Outcome Measurement Information System (PROMIS) Pediatric symptom and function measures were examined using the established minimally important difference (MID) value (three points) derived from these measures. Latent class analysis (LCA) was then used to analyze the status/patterns of response agreement. A total of 430 dyadic cases (dyads) were used for LCA modeling. Three classes of child-caregiver concordance resulted: (a) Agreement-caregiver and child within one MID (200 dyads); (b) Disagreement-caregiver underestimating symptoms (47 dyads with caregiver scores more than 1 MID lower than the child scores); and (c) Disagreement-caregiver overestimating symptoms (183 dyads with caregiver scores more than 1 MID higher than child scores). As PRO use increases in pediatric oncology, clinician identification of child/caregiver differences on symptom and function reports will help to distinguish family perceptions of cancer treatment impact. The treatment goal, in addition to mitigating symptom and function treatment-related burdens for the child and caregiver, may now also be to discern the direction and clinical meaning of the difference within the child-parent dyad.

Patterns of Familial and Racial Trauma and Their Associations with Substance Use Disorders among Racial/Ethnic Minority Adults.

Racial/ethnic minorities in the United States often experience many different types of traumatic events. We examine the patterns of familial and racial trauma and their associations with substance use disorders (SUDs) among racial/ethnic minority adults. We used data from the National Epidemiologic Survey of Alcohol and Related Conditions-III. The study sample included 17,115 individuals who were Hispanic (43.6%), Black (34.9%), Asian American and Pacific Islander (17.0%), and American Indian or Alaska Native (AI/AN, 4.6%). Latent class analysis models with covariates and distal outcomes were analyzed to investigate patterns of trauma exposure and estimate binary outcomes of SUDs. Familial and racial trauma was measured by ten areas of adverse childhood experiences (ACEs) and six items of racial discrimination. We found four distinctive groups: low trauma (Class 1, 62.1%), high discrimination (Class 2, 17.2%), high ACEs (Class 2, 14.9%), and high trauma (Class 4, 5.9%). Compared to Class 1, other groups were more likely to include Black and AI/AN adults. Participants in Class 2 reported greater risks for alcohol and other drug use disorders. Those in Class 3 and 4 reported greater risks for alcohol, opioid, stimulant, and other drug use disorders. Given a higher risk of trauma exposure in Black and AI/AN adults, racially and ethnically sensitive trauma-focused interventions may help prevent and reduce SUDs in those populations.

Long-term symptom profiles after COVID-19 vs other acute respiratory infections: an analysis of data from the COVIDENCE UK study

Long COVID is a well recognised, if heterogeneous, entity. Acute respiratory infections (ARIs) due to other pathogens may cause long-term symptoms, but few studies compare post-acute sequelae between SARS-CoV-2 and other ARIs. We aimed to compare symptom profiles between people with previous SARS-CoV-2 infection, people with previous non-COVID-19 ARIs, and contemporaneous controls, and to identify clusters of long-term symptoms. COVIDENCE UK is a prospective, population-based UK study of ARIs in adults. We analysed data for 16potential long COVID symptoms and health-related quality of life (HRQoL), reported between January 21 and February 15, 2021, by participants unvaccinated against SARS-CoV-2. We classified participants as having previous SARS-CoV-2 infection or previous non-COVID-19 ARI (≥4 weeks prior) or no reported ARI. We compared symptoms by infection status using logistic and fractional regression, and identified symptom clusters using latent class analysis (LCA). This study is registered with ClinicalTrials.gov, NCT04330599. We included 10,171 participants (1311 [12.9%] with SARS-CoV-2 infection, 472 [4.6%] with non-COVID-19 ARI). Both types of infection were associated with increased prevalence/severity of most symptoms and decreased HRQoL compared with no infection. Participants with SARS-CoV-2 infection had increased odds of problems with taste/smell (odds ratio 19.74, 95% CI 10.53-37.00) and lightheadedness or dizziness (1.74, 1.18-2.56) compared with participants with non-COVID-19 ARIs. Separate LCA models identified three symptom severity groups for each infection type. In the most severe groups (representing 22% of participants for both SARS-CoV-2 and non-COVID-19 ARI), SARS-CoV-2 infection presented with a higher probability of problems with taste/smell (probability 0.41 vs 0.04), hair loss (0.25 vs 0.16), unusual sweating (0.38 vs 0.25), unusual racing of the heart (0.43 vs 0.33), and memory problems (0.70 vs 0.55) than non-COVID-19 ARI. Both SARS-CoV-2 and non-COVID-19 ARIs are associated with a wide range of symptoms more than 4 weeks after the acute infection. Research on post-acute sequelae of ARIs should extend from SARS-CoV-2 to include other pathogens. Barts Charity.