Depression at the intersection of race/ethnicity, sex/gender, and sexual orientation in a nationally representative sample of US adults: A design-weighted intersectional MAIHDA.

This study examined how race/ethnicity, sex/gender, and sexual orientation intersect under interlocking systems of oppression to socially pattern depression among US adults. With cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (NSDUH; n=234,722), we conducted design-weighted multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) under an intersectional framework to predict past-year and lifetime major depressive episode (MDE). With 42 intersectional groups constructed from seven race/ethnicity, two sex/gender, and three sexual orientation categories, we estimated age-standardized prevalence and excess/reduced prevalence attributable to two-way or higher interaction effects. Models revealed heterogeneity across groups, with prevalence ranging from 1.9-19.7% (past-year) and 4.5-36.5% (lifetime). Approximately 12.7% (past-year) and 12.5% (lifetime) of total individual variance were attributable to between-group differences, indicating key relevance of intersectional groups in describing the population distribution of depression. Main effects indicated, on average, people who were White, women, gay/lesbian, or bisexual had greater odds of MDE. Main effects explained most between-group variance. Interaction effects (past-year: 10.1%; lifetime: 16.5%) indicated a further source of heterogeneity around averages with groups experiencing excess/reduced prevalence compared to main effects expectations. We extend the MAIHDA framework to calculate nationally representative estimates from complex sample survey data using design-weighted, Bayesian methods.

Test-retest reliability of the play-or-pass version of the Iowa Gambling Task.

The Iowa Gambling Task (IGT) is used to assess decision-making in clinical populations. The original IGT does not disambiguate reward and punishment learning; however, an adaptation of the task, the "play-or-pass" IGT, was developed to better distinguish between reward and punishment learning. We evaluated the test-retest reliability of measures of reward and punishment learning from the play-or-pass IGT and examined associations with self-reported measures of reward/punishment sensitivity and internalizing symptoms. Participants completed the task across two sessions, and we calculated mean-level differences and rank-order stability of behavioral measures across the two sessions using traditional scoring, involving session-wide choice proportions, and computational modeling, involving estimates of different aspects of trial-level learning. Measures using both approaches were reliable; however, computational modeling provided more insights regarding between-session changes in performance, and how performance related to self-reported measures of reward/punishment sensitivity and internalizing symptoms. Our results show promise in using the play-or-pass IGT to assess decision-making; however, further work is still necessary to validate the play-or-pass IGT.

A regional genomic surveillance program is implemented to monitor the occurrence and emergence of SARS-CoV-2 variants in Yubei District, China.

BACKGROUND: In December 2022, Chongqing experienced a significant surge in coronavirus disease 2019 (COVID-19) epidemic after adjusting control measures in China. Given the widespread immunization of the population with the BA.5 variant, it is crucial to actively monitor severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant evolution in Chongqing's Yubei district. METHODS: In this retrospective study based on whole genome sequencing, we collected oropharyngeal and nasal swab of native COVID-19 cases from Yubei district between January to May 2023, along with imported cases from January 2022 to January 2023. Through second-generation sequencing, we generated a total of 578 genomes. RESULTS: Phylogenetic analyses revealed these genomes belong to 47 SARS-CoV-2 Pango lineages. BA.5.2.48 was dominant from January to April 2023, rapidly replaced by XBB* variants from April to May 2023. Bayesian Skyline Plot reconstructions indicated a higher evolutionary rate (6.973 × 10-4 subs/site/year) for the XBB.1.5* lineage compared to others. The mean time to the most recent common ancestor (tMRCA) of BA.5.2.48* closely matched BA.2.75* (May 27, 2022). Using multinomial logistic regression, we estimated growth advantages, with XBB.1.9.1 showing the highest growth advantage (1.2, 95% HPI:1.1-1.2), followed by lineage FR.1 (1.1, 95% HPI:1.1-1.2). CONCLUSIONS: Our monitoring reveals the rapid replacement of the previously prevalent BA.5.2.48 variant by XBB and its sub-variants, underscoring the ineffectiveness of herd immunity and breakthrough BA.5 infections against XBB variants. Given the ongoing evolutionary pressure, sustaining a SARS-CoV-2 genomic surveillance program is imperative.

Aspirin 162 mg versus 81 mg for Preeclampsia Prophylaxis in High-risk Obese Individuals: A Comparative Effectiveness Open Label Randomized Trial (ASPREO).

BACKGROUND: In the United States, leading medical societies recommend 81 mg of aspirin daily for the prevention of preeclampsia (PE) in women at risk, whereas the NICE guidelines in the UK recommend a dose as high as 150 mg of aspirin. Recent data also suggest that in the obese population, inadequate dosing or aspirin resistance may impact the efficacy of aspirin at the currently recommend doses. OBJECTIVE: We evaluated whether daily administration of 162 mg aspirin would be more effective compared to 81 mg in decreasing the rate of PE with severe features in high-risk obese pregnant individuals. STUDY DESIGN: We performed a randomized trial between May 2019 and November 2022. Individuals at 12 to 20-weeks gestational age (GA) with a BMI ≥ 30 kg/m2 at time to enrollment, and at least one of three high risk factors: history of PE in a prior pregnancy, at least stage I hypertension documented in the index pregnancy, pre-gestational diabetes or gestational diabetes diagnosed prior to 20 weeks GA were randomized to either 162 mg or 81 mg of aspirin daily till delivery, participants were not blinded to treatment allocation. Exclusion criteria were: multifetal gestation, known major fetal anomalies, seizure disorder, baseline proteinuria, on aspirin due to other indications, or contraindication to aspirin. The primary outcome was PE with severe features (PE or superimposed PE with severe features, eclampsia, or HELLP). Secondary outcomes included rates of preterm birth due to PE, small for gestational age (SGA), postpartum hemorrhage, abruption, and medication side effects. A sample size of 220 was needed using a preplanned Bayesian analysis of the primary outcome to estimate the posterior probability of benefit or harm with a neutral informative prior. RESULTS: Of 343 eligible individuals, 220 (64.1%) were randomized. The primary outcome was available for 209/220 (95%). Baseline characteristics were similar between groups, median gestational age at enrollment was 15.9 weeks in the 162 mg aspirin group and 15.6 weeks in the 81 mg aspirin group. Enrollment prior to 16 weeks occurred in 55/110 of those assigned to 162 mg and 58/110 of those assigned to 81 mg of aspirin. The primary outcome occurred in 35% in the 162 mg aspirin group and in 40% in the 81 mg aspirin group (posterior relative risk, 0.88; 95% credible interval, 0.64-1.22). Bayesian analysis indicated a 78% probability of a reduction in the primary outcome with 162 mg aspirin compared to 81 mg aspirin dose. Rates of indicated preterm birth due to preeclampsia (21% vs 21%), SGA (6.5% vs 2.9%), abruption (2.8% vs 3.0%) and postpartum hemorrhage (10% vs 8.8%) were similar between groups. Medication adverse effects were also similar. CONCLUSION: Among high-risk obese individuals, there was 78% probability of benefit that 162 mg aspirin compared to 81 mg will decrease the rate of PE with severe features. With a best estimate of a 12% reduction when using 162 mg of aspirin in comparison to 81 mg of aspirin in this population. This trial supports doing a larger multicenter trial.

Implementing a Bundle for Evidence-based Cesarean Delivery May Not Be As Beneficial as Expected: A Multi-center, Pre- and Post- Study.

BACKGROUND: Standardization of procedures improves outcomes. Though systematic reviews have summarized the evidence-based steps (EBS) of cesarean delivery (CD), their bundled implementation has not been investigated. OBJECTIVE: In this pre- and post-implementation trial, we sought to ascertain if bundled EBS of CD, compared to surgeon's preference, improves outcomes. STUDY DESIGN: A StaRI (Standards for Reporting Implementation Studies) compliant, multi-center pre- and post-implementation trial at 4 teaching hospitals was conducted. The pre-implementation period consisted of CD done based on the physicians' preferences for 3 months; educational intervention (e.g., didactics, badge cards, posters, video) occurred at the 4th month. CDs in post-implementation period employed the bundled EBS. A pre-planned 10% randomized audit of both groups assessed adherence and uptake of EBS. The primary outcome was a composite maternal morbidity (CMM), which included estimated blood loss > 1,000 mL, blood transfusion, endometritis, post-partum fever, wound complications, sepsis, thrombosis, ICU admission, hysterectomy, or death. The secondary outcome was a composite neonatal morbidity (CNM) and some of its components were 5-min Apgar score < 7, positive pressure oxygen use, hypoglycemia, or sepsis. A priori Bayesian sample size calculation indicated 700 CD in each group was needed to demonstrate 20% relative reduction (from 15% to 12%) of CMM with 75% certainty. Bayesian logistic regression with neutral priors was used to calculate likelihood of net-improvement in adjusted relative risk (aRR) with 95% credible intervals (CrI). RESULTS: A total of 1,425 consecutive CD (721 in pre- and 704 in post-implementation group) were examined. Audited data indicated that the baseline EBS utilization rate during the pre-implementation period was 79%; after the implementation bundled EBS of CD the audited adherence was 89%-an uptake of 10.0% of the EBS. In four aspects, the maternal characteristics differed significantly in the pre- and post-implementation periods: race/ethnicity, hypertensive disorder, and the relative contribution of the 4 centers to the cohorts and the gestational age at delivery, but the indications for CD and whether its duration was < versus > 60 min did not. The rates of CMM in the pre- and post-implementation groups were 26% and 22%, respectively (aRR, 0.88; 95% CrI, 0.73-1.04), with a 94 % Bayesian probability of a reduction in CMM. The CNM occurred in 37% of the pre- and in 41% of the post-implementation group (aRR, 1.12; 95% CrI 0.98-1.39), with a 95% Bayesian probability of worsening in CNM. When CMM were segregated by preterm (<37 wks) and term (> 37 weeks) CD, the improvement in maternal outcomes persisted; when CNM were segregated by gestational age subgroupsthe potential for worsening neonatal outcomes persisted as well. CONCLUSIONS: Standardization of the evidence-based bundled steps of cesarean delivery resulted in a modest reduction of the composite maternal outcome; however, a paradoxical increase in neonatal composite morbidity was noted. Although individual evidence-based steps may be of value, while awaiting additional intervention trials a formal bundling of such steps is currently not recommended.

Bayesian spatio-temporal analysis of malaria prevalence in children between 2 and 10 years of age in Gabon.

BACKGROUND: Gabon still bears significant malaria burden despite numerous efforts. To reduce this burden, policy-makers need strategies to design effective interventions. Besides, malaria distribution is well known to be related to the meteorological conditions. In Gabon, there is limited knowledge of the spatio-temporal effect or the environmental factors on this distribution. This study aimed to investigate on the spatio-temporal effects and environmental factors on the distribution of malaria prevalence among children 2-10 years of age in Gabon. METHODS: The study used cross-sectional data from the Demographic Health Survey (DHS) carried out in 2000, 2005, 2010, and 2015. The malaria prevalence was obtained by considering the weighting scheme and using the space-time smoothing model. Spatial autocorrelation was inferred using the Moran's I index, and hotspots were identified with the local statistic Getis-Ord General Gi. For the effect of covariates on the prevalence, several spatial methods implemented in the Integrated Nested Laplace Approximation (INLA) approach using Stochastic Partial Differential Equations (SPDE) were compared. RESULTS: The study considered 336 clusters, with 153 (46%) in rural and 183 (54%) in urban areas. The prevalence was highest in the Estuaire province in 2000, reaching 46%. It decreased until 2010, exhibiting strong spatial correlation (P < 0.001), decreasing slowly with distance. Hotspots were identified in north-western and western Gabon. Using the Spatial Durbin Error Model (SDEM), the relationship between the prevalence and insecticide-treated bed nets (ITNs) coverage was decreasing after 20% of coverage. The prevalence in a cluster decreased significantly with the increase per percentage of ITNs coverage in the nearby clusters, and per degree Celsius of day land surface temperature in the same cluster. It slightly increased with the number of wet days and mean temperature per month in neighbouring clusters. CONCLUSIONS: In summary, this study showed evidence of strong spatial effect influencing malaria prevalence in household clusters. Increasing ITN coverage by 20% and prioritizing hotspots are essential policy recommendations. The effects of environmental factors should be considered, and collaboration with the national meteorological department (DGM) for early warning systems is needed.

An integrated Bayesian framework for multi-omics prediction and classification.

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code.

BACKGROUND: The rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings. METHODS: We present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator's descriptive accuracy. RESULTS: In this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8-22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65-5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator. CONCLUSIONS: The emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

Spatial-temporal distribution of preterm birth in China, 1990-2020: A systematic review and modelling analysis.

BACKGROUND: Little is known about the long-term trends of preterm birth rates in China and their geographic variation by province. OBJECTIVES: To estimate the annual spatial-temporal distribution of preterm birth rates in China by province from 1990 to 2020. DATA SOURCES: We searched PubMed, EMBASE, Web of Science, CNKI, WANFANG and VIP from January 1990 to September 2023. STUDY SELECTION AND DATA EXTRACTION: Studies that provided data on preterm births in China after 1990 were included. Data were extracted following the Guidelines for Accurate and Transparent Health Estimates Reporting. SYNTHESIS: We assessed the quality of each survey using a 9-point checklist. We estimated the annual preterm birth risk by province using Bayesian multilevel logistic regression models considering potential socioeconomic, environmental, and sanitary predictors. RESULTS: Based on 634 survey data from 343 included studies, we found a gradual increase in the preterm birth risk in most provinces in China since 1990, with an average annual increase of 0.7% nationally. However, the preterm birth rates in Inner Mongolia, Hubei, and Fujian Province showed a decline, while those in Sichuan were quite stable since 1990. In 2020, the estimates of preterm birth rates ranged from 2.9% (95% Bayesian credible interval [BCI] 2.1, 3.8) in Inner Mongolia to 8.5% (95% BCI 6.6, 10.9) in Jiangxi, with the national estimate of 5.9% (95% BCI 4.3, 8.1). Specifically, some provinces were identified as high-risk provinces for either consistently high preterm birth rates (e.g. Jiangxi) or relatively large increases (e.g. Shanxi) since 1990. CONCLUSIONS: This study provides annual information on the preterm birth risk in China since 1990 and identifies high-risk provinces to assist in targeted control and intervention for this health issue.

Gender Dysphoria and Sexual Euphoria: A Bayesian Perspective on the Influence of Gender-Affirming Hormone Therapy on Sexual Arousal.

Self-reported sexual orientation of transgender individuals occasionally changes over transition. Using functional magnetic resonance imaging, we tested the hypothesis that neural and behavioral patterns of sexual arousal in transgender individuals would shift from the assigned to the experienced gender (e.g., trans women's responses becoming more dissimilar to those of cis men and more similar to those of cis women). To this aim, trans women (N = 12) and trans men (N = 20) as well as cisgender women (N = 24) and cisgender men (N = 14) rated visual stimuli showing male-female, female-female or male-male intercourse for sexual arousal before and after four months of gender-affirming hormone therapy. A Bayesian framework allowed us to incorporate previous behavioral findings. The hypothesized changes could indeed be observed in the behavioral responses with the strongest results for trans men and female-female scenes. Activation of the ventral striatum supported our hypothesis only for female-female scenes in trans women. The respective application or depletion of androgens in trans men and trans women might partly explain this observation. The prominent role of female-female stimuli might be based on the differential responses they elicit in cis women and men or, in theory, the controversial concept of autogynephilia. We show that correlates of sexual arousal in transgender individuals might change in the direction of the experienced gender. Future investigations should elucidate the mechanistic role of sex hormones and the cause of the differential neural and behavioral findings.The study was registered at ClinicalTrials.gov (NCT02715232), March 22, 2016.

Assessing the use of GEE methods for analyzing binary outcomes in family studies: the Strong Heart Family Study.

The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.

Effect of digital game intervention on cognitive functions in older adults: a multiple baseline single case experimental design study.

BACKGROUND: Residents in nursing homes are prone to cognitive decline affecting memory, visuospatial cognition, and executive functions. Cognitive decline can lead to dementia, necessitating prioritized intervention. METHODS: The current study aimed to investigate whether an intervention using a digital game was effective for preserving and improving the cognitive function of residents in nursing homes. An intervention study was conducted using a single-case AB design with multiple baselines. The participants in the study were five older adults aged 65 and over who do not play digital games regularly. The study ran for 15 weeks, including a baseline (phase A) and an intervention phase (phase B). Phase A had five baselines (5 to 9 weeks) with random participant assignment. In phase B, participants engaged in a digital game (Space Invaders) individually. Cognitive function was assessed as the outcome, measured using the Brain Assessment (performed on a tablet through the Internet) at 16 measurement points. Four of five participants (two female and two male) were included in the analysis, using visual inspection and Bayesian statistics with multi-level modeling. RESULTS: Visual inspection of the graphs revealed cognitive function score improvements after the intervention for most layers in terms of memory of numbers, memory of words, mental rotation test (visuospatial ability), and total scores in the Brain Assessment. These effects were also significant in the analysis by multi-level modeling. CONCLUSIONS: The results suggest that the use of digital games may be effective for preserving and improving cognitive function among residents of nursing home. TRIAL REGISTRATION: This study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000048677; public title: Effect of a Digital Game Intervention for Cognitive Functions in Older People; registration date: August 30, 2022).

Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis: a cross-sectional and longitudinal study.

BACKGROUND: Frailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults. METHODS: We conducted a cross-sectional and longitudinal study using data from the "Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC)," which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs). RESULTS: At baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model's AUC was 0.943 (95%CI 0.913-0.974), indicating good discrimination, with Hosmer-Lemeshow (H-L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model's AUC was 0.722 (95%CI 0.656-0.788), indicating moderate discrimination, with H-L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%. CONCLUSION: Frailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

An implementation analysis of a quality improvement project to reduce cesarean section in Brazilian private hospitals.

BACKGROUND: Brazil has one of the highest prevalence of cesarean sections in the world. The private health system is responsible for carrying out most of these surgical procedures. A quality improvement project called Adequate Childbirth Project ("Projeto Parto Adequado"- PPA) was developed to identify models of care for labor and childbirth, which place value on vaginal birth and reduce the frequency of cesarean sections without a clinical indication. This research aims to evaluate the implementation of PPA in private hospitals in Brazil. METHOD: Evaluative hospital-based survey, carried out in 2017, in 12 private hospitals, including 4,322 women. We used a Bayesian network strategy to develop a theoretical model for implementation analysis. We estimated and compared the degree of implementation of two major driving components of PPA-"Participation of women" and "Reorganization of care" - among the 12 hospitals and according to type of hospital (belonging to a health insurance company or not). To assess whether the degree of implementation was correlated with the rate of vaginal birth data we used the Bayesian Network and compared the difference between the group "Exposed to the PPA model of care" and the group "Standard of care model". RESULTS: PPA had a low degree of implementation in both components "Reorganization of Care" (0.17 - 0.32) and "Participation of Women" (0.21 - 0.34). The combined implementation score was 0.39-0.64 and was higher in hospitals that belonged to a health insurance company. The vaginal birth rate was higher in hospitals with a higher degree of implementation of PPA. CONCLUSION: The degree of implementation of PPA was low, which reflects the difficulties in changing childbirth care practices. Nevertheless, PPA increased vaginal birth rates in private hospitals with higher implementation scores. PPA is an ongoing quality improvement project and these results demonstrate the need for changes in the involvement of women and the care offered by the provider.

Bayesian estimates of genetic parameters for growth traits in Harnali sheep.

The objective of the study was to estimate genetic parameters of the growth traits under Bayesian inference in Harnali sheep. The information of pedigree and targeted traits of 2404 Harnali animals born to 159 sires and 695 dams was collected for the period from 1998 to 2021. The growth traits included weight at birth (BWT), 3 (WWT), 6 (6WT) and 12 (YWT) months of age. The genetic evaluation was carried out using six univariate animal models comprising direct and maternal effects using THRGIBBS1F90 and POSTGIBBSF90 programs. The fixed factors adjusted in the analysis were period of birth, sex of lamb and dam's weight at lambing. Bayesian estimates of direct heritability under best model for BWT, WWT, 6WT and YWT traits were 0.16 ± 0.04, 0.10 ± 0.04, 0.18 ± 0.04, and 0.05 ± 0.03, respectively. The significant maternal influences observed for BWT and WWT traits with 9% and 8% contribution to total phenotypic variances, respectively. Additionally, maternal permanent environmental influences were observed to BWT (4%) and YWT trait (3%). The genetic and phenotypic correlations among studied traits were high and positive. The genetic changes were positive and significant for WWT only. It was concluded that the weight at 6 months of age can be continued as selection criterion for further genetic improvement through selection. Also, maternal effects should be considered in breeding programme for enhancing early growth performance in Harnali sheep.

Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity.

The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.

Futility Interim Analysis Based on Probability of Success Using a Surrogate Endpoint.

In clinical trials with time-to-event data, the evaluation of treatment efficacy can be a long and complex process, especially when considering long-term primary endpoints. Using surrogate endpoints to correlate the primary endpoint has become a common practice to accelerate decision-making. Moreover, the ethical need to minimize sample size and the practical need to optimize available resources have encouraged the scientific community to develop methodologies that leverage historical data. Relying on the general theory of group sequential design and using a Bayesian framework, the methodology described in this paper exploits a documented historical relationship between a clinical "final" endpoint and a surrogate endpoint to build an informative prior for the primary endpoint, using surrogate data from an early interim analysis of the clinical trial. The predictive probability of success of the trial is then used to define a futility-stopping rule. The methodology demonstrates substantial enhancements in trial operating characteristics when there is a good agreement between current and historical data. Furthermore, incorporating a robust approach that combines the surrogate prior with a vague component mitigates the impact of the minor prior-data conflicts while maintaining acceptable performance even in the presence of significant prior-data conflicts. The proposed methodology was applied to design a Phase III clinical trial in metastatic colorectal cancer, with overall survival as the primary endpoint and progression-free survival as the surrogate endpoint.

Comparative Analysis of the Mitochondrial Genomes of Chloropidae and Their Implications for the Phylogeny of the Family.

Chloropidae, commonly known as grass flies, represent the most taxonomically diverse family of Diptera Carnoidea, comprising over 3000 described species worldwide. Previous phylogenetic studies of this family have predominantly relied on morphological characters, with mitochondrial genomes being reported in a few species. This study presents 11 newly sequenced mitochondrial genomes (10 Chloropidae and 1 Milichiidae) and provides the first comprehensive comparative analysis of mitochondrial genomes for Chloropidae. Apart from 37 standard genes and the control region, three conserved intergenic sequences across Diptera Cyclorrhapha were identified in all available chloropid mitochondrial genomes. Evolutionary rates within Chloropidae exhibit significant variation across subfamilies, with Chloropinae displaying higher rates than the other three subfamilies. Phylogenetic relationships based on mitochondrial genomes were inferred using maximum likelihood and Bayesian methods. The monophyly of Chloropidae and all four subfamilies is consistently strongly supported, while subfamily relationships within Chloropidae remain poorly resolved, possibly due to rapid evolution.

Everything, altogether, all at once: Addressing data challenges when measuring speech intelligibility through entropy scores.

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.

Symptom-specific improvement across therapies and their putative mediators: A mediation network intervention analysis.

OBJECTIVE: We evaluated differential treatment effects on specific symptoms and their mediators for Cognitive Behavioral Analysis System of Psychotherapy (CBASP) and Supportive Psychotherapy (SP) in persistently depressed patients. METHOD: We conducted a Bayesian mediation network intervention analysis with data from a randomized controlled trial comparing CBASP and SP. Three networks were calculated to investigate (1) differential treatment effects on specific symptoms, (2) differential treatment effects on the potential mediators interpersonal problems and social functioning, and (3) associations between change in symptoms and change in the potential mediators. RESULTS: First, we found no evidence that CBASP more strongly improves most depressive symptoms specifically, except minimal evidence of symptom-specific effects on sleeping problems and self-esteem. Second, no and minimal evidence for differential treatment effects on interpersonal problems and social functioning was shown, respectively. Third, interpersonal problems and social functioning were strongly related to depressive symptoms. CONCLUSION: While CBASP showed superior treatment effects for overall symptom severity, this treatment might not be superior in improving specific symptoms and the potential mediators interpersonal problems and social functioning. Still, interpersonal problems and social functioning seem to play an important role for depression symptoms. Future research needs to further investigate potential working mechanisms of CBASP.Trial registration: ClinicalTrials.gov identifier: NCT00970437.