Ecological Predictors of Organelle Genome Evolution: Phylogenetic Correlations with Taxonomically Broad, Sparse, Unsystematized Data.

Comparative analysis of variables across phylogenetically linked observations can reveal mechanisms and insights in evolutionary biology. As the taxonomic breadth of the sample of interest increases, challenges of data sparsity, poor phylogenetic resolution, and complicated evolutionary dynamics emerge. Here, we investigate a cross-eukaryotic question where all these problems exist: which organismal ecology features are correlated with gene retention in mitochondrial and chloroplast DNA (organelle DNA or oDNA). Through a wide palette of synthetic control studies, we first characterize the specificity and sensitivity of a collection of parametric and non-parametric phylogenetic comparative approaches to identify relationships in the face of such sparse and awkward datasets. This analysis is not directly focused on oDNA, and so provides generalizable insights into comparative approaches with challenging data. We then combine and curate ecological data coupled to oDNA genome information across eukaryotes, including a new semi-automated approach for gathering data on organismal traits from less systematized open-access resources including encyclopedia articles on species and taxa. The curation process also involved resolving several issues with existing datasets, including enforcing the clade-specificity of several ecological features and fixing incorrect annotations. Combining this unique dataset with our benchmarked comparative approaches, we confirm support for several known links between organismal ecology and organelle gene retention, identify several previously unidentified relationships constituting possible ecological contributors to oDNA genome evolution, and provide support for a recently hypothesized link between environmental demand and oDNA retention. We, with caution, discuss the implications of these findings for organelle evolution and of this pipeline for broad comparative analyses in other fields.

Maintenance of Behavioral Variation under Predation Risk: Effects on Personality, Plasticity, and Predictability.

AbstractClassic evolutionary theory predicts that predation will shift trait means and erode variance within prey species; however, several studies indicate higher behavioral trait variance and trait integration in high-predation populations. These results come predominately from field-sampled animals comparing low- and high-predation sites and thus cannot isolate the role of predation from other ecological factors, including density effects arising from higher predation. Here, we study the role of predation on behavioral trait (co)variation in experimental populations of guppies (Poecilia reticulata) living with and without a benthic ambush predator (Jaguar cichlid) to better evaluate the role of predation and where density was equalized among replicates twice per year. At 2.5 years after introduction of the predators (∼10 overlapping generations), 40 males were sampled from each of the six replicate populations and extensively assayed for activity rates, water column use, and latency to feed following disturbance. Individual variation was pronounced in both treatments, with substantial individual variation in means, temporal plasticity, and predictability (inverse residual variance). Predators had little effect on mean behavior, although there was some evidence for greater use of the upper water column in predator-exposed fish. There was greater variance among individuals in water column use in predator-exposed fish, and they habituated more quickly over time; individuals higher in the water column fed slower and had a reduced positive correlation with activity, although again this effect was time specific. Predators also affected the integration of personality and plasticity-among-individual variances in water column use increased, and those in activity decreased, through time-which was absent in controls. Our results contrast with the extensive guppy literature showing rapid evolution in trait means, demonstrating either increases or maintenance of behavioral variance under predation.

CellDrift: inferring perturbation responses in temporally sampled single-cell data.

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.

Association analysis of self-reported outcomes with a validated subset.

In health-science research, outcomes ascertained through surveys and interviews are subject to potential bias with respect to the true outcome status, which is only ascertainable with clinical and laboratory assessment. This measurement error may lead to biased inference when evaluating associations between exposures and outcomes of interest. Here, we consider a cohort study in which the outcome of interest is ascertained via questionnaire, subject to imperfect ascertainment, but where a subset of participants also have a clinically assessed, validated outcome available. This presents a methodological opportunity to address potential bias. Specifically, we constructed the likelihood in two parts, one using the validated subset and the other using a subset without validation. This work expands on that proposed by Pepe and enables inference with standard statistical software. Weighted generalized linear model estimates for our method and maximum likelihood estimates (MLE) for Pepe's method were computed, and the statistical inference was based on the standard large-sample likelihood theory. We compare the finite sample performance of two approaches through Monte Carlo simulations. This methodological work was motivated by a large cohort study of long-term childhood cancer survivors, allowing us to provide a relevant application example where we examined the association between clinical factors and chronic health conditions.

A generalized Hosmer-Lemeshow goodness-of-fit test for a family of generalized linear models.

Generalized linear models (GLMs) are very widely used, but formal goodness-of-fit (GOF) tests for the overall fit of the model seem to be in wide use only for certain classes of GLMs. We develop and apply a new goodness-of-fit test, similar to the well-known and commonly used Hosmer-Lemeshow (HL) test, that can be used with a wide variety of GLMs. The test statistic is a variant of the HL statistic, but we rigorously derive an asymptotically correct sampling distribution using methods of Stute and Zhu (Scand J Stat 29(3):535-545, 2002) and demonstrate its consistency. We compare the performance of our new test with other GOF tests for GLMs, including a naive direct application of the HL test to the Poisson problem. Our test provides competitive or comparable power in various simulation settings and we identify a situation where a naive version of the test fails to hold its size. Our generalized HL test is straightforward to implement and interpret and an R package is publicly available. Supplementary Information: The online version contains supplementary material available at 10.1007/s11749-023-00912-8.

Fine-Scale Spatial Prediction on the Risk of Plasmodium vivax Infection in the Republic of Korea.

BACKGROUND: Malaria elimination strategies in the Republic of Korea (ROK) have decreased malaria incidence but face challenges due to delayed case detection and response. To improve this, machine learning models for predicting malaria, focusing on high-risk areas, have been developed. METHODS: The study targeted the northern region of ROK, near the demilitarized zone, using a 1-km grid to identify areas for prediction. Grid cells without residential buildings were excluded, leaving 8,425 cells. The prediction was based on whether at least one malaria case was reported in each grid cell per month, using spatial data of patient locations. Four algorithms were used: gradient boosted (GBM), generalized linear (GLM), extreme gradient boosted (XGB), and ensemble models, incorporating environmental, sociodemographic, and meteorological data as predictors. The models were trained with data from May to October (2019-2021) and tested with data from May to October 2022. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: The AUROC of the prediction models performed excellently (GBM = 0.9243, GLM = 0.9060, XGB = 0.9180, and ensemble model = 0.9301). Previous malaria risk, population size, and meteorological factors influenced the model most in GBM and XGB. CONCLUSION: Machine-learning models with properly preprocessed malaria case data can provide reliable predictions. Additional predictors, such as mosquito density, should be included in future studies to improve the performance of models.

Factors affecting consonant production accuracy in children with cochlear implants: Expressive vocabulary and maternal education.

BACKGROUND: Despite the ability of cochlear implants (CIs) to provide children with access to speech, there is considerable variability in spoken language outcomes. Research aimed at identifying factors influencing speech production accuracy is needed. AIMS: To characterize the consonant production accuracy of children with cochlear implants (CWCI) and an age-matched group of children with typical hearing (CWTH) and to explore several factors that potentially affect the ability of both groups to accurately produce consonants. METHODS & PROCEDURES: We administered the Bankson-Bernthal Test of Phonology (BBTOP) to a group of 25 CWCI (mean age = 4;9, SD = 1;6, range = 3;2-8;5) implanted prior to 30 months of age with a mean duration of implant usage of 3;6 and an age-matched group of 25 CWTH (mean age = 5;0, SD = 1;6, range = 3;1-8;6). The recorded results were transcribed, and the accuracy of the target consonants was determined. Expressive vocabulary size estimates were obtained from a language sample using the number of different words (NDW). A parent questionnaire provided information about maternal education, duration of CIs experience and other demographic characteristics of each child. OUTCOMES & RESULTS: The CWCI group demonstrated some similarities to, and some differences from, their hearing peers. The CWCI demonstrated poorer consonant production accuracy overall and in various phonetic categories and word positions. However, both groups produced initial consonants more accurately than final consonants. Whilst CWCI had poorer production accuracy than CWTH for all phonetic categories (stops, nasals, fricatives, affricates, liquids and glides and consonant clusters), both groups exhibited similar error patterns across categories. For CWCI, the factors most related to consonant production accuracy when considered individually were expressive vocabulary size, followed by duration of CI experience, chronological age, maternal education and gender. The combination of maternal education and vocabulary size resulted in the best model of consonant production accuracy for this group. For the CWTH, chronological age followed by vocabulary size were most related to consonant production accuracy. No combination of factors yielded an improved model for the CWTH. CONCLUSIONS & IMPLICATIONS: Whilst group differences in production accuracy between the CWCI and CWTH were found, the pattern of errors was similar for the two groups of children, suggesting that the children are at earlier stages of overall consonant production development. Although duration of CI experience was a significant covariate in a single-variable model of consonant production accuracy for CWCI, the best multivariate model of consonant production accuracy for these children was based on the combination of expressive vocabulary size and maternal education. WHAT THIS PAPER ADDS: What is already known on the subject Research has shown that a range of factors is associated with consonant production accuracy by CWCIs, including factors such as the age at implant, duration of implant use, gender, other language skills and maternal education. Despite numerous studies that have examined speech sound production in these children, most have explored a limited number of factors that might explain the variability in scores obtained. Research that examines the potential role of a range of child-related and environmental factors in the same children is needed to determine the predictive role of these factors in speech production outcomes. What this paper adds to the existing knowledge Whilst the consonant production accuracy was lower for the CWCIs than for their typically hearing peers, there were some similarities suggesting that these children are experiencing similar, but delayed, acquisition of consonant production skills to that of their hearing peers. Whilst several factors are predictive of consonant production accuracy in children with implants, vocabulary diversity and maternal education, an indirect measure of socio-economic status, were the best combined predictors of consonant production accuracy. What are the potential or actual clinical implications of this work? Understanding the factors that shape individual differences in CWCI speech production is important for effective clinical decision-making and intervention planning. The present findings point to two potentially important factors related to speech sound production beyond the duration of robust hearing in CWCI, namely, a lexical diversity and maternal education. This suggests that intervention is likely most efficient that addresses both vocabulary development and speech sound development together. The current findings further suggest the importance of parental involvement and commitment to spoken language development and the importance of receiving early and consistent intervention aimed both at skill development and parental efficacy.

Associations between short-term exposure to ambient PM2.5 and incident cases of cardiovascular disease in Yantai, China.

There are limited studies investigating the association between short-term exposure to PM2.5 and incident cardiovascular disease (CVD) cases in China. This study aims to examine the short-term effects of PM2.5 on the incidence of cardiovascular diseases. A combination of Poisson-distribution generalized linear model and distributed lag non-linear model was used to examine the association between short-term exposure to PM2.5 and incident cases of CVD. The results revealed that per 10 µg/m3 increment of PM2.5 would increase the incident CVD cases by 0.147% (Relative Risk: 1.00147, 95% Confidence Interval: 1.00008-1.00286) at a lag of 2 days. The stratified analyses showed higher effects risk in females, older residents (aged 60-75 years), and acute myocardial infarction group (p-value for difference <0.05). This study indicates that short-term exposure to PM2.5 may increase the risk of CVD and highlights the necessity for a higher air quality standard in Yantai, China.

Long-term effects of air pollutants on deaths in a semi-urban city in Northwestern Turkey: a time series analysis.

To examine the associations between daily variations of coarse Particulate Matter(PM10) and/or sulfur dioxide(SO2) and mortality. The Poisson Generalized Linear Model(GLM) was employed to analyze the relationship between ambient air pollutants such as PM10 and SO2 and mortality. For each 10 µg/m3 increase in PM10, the overall mortality risk was found to be 1.022-fold high on the previous-eighth-day(lag 7) (RR, 95%CI:1.002-1.042) in the unadjusted model; 1.031-fold high in men (RR, 95%CI:1.005-1.058); 1.024-fold high in those aged 65 and over (RR, 95%CI:1.001-1.048). Also, the risk of death in men was 1.028-fold high in the model adjusted on the previous- eighth-day(lag 7) (RR, 95%CI:1.002-1.055). Mortality risk was found to be 1.088-fold high in 10 µg/m3 increase in SO2 under 65 years in males in the previous-third-day(lag 2) in the unadjusted model, and the risk of death was found to be 1.086-fold (RR, 95%CI:1.007-1.164) high in males in the adjusted model. .

Random effect generalized linear model-based predictive modelling of traffic noise.

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru.

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.

Phylogeography Reveals Association between Swine Trade and the Spread of Porcine Epidemic Diarrhea Virus in China and across the World.

The ongoing SARS (severe acute respiratory syndrome)-CoV (coronavirus)-2 pandemic has exposed major gaps in our knowledge on the origin, ecology, evolution, and spread of animal coronaviruses. Porcine epidemic diarrhea virus (PEDV) is a member of the genus Alphacoronavirus in the family Coronaviridae that may have originated from bats and leads to significant hazards and widespread epidemics in the swine population. The role of local and global trade of live swine and swine-related products in disseminating PEDV remains unclear, especially in developing countries with complex swine production systems. Here, we undertake an in-depth phylogeographic analysis of PEDV sequence data (including 247 newly sequenced samples) and employ an extension of this inference framework that enables formally testing the contribution of a range of predictor variables to the geographic spread of PEDV. Within China, the provinces of Guangdong and Henan were identified as primary hubs for the spread of PEDV, for which we estimate live swine trade to play a very important role. On a global scale, the United States and China maintain the highest number of PEDV lineages. We estimate that, after an initial introduction out of China, the United States acted as an important source of PEDV introductions into Japan, Korea, China, and Mexico. Live swine trade also explains the dispersal of PEDV on a global scale. Given the increasingly global trade of live swine, our findings have important implications for designing prevention and containment measures to combat a wide range of livestock coronaviruses.

Generalized models for quantifying laterality using functional transcranial Doppler ultrasound.

We consider how analysis of brain lateralization using functional transcranial Doppler ultrasound (fTCD) data can be brought in line with modern statistical methods typically used in functional magnetic resonance imaging (fMRI). Conventionally, a laterality index is computed in fTCD from the difference between the averages of each hemisphere's signal within a period of interest (POI) over a series of trials. We demonstrate use of generalized linear models (GLMs) and generalized additive models (GAM) to analyze data from individual participants in three published studies (N = 154, 73 and 31), and compare this with results from the conventional POI averaging approach, and with laterality assessed using fMRI (N = 31). The GLM approach was based on classic fMRI analysis that includes a hemodynamic response function as a predictor; the GAM approach estimated the response function from the data, including a term for time relative to epoch start (simple GAM), plus a categorical index corresponding to individual epochs (complex GAM). Individual estimates of the fTCD laterality index are similar across all methods, but error of measurement is lowest using complex GAM. Reliable identification of cases of bilateral language appears to be more accurate with complex GAM. We also show that the GAM-based approach can be used to efficiently analyze more complex designs that incorporate interactions between tasks.

Semiparametric count data regression for self-reported mental health.

''For how many days during the past 30 days was your mental health not good?" The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: The data are overdispersed, zero-inflated, bounded by 30, and heaped in 5- and 7-day increments. To address these challenges-which are especially common for health questionnaire data-we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (star) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an expectation-maximization (EM) algorithm that is compatible with any continuous data model estimable by least squares. star regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. Using star regression, we identify key factors associated with self-reported mental health and demonstrate substantial improvements in goodness-of-fit compared to existing count data regression models.

Supervised convex clustering.

Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to their unsupervised nature. Meanwhile, in many real-world scenarios, there are some noisy supervising auxiliary variables, for instance, subjective diagnostic opinions, that are related to the observed heterogeneity of the unlabeled data. By leveraging information from both supervising auxiliary variables and unlabeled data, we seek to uncover more scientifically interpretable group structures that may be hidden by completely unsupervised analyses. In this work, we propose and develop a new statistical pattern discovery method named supervised convex clustering (SCC) that borrows strength from both information sources and guides towards finding more interpretable patterns via a joint convex fusion penalty. We develop several extensions of SCC to integrate different types of supervising auxiliary variables, to adjust for additional covariates, and to find biclusters. We demonstrate the practical advantages of SCC through simulations and a case study on Alzheimer's disease genomics. Specifically, we discover new candidate genes as well as new subtypes of Alzheimer's disease that can potentially lead to better understanding of the underlying genetic mechanisms responsible for the observed heterogeneity of cognitive decline in older adults.

Modelling count, bounded and skewed continuous outcomes in physical activity research: beyond linear regression models.

BACKGROUND: Inference using standard linear regression models (LMs) relies on assumptions that are rarely satisfied in practice. Substantial departures, if not addressed, have serious impacts on any inference and conclusions; potentially rendering them invalid and misleading. Count, bounded and skewed outcomes, common in physical activity research, can substantially violate LM assumptions. A common approach to handle these is to transform the outcome and apply a LM. However, a transformation may not suffice. METHODS: In this paper, we introduce the generalized linear model (GLM), a generalization of the LM, as an approach for the appropriate modelling of count and non-normally distributed (i.e., bounded and skewed) outcomes. Using data from a study of physical activity among older adults, we demonstrate appropriate methods to analyse count, bounded and skewed outcomes. RESULTS: We show how fitting an LM when inappropriate, especially for the type of outcomes commonly encountered in physical activity research, substantially impacts the analysis, inference, and conclusions compared to a GLM. CONCLUSIONS: GLMs which more appropriately model non-normally distributed response variables should be considered as more suitable approaches for managing count, bounded and skewed outcomes rather than simply relying on transformations. We recommend that physical activity researchers add the GLM to their statistical toolboxes and become aware of situations when GLMs are a better method than traditional approaches for modeling count, bounded and skewed outcomes.

The Healthcare Cost Burden of Asthma in Children: A Longitudinal Population-Based Study.

OBJECTIVES: This study aimed to investigate the extent of healthcare cost increase at population level due to childhood asthma. We aimed to investigate the cross-sectional relationship between asthma and healthcare costs among children aged 2 to 18 years and, in longitudinal analyses, whether costs increase with an increase in the duration of asthma prevalence. METHODS: Study participants are 4175 and 4482 children of birth and kindergarten cohorts from the nationally representative Longitudinal Study of Australian Children for whom the linked Medicare cost data are available. The children were followed in all waves from the year 2004 to 2018. Generalized linear models were used to estimate the excess healthcare costs associated with asthma. The sum of Medicare Benefits Schedule and Pharmaceutical Benefits Scheme costs constitutes the total healthcare costs. RESULTS: Total excess healthcare costs associated with asthma among the 2- to 18-year-old children were A$4316 per child. At the population level, the estimated total excess Medicare costs associated with current asthma treatment among 2- to 18-year-old children were, on average, A$190.6 million per year (2018 population and price). Compared with the non-asthmatic children, peers with persistent asthma morbidity and treatment requirements had excess costs up to A$20 727 for the B cohort children until 14 years of age, whereas excess costs for the K cohort children were A$19 571 until 18 years of age. CONCLUSIONS: Asthma in children imposes a significant financial burden on the public health system. Higher excess healthcare costs of all asthmatic children than the costs of nonasthmatic children provide further economic justification for promoting preventive efforts at early ages.

Robust and efficient estimation of nonparametric generalized linear models.

Generalized linear models are flexible tools for the analysis of diverse datasets, but the classical formulation requires that the parametric component is correctly specified and the data contain no atypical observations. To address these shortcomings, we introduce and study a family of nonparametric full-rank and lower-rank spline estimators that result from the minimization of a penalized density power divergence. The proposed class of estimators is easily implementable, offers high protection against outlying observations and can be tuned for arbitrarily high efficiency in the case of clean data. We show that under weak assumptions, these estimators converge at a fast rate and illustrate their highly competitive performance on a simulation study and two real-data examples. Supplementary Information: The online version contains supplementary material available at 10.1007/s11749-023-00866-x.

Pneumococcal vaccination coverage and adherence to recommended dosing schedules in adults: a repeated cross-sectional study of the INTEGO morbidity registry.

BACKGROUND: Since 2014, Belgium's Superior Health Council has recommended pneumococcal vaccination for adults aged 19-85 years at increased risk for pneumococcal diseases with a specific vaccine administration sequence and timing. Currently, Belgium has no publicly funded adult pneumococcal vaccination program. This study investigated the seasonal pneumococcal vaccination trends, evolution of vaccination coverage and adherence to the 2014 recommendations. METHODS: INTEGO is a general practice morbidity registry in Flanders (Belgium) that represents 102 general practice centres and comprised over 300.000 patients in 2021. A repeated cross-sectional study was performed for the period between 2017 and 2021. Using adjusted odds ratios computed via multiple logistic regression, the association between an individual's characteristics (gender, age, comorbidities, influenza vaccination status and socioeconomic status) and schedule-adherent pneumococcal vaccination status was assessed. RESULTS: Pneumococcal vaccination coincided with seasonal flu vaccination. The vaccination coverage in the population at risk decreased from 21% in 2017 to 18.2% in 2018 and then started to increase to 23.6% in 2021. Coverage in 2021 was highest for high-risk adults (33.8%) followed by 50- to 85-year-olds with comorbidities (25.5%) and healthy 65- to 85-year-olds (18.7%). In 2021, 56.3% of the high-risk adults, 74.6% of the 50+ with comorbidities persons, and 74% of the 65+ healthy persons had an adherent vaccination schedule. Persons with a lower socioeconomic status had an adjusted odds ratio of 0.92 (95% Confidence Interval (CI) 0.87-0.97) for primary vaccination, 0.67 (95% CI 0.60-0.75) for adherence to the recommended second vaccination if the 13-valent pneumococcal conjugate vaccine was administered first and 0.86 (95% CI 0.76-0.97) if the 23-valent pneumococcal polysaccharide vaccine was administered first. CONCLUSION: Pneumococcal vaccine coverage is slowly increasing in Flanders, displaying seasonal peaks in sync with influenza vaccination campaigns. However, with less than one-fourth of the target population vaccinated, less than 60% high-risk and approximately 74% of 50 + with comorbidities and 65+ healthy persons with an adherent schedule, there is still much room for improvement. Furthermore, adults with poor socioeconomic status had lower odds of primary vaccination and schedule adherence, demonstrating the need for a publicly funded program in Belgium to ensure equitable access.

Social capital and the nonprofit infrastructure; an ecological study of child maltreatment.

Child maltreatment is a significant social problem that responds to neighborhood conditions, including disorder and support. Using administrative sources with the census response rate and geocoded nonprofit tax forms in a cross-sectional ecological design (N = 443), this article explores two understudied supportive factors in neighborhoods: aggregate social capital and nonprofit organizations. A series of Poisson models show aggregate social capital and nonprofit density are negatively related to child maltreatment rates, while the relationship between social capital and child maltreatment rates varies by the number of nonprofits present in the neighborhood. The results provide new insights into the ecology of child maltreatment and illustrate the importance of norms and formal organizations when addressing collective action problems.