BACKGROUND: Single parent families are at higher risk of re-report to Child Protective Services (CPS) than two-parent families. Yet, how single-family homes differ in risk from two-parent families remains under researched.
OBJECTIVE: To identify heterogenous patterns of child and caregiver factors among CPS-involved families and the subsequent risk for CPS re-report based on child and family characteristics (i.e., sociodemographic information, family structure, and risk indicators).
METHODS: Data were from the 2017 National Child Abuse and Neglect Data System Child File (N = 249,026).
METHODS: We conducted latent class analysis (LCA) to identify discrete patterns (i.e., classes) based on child and caregiver risk indicators (e.g., substance use, behavioral health). We then used logistic regression to examine family structure and other family characteristics and CPS indicators predicted CPS re-report for each class.
RESULTS: Results yielded five distinct classes: 1) Financial Stressors (25 % of the sample); 2) Caregiver Substance Use (16 %); 3) Complex Household Stressors (3 %); 4) Child Disabilities (4 %); and 5) Minimal Household Stressors (53 %). Family structure was significantly associated with CPS re-reports for Classes 1, 2, and 5. For Class 1, single father families had increased odds of CPS re-report compared to other family structures. For Classes 2 and 5, single father families\' odds of CPS re-reports were greater than those of married families, but lower than single mother families.
CONCLUSIONS: Children growing up in single father families have different likelihoods of repeat CPS involvement compared to those in single mother and married families. Financial stressors and parental substance use within single father families should be addressed.

This study focuses on understanding pharmacovigilance knowledge, attitudes, and practices (KAP) in Yunnan Province, employing Structural Equation Modeling (SEM) and network analysis. It aims to evaluate the interplay of these factors among healthcare personnel and the public, assessing the impact of demographic characteristics to inform policy and educational initiatives.
A cross-sectional survey was conducted in Yunnan, targeting healthcare personnel and the public. Data collection was through questionnaires, with subsequent analysis involving correlation matrices, network visualization, and SEM. The data analysis utilized SPSS 27.0, AMOS 26.0, and Gephi software for network analysis.
This study evaluated pharmacovigilance KAP among 209 public participants and 823 healthcare personnel, uncovering significant differences. Public respondents scored averages of 4.62 ± 2.70 in knowledge, 31.99 ± 4.72 in attitudes, and 12.07 ± 4.96 in practices, while healthcare personnel scored 4.38 ± 3.06, 27.95 ± 3.34, and 7.75 ± 2.77, respectively. Statistically significant correlations across KAP elements were observed in both groups, highlighting the interconnectedness of these factors. Demographic influences were more pronounced among healthcare personnel, emphasizing the role of professional background in pharmacovigilance competency. Network analysis identified knowledge as a key influencer within the pharmacovigilance KAP network, suggesting targeted education as a vital strategy for enhancing pharmacovigilance engagement.
The research reveals a less-than-ideal state of pharmacovigilance KAP among both healthcare personnel and the public in Yunnan, with significant differences between the two groups. SEM and network analysis confirmed a strong positive link among KAP components, moderated by demographics like age, occupation, and education level. These insights emphasize the need to enhance pharmacovigilance education and awareness, thereby promoting safer drug use.

Lumbar spondylolysis is the most common underlying cause of lower back pain (LBP) in young athletes. Conservative treatment methods are often used to reduce pain and promote healing. Several parameters may affect the duration of conservative treatment, such as the time to return to play (RTP), patient behavior, and physical parameters; however, no study has comprehensively assessed the factors that affect the time to RTP.
This study aimed to determine the factors associated with the time required for RTP among young athletes with early-stage spondylolysis receiving conservative treatment using structural equation modeling (SEM).
In this retrospective case series, 137 young athletes (128 males and 9 females, aged 9-18 years) with early-stage lumbar spondylolysis were enrolled. All patients were examined using plain radiography and magnetic resonance imaging and treated conservatively (sports cessation, wearing a corset, therapeutic exercises, and low-intensity pulsed ultrasound radiation). SEM was used to investigate the factors affecting the time to RTP in these patients.
The final model included the following factors: spondylolysis laterality, symptom duration, lower-extremity flexibility, treatment interval, patient adherence, and residual LBP. SEM revealed that patient adherence to physician orders (p < 0.01), treatment interval (p < 0.001), and spondylolysis laterality (p < 0.001) contributed directly to shortened RTP.
Patient adherence is essential for reducing the time to RTP among young athletes receiving conservative treatment for early-stage spondylolysis.

Food addiction (FA) and disordered eating behaviors related to obesity are gaining attention in clinical and research fields. The modified Yale Food Addiction Scale 2.0 (mYFAS2.0) is the gold standard questionnaire to measure FA, while another tool is the Measure of Eating Compulsivity 10 (MEC10). Discriminant validity is present when two measures of similar but distinct constructs show a correlation that is low enough for the factors to be regarded as distinct. However, the discriminant validity of these measures has never been tested. Through a cross-sectional study design, 717 inpatients (females: 56.20%, age: 53.681 ± 12.74) with severe obesity completed the MEC10, Binge Eating Scale (BES), and mYFAS2.0. A structural equation model (SEM) was fitted, freely estimating latent correlations with 95% confidence intervals (95% CI). The results confirmed the scales\' excellent psychometric properties. Importantly, latent factor correlations between MEC10 and mYFAS2.0 (est = 0.783, 95% CI [0.76, 0.80]) supported their discriminant validity. In contrast, the latent correlation of MEC10 and BES (est = 0.86, 95% CI [0.84, 0.87]) exceeded the recommended thresholds, indicating the absence of discriminant validity and suggesting a potential overlap, consistent with previous evidence. In conclusion, MEC10 demonstrates excellent psychometric properties but is more a measure of BED and not FA.

Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.

BACKGROUND: The Coronavirus Disease (COVID-19) caused by SARS-CoV-2 infections remains a significant health challenge worldwide. There is paucity of evidence on the influence of the universal health coverage (UHC) and global health security (GHS) nexus on SARS-CoV-2 infection risk and outcomes. This study aimed to investigate the effects of UHC and GHS nexus and interplay on SARS-CoV-2 infection rate and case-fatality rates (CFR) in Africa.
METHODS: The study employed descriptive methods to analyze the data drawn from multiple sources as well used structural equation modeling (SEM) with maximum likelihood estimation to model and assess the relationships between independent and dependent variables by performing path analysis.
RESULTS: In Africa, 100% and 18% of the effects of GHS on SARS-CoV-2 infection and RT-PCR CFR, respectively were direct. Increased SARS-CoV-2 CFR was associated with median age of the national population (β = -0.1244, [95% CI: -0.24, -0.01], P = 0.031 ); COVID-19 infection rate (β = -0.370, [95% CI: -0.66, -0.08], P = 0.012 ); and prevalence of obesity among adults aged 18 + years (β = 0.128, [95% CI: 0.06,0.20], P = 0.0001) were statistically significant. SARS-CoV-2 infection rates were strongly linked to median age of the national population (β = 0.118, [95% CI: 0.02,0.22 ], P = 0.024); population density per square kilometer, (β = -0.003, [95% CI: -0.0058, -0.00059], P = 0.016 ) and UHC for service coverage index (β = 0.089, [95% CI: 0.04,0.14, P = 0.001 ) in which their relationship was statistically significant.
CONCLUSIONS: The study shade a light that UHC for service coverage, and median age of the national population, population density have significant effect on COVID-19 infection rate while COVID-19 infection rate, median age of the national population and prevalence of obesity among adults aged 18 + years were associated with COVID-19 case-fatality rate. Both, UHC and GHS do not emerge to protect against COVID-19-related case fatality rate.

Promoting soil functioning by maintaining soil microbial diversity and activity is central for sustainable agriculture. In viticulture, soil management often includes tillage, which poses a multifaceted disturbance to the soil environment and has direct and indirect effects on soil microbial diversity and soil functioning. However, the challenge of disentangling the effects of different soil management practices on soil microbial diversity and functioning has rarely been addressed. In this study, we investigated the effects of soil management on soil bacterial and fungal diversity as well as soil functions (soil respiration and decomposition) using a balanced experimental design with four soil management types in nine vineyards in Germany. Application of structural equation modelling enabled us to investigate the causal relationships of soil disturbance, vegetation cover, and plant richness on soil properties, microbial diversity, and soil functions. We could show that soil disturbance by tillage increased bacterial diversity but decreased fungal diversity. We identified a positive effect of plant diversity on bacterial diversity. Soil respiration showed a positive response to soil disturbance, while decomposition was negatively affected in highly disturbed soils via mediated effects of vegetation removal. Our results contribute to the understanding of direct and indirect effects of vineyard soil management on soil life and aids designing targeted recommendations for agricultural soil management.

[This corrects the article DOI: 10.3389/fpsyg.2022.1000043.].

The impact of antimicrobials on the human microbiome and its relationship to human health are of great interest. How antimicrobial exposure might drive change within specific constituents of the microbiome to effect clinically relevant endpoints is difficult to study. Clinical investigation of each step within a network of causation would be challenging if done \'step-by-step\'. An analytic tool of great potential to clinical microbiome research is structural equation modelling (SEM), which has a long history of applications to research questions arising within subject areas as diverse as psychology and econometrics. SEM enables postulated models based on a network of causation to be tested en bloc by confrontation with data derived from the literature. Case studies for the potential application of SEM techniques are colonization resistance (CR) and its counterpart, colonization susceptibility (CS), wherein specific microbes within the microbiome are postulated to either impede (CR) or facilitate (CS) invasive infection with pathogenic bacteria. These postulated networks have three causation steps: exposure to specific antimicrobials are key drivers, clinically relevant infection endpoints are the measurable observables and the activity of key microbiome constituents mediating CR or CS, which may be unobservable, appear as latent variables in the model. SEM methods have potential application towards evaluating the activity of specific antimicrobial agents within postulated networks of causation using clinically derived data.

This study focuses on factors that shape vaccine attitudes and behaviours in the context of a low-trust society. Our analysis focuses on the Polish vaccination programme against COVID-19, primarily on (1) the evaluation of the information campaign, (2) trust in the institutions, (3) trust in other people, (4) attitudes toward vaccine safety and efficacy, (5) attitudes toward restrictions related to vaccination (e.g., restricted access to certain services for unvaccinated persons) and the introduction of mandatory vaccination, (6) the evaluation of the government\'s actions during the pandemic, and (7) political preferences. The study was conducted with a sample of 1143 adult residents in Poland (CATI). The explanation of the factors determining the COVID-19 vaccine was based on structural equation modelling (SEM). The model showed that the declared fact of vaccination was largely determined by a positive attitude toward restrictions related to vaccination and trust in vaccines. The formation of the provaccine attitude was to an extent determined by the assessment of the government\'s campaign and actions during pandemic. While institutional trust had a positive effect on support for the ruling coalition (0.56), the latter on its own had the opposite effect (-0.61) on the formation of provaccine attitude. In the group who both trust institutions and support the parties currently in power, there are more of those who simultaneously reject the restrictions and mandatory vaccination and remain sceptical about the safety and efficacy of COVID-19 vaccines than those who both trust in the vaccine safety and efficacy and accept the restrictions and mandatory vaccination. This indicates that in the context of strong political polarisation, ideological affiliations may play a greater role in shaping vaccine attitudes and behaviours than institutional trust.