Depression Severity and Sexual Satisfaction in Fertile and Infertile Women in Iran: A Population-Based Case-Control Study

Background: Depression and sexual dissatisfaction are among the most common psychological factors caused by infertility. Infertility is an essential topic in the Iranian culture, and many studies have already investigated it. Objectives: This study aimed to compare the depression severity and sexual dissatisfaction between fertile and infertile women in Iran. Methods: This case-control study enrolled 180 infertile women and 540 fertile women in 2019. The participants were selected through multistage stratified and cluster sampling methods. For each infertile woman, three fertile women were randomly selected. The data collection instruments consisted of a demographic form, the Depression Inventory Scale (Second Edition), and the Linda Berg Sexual Satisfaction Questionnaire. The multivariate marginal model and SPSS version 21 were used for data analysis at a significance level of 0.05. Results: After adjustment for confounding variables, the marginal model showed that the odds of depression increased by approximately 21.305 times among cases compared to controls (OR = 21.305, 95% CI = 14.75 - 32.021, P < 0.001). This model also found that by moderating the effects of confounding variables, infertility increased the odds of low sexual satisfaction by approximately 15.560 times (OR = 15.560, 95% CI = 5.089 - 47.571, P < 0.001). The chi-square test showed a significant relationship between infertility treatment and depression severity in infertile women (P = 0.001). Conclusions: The overall depression severity and sexual dissatisfaction were higher in the infertile group than in the fertile one. Most cases of severe depression were observed in IVF clinics with higher depression levels. The study may help reveal infertility's psychological and social aspects in Iran.

Plasma metabolomic profiling in subclinical atherosclerosis: the Diabetes Heart Study

Background Incidence rates of cardiovascular disease (CVD) are increasing, partly driven by the diabetes epidemic. Novel prediction tools and modifiable treatment targets are needed to enhance risk assessment and management. Plasma metabolite associations with subclinical atherosclerosis were investigated in the Diabetes Heart Study (DHS), a cohort enriched for type 2 diabetes (T2D). Methods The analysis included 700 DHS participants, 438 African Americans (AAs), and 262 European Americans (EAs), in whom coronary artery calcium (CAC) was assessed using ECG-gated computed tomography. Plasma metabolomics using liquid chromatography-mass spectrometry identified 853 known metabolites. An ancestry-specific marginal model incorporating generalized estimating equations examined associations between metabolites and CAC (log-transformed (CAC + 1) as outcome measure). Models were adjusted for age, sex, BMI, diabetes duration, date of plasma collection, time between plasma collection and CT exam, low-density lipoprotein cholesterol (LDL-C), and statin use. Results At an FDR-corrected p-value < 0.05, 33 metabolites were associated with CAC in AAs and 36 in EAs. The androgenic steroids, fatty acid, phosphatidylcholine, and bile acid metabolism subpathways were associated with CAC in AAs, whereas fatty acid, lysoplasmalogen, and branched-chain amino acid (BCAA) subpathways were associated with CAC in EAs. Conclusions Strikingly different metabolic signatures were associated with subclinical coronary atherosclerosis in AA and EA DHS participants.

81 Safety of sacubitril/valsartan in the elderly population with heart failure and reduced ejection fraction: a real-life observational study

Aims Prevalence of heart failure (HF) increases with age, but the elderly population is underrepresented in trials and the only data on the efficacy and safety of sacubitril/valsartan (S/V) in this group provided by the subanalysis of the PARADIGM trial, which confirms a favourable benefit-risk profile in all age groups. Information in a real-world setting is still lacking. Our study aims to evaluate the safety profile of S/V, in terms of risk of symptomatic hypotension, renal failure, and hyperkalaemia, in a group of real-life patients (pts) &gt; 75 years old (yo) presenting with HF and reduced Ejection Fraction (HFrEF). Methods and results 59 patients on S/V therapy older than 75 years (average age 78.9 ± 2.6) were identified out of a total number of 148 patients with HFrEF (39.8%), followed at our Heart Failure Clinic from December 2016 to April 2019. They were included in our retrospective observational cohort study. We compared their baseline characteristics (cardiovascular risk factors, mean serum creatinine, atrial fibrillation, EF) with the PARADIGM subgroup (1563pts, 18.6%) (average age: 79.1 ± 3.5) At time 0 (T0), and at 6 (T1), 12 (T2), and 18 (T3) months we assessed their serum creatinine, potassium and Nt-proBNP levels, blood pressure (BP), heart rate, NYHA class, eGFR (with MDRD), and the occurrence of death, hospital admissions for HF, symptomatic hypotension and angioedema. For the statistical analysis we used a marginal model with an unstructured covariance structure. Compared to the PARADIGM subgroup our study population showed a higher prevalence of AF (64.4% vs. 50.7%, P 0.039), a worse baseline creatinine (1.46 mg/dl vs. 1.22 mg/dl, P &lt; 0.001) and eGFR (51.5 vs. 57.5 ml/min/1.73 m2, P 0.005) (Figure 1). During the 18 months follow-up there was no significant increase in serum creatinine (P 0.092) and potassium (P 0.799). There was however a significant decrease in eGFR (from 51.46 to 45.36; P 0.015) and systolic BP (from 123.6 to 116.4 mmHg; P 0.021) and a higher rate of treatment discountination for symptomatic hypotension (5.1% vs. 1.8%, P 0.082) (Figure 2). Conclusions The present study confirmed the safety of S/V already shown in the PARADIGM trial, despite the worse baseline clinical characteristics of our elderly population. Our population had no significance increase in serum creatinine and potassium, similarly to the trial subgroup. Nevertheless there was a trend towards a higher rate of symptomatic hypotension leading more frequently to treatment discontinuation. Whether a more personalized implementation of S/V therapy in the ageing HF population (minimum starting dose, slower uptitration) would determine a higher tolerability of the drug should be specifically addressed by focused, prospective clinical trials.

Analysis and Diagnostics of Categorical Variables with Multiple Outcomes

<p>Surveys often contain qualitative variables for which respondents may select any number of the outcome categories. For instance, for the question "What type of contraceptive have you used?" with possible responses (oral, condom, lubricated condom, spermicide, and diaphragm), respondents would be instructed to select as many of the J = 5 outcomes as apply. This situation is known as multiple responses and outcomes are referred to as items. This thesis discusses several approaches to analysing such data. For stratified multiple response data, we consider three ways of defining the common odds ratio, a summarising measure for the conditional association between a row variable and the multiple response variable, given a stratification variable. For each stratum, we define the odds ratio in terms of: 1 item and 2 rows, 2 items and 2 rows, and 2 items and 1 row. Then we consider two estimation approaches for the common odds ratio and its (co)variance estimators for these types of odds ratios. The model-based approach treats the J items as a Jdimensional binary response and then uses logit models directly for the marginal distribution of each item by applying the generalised estimating equation (GEE) (Liang and Zeger 1986) method. The non-model-based approach uses Mantel-Haenszel (MH) type estimators. The model-based (or marginal model) approach is still applicable for more than two explanatory variables. Preisser and Qaqish (1996) proposed regression diagnostics for GEE. Another model fitting approach is the homogeneous linear predictor model (HLP) based on maximum likelihood (ML) introduced by Lang (2005). We investigate deletion diagnostics as the Cook distance and DBETA for multiple response data using HLPmodels (Lang 2005), which have not been considered yet, and propose a simple "delete=replace" method as an alternative approach for deletion. Methods are compared with the GEE approach. We also discuss the modelling of a repeated multiple response variable, a categorical variable for which subjects can select any number of categories on repeated occasions. Multiple responses have been considered in the literature by various authors; however, repeated multiple responses have not been considered yet. Approaches include the marginal model approach using the GEE and HLP methods, and generalised linear mixed models (GLMM). For the GEE method, we also consider possible correlation structures and propose a groupwise correlation estimation method yielding more efficient parameter estimates if the correlation structure is indeed different for different groups, which is confirmed by a simulation study. Ordered categorical variables occur in many applications and can be seen as a special case of multiple responses. The proportional odds model, which uses logits of cumulative probabilities, is currently the most popular model. We consider two approaches focusing on the mis-specification of a covariate. The binary approach considers the proportional oddsmodel as J-1 logistic regression models and applies the cumulative residual process introduced by Arbogast and Lin (2005) for logistic regression. The multivariate approach views the proportional odds model as a member of the class of multivariate generalised linear models (MGLM), where the response variable is a vector of indicator responses.</p>

Analysis and Diagnostics of Categorical Variables with Multiple Outcomes

<p>Surveys often contain qualitative variables for which respondents may select any number of the outcome categories. For instance, for the question "What type of contraceptive have you used?" with possible responses (oral, condom, lubricated condom, spermicide, and diaphragm), respondents would be instructed to select as many of the J = 5 outcomes as apply. This situation is known as multiple responses and outcomes are referred to as items. This thesis discusses several approaches to analysing such data. For stratified multiple response data, we consider three ways of defining the common odds ratio, a summarising measure for the conditional association between a row variable and the multiple response variable, given a stratification variable. For each stratum, we define the odds ratio in terms of: 1 item and 2 rows, 2 items and 2 rows, and 2 items and 1 row. Then we consider two estimation approaches for the common odds ratio and its (co)variance estimators for these types of odds ratios. The model-based approach treats the J items as a Jdimensional binary response and then uses logit models directly for the marginal distribution of each item by applying the generalised estimating equation (GEE) (Liang and Zeger 1986) method. The non-model-based approach uses Mantel-Haenszel (MH) type estimators. The model-based (or marginal model) approach is still applicable for more than two explanatory variables. Preisser and Qaqish (1996) proposed regression diagnostics for GEE. Another model fitting approach is the homogeneous linear predictor model (HLP) based on maximum likelihood (ML) introduced by Lang (2005). We investigate deletion diagnostics as the Cook distance and DBETA for multiple response data using HLPmodels (Lang 2005), which have not been considered yet, and propose a simple "delete=replace" method as an alternative approach for deletion. Methods are compared with the GEE approach. We also discuss the modelling of a repeated multiple response variable, a categorical variable for which subjects can select any number of categories on repeated occasions. Multiple responses have been considered in the literature by various authors; however, repeated multiple responses have not been considered yet. Approaches include the marginal model approach using the GEE and HLP methods, and generalised linear mixed models (GLMM). For the GEE method, we also consider possible correlation structures and propose a groupwise correlation estimation method yielding more efficient parameter estimates if the correlation structure is indeed different for different groups, which is confirmed by a simulation study. Ordered categorical variables occur in many applications and can be seen as a special case of multiple responses. The proportional odds model, which uses logits of cumulative probabilities, is currently the most popular model. We consider two approaches focusing on the mis-specification of a covariate. The binary approach considers the proportional oddsmodel as J-1 logistic regression models and applies the cumulative residual process introduced by Arbogast and Lin (2005) for logistic regression. The multivariate approach views the proportional odds model as a member of the class of multivariate generalised linear models (MGLM), where the response variable is a vector of indicator responses.</p>

Risk Factors for Salmonella Contamination of Whole Chicken Carcasses following Changes in U.S. Regulatory Oversight

Salmonella is a common cause of foodborne illness in the U.S. and often is attributed to chicken products. Previous studies have associated Salmonella contamination with meat processing facility characteristics such as the number of establishment employees (i.e., HACCP size). An evaluation of risk factors for Salmonella contamination in U.S. poultry has not been performed since implementation of the New Poultry Inspection System (NPIS) in 2014. The goal of this study was to determine if risk factors for Salmonella contamination changed following implementation of NPIS. Presence/absence of Salmonella in whole chicken carcasses was modeled using microbiological testing data collected from 203 poultry processing establishments by the U.S. Department of Agriculture’s Food Safety and Inspection Service (USDA-FSIS) between May 2015 and December 2019. A model was fit using generalized estimating equations for weekly presence/absence of Salmonella with production volume, geographic location, and season included as potential covariates, among other establishment demographics. Odds ratios (OR) and 95% confidence intervals (CI) were calculated from the marginal model. Of the 40,497 analyzable samples, 1,725 (4.26%) were positive for Salmonella. Odds of contamination was lower among establishments slaughtering ≥ 10,000,000 birds per year (OR = 0.466; 95% CI: [0.307,0.710]) and establishments producing ready-to-eat (RTE) finished products (OR = 0.498; 95% CI: [0.298,0.833]) while higher among establishments historically (previous 84-days) noncompliant with HACCP (OR = 1.249; 95% CI: [1.071,1.456]). Contamination also significantly varied by season and geographic region, with higher odds of contamination during summer and outside the Mid-East Central region. These results support continuation of targeted food safety policies and initiatives promoting pathogen reduction by smaller-volume establishments and those noncompliant with HACCP regulations.

Nonlinear ridge regression improves cell-type-specific differential expression analysis

Background Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity. Results First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data. Conclusion Nonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas

Statistical modelling of extreme temperatures on the Greenland Ice Sheet

&lt;p&gt;The Greenland ice sheet has experienced significant melt over the past 6 decades, with extreme melt events covering large areas of the ice sheet. Melt events are typically analysed using summary statistics, but the nature and characteristics of the events themselves are less frequently analysed. Our work aims to examine melt events from a statistical perspective by modelling 20 years of MODIS surface temperature data with a Spatial Conditional Extremes model. We use a Gaussian mixture model for the distribution of temperatures at each location with separate model components for ice and meltwater temperatures. This is used as a marginal model in the full spatial model and gives a more location-specific threshold to define melt at each location. The fitted model allows us to simulate melt events given that we observe an extreme temperature at a particular location, allowing us to analyse the size and magnitude of melt events across the ice sheet.&lt;/p&gt;

Yale Global Tic Severity Scale (YGTSS): Psychometric Quality of the Gold Standard for Tic Assessment Based on the Large-Scale EMTICS Study

The Yale Global Tic Severity Scale (YGTSS) is a clinician-rated instrument considered as the gold standard for assessing tics in patients with Tourette's Syndrome and other tic disorders. Previous psychometric investigations of the YGTSS exhibit different limitations such as small sample sizes and insufficient methods. To overcome these shortcomings, we used a subsample of the large-scale “European Multicentre Tics in Children Study” (EMTICS) including 706 children and adolescents with a chronic tic disorder and investigated convergent, discriminant and factorial validity, as well as internal consistency of the YGTSS. Our results confirm acceptable convergent and good to very good discriminant validity, respectively, indicated by a sufficiently high correlation of the YGTSS total tic score with the Clinical Global Impression Scale for tics (rs = 0.65) and only low to medium correlations with clinical severity ratings of attention deficit/hyperactivity symptoms (rs = 0.24), obsessive–compulsive symptoms (rs = 27) as well as internalizing symptoms (rs = 0.27). Internal consistency was found to be acceptable (Ω = 0.58 for YGTSS total tic score). A confirmatory factor analysis supports the concept of the two factors “motor tics” and “phonic tics,” but still demonstrated just a marginal model fit (root mean square error of approximation = 0.09 [0.08; 0.10], comparative fit index = 0.90, and Tucker Lewis index = 0.87). A subsequent analysis of local misspecifications revealed correlated measurement errors, suggesting opportunities for improvement regarding the item wording. In conclusion, our results indicate acceptable psychometric quality of the YGTSS. However, taking the wide use and importance of the YGTSS into account, our results suggest the need for further investigations and improvements of the YGTSS. In addition, our results show limitations of the global severity score as a sum score indicating that the separate use of the total tic score and the impairment rating is more beneficial.