BACKGROUND: Angina is a crucial risk signal for cardiovascular disease. However, few studies have evaluated the effects of ambient air pollution exposure on angina.
OBJECTIVE: We aimed to explore the short-term effects of air pollution on hospitalization for angina and its lag effects.
METHODS: We collected data on air pollutant concentrations and angina hospitalizations from 2013 to 2020. Distributed lag nonlinear model (DLNM) was used to evaluate the short-term effects of air pollutants on angina hospitalization under different lag structures. Stratified analysis by sex, age and season was obtained.
RESULTS: A total of 39,110 cases of angina hospitalization were included in the study. The results showed a significant positive correlation between PM2.5, SO2, NO2, and CO and angina hospitalization. Their maximum harmful effects were observed at lag0-7 (RR = 1.042; 95% CI: 1.017, 1.068), lag0-3 (RR = 1.067; 95% CI: 1.005, 1.133), lag0-6 (RR = 1.078; 95% CI: 1.041, 1.117), and lag0-6 (RR = 1.244; 95% CI: 1.109, 1.397), respectively. PM10 did not have an overall risk effect on angina hospitalization, but it did have a risk effect on women and the elderly. O3 was significantly negatively correlated with angina hospitalization, with the most pronounced effect observed at lag0-6 (RR = 0.960; 95% CI: 0.940, 0.982). Stratified analysis results showed that women and the elderly were more susceptible to pollutants, and the adverse effects of pollutants were stronger in the cold season.
CONCLUSIONS: Short-term exposure to PM2.5, SO2, NO2, and CO increases the risk of hospitalization for angina.

Epilepsy is one of the common neurological diseases which affects 65-70 million people worldwide. Modified Atkins diet (MAD) as a therapy is used as one of the treatments to reduce the seizures occurrence in epileptic patients. The purpose of this purpose is to review all evidence regarding the efficacy of the MAD from randomized controlled trials (RCTs) in adolescents and adults with drug resistant epilepsy (DRE). The total of three databases were searched (PubMed, Embase, and Cochrane Library) till 31 January 2023. Only RCTs with MAD as a one of the treatment arms were included in meta-analysis. The proportion of reduction of seizures in patients with epilepsy and relative risk to identify the relationship between MAD (as risk) to decrease the epileptic seizure was used as outcomes. The Jadad score with three domains was used to estimate the quality of RCTs included for meta-analysis. Only three RCTs were included following the stringent inclusion criteria in current meta-analysis. The pooled proportion from 142 patients going through MAD therapy shows the reduction in epileptic seizure ≥50%, by the random effect model was 0.23 (95% confidence interval [CI], 0.10 to 0.37). Our meta-analysis underlines a significant efficacy of MAD compared to the control group in seizure reduction ≥50%, The pooled relative risk was 6.47 (95% CI, 1.60 to 26.14; p-value <0.05). MAD therapy was efficacious and had better compliance for seizure reduction in subjects with DRE.

暂无摘要。

The five discussions of our paper provide several modeling alternatives, extensions, and generalizations that can potentially guide future research in meta-analysis. In this rejoinder, we briefly summarize and comment on some of those points.

Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these patients about appropriate risk-reducing management strategies. Counseling hinges on accurate estimates of age-specific risks of developing various cancers associated with mutations in a specific gene, ie, penetrance estimation. We propose a meta-analysis approach based on a Bayesian hierarchical random-effects model to obtain penetrance estimates by integrating studies reporting different types of risk measures (eg, penetrance, relative risk, odds ratio) while accounting for the associated uncertainties. After estimating posterior distributions of the parameters via a Markov chain Monte Carlo algorithm, we estimate penetrance and credible intervals. We investigate the proposed method and compare with an existing approach via simulations based on studies reporting risks for two moderate-risk breast cancer susceptibility genes, ATM and PALB2. Our proposed method is far superior in terms of coverage probability of credible intervals and mean square error of estimates. Finally, we apply our method to estimate the penetrance of breast cancer among carriers of pathogenic mutations in the ATM gene.

Investigators often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are \"transportable\" across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions that imply the transportability of both relative and difference effect measures. We then describe how marginal (population-averaged) causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., an assumption of no unmeasured confounding in the target population with respect to potential outcomes under the control treatment in the trial). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software and illustrate their use using data from a comprehensive cohort study of stable ischemic heart disease.

UNASSIGNED: The population attributable risk (PAR) is a statistic commonly used for quantifying preventability of cancer. We report here PAR estimates for the United Kingdom (UK) along with its constituent countries for up-to-date risk factor-attributable colorectal cancer (CRC) and breast cancer (BC), focusing on diet and nutrition related factors and tobacco (CRC) using representative national surveys.
UNASSIGNED: The PAR was calculated using established, modifiable risk factors by the World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR): physical activity, body mass index (BMI), alcoholic drinks, red meat, processed meat, dietary fiber, dietary calcium, as well as cigarette smoking for CRC, and physical activity, BMI, alcoholic drinks, and fruits and vegetable consumption for BC. National prevalence estimates and relative risks (RRs) for CRC and BC were obtained from meta-analyses or large pooled analyses.
UNASSIGNED: Based on eight dietary and lifestyle risk factors, the estimates for attributable cases of CRC for males and females, respectively, were as follows: England: 67% and 60%; Scotland: 68% and 59%, Wales: 66% and 61%; Northern Ireland: 67% and 61%; and UK: 67% and 60%. Excluding smoking, the PAR for the UK was 61% for men and 52% for women. Based on four dietary and lifestyle risk factors, the estimates for BC were as follows: England: 26%, Scotland: 27%; Wales: 25%; Northern Ireland: 26%; and UK: 27%.
UNASSIGNED: Up to 67% for CRC and 27% of BC were attributable to modifiable dietary and lifestyle factors in the UK. Moderate differences in PAR are observed between countries due to different prevalence of exposure to risk factors.
A number of lifestyle factors, such as smoking tobacco, heavy alcohol, physical inactivity, excess body weight, and poor diet can cause cancer. Estimating the future preventable burden of cancers through modifications to these behaviors is critical. Population attributable risk has been extensively used in the literature for quantifying the preventable burden of cancer. Based on some dietary and lifestyle risk factors, we report here United Kingdom (UK) cancer preventability estimates along with its constituent countries on the number of risk factor-attributable colorectal cancer and breast cancer.

BACKGROUND: Vaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities.
METHODS: This work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome.
RESULTS: Clinical trial simulations, in which the true relationship between CoP data and clinical outcome probability is a sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy.
CONCLUSIONS: This study proposes and evaluates a novel approach for assessing baseline demographic covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions.

We used Monte Carlo simulations to compare the performance of marginal structural models (MSMs) based on weighted univariate generalized linear models (GLMs) to estimate risk differences and relative risks for binary outcomes in observational studies. We considered four different sets of weights based on the propensity score: inverse probability of treatment weights with the average treatment effect as the target estimand, weights for estimating the average treatment effect in the treated, matching weights and overlap weights. We considered sample sizes ranging from 500 to 10,000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We examined both the robust variance estimator when using generalized estimating equations with an independent working correlation matrix and a bootstrap variance estimator for estimating the standard error of the risk difference and the log-relative risk. The performance of these methods was compared with that of direct weighting. Both the direct weighting approach and MSMs based on weighted univariate GLMs resulted in the identical estimates of risk differences and relative risks. When sample sizes were small to moderate, the use of an MSM with a bootstrap variance estimator tended to result in the most accurate estimates of standard errors. When sample sizes were large, the direct weighting approach and an MSM with a bootstrap variance estimator tended to produce estimates of standard error with similar accuracy. When using a MSM to estimate risk differences and relative risks, in general it is preferable to use a bootstrap variance estimator than the robust variance estimator. We illustrate the application of the different methods for estimating risks differences and relative risks using an observational study on the effect on mortality of discharge prescribing of a beta-blocker in patients hospitalized with acute myocardial infarction.

Null hypothesis significance testing (NHST) in medical research is increasingly being supplemented by estimation statistics, focusing on effect sizes (ESs) and confidence intervals (CIs). This study evaluates the expression of ESs and CIs for binary outcomes. A utilitarian framework is proposed, emphasizing the number of beneficiaries and the impact level. To evaluate clinical significance, minimal clinically important risk difference (MCIRD) is proposed based on event magnitude (EM). Within this framework, risk difference (RD) is introduced as the primary measure. To assess the performance of RD, we compared its statistical power against other measures (risk ratio, RR; odds ratio, OR; Cohen\'s h) in individual study scenarios, and visual information conveyance in meta-analysis scenarios. RDs maintain statistical power in comparison to other measures in individual studies. They provide clarity on the true impact of clinical interventions without compromising statistical integrity. Meta-analytic results indicate that using RDs directly enhances transparency, uncovers heterogeneity, and addresses misaligned assumptions. This approach, by quantifying clinical effectiveness under a utilitarian perspective, facilitates the applicability of research to patient care and encourages shared decision-making. The study advocates for reporting baseline risks (BRs) with RDs and recommends a standardized presentation of these statistics. In a utilitarian perspective, adopting RD as the preferred ES can foster a transparent, patient-focused research ethos. This aids in accurately presenting the magnitude and variability of treatment effects, offering a new direction in methodology.