Marginal Models Research Articles

Efficacy of sotagliflozin by diabetes duration: a secondary analysis of SCORED

Abstract Background Sotagliflozin is a sodium-glucose cotransporter (SGLT) 1 and SGLT2 inhibitor. SCORED randomized patients with type 2 diabetes (DM) and high cardiovascular (CV) risk to sotagliflozin or placebo. Purpose We evaluated if diabetes duration affects HF and CV outcomes when comparing sotagliflozin with placebo in SCORED, as diabetes duration can modify CV risk. Methods SCORED enrolled patients with DM (hemoglobin A1c ≥7%), chronic kidney disease (glomerular filtration rate of 25-60 mL/minute per 1.73 m2), and CV risk factors. Patients were randomized to sotagliflozin compared with placebo. Patients in SCORED were stratified by diabetes duration, into categories of <10 years, 10-19 years, or ≥20 years. The primary endpoint was total CV death, hospitalization for HF, or urgent visit for HF. Secondary endpoints were total hospitalization for HF or urgent visit for HF, as well as total CV death, nonfatal myocardial infarction, and nonfatal stroke (MACE). Outcomes were assessed using marginal proportional hazard models stratified according to HF criteria and geographic region where non-CV death was a competing terminal event. Subgroup analyses were assessed with natural cubic splines of interaction between treatment and continuous diabetes duration. Results In SCORED, 10,579 (99.9%) of 10,584 patients had complete data on diabetes duration, with 2412 (22.8%), 4424 (41.8%), 3743 (25.9%) having diabetes duration of <10, 10-19, and ≥20 years respectively. Median (Q1, Q3) diabetes duration was 16.4 years (10.4, 22.4 years), while mean hemoglobin A1c was 8.6%, 8.7%, and 8.7% respectively by diabetes duration. The rate of the primary endpoint was lower in the sotagliflozin group (5.6 events per 100 patient-years [p-y]) compared with the placebo group (7.5 events per 100 p-y) (HR: 0.74; 95% CI: 0.63, 0.88). Event rates among diabetes duration subgroups indicated that rates in both the placebo group and relative treatment benefit increased with increasing diabetes duration, with 5.6 vs 5.8 events per 100 p-y, 6.1 vs 7.4 events per 100 p-y, and 4.6 vs 8.5 events per 100 p-y for diabetes duration <10, 10-19, and ≥20 years respectively. Spline analysis indicated increasing treatment benefit with increasing duration when modeled continuously (Figure Panel A; PSpline=0.02). Similar findings were observed for the secondary HF outcome of hospitalization for HF or urgent visit for HF, with increased treatment benefit with duration (Figure Panel B; PSpline=0.10). Total MACE was lower in the sotagliflozin group (4.8 events per 100 patient-years) than in the placebo group (6.3 events per 100 patient-years) (HR: 0.77; 95% CI: 0.65, 0.91), without significant difference in relative treatment benefit by duration (Figure Panel C; PSpline=0.30). Conclusions Sotagliflozin improved HF and MACE outcomes in patients with high CV risk, with increasing treatment benefit for HF outcomes among patients with longer diabetes duration.

Increase in PVC-BSI during the second COVID-19 pandemic year: analysis of catheter and patient characteristics.

Increasing nosocomial infections during the COVID-19 pandemic have been reported. However, data describing peripheral venous catheter associated bloodstream infections (PVC-BSI) are limited. To describe the epidemiology and risk factors for PVC-BSI during the COVID-19 pandemic. We conducted retrospective cohort study of prospectively collected PVC-BSI data in a 2100 bed hospital network in Switzerland. Adult patients with a PVC inserted between January 1, 2020 and December 31, 2021 were included. Risk factors for PVC-BSI were identified through descriptive analysis of patient and catheter characteristics, and univariable marginal Cox models. 206,804 PVCs and 37 PVC-BSI were analysed. Most PVC-BSI were attributed to catheters inserted in the Emergency department (76%) or surgical wards (22%). PVC-BSI increased in 2021 compared to 2020 (hazard ratio 2021 vs. 2020 = 2.73; 95% confidence interval 1.19-6.29), with a numerically higher rate ofStaphylococcus aureus (1/10, 10%, vs. 5/27, 19%) and polymicrobial infection (0/10, 0% vs. 4/27, 15%). PVC insertions, patient characteristics, and catheter characteristics remained similar across the study period. PVC-BSI risk was associated with admission to the intensive care unit (ICU), and use of wide gauge catheter ( < = 16G). Increased PVC-BSI during the COVID-19 pandemic was not explained by catheter or patient factors alone, and may result from system-wide changes. PVC-BSI events are primarily attributed to acute care settings, including the emergency department, surgical wards, and the ICU.

Unveiling the causal effects of China’s minimum living standard guarantee on household transportation expenditures: A causal forest analysis

We investigate the causal effects of China’s Minimum Living Standard Guarantee (MLSG, also called Dibao) subsidy on household transportation expenditures. Utilizing data from the Chinese Social Survey, we employ a combination of the propensity score matching (PSM), causal forest (CF) and marginal treatment effect model (MTE) methodologies to rigorously estimate the subsidy’s effects. The PSM allows us to mitigate selection bias by matching MLSG recipients with comparable non-recipients. While the causal forest captures the heterogeneity of treatment effects across various household profiles. The result of MTE indicate that observable and essential heterogeneity are present to influence the effect of their subsidies, which present the consistent with PSM and CF. The causal mediation analysis indicates the mediating mechanism that MLSG impacts on household transportation expenditures, while also revealing significant variations among different regions. The study not only refines our understanding of the MLSG’s effects on household spending but also offers novel insights into applicational advancements by incorporating machine-learning techniques for policy evaluation. These results have important implications for policy formulation and refinement, particularly in the urban-rural differences.

Effect of educational intervention based on the theory of planned behavior (TPB) on amount of salt intake in pregnant women with PreHypertension

BackgroundPrehypertension is one of the common disorders during pregnancy. Reducing salt intake is among the best cost-effective interventions to reduce the risk of hypertension. The present study aimed to explore the effect of an educational intervention based on the Theory of Planned Behavior (TPB) on amount of salt consumed by prehypertensive pregnant women.MethodsThe present quasi-experimental study was conducted in 2022 on 61 prehypertensive pregnant women visiting the Comprehensive Health Services Centers in Birjand, Iran. Pregnant women were selected through a convenience sampling method and randomly assigned to intervention and control groups. A blood pressure test and a 24-hour urine test were taken in 3 stages (before, immediately after, and one month after the intervention). A reliable and valid questionnaire based on the TPB was used to measure the cognitive variables. The intervention program included three training sessions based on the TPB held on an online platform (WhatsApp social network). Data were analyzed using repeated measures ANOVA and longitudinal marginal model with the GEE approach in SPSS19. The significance level for all statistical tests was set at p < 0 0.05.ResultsAfter intervention, the mean score of perceived behavioral control (p = 0.02), intention (p = 0.004), and salt consumption behavior (p = 0.03) increased significantly in the intervention group, and the mean score of systolic blood pressure (p < 0.001) and diastolic blood pressure (P < 0.01) decreased significantly in this group. In the control group, a statistically significant difference was observed in the systolic and diastolic blood pressure of the subjects (p < 0.01). However, the score of attitude, subjective norms, perceived behavioral control, intention, salt consumption behavior and the daily salt intake did not show a statistically significant difference over time (P < 0.05).ConclusionThe present findings showed that the perceived behavioral control, intention, behavior, systolic and diastolic blood pressure can be modifiable variables to improve the amount of salt consumed by pre-hypertensive pregnant women. Therefore, we recommend that the present model be used to develop interventions to improve health indicators in pregnant women as a highly susceptible group in society.

Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.

Pooled analysis of the MANTICO2 and MONET randomized controlled trials comparing drug efficacy for early treatment of COVID-19 during Omicron waves

BackgroundThe clinical effectiveness of early therapies for mild-to-moderate COVID-19, comparing antivirals and monoclonal antibodies (mAbs) during the Omicron era, has not been conclusively assessed through a post-approval comparative trial. We present a pooled analysis of two randomized clinical trials conducted during Omicron waves. Methods The MANTICO2/MONET trial is a pooled analysis of two multicentric, independent, phase-4, three-arm, superiority, randomized, open-label trials. Nonhospitalized patients with early mild-to-moderate COVID-19 (≤5 days after symptoms’ onset) and at least one risk factor for disease progression were randomized 1:1:1 to receive 500mg of intravenous sotrovimab (SOT) or 600mg of intramuscular tixagevimab/cilgavimab (TGM/CGM) or oral 5-days course of nirmatrelvir/ritonavir (NMV/r) 300/100mg BID. Primary outcome was COVID-19-related hospitalization or death within 29 days after randomization. Fisher’s exact test for pooled data and incidence of failure was reported as overall and by arm with respective 95% CI.Pairwise comparisons across the arms were conducted using unadjusted exact logistic regression. An analysis by means of a doubly robust marginal model using augmented inverse probability weighting (AIPW) was also conducted to estimate the potential outcomes (Pom) in each treatment group and their difference by the average treatment effect (ATE). Analysis of symptom persistence within 30 days after randomization was performed using a 2-level hierarchical mixed-effects logistic model with a random intercept at the patient’s level. Point estimates and 95% confidence intervals were adjusted for age and sex and calculated using ANOVA-like methods for the mixedeffects logistic model. These trials are registred with the European Clinical Trials Database, EudraCT2021-002612-31 (MANTICO2) and EudraCT2021-004188-28 (MONET) and ClinicalTrials.gov, NCT05321394 (MANTICO2) Findings Between March 2022 and February 2023, 991 patients (SOT=332, TGM/CGM =327, NMV/r=332) were enrolled in 15 Italian centersThe overall mean age was 66 years; 482 participants (48.80%) were male and 856 vaccinated with at least a primary course ( 86%). Among the 8/991 hospitalizations observed, one resulted in death. The overall estimate of failure was 0.81% (95%CI; 0.35-1.58%). The odds ratio (OR) for the primary outcome in the NMV/r arm compared to the TGM/CGM and SOT arms was 8.41 (95% CI 1.21 to infinity; p=0.015) and 2.42 (95% CI 0.19 to infinity; p=0.499), respectively. No significant difference was observed between SOT and TGM/CGM (OR 0.32; 95% CI 0.032-1.83; p=0.174). Results were similar when we applied the marginal weighted model accounting for potential residual confounding bias. There was no evidence for a difference in the prevalence of symptoms between treatment groups, except for cough, which was higher in the SOT group compared to the other two groups at the 21-day follow-up (P=0.039) and a higher prevalence of nausea at the 7-day follow-up in the NMV/r group compared to the mAbs group (p=0.036). Interpretation NMV/r was superior to TGM/CGM in reducing hospital admission or death in clinical vulnerable patients with SARS-CoV-2 infection treated within 5 days of symptoms’ onset. No significant difference in symptom prevalence over time across the arms was found. Funding: The trials were funded by the Italian Agency of Drugs (AIFA) in 2021.

The inverse association between cancer history and incident cognitive impairment: Addressing attrition bias.

Cancer is inversely associated with cognitive impairment. Whether this is due to statistical handling of attrition (death and censoring) is unknown. We quantified associations between cancer history and incident cognitive impairment among Health, Aging, and Body Composition Study participants without baseline cognitive impairment or stroke (n=2604) using multiple competing-risks models and their corresponding estimands: cause-specific, subdistribution, and marginal hazards, plus composite-outcome (cognitive impairment or all-cause mortality) hazards. All-cause mortality was also modeled. After covariate adjustment (demographics, apolipoprotein E ε4, lifestyle, health conditions), cause-specific and marginal hazard ratios (HRs) were similar to each other (≈ 0.84; P values<0.05). The subdistribution HR was 0.764 (95% confidence interval [CI]=0.645-0.906), and composite-outcome Cox model HR was 1.149 (95% CI=1.016-1.299). Cancer history was positively associated with all-cause mortality (HR=1.813; 95% CI=1.525-2.156). Cause-specific, subdistribution, and marginal hazards models produced inverse associations between cancer and cognitive impairment. Competing risk models answer slightly different questions, and estimand choice influenced findings here. Cancer history is inversely associated with incident cognitive impairment. Findings were robust to handling of competing risks of death. All models also addressed possible informative censoring bias. Cancer history was associated with 16% lower hazard of cognitive impairment. Cancer history was associated with 81% higher all-cause mortality hazard.

Techno-economic analysis of the direct solar conversion of carbon dioxide into renewable fuels

Direct conversion of carbon dioxide (CO2) using sunlight into commercially viable renewable fuels will be one key solution for decarbonization and storing renewable solar energy. However, the direct conversion of CO2 using sunlight faces uphill challenges, especially the techno-economic viability (TEV), as the produced fuels must compete with fossil fuels or at least with fossil fuel alternatives, which are cheap. This work proposes an innovative structure design for photoelectrochemical reduction of CO2 into renewable fuels and performs a detailed techno-economic analysis (TEA) using a generalized gross margin (GM) model. The proposed structure uses low-cost and earth-abundant crystalline silicon-based photoanode with triangle nano-strips on a thin-film substrate to efficiently convert solar energy into renewable fuels. The proposed structure shows 20.01% of power-conversion efficiency (ηPEC). The techno-economic GM model considers all relevant cost parameters to assess the TEV of the produced hydrogen (H2) and hydrocarbon (C1–C3) fuels from CO2 reduction processes. The TEV of the produced renewable fuels is analyzed and presented against critical device parameters, such as the photocurrent density (J), catalyst’s durability (tdur), Faradaic efficiency (FE), and catalyst cost (Ccat). We also analyzed how sensitively the TEV of the produced fuels depends on the uncertainty of different cost parameters assuming base-, worst-, and optimistic-case scenarios. We find that carbon monoxide (CO) and formic acid (HCOOH) fuels will be commercially viable in the base case, while H2 and propanol (C3H7OH) will be only in the optimistic case.

Modern extreme value statistics for Utopian extremes. EVA (2023) Conference Data Challenge: Team Yalla

AbstractCapturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapolation into the upper tails of the data-generating distribution. We present a modern toolbox of four methodological frameworks, motivated by classical extreme value theory, that can be used to accurately estimate extreme exceedance probabilities or the corresponding level in either a univariate or multivariate setting. Our frameworks were used to facilitate the winning contribution of Team Yalla to the EVA (2023) Conference Data Challenge, which was organised for the 13$$^\text {th}$$ th International Conference on Extreme Value Analysis. This competition comprised seven teams competing across four separate sub-challenges, with each requiring the modelling of data simulated from known, yet highly complex, statistical distributions, and extrapolation far beyond the range of the available samples in order to predict probabilities of extreme events. Data were constructed to be representative of real environmental data, sampled from the fantasy country of “Utopia”.

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks

AbstractA new modeling framework for bipartite social networks arising from a sequence of partially time-ordered relational events is proposed. We directly model the joint distribution of the binary variables indicating if each single actor is involved or not in an event. The adopted parametrization is based on first- and second-order effects, formulated as in marginal models for categorical data and free higher order effects. In particular, second-order effects are log-odds ratios with meaningful interpretation from the social perspective in terms of tendency to cooperate, in contrast to first-order effects interpreted in terms of tendency of each single actor to participate in an event. These effects are parametrized on the basis of the event times, so that suitable latent trajectories of individual behaviors may be represented. Inference is based on a composite likelihood function, maximized by an algorithm with numerical complexity proportional to the square of the number of units in the network. A classification composite likelihood is used to cluster the actors, simplifying the interpretation of the data structure. The proposed approach is illustrated on simulated data and on a dataset of scientific articles published in four top statistical journals from 2003 to 2012.

Generalized Marginal Cumulative Homogeneity Model for Multi-way Contingency Tables

Generalized Marginal Cumulative Homogeneity Model for Multi-way Contingency Tables

Validating locational marginal emissions models with wind generation

Validating locational marginal emissions models with wind generation

Financial impact of low pathogenic avian influenza virus subtype H9N2 on commercial broiler chicken and egg layer production systems in Pakistan

Low Pathogenic Avian Influenza (LPAI) subtype H9N2 is endemic in Pakistan and impacts poultry farming through disease related mortality, poor weight gain and reduced egg production. This study aims to estimate the farm-level financial impact of LPAI H9N2 infection on commercial broiler and layer production systems in Pakistan.A questionnaire based cross-sectional survey of 138 broiler farms and 136 layer farms in Pakistan was conducted in 2019. Primary data collected by cross-sectional survey along with expert opinion and published literature were used to parameterize five stochastic production and gross margin models for three broiler and two layer production systems: fully integrated production (FIP), partially integrated production (PIP) and independent farming production (IP) systems. Partial budget analysis were then carried out to estimate the financial impact of LPAI H9N2.Results indicate that in broiler production systems, starting with 35,000 day old chicks (DOC) per batch, the net cost of disease (million PKR/production cycle) was estimated at 4.10 (14,862 USD), 4.62 (16,747 USD) and 2.46 (8917 USD) for IP, PIP and FIP systems, respectively. The disease produced a negative gross margin (defined here as revenue minus replacement and variable costs) in IP (-53 PKR (-0.19 USD)/DOC bought) and PI (-25 PKR (-0.091 USD)/DOC bought) systems, while remained positive for FIP systems (87 PKR (0.32 USD)/DOC bought). For layer production systems, (mean flock size as 48,000 DOCs) the net cost (million PKR/production cycle) was 29.75 (107,095.21 USD) and 29.51 (106,223.45 USD) IP and PIP systems, respectively, and produced negative gross margin in both systems.The outcomes of the study highlight the vulnerability of independent and partially integrated production systems to the disease. These findings also offer a decision-making tool to the farmers and policy makers to evaluate avian influenza surveillance systems and control interventions in Pakistan.

Simultaneous Inference Using Multiple Marginal Models.

This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.

Trends, risk factors and interventions for some mental health problems in the US children and adolescents: evidence from the National Survey of Children’s Health, 2016-2022

PurposeThe purpose of this study is threefold: Determine recent trends in several mental health problems in the USA, identify risk factors that may be responsible for the trends and evaluate intervention policies to reduce the consequences of these problems.Design/methodology/approachThis study used data from the National Survey of Children's Health (NSCH), a nationally representative survey of children under the age of 17 that was conducted between 2016 and 2022. Prevalence rates in the data take into account the probability of selection and nonresponse. Because of the possible correlation in the longitudinal responses in the data, an appropriate extension of the generalized linear models (the marginal models) was used. Marginal models, also known as population-average models, do not require distributional assumptions for the observations, only a regression model for the mean response. The avoidance of distributional assumptions leads to the use of the generalized estimating equations (GEE) method.FindingsThe author found that the odds of children and adolescents experiencing mental health problems in the USA changed over a seven-year period, from 2016 to 2022. Anxiety and depression, in particular, have both increased, with anxiety increasing faster than depression; however, behavioral issues and attention deficit disorder/attention deficit hyperactivity disoder (ADD/ADHD) remained stable until 2020 (the start of COVID-19), when they began to rise. This paper also found a link between increased social media use and increased mental health problems, and bullying has a negative impact on the mental health of children and adolescents.Originality/valueThe NSCH, an annual representative survey, was used in this study to assess mental health problems among children and adolescents in the USA. Marginal models, which enable the capture of potential correlations among observations of the same subject, were used in conjunction with the GEE method. This study differs from previous research, which used other surveys, pre-COVID-19 data points and logistic regressions that assumed independence in repeated observations.

GLP-1RA vs DPP-4i Use and Rates of Hyperkalemia and RAS Blockade Discontinuation in Type 2 Diabetes

Hyperkalemia is a common complication in people with type 2 diabetes (T2D) that may limit the use of guideline-recommended renin-angiotensin system inhibitors (RASis). Emerging evidence suggests that glucagon-like peptide-1 receptor agonists (GLP-1RAs) increase urinary potassium excretion, which may translate into reduced hyperkalemia risk. To compare rates of hyperkalemia and RASi persistence among new users of GLP-1RAs vs dipeptidyl peptidase-4 inhibitors (DPP-4is). This cohort study included all adults with T2D in the region of Stockholm, Sweden, who initiated GLP-1RA or DPP-4i treatment between January 1, 2008, and December 31, 2021. Analyses were conducted between October 1, 2023, and April 29, 2024. GLP-1RAs or DPP-4is. The primary study outcome was time to any hyperkalemia (potassium level >5.0 mEq/L) and moderate to severe (potassium level >5.5 mEq/L) hyperkalemia. Time to discontinuation of RASi use among individuals using RASis at baseline was assessed. Inverse probability of treatment weights served to balance more than 70 identified confounders. Marginal structure models were used to estimate per-protocol hazard ratios (HRs). A total of 33 280 individuals (13 633 using GLP-1RAs and 19 647 using DPP-4is; mean [SD] age, 63.7 [12.6] years; 19 853 [59.7%] male) were included. The median (IQR) time receiving treatment was 3.9 (1.0-10.9) months. Compared with DPP-4i use, GLP-1RA use was associated with a lower rate of any hyperkalemia (HR, 0.61; 95% CI, 0.50-0.76) and moderate to severe (HR, 0.52; 95% CI, 0.28-0.84) hyperkalemia. Of 21 751 participants who were using RASis, 1381 discontinued this therapy. The use of GLP-1RAs vs DPP-4is was associated with a lower rate of RASi discontinuation (HR, 0.89; 95% CI, 0.82-0.97). Results were consistent in intention-to-treat analyses and across strata of age, sex, cardiovascular comorbidity, and baseline kidney function. In this study of patients with T2D managed in routine clinical care, the use of GLP-1RAs was associated with lower rates of hyperkalemia and sustained RASi use compared with DPP-4i use. These findings suggest that GLP-1RA treatment may enable wider use of guideline-recommended medications and contribute to clinical outcomes in this population.

Anti-SARS-CoV-2 mRNA vaccination among patients living with SLE in Sweden: Coverage and clinical effectiveness.

To describe the uptake of anti-SARS-CoV2 vaccination in 2021 and investigate vaccine effectiveness in systemic lupus erythematosus (SLE) patients in Sweden. The cumulative incidence of first anti-SARS-CoV2 vaccination was estimated among SLE patients from the Swedish National Patient Register and matched comparators living in Sweden on January 1, 2021. To assess vaccine effectiveness, we included the individuals who received two doses of anti-SARS-CoV2 mRNA vaccines before year 2022, with no COVID-19 diagnosis code before the 2nd vaccine dose. Hospitalization rates with COVID-19 as main diagnosis during the year after second dose were compared between SLE patients and comparators in multivariable-adjusted marginal Cox models, overall and stratified by immunosuppressive treatment received during the year before second vaccine dose. Vaccination uptake was similar between SLE patients and comparators. By December 2021, 9% of both SLE and comparators had not received any vaccine doses. Among 5585 SLE patients and 37,102 comparators, 11 COVID-19 hospitalizations in the SLE group and 20 in the comparators occurred. SLE was associated with a higher risk of COVID-19 hospitalization (HR = 3.47, 95%CI 1.63-7.39). The HR was higher for immunosuppressive-treated SLE (7.03 95%CI 3.00-16.46) than for immunosuppressive-untreated (1.50 95%CI 0.34-6.60). Vaccination of immunosuppressive-untreated SLE patients had similar effectiveness as comparators. Anti-SARS-CoV2 vaccination coverage was similar between SLE patients and the general population in Sweden. Even though the incidence of post-vaccination COVID-19 hospitalization was very low, vaccine effectiveness was diminished in SLE patients compared to the general population and lowest in those treated with immunosuppressants.

Assessing genetic and environmental components for pterygium: a nationwide study in Taiwan

This study aims to estimate the familial risks of pterygium and assess its relative contributions to environmental and genetic factors using the 2000–2017 Taiwan National Health Insurance Research Database. The marginal Cox's model and the polygenic liability model were made. In Taiwan, the prevalence rate of pterygium in 2017 was 1.64% for individuals with affected first-degree relatives, higher than the general population (1.34%). The adjusted relative risk (RR) for pterygium was highest for twins of the same sex (15.54), followed by siblings of the same sex (4.69), offsprings (3.39), siblings of the different sex (2.88), spouse (2.12), parents (1.86), twins of the different sex (1.57), respectively. The phenotypic variance of pterygium was 21.6% from additive genetic variance, 24.3% from common environmental factors shared by family members, and 54.1% from non-shared environmental factors, respectively. Sensitivity analysis by restricting those with surgical pterygium reveals that aRRs and the three components were similar to those of the overall pterygium. In summary, the prevalence rate of pterygium was higher for individuals with affected first-degree relatives than for the general population. The non-shared environmental factors account for half of the phenotypic variance of pterygium; genetic and shared environmental factors explain the rest.

A Copula Discretization of Time Series-Type Model for Examining Climate Data

The study presents a comparative analysis of climate data under two scenarios: a Gaussian copula marginal regression model for count time series data and a copula-based bivariate count time series model. These models, built after comprehensive simulations, offer adaptable autocorrelation structures considering the daily average temperature and humidity data observed at a regional airport in Mobile, AL.

Assessment of Baseline Ultrawidefield Fluorescein Angiographic Quantitative Leakage Parameters with Ultrawidefield Fundus Features and Clinical Parameters in Diabetic Retinopathy in Protocol AA

PurposeEvaluate quantitative leakage parameters on ultra-widefield fluorescein angiography (UWF-FA) images and explore their association with Diabetic Retinopathy Severity Scale (DRSS), predominantly peripheral lesions (PPLs), visual acuity, and clinical characteristics. DesignA post-hoc analysis of baseline UWF-FA images in the DRCR Retina Network observational study Protocol AA. ParticipantsN = 575 eyes from 384 adults across 38 sites in the US and Canada with gradable UWF-FA. MethodsA machine learning-enhanced feature extraction platform provided initial leakage segmentation of UWF-FA images sequentially reviewed and corrected by two certified readers for segmentation accuracy. UWF-FA leakage was measured in five retinal zones: panretinal (whole retina), central macular (3-disc diameter fovea-centered circle), posterior pole (6-disc diameter fovea-centered circle), peripheral (outside 6-disc diameter circle) and widefield far peripheral (outside 9-disc diameter circle); associations with clinical factors were evaluated with marginal beta regression models. Main Outcome MeasuresUWF-FA leakage index, calculated as the area with leakage divided by the analyzable retinal area. ResultsThe mean quantitative leakage index was 3.5% for panretinal, 6.6% for macular, 4.8% for posterior pole, 3.3% for peripheral and 2.8% for wide-field far peripheral retinal zones. Panretinal leakage was associated with DRSS (mean 2.2% for no to mild NPDR, 3.4% for moderate NPDR, 4.2% for moderately severe NPDR, 4.8% for severe NPDR and 5.1% for PDR; P<.001), HbA1c (3.2% for HbA1c <8% vs. 3.8% for HbA1c ≥8%; P=.01 for continuous HbA1c), visual acuity (3.3% for 20/25 or better vs. 4.7% for 20/32 or worse; continuous P<.001), and UWF-FA-PPL types of IRMA (4.3% vs. 3.3%; P=.005) or NVE (5.7% vs. 3.4%; P=.003). DRSS was also statistically significant for leakage within all retinal zones (P<.001); eyes with non-central DME versus no DME had higher mean leakage in the central macular (11.2% vs. 5.9%; P=0.005) and posterior pole regions (9.2% vs. 4.2%; P=.002). ConclusionQuantitative UWF-FA leakage analysis identified associations between leakage and DRSS, visual acuity, and presence of DME. In the future, quantitative UWF-FA leakage parameters may be explored as potential biomarkers for disease progression risk.