Population Datasets Research Articles

Living in darkness: rural poverty in Venezuela

ABSTRACT This paper uses nighttime lights imagery and gridded population datasets to estimate 2000–2020 rural poverty rates for Venezuela at the state and municipality levels. Then, I examine if there has been a significant change in rural poverty during the economic collapse ongoing since 2013–2014. The main finding reveals that most of the Venezuelan territory experienced a considerable increase in rural poverty rates between 2014 and 2020. Furthermore, I confirm how new rural poor areas appear across the country in clusters, surrounding municipalities with moderate to high poverty rates. This suggests that in recent years, more Venezuelans have sunk in darkness.

P557 Analysis of colectomy rates and advanced therapy prescribing for ulcerative colitis in Lothian; Scotland an updated population based cohort study

Abstract Background The impact of advanced therapies on colectomy rates in UC remains unclear. We have published a population-based cohort reporting patterns of advanced therapy prescribing and the impact on colectomy rates complete to 2018 The aim of this study was to describe recent trends by extending the study period to 2021. Methods The Lothian IBD registry (LIBDR) is a manually validated, comprehensive, population dataset of the Edinburgh area . The LIBDR contains measured point prevalence between 2008-2018 and estimated prevalence from 2018-2028 (derived from AARIMA modelling). All patients with a diagnosis of UC who commenced advanced therapy or underwent a colectomy in Lothian, Scotland between 01/01/2005 and 31/12/2021 were included interrogating multiple clinical and administrative databases. Linear and segmental regression analyses were used to identify the annual percentage change (APC) and temporal trends (statistical joinpoints) in colectomy rates and drug initiation. Kaplin Meier curves were created for drug ersistence and compared with the log rank test. Results 484 patients underwent colectomy for UC between 2005 and 2021. Colectomy rates per 100 UC patients fell from 1.47 colectomies in 2005 to 0.25 in 2021 (p=0.009). Temporal trend analysis (2005-2021) identified a significant joinpoint in colectomy rates in 2014 (p=0.009) (Figure 2). Figure 1, Colectomy rates per 100 UC patients from 2005-2021. Joinpoint regression separated at inflection point at 2014 (dashed line). 383 patients were treated with their first advanced therapy for UC between 2005 and 2021, of whom 165 progressed to second line therapy. The annual percentage change in first line therapy was 34.7%, with joinpoints at 2013 and 2015 (p<0.001). Figure 2, First line advanced therapy prescription rate per 100 UC patients per year between 2005-2021 stratified by agent used. Figure 3, Second line advanced therapy prescription rate per 100 UC patients per year between 2005-2021 stratified by agent used. The 1 and 2 year persistence on first line advanced therapy was; 44.8% and 24.1% for adalimumab, 60.7% and 54.4% for infliximab and 79.8% and 67% for vedolizumab (p=0.001) (Fig 3). Fig 4, Persistence on first line advanced therapy. Conclusion This population based cohort study has shown a significant increase in advanced therapy prescription for UC. Whilst we cannot ascribe causality this has been paralleled by a significant reduction in the rate of colectomy that has been maintained in recent years.

Pharmacokinetic comparability between two populations using nonlinear mixed effect models: a Monte Carlo study.

'Are two populations the same or are they different' is a question that is often faced in clinical pharmacology trials e.g., a pharmacokinetic trial studying a particular drug in racially different groups. To address this question, concentration-time data were simulated from a reference and test population, where in the latter the clearance, sample size, and sampling design were systematically varied. It was of interest to determine whether the estimates of clearance from the two groups were the same or different. Two approaches were used to estimate the empirical Bayes estimates (EBEs) for clearance. One approach developed a population pharmacokinetic model for the reference population and the EBEs for the reference population were estimated from this model. The parameters of the reference population were fixed to their maximum likelihood estimates. The model was then applied to the test population dataset to estimate the EBEs of the test population using the MAXEVAL = 0 option in NONMEM. A second approach, the theta approach, combined the reference and test datasets into a single dataset and used population as a covariate in the model; the EBEs were estimated from this combined model. The power and type I error rate of each approach were calculated for each treatment combination using a variety of statistical tests to determine whether there was a difference in the distribution of the EBEs in the reference population compared to the test population. Our results suggest that either MAXEVAL or theta approaches can be used with informative sampling designs. In addition to reasonable power and type I error, both approaches gave almost identical results under a dense sampling design. To statistically compare the distribution of EBEs of pharmacokinetic parameters from a reference group to that of a test group, a T-test and DTS eCDF test are equally useful.

A population-based model for rationing COVID-19 vaccine

BACKGROUND As COVID-19 vaccines develop, methods for identifying vulnerability within groups to prioritized vaccination remain un-established. This paper presents a novel approach based on population-based analysis of viral pneumonia vulnerability, as an example. METHODS The analysis employed an anonymous, 16-year, population dataset (n = 768,460) consisting of International Classification of Diseases (ICD-9) diagnoses, demographics, and dates identifying those with viral pneumonia and permitting linkage of these individuals to all their associated diagnoses for calculation of odds ratios and proportions of disorders before and after the index viral pneumonia diagnosis. RESULTS Females and males had results of differing magnitude. For those with viral pneumonia, the mean number of diagnoses was greater in both the subsample and whole sample, with associated diagnoses arising about 4 years on average before the viral pneumonia index diagnosis. Within the subsample, compared to those without, the temporal analysis revealed distinct over-representation for those with viral pneumonia at visit one and over the first fifty visits. Further, those with viral pneumonia had diagnoses not represented in the group without viral pneumonia. CONCLUSIONS The population-based analysis of temporal hyper-morbidity may be a viable and economical approach to identifying viral pneumonia vulnerability. The approach presented in this paper may provide an economical means of identifying vulnerability to COVID-19 in regions where comparable data are available for analysis. Rational approaches may optimize vaccination and help to limit the spread of the disease and to some extent alleviate the health service burden.

Tough times never last too long: the future of psychopharmacology.

Tough times never last too long: the future of psychopharmacology.

Evaluation and limitations of different approaches among COVID-19 fatal cases using whole-exome sequencing data

BackgroundCOVID-19 caused by the SARS-CoV-2 infection may result in various disease symptoms and severity, ranging from asymptomatic, through mildly symptomatic, up to very severe and even fatal cases. Although environmental, clinical, and social factors play important roles in both susceptibility to the SARS-CoV-2 infection and progress of COVID-19 disease, it is becoming evident that both pathogen and host genetic factors are important too. In this study, we report findings from whole-exome sequencing (WES) of 27 individuals who died due to COVID-19, especially focusing on frequencies of DNA variants in genes previously associated with the SARS-CoV-2 infection and the severity of COVID-19.ResultsWe selected the risk DNA variants/alleles or target genes using four different approaches: 1) aggregated GWAS results from the GWAS Catalog; 2) selected publications from PubMed; 3) the aggregated results of the Host Genetics Initiative database; and 4) a commercial DNA variant annotation/interpretation tool providing its own knowledgebase. We divided these variants/genes into those reported to influence the susceptibility to the SARS-CoV-2 infection and those influencing the severity of COVID-19. Based on the above, we compared the frequencies of alleles found in the fatal COVID-19 cases to the frequencies identified in two population control datasets (non-Finnish European population from the gnomAD database and genomic frequencies specific for the Slovak population from our own database). When compared to both control population datasets, our analyses indicated a trend of higher frequencies of severe COVID-19 associated risk alleles among fatal COVID-19 cases. This trend reached statistical significance specifically when using the HGI-derived variant list. We also analysed other approaches to WES data evaluation, demonstrating its utility as well as limitations.ConclusionsAlthough our results proved the likely involvement of host genetic factors pointed out by previous studies looking into severity of COVID-19 disease, careful considerations of the molecular-testing strategies and the evaluated genomic positions may have a strong impact on the utility of genomic testing.

Population genetic analysis of 12 X-STR markers in Slovakia

Background: During the last 20 years, X-chromosomal STR markers have become widely used in forensic genetics and paternity testing. Nevertheless, to exploit their full potential in any given population, a reliable reference dataset needs to be established. Since no relevant studies concerning these markers have been performed on the Slovak population so far, we decided to analyse several commonly used markers in this population. Aim: To create an informative set of Slovak population data concerning X-STR markers. Subjects and methods: We genotyped 378 individuals and analysed 12 loci (DXS10148, DX10135, DXS8378, DXS7132, DXS10079, DXS10074, DXS10103, HPRTB, DXS10101, DXS10146, DXS10134 and DXS742) localised in four distinct linkage groups. Results: Our analysis showed that the most informative marker is DXS10135 (PIC = 0,927) and the most informative linkage group (LG) is LG1 with 149 different haplotypes. This analysis also confirmed linkage disequilibrium for two pairs of markers (DX10101-DX10103 and DX10101-HPRTB) within LG3 in female samples. No statistically significant departure from HWE was observed for any locus. Moreover, the interpopulation comparison of 8 European populations based on haplotype frequencies showed no statistically significant FST values in any LG, except for LG2 in comparison with the German population. Conclusion: We created a haplotype database for forensic analyses and kinship testing in Slovakia, as well as the CE dataset which can be used to further increase the decision power in similar analyses in the future.

The causal effect of hot beverage temperature preference on esophageal cancer risk: a Mendelian randomization study.

Previous research has found a link between the temperature of food and beverages and the risk of esophageal cancer (EC). A causal relationship between the two has not been well established. Herein, we used Mendelian randomization (MR) analysis to assess the causal effect of temperature preference for hot beverages on EC risk. Genome-wide association studies (GWAS) data for hot beverage temperature preference were obtained from the UK biobank. There were 457,873 European and 2,617 East Asian participants included. GWAS data for EC were obtained from the Integrative Epidemiology Unit (IEU) project database. Two datasets from the European population and two datasets from the East Asian population were included. Totally, 4,426 EC cases and 1,202,270 control subjects were included. The "TwoSampleMR" R package was used to conduct a two-sample MR analysis. A random-effect inverse variance weighted (IVW) was used as the main analytical method to estimate the causal effect, and various sensitivity analyses, including MR Egger, weighted median, simple mode, and weighted mode, were used to examine the potential violation of the second and third MR assumptions. Meta-analyses were performed to further confirm the results. Sixty-eight single nucleotide polymorphisms (SNPs) from the European population and 11 SNPs from the East Asian population were used for MR analysis. No significant causal effect was found between hot beverage temperature preference and EC risk in the European population {for the ieu-b-4960 dataset, inverse variance weighted odds ratio (ORIVW) =1.00 [95% confidence interval (CI): 0.99-1.00], P=0.54; for the ebi-a-GCST90018841 dataset, ORIVW =0.35 (95% CI: 0.10-1.29), P=0.12} or in the East Asian population [for the bbj-a-117 dataset, ORIVW =1.09 (95% CI: 0.80-1.48), P=0.59; for the ebi-a-GCST90018621 dataset, ORIVW =0.11 (95% CI: 0.82-1.50), P=0.49]. Meta-analyses of the European population datasets and the Asian population datasets showed consistent results. The current MR analysis provides new genetic evidence for a null causal relationship between hot beverage temperature preference and EC, both in the European population and the East Asian population. Evidence to prevent EC by reducing the intake of hot beverages is insufficient.

Batch Hard Contrastive Loss and Its Application to Cross-View Gait Recognition

Biometric person authentication comprises two tasks: the identification task (i.e., one-to-many matching) and the verification task (i.e., one-to-one matching). In this paper, we propose a loss function called batch hard contrastive loss (BHCn) for the deep learning-based verification task. For this purpose, we consider batch mining techniques developed in the identification task and translate them to the verification task. More specifically, inspired by batch mining triplet losses to learn a relative distance for the identification task, we propose BHCn to learn an absolute distance that better represents verification in general. Our method preserves the identity-agnostic nature of the contrastive loss by selecting the hardest pair of samples for each pair of identities in a batch instead of selecting the hardest pair for each sample. We validate the effectiveness of the proposed method in cross-view gait recognition using three networks: a lightweight input, structure, and output network we call GEI + CNN (Gait Energy Image Convolutional Neural Network) as well as the widely used GaitSet and GaitGL, which have sophisticated inputs, structures, and outputs. We trained these networks with the publicly available silhouette-based datasets, the OU-ISIR Gait Database Multi-View Large Population (OU-MVLP) dataset and the Institute of Automation Chinese Academy of Sciences Gait Database Multiview (CASIA-B) dataset. Experimental results show that the proposed BHCn outperforms other loss functions, such as a triplet loss with batch mining as well as the conventional contrastive loss.

Understanding the urban structure of the Algerian cities using emerging data: Case study of Algiers

Despite the identification of urban structure and urban centers being crucial for effective policy making (transport policies, urban risk management policies etc) there is a serious lack of studies covering developing countries' urban structure changes, especially north African cities. This is mainly due to the unavailability of the necessary data for urban structure identification. In this contribution we investigate the polycentric development in the Algerian capital, Algiers, using innovative data resources such as LandScanTM High Resolution Global Population Dataset and the POI approach, and spatial statistics. The findings suggest that the structure of Algiers is indeed developing into a polycentric city. The emerging data helped us overcome a serious methodological problem and objectively identify the city structure for better planning.

Higher Non-High-Density Lipoprotein Cholesterol Was Higher Associated With Cardiovascular Disease Comparing Higher LDL-C in Nine Years Follow Up: Cohort Study.

Non-high-density lipoprotein cholesterol (non-HDL-C) may be equivalent to or superior to low-density lipoprotein cholesterol (LDL-C) for the prediction of cardiovascular disease (CVD). However, studies comparing the predictive values of LDL-C and non-HDL-C levels for CVD have yielded conflicting results. In this study, we evaluated the relationship between non-HDL-C, LDL-C, and CVD using a large-scale population dataset from the National Health Information Database (NHID). We performed a retrospective observational cohort study of 3,866,366 individuals ≥ 20 years, from 2009 to 2018, using the NHID. The participants were divided into LDL-C and non-HDL-C quartiles. The outcome variables included stroke, myocardial infarction (MI), and both. All outcomes were analyzed using Cox proportional hazards regression analysis while controlling for baseline covariates (age, sex, smoking, drinking, regular exercise, body mass index, diabetes, hypertension, and statin use). During 9.1 years of mean follow-up, stroke was diagnosed in 60,081 (1.55%), MI in 31,234 (0.81%), and both stroke and MI in 88,513 (2.29%) participants. Multivariate-adjusted hazard ratios (HRs) for patients in the highest non-HDL-C quartile demonstrated that these patients had a higher risk of stroke (HR, 1.254; 95% confidence interval [CI], 1.224-1.285), MI (HR, 1.918; 95% CI, 1.853-1.986), and both (HR, 1.456; 95% CI, 1.427-1.486) compared with participants in the lowest quartile. These were higher than the HRs for patients in the highest LDL-C quartile for stroke (HR, 1.134; 95% CI, 1.108-1.160), MI (HR, 1.601; 95% CI, 1.551-1.653), and both (HR, 1.281; 95% CI, 1.257-1.306). In our large population study, higher non-HDL-C levels were associated with CVD than LDL-C levels.

Machine learning model to predict the width of maxillary central incisor from anthropological measurements.

To improve smile esthetics, clinicians should comprehensively analyze the face and ensure that the sizes selected for the maxillary anterior teeth are compatible with the available anthropological measurements. The inter commissural (ICW), interalar (IAW), intermedial-canthus (MCW), interlateral-canthus (LCW), and interpupillary (IPW) widths are used to determine the width of maxillary central incisors (CW). The aim of this study was to develop an automated approach using machine learning (ML) algorithms to predict central incisor width in a young Turkish population using anthropological measurements. This automation can contribute to digital dentistry and clinical decision-making. In the initial phase of this cross-sectional study, several ML regression models-including multiple linear regression (MLR), multi-layer-perceptron (MLP), decision-tree (DT), and random forest (RF) models-were validated to confirm the central width prediction accuracy. Datasets containing only male and female measurements, as well as combined were considered for ML model implementation, and the performance of each model was evaluated for an unbiased population dataset. Compared with the other algorithms, the RF algorithm showed improved performance for all cases, with an accuracy of 96%, which represents the percentage of correct predictions. The plot reveals the applicability of the RF model in predicting the CW from anthropological measurements irrespective of the candidate's sex. These results demonstrated the possibility of predicting central incisor widths based on anthropometric measurements using ML models. The accurate central incisor width prediction from these trials also indicates the applicability of the proposed model to be deployed for enhanced clinical decision-making.

Impact of Corporate Social Responsibility on Economic Value Added: The Role of the Supply Chain Management Environment at LQ45 Indonesia

Purpose – This research examined the effect and strategy of CSR (Corporate Social Responsibility) on EVA (Economic Value Added) using the SCME mechanism. Methodology – Population data sets were derived from the index LQ45 for the period of August 2022 and compared to the previous six years. Furthermore, a purposive sampling technique was applied to conduct a regression analysis, while mediation used the Hayes process version 4.2, Model 8. The data set was compiled using information from IDX, Bloomberg, and Eikon platforms (Thomson Reuters). Findings – The results highlighted four key conclusions, as follows: High CSR index companies observed a rise in EVA; SCME mediates the effects of CSR on EVA; Company size considerably modifies the effect of CSR on EVA; Company size significantly modifies the effect of CSR on SCME. Originality – This research represents a novel attempt to investigate the relationship between CSR and EVA, the company size dimension's mediative role, and the SCME mediating role in Indonesia's LQ45 Index. The results present intriguing management, policy, and literature implications.

Childhood Prevalence of Involvement with the Child Protection System in Quebec: A Longitudinal Study

The goal of this study, the first of its kind in Canada, was to estimate the child lifetime prevalence of child protection involvement in Quebec. Using administrative and population data spanning 17 years, we performed a survival analysis of initial incidents of child protection reports, confirmed reports, confirmation of a child's security or development being compromised, and placement outside the home for one day or more. We found that before reaching the age of 18 years, over 18% of children were reported to child protection at least once, one in every ten children (10.1%) in the province had a report that led to the finding of their security or development being compromised, and over 5% were placed outside the home. We found that neglect was a primary concern in close to half (47.6%) of cases. By using a full population dataset, we obtained a more accurate prevalence estimate than studies using synthetic cohort life tables. These findings only captured initial incidents of involvement with child protection, meaning this study does not show the extent of recurrent involvement for some children. The findings reflect prior results showing that neglect is common in initial child protection involvement but less pervasive than has been shown in incidence studies, suggesting that recurrent child protection involvement is more driven by neglect than initial incidents are.

DEVELOPING A RESOURCE FOR LEVERAGING POPULATION DATA SETS TO ADVANCE CAREGIVING SCIENCE

Abstract Older adults often rely on other people to help care for them as they age. Caregiving has predominantly been studied through a primary caregiver lens with few studies focusing on the wider caregiving network. However, it is challenging to collect new data about family caregivers, particularly in contexts of serious illness and end of life. Selection and attrition effects, underrepresentation of marginalized or minoritized racial and ethnic groups, and low response rates can bias findings in prospective clinical research. Analyzing existing datasets, particularly population-based data (e.g., registries with linked administrative, health, and vital records) may hold promise to advance our understanding of the role families play throughout the caregiving and bereavement continuum to optimize the health and well-being of older adults. Little is known, however, about existing population-based datasets available for research on family caregiving. We conducted a scoping review of published literature and data repositories to identify datasets relevant for family caregiving research, explore the research reuse of these datasets, and describe potential validity and reliability concerns. Synthesized findings reveal: 1) methodological approaches for identifying the presence and type of caregivers and their level of engagement; 2) inclusion and measurement of key outcome variables; and 3) sampling and study design issues. We describe and compare a selection of high-value existing datasets relevant to family caregiving research. This new research information resource will advance research use of population datasets to improve care for older adults and their family caregivers across the lifespan.

Genetic landscape of human neutrophil antigen variants in India from population-scale genomes.

Antibodies against human neutrophil antigens (HNAs) play a significant role in various clinical conditions such as neonatal alloimmune neutropenia, transfusion-related acute lung injury, and other nonhemolytic transfusion reactions. This study aims to identify the genotype and allele frequencies of HNAs in the healthy Indian population. Ten genetic variants in four human genes encoding alleles of HNAs class I-V approved by the International Society of Blood Transfusion-Granulocyte Immunobiology Working Party were used in the analysis. Genetic variants from whole genome sequences of 1029 healthy Indian individuals corresponding to HNA alleles were analyzed. The frequencies of the variants were compared with global population datasets using an in-house computational pipeline. In HNA class I, allele frequencies of FCGR3B*01, FCGR3B*02, and FCGR3B*03 encoding HNA-1a, HNA-1b, and HNA-1c were 0.07, 0.8, and 0.13, respectively. HNA class 3 alleles namely SLC44A2*01 (encoding HNA-3a) and SLC44A2*02 (encoding HNA-3b) were found at allele frequencies of 0.78 and 0.22, respectively. The frequencies of ITGAM*01 encoding HNA-4a and ITGAM*02 encoding HNA-4a were 0.95 and 0.05, respectively. Furthermore, allele frequencies of HNA class 5 alleles were 0.32 for ITGAL*01 (encoding HNA-5a) and 0.68 for ITGAL*02 (encoding HNA-5b). Interestingly, it was also found that rs2230433 variant deciding the HNA class 5 alleles, was highly prevalent (78.2%) in the Indian population compared with other global populations. This study presents the first comprehensive report of HNA allele and genotype frequencies in the Indian population using population genome datasets of 1029 individuals. Significant difference was observed in the prevalence of HNA5a and HNA5b in India in comparison to other global populations.

TogoVar: A comprehensive Japanese genetic variation database

TogoVar (https://togovar.org) is a database that integrates allele frequencies derived from Japanese populations and provides annotations for variant interpretation. First, a scheme to reanalyze individual-level genome sequence data deposited in the Japanese Genotype-phenotype Archive (JGA), a controlled-access database, was established to make allele frequencies publicly available. As more Japanese individual-level genome sequence data are deposited in JGA, the sample size employed in TogoVar is expected to increase, contributing to genetic study as reference data for Japanese populations. Second, public datasets of Japanese and non-Japanese populations were integrated into TogoVar to easily compare allele frequencies in Japanese and other populations. Each variant detected in Japanese populations was assigned a TogoVar ID as a permanent identifier. Third, these variants were annotated with molecular consequence, pathogenicity, and literature information for interpreting and prioritizing variants. Here, we introduce the newly developed TogoVar database that compares allele frequencies among Japanese and non-Japanese populations and describes the integrated annotations.

Health Benefits of Residence at Moderate Altitude Do Not Reduce COVID-19 Mortality.

Moderate altitude (1000−2000 m above sea level) residence is emerging as a protective factor from the mortality of various causes, including of cardiovascular diseases. Conversely, mortality from certain respiratory diseases is higher at these altitudes than in lowlands. These divergent outcomes could indicate either beneficial or detrimental effects of altitude on the mortality of COVID-19 that primarily infects the respiratory tract but results in multi-organ damage. Previous epidemiological data indeed suggest divergent outcomes of moderate to high altitude residence in various countries. Confounding factors, such as variations in the access to clinical facilities or selection biases of investigated populations, may contribute to the equivocation of these observations. We interrogated a dataset of the complete population of an Alpine country in the center of Europe with relatively similar testing and clinical support conditions across altitude-levels of residence (up to around 2000 m) to assess altitude-dependent mortality from COVID-19 throughout 2020. While a reduced all-cause mortality was confirmed for people living higher than 1000 m, no differences in the mortality from COVID-19 between the lowest and the highest altitude regions were observed for the overall population and the population older than 60 years as well. Conversely, COVID-19 mortality seems to have been reduced in the very old (>85 years) women at moderate altitudes.

Assessment of United States Department of Agriculture Food Safety Inspection Service Humane Handling Enforcement Actions: 2018-2020.

Federally inspected slaughter establishments in the United States must adhere to the Humane Methods of Slaughter Act and regulations that enforce it. Failure to comply with this law results in a Humane Handling Enforcement Action (HHEA) issued by the United States Department of Agriculture Food Safety Inspection Service (USDA FSIS). The objective of this study was to systematically analyze and describe HHEAs issued between 2018 and 2020. Enforcement action notification letters were accessed from the USDA FSIS website and date, location, regulatory action, reason for noncompliance, species, and follow up action for each HHEA was recorded. Summary statistics (proportions and percentages) were calculated for the entire population dataset. Between 2018 and 2020, FSIS issued 293 HHEAs; 109 in 2018, 85 in 2019, and 99 in 2020. The majority of HHEAs (64.16%; 188 of 293) were related to the mechanical stunning of bovine (39.93%; 117 of 293) and porcine (24.23%; 71 of 293) species. The majority (50.23%; 107 of 213) of causative reasons for mechanical stun failure across all species were not clearly described; however, of those that were, most (39.12%; 68 of 213) were related to the placement of mechanical stuns. Addressing these issues through improved training and research would help to reduce the total number of HHEAs. Additional detail in reporting the events that result in HHEAs from USDA FSIS would aid in guiding corrective actions on an industry-wide scale.

Academic resilience from school entry to third grade: Child, parenting, and school factors associated with closing competency gaps.

There is substantial evidence confirming that children who begin school with strong developmental skills tend to maintain positive academic trajectories across the elementary school years. Much less is known about children who begin school with poorer developmental competencies yet go on to achieve academically on par with, or above, their initially more competent peers, demonstrating academic resilience. This study used a large population dataset, the Longitudinal Study of Australian Children (n = 2,118) to identify children who demonstrated academic resilience between school entry and third grade, and the child, parenting, and school characteristics associated with this resilience. Findings show that two in five children who were initially identified as academically vulnerable on a school entry measure of language and cognitive skills were classified as academically resilient by Grade 3. In multivariate analysis, higher attentional regulation and receptive vocabulary skills were key factors associated with academic resilience in reading and numeracy, along with paternal consistency (for reading resilience) and fewer sleep problems (for numeracy resilience). Bivariate relations (ANOVAs) showed that resilient children, when compared to children who remained vulnerable, also showed fewer peer problems, fewer behavioral sleep problems, higher levels of parenting consistency and lower levels of parenting anger by mothers and fathers, higher levels of parental engagement in children's school, and higher levels of teacher self-efficacy. Supporting resilient pathways for children who are identified as vulnerable at school entry should include a particular focus on vocabulary development and attentional regulation, along with social skills and peer relationships, school-based parental engagement, and positive parenting support.