Random intercept hierarchical linear model for multi-regional clinical trials.

In multi-regional clinical trials, hierarchical linear models have been actively studied because they can reflect that patients in the same region share common intrinsic and extrinsic factors. In this paper, we investigate the statistical properties of the hierarchical linear model including a random effect in the intercept. The big advantage of the random intercept hierarchical linear model is that it can control the type I error rates of testing the overall treatment effect when there are no or clinically negligible regional differences in the treatment effect. Moreover, we compare the pros and cons with the hierarchical linear model in which the random effect is included in the slope. For the two hierarchical linear models, the model selection criteria are determined according to the magnitude of the difference in treatment effect across the regions, and we provide the criteria through simulation studies.

Differences in Covid-19 mortality among persons 70 years and older in an integrated care setting in region Stockholm: a multi-level analysis between March 2020-February 2021.

BACKGROUND: In Norrtälje municipality, within Region Stockholm, there is a joint integrated care organisation providing health and social care, which may have facilitated a more coordinated response to the covid-19 pandemic compared to the otherwise decentralised Swedish system. This study compares the risk of covid-19 mortality among persons 70 years and older, in the municipalities of Stockholm, Södertälje, and Norrtälje, while considering area and individual risk factors. METHODS: A population-based study using linked register data to examine covid-19 mortality among those 70 + years (N = 127,575) within the municipalities of interest between the periods March-August 2020 and September 2020-February 2021. The effect of individual and area level variables on covid-19 mortality among inhabitants in 68 catchment areas were examined using multi-level logistic models. RESULTS: Individual factors associated with covid-19 mortality were sex, older age, primary education, country of birth and poorer health as indicated by the Charlson Co-morbidity Index. The area-level variables associated were high deprivation (OR: 1.56, CI: 1.18-2.08), population density (OR: 1.14, CI: 1.08-1.21), and usual care. Together, this explained 85.7% of the variation between catchment areas in period 1 and most variation was due to individual risk factors in period 2. Little of the residual variation was attributed to differences between catchment areas. CONCLUSION: Integrated care in Norrtälje may have facilitated a more coordinated response during period 1, compared to municipalities with usual care. In the future, integrated care should be considered as an approach to better protect and meet the care needs of older people during emergency situations.

Assessing complier average causal effects from longitudinal trials with multiple endpoints and treatment noncompliance: An application to a study of Arthritis Health Journal.

Treatment noncompliance often occurs in longitudinal randomized controlled trials (RCTs) on human subjects, and can greatly complicate treatment effect assessment. The complier average causal effect (CACE) informs the intervention efficacy for the subpopulation who would comply regardless of assigned treatment and has been considered as patient-oriented treatment effects of interest in the presence of noncompliance. Real-world RCTs evaluating multifaceted interventions often employ multiple study endpoints to measure treatment success. In such trials, limited sample sizes, low compliance rates, and small to moderate effect sizes on individual endpoints can significantly reduce the power to detect CACE when these correlated endpoints are analyzed separately. To overcome the challenge, we develop a multivariate longitudinal potential outcome model with stratification on latent compliance types to efficiently assess multivariate CACEs (MCACE) by combining information across multiple endpoints and visits. Evaluation using simulation data shows a significant increase in the estimation efficiency with the MCACE model, including up to 50% reduction in standard errors (SEs) of CACE estimates and 1-fold increase in the power to detect CACE. Finally, we apply the proposed MCACE model to an RCT on Arthritis Health Journal online tool. Results show that the MCACE analysis detects significant and beneficial intervention effects on two of the six endpoints while estimating CACEs for these endpoints separately fail to detect treatment effect on any endpoint.

Intensity of COVID-19 in care homes following hospital discharge in the early stages of the UK epidemic.

BACKGROUND: defining features of the COVID-19 pandemic in many countries were the tragic extent to which care home residents were affected and the difficulty in preventing the introduction and subsequent spread of infection. Management of risk in care homes requires good evidence on the most important transmission pathways. One hypothesised route at the start of the pandemic, prior to widespread testing, was the transfer of patients from hospitals that were experiencing high levels of nosocomial events. METHODS: we tested the hypothesis that hospital discharge events increased the intensity of care home cases using a national individually linked health record cohort in Wales, UK. We monitored 186,772 hospital discharge events over the period from March to July 2020, tracking individuals to 923 care homes and recording the daily case rate in the homes populated by 15,772 residents. We estimated the risk of an increase in case rates following exposure to a hospital discharge using multi-level hierarchical logistic regression and a novel stochastic Hawkes process outbreak model. FINDINGS: in regression analysis, after adjusting for care home size, we found no significant association between hospital discharge and subsequent increases in care home case numbers (odds ratio: 0.99, 95% CI: 0.82, 1.90). Risk factors for increased cases included care home size, care home resident density and provision of nursing care. Using our outbreak model, we found a significant effect of hospital discharge on the subsequent intensity of cases. However, the effect was small and considerably less than the effect of care home size, suggesting the highest risk of introduction came from interaction with the community. We estimated that approximately 1.8% of hospital discharged patients may have been infected. INTERPRETATION: there is growing evidence in the UK that the risk of transfer of COVID-19 from the high-risk hospital setting to the high-risk care home setting during the early stages of the pandemic was relatively small. Although access to testing was limited to initial symptomatic cases in each care home at this time, our results suggest that reduced numbers of discharges, selection of patients and action taken within care homes following transfer all may have contributed to the mitigation. The precise key transmission routes from the community remain to be quantified.

[Multilevel study on the effects of dietary nutrients on body mass index of Chinese children and adolescents aged 7-17].

OBJECTIVE: Analyze the relationship between dietary nutrients and body mass index of children and adolescents aged 7-17 in China. METHODS: The data comes from the "China Health and Nutrition Survey", and 5562 children and adolescents aged 7-17 who participated in at least one round(2000, 2006, 2011 and 2015) of the survey and had complete dietary and physical measurement survey data were selected as the research objects. A three-level(community-individual-observation level) linear random intercept mixed effect model of body mass index was constructed to analyze the influence of dietary nutrient intake of children and adolescents of different genders in urban and rural areas on their body mass index(BMI).24 hours for 3 consecutive days and family weight accounting were used to evaluate the dietary nutrient intake. RESULTS: BMI of urban children and adolescents is higher than that of rural children and adolescents. The BMI of children and adolescents aged 12-17 is higher than that of children and adolescents aged 7-11. BMI of boys was higher than that of girls, but the difference was statistically significant only in 2011 and 2015. After controlling for confounding factors such as individual level(survey year, age, physical activity and family per capita income) and community level(community urbanization index), the three-level model showed that the BMI of rural boys increased with the increase of cholesterol intake(P<0.01). BMI of urban girls increased with the increase of vitamin B_1 intake(P<0.05) and iron intake(P<0.01). BMI of rural girls increased with the increase of vitamin E intake(P<0.001) and sodium intake(P<0.05). CONCLUSION: There are some differences in dietary nutrients that affect the BMI level of 7-17 years old children and adolescents between urban and rural areas.

Association of adiposity indicators with hypertension among Chinese adults.

BACKGROUND AND AIMS: Obesity has been linked to the development of hypertension, but the comparison of relationships between different obesity parameters with hypertension are scarcely studied with nationally representative Chinese adults samples. We sought to compare the predictive strengths of different obesity indicators to hypertension. METHODS AND RESULTS: Data in this study were obtained from the Chinese National Stroke Prevention Project with a nationally representative sample of Chinese aged 40 years and older. A total of 162,880 individuals were included. Multi-level analyses and Receiver Operating Characteristic (ROC) curves were used to examine the risk of hypertension in relation to different obesity parameters, including body mass index (BMI), waist circumference (WC), lipid accumulation product index (LAP), visceral adiposity index (VAI), and body adiposity index (BAI). As results, the BMI, WC, LAP, VAI, and BAI were positively associated with the risk of hypertension (P < 0.001). In total, BMI had the strongest association with hypertension when compared with other obesity indicators, and one SD up of BMI would increase the risk of hypertension by 53.9% (95% CI: 1.514-1.566). For men, WC was most associated with hypertension, and one SD up of WC would increase the risk of hypertension by 73.3% (95% CI: 1.685-1.782). For women, BMI showed the strongest predictive power, one SD up of BMI would increase the risk of hypertension by 51.0% (95% CI: 1.479-1.543). CONCLUSIONS: BMI, WC, LAP, VAI, and BAI are all positively corrected to hypertension, but gender disparities should be considered in predicting hypertension by obesity indicators.

Ensuring identifiability in hierarchical mixed effects Bayesian models.

Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

Household income, active travel, and their interacting impact on body mass index in a sample of urban Canadians: a Bayesian spatial analysis.

BACKGROUND: Active travel for utilitarian purposes contributes to total physical activity and may help counter the obesity epidemic. However, the evidence linking active travel and individual-level body weight is equivocal. Statistical modeling that accounts for spatial autocorrelation and unmeasured spatial predictors has not yet used to explore whether the health benefits of active travel are shared equally across socioeconomic groups. METHODS: Bayesian hierarchical models with spatial random effects were developed using travel survey data from Saskatoon, Canada (N = 4625). Differences in log-transformed body mass index (BMI) were estimated for levels of active travel use (vehicular travel only, mixed vehicular/active travel, and active travel only), household income, and neighbourhood deprivation after controlling for sociodemographic and physical activity variables. The modifying effect of household income on the association between active travel and BMI was also evaluated. RESULTS: Significant and meaningful decreases in BMI were observed for mixed (ß = - 0.02, CrI - 0.036 to - 0.004) and active only (ß = - 0.043, CrI - 0.06 to - 0.025) compared to vehicular only travelers. BMI was significantly associated with levels of household income and neighbourhood deprivation. Accounting for the interaction between travel mode and household income, decreases in BMI were observed for active only compared to vehicular only travellers in the highest income category (ß = - 0.061, CrI - 0.115 to - 0.007). CONCLUSION: Strategies to increase active travel use can support healthy weight loss and maintenance, but the opportunity to benefit from active travel use may be limited by low income. Considerations should be given to how interventions to increase active transportation might exacerbate social inequalities in BMI. Spatial statistical models are needed to account for unmeasured but spatially structured neighbourhood factors.

[Geographical variation and related factors in prediabetes prevalence in Chinese adults in 2013].

Objective: To investigate the geographical variation of prediabetes in adults in different regions of China, and to analyze the related factors of prediabetes. Methods: Data was obtained from China Chronic Disease and Related Risk Factor Surveillance in 2013. The surveillance adopted multiple-stage stratified cluster random sampling method, which sampled 177 099 residents aged above 18 years old among 298 surveillance points in 31 provinces of Chinese Mainland. Questionnaire interview was used to obtain demographic variables, personal living style, and socio-economical information. Physical examination was conducted and fasting venous blood sample and (oral glucose tolerance test-2 hours, OGTT-2 h) venous blood sample were obtained from the participants. A total of 171 567 residents aged 18 and above were included in the analysis. The prevalence of prediabetes was analyzed by provinces and by China's geographical regions, after complex weighting. Multilevel logistic models were established to explore the related factors of prediabetes on the area level and individual level. Results: The prevalence of prediabetes among residents aged 18 and above was 16.6% (95%CI: 15.6%-17.6%) in China. The prevalence of prediabetes was the highest (18.3%) in the south China and lowest (13.1%) in the northwest area. The difference of the prevalence in different areas were not statistically significant (P=0.510). If categorized the prevalence of prediabetes into 5 groups by quintile, Hainan, Jilin, Shandong, Anhui, Hunan and Chongqing were in the highest group of prevalence of prediabetes (18.6%-22.7%), and Tibet, Qinghai, Gansu, Ningxia, Guizhou, and Jiangxi were in the lowest group (7.6%-12.6%). The variance of prevalence of prediabetes on the county level (MOR: 1.60 (95%CI:1.53-1.67)) was more diverse than the province level (MOR: 1.21(95%CI:1.08-1.29)) and higher than the street level (1.23 (95%CI:1.14-1.30)). Several factors increased risk of pre-diabetes, including smoking, hazardous drinking and harmful drinking, drinking in the past 30 days, overweight, obesity, central obesity, sugary drink intake, hypertension, high total cholesterol, high triglycerides, high blood low-density lipoprotein cholesterol, low blood high-density lipoprotein cholesterol (all P<0.05). After adjusted the above variables, 92.5% of variance of prediabetes prevalence conld be explained on the provincial level. Conclusion: The geographical distribution of prediabetes in adults in China differed by geographic areas, and it significantly varied on the county level. The related variables included demographic variables, personal behavior, and geographic related variables.

Bayesian mixed hidden Markov models: a multi-level approach to modeling categorical outcomes with differential misclassification.

Questionnaire-based health status outcomes are often prone to misclassification. When studying the effect of risk factors on such outcomes, ignoring any potential misclassification may lead to biased effect estimates. Analytical challenges posed by these misclassified outcomes are further complicated when simultaneously exploring factors for both the misclassification and health processes in a multi-level setting. To address these challenges, we propose a fully Bayesian mixed hidden Markov model (BMHMM) for handling differential misclassification in categorical outcomes in a multi-level setting. The BMHMM generalizes the traditional hidden Markov model (HMM) by introducing random effects into three sets of HMM parameters for joint estimation of the prevalence, transition, and misclassification probabilities. This formulation not only allows joint estimation of all three sets of parameters but also accounts for cluster-level heterogeneity based on a multi-level model structure. Using this novel approach, both the true health status prevalence and the transition probabilities between the health states during follow-up are modeled as functions of covariates. The observed, possibly misclassified, health states are related to the true, but unobserved, health states and covariates. Results from simulation studies are presented to validate the estimation procedure, to show the computational efficiency due to the Bayesian approach and also to illustrate the gains from the proposed method compared to existing methods that ignore outcome misclassification and cluster-level heterogeneity. We apply the proposed method to examine the risk factors for both asthma transition and misclassification in the Southern California Children's Health Study.

Area-level unemployment and perceived job insecurity: evidence from a longitudinal survey conducted in the Australian working-age population.

UNLABELLED: RESEARCH SIGNIFICANCE: Job insecurity, the subjective individual anticipation of involuntary job loss, negatively affects employees' health and their engagement. Although the relationship between job insecurity and health has been extensively studied, job insecurity as an 'exposure' has received far less attention, with little known about the upstream determinants of job insecurity in particular. This research sought to identify the relationship between self-rated job insecurity and area-level unemployment using a longitudinal, nationally representative study of Australian households. METHODS: Mixed-effect multi-level regression models were used to assess the relationship between area-based unemployment rates and self-reported job insecurity using data from a longitudinal, nationally representative survey running since 2001. Interaction terms were included to test the hypotheses that the relationship between area-level unemployment and job insecurity differed between occupational skill-level groups and by employment arrangement. Marginal effects were computed to visually depict differences in job insecurity across areas with different levels of unemployment. RESULTS: Results indicated that areas with the lowest unemployment rates had significantly lower job insecurity (predicted value 2.74; 95% confidence interval (CI) 2.71-2.78, P < 0.001) than areas with higher unemployment (predicted value 2.81; 95% CI 2.79-2.84, P < 0.001). There was a stronger relationship between area-level unemployment and job insecurity among precariously and fixed-term employed workers than permanent workers. CONCLUSION: These findings demonstrate the independent influences of prevailing economic conditions, individual- and job-level factors on job insecurity. Persons working on a casual basis or on a fixed-term contract in areas with higher levels of unemployment are more susceptible to feelings of job insecurity than those working permanently.

Shyness, Sport Engagement, and Internalizing Problems in Chinese Children: The Moderating Role of Class Sport Participation in a Multi-Level Model.

The relations between shyness and internalizing problems have been mainly explored at the individual level, with little known about its dynamics at the group level. This study aims to examine the mediating effect of individual-level sport engagement and the moderating effect of class-level sport participation in the relations between shyness and internalizing problems. The participants were 951 children attending primary and middle school from grade 3 to grade 7 (Mage = 11 years, 509 boys) in urban areas of China. Cross-sectional data were collected using self-report assessments. Multi-level analysis indicated that (1) shyness was positively associated with internalizing problems; (2) sport engagement partially mediated the relations between shyness and internalizing problems; and (3) class sport participation was a cross-level moderator in the mediating relations between shyness, sport engagement, and internalizing problems. Shy children in classes with a higher level of sport participation tend to have less sport engagement and more internalizing problems than those in classes with a lower level of sport participation. These findings illuminate implications from a multi-level perspective for shy children's adjustment in a Chinese context. The well-being of shy children could be improved by intervening in sport activity, addressing both individual engagement and group dynamics, such as class participation.

Fieldworker effects on quality of data collected on sensitive questions in National Family Health Surveys: An analysis based on intimate partner violence in India.

National Family Health Survey (NFHS) has played a pivotal role in formulating policies and programs by providing nationally representative data on a wide range of monitoring and impact evaluation indicators in population, health and nutrition. However, due to measurement errors resulting in misreporting, the collection of accurate data on sensitive issues in sample surveys has always been a matter of concern. This study examines the fieldworker effect on the reporting of physical, sexual, and emotional Intimate Partner Violence (IPV) using fifth round of NFHS (2019-21) data in India. The cross-classified multi-level model was used to examine the fieldworker's effect on the reporting of IPV. The fieldworker effect accounted for around 32% of total variation in the reporting of intimate partner violence. The fieldworker's effect varied from 26% to 41 % for physical violence, 29% to 33% for emotional IPV and 30%-36% for sexual IPV. It was observed that variation due to fieldworker for non-sensitive outcomes was negligible.Of total variation, only 1.6% for information related to ever attending school, 4.8% for ever given birth, 5% for currently pregnant and 8.5% for information on ever terminated pregnancy was being explained by the fieldworker. Our results indicate a significant fieldworker influence on the likelihood of reporting intimate partner violence, underlining the need for an increased understanding of the impact of fieldworkers on data outputs, especially-but not exclusively-when queries contain delicate or stigmatized themes. This study highlights the importance of providing extra guidance and special training to the fieldworkers when collecting sensitive information related to IPV.

The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model.

Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models. In this study, we derive the BIC's penalty term for level-specific fixed- and random-effect selection in a two-level nested design. In this new version of BIC, called BIC E 1 , this penalty term is decomposed into two parts if the random-effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called BIC E 2 , in the presence of redundant random effects. We show that the derived formulae, BIC E 1 and BIC E 2 , adhere to empirical values via numerical demonstration and that BIC E ( E indicating either E 1 or E 2 ) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi-level conditions through a simulation study. In addition, the use of BIC E 1 is illustrated with a textbook example dataset.

The availability and distribution of vaping retailers across Ontario neighbourhoods, 2016-2019.

OBJECTIVES: To characterize the distribution of vaping retailers and examine the association between neighbourhood income and vaping retailer availability in Ontario prior to and after regulatory changes to the vaping market in 2018. METHODS: We quantified vaping access by number of vaping retailers for 19,964 dissemination areas (DAs) in Ontario and percentage of schools near a vaping retailer. We used mixed-effects regression models to examine the associations between vaping access and neighbourhood income in 2018 and 2019. RESULTS: Between 2016 and 2019, the number of vaping retailers in Ontario increased by 22.6% (5999 to 7355), despite a mild drop from 2016 to 2018. In 2019, 59.7% of urban neighbourhoods had one or more vaping retailers within 1000 m of their geographic centre, and 79.4% of elementary, 82.8% of secondary, and 84.2% of post-secondary schools had at least one within 1000 m. Neighbourhood income was associated with access to vaping retailers, with a greater number in low-income regions. In 2019, neighbourhoods in the lowest income quintile had over twice the number of vaping retailers per capita within 1000 m compared to the highest income quintile (adjusted incidence rate ratio 2.40; 95% CI 2.24-2.58). Increases over time in access to vaping retailers did not differ by geographic region, neighbourhood income quintile, or school type. CONCLUSION: We observed a substantial increase in access to vaping retailers in Ontario including proximity to elementary and secondary schools following the 2018 provincial marketing regulations and federal nicotine regulations. Access to vaping was greatest in low-income neighbourhoods and may contribute to established inequities in vaping-related adverse events.

RéSUMé: OBJECTIFS: Caractériser la répartition des détaillants de vapotage et examiner l'association entre le revenu du quartier et la disponibilité des détaillants de vapotage en Ontario avant et après les modifications réglementaires du marché du vapotage en 2018. MéTHODES: Nous avons quantifié l'accès au vapotage selon le nombre de détaillants de vapotage pour 19 964 aires de diffusion (DA) en Ontario et le pourcentage d'écoles à proximité d'un détaillant de vapotage. Nous avons utilisé des modèles de régression à effets mixtes pour examiner les associations entre l'accès au vapotage et le revenu du quartier en 2018 et 2019. RéSULTATS: Entre 2016 et 2019, le nombre de détaillants de vapotage en Ontario a augmenté de 22,6 % (5 999 à 7 355), malgré une légère baisse de 2016 à 2018. En 2019, 59,7 % des quartiers urbains avaient un ou plusieurs détaillants de vapotage à moins de 1 000 m de leur centre géographique, et 79,4 % des écoles élémentaires, 82,8 % des écoles secondaires et 84,2 % des écoles postsecondaires en avaient au moins un détaillant à moins de 1 000 m. Le revenu du quartier était associé à l'accès aux détaillants, avec plus de détaillants dans les régions à faible revenu. En 2019, les quartiers du quintile de revenu le plus bas comptaient plus de deux fois plus de détaillants de vapotage par habitant dans un rayon de 1 000 m par rapport au quintile le plus élevé (rapport de taux d'incidence ajusté 2,40; IC à 95 % 2,24-2,58). Les augmentations au fil du temps de l'accès aux détaillants de vapotage ne différaient pas selon la région géographique, le quintile de revenu du quartier ou le type d'école. CONCLUSION: Nous avons observé une augmentation significative de l'accès aux détaillants de vapotage en Ontario, y compris la proximité des écoles primaires et secondaires à la suite des nouvelles réglementations provinciales sur le marketing et de la réglementation fédérale sur la nicotine en 2018. L'accès au vapotage était le plus élevé dans les quartiers à faible revenu et pourrait contribuer aux inégalités par rapport aux événements indésirables lié au vapotage et tabac.

Drivers of vaccine hesitancy among vulnerable populations in India: a cross-sectional multi-state study.

Objectives: India's Covid-19 vaccination campaign engaged frontline workers (FLWs) to encourage vaccination among vulnerable segments of society. The FLWs report encountering a variety of barriers to vaccination and are often unsuccessful despite multiple visits to the same person. This cross-sectional study aims to pinpoint which of these barriers drive vaccine hesitancy among these segments, to help streamline vaccine communication, including FLW training, to better safeguard the population. Methods: Trained field enumerators contacted 893 individuals from five states across India and collected self-reported assessments of fifteen vaccination barriers (identified through discussions with FLWs), current vaccination status and future vaccination intentions, and covariates (demographics/comorbidities). Factor analysis of the fifteen barriers yielded two factors, one relating to fear of vaccine adverse effects and a second focused on peripheral concerns regarding the vaccine. The covariates significantly associated with current vaccination status were combined under a latent class regime to yield three cluster types (health access, financial strength, and demographics). The primary analysis examined the effect of the two barrier factors, the covariate clusters, and comorbidity, on current vaccination status and future vaccine intentions. Results: Fear of vaccine adverse effects was the primary driver of vaccine hesitancy; peripheral concerns frequently mentioned by the FLWs had no impact. Although cluster membership and the presence of comorbidities predicted vaccine uptake, neither of them materially altered the effect of fear of vaccine adverse effects with the following exception: fear of adverse effects was not associated with vaccination status among young Muslim men. Conclusion: Subject to limitations, these results indicate that interventions to decrease vaccine hesitancy should focus primarily on fear associated with vaccines rather than spend resources trying to address peripheral concerns.

What Can Brinley Plots Tell Us About Cognitive Aging? Exploring Simulated Data and Modified Brinley Plots.

Cognitive aging researchers have been challenged with demonstrating age-related effects above and beyond global slowing ever since Cerella raised this issue in 1990. As the literature has made clear, this has indeed proved to be a difficult task and continues to plague the field. One way that researchers have attempted to test for disproportionate age differences across task conditions is by using Brinley plots, or plotting the mean response latencies of older adults against the mean latencies for younger adults. The simplicity and large proportion of variance accounted for by these models has led to the widespread use of Brinley plots over the years. However, as systematically tested here through eight cases of simulated data, it is clear that the Brinley technique is not well suited to either identify or display the underlying structure of datasets examining age-related differences in attentional control. Some of the problems with conventional Brinley plots can be resolved by using a modified Brinley plot that includes study-specific slopes linking trial types and a no-age-difference reference line. Multilevel models find all of the relevant effects, especially if applied to trial-level data, and have the advantage of incorporating study-level moderators that might account for slope heterogeneity. Ultimately, we encourage fellow cognitive aging researchers to access the code and data for this project on OSF (https://osf.io/zxus8/) and employ the use of multilevel models over Brinley plots.

Strengthening networks for healthcare integration: A commentary.

In their paper "Integrating Network Theory into the Study of Integrated Healthcare," Burns, Nembhard and Shortell set out to change how we think about healthcare, and ultimately how we design and deliver healthcare. They aim to do this by focusing attention on the networks through which care is delivered, with particular attention to the relational dimensions of those networks. Inspired by social network, care integration, and relational coordination theories, Burns et al. (2022) offer recommendations for moving the healthcare sector toward a fresh approach to care integration that reflects the realities of relationships and networks. In this commentary, we analyze the main recommendations by Burns et al. and present our view of the field's current standing with regards to each of them. We then suggest potential research questions, contexts and designs to move this proposed work forward, drawing upon insights from a conversation with the authors in addition to their original article. We end by recommending the formation of a research collaborative to carry out the work.

Introspective Meditation before Seeking Pleasurable Activities as a Stress Reduction Tool among College Students: A Multi-Theory Model-Based Pilot Study.

In the realm of behavioral interventions, a combined approach of yoga and a cognitive-behavioral strategy in the form of introspective meditation (manan-dhyana) may offer benefits as a stress management tool. This pilot study focuses on introspective meditation performed before seeking pleasurable activities, which is a self-reflection about whether to pursue a goal that will bring sensory pleasure in life. A non-probability sample of college students was recruited from a mid-sized Southern University of the United States using a 52-items web-based survey built in Qualtrics. Univariate, bivariate, and multivariate statistics were used to analyze data. Of total 65 students, only 21.5% students reported being engaged in the introspective meditation. The sample constituted predominantly females (75.4%), White (64.6%), and undergraduate students (87.7%). The proportions of anxiety, depression, and moderate/high stress were 50.8%, 40.0%, 86.1% respectively. In the hierarchical regression for initiation, the final model explained nearly 21.1% of variance in initiating introspective meditation among participants (n = 51) who had not been practicing it. With each unit increment in subscales of initiation (i.e., changes in physical environment), the conditional mean for initiating introspective meditation behavior increased by 0.373 units. In the hierarchical regression for sustenance, the final model explained nearly 50.5% of variance in sustaining introspective meditation behavior among participants (n = 51) who had not been practicing it. With each unit increment in subscales of sustenance (i.e., emotional transformation), the conditional mean for sustaining introspective meditation behavior increased by 0.330 units. This study can pave a way for designing interventions for college students to promote introspective meditation directed toward seeking pleasurable activities before engaging in them. This has implications for the reduction of stress as well as a preemptive measure for sexual risk-taking, indulgence in maladaptive behaviors such as smoking, vaping, alcohol, and substance use.

The Economic Impact of Climate Change on Wheat and Maize Yields in the North China Plain.

Climate change has significantly affected agricultural production. As one of China's most important agricultural production regions, the North China Plain (NCP) is subject to climate change. This paper examines the influence of climate change on the wheat and maize yields at household and village levels, using the multilevel model based on a large panel survey dataset in the NCP. The results show that: (i) Extreme weather events (drought and flood) would significantly reduce the wheat and maize yields. So, the governments should establish and improve the emergency service system of disaster warning and encourage farmers to mitigate the adverse effects of disasters. (ii) Over the past three decades, the NCP has experienced climate change that affects its grain production. Therefore, it is imperative to build the farmers' adaptive capacity to climate change. (iii) Spatial variations in crop yield are significantly influenced by the household characteristics and the heterogeneity of village economic conditions. Therefore, in addition to promoting household production, it is necessary to strengthen and promote China's development of the rural collective economy, especially the construction of rural irrigation and drainage infrastructures.