The current 'dramatically' high paternal ages at childbirth are not unprecedented.

There is strong individual-level evidence that late fatherhood is related to a wide range of health disorders and conditions in offspring. Over the last decades, mean paternal ages at childbirth have risen drastically. This has alarmed researchers from a wide range of fields. However, existing studies have an important shortcoming in that they lack a long-term perspective. This article is a step change in providing such a long-term perspective. We unveil that in many countries the current mean paternal ages at childbirth and proportions of fathers of advanced age at childbirth are not unprecedented. Taking the detected U-shaped trend pattern into account, we discuss individual- and population-level implications of the recent increases in paternal ages at childbirth and highlight important knowledge gaps. At the individual level, some of the biological mechanisms that are responsible for the paternal age-related health risk might, at least to some degree, be counterbalanced by various social factors. Further, how these individual-level effects are linked to population health and human cognitive development might be influenced by various factors, including technical advances and regulations in prenatal diagnostics.

Bayesian modeling of spatial ordinal data from health surveys.

Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this article, we propose a Bayesian individual-level model for small-area estimation of survey-based health indicators. A categorical likelihood is used at the first level of the model hierarchy to describe the ordinal data, and spatial dependence among small areas is taken into account by using a conditional autoregressive distribution. Post-stratification of the results of the proposed individual-level model allows extrapolating the results to any administrative areal division, even for small areas. We apply this methodology to describe the geographical distribution of a self-perceived health indicator from the Health Survey of the Region of Valencia (Spain) for the year 2016.

Association between event-free survival and overall survival in early-stage triple-negative breast cancer.

Aim: This study evaluated event-free survival (EFS) as a surrogate outcome for overall survival (OS) in neoadjuvant therapy for early-stage triple-negative breast cancer (eTNBC). Methods: Meta-regression analyses based on a targeted literature review were used to evaluate the individual- and trial-level associations between EFS and OS. Results: In the individual-level analyses, 3-year EFS was a significant predictor of 5-year OS (p < 0.01; coefficient of determinations [R2]: 0.82 [95% CI: 0.68-0.91]). Additionally, there was a statistically significant association between the treatment effect on EFS and OS at the trial level (p < 0.001; R2: 0.64 [95% CI: 0.45-0.82]). Conclusion: This study demonstrates significant associations between EFS and OS and suggests that EFS is a valid surrogate for OS following neoadjuvant therapy for eTNBC.

What is this article about? Studies of cancer therapies typically use patient survival to understand whether a treatment is helpful, such as overall survival (time from treatment to death) and event-free survival (time from treatment until the cancer progresses). Only using overall survival can slow clinical trials and the ability to assess whether new treatments may be useful. This study examined whether event-free survival was a good surrogate outcome for overall survival in studies of neoadjuvant therapy for early stage, triple-negative breast cancer (eTNBC). Neoadjuvant therapy is used to shrink a tumor before the definitive surgery, and TNBC is a type of breast cancer lacking three common hormone receptors that treatments target. To accomplish this, we first searched for published clinical trials and observational studies that reported overall and event-free survival and extracted their data. Then we tested the association between the two survival outcomes to determine if event-free survival could be used to accurately predict overall survival. Using data from randomized clinical trials, we also tested whether a treatment's effect on event-free survival could predict its effect on overall survival. What did this study find? We found that event-free survival at three years could predict overall survival at 5 years, and that there was a meaningful relationship between a treatment's effect on event-free and overall survival for eTNBC following neoadjuvant treatment. What do the results of the study mean? The results suggest that event-free survival is an accurate and useful surrogate for overall survival following neoadjuvant treatment of eTNBC.

Individual-level functional connectomes predict the motor symptoms of Parkinson's disease.

Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.

An individual-level weighted artificial neural network method to improve the systematic bias in BrainAGE analysis.

BrainAGE is a commonly used machine learning technique to measure the accelerated/delayed development pattern of human brain structure/function with neuropsychiatric disorders. However, recent studies have shown a systematic bias ("regression toward mean" effect) in the BrainAGE method, which indicates that the prediction error is not uniformly distributed across Chronological Ages: for the older individuals, the Brain Ages would be under-estimated but would be over-estimated for the younger individuals. In the present study, we propose an individual-level weighted artificial neural network method and apply it to simulation datasets (containing 5000 simulated subjects) and a real dataset (containing 135 subjects). Results show that compared with traditional machine learning methods, the individual-level weighted strategy can significantly reduce the "regression toward mean" effect, while the prediction performance can achieve the comparable level with traditional machine learning methods. Further analysis indicates that the sigmoid active function for artificial neural network shows better performance than the relu active function. The present study provides a novel strategy to reduce the "regression toward mean" effect of BrainAGE analysis, which is helpful to improve accuracy in exploring the atypical brain structure/function development pattern of neuropsychiatric disorders.

Geographically dependent individual-level models for infectious diseases transmission.

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.

A hierarchical model for analyzing multisite individual-level disease surveillance data from multiple systems.

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two-stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias-corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.

Calibrated meta-analysis to estimate the efficacy of mental health treatments in target populations: an application to paliperidone trials for treatment of schizophrenia.

BACKGROUNDS: Meta-analyses can be a powerful tool but need to calibrate potential unrepresentativeness of the included trials to a target population. Estimating target population average treatment effects (TATE) in meta-analyses is important to understand how treatments perform in well-defined target populations. This study estimated TATE of paliperidone palmitate in patients with schizophrenia using meta-analysis with individual patient trial data and target population data. METHODS: We conducted a meta-analysis with data from four randomized clinical trials and target population data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Efficacy was measured using the Positive and Negative Syndrome Scale (PANSS). Weights to equate the trial participants and target population were calculated by comparing baseline characteristics between the trials and CATIE. A calibrated weighted meta-analysis with random effects was performed to estimate the TATE of paliperidone compared to placebo. RESULTS: A total of 1,738 patients were included in the meta-analysis along with 1,458 patients in CATIE. After weighting, the covariate distributions of the trial participants and target population were similar. Compared to placebo, paliperidone palmitate was associated with a significant reduction of the PANSS total score under both unweighted (mean difference 9.07 [4.43, 13.71]) and calibrated weighted (mean difference 6.15 [2.22, 10.08]) meta-analysis. CONCLUSIONS: The effect of paliperidone palmitate compared with placebo is slightly smaller in the target population than that estimated directly from the unweighted meta-analysis. Representativeness of samples of trials included in a meta-analysis to a target population should be assessed and incorporated properly to obtain the most reliable evidence of treatment effects in target populations.

Individual-Level Socioeconomic Position and Long-Term Prognosis in Danish Heart-Transplant Recipients.

Socioeconomic deprivation can limit access to healthcare. Important gaps persist in the understanding of how individual indicators of socioeconomic disadvantage may affect clinical outcomes after heart transplantation. We sought to examine the impact of individual-level socioeconomic position (SEP) on prognosis of heart-transplant recipients. A population-based study including all Danish first-time heart-transplant recipients (n = 649) was conducted. Data were linked across complete national health registers. Associations were evaluated between SEP and all-cause mortality and first-time major adverse cardiovascular event (MACE) during follow-up periods. The half-time survival was 15.6 years (20-year period). In total, 330 (51%) of recipients experienced a first-time cardiovascular event and the most frequent was graft failure (42%). Both acute myocardial infarction and cardiac arrest occurred in ≤5 of recipients. Low educational level was associated with increased all-cause mortality 10-20 years post-transplant (adjusted hazard ratio [HR] 1.95, 95% confidence interval [CI] 1.19-3.19). During 1-10 years post-transplant, low educational level (adjusted HR 1.66, 95% CI 1.14-2.43) and low income (adjusted HR 1.81, 95% CI 1.02-3.22) were associated with a first-time MACE. In a country with free access to multidisciplinary team management, low levels of education and income were associated with a poorer prognosis after heart transplantation.

Functional traits and habitat heterogeneity explain tree growth in a warm temperate forest.

To explore how traits determine demographic performance is an important goal of plant community ecology in explaining the assembly and dynamics of ecological communities. However, whether the prediction of individual-level trait data is more precise compared to species average trait data is questioned. Here, we analyzed the growth and trait data for 11 species collected from October 2018 to October 2020 in a temperate forest, Donglingshan, Beijing. To quantify the relationships between traits and growth rate, we conducted linear regression models at both the species and individual levels, as well as developed structural equation models at both levels. We found there was a clear difference in growth between the warm and cold seasons, with tree growth mainly concentrated in the warm season. Growth rate was positively correlated with the specific leaf area, while negatively correlated with leaf thickness and wood density without considering environmental information. Adding important contextual information in the analysis of species-level structural equation modeling, growth rates were positively correlated with specific leaf area and leaf thickness. However, in the individual-level, there was a negative correlation between growth rate and wood density. Our study showed that individual-level trait data have better predictions for individual growth than species-level data. When we use multiple traits and establish links between traits and tree size, we generated strong predictive relationships between traits and growth rates. Furthermore, our study highlighted that the importance of incorporating topographical factors and considering different seasons to assess the relationship between tree growth and functional traits.

A secondary analysis of the factors associated with women´s adequate utilization of antenatal care services during their last pregnancy in Uganda.

BACKGROUND: Adequate antenatal care services (ANC) use is critical to identifying and reducing pregnancy risks. Despite the importance placed on adequate antenatal care service utilization, women in Uganda continue to underutilize antenatal care services. The primary goal of this study is to identify the factors associated with women's adequate utilization of antenatal care services in Uganda. METHODS: Secondary data from the 2016 Uganda Demographic and Health Survey were used in this study. The study sample consists of 9,416 women aged 15 to 49 who reported giving birth in the five years preceding the survey. The adequate use of antenatal care services is the dependent variable. A woman who used antenatal care services at least four times is considered to have adequately used antenatal care services. We used univariate, bivariate, and multilevel logistic regression modelling to identify the factors associated with adequate utilization of antenatal care services. STATA version 14.2 was used to analyze the data. RESULTS: The prevalence of adequate utilization of antenatal care services in Uganda was found to be 61.4%. Women with secondary or higher education were 32.0% (AOR = 1.32, 95% CI;1.07-1.63), employed women were 26.0% (AOR = 1.26, 95% CI;1.10-1.44), women who received high-quality antenatal care content were 78.0% (AOR = 1.78, 95% CI;1.58-2.02), and women who belong to the rich category of the wealth index bracket were 27.0% (AOR = 1.27, 95% CI;1.09-1.49), more likely to use antenatal care services adequately. Finally, the study discovered that women from less diverse ethnic communities were 15.0% (AOR, 0.85, 95%CI; 0.73-0.99) less likely to use antenatal care services adequately. CONCLUSION: Women's adequate utilization of antenatal care was influenced by both community and individual-level characteristics. Policymakers must use a multi-sectoral approach to develop policies that address both individual and community-level characteristics.

Assessing the association between overcrowding and human physiological stress response in different urban contexts: a case study in Salzburg, Austria.

Overcrowding in densely populated urban areas is increasingly becoming an issue for mental health disorders. Yet, only few studies have examined the association between overcrowding in cities and physiological stress responses. Thus, this study employed wearable sensors (a wearable camera, an Empatica E4 wristband and a smartphone-based GPS) to assess the association between overcrowding and human physiological stress response in four types of urban contexts (green space, transit space, commercial space, and blue space). A case study with 26 participants was conducted in Salzburg, Austria. We used Mask R-CNN to detect elements related to overcrowding such as human crowds, sitting facilities, vehicles and bikes from first-person video data collected by wearable cameras, and calculated a change score (CS) to assess human physiological stress response based on galvanic skin response (GSR) and skin temperature from the physiological data collected by the wristband, then this study used statistical and spatial analysis to assess the association between the change score and the above elements. The results demonstrate the feasibility of using sensor-based measurement and quantitative analysis to investigate the relationship between human stress and overcrowding in relation to different urban elements. The findings of this study indicate the importance of considering human crowds, sitting facilities, vehicles and bikes to assess the impact of overcrowding on human stress at street level.

People's political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data.

BACKGROUND: As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people's residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people's concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people's private locations. METHODS: We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people's political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables. RESULTS: We observed a tradeoff relationship between people's privacy concerns and the acceptance (and perceived social benefits) of the control measures. People's perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns. CONCLUSIONS: Our results first suggest that people's privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people's acceptance and perceived social benefits of the control measures but may not reduce people's privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people's privacy concerns tend to increase with their conservatism and individualism.

The association of intimate Partner violence and decision making power on nutritional status of married women in Ethiopia: a multilevel mixed-effect analysis.

BACKGROUND: A growing body of research has established a link between intimate partner violence (IPV) and decision-making autonomy on women's mental, physical, and reproductive health consequences, as well as child nutritional status. However, there is a scarcity of research on the effects of IPV and decision-making autonomy on women's nutritional status. To date, no research has been conducted in Ethiopia to investigate the impact of IPV and decision-making autonomy on women's nutritional status. Therefore, the purpose of this study was to investigate the relationship between IPV and decision-making power at both the individual and community levels on women's nutritional status. METHODS: We analyzed data from the 2016 Ethiopian demographic and health survey. Our study focused on 3,660 married non-pregnant women of reproductive age. We used the chi-squared test and spearman correlation coefficients for bivariate analysis. The relationship between IPV and decision making power with nutritional status was evaluated using multilevel binary logistic regression models while controlling other determinants. RESULTS: Around 28% of women reported at least one of the four types of IPV. Approximately 32% of women had no decision-making power at home. About 27.1% of women were underweight (BMI < 18.5), while 10.6% were overweight/obese (BMI ≥ 25). Women who had sexual IPV had a higher chance of being underweight (AOR = 2.97; 95% CI: 2.02-4.38) than women who had no sexual IPV. While women who had decision-making power at home were at lower odds of being underweight (AOR = 0.83; 95% CI: 0.69-0.98) than their counterparts. The findings also revealed a negative relationship between being overweight/obese and community-level women's decision-making autonomy (AOR = 0.75; 95% CI 0.34-0.89). CONCLUSIONS: Our findings show the presence of a significant association between IPV & decision-making autonomy with women's nutritional status. Therefore, effective policies and programs to end violence against women and encourage women to participate in decision-making are required. This will help to improve the nutritional status of women as well as the nutritional outcomes of their families. This study suggests that efforts to achieve Sustainable Development Goal (SDG5) may have an impact on other SDGs particularly SDG2.

Homogeneity Assumptions in the Analysis of Dynamic Processes.

With the increased use of time series data in human research, ranging from ecological momentary assessments to data passively obtained, researchers can explore dynamic processes more than ever before. An important question researchers must ask themselves is, do I think all individuals have similar processes? If not, how different, and in what ways? Dr. Peter Molenaar's work set the foundation to answer these questions by providing insight into individual-level analysis for processes that are assumed to differ across individuals in at least some aspects. Currently, such assumptions do not have a clear taxonomy regarding the degree of homogeneity in the patterns of relations among variables and the corresponding parameter values. This paper provides the language with which researchers can discuss assumptions inherent in their analyses. We define strict homogeneity as the assumption that all individuals have an identical pattern of relations as well as parameter values; pattern homogeneity assumes the same pattern of relations but parameter values can differ; weak homogeneity assumes there are some (but not all) generalizable aspects of the process; and no homogeneity explicitly assumes no population-level similarities in dynamic processes across individuals. We demonstrate these assumptions with an empirical data set of daily emotions in couples.

Area-level and individual-level socio-economic differences in health-related quality of life trajectories: Results from a 10-year longitudinal stroke study.

BACKGROUND: We examined area-level (aSES) and individual-level (iSES) socio-economic status on trajectories of HRQoL to 10 years following stroke. METHODS: Participants with strokes between 1/5/1996 and 30/4/1999 completed the Assessment of Quality of Life instrument (AQoL, range: -0.04 [worse than death] to 0 [death] to 1 [full health]) at ≥one of 3month, 6-month, 1-year, 2-year, 3-year, 4-year, 5-year, 7-year and 10-year interviews after stroke. Sociodemographic and health information were collected at baseline. We derived aSES from postcode using the Australian Socio-Economic Indexes For Area (2006) (categories: high, medium, low), and iSES from lifetime occupation (categories: non-manual, manual). Multivariable linear mixed effects modelling was used to estimate trajectories of HRQoL over 10 years, by aSES and iSES, adjusting for age, sex, cardiovascular disease, smoking, diabetes, stroke severity, stroke type, and the time influence on age and health conditions. RESULTS: Of 1,686 participants enrolled, we excluded 239 with 'possible' stroke and 284 with missing iSES. Among the remaining 1,163 participants, 1,123 (96.6%) had AQoL assessed at ≥3 timepoints. In multivariable analysis, over time, people in the medium aSES group had mean 0.02 (95% CI -0.06, 0.02) greater reduction in AQoL score, and people in the low aSES group had mean 0.04 (95% CI, -0.07, -0.001) greater reduction, than those in the high aSES group. Manual workers had an average 0.04 (95% CI, -0.07, -0.01) greater reduction in AQoL score over time than non-manual workers. CONCLUSIONS: Over time, HRQoL declines in all people with stroke, declining most rapidly in lower SES groups.

Comparing individual-, family-, and community-level effects on the oral health of preschool children: a multilevel analysis of national survey data.

BACKGROUND: Early childhood is a critical stage for the prevention of dental caries. The prevalence of caries in preschool children is still high in Taiwan, where National Health Insurance covers 99% of the population. The effort to improve the oral health of preschool children should be based on conceptual model that encompasses more than individual-level factors. This study input nationwide survey data in a conceptual model to evaluate the effects of comprehensive factors related to the high prevalence of caries in preschool children. METHODS: This observation study examined factors related to the oral health of preschool children by employing a comprehensive multilevel model to analyse nationally representative data from the Taiwan Oral Health Survey of Preschool Children (TOHPC) 2017-2018. Individual-level, family-level and community-level contextual effects were evaluated through multilevel analysis in this study. The proportional change in variance (PCV) was used to compare the multilevel model with the null model and individual-level, family-level, and community-level context effects. RESULTS: The estimated deft index for preschool children was 1.34 (1.22-1.47) at age 3, 2.20 (2.08-2.32) at age 4, and 3.05 (2.93-3.18) at age 5. The overall prevalence of caries in preschool children in Taiwan was 34.27% (30.76%, 37.78%) at age 3, 51.67% (48.99%, 54.35%) at age 4, and 62.05% (59.66%, 64.44%) at age 5. The model that included the individual-, family-, and community-context levels exhibited the highest reduction of variance (PCV = 53.98%). The PCV was further reduced to 35.61% when only the level of accessibility to dental services for individuals, families, and the community was considered. For the model in which no community-context cofactors were considered and the model considering only the individual level, the PCVs were 20.37% and 5.52%, respectively. CONCLUSIONS: Our findings indicate the key components that affect oral health in preschool children and can serve as a reference for policy makers. The most notable finding of this study is that to improve the oral health of preschool children, community-level factors should be targeted. To rely solely on dentists for leading oral health education programs for children is impractical and inefficient. Training more professional oral health educators to provide additional community-based oral health promotion campaigns is critical. We suggest training more professional oral health educators to provide more community-based oral health promotion campaigns.

Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: A study using individual-level GPS-based real-time sensing data.

Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using individual-level data collected with GPS-tracking smartphones, mobile air-pollutant and noise sensors, an activity-travel diary, and a questionnaire from two typical neighborhoods in a dense and well-developed city (i.e., Hong Kong), this study seeks to examine 1) the associations between multiple environmental exposures (i.e., different types of greenspace, PM2.5, and noise) and housing conditions (i.e., housing types, ownership, and overcrowding) with individuals' COVID-19 risk both in residential neighborhoods and along daily mobility trajectories; 2) which social groups are disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple linear regression and logistical regression models, we found a significant negative association between COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along people's daily mobility trajectories. Meanwhile, we also found that high open space and recreational land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise exposure was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with work places in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk.

Patterns and predictors of adolescent life change during the COVID-19 pandemic: a person-centered approach.

The present study investigated patterns of adolescent life changes across multiple life domains and utilized a holistic-interactionistic perspective to examine their individual, familial, and societal correlates with a sample of 2544 Chinese parent-adolescent dyads. Adolescents were aged from 10 to 19 years old (50.16% girls). Latent profile analysis revealed five life change profiles, including three improved profiles at various degrees, one unchanged profile, and one worsened profile. The majority of adolescents had an improved or unchanged life. Multinomial logistic regression analyses found that most of the individual, familial, and societal factors predicted the group memberships. Notably, parent-adolescent conflict was a significant factor that predicted memberships of all patterns. These findings show the resilience of adolescents and indicate the need for policies and interventions that consider the holistic nature of adolescents' person-context system, especially during a global crisis such as the COVID-19 pandemic.

Subjective difficulty in a verbal recognition-based memory task: Exploring brain-behaviour relationships at the individual level in healthy young adults.

The vast majority of fMRI studies of task-related brain activity utilize common levels of task demands and analyses that rely on the central tendencies of the data. This approach does not take into account perceived difficulty nor regional variations in brain activity between people. The results are findings of brain-behavior relationships that weaken as sample sizes increase. Participants of the current study included twenty-six healthy young adults evenly split between the sexes. The current work utilizes five parametrically modulated levels of memory load centered around each individual's predetermined working memory cognitive capacity. Principal components analyses (PCA) identified the group-level central tendency of the data. After removing the group effect from the data, PCA identified individual-level patterns of brain activity across the five levels of task demands. Expression of the group effect significantly differed between the sexes across all load levels. Expression of the individual level patterns demonstrated a significant load by sex interaction. Furthermore, expressions of the individual maps make better predictors of response time behavior than group-derived maps. We demonstrated that utilization of an individual's unique pattern of brain activity in response to increasing a task's perceived difficulty is a better predictor of brain-behavior relationships than study designs and analyses focused on identification of group effects. Furthermore, these methods facilitate exploration into how individual differences in patterns of brain activity relate to individual differences in behavior and cognition.