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With SCAview, we present a prompt and comprehensive tool that enables scientists to browse large datasets of the most common spinocerebellar ataxias intuitively and without technical effort. Basic concept is a visualization of data, with a graphical handling and filtering to select and define subgroups and their comparison. Several plot types to visualize all data points resulting from the selected attributes are provided. The underlying synthetic cohort is based on clinical data from five different European and US longitudinal multicenter cohorts in spinocerebellar ataxia type 1, 2, 3, and 6 (SCA1, 2, 3, and 6) comprising > 1400 patients with overall > 5500 visits. First, we developed a common data model to integrate the clinical, demographic, and characterizing data of each source cohort. Second, the available datasets from each cohort were mapped onto the data model. Third, we created a synthetic cohort based on the cleaned dataset. With SCAview, we demonstrate the feasibility of mapping cohort data from different sources onto a common data model. The resulting browser-based visualization tool with a thoroughly graphical handling of the data offers researchers the unique possibility to visualize relationships and distributions of clinical data, to define subgroups and to further investigate them without any technical effort. Access to SCAview can be requested via the Ataxia Global Initiative and is free of charge.

The results of observational studies using real-world data, known as real-world evidence, have gradually started to be used in drug development and decision-making by policymakers. A good quality management system—a comprehensive system of process, data, and documentation to ensure quality—is important in obtaining real-world evidence. A risk-based approach is a common quality management system used in interventional studies. We used a quality management system and risk-based approach in an observational study on a designated intractable disease. Our multidisciplinary team assessed the risks of the real-world data study comprehensively and systematically. When using real-world data and evidence to support regulatory decisions, both the quality of the database and the validity of the outcome are important. We followed the seven steps of the risk-based approach for both database selection and research planning. We scored the risk of two candidate databases and chose the Japanese National Database of designated intractable diseases for this study. We also conducted a quantitative assessment of risks associated with research planning. After prioritizing the risks, we revised the research plan and outcomes to reflect the risk-based approach. We concluded that implementing a risk-based approach is feasible for an observational study using real-world data. Evaluating both database selection and research planning is important. A risk-based approach can be essential to obtain robust real-world evidence. [ABSTRACT FROM AUTHOR]

Background: Comprehensive summaries on real-world outcomes in pulmonary arterial hypertension (PAH)—a rare, incurable condition, are lacking. We conducted a systematic literature review to describe current survival, morbidity, and quality of life (QoL) outcomes in adult and pediatric PAH patients. We searched Medline and Embase electronic databases, clinicaltrials.gov, and encepp.eu entries, and grey literature to identify outcome estimates for right-heart catheterization-confirmed PAH patients from population-based observational studies (search date: 25 Nov 2021). Data were synthesized using a narrative approach and post-hoc subgroup meta-analyses were conducted to explore adult survival by region, disease severity, representativeness, and study period. The search yielded 7473 records. Following screening and full text review, 22 unique studies with 31 individual reports of outcomes were included. Studies were mostly national registries (n = 21), European (n = 13) and covering adults (n = 17); only six had systematic countrywide coverage of centers. Survival was the most frequently reported outcome (n = 22). Global adult 1-, 3-, and 5-year survival ranged from 85 to 99% (n = 15), 65 to 95% (n = 14), and 50 to 86% (n = 9), respectively. Subgroup meta-analysis showed that 1-, 3-, and 5-year survival in Europe was 90% (95% CI 86–94%; n = 8), 78% (95% CI 68–86%; n = 8), and 61% (95% CI 49–72%; n = 6), respectively; 1-year survival in North America was 88% (95% CI 83–93%; n = 3) and 3-year survival in Asia was 85% (95% CI 82–88%; n = 3). No difference in survival between regions was observed. Subgroup analysis suggested higher survival in patients with better baseline functional class; however, interpretation should be cautioned due to large subgroup heterogeneity and potential missingness of data. Short conclusion: This review describes current disease outcomes based on well-defined and representative PAH populations. There is an overall lack of follow-up data for morbidity and QoL outcomes; survival estimates for pediatric patients are scarce and may not be generalizable to the current treatment era, although publications from large pediatric registries became available after our search date. This study demonstrated a remaining unmet need world-wide to improve long-term prognosis in PAH in the current era. [ABSTRACT FROM AUTHOR]

Many Veterans receive Department of Veterans Affairs (VA)-purchased care from non-VA facilities but little is known about factors that Veterans consider for this choice. Between May 2020 and August 2021, we surveyed VA-purchased care-eligible VA patients about barriers and facilitators to choosing where to receive care. We examined the association between travel time to their VA facility and their choice of VA-purchased care (VA-paid health care received in non-VA settings) versus VA facility and whether this association was modified by distrust. We received 1,662 responses and 692 (42%) chose a VA facility. Eighty percent reported quality care was in their top three factors that influenced their decision. Respondents with the highest distrust and who lived >1 hr from the nearest VA facility had the lowest predicted probability (PP) of choosing VA (PP 15%; 95% confidence interval: 10%–20%). Veterans value quality of care. VA and other health care systems should consider patient-centered ways to improve and publicize quality and reduce distrust. [ABSTRACT FROM AUTHOR]

Valid inference of cause-and-effect relations in observational studies necessitates adjusting for common causes of the focal predictor (i.e., treatment) and the outcome. When such common causes, henceforth termed confounders, remain unadjusted for, they generate spurious correlations that lead to biased causal effect estimates. But routine adjustment for all available covariates, when only a subset are truly confounders, is known to yield potentially inefficient and unstable estimators. In this article, we introduce a data-driven confounder selection strategy that focuses on stable estimation of the treatment effect. The approach exploits the causal knowledge that after adjusting for confounders to eliminate all confounding biases, adding any remaining non-confounding covariates associated with only treatment or outcome, but not both, should not systematically change the effect estimator. The strategy proceeds in two steps. First, we prioritize covariates for adjustment by probing how strongly each covariate is associated with treatment and outcome. Next, we gauge the stability of the effect estimator by evaluating its trajectory adjusting for different covariate subsets. The smallest subset that yields a stable effect estimate is then selected. Thus, the strategy offers direct insight into the (in)sensitivity of the effect estimator to the chosen covariates for adjustment. The ability to correctly select confounders and yield valid causal inferences following data-driven covariate selection is evaluated empirically using extensive simulation studies. Furthermore, we compare the introduced method empirically with routine variable selection methods. Finally, we demonstrate the procedure using two publicly available real-world datasets. A step-by-step practical guide with user-friendly R functions is included. A central goal of psychological research is to understand cause-and-effect relations. Thoughtfully designed and meticulously conducted randomized experiments are the gold standard for examining the causal impact of an intervention, or treatment, on an outcome. But such randomized studies are often practically unfeasible for ethical and logistical reasons. In such contexts, observational or nonexperimental studies where individuals are exposed to nonrandomized treatments become the only viable option for causal inference. However, the statistical correlation between a nonrandomized treatment and an outcome can be due to noncausal (or "spurious") correlations generated by their mutual associations with common causes. Thus, adjusting, or controlling, for such common causes is necessary to eliminate the spurious correlations toward valid inference of the treatment effect. But correctly selecting covariates can be a challenging task. The common practice of routinely including all available (pretreatment) variables that covary with either treatment or outcome can result in effect estimators with poor statistical qualities. This article introduces a data-driven covariate (or confounder) selection strategy to help researchers avoid such concerns. It directly evaluates the impact of covariate selection on the quality of the effect estimator as part of the selection process. Covariates are first ordered by decreasing priority for confounding adjustment. The effect estimator adjusting for different covariate subsets is then calculated. Finally, the smallest subset that yields a stable estimator (visually or numerically), even as more covariates are included, is selected. The introduced strategy offers insight into the (in)sensitivity of the estimator to the covariates chosen and provides a resilient justification for valid causal inference. [ABSTRACT FROM AUTHOR]

ObjectivesMethodResultsConclusionWe conducted the current systematic review to investigate the association between dietary inflammatory index (DII) and severe headaches or migraine among adults via synthesizing observational evidence.We conducted a systematic literature search of observational studies through PubMed, Scopus, and Web of Science databases from inception until July 2024. The PECO framework was implemented to select eligible studies as follows: Population (adults with severe headache or migraine), Exposure (individuals with the highest adherence to a pro-inflammatory diet), Comparison (individuals with the lowest adherence to a pro-inflammatory diet), Outcome (risk of developing severe headache or migraine, headaches frequency, duration, severity, and migraine-related disability).After reviewing six studies involving 31,958 individuals, we found that following an anti-inflammatory diet is associated with a lower frequency and severity of migraine headaches. Additionally, our research revealed that individuals with migraines tend to have lower adherence to an anti-inflammatory diet when compared to people without migraines. Surprisingly, adherence to a pro-inflammatory diet was linked to a reduced risk of chronic daily headaches.Present findings imply a negative link between an inflammatory diet and severe headaches or migraine. However, further well-designed longitudinal studies are needed to interpret the causality and shed light on the underlying mechanisms. [ABSTRACT FROM AUTHOR]

The combination of propensity score analysis and multiple imputation has been prominent in epidemiological research in recent years. However, studies on the evaluation of balance in this combination are limited. In this paper, we propose a new method for assessing balance in propensity score analysis following multiple imputation. A simulation study was conducted to evaluate the performance of balance assessment methods (Leyrat's, Leite's, and new method). Simulated scenarios varied regarding the presence of missing data in the control or treatment and control group, and the imputation model with/without outcome. Leyrat's method was more biased in all the studied scenarios. Leite's method and the combine method yielded balanced results with lower mean absolute difference, regardless of whether the outcome was included in the imputation model or not. Leyrat's method had a higher false positive ratio and Leite's and combine method had higher specificity and accuracy, especially when the outcome was not included in the imputation model. According to simulation results, most of time, Leyrat's method and Leite's method contradict with each other on appraising the balance. This discrepancy can be solved using new combine method. [ABSTRACT FROM AUTHOR]

What is the best way to split one stratum into two to maximally reduce the within-stratum imbalance in many covariates? We formulate this as an integer program and approximate the solution by randomized rounding of a linear program. A linear program may assign a fraction of a person to each refined stratum. Randomized rounding views fractional people as probabilities, assigning intact people to strata using biased coins. Randomized rounding is a well-studied theoretical technique for approximating the optimal solution of certain insoluble integer programs. When the number of people in a stratum is large relative to the number of covariates, we prove the following new results: (i) randomized rounding to split a stratum does very little randomizing, so it closely resembles the linear programming relaxation without splitting intact people; (ii) the linear relaxation and the randomly rounded solution place lower and upper bounds on the unattainable integer programming solution; and because of (i), these bounds are often close, thereby ratifying the usable randomly rounded solution. We illustrate using an observational study that balanced many covariates by forming matched pairs composed of 2016 patients selected from 5735 using a propensity score. Instead, we form 5 propensity score strata and refine them into 10 strata, obtaining excellent covariate balance while retaining all patients. An R package optrefine at CRAN implements the method. Supplementary materials are available online. [ABSTRACT FROM AUTHOR]

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity analyses. We focus on a class of factor models for which we can bound the causal effects for all outcomes conditional on a single sensitivity parameter that represents the fraction of treatment variance explained by unobserved confounders. We characterize how causal ignorance regions shrink under additional prior assumptions about the presence of null control outcomes, and provide new approaches for quantifying the robustness of causal effect estimates. Finally, we illustrate our sensitivity analysis workflow in practice, in an analysis of both simulated data and a case study with data from the National Health and Nutrition Examination Survey (NHANES). for this article are available online. [ABSTRACT FROM AUTHOR]

When estimating treatment effects, the gold standard is to conduct a randomized experiment and then contrast outcomes associated with the treatment group and the control group. However, in many cases, randomized experiments are either conducted with a much smaller scale compared to the size of the target population or accompanied with certain ethical issues and thus hard to implement. Therefore, researchers usually rely on observational data to study causal connections. The downside is that the unconfoundedness assumption, which is the key to validating the use of observational data, is untestable and almost always violated. Hence, any conclusion drawn from observational data should be further analyzed with great care. Given the richness of observational data and usefulness of experimental data, researchers hope to develop credible methods to combine the strength of the two. In this paper, we consider a setting where the observational data contain the outcome of interest as well as a surrogate outcome, while the experimental data contain only the surrogate outcome. We propose an easy-to-implement estimator to estimate the average treatment effect of interest using both the observational data and the experimental data. [ABSTRACT FROM AUTHOR]

Introduction There is no consensus on the questions that should be included in questionnaires to properly ascertain exposure to secondhand tobacco smoke (SHS). The objective of this study is to analyze the questions included in studies which have assessed SHS exposure in Spain. Methods A scoping review was performed, using PubMed, Embase and Web of Science databases, selecting original articles published in English and Spanish, across the period 2012–2021. We extracted data from each study regarding its design, target population, sample size or geographical scope; we also collected data regarding how studies dealt with exposure to SHS including assessment and intensity of SHS, exposure setting, geographical scope, and the verbatim questions used. Results Finally, 75 studies were identified. In the 23 studies carried out in children, verbatim questions were included in 8 studies, and the setting most studied was the home. SHS exposure was assessed during pregnancy and postnatally by 8 studies, the verbatim questions used were described in 2 studies, being exposure ascertained at home and workplace. In the adult population, 14 of 44 studies described the verbatim questions; the setting most studied was the home. Verbatim questions varied among studies. Conclusions Questionnaire-based assessment of SHS exposure is highly heterogeneous, hindering comparability between studies. Therefore, it is necessary to set a standard questionnaire to assess exposure to SHS.

Abstract Background Comprehensive summaries on real-world outcomes in pulmonary arterial hypertension (PAH)—a rare, incurable condition, are lacking. Main body of the abstract We conducted a systematic literature review to describe current survival, morbidity, and quality of life (QoL) outcomes in adult and pediatric PAH patients. We searched Medline and Embase electronic databases, clinicaltrials.gov, and encepp.eu entries, and grey literature to identify outcome estimates for right-heart catheterization-confirmed PAH patients from population-based observational studies (search date: 25 Nov 2021). Data were synthesized using a narrative approach and post-hoc subgroup meta-analyses were conducted to explore adult survival by region, disease severity, representativeness, and study period. The search yielded 7473 records. Following screening and full text review, 22 unique studies with 31 individual reports of outcomes were included. Studies were mostly national registries (n = 21), European (n = 13) and covering adults (n = 17); only six had systematic countrywide coverage of centers. Survival was the most frequently reported outcome (n = 22). Global adult 1-, 3-, and 5-year survival ranged from 85 to 99% (n = 15), 65 to 95% (n = 14), and 50 to 86% (n = 9), respectively. Subgroup meta-analysis showed that 1-, 3-, and 5-year survival in Europe was 90% (95% CI 86–94%; n = 8), 78% (95% CI 68–86%; n = 8), and 61% (95% CI 49–72%; n = 6), respectively; 1-year survival in North America was 88% (95% CI 83–93%; n = 3) and 3-year survival in Asia was 85% (95% CI 82–88%; n = 3). No difference in survival between regions was observed. Subgroup analysis suggested higher survival in patients with better baseline functional class; however, interpretation should be cautioned due to large subgroup heterogeneity and potential missingness of data. Short conclusion This review describes current disease outcomes based on well-defined and representative PAH populations. There is an overall lack of follow-up data for morbidity and QoL outcomes; survival estimates for pediatric patients are scarce and may not be generalizable to the current treatment era, although publications from large pediatric registries became available after our search date. This study demonstrated a remaining unmet need world-wide to improve long-term prognosis in PAH in the current era.

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-μg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors.

Against the background of low women's participation in farm decision making processes, Malawi's agriculture sector adapted and up- scaled Household Approach, a gender transformative tool known for its impact in enhancing participation of women in farm decision making processes. In this study, participation in farm decision making is defined as the involvement of women, men and youth in decision making process in relation to visioning, planning, implementation, monitoring and evaluation. The objective of this study was to determine level of participation of women under Household Approach in farm decision making processes. This was a cross- sectional study. Using a semi-structured interview, a quantification of the extent of participation of various gender categories in decision making in various areas was determined. Thirty- three households were purposively selected due to their participation in Household Approach. Analysis of women participation in decision making, was done by computing women's decision score. Decision score for men and youth was also computed so that a comparison is made to women's level of participation. Women took joint decisions in all farm decision making processes with a higher participation score in household visioning (0.91) compared to men (0.83) and youth (0.44). When it came to implementing production activities of an enterprise (maize) which is a major source of their livelihood determining fulfilment of their visions, there were significant variations among their participation score in decision making in relation to maize enterprise production ([chi]2 (2) = 40.282, p = 0.000) with a mean rank participation score of 25.00, 39.94 and 8.56 in men, women and youth, respectively. This implies that much as implementation of maize production activities are jointly done with men, women still do a greater part of maize production work. When it came to monitoring, women had a lower participation score (0.68) compared to men (0.81), but higher than the youth (0.33). The lower participation score in monitoring among women was due to multiple roles the women have. This study recommends that sharing of roles be encouraged among peer households. Key words: Level, Women, participation, Household Approach, Farm, Decision making, Pragmatic, Cross- sectional, INTRODUCTION Agriculture accounts for 30% of Gross Domestic Product (GDP), generates over 80% of national export earnings in Malawi [1] and employs 76% of the country's workforce [2]. In Malawi, [...]

Introduction: This exploratory review aimed to provide empirical evidence on the definitions of labor, the statistical approaches and measures reported in randomized controlled trials (RCTs) and observational studies measuring the duration of labor. Methods: A systematic electronic literature search was conducted using different databases. An extraction form was designed and used to extract relevant data. English, French, and German studies published between 1999 and 2019 have been included. Only RCTs and observational studies analyzing labor duration (or a phase of labor duration) as a primary outcome have been included. Results: Ninety‐two RCTs and 126 observational studies were eligible. No definition of the onset of labor was provided in 21.7% (n = 20) of the RCTs and 23.8% (n = 30) of the observational studies. Mean was the most frequently applied measure of labor duration in the RCTs (89.1%, n = 82), and median in the observational studies (54.8%, n = 69). Most RCTs (83%, n = 76) and observational studies (70.6%, n = 89) analyzed labor duration using a bivariate method, with the t‐test being the most frequently applied (45.7% and 27%, respectively). Only 10.8% (n = 10) of the RCTs and 52.4% (n = 66) of the observational studies conducted a multivariable regression: 3 (30%; out of 10) RCTs and 37 (56%; out of 66) observational studies used a time‐to‐event adapted model. Conclusion: This survey reports a lack of agreement with respect to how the onset of labor and phases of labor duration are presented. Concerning the statistical approaches, few studies used survival analysis, which is the appropriate statistical framework to analyze time‐to‐event data. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the efficacy and safety of sacubitril/valsartan in abnormal renal function (eGFR < 60 ml/min/1.73m2) patients combined with heart failure based on randomized controlled trials (RCTs) and observational studies. The Embase, PubMed and the Cochrane Library were searched for relevant studies from inception to December 2023. Dichotomous variables were described as event counts with the odds ratio (OR) and 95% confidence interval (CI) values. Continuous variables were expressed as mean standard deviation (SD) with 95% CIs. A total of 6 RCTs and 8 observational studies were included, involving 17335 eGFR below 60 ml/min/1.73m2 patients combined with heart failure. In terms of efficacy, we analyzed the incidence of cardiovascular events and found that sacubitril/valsartan significantly reduced the risk of cardiovascular death or heart failure hospitalization in chronic kidney disease (CKD) stages 3–5 patients with heart failure (OR: 0.65, 95%CI: 0.54–0.78). Moreover, sacubitril/valsartan prevented the serum creatinine elevation (OR: 0.81, 95%CI: 0.68–0.95), the eGFR decline (OR: 0.83, 95% CI: 0.73–0.95) and the development of end-stage renal disease in this population (OR:0.73, 95%CI:0.60–0.89). As for safety outcomes, we did not find that the rate of hyperkalemia (OR:1.31, 95%CI:0.79–2.17) and hypotension (OR:1.57, 95%CI:0.94–2.62) were increased in sacubitril/valsartan group among CKD stages 3–5 patients with heart failure. Our meta-analysis proves that sacubitril/valsartan has a favorable effect on cardiac function without obvious risk of adverse events in abnormal renal function patients combined with heart failure, indicating that sacubitril/valsartan has the potential to become perspective treatment for these patients. [ABSTRACT FROM AUTHOR]

Abstract Background Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network. Results We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments. Conclusions The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias.

Background Aims Materials and Methods Results Discussion Conclusion The target trial framework was developed as a strategy to design and analyze observational epidemiologic studies with the aim of reducing bias due to analytic decisions. It involves designing a hypothetical randomized trial to answer a question of interest and systematically considering how to use observational data to emulate each trial component.The primary aim of this paper is to provide a detailed example of the application of the target trial framework to a research question in oral epidemiology.We describe the development of a hypothetical target trial and emulation protocol to evaluate the effect of preconception periodontitis treatment on time‐to‐pregnancy. We leverage data from Pregnancy Study Online (PRESTO), a preconception cohort, to ground our example in existing observational data. We discuss the decision‐making process for each trial component, as well as limitations encountered.Our target trial application revealed data limitations that precluded us from carrying out the proposed emulation. Implications for data quality are discussed and we provide recommendations for researchers interested in conducting trial emulations in the field of oral epidemiology.The target trial framework has the potential to improve the validity of observational research in oral health, when properly applied.We encourage the broad adoption of the target trial framework to the field of observational oral health research and demonstrate its value as a tool to identify directions for future research. [ABSTRACT FROM AUTHOR]

Background Methods Conclusion Practice‐Based Research Network (PBRN) studies typically assess the effectiveness of clinical interventions in settings that match real‐world conditions. Dental PBRNs have the capacity to amass, identify, and analyze vast amounts of data from patient populations that include diverse racial, ethnic, socio‐economic, and geographic backgrounds. These dental studies encompass a broad spectrum of healthcare aspects, including prevention, diagnosis, symptom and disease treatment, quality enhancement, and care coordination.An extensive range of research methodologies can be employed within dental PBRNs to investigate these topics, including randomized controlled trials. Dental PBRNs have evolved from primarily focusing on case observations to leveraging advanced network infrastructure and collaborating across multiple regional and national sites. In addition to producing numerous high‐impact peer‐reviewed publications, study results have led to improved clinical care. However, PBRNs encounter challenges, such as the sustainability of research capacity (relying heavily on ongoing support from funding agencies), diverse research cultures, and an imperative to design studies that are both feasible and relevant to everyday clinical practice. Recognizing the pivotal role of real‐world evidence, it is important to have sustained investment in dental PBRN infrastructure and feasible opportunities for practitioners to participate in network activities nationwide.Practice‐Based Research Network studies capitalize on an important research context within which to investigate a range of clinical topics that can employ multiple research methodologies. However, sustaining productive networks requires strategic effort, ongoing financial support, and customized organizational skills. [ABSTRACT FROM AUTHOR]

Background: Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network. Results: We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments. Conclusions: The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias. [ABSTRACT FROM AUTHOR]

Background: Comparisons between cladribine and other potent immunotherapies for multiple sclerosis (MS) are lacking. Objectives: To compare the effectiveness of cladribine against fingolimod, natalizumab, ocrelizumab and alemtuzumab in relapsing-remitting MS. Methods: Patients with relapsing-remitting MS treated with cladribine, fingolimod, natalizumab, ocrelizumab or alemtuzumab were identified in the global MSBase cohort and two additional UK centres. Patients were followed for ⩾6/12 and had ⩾3 in-person disability assessments. Patients were matched using propensity score. Four pairwise analyses compared annualised relapse rates (ARRs) and disability outcomes. Results: The eligible cohorts consisted of 853 (fingolimod), 464 (natalizumab), 1131 (ocrelizumab), 123 (alemtuzumab) or 493 (cladribine) patients. Cladribine was associated with a lower ARR than fingolimod (0.07 vs. 0.12, p = 0.006) and a higher ARR than natalizumab (0.10 vs. 0.06, p = 0.03), ocrelizumab (0.09 vs. 0.05, p = 0.008) and alemtuzumab (0.17 vs. 0.04, p < 0.001). Compared to cladribine, the risk of disability worsening did not differ in patients treated with fingolimod (hazard ratio (HR) 1.08, 95% confidence interval (CI) 0.47–2.47) or alemtuzumab (HR 0.73, 95% CI 0.26–2.07), but was lower for patients treated with natalizumab (HR 0.35, 95% CI 0.13–0.94) and ocrelizumab (HR 0.45, 95% CI 0.26–0.78). There was no evidence for a difference in disability improvement. Conclusion: Cladribine is an effective therapy that can be viewed as a step up in effectiveness from fingolimod, but is less effective than the most potent intravenous MS therapies. [ABSTRACT FROM AUTHOR]

The debate about the characteristics and advantages of quantitative and qualitative methods is decades old. In their seminal monograph, A Tale of Two Cultures (2012, ATTC), Gary Goertz and James Mahoney argue that methods and research design practices for causal inference can be distinguished as two cultures that systematically differ from each other along 25 specific characteristics. ATTC's stated goal is a description of empirical patterns in quantitative and qualitative research. Yet, it does not include a systematic empirical evaluation as to whether the 25 are relevant and valid descriptors of applied research. In this paper, we derive five observable implications from ATTC and test the implications against a stratified random sample of 90 qualitative and 90 quantitative articles published in six journals between 1990–2012. Our analysis provides little support for the two-cultures hypothesis. Quantitative methods are largely implemented as described in ATTC, whereas qualitative methods are much more diverse than ATTC suggests. While some practices do indeed conform to the qualitative culture, many others are implemented in a manner that ATTC characterizes as constitutive of the quantitative culture. We find very little evidence for ATTC's anchoring of qualitative research with set-theoretic approaches to empirical social science research. The set-theoretic template only applies to a fraction of the qualitative research that we reviewed, with the majority of qualitative work incorporating different method choices. [ABSTRACT FROM AUTHOR]

Purpose: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency–funded project (EUPAS39322), defined a set of metadata to describe real‐world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. Methods: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. Results: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. Conclusions: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe. [ABSTRACT FROM AUTHOR]

The problems of generalization and transportation of treatment effect estimates from a study sample to a target population are central to empirical research and statistical methodology. In both randomized experiments and observational studies, weighting methods are often used with this objective. Traditional methods construct the weights by separately modeling the treatment assignment and study selection probabilities and then multiplying functions (e.g., inverses) of their estimates. In this work, we provide a justification and an implementation for weighting in a single step. We show a formal connection between this one-step method and inverse probability and inverse odds weighting. We demonstrate that the resulting estimator for the target average treatment effect is consistent, asymptotically Normal, multiply robust, and semiparametrically efficient. We evaluate the performance of the one-step estimator in a simulation study. We illustrate its use in a case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California. We provide R code implementing the methodology. [ABSTRACT FROM AUTHOR]

Assessing population‐level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person's treatment may affect another individual's outcome, that is, there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, that is, there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g‐formula is extended to allow for partial interference. G‐formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g‐null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as 'weak stratified interference'). Simulation studies are presented demonstrating the finite‐sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g‐formula estimators to assess the effects of bed net use on malaria. [ABSTRACT FROM AUTHOR]

Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates. [ABSTRACT FROM AUTHOR]

Cereals are the basis of much of the world’s daily diet. Recently, there has been considerable interest in the beneficial properties of wholegrains due to their content of phytochemicals, particularly polyphenols. Despite this, the existing data on polyphenolic composition of cereal-based foods reported in the most comprehensive databases are still not updated. Many cereal-based foods and phenolic compounds are missing, including pigmented ones. Observational epidemiological studies reporting the intake of polyphenols from cereals are limited and inconsistent, although experimental studies suggest a protective role for dietary polyphenols against cardiovascular disease, diabetes, and cancer. Estimating polyphenol intake is complex because of the large number of compounds present in foods and the many factors that affect their levels, such as plant variety, harvest season, food processing and cooking, making it difficult matching consumption data with data on food composition. Further, it should be taken into account that food composition tables and consumed foods are categorized in different ways. The present work provides an overview of the available data on polyphenols content reported in several existing databases, in terms of presence, missing and no data, and discusses the strengths and weaknesses of methods for assessing cereal polyphenol consumption. Furthermore, this review suggests a greater need for the inclusion of most up-to-date cereal food composition data and for the harmonization of standardized procedures in collecting cereal-based food data and adequate assessment tools for dietary intake. [ABSTRACT FROM AUTHOR]

Purpose: To compare the performance (covariate balance, effective sample size [ESS]) of stable balancing weights (SBW) versus propensity score weighting (PSW). Two applied cases were used to compare performance: (Case 1) extreme imbalance in baseline covariates between groups and (Case 2) substantial discrepancy in sample size between groups. Methods: Using the Premier Healthcare Database, we selected patients who (Case 1) underwent a surgical procedure with one of two different bipolar forceps between January 2000 and June 2020, or (Case 2) a neurological procedure using one of two different nonabsorbable surgical sutures between January 2000 and March 2020. Average treatment effects on the treated (ATT) weights were generated based on selected covariates. SBW was implemented using two techniques: (1) "grid search" to find weights of minimum variance at the lowest target absolute standardized mean difference (SMD); (2) finding weights of minimum variance at prespecified SMD tolerance. PSW and SBW methods were compared on postweighting SMDs, the number of imbalanced covariates, and ESS of the ATT‐weighted control group. Results: In both studies, improved covariate balance was achieved with both SBW techniques. All methods suffered from postweighting ESS that was lower than the unweighted control group's original sample size; however, SBW methods achieved higher ESS for the control groups. Sensitivity analyses using SBW to apply variable‐specific SMD thresholds increased ESS, outperforming PSW. Conclusions: In this applied example, the optimization‐based SBW method provided ample flexibility with respect to prespecification of covariate balance goals and resulted in better postweighting covariate balance and larger ESS as compared with PSW. [ABSTRACT FROM AUTHOR]

Background: Quality-of-life metrics are increasingly important for oncological patients alongside traditional endpoints like mortality and disease progression. Statistical tools such as Win Ratio, Win Odds, and Net Benefit prioritize clinically significant outcomes using composite endpoints. In randomized trials, Win Statistics provide fair comparisons between treatment and control groups. However, their use in observational studies is complicated by confounding variables. Propensity score (PS) matching mitigates confounding variables but may reduce the sample size, affecting the power of win statistics analyses. Alternatively, PS matching can stratify samples, preserving the sample size. This study aims to assess the long-term impact of these methods on decision making, particularly in colorectal cancer patients. Methods: A motivating example involves a cohort of patients from the ReSARCh observational study (2016–2021) with locally advanced adenocarcinoma of the rectum, situated up to 12 cm from the anal verge. These patients underwent either a watch-and-wait approach (WW) or trans-anal local excision (LE). Win statistics compared the effects of WW and LE on a composite outcome (overall survival, recurrence, presence of ostomy, and rectum excision). For matched win statistics, we used robust inference techniques proposed by Matsouaka et al. (2022), and for stratified win statistics, we applied the method proposed by Dong et al. (2018). A simulation study assessed the coverage probability of matched and stratified win statistics in balanced and unbalanced groups, calculating how often the confidence intervals included the true values of WR, NB, and WO across 1000 simulations. Results: The results suggest a better efficacy of the LE approach when considering efficacy outcomes alone (WR: 0.47 (0.01 to 1.14); NB: −0.16 (−0.34 to 0.02); and WO: 0.73 (0.5 to 1.05)). However, when QoL outcomes are included in the analyses, the estimates are closer to 1 (WR: 0.87 (0.06 to 2.06); WO: 0.93 (0.61 to 1.4)) and to 0 (NB: −0.04 (−0.25 to 0.17)), indicating a negative impact of the treatment effect of LE regarding the presence of ostomy and the excision of the rectum. Moreover, based on the simulation study, our findings underscore the superior performance of matched compared to stratified win statistics in terms of coverage probability (matched WR: 97% vs. stratified WR: 33.3% in a high-imbalance setting; matched WR: 98% vs. stratified WR: 34.4% in a medium-imbalance setting; and matched WR: 99.2% vs. stratified WR: 37.4% in a low-imbalance setting). Conclusions: In conclusion, our study sheds light on the interpretation of the results of win statistics in terms of statistical significance, providing insights into the application of pairwise comparison in observational settings, promoting its use to improve outcomes for cancer patients. [ABSTRACT FROM AUTHOR]

• The prevalence of pSS in Italy may be consistent with the definition of rare disease. • Patients with pSS show higher rates of hospitalization, pharmacological treatment, and outpatient care needs. • Economic impact of pSS patients' health care is three-times higher than the general population. Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease with significant impact on morbidity, mortality, and quality of life. This study aimed to evaluate epidemiology, healthcare needs and related costs of pSS patients from the Italian National Health Service perspective. From the Fondazione Ricerca e Salute's database (∼5 million inhabitants/year), pSS prevalence in 2018 was calculated. Demographics, mean healthcare consumptions and direct costs at one year following index date (first in-hospital diagnosis/disease waiver claim) were analysed through an individual direct matched pair case-control analysis (age, sex, residency). In Italy, 3.8/10,000 inhabitants were identified as affected by pSS (1,746 case: 1,746 controls) in 2018. In the year following index date, 53.7% of cases and 42.7% of controls received ≥1 drug (p <0.001); mean per capita cost was €501 and €161, respectively (p <0.01). At least one hospitalization occurred to 7.8% of cases and 3.9% of controls (p <0.001) with mean per capita costs of €416 and €129, respectively (p = 0.46). At least one outpatient specialist service was performed in 49.8% of cases and 30.6% of controls (p <0.001); mean per capita costs were €200 and €75, respectively (p <0.01). Overall, mean annual costs were €1,171 per case and €372 per control (p < 0.01). According to results of this population-based study, the prevalence of pSS in Italy appears to be consistent with the definition of rare disease. Patients with pSS have higher pharmacological, in-hospital and outpatient specialist care needs, leading to three-times higher overall cost for the INHS, compared to the general population. [ABSTRACT FROM AUTHOR]

Background: Palliative care is characterised by heterogeneous patient and caregiver populations who are provided care in different health systems and a research base including a large proportion of observational, mostly retrospective studies. The inherent diversity of palliative care populations and the often inadequate study descriptions challenge the application of new knowledge into practice and reproducibility for confirmatory studies. Being able to define systematically study populations would significantly increase their generalisability and effective translation into practice. Proposal: Based on an informal consensus process by active palliative care researchers challenged by this problem and a review of the current evidence, we propose an approach to creating more comparable cohorts in observational (non-randomised) palliative care studies that relies on defining the study population in relation to a fixed, well-defined event from which analyses are built ('anchoring'). In addition to providing a detailed and complete description of the study population, anchoring is the critical step in creating more comparable cohorts in observational palliative care studies. Anchoring can be done with respect to a single or multiple data points, and can support both prospective and retrospective data collection and analysis. Discussion: Anchoring the cohort to reproducible data points will help create more comparable cohorts in palliative care whilst mitigating its inherent heterogeneity. This, in turn, will help optimise the generalisability, applicability and reproducibility of observational palliative care studies to strengthen the evidence base and improve practice. [ABSTRACT FROM AUTHOR]

We propose a semiparametric method to estimate average treatment effects in observational studies based on the assumption of unconfoundedness. Assume that the propensity score model and outcome model are a general single index model, which are estimated by the kernel method and the unknown index parameter is estimated via linearized maximum rank correlation method. The proposed estimator is computationally tractable, allows for large dimension covariates and not involves the approximation of link functions. We showed that the proposed estimator is consistent and asymptotically normally distributed. In general, the proposed estimator is superior to existing methods when the model is incorrectly specified. We also provide an empirical analysis on the average treatment effect and average treatment effect on the treated of 401(k) eligibility on net financial assets. [ABSTRACT FROM AUTHOR]

Transcranial direct current stimulation (tDCS) is known to modulate the brain excitability and enhance cognitive functions and neuroplasticity, although adherence to its administration in post-stroke rehabilitation is still being understudied. This study set out to develop and content validate a questionnaire to assess clinical physiotherapists’ knowledge, attitude, and practices regarding the use of tDCS following a stroke. In order to gather expert opinion and come to a consensus on a certain topic, the Delphi technique was employed. The measure was validated using both qualitative (cognitive interviewing) and quantitative (content validity) methods on a panel of 32 experts. Calculations were made for the content validity ratio (CVR), content validity index (CVI), item-level CVI (I-CVI), and scale-level CVI universal agreement (S-CVI/UA). A total of 48 interdisciplinary experts in the field of neurosciences were invited. In all, 32 specialists from Neurology, General Medicine, Neurophysiotherapy, and Physiology departments accepted the invitation and provided their opinion for instrument analysis. After two rounds, early iterations of this instrument demonstrated an acceptable CVR value of 1, high overall content validity (S-CVI/UA = 0.86), and high content validity of individual items (I-CVI range: 0.83-1.00). The kappa value varied between 0.75 and 1, which is excellent. Its content is therefore deemed validated. Through an iterative process, its development and assessment revealed strong item-content validity for determining the domains of the questionnaire. It is anticipated that this metric could be utilized to increase the adherence rate of post-stroke tDCS application in Saudi Arabia. [ABSTRACT FROM AUTHOR]

This study summarizes a presentation at the symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to the first author. As epidemiologists, we are taught that “correlation does not imply causation.” While true, identifying causes is a key objective for much of the research that we conduct. There is empirical evidence that veterinary epidemiologists are conducting observational research with the intent to identify causes; many studies include control for confounding variables, and causal language is often used when interpreting study results. Frameworks for studying causes include the articulation of specific hypotheses to be tested, approaches for the selection of variables, methods for statistical estimation of the relationship between the exposure and the outcome, and interpretation of that relationship as causal. When comparing observational studies in veterinary populations to those conducted in human populations, the application of each of these steps differs substantially. The a priori identification of exposure- outcome pairs of interest are less common in observational studies in the veterinary literature compared to the human literature, and prior knowledge is used to select confounding variables in most observational studies in human populations, whereas data-driven approaches are the norm in veterinary populations. The consequences of not having a defined exposure- outcome hypotheses of interest and using data-driven analytical approaches include an increased probability of biased results and poor replicability of results. A discussion by the community of researchers on current approaches to studying causes in observational studies in veterinary populations is warranted. [ABSTRACT FROM AUTHOR]

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite‐sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real‐world data examples marked by violations of the positivity assumption. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a disease demonstrating increasing morbidity and mortality, especially in patients with chronic viral hepatitis. Studies have shown that aspirin can reduce the incidence of liver cancer; however, the degree of benefit in patients with viral hepatitis is unclear. This study focused on the association between aspirin use and HCC risk in patients with chronic viral hepatitis. Methods: A systematic search of the PubMed, Embase, Web of Science, and Cochrane Library databases was performed from the earliest available date to December 16, 2023. The primary outcome was HCC incidence, and the secondary outcome was gastrointestinal bleeding. The results were expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Meta-analyses were performed by using random or fixed-effects models based on the heterogeneity assessed via the I2 statistic. Results: A total of 13 articles (303,414 participants and 14,423 HCC patients) were included in the analysis. The incidence of HCC in aspirin users was lower than that in non-aspirin users (HR 0.75; 95% CI, 0.68–0.83; P < 0.001; I2 = 90.0%). Subgroup analysis further showed that this effect may be more obvious in HCV patients, non-cirrhotic patients, patients with statins, and long-term aspirin users, but it may have the risk of gastrointestinal bleeding (HR 1.13; 95% CI, 1.07–1.20; P = 0.906; I2 = 0.0%). Conclusions: Our meta-analysis shows that in patients with chronic viral hepatitis, aspirin use is associated with a significantly reduced risk of liver cancer, but attention should be paid to the possible risk of gastrointestinal bleeding, and this conclusion needs further validation in the future. [ABSTRACT FROM AUTHOR]

IntroductionIn the context of migraine preventive therapy, new therapeutic modalities such as monoclonal antibodies targeting the calcitonin gene-related peptide receptor (CGRP) or ligand offer potential advantages over traditional preventive treatments.MethodsThis systematic literature review gathered recent real-world evidence from Spain on the use of galcanezumab, a CGRP-targeting treatment, in migraine patients. The review included observational studies in English or Spanish, published from August 2020 to August 2023, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Cochrane guidelines.ResultsA total of 29 publications involving 2,592 Spanish adult patients were identified, reporting relevant information on clinical outcomes (treatment effectiveness and safety), treatment persistence and patterns (switching from other therapies and time to discontinuation and restart), and patient-reported outcomes (including satisfaction with treatment). The most frequently reported variables were related to the clinical effectiveness of galcanezumab, demonstrating a significant reduction in monthly migraine days and monthly headache days. Additionally, adverse impact of headache per HIT-6 (Headache Impact Test-6) and disability per MIDAS (Migraine Disability Assessment) also improve. Studies also showed that up to 12-month persistence to galcanezumab ranged from 76.8 to 59.8%. Serious adverse events were rare. None of the publications included health-related quality of life data, either generic or migraine-specific. One study highlighted that galcanezumab treatment would offer high patient satisfaction for people with migraine.ConclusionThe real-world evidence on the use of galcanezumab treatment among the Spanish population shows that its effectiveness, persistence, safety, and impact on health burden align with findings from clinical trials and observational studies conducted in other countries. Future studies should incorporate health-related quality of life data to gain a more holistic understanding of this treatment’s impact.

BackgroundPrevious studies have reported the association between dietary total antioxidant capacity (TAC) and risk of stroke, but these findings have been inconsistent. We therefore performed this systematic review and dose–response meta-analysis of observational studies to evaluate the association between dietary TAC and risk of stroke.MethodsA systematic literature search was carried out through PubMed, ISI Web of Science, EBSCO, Scopus and China National Knowledge Infrastructure (CNKI) databases, to find the relevant articles published up to 31 May, 2024. Random-effects or fixed-effects models were used to pool the relative risks (RRs) and their 95% confidence intervals (CIs) where appropriate. Heterogeneity across studies were determined using the Cochran’s Q test and I-square (I2) statistics.ResultsEight observational studies (six cohort and two case–control studies) were included in the final analysis. The pooled results showed that higher intake of dietary TAC was associated with a lower risk of stroke (RR = 0.88; 95%CI: 0.81–0.95, p = 0.002). Additionally, dose–response analysis of cohort studies demonstrated a linear association between dietary TAC intake and risk of stroke (RR = 0.994; 95%CI: 0.990–0.999, Pnon-linearity = 0.329, Pdose–response = 0.014). Subgroup analyses showed the inverse association between dietary TAC intake and risk of stroke in the studies with mean age

Abstract Background Vitiligo is a disease that affects people of all skin shades and can impact their quality of life. Reliable evidence on the effectiveness and adverse events associated with the recent use of Janus kinase (JAK) inhibitors to treat vitiligo is needed. This protocol for a systematic review and meta-analysis seeks to collect evidence from both randomized controlled trials (RCTs) and observational studies to determine the effectiveness and patient-centered outcomes concerning treatment with JAK inhibitors. Methods We will conduct a systematic review of the literature for RCTs and observational studies that used upadacitinib, ritlecitinib, brepocitinib, ifidancitinib, cerdulatinib, deglocitinib, baricitinib, tofacitinib, and ruxolitinib JAK inhibitors as treatments for vitiligo compared to placebo, no treatment, or combination therapies. We will systematically search from inception in Epistemonikos, MEDLINE, Scopus, Cochrane Central Register of Controlled Trials, EMBASE, ClinicalTrials.gov, PsycINFO, Allied and Complementary Medicine Database, Latin American and Caribbean Health Sciences Literature, Web of Science Core Collection, relevant preprint servers, and the gray literature. Ethics approval was not sought as the protocol and systematic review will not involve human participants, but rather summarized and anonymous data from studies. Primary outcomes include quality of life, percentage repigmentation, decreased vitiligo within 1 year or more, lasting repigmentation after a 2-year follow-up, cosmetic acceptability of repigmentation and tolerability or burden of treatment, and adverse events. Secondary outcomes are patient and study characteristics. We will include full-text articles, preprints, and clinical trial data in any language and all geographic regions. For data sources unavailable in English, we will obtain translations from global collaborators via the Cochrane Engage network. We will exclude articles for which sufficient information cannot be obtained from the authors of articles and systematic reviews. At least two investigators will independently assess articles for inclusion and extract data; reliability will be assessed before subsequent selection and data extraction of remaining studies. The risk of bias and certainty of evidence with Grading of Recommendations Assessment, Development, and Evaluation guidelines will be assessed independently by at least two investigators. We will estimate treatment effects by random-effects meta-analyses and assess heterogeneity using I 2. Data that cannot be included in the meta-analysis will be reported narratively using themes. Discussion The proposed systematic review and meta-analysis describe the methods for summarizing and synthesizing the evidence on the effectiveness and patient-centered outcomes concerning the treatment of vitiligo with JAK inhibitors that were recently approved for this indication. To disseminate further the results of our systematic review, we plan to present them at international conferences and meetings. Our findings will provide robust evidence to facilitate decision-making at the policy or practitioner level. Systematic review registration PROSPERO CRD42023383920.

The objective of this scoping review is to evaluate the updated evidence on the consumption of alcohol and health outcomes regarded as relevant for the Nordic and Baltic countries, including cardiovascular disease, cancer, and all-cause mortality. It is based on the previous Nordic Nutrition Recommendations of 2012 and relevant papers published until 31 May 2021. Current evidence from mainly observational epidemiological studies suggests that regular, moderate alcohol consumption may confer protective effects against myocardial infarction (MI) and type 2 diabetes. Mendelian randomization analyses do not fully support these findings, possibly because these analyses may fail to identify low alcohol intake. For several cancers, it is not possible to set any safe limit. All-cause mortality is not increased with light to moderate alcohol intake in middle-aged and older adults who do not engage in binge drinking. Total abstinence is associated with the lowest risk of mortality in young adults. Observational studies on alcohol consumption are hampered by a number of inherent methodological issues such as ascertainment of alcohol intake, selection of appropriate exposure groups, and insufficient control of confounding variables, colliders, and mediators. It should also be emphasized that there is a socio-economic contribution to the alcohol-health axis with a stronger detrimental effect of alcohol in the lower social classes. The above issues contribute to the complexity of unravelling the causal web between alcohol, mediators, confounders, and health outcome.

Abstract Background Randomized controlled trials and real-world studies suggest that combination therapy with sodium–glucose transport protein 2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) is associated with improvement in fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), systolic blood pressure (SBP), body mass index (BMI), and total cholesterol levels. However, a systematic review of available real-world evidence may facilitate clinical decision-making in the real-world scenario. This meta-analysis assessed the safety and effectiveness of combinations of SGLT2is + GLP-1RAs with a focus on their cardioprotective effects along with glucose-lowering ability in patients with type 2 diabetes mellitus (T2DM) in a real-world setting. Methods Electronic searches were performed in the PubMed/MEDLINE, PROQuest, Scopus, CINAHL, and Google Scholar databases. Qualitative analyses and meta-analyses were performed using the Joanna Briggs Institute SUMARI software package and Review Manager v5.4, respectively. Results The initial database search yielded 1445 articles; of these, 13 were included in this study. The analyses indicated that SGLT2is + GLP-1RAs combinations were associated with significantly lower all-cause mortality when compared with individual therapies (odds ratio [95% confidence interval [CI] 0.49 [0.41, 0.60]; p

ABSTRACT Aims/Introduction An overrepresentation of epilepsy has been suggested in patients with type 1 diabetes (T1D). This meta‐analysis was conducted to evaluate if type 1 diabetes is associated with a higher incidence of epilepsy. Materials and Methods Longitudinal observational studies which are relevant to the purpose of the meta‐analysis were screened and obtained by searching PubMed, Embase, and Web of Science databases. Random‐effects models were used when significant heterogeneity was observed; otherwise, fixed‐effects models were used. Results Six observational studies involving 10 datasets of 8,001,899 participants were included, with six datasets including children and only one dataset including older people. Among them, 100,414 (1.25%) had type 1 diabetes. During the follow‐up duration of 5.4–15.2 years (mean: 9.5 years), 98,644 cases (1.23%) of epilepsy were observed. Compared with participants with normoglycemia, those with type 1 diabetes were shown to have a higher incidence of epilepsy (risk ratio [RR]: 2.41, 95% confidence interval 1.69–3.44, P

Aims This study aimed to investigate the efficacy and safety of sacubitril/valsartan in abnormal renal function (eGFR < 60 ml/min/1.73m2) patients combined with heart failure based on randomized controlled trials (RCTs) and observational studies.Methods The Embase, PubMed and the Cochrane Library were searched for relevant studies from inception to December 2023. Dichotomous variables were described as event counts with the odds ratio (OR) and 95% confidence interval (CI) values. Continuous variables were expressed as mean standard deviation (SD) with 95% CIs.Results A total of 6 RCTs and 8 observational studies were included, involving 17335 eGFR below 60 ml/min/1.73m2 patients combined with heart failure. In terms of efficacy, we analyzed the incidence of cardiovascular events and found that sacubitril/valsartan significantly reduced the risk of cardiovascular death or heart failure hospitalization in chronic kidney disease (CKD) stages 3–5 patients with heart failure (OR: 0.65, 95%CI: 0.54–0.78). Moreover, sacubitril/valsartan prevented the serum creatinine elevation (OR: 0.81, 95%CI: 0.68–0.95), the eGFR decline (OR: 0.83, 95% CI: 0.73–0.95) and the development of end-stage renal disease in this population (OR:0.73, 95%CI:0.60–0.89). As for safety outcomes, we did not find that the rate of hyperkalemia (OR:1.31, 95%CI:0.79–2.17) and hypotension (OR:1.57, 95%CI:0.94–2.62) were increased in sacubitril/valsartan group among CKD stages 3–5 patients with heart failure.Conclusions Our meta-analysis proves that sacubitril/valsartan has a favorable effect on cardiac function without obvious risk of adverse events in abnormal renal function patients combined with heart failure, indicating that sacubitril/valsartan has the potential to become perspective treatment for these patients.