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.

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.

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.

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.

Factors associated to influenza vaccination among hospital's healthcare workers in the Autonomous Community of Madrid, Spain 2021-2022.

Influenza vaccination (IV) aims primarily to protect high-risk groups against severe influenza-associated disease and death. It is recommended in Spain for high-risk groups, including Healthcare workers (HCWs). However, vaccination uptake has been consistently below desirable levels. The objective of this study were to evaluate influenza vaccination coverage using data from the regional vaccination registry, during 2021-2022 season, among HCWs in public hospitals in Autonomous Community of Madrid (CAM) and to explore factors associated with influenza vaccination. We conducted a cross-sectional study using administrative data retrieved from the Regional Department of Health data repositories. A multilevel logistic regression model was designed to estimate Level 1 (individual) and Level 2 (contextual) variables that are associated with HCWs vaccination. The study population was HCWs from 25 public hospitals in CAM who were active during the period of the influenza 2021-2022 vaccination campaign. The global influenza vaccination coverage (IVC) was 52.1 %. All study Level 1 variables were significantly associated with vaccine uptake. Greater IVC is associated with individual characteristics: female (OR1.14; 95 %CI 1.09-1.19), older age 59-69 years old (OR1.72; 95 %CI 1.60-1.84), born in Spain, medical staff, more than one high-risk condition (OR1.24; 95 %CI 1.10-1.40; respectively) and vaccinated in two previous campaigns (OR25.64; 95 %CI 24.27-27.09). IVC was highest among HCWs celiac disease (65.5 %) followed by diabetes mellitus (65.1 %) and chronic inflammatory disease (64.3 %). The hospital where the study subject worked also showed an effect on the vaccination uptake, although Level 2 variable (hospital complexity) was not positively associated. IVC in HCWs remains suboptimal. These findings may prove useful to tailor strategies to reach specific groups. It is recommended to delve into the identification of factors associated with the HCWs workplace that may have a positive impact on IV.

Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis.

BACKGROUND AND HYPOTHESIS: Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease. STUDY DESIGN: We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes. STUDY RESULTS: All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 ß = -0.26, DTI ß = -0.15, MM ß = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive ß = 0.21, Negative ß = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (ß = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05). CONCLUSIONS: Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.

A continuum of individual-level factors that influence modern contraceptive uptake and use: perspectives from community members and healthcare providers in Durban, South Africa.

BACKGROUND: South Africa faces numerous sexual and reproductive health challenges that can be mitigated with contraceptive use. Contraceptive use is defined and measured as use, non-use, or discontinued use. Research has shown that there are expanded definitions of use beyond these categories. Identifying such categories may assist in a better understanding of factors that influence contraceptive use. SETTING AND METHODOLOGY: This qualitative study was conducted in the eThekwini Municipality in KwaZulu-Natal, South Africa. The aim was to explore the factors influencing the uptake and use of modern contraception. One hundred and twenty-seven participants were enrolled in this study. One hundred and three of those were community members, and twenty-five were healthcare providers. Focus group discussions and in-depth interviews were conducted to gather the data. Data analysis was facilitated using NVivo 10 software. RESULTS: The data show that numerous factors influence contraceptive uptake and use. From these factors, a continuum of use that captures a variety of states of use emerged. Five different states of use were uncovered: no-use, vulnerable use, compelled use, conditional use, and autonomous use. The development of the model illustrates the complexity of contraceptive needs and that it extends beyond definitions found in policies and large-scale surveys. Expanding conceptions of use can aid in developing counselling and information support tools that can improve the uptake and continued use of modern contraception.

Nonstationary relationships among individuals' concurrent exposures to noise, air pollution and greenspace: A mobility-based study using GPS and mobile sensing data.

Individuals are often exposed to multiple environmental factors simultaneously. Understanding their joint effects is essential for developing effective public health policies. However, there has been a lack of research examining individuals' concurrent exposures to multiple environmental factors during people's daily mobility. To address this gap, this study investigated the relationships between and geographic patterns of individual exposures to air pollution (PM2.5), noise and greenspace using individual-level real-time GPS and mobile sensing data collected in outdoor environments. The findings indicate that the relationships between individual exposures to air pollution, noise and greenspace vary across different value ranges of exposures. The study also reveals that people's concurrent exposures to multiple environmental factors exhibit spatial nonstationary and strong clustering patterns. These results highlight the importance of considering spatial nonstationary and spatial heterogeneity of environmental exposures in understanding the relationships between multiple exposures in environmental health research.

Application of Causal Inference Methods to Pooled Longitudinal Non- Randomized Studies: A Methodological Systematic Review.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

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.

Prevention of Hip Fractures: Trade-off between Minor Benefits to Individuals and Large Benefits to the Community.

Goeffrey Rose postulated that a population-based measure bringing a small benefit to each individual can yield large benefits to the community. We aimed to test this axiom by quantifying the relationship between change in bone mineral density (BMD) and hip fracture incidence between two prospective cohorts separated by ~10 years. In this prospective population-based Dubbo Osteoporosis Epidemiology Study (DOES), the participants aged 60+ were recruited in two waves: the initial cohort (1311 women, 842 men) in 1989 to 1992 and the second cohort (974 women, 544 men) in 1999 to 2001. The incident hip fracture was radiologically ascertained. Femoral neck BMD was measured biannually. Multivariable-adjusted Cox's proportional hazards models were adjusted for the predefined covariates such as age, BMI, lifestyle factors, falls, and prior fracture. Compared with the initial cohort, the second cohort had a higher femoral neck BMD by ~0.04 g/cm2 in women and 0.03 g/cm2 in men. However, the prevalence of osteoporosis in the second cohort was halved (prevalence ratio 0.51, 95% CI 0.36 to 0.73 in women; 0.45, 0.24 to 0.84 in men), and its hip fracture incidence was significantly reduced (hazard ratio 0.54, 95% CI, 0.38 to 0.78 in women; 0.39, 0.19 to 0.80 in men). Sensitivity analyses indicated that the "effect" was unlikely due to unmeasured confounders. These findings suggest that a population-wide strategy aimed at enhancing BMD across the entire population could lead to a substantial decrease in the incidence of hip fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).