Assessing associations between individual-level social determinants of health and COVID-19 hospitalizations: Investigating racial/ethnic disparities among people living with human immunodeficiency virus (HIV) in the U.S. National COVID Cohort Collaborative (N3C).

Background: Leveraging the National COVID-19 Cohort Collaborative (N3C), a nationally sampled electronic health records repository, we explored associations between individual-level social determinants of health (SDoH) and COVID-19-related hospitalizations among racialized minority people with human immunodeficiency virus (HIV) (PWH), who have been historically adversely affected by SDoH. Methods: We retrospectively studied PWH and people without HIV (PWoH) using N3C data from January 2020 to November 2023. We evaluated SDoH variables across three domains in the Healthy People 2030 framework: (1) healthcare access, (2) economic stability, and (3) social cohesion with our primary outcome, COVID-19-related hospitalization. We conducted hierarchically nested additive and adjusted mixed-effects logistic regression models, stratifying by HIV status and race/ethnicity groups, accounting for age, sex, comorbidities, and data partners. Results: Our analytic sample included 280,441 individuals from 24 data partner sites, where 3,291 (1.17%) were PWH, with racialized minority PWH having higher proportions of adverse SDoH exposures than racialized minority PWoH. COVID-19-related hospitalizations occurred in 11.23% of all individuals (9.17% among PWH, 11.26% among PWoH). In our initial additive modeling, we observed that all three SDoH domains were significantly associated with hospitalizations, even with progressive adjustments (adjusted odds ratios [aOR] range 1.36-1.97). Subsequently, our HIV-stratified analyses indicated economic instability was associated with hospitalization in both PWH and PWoH (aOR range 1.35-1.48). Lastly, our fully adjusted, race/ethnicity-stratified analysis, indicated access to healthcare issues was associated with hospitalization across various racialized groups (aOR range 1.36-2.00). Conclusion: Our study underscores the importance of assessing individual-level SDoH variables to unravel the complex interplay of these factors for racialized minority groups.

Inter-relationships among individual views of COVID-19 control measures across multi-cultural contexts.

Individual-level georeferenced data have been widely used in COVID-19 control measures around the world. Recent research observed that there is a trade-off relationship between people's privacy concerns and their acceptance of these control measures. However, whether this trade-off relationship exists across different cultural contexts is still unaddressed. Using data we collected via an international survey (n = 4260) and network analysis, our study found a substantial trade-off inter-relationship among people's privacy concerns, perceived social benefits, and acceptance across different control measures and study areas. People's privacy concerns in culturally tight societies (e.g., Japan) have the smallest negative impacts on their acceptance of pandemic control measures. The results also identify people's key views of specific control measures that can influence their views of other control measures. The impacts of these key views are heightened among participants with a conservative political view, high levels of perceived social tightness, and vertical individualism. Our results indicate that cultural factors are a key mechanism that mediate people's privacy concerns and their acceptance of pandemic control measures. These close inter-relationships lead to a double-edged sword effect: the increased positive impacts of people's acceptance and perceived social benefits also lead to increased negative impacts of privacy concerns in different combinations of control strategies. The findings highlight the importance of cultural factors as key determinants that affect people's acceptance or rejection of specific pandemic control measures.

Individual and relational dynamics perceived to influence the sexual behaviour of adolescents in Ethiopia: a qualitative study.

Background: There are 1.2 billion adolescents in the world today, more than ever before, making up 16% of the world's population and nearly one-fourth of the total population in Sub-Saharan Africa. Adolescents are facing life-threatening health challenges attributed to sexual and reproductive health issues such as unwanted pregnancies, unsafe abortions, and sexually transmitted infections, including the human immunodeficiency virus, and acquired immunodeficiency syndrome. The aim of this research is to explore the individual and relational levels of factors that drive adolescents to engage in risky sexual behaviour. Methods: A qualitative phenomenological study design was used from February to June 2020. Adolescents and health professionals were selected purposefully. A total of 12 individual in-depth interviews, five focus group discussions with adolescents, and eight key informant interviews with health professionals were conducted using a semi-structured guide. Data analysis was performed using thematic analysis with ATLAS Ti version 7 software. Credibility, dependability, transferability, and confirmability were used to ensure the trustworthiness of the data. Results: In this study, two themes were identified; individual level factors such as sexual desire and emotion driven sex, limited knowledge of sexual and reproductive health, and a permissive attitude towards sexual activities drive adolescents to engage in risky sexual behaviour; and relational level factors such as, limited family support and involvement, negative peer pressure and influence, male partner dominance during the partnership, and pressuring females to engage in sexual intercourse were perceived factors influencing adolescents to engage in risky sexual behaviour. Conclusion: Various individual-level and relational-level factors are influencing adolescents to engage in risky sexual behaviour. Socially and culturally acceptable, comprehensive sexual education should be provided for in-school and out-school adolescents to enhance their knowledge, attitude, and skill about sexual and reproductive health. Interventions at the peer and partner level should be considered to enhance the life skills that enable them to resist pressure from peers and their partners. Child-parent communication on sexual and reproductive health matters should be promoted.

Edge effects in spatial infectious disease models.

Epidemic models serve as a useful analytical tool to study how a disease behaves in a given population. Individual-level models (ILMs) can incorporate individual-level covariate information including spatial information, accounting for heterogeneity within the population. However, the high-level data required to parameterize an ILM may often be available only for a sub-population of a larger population (e.g., a given county, province, or country). As a result, parameter estimates may be affected by edge effects caused by infection originating from outside the observed population. Here, we look at how such edge effects can bias parameter estimates for within the context of spatial ILMs, and suggest a method to improve model fitting in the presence of edge effects when some global measure of epidemic severity is available from the unobserved part of the population. We apply our models to simulated data, as well as data from the UK 2001 foot-and-mouth disease epidemic.

Developing and validating intracity spatiotemporal air quality health index in eastern China.

BACKGROUND: Recently pilot published city-level air quality health index (AQHI) provides a useful tool for communicating short-term health risks of ambient air pollution, but fails to account for intracity spatial heterogeneity in exposure and associated population health impacts. This study aims to develop the intracity spatiotemporal AQHI (ST-AQHI) via refined air pollution-related health risk assessments. METHODS: A three-stage analysis was conducted through integrating province-wide death surveillance data and high-resolution gridded estimates of air pollution and climate factors spanning 2016-2019 in Jiangsu Province, eastern China. First, an individual-level case-crossover design was employed to quantify the short-term risk of nonaccidental mortality associated with residential exposure to individual pollutant (i.e., PM2.5, NO2, O3, and SO2). Second, we accumulated and scaled the excess risks arising from multiple pollutants to formulate daily gridded ST-AQHI estimates at 0.1° × 0.1°, dividing exposure-related risks into low (0-3), moderate (4-6), high (7-9), and extreme high (10+) levels. Finally, the effectiveness of ST-AQHI as composite risk communication was validated through checking the dose-response associations of individual ST-AQHI exposure with deaths from nonaccidental and major cardiopulmonary causes via repeating case-crossover analyses. RESULTS: We analyzed a total of 1,905,209 nonaccidental death cases, comprising 785,567 from circulatory diseases and 247,336 from respiratory diseases. In the first-stage analysis, for each 10-µg/m3 rise in PM2.5, NO2, O3, and SO2 exposure at lag-01 day, population risk of nonaccidental death was increased by 0.8% (95% confidence interval: 0.7%, 0.9%), 1.9% (1.7%, 2.0%), 0.4% (0.3%, 0.5%), and 4.1% (3.7%, 4.5%), respectively. Spatiotemporal distribution of ST-AQHI exhibited a consistent declining trend throughout the study period (2016-2019), with annual average ST-AQHI decreasing from 5.2 ± 1.3 to 4.0 ± 1.0 and high-risk days dropping from 15.8% (58 days) to 1.6% (6 days). Exposure associated health risks showed great intracity- and between-city heterogeneities. In the validation analysis, ST-AQHI demonstrated approximately linear, threshold-free associations with multiple death events from nonaccidental and major cardiopulmonary causes, suggesting excellent performance in predicting exposure-related health risks. Specifically, each 1-unit rise in ST-AQHI was significantly associated with an excess risk of 2.0% (1.8%, 2.1%) for nonaccidental mortality, 2.3% (2.1%, 2.6%) for overall circulatory mortality, and 2.7% (2.3%, 3.1%) for overall respiratory mortality, as well as 1.7%-3.0% for major cardiopulmonary sub-causes. CONCLUSIONS: ST-AQHI developed in this study performed well in predicting intracity spatiotemporal heterogeneity of death risks related to multiple air pollutants, and may hold significant practical importance in communicating air pollution-related health risks to the public at the community scales.

Model-based estimation of individual-level social determinants of health and its applications in All of Us.

OBJECTIVES: We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program. MATERIALS AND METHODS: Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes. RESULTS: Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900. DISCUSSION AND CONCLUSION: The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.

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.

Integrated multilevel investigation of photosynthesis revealed the algal response distinction to differentially charged nanoplastics.

Nanoplastics (NPs), especially those with different charges, as one of emerging contaminants pose a threat to aquatic ecosystems. Although differentially charged NPs could induce distinct biological effects, mechanistic understanding of the critical physiological processes of aquatic organisms from an integrated multilevel perspective on aquatic organisms is still uncertain. Herein, multi-effects of differentially charged nanosized polystyrene (nPS) including neutral nPS, nPS-COOH, and nPS-NH2 on the photosynthesis-related physiological processes of algae were explored at the population, individual, subcellular, protein, and transcriptional levels. Results demonstrated that both nPS and nPS-COOH exhibited hormesis to algal photosynthesis but nPS-NH2 triggered severe inhibition. As for nPS-NH2, the integrity of algal subcellular structure, chlorophyll biosynthesis, and expression of photosynthesis-related proteins and genes were interfered. Intracellular NPs' content in nPS treatment was 25.64 % higher than in nPS-COOH treatment, and the content of chloroplasts in PS and nPS-COOH treatment were 3.09 % and 4.56 % higher than control, respectively. Furthermore, at the molecular levels, more photosynthesis-related proteins and genes were regulated under nPS-COOH exposure than those exposed to nPS. Light-harvesting complex II could be recognized as an underlying explanation for different effects between nPS and nPS-COOH. This study first provides a novel approach to assess the ecological risks of NPs at an integrated multilevel.

Racial Fairness of Individual- and Community-Level Proxies of Socioeconomic Status Among Birthing Parent-Child Dyads.

BACKGROUND: While precision medicine algorithms can be used to improve health outcomes, concerns have been raised about racial equity and unintentional harm from encoded biases. In this study, we evaluated the fairness of using common individual- and community-level proxies of pediatric socioeconomic status (SES) such as insurance status and community deprivation index often utilized in precision medicine algorithms. METHODS: Using 2012-2021 vital records obtained from the Ohio Department of Health, we geocoded and matched each residential birth address to a census tract to obtain community deprivation index. We then conducted sensitivity and specificity analyses to determine the degree of match between deprivation index, insurance status, and birthing parent education level for all, Black, and White children to assess if there were differences based on race. RESULTS: We found that community deprivation index and insurance status fail to accurately represent individual SES, either alone or in combination. We found that deprivation index had a sensitivity of 61.2% and specificity of 74.1%, while insurance status had a higher sensitivity of 91.6% but lower specificity of 60.1%. Furthermore, these inconsistencies were race-based across all proxies evaluated, with greater sensitivities for Black children but greater specificities for White children. CONCLUSION: This may explain some of the racial disparities present in precision medicine algorithms that utilize SES proxies. Future studies should examine how to mitigate the biases introduced by using SES proxies, potentially by incorporating additional data on housing conditions.

Psychological Traditionality and Modernity and Happiness: The Different Happiness Levels in Chinese Students.

The conceptualization of happiness varies across different cultures. In Chinese culture, happiness includes oneself and hinges on others. Chinese social development has influenced psychological traditionality (PT), psychological modernity (PM), and personal happiness. Our study recruited 450 participants to examine the different happiness levels in Chinese students with diverse PT and PM. The results indicate that individuals scoring higher in PT and PM reported higher life satisfaction. Moreover, individuals scoring higher in PT reported more positive emotions, fewer negative emotions, and greater social well-being, while those scoring higher in PM reported more negative emotions and greater relationship happiness. The happiness of Chinese students comprised individual, relational, and societal levels and happiness at different levels related to Chinese PT and PM. The present study may promote cross-cultural understanding and potentially inform interventions for individual happiness within positive psychology.

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.

Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis.

While genetic factors, behavior, and environmental exposures form a complex web of interrelated associations in type 2 diabetes (T2D), their interaction is poorly understood. Here, using data from ~500K participants of the UK Biobank, we identify the genetic determinants of a "polyexposure risk score" (PXS) a new risk factor that consists of an accumulation of 25 associated individual-level behaviors and environmental risk factors that predict longitudinal T2D incidence. PXS-T2D had a non-zero heritability (h2 = 0.18) extensive shared genetic architecture with established clinical and biological determinants of T2D, most prominently with body mass index (genetic correlation [rg] = 0.57) and Homeostatic Model Assessment for Insulin Resistance (rg = 0.51). Genetic loci associated with PXS-T2D were enriched for expression in the brain. Biobank scale data with genetic information illuminates how complex and cumulative exposures and behaviors as a whole impact T2D risk but whose biology have been elusive in genome-wide studies of T2D.

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.

Using individual approach to examine the association between urban heat island and preterm birth: A nationwide cohort study in China.

BACKGROUND: Evidence suggests that maternal exposure to heat might increase the risk of preterm birth (PTB), but no study has investigated the effect from urban heat island (UHI) at individual level. AIMS: Our study aimed to investigate the association between individual UHI exposure and PTB. METHODS: We utilized data from the ongoing China Birth Cohort Study (CBCS), encompassing 103,040 birth records up to December 2020. UHI exposure was estimated for each participant using a novel individual assessment method based on temperature data and satellite-derived land cover data. We used generalized linear mixed-effects models to estimate the association between UHI exposure and PTB, adjusting for potential confounders including maternal characteristics and environmental factors. RESULTS: Consistent and statistically significant associations between UHI exposure and PTB were observed up to 21 days before birth. A 5 °C increment in UHI exposure was associated with 27 % higher risk (OR = 1.27, 95 % confident interval: 1.20, 1.34) of preterm birth in lagged day 1. Stratified analysis indicated that the associations were more pronounced in participants who were older, had higher pre-pregnancy body mass index level, of higher socioeconomic status and living in greener areas. CONCLUSION: Maternal exposure to UHI was associated with increased risk of PTB. These findings have implications for developing targeted interventions for susceptible subgroups of pregnant women. More research is needed to validate our findings of increased risk of preterm birth due to UHI exposure among pregnant women.

Disparities in COVID-19 Hospitalization at the Intersection of Race and Ethnicity and Income.

BACKGROUND: Existing studies have elucidated racial and ethnic disparities in COVID-19 hospitalizations, but few have examined disparities at the intersection of race and ethnicity and income. METHODS: We used a population-based probability survey of non-institutionalized adults in Michigan with a polymerase chain reaction-positive SARS-CoV-2 test before November 16, 2020. We categorized respondents by race and ethnicity and annual household income: low-income (< $50,000) Non-Hispanic (NH) Black, high-income (≥ $50,000) NH Black, low-income Hispanic, high-income Hispanic, low-income NH White, and high-income NH White. We used modified Poisson regression models, adjusting for sex, age group, survey mode, and sample wave, to estimate COVID-19 hospitalization prevalence ratios by race and ethnicity and income. RESULTS: Over half of the analytic sample (n = 1593) was female (54.9%) and age 45 or older (52.5%), with 14.5% hospitalized for COVID-19. Hospitalization was most prevalent among low-income (32.9%) and high-income (31.2%) Non-Hispanic (NH) Black adults, followed by low-income NH White (15.3%), low-income Hispanic (12.9%), high-income NH White (9.6%), and high-income Hispanic adults (8.8%). In adjusted models, NH Black adults, regardless of income (low-income prevalence ratio [PR]: 1.86, 95% CI: 1.36-2.54; high-income PR: 1.57, 95% CI: 1.07-2.31), and low-income NH White adults (PR: 1.52, 95% CI: 1.12-2.07), had higher prevalence of hospitalization compared to high-income NH White adults. We observed no significant difference in the prevalence of hospitalization among Hispanic adults relative to high-income NH White adults. CONCLUSIONS: We observed disparities in COVID-19 hospitalization at the intersection of race and ethnicity and income for NH Black adults and low-income NH White adults relative to high-income NH White adults, but not for Hispanic adults.

A personalized network framework reveals predictive axis of anti-TNF response across diseases.

Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.

Variable screening methods in spatial infectious disease transmission models.

Data-driven mathematical modelling can enrich our understanding of infectious disease spread enormously. Individual-level models of infectious disease transmission allow the incorporation of different individual-level covariates, such as spatial location, vaccination status, etc. This study aims to explore and develop methods for fitting such models when we have many potential covariates to include in the model. The aim is to enhance the performance and interpretability of models and ease the computational burden of fitting these models to data. We have applied and compared multiple variable selection methods in the context of spatial epidemic data. These include a Bayesian two-stage least absolute shrinkage and selection operator (Lasso), forward and backward stepwise selection based on the Akaike information criterion (AIC), spike-and-slab priors, and random variable selection (boosting) methods. We discuss and compare the performance of these methods via simulated datasets and UK 2001 foot-and-mouth disease data. While comparing the variable selection methods all performed consistently well except the two-stage Lasso. We conclude that the spike-and-slab prior method is to be recommended, consistently resulting in high accuracy and short computational time.

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.

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.

Greenspace exposure may increase life expectancy of elderly adults, especially for those with low socioeconomic status.

With an increasing aging population in many cities worldwide, promoting and maintaining the health of elderly individuals has become a pressing public health issue. Although greenspaces may deliver many health outcomes for the elderly population, existing evidence remains inconsistent, partly due to discrepancies in the measure of greenspace and health outcomes. In addition, few studies examined the effect of greenspace exposure on life expectancy at the individual level. Thus, this study comprehensively investigated the association between greenspace exposure and life expectancy among elderly adults in Guangzhou, China, based on the individual-level mortality dataset. The data were analyzed at both the individual level and aggregate level, and two types of buffers (straight-line vs. street-network buffer) were used to define individual greenspace exposure. After controlling for the random effects and multiple types of covariates, we found that 1) elderly individuals with higher greenspace exposure were associated with an increased life expectancy; 2) elderly individuals with lower socioeconomic status benefit more from greenspace (i.e., equigenesis hypothesis); 3) different greenspace measurements lead to different results; 4) greenspace had the highest effects on life expectancy and equigenesis within the street-network buffer distances of 3000 m and 2500 m, respectively. This study underscores the potential health benefits of greenspace exposure on elderly individuals and the importance of provision and upkeep of greenspace, especially among socially disadvantaged groups.