Association between P M 10 and respiratory diseases admission in peninsula Malaysia during haze.

Numerous studies have been conducted in other countries on the health effects of exposure to particulate matter with a diameter of 10 microns or less P M 10 , but little research has been conducted in Malaysia, particularly during the haze season. This study intends to investigate how exposure of P M 10 influenced hospital admissions for respiratory diseases during the haze period in peninsula Malaysia and it was further stratified by age group, gender and respiratory diseases categories. The study includes data from all patients with respiratory diseases in 92 government hospitals, as well as P M 10 concentration and meteorological data from 92 monitoring stations in Peninsula Malaysia starting from 1st January 2000 to 31st December 2019. A quasi-poison time series regression with distributed lag nonlinear model (DLNM) was employed in this study to examine the relationship between exposure of P M 10 and hospital admissions for respiratory diseases during the haze period. Haze period for this study has been defined from June to September each year. According to the findings of this study, P M 10 was positively associated with hospitalisation of respiratory disease within 30 lag days under various lag patterns, with lag 25 showing the strongest association (RR = 1.001742, CI 1.001029,1.002456). Using median as a reference, it was discovered that females were more likely than males to be hospitalized for P M 10 exposure. Working age group will be the most affected by the increase in P M 10 exposure with a significant cumulative RR from lag 010 to lag 030. The study found that P M 10 had a significant influence on respiratory hospitalisation in peninsula Malaysia, particularly for lung diseases caused by external agents(CD5). Therefore, it is important to implement effective intervention measures to control P M 10 and reduce the burden of respiratory disease admissions.

Choice of needleless connector technology as a risk reduction strategy for catheter related bloodstream infection, mortality, and cost: A secondary data analysis.

OBJECTIVE: To compare relative risk (RR) of central line-associated bloodstream infection (CLABSI) between hospitals using Clave needleless connector technologies (NCT) and comparator hospitals using non-Clave NCT. And, to estimate avoided CLABSIs, mortality, and cost savings. BACKGROUND: Needleless connectors, while protective against needlestick injury, have long been implicated as a potential risk for CLABSI. Significant RR reduction of CLABSI among the many NCT has not been clinically demonstrated. METHODS: The U.S. Healthcare-Associated Infections database was accessed for CLABSI data for calendar year 2019 via the Centers for Medicare and Medicaid Services website. This dataset was merged with the Clave NCT manufacturer's 2019 database to identify hospitals purchasing Clave NCT (MicroClave™, NanoClave™, Clave Neutron™, Clave™, Microclave™ Clear) and non-Clave NCT comparator hospitals. Sub-analysis of Clave NCT hospitals included: (1) Clave NCT mixed-use and (2) Clave high-volume use hospitals. The standardized infection ratio (SIR) was generated to estimate CLABSI RR after adjusting for intern/resident-to-bed-ratio (IRB), care location, and hospital demographics. Using the RR, avoided CLABSIs, mortality, and cost savings were calculated. RESULTS: A total of 2987 eligible hospitals (1288 Clave NCT, 1699 non-Clave NCT) with 17,452,575 central line-days were evaluated. All three Clave NCT hospital groups showed a statistically significant reduction in RR compared to the non-Clave NCT hospitals. The RR in Clave NCT hospitals was 0.93, a 7% decrease in CLABSI risk (p = 0.02). In the Clave NCT subgroups, mixed-use hospitals RR was 0.93, a 7% reduction (p = 0.04), while the Clave NCT high-volume hospitals experienced a 19% reduction, RR 0.81 (p = 0.04). An estimated 563 CLABSIs and 84 related deaths were avoided with use of Clave NCT and $27,095,231 in cost savings. CONCLUSION: The use of the Clave NCT, in and of itself, is an effective risk reduction strategy for CLABSI prevention, reduced mortality, and substantial cost savings.

Impact of deforestation and climate on spatio-temporal spread of dengue fever in Mexico.

Dengue prevalence results from the interaction of multiple socio-environmental variables which influence its spread. This study investigates the impact of forest loss, precipitation, and temperature on dengue incidence in Mexico from 2010 to 2020 using a Bayesian hierarchical spatial model. Three temporal structures-AR1, RW1, and RW2-were compared, with RW2 showing superior performance. Findings indicate that a 1 % loss of municipal forest cover correlates with a 16.9 % increase in dengue risk. Temperature also significantly affects the vectors' ability to initiate and maintain outbreaks, highlighting the significant role of environmental factors. The research emphasizes the importance of multilevel modeling, finer temporal data resolution, and understanding deforestation causes to enhance the predictability and effectiveness of public health interventions. As dengue continues affecting global populations, particularly in tropical and subtropical regions, this study contributes insights, advocating for an integrated approach to health and environmental policy to mitigate the impact of vector-borne diseases.

Modified Atkins Diet in Adolescents and Adults with Drug Resistant Epilepsy: A Systematic Review and Meta-Analysis.

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.

Effects of major air pollutants on angina hospitalizations: a correlation study.

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. CONCLUSION: Short-term exposure to PM2.5, SO2, NO2, and CO increases the risk of hospitalization for angina.

Estimating causal effects for binary outcomes using per-decision inverse probability weighting.

Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW." The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.

Estimating geographical spread of Streptococcus pneumoniae within Israel using genomic data.

Understanding how pathogens spread across geographical space is fundamental for control measures such as vaccination. Streptococcus pneumoniae (the pneumococcus) is a respiratory bacterium responsible for a large proportion of infectious disease morbidity and mortality globally. Even in the post-vaccination era, the rates of invasive pneumococcal disease (IPD) remain stable in most countries, including Israel. To understand the geographical spread of the pneumococcus in Israel, we analysed 1174 pneumococcal genomes from patients with IPD across multiple regions. We included the evolutionary distance between pairs of isolates inferred using whole-genome data within a relative risk (RR) ratio framework to capture the geographical structure of S. pneumoniae. While we could not find geographical structure at the overall lineage level, the extra granularity provided by whole-genome sequence data showed that it takes approximately 5 years for invasive pneumococcal isolates to become fully mixed across the country.This article contains data hosted by Microreact.

Associations between mosaic loss of sex chromosomes and incident hospitalization for atrial fibrillation in the United Kingdom.

Background: Mosaic loss of chromosome Y (mLOY) in leukocytes of men reflects genomic instability from aging, smoking, and environmental exposures. A similar mosaic loss of chromosome X (mLOX) occurs among women. However, the associations between mLOY, mLOX, and risk of incident heart diseases are unclear. Methods: We estimated associations between mLOY, mLOX, and risk of incident heart diseases requiring hospitalization, including atrial fibrillation, myocardial infarction, ischemic heart disease, cardiomyopathy, and heart failure. We analyzed 190,613 men and 224,853 women with genotyping data from the UK Biobank. Among these participants, we analyzed 37,037 men with mLOY and 13,978 women with mLOX detected using Mosaic Chromosomal Alterations caller. Multivariable Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of each incident heart disease in relation to mLOY in men and mLOX in women. Additionally, Mendelian randomization (MR) was conducted to estimate causal associations. Results: Among men, detectable mLOY was associated with elevated risk of atrial fibrillation (HR=1.06, 95%CI:1.03-1.11). The associations were apparent in both never-smokers (HR=1.07, 95%:1.01-1.14) and ever-smokers (HR=1.05, 95%CI:1.01-1.11) as well as men > and ≤60 years of age. MR analyses supported causal associations between mLOY and atrial fibrillation (HRMR-PRESSO=1.15, 95%CI:1.13-1.18). Among post-menopausal women, we found a suggestive inverse association between detectable mLOX and atrial fibrillation risk (HR=0.90, 95%CI:0.83-0.98). However, associations with mLOY and mLOX were not found for other heart diseases. Conclusions: Our findings suggest that mLOY and mLOX reflect sex-specific biological processes or exposure profiles related to incident atrial fibrillation requiring hospitalization.

Learning about treatment effects in a new target population under transportability assumptions for relative effect measures.

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.

Bayesian meta-analysis of penetrance for cancer risk.

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.

Rejoinder to the discussion on "Bayesian meta-analysis of penetrance for cancer risk".

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.

The performance of marginal structural models for estimating risk differences and relative risks using weighted univariate generalized linear models.

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.

A Utilitarian Perspective on Risk Quantification for Clinical Significance in Binary Outcomes.

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.

Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression.

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.

Meta-analysis of breast cancer risk for individuals with PALB2 pathogenic variants.

Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these estimates is necessary to accurately counsel patients with this mutation. However, this is not trivial due to heterogeneity of studies in terms of study design and risk measure. We utilized a recently proposed Bayesian random-effects meta-analysis method that can synthesize estimates from such heterogeneous studies. We applied this method to combine estimates from 12 studies on BC risk for carriers of pathogenic PALB2 mutations. The estimated overall (meta-analysis-based) risk of BC is 12.80% (6.11%-22.59%) by age 50 and 48.47% (36.05%-61.74%) by age 80. Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.

A new method for vascular age estimation based on relative risk difference in vascular aging.

OBJECTIVE: The current models of estimating vascular age (VA) primarily rely on the regression label expressed with chronological age (CA), which does not account individual differences in vascular aging (IDVA) that are difficult to describe by CA. This may lead to inaccuracies in assessing the risk of cardiovascular disease based on VA. To address this limitation, this work aims to develop a new method for estimating VA by considering IDVA. This method will provide a more accurate assessment of cardiovascular disease risk. METHODS: Relative risk difference in vascular aging (RRDVA) is proposed to replace IDVA, which is represented as the numerical difference between individual predicted age (PA) and the corresponding mean PA of healthy population. RRDVA and CA are regard as the influence factors to acquire VA. In order to acquire PA of all samples, this work takes CA as the dependent variable, and mines the two most representative indicators from arteriosclerosis data as the independent variables, to establish a regression model for obtaining PA. RESULTS: The proposed VA based on RRDVA is significantly correlated with 27 indirect indicators for vascular aging evaluation. Moreover, VA is better than CA by comparing the correlation coefficients between VA, CA and 27 indirect indicators, and RRDVA greater than zero presents a higher risk of disease. CONCLUSION: The proposed VA overcomes the limitation of CA in characterizing IDVA, which may help young groups with high disease risk to promote healthy behaviors.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups.

BACKGROUND: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data. METHODS: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. FINDINGS: Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. INTERPRETATION: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity. FUNDING: National Institute for Health and Care Research.

Post stroke pain: Is there under-diagnosis in Black versus White patients?

Stroke incidence is higher and stroke outcomes are poorer in Black patients compared to White patients. Poststroke pain, however, is not a well understood stroke outcome. Using the National Institutes of Health All of Us Research Program database, we hypothesized that the dataset would demonstrate proportionately higher relative risk of poststroke pain in the Black poststroke patient population compared to the White poststroke patient population. However, our analysis showed that Black stroke patients were diagnosed with poststroke pain at a similar rate as White stroke patients. As our results are not consistent with other poststroke outcomes in the literature, this study identifies a potentially underdiagnosed patient population, highlighting the need for further research.

Visceral adiposity index as a predictor of type 2 diabetes mellitus risk: A systematic review and dose-response meta-analysis.

AIMS: Considering the positive association between visceral adiposity index (VAI) and type 2 diabetes mellitus (T2DM), no comprehensive assessment on the summarized and dose-response relationship between VAI and T2DM has yet been reported. Therefore, we performed a meta-analysis, including dose-response analysis, to quantitively elucidate this association. DATA SYNTHESIS: MEDLINE via PubMed and Embase databases were searched for relevant articles up to December 14, 2021. Random-effects generalized least squares regression models were used to assess the quantitative association between VAI and T2DM risk across studies. Restricted cubic splines were used to model the dose-response association. A total of 9 prospective cohort studies and 5 cross sectional studies were included in our review. Based on the meta-analysis, the pooled RR of T2DM was 2.05 (95% CI 1.74-2.41) for the highest versus reference VAI category. We found that the risk of T2DM was increased by 44% (RR, 1.44; 95% CI, 1.23-1.68) with each 1-unit increment of VAI. While, we found no evidence of a nonlinear dose-response association of VAI and T2DM (Pnon-linearity = 0.428). With the linear cubic spline model, when compared to population with VAI at 0.6, for those with VAI at 2.0, the risk of T2DM was increased by 81% (RR, 1.81; 95% CI 1.55-2.12). CONCLUSIONS: Our meta-analysis provides quantitative data suggesting that VAI is associated with an increased risk of T2DM. Public health strategies focusing on weight loss among obesity, especially the people characterized by the thin-on-the-outside--fat-on-the-inside phenotype could possibly reduce a substantial risk of T2DM. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022372666.