Search

White spot syndrome virus (WSSV) has emerged as a significant viral pathogen in shrimp aquaculture since it was first identified in 1992. Similar to most crustacean pathogens, cannibalism is the most crucial mechanism for the horizontal transmission of WSSV. While several studies have reported the impact of cannibalism on WSSV transmission, a comprehensive investigation that reflects varying conditions has not yet been conducted. Herein, we investigated the impact of cannibalism on WSSV transmission in whiteleg shrimp (Penaeus vannamei) from the perspective of a relative risk assessment. In Experiment 1, cannibalistic behaviors of healthy whiteleg shrimp were monitored at three different stocking densities. The number of cannibalism incidents increased threefold at higher densities, whereas the odds ratios were not significantly different. In Experiment 2, WSSV infection and mortality were observed in shrimp administered high-severity (G2–G3) tissues (1 to 20% biomass inoculum), whereas shrimp administered low-severity (G0–G1) tissues were not infected with WSSV, regardless of the amount of inoculum. However, shrimp administered high-severity (G2–G3) tissue showed a 3.47-fold increase in hazard as biomass increased from 1 to 20%. Experiment 3 compared feeding challenge tests at 30 °C and 25 °C. When shrimp were administered 10% biomass of high-severity (G2–G3) tissue, the hazard ratio increased 3.88-fold at 25 °C compared with 30 °C. This study found a direct correlation between WSSV severity grade, prevalence, water temperature, and transmission through the cannibalism route. These findings provide insights into WSSV transmission via cannibalism and clarify that proper carcass removal measures can directly aid in inhibiting white spot disease progression. [ABSTRACT FROM AUTHOR]

The risk of treatment‐related toxicities with programmed cell death 1 and its ligand (PD‐1/PD‐L1) inhibitors in patients with lung cancer is unclear and inconclusive. PubMed, EMBASE, and the Cochrane Library databases were systematically searched without language restrictions from inception to May 31, 2024 to identify Phase 3 randomized controlled trials of lung cancer comparing PD‐1/PD‐L1 inhibitors versus placebo/best supportive care (alone or in combination with nontargeted chemotherapy) that had available data regarding treatment‐related adverse events (TRAEs) or incidence and sample size. Random‐effect models were employed to study the pooled relative risk (RR) and 95% confidence intervals (CIs). Finally, 36 trials, involving 19,693 participants, fulfilled the inclusion criteria. PD‐1/PD‐L1 inhibitors significantly augmented the likelihood of developing all‐grade (RR, 1.03; 95% CI, 1.01–1.04, p <.01) and grade ≥3 TRAEs (RR, 1.16; 95% CI, 1.10 to 1.23, p <.01). PD‐1/PD‐L1 inhibitors substantially augmented the odds of developing treatment‐related serious adverse events (SAEs) (RR, 1.48; 95% CI, 1.27–1.71, p <.01) and fatal adverse events (FAEs) (RR, 1.42; 95% CI, 1.11–1.82, p <.01). Subgroup analyses indicated that the RR of SAEs and FAEs were generally consistent, regardless of treatment type, tumor type, treatment setting, PD‐1/PD‐L1 inhibitors type and study design. The most common causes of FAEs were respiratory failure/insufficiency (33.3%), cardiac events (16.1%), and hematological disorders (10.1%). We demonstrated that PD‐1/PD‐L1 inhibitors were significantly correlated with higher possibility of developing treatment‐related toxicities, especially SAEs and FAEs, compared with placebo/best supportive care controls. [ABSTRACT FROM AUTHOR]

Background: The aim of this paper was to evaluate the distribution of HIV and TB in Ethiopia during four years (2015-2018) at the district level, considering both spatial and temporal patterns. Methods: Consolidated data on the count of TB case notifications and the number of patients with HIV for four years, 2015-2018, were provided by the Ethiopian Federal Ministry of Health. The data was analyzed using the Bayesian hierarchical approach, employing joint spatiotemporal modelling. The integrated nested Laplace approximation available in the R-INLA package was used to fit six models, each with different priors, for the precision parameters of the random effects variances. The best-fitting model with the best predictive capacity was selected using the Deviance Information Criterion and the negative sum of cross-validatory predictive log-likelihood. Results: According to the findings of the selected model, about 53% of the variability in TB and HIV incidences in the study period was explained by the shared temporal component, disease-specific spatial effect of HIV, and space-time interaction effect. The shared temporal trend and disease-specific temporal trend of HIV risk showed a slight upward trend between 2015 and 2017, followed by a slight decrease in 2018. However, the disease-specific temporal trend of TB risk had almost constant trend with minimal variation over the study period. The distribution of the shared relative risks was similar to the distribution of disease-specific TB relative risk, whereas that of HIV had more districts as high-risk areas. Conclusions: The study showed the spatial similarity in the distribution of HIV and TB case notifications in specific districts within various provinces. Moreover, the shared relative risks exhibit a temporal pattern and spatial distribution that closely resemble those of the relative risks specific to HIV illness. The existence of districts with shared relative risks implies the need for collaborative surveillance of HIV and TB, as well as integrated interventions to control the two diseases jointly. [ABSTRACT FROM AUTHOR]

Background: To date, several studies have demonstrated that erroneous labeling of Penicillin allergy (PAL) can significantly impact treatment options and result in adverse clinical outcomes, while other studies have reported no negative effects. Therefore, to systematically evaluate these effects and investigate the association between adverse clinical outcomes and the Penicillin label, we conducted this meta-analysis. Method: Searches were conducted in the PubMed, Embase, Cochrane Library, and Web of Science databases from inception to 13 July 2024. The search strategy utilized terms ("antibiotic allergy label," "penicillin allergy label," and "allergy label") and ("death," "readmission," "adverse outcome," and "clinical adverse outcome"). In the study selection process, the PICOS framework and stringent inclusion/exclusion criteria were applied. The quality of the initially included studies was independently assessed using the Newcastle-Ottawa Scale (NOS). Data from the included studies, including relative risk (RR) and 95% confidence intervals (CI), were extracted and analyzed using Stata 16.0. Sensitivity analyses were conducted to validate the results. Heterogeneity was assessed using the I2 and Cochrane Q tests, and publication bias was evaluated using Egger's test and funnel plot analysis. Results: A total of 497 relevant studies were identified through four databases. Following a thorough screening process, 11 studies encompassing 1,200,785 participants were ultimately included. The combined evidence suggests that penicillin allergy labeling is associated with increased mortality RR = 1.06 (95% CI 1.06–1.07, I2 = 0.00%), acute heart failure (RR = 1.19, 95% CI 1.09–1.30, τ2 = 0.00, I2 = 92.39%), ICU events (RR = 1.10, 95% CI 1.01–1.19, τ2 = 0.00, I2 = 57.09%), and mechanical ventilation events (RR = 1.16, 95% CI 1.09–1.24, τ2 = 0.00, I2 = 23.11%). Additionally, there was no significant association with readmissions (RR = 1.05, 95% CI 0.95–1.16, I2 = 0.00%). Conclusion: Our findings indicate that penicillin allergy labels are associated with an increased risk of mortality in patients, as well as being linked to acute heart failure, heightened ICU requirements, and mechanical ventilation. Systematic Review Registration: PROSPERO, identifier CRD42024571535. Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD4202457153. [ABSTRACT FROM AUTHOR]

Spatial, temporal, and space–time scan statistics can be used for geographical surveillance, identifying temporal and spatial patterns, and detecting outliers. While statistical cluster analysis is a valuable tool for identifying patterns, optimizing resource allocation, and supporting decision-making, accurately predicting future spatial clusters remains a significant challenge. Given the known relative risks of spatial clusters over the past k time intervals, the main objective of the present study is to predict the relative risks for the subsequent interval, k + 1 . Building on our prior research, we propose a predictive Markov chain model with an embedded corrector component. This corrector utilizes either multiple linear regression or an exponential smoothing method, selecting the one that minimizes the relative distance between the observed and predicted values in the k-th interval. To test the proposed method, we first calculated the relative risks of statistically significant spatial clusters of COVID-19 mortality in the U.S. over seven time intervals from May 2020 to March 2023. Then, for each time interval, we selected the top 25 clusters with the highest relative risks and iteratively predicted the relative risks of clusters from intervals three to seven. The predictive accuracies ranged from moderate to high, indicating the potential applicability of this method for predictive disease analytic and future pandemic preparedness. [ABSTRACT FROM AUTHOR]

Background: Guidelines for randomised controlled trials (RCTs) recommend reporting relative and absolute measures of effect for binary outcomes while adjusting for covariates. There are a number of different ways covariate-adjusted relative risks and risk differences can be estimated. Objectives: Our goal was to identify methods used to estimate covariate-adjusted relative risk and risk differences in RCTs published in high-impact journals with binary outcomes. Other secondary objectives included the identification of how covariates are chosen for adjustment and whether covariate adjustment results in an increase in statistical precision in practice. Methods: We included two-arm parallel RCTs published in JAMA, NEJM, Lancet, or the BMJ between January 1, 2018, and March 11, 2023, reporting relative risks or risk differences as a summary measure for a binary primary outcome. The search was conducted in Ovid-MEDLINE. Results: Of the 308 RCTs identified, around half (49%; 95% CI: 43–54%) reported a covariate-adjusted relative risk or risk difference. Of these, 82 reported an adjusted relative risk. When the reporting was clear (n = 65, 79%), the log-binomial model (used in 65% of studies; 95% CI: 52–76%) and modified Poisson (29%; 95% CI: 19–42%) were most commonly used. Of the 92 studies that reported an adjusted risk difference, when the reporting was clear (n = 56, 61%), the binomial model (used in 48% of studies; 95% CI: 35–62%) and marginal standardisation (21%; 95% CI: 12–35%) were the common approaches used. Conclusions: Approximately half of the RCTs report either a covariate-adjusted relative risk or risk difference. Many RCTs lack adequate details on the methods used to estimate covariate-adjusted effects. Of those that do report the approaches used, the binomial model, modified Poisson and to a lesser extent marginal standardisation are the approaches used. [ABSTRACT FROM AUTHOR]

PurposeMethodsResultsConclusionTo evaluate the incidence, refractive error (RE) association, and distribution of atraumatic rhegmatogenous retinal detachment (RRD) in U.S. military service members (SMs).This study used data from the Military Health System (MHS) M2 database to identify active U.S. military and National Guard SMs diagnosed with RRD from 2017 to 2022. The RE in diopters (D) was manually extracted from available medical charts for 518 eyes. The annual incidence rate of RRD was calculated overall and evaluated in terms of age, gender, and RE. A multivariate Poisson regression model was used to estimate the relative risk (RR) for RRD with RE.From 2017 to 2022, 1,537 SMs were diagnosed with RRD and 1,243,189 were diagnosed with RE. One thousand two hundred seventy-five SMs had both diagnoses: RRD and RE. The overall incidence rate of RRD over the 6-year study was 16.3 per 100,000 people (16.4 and 15.9 for males and females, respectively). In all study groups, the incidence of RRD increased with age. SMs with RE had an overall 25-fold increased risk for RRD compared to SMs without RE. RE was present in 83.0% of cases of RRD. Myopia accounted for 93.3% of cases for eyes with detailed refractive data.The incidence of RRD in U.S. SMs is comparable to other studies and is similar among male and female SMs. RE is present in most cases of RRD in SMs, with the most common type being low to moderate amounts of myopia. [ABSTRACT FROM AUTHOR]

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two‐step meta‐analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one‐shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP‐B. We evaluate the performance of our method through both simulation studies and real‐world case analyses of postacute sequelae of SARS‐CoV‐2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta‐analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication‐efficient and privacy‐preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two‐step meta‐analysis methods. Overall, ODAP‐B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator. [ABSTRACT FROM AUTHOR]

ABSTRACT: Background: Smokeless tobacco (SLT) use, particularly gutka and khaini, is a significant public health concern in India, mainly in rural areas. This prospective cohort study aimed to investigate the association between SLT use and oral cancer incidence among rural Indian men. Methods: A cohort of 300 rural Indian men without a history of oral cancer was recruited and followed for five years. Baseline data on SLT use (type, frequency, and duration) and other risk factors were collected. Annual oral examinations and medical record reviews were conducted to identify incident cases of oral cancer. Incidence rates, relative risks, and hazard ratios (HR) were calculated to assess the association between SLT use and oral cancer. Results: Among the 300 participants, 185 (61.7%) were SLT users at baseline. During follow-up, 25 incident cases of oral cancer were diagnosed. The incidence rate of oral cancer was significantly higher among SLT users (24.3 per 1,000 person-years) compared to non-users (5.4 per 1,000 person-years). After adjusting for confounders, the HR for oral cancer associated with SLT use was 4.51 (95% CI: 2.03–9.98), indicating a significantly increased risk. A dose–response relationship was observed, with higher frequency and longer duration of SLT use associated with increased risk. Conclusions: This study provides strong evidence for a significant association between SLT use and increased risk of oral cancer among rural Indian men. The findings underscore the urgent need for targeted interventions to reduce SLT use and promote oral cancer awareness in this high-risk population. [ABSTRACT FROM AUTHOR]

Background and objectives: Although recent meta-analyses have shown that the association between physical activity (PA) and the risk of developing Parkinson's disease (PD) is influenced by gender differences, a growing number of studies are revealing the general applicability of this association across genders. This study aimed to reassess the association and dose–response relationship between PA and PD risk in populations. Methods: A systematic search of PubMed, Embase, Cochrane Library, and Web of Science databases was conducted in this study from inception to February 1, 2024, without language restrictions. Stratified analyses were conducted to explore the association between PA and PD risk, combining multivariate-adjusted effect estimates via random-effects models, and to validate the dose–response relationship between the two. Results: This study included 21 observational studies, comprising 13 cohort studies and 8 case–control studies. The pooled analysis revealed that PA significantly reduced the risk of developing PD [relative risk (RR) = 0.77, 95% CI 0.70–0.85]. In addition, the dose–response analysis revealed both linear and nonlinear associations, with linear results indicating a 9% reduction in PD risk for every 10 MET-h/wk increase in PA. The study also demonstrated that the protective effect of PA against PD was significant for both sexes. Moreover, no statistically significant effects of PA on preventing PD were observed in individuals with a BMI > 26 (RR = 0.35, 95% CI 0.12–1.02) or in Asian populations (RR = 0.78, 95% CI 0.60–1.01); however, the trends suggest potential protective effects, warranting further investigation. Sensitivity analyses confirmed the robustness of these findings. Conclusion: This meta-analysis produced substantial evidence to reaffirm the protective effect of high PA on PD across various population groups and the inverse dose–response relationship with PD risk, and to validate the protective effect of PA among different demographic groups. [ABSTRACT FROM AUTHOR]

Abstract Background Thyroid dysfunction in pregnancy can adversely impact maternal and fetal outcomes. However, the association between thyroid status and specific adverse outcomes needs clarity, especially in understudied regions. Objective This prospective cohort study aimed to illuminate the multifaceted associations between maternal thyroid dysfunction and feto-maternal outcomes in Gujarat, India. Methods This hospital-based cohort study recruited and monitored 500 euthyroid, 250 hypothyroid, and 150 hyperthyroid pregnant women until delivery. Maternal thyroid status was determined by serum thyroid stimulating hormone (TSH) and free thyroxine (fT4) levels. Adverse fetal outcomes included preterm birth, neonatal intensive care unit (NICU) admission, respiratory issues, and low APGAR scores. Maternal outcomes included preeclampsia, haemorrhage, hypertension, postpartum thyroiditis, and thromboembolism. Relative risks quantified associations between thyroid dysfunction and outcomes. Results Compared to euthyroid women, hypothyroid women had a higher RR for preterm birth (RR 1.8, 95% CI 1.1–3.0), low APGAR score (RR 2.5, 95% CI 1.5–4.1), preeclampsia (RR 3.0, 95% CI 1.9–4.8), postpartum haemorrhage (RR 1.6, 95% CI 1.2–2.1), and venous thromboembolism (RR 3.1, 95% CI 1.7–5.7). Hyperthyroid women had over twice the risk of low APGAR score (RR 1.8, 95% CI 0.9–3.5), neonatal hypoglycemia (RR 1.5, 95% CI 0.5–4.3), respiratory distress (RR 1.4, 95% CI 0.7–2.8), and postpartum thyroiditis (RR 2.3, 95% CI 1.1–4.8). Conclusion Maternal thyroid dysfunction escalates risks for adverse fetal and maternal outcomes. Thyroid monitoring and management during pregnancy are critical to mitigate complications.

Abstract Background With shared modes of transmission and clinical symptoms the convergence of COVID-19 and tuberculosis (TB) might lead to reduced diagnosis and detection of TB, which is challenging for healthcare systems already strained by the pandemic's reach. Methods This ecological study investigated the impact of the COVID-19 pandemic on TB surveillance over the first 2 years of the pandemic (March 2020 to February 2022) in southeastern Iran. Interrupted Time Series (ITS) analysis with the quasi-Poisson regression models was used to estimate the relative risk (RR) of TB diagnosis and treatment outcome counts, stratified by gender, case definition, involvement type, and treatment outcomes. Results The ITS analyses showed a significant decrease in TB total cases (RR: 0.622 [95% CI: 0.487, 0.793], P

Abstract Background Air pollution is a major environmental risk factor. Hypertension is one of the most important modifiable risk factors for cardiovascular diseases. However, few studies have evaluated the impact of exposure to ambient air pollution on hypertension hospitalizations. Objective This study aims to explore the correlation between exposure to air pollution and hospital admissions for hypertension, to evaluate the short-term effects of air pollution on hypertension hospitalizations and its lag effects. Methods We collected air pollution concentration and hypertension hospitalization data from 2013 to 2020. Distributed lag non-linear models were employed to assess the impact of air pollution on hypertension hospitalizations in Lanzhou City. We also performed subgroup analyses and sensitivity analyses. Results A total of 47,884 cases of hypertension hospitalizations were included. Short-term exposure to NO2 and CO increased the risk of hypertension hospitalization. For each 10 µg/m3 increase in NO2 and each 1 mg/m3 increase in CO, the relative risk (RR) for hypertension hospitalization were highest at lag0-3 (RR: 1.0427; 95% CI: 1.0196, 1.0663) and lag3 (RR: 1.0986; 95% CI: 1.0350, 1.1661), respectively. SO2 at different lag structures showed opposite effects on hypertension hospitalization. O3 was significantly negatively associated with hypertension hospitalization. No correlation was observed between PM2.5 and PM10 and hypertension hospitalization. Females and individuals aged ≥ 65 were more susceptible to air pollution. The harmful effects of air pollution are greater in the cold season. Conclusion Short-term exposure to ambient air pollution increases risk of hypertension hospitalization.

Abstract Background A Bayesian approach may be useful in the study of possible treatment-related rare serious adverse events, particularly when there are strongly held opinions in the absence of good quality previous data. We demonstrate the application of a Bayesian analysis by integrating expert opinions with population-based epidemiologic data to investigate the association between chiropractic care and acute lumbar disc herniation (LDH) with early surgery. Methods Experts’ opinions were used to derive probability distributions of the incidence rate ratio (IRR) for acute LDH requiring early surgery associated with chiropractic care. A ‘community of priors’ (enthusiastic, neutral, and skeptical) was built by dividing the experts into three groups according to their perceived mean prior IRR. The likelihood was formed from the results of a population-based epidemiologic study comparing the relative incidence of acute LDH with early surgery after chiropractic care versus primary medical care, with sensitive and specific outcome case definitions and surgery occurring within 8- and 12-week time windows after acute LDH. The robustness of results to the community of priors and specific versus sensitive case definitions was assessed. Results The most enthusiastic 25% of experts had a prior IRR of 0.42 (95% credible interval [CrI], 0.03 to 1.27), while the most skeptical 25% of experts had a prior IRR of 1.66 (95% CrI, 0.55 to 4.25). The Bayesian posterior estimates across priors and outcome definitions ranged from an IRR of 0.39 (95% CrI, 0.21 to 0.68) to an IRR of 1.40 (95% CrI, 0.52 to 2.55). With a sensitive definition of the outcome, the analysis produced results that confirmed prior enthusiasts’ beliefs and that were precise enough to shift prior beliefs of skeptics. With a specific definition of the outcome, the results were not strong enough to overcome prior skepticism. Conclusion A Bayesian analysis integrating expert beliefs highlighted the value of eliciting informative priors to better understand how new evidence ought to update prior existing beliefs. Clinical epidemiologists are encouraged to integrate informative and expert opinions representing the end-user community of priors in Bayesian analyses, particularly when there are strongly held opinions in the absence of definitive scientific evidence.

Abstract Background Specific food preferences can determine an individual’s dietary patterns and therefore, may be associated with certain health risks and benefits. Methods Using food preference questionnaire (FPQ) data from a subset comprising over 180,000 UK Biobank participants, we employed Latent Profile Analysis (LPA) approach to identify the main patterns or profiles among participants. blood biochemistry across groups/profiles was compared using the non-parametric Kruskal–Wallis test. We applied the Limma algorithm for differential abundance analysis on 168 metabolites and 2923 proteins, and utilized the Database for Annotation, Visualization and Integrated Discovery (DAVID) to identify enriched biological processes and pathways. Relative risks (RR) were calculated for chronic diseases and mental conditions per group, adjusting for sociodemographic factors. Results Based on their food preferences, three profiles were termed: the putative Health-conscious group (low preference for animal-based or sweet foods, and high preference for vegetables and fruits), the Omnivore group (high preference for all foods), and the putative Sweet-tooth group (high preference for sweet foods and sweetened beverages). The Health-conscious group exhibited lower risk of heart failure (RR = 0.86, 95%CI 0.79–0.93) and chronic kidney disease (RR = 0.69, 95%CI 0.65–0.74) compared to the two other groups. The Sweet-tooth group had greater risk of depression (RR = 1.27, 95%CI 1.21–1.34), diabetes (RR = 1.15, 95%CI 1.01–1.31), and stroke (RR = 1.22, 95%CI 1.15–1.31) compared to the other two groups. Cancer (overall) relative risk showed little difference across the Health-conscious, Omnivore, and Sweet-tooth groups with RR of 0.98 (95%CI 0.96–1.01), 1.00 (95%CI 0.98–1.03), and 1.01 (95%CI 0.98–1.04), respectively. The Health-conscious group was associated with lower levels of inflammatory biomarkers (e.g., C-reactive Protein) which are also known to be elevated in those with common metabolic diseases (e.g., cardiovascular disease). Other markers modulated in the Health-conscious group, ketone bodies, insulin-like growth factor-binding protein (IGFBP), and Growth Hormone 1 were more abundant, while leptin was less abundant. Further, the IGFBP pathway, which influences IGF1 activity, may be significantly enhanced by dietary choices. Conclusions These observations align with previous findings from studies focusing on weight loss interventions, which include a reduction in leptin levels. Overall, the Health-conscious group, with preference to healthier food options, has better health outcomes, compared to Sweet-tooth and Omnivore groups. Graphical Abstract

Abstract Background Scabies is one of public health concerns among communicable disease in Ethiopia, especially among disadvantaged and the poor. This current study aims to detect the spatiotemporal patterns of scabies in Tigray from 2018 to 2023 using scabies data aggregated at the zonal level. The study also examined the persistent patterns in the spatial variation of scabies incidence across the administrative regions during the study period. Method We collected scabies data using a weekly disease surveillance reporting format of the country from 2018 to 2023 across all accessible district health facilities in Tigray region, Ethiopia. We conducted retrospective analyses using both purely spatial and spatiotemporal scan statistic approaches, employing a discrete Poisson probability model to identify statistically significant clusters of high scabies rates throughout the Tigray regional zones in Ethiopia. Our methodology involved the use of Kulldorff’s spatial scan statistic software (SaTScan v10.1.3), R programming software version 4.3.1, and ArcGIS Pro for all analyses. Results A total of 101,116 cases of scabies were reported from 2018 to 2023. Our study indicated a spatial heterogeneity in the pattern of scabies across Tigray region as well as its localization among geographically contiguous zones across space, except for the Western zone of Tigray where no data was collected. The detected statistically significant spatial clusters $$\:p-value

Abstract 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 $$PM_{10}$$ P M 10 , but little research has been conducted in Malaysia, particularly during the haze season. This study intends to investigate how exposure of $$PM_{10}$$ 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 $$PM_{10}$$ 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 $$PM_{10}$$ 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, $$PM_{10}$$ 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 $$PM_{10}$$ P M 10 exposure. Working age group will be the most affected by the increase in $$PM_{10}$$ P M 10 exposure with a significant cumulative RR from lag 010 to lag 030. The study found that $$PM_{10}$$ 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 $$PM_{10}$$ P M 10 and reduce the burden of respiratory disease admissions.

Background: With shared modes of transmission and clinical symptoms the convergence of COVID-19 and tuberculosis (TB) might lead to reduced diagnosis and detection of TB, which is challenging for healthcare systems already strained by the pandemic's reach. Methods: This ecological study investigated the impact of the COVID-19 pandemic on TB surveillance over the first 2 years of the pandemic (March 2020 to February 2022) in southeastern Iran. Interrupted Time Series (ITS) analysis with the quasi-Poisson regression models was used to estimate the relative risk (RR) of TB diagnosis and treatment outcome counts, stratified by gender, case definition, involvement type, and treatment outcomes. Results: The ITS analyses showed a significant decrease in TB total cases (RR: 0.622 [95% CI: 0.487, 0.793], P < 0.001), new cases (RR: 0.632 [95% CI: 0.493, 0.810], P < 0.001) and recurrent cases (RR: 0.491 [95% CI: 0.247, 0.974], P < 0.001). In addition, recovery and treatment failure counts also showed significant decreases (RR: 0.751 [95% CI: 0.566, 0.996], P = 0.05; RR: 0.201 [95% CI: 0.054, 0.738], P = 0.02). Moreover, significant decreases are observed in both genders and involvement types (pulmonary and extrapulmonary TB). No significant change was observed for absent to treatment and death counts. Conclusion: The COVID-19 pandemic has negatively impacted TB diagnosis and treatments. Concerns are risen about the progress achieved in TB control. [ABSTRACT FROM AUTHOR]

Background: A Bayesian approach may be useful in the study of possible treatment-related rare serious adverse events, particularly when there are strongly held opinions in the absence of good quality previous data. We demonstrate the application of a Bayesian analysis by integrating expert opinions with population-based epidemiologic data to investigate the association between chiropractic care and acute lumbar disc herniation (LDH) with early surgery. Methods: Experts' opinions were used to derive probability distributions of the incidence rate ratio (IRR) for acute LDH requiring early surgery associated with chiropractic care. A 'community of priors' (enthusiastic, neutral, and skeptical) was built by dividing the experts into three groups according to their perceived mean prior IRR. The likelihood was formed from the results of a population-based epidemiologic study comparing the relative incidence of acute LDH with early surgery after chiropractic care versus primary medical care, with sensitive and specific outcome case definitions and surgery occurring within 8- and 12-week time windows after acute LDH. The robustness of results to the community of priors and specific versus sensitive case definitions was assessed. Results: The most enthusiastic 25% of experts had a prior IRR of 0.42 (95% credible interval [CrI], 0.03 to 1.27), while the most skeptical 25% of experts had a prior IRR of 1.66 (95% CrI, 0.55 to 4.25). The Bayesian posterior estimates across priors and outcome definitions ranged from an IRR of 0.39 (95% CrI, 0.21 to 0.68) to an IRR of 1.40 (95% CrI, 0.52 to 2.55). With a sensitive definition of the outcome, the analysis produced results that confirmed prior enthusiasts' beliefs and that were precise enough to shift prior beliefs of skeptics. With a specific definition of the outcome, the results were not strong enough to overcome prior skepticism. Conclusion: A Bayesian analysis integrating expert beliefs highlighted the value of eliciting informative priors to better understand how new evidence ought to update prior existing beliefs. Clinical epidemiologists are encouraged to integrate informative and expert opinions representing the end-user community of priors in Bayesian analyses, particularly when there are strongly held opinions in the absence of definitive scientific evidence. [ABSTRACT FROM AUTHOR]

Binary logistic regression is one of the most widely used statistical tools. The method uses odds, log odds, and odds ratios, which are difficult to understand and interpret. Understanding of logistic regression tends to fall down in one of three ways: (1) Many students and researchers come to believe that an odds ratio translates directly into relative probabilities. (2) Alternatively, they learn that coefficients tell us whether the variables make the outcome more or less likely, without knowing how to interpret changes in the odds. (3) They may be instructed in how to calculate predicted probabilities, but the additional steps are too complicated for them to follow. Our key aim is to highlight and correct the common mistake of confusing differences in odds with relative risks. Simply reporting the odds ratio is unhelpful, however, so we describe an easy method of estimating probabilities for both binary and continuous variables. [ABSTRACT FROM AUTHOR]

INTRODUCTION: To study digestive system cancer risks in individuals with inflammatory bowel diseases (IBDs) in the biologic era. METHODS: We used population-level administrative and cancer registry data from Ontario, Canada, (1994-2020) to compare people with IBD to matched controls (1:10 by sex and birth year) on trends in age-sex standardized cancer incidence and risk ratios of incident cancers and cancer-related deaths. RESULTS: Among 110,919 people with IBD and 1,109,190 controls, colorectal cancer incidence (per 100,000 person-years) declined similarly in people with ulcerative colitis (average annual percentage change [AAPC] 21.81; 95% confidence interval [CI] 22.48 to 21.156) and controls (AAPC 22.79; 95% CI 23.44 to 22.14), while small bowel cancer incidence rose faster in those with Crohn's disease (AAPC 9.68; 95% CI 2.51-17.3) than controls (AAPC 3.64; 95% CI 1.52-5.80). Extraintestinal digestive cancer incidence rose faster in people with IBD (AAPC 3.27; 95% CI 1.83-4.73) than controls (AAPC 21.87;95%CI22.33to21.42),particularly for liver (IBDAAPC8.48;95%CI 4.11-13.1) and bile duct (IBD AAPC 7.22; 95% CI 3.74-10.8) cancers. Beyond 2010, the incidences (and respective mortality rates) of colorectal (1.60; 95% CI 1.46-1.75), small bowel (4.10; 95% CI 3.37-4.99), bile duct (2.33; 95%CI 1.96-2.77), and pancreatic (1.19; 95% CI 1.00-1.40) cancers were higher in people with IBD. DISCUSSION: Cancer incidence is declining for colorectal cancer and rising for other digestive cancers in people with IBD. Incidence and mortality remain higher in people with IBD than controls for colorectal, small bowel, bile duct, and pancreatic cancers. [ABSTRACT FROM AUTHOR]

Background: Ambient particulate matter of ≤2.5 μm in diameter (PM2.5) is named as a risk factor for cerebrovascular diseases. This investigation aimed to evaluate the impact of ambient air pollution with PM2.5 on stroke occurrence. Methods: The study was performed in Kaunas, Lithuania, from 2010 to 2022. The daily numbers of ISs, subarachnoid hemorrhages (SAHs), and intracerebral hemorrhages (ICHs) were obtained from the Kaunas Stroke Register. The association between stroke occurrence and PM2.5 exposure was assessed by time- and seasonally stratified Poisson regression. Results: Among middle-aged persons, 3377 had a stroke, of which 2686 (79.5%) had an IS, 469 (13.9%) had an ICH, and 222 (6.6%) had SAH. The relative risk (RR) of SAH was increased by 1.7% with an increase in daily PM2.5 by 1 μg/m3 on the same day and at a lag of 1 day, and by 2.2% with an increase in mean PM2.5 concentration at a lag 0–1 days by 1 μg/m3. The RR of having a SAH was increased by 0.7% with an increase in daily PM2.5 by 1 μg/m3 on the same day. Conclusions: Significant associations between stroke occurrence and air pollution with PM2.5 were found in the SAH and HS patients, and only in middle-aged subjects. [ABSTRACT FROM AUTHOR]

Background: Scabies is one of public health concerns among communicable disease in Ethiopia, especially among disadvantaged and the poor. This current study aims to detect the spatiotemporal patterns of scabies in Tigray from 2018 to 2023 using scabies data aggregated at the zonal level. The study also examined the persistent patterns in the spatial variation of scabies incidence across the administrative regions during the study period. Method: We collected scabies data using a weekly disease surveillance reporting format of the country from 2018 to 2023 across all accessible district health facilities in Tigray region, Ethiopia. We conducted retrospective analyses using both purely spatial and spatiotemporal scan statistic approaches, employing a discrete Poisson probability model to identify statistically significant clusters of high scabies rates throughout the Tigray regional zones in Ethiopia. Our methodology involved the use of Kulldorff's spatial scan statistic software (SaTScan v10.1.3), R programming software version 4.3.1, and ArcGIS Pro for all analyses. Results: A total of 101,116 cases of scabies were reported from 2018 to 2023. Our study indicated a spatial heterogeneity in the pattern of scabies across Tigray region as well as its localization among geographically contiguous zones across space, except for the Western zone of Tigray where no data was collected. The detected statistically significant spatial clusters persisted mainly in the Central, Eastern and Northwestern zones of Tigray over the six years of the study period. The highest relative risk (RR) was recorded in year 2021 (. The central zone had the major clusters of scabies at district level from 2018 to 2023. The heterogeneous distribution of scabies across Tigray could be due to the spatial variations in the determinants of scabies (such as socioeconomic status, demographics, and material deprivation) across the region. Conclusion: An enormous burden of scabies was reported over a period of six years. The present study found localized clusters of high scabies rates at district and zonal levels in Tigray, Ethiopia, possibly due to differences in various determinants of scabies such as access to WASH services. The findings could help the government and health authorities to develop and implement scabies control strategies in Tigray, with a focus on high-risk districts and zones to ensure optimal resource allocation. [ABSTRACT FROM AUTHOR]

The social defeat hypothesis posits that low status and repeated humiliation increase the risk for psychotic disorders (PDs) and psychotic experiences (PEs). The purpose of this paper was to provide a systematic review of studies on risk of PDs and PEs among lesbian, gay, or bisexual (LGB) people and a quantitative synthesis of any difference in risk. PubMed, PsycINFO, Embase, and Web of Science were searched from database inception until January 30, 2024. Two independent reviewers assessed the eligibility and quality of studies, extracted effect sizes, and noted the results of mediation analyses. Using a random effects model we computed pooled odds ratios (ORs). Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The search identified seven studies of PDs and six of PEs. As for PDs, the unadjusted (2.13; 95% confidence interval 0.72–6.34) and covariate-adjusted pooled OR (2.24; 1.72–3.53) were not significantly increased for LGB individuals. After exclusion of a study of limited quality, both the unadjusted pooled OR (2.77; 1.21–6.32) and the covariate-adjusted pooled OR (2.67; 1.53–4.66) were significantly increased. The pooled ORs were increased for PEs: unadjusted, pooled OR = 1.97 (1.47–2.63), covariate-adjusted, pooled OR = 1.85 (1.50–2.28). Studies of PE that examined the mediating role of several variables reported that the contribution of drug abuse was small compared to that of psychosocial stressors. The results of a study in adolescents suggested a protective effect of parental support. These findings suggest an increased psychosis risk for LGB people and support the social defeat hypothesis. [ABSTRACT FROM AUTHOR]

The severity of food insecurity in Ethiopia is among the worst globally, with recordbreaking food assistance needs by various factors. This study tries to uncover various aspects of food security related to food availability, food access, food utilization and food stability dimensions of food security. The study was executed in the west Hararghe zone of the Oromia region of Ethiopia. Its main objective was to determine the food security status of households, with specific objectives aiming at computing individual daily kilo calorie intake, determining the average food requirement, comparing the relative risk between food secure and insecure households and identifying factors that affect household food security status. A multistage sampling procedure, involving the purposive selection of; the Zone and Woreda, and a simple random selection of households, to draw the required number of representative sample respondents, was adopted. Hence, from 4163 total households, 134 were selected for collecting primary data. Data were analyzed using descriptive statistics, relative risk analysis using Cohorts and independent tests using ANOVA. Food security status was measured using the Household Food Balance Model (HFBM). Using this model, the study obtained 1973 kilo calories per individual per day intake and 2108 kilo calorie as their requirement, which indicated households were energy deficient by 135 kilo calorie with 2-sided-value < 0.1 critical level (t= -1.747, df =133 & sig2-sided. 0.083). Independent test results showed that farm income (at p-value < 0.1), gross product (at p-value < 0.05), age of household head (at p-value < 0.1), years of owning land (at p < 0.1) and family size (at p-value < 0.1) contributed significantly to food security status. Cross-tabulation showed a linear relationship between food security and higher education level (at p-value < 0.05), which urges a concern in improving farming societies' literacy to the desired level. In the study areas, about 91.8% of the population produces crops depending on the seasonal rainfall and their daily kilo calorie consumed depends on it; this is statistically correlated with the food insecurity problem at p-value < 0.1 critical level (lambada = 1.75 & sig. = 0.08). Therefore, to tackle the food insecurity problem, alternative method of crop production should be devised such as the establishment of irrigation schemes, water harvesting structures, and water conservation practices, among others. [ABSTRACT FROM AUTHOR]

Background: Specific food preferences can determine an individual's dietary patterns and therefore, may be associated with certain health risks and benefits. Methods: Using food preference questionnaire (FPQ) data from a subset comprising over 180,000 UK Biobank participants, we employed Latent Profile Analysis (LPA) approach to identify the main patterns or profiles among participants. blood biochemistry across groups/profiles was compared using the non-parametric Kruskal–Wallis test. We applied the Limma algorithm for differential abundance analysis on 168 metabolites and 2923 proteins, and utilized the Database for Annotation, Visualization and Integrated Discovery (DAVID) to identify enriched biological processes and pathways. Relative risks (RR) were calculated for chronic diseases and mental conditions per group, adjusting for sociodemographic factors. Results: Based on their food preferences, three profiles were termed: the putative Health-conscious group (low preference for animal-based or sweet foods, and high preference for vegetables and fruits), the Omnivore group (high preference for all foods), and the putative Sweet-tooth group (high preference for sweet foods and sweetened beverages). The Health-conscious group exhibited lower risk of heart failure (RR = 0.86, 95%CI 0.79–0.93) and chronic kidney disease (RR = 0.69, 95%CI 0.65–0.74) compared to the two other groups. The Sweet-tooth group had greater risk of depression (RR = 1.27, 95%CI 1.21–1.34), diabetes (RR = 1.15, 95%CI 1.01–1.31), and stroke (RR = 1.22, 95%CI 1.15–1.31) compared to the other two groups. Cancer (overall) relative risk showed little difference across the Health-conscious, Omnivore, and Sweet-tooth groups with RR of 0.98 (95%CI 0.96–1.01), 1.00 (95%CI 0.98–1.03), and 1.01 (95%CI 0.98–1.04), respectively. The Health-conscious group was associated with lower levels of inflammatory biomarkers (e.g., C-reactive Protein) which are also known to be elevated in those with common metabolic diseases (e.g., cardiovascular disease). Other markers modulated in the Health-conscious group, ketone bodies, insulin-like growth factor-binding protein (IGFBP), and Growth Hormone 1 were more abundant, while leptin was less abundant. Further, the IGFBP pathway, which influences IGF1 activity, may be significantly enhanced by dietary choices. Conclusions: These observations align with previous findings from studies focusing on weight loss interventions, which include a reduction in leptin levels. Overall, the Health-conscious group, with preference to healthier food options, has better health outcomes, compared to Sweet-tooth and Omnivore groups. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article deal with the proposal of suggest and study of the properties of pre-test shrinkage estimators of Reliability Function for the Burr XII distribution using Progressive Type II censored sample. Since some difficulties to derive equations of risk function for proposed shrinkage estimators of reliability function under Precautionary Loss Function (PLF), we to study properties by using Monte-Carlo simulation. The numerical and Monte-Carlo simulations show that the performance of the proposed estimators is better than classical estimators in terms of relative risk. [ABSTRACT FROM AUTHOR]

Background: Hypertension is an important cause of cardiovascular-related morbidity and mortality. This study aimed to evaluate the effect of demographic, biochemical, and lifestyle variables on hypertension in the last five years in Kerman, Iran. Methods: About 2055 adults were randomly selected through single-stage cluster sampling. Demographic characteristics, biochemical variables, tobacco use, opium use, mood, and physical activity were examined. The effect of the studied variables on hypertension was also measured. COPY methods were used to estimate relative risk (RR) and create confidence intervals. All statistical analysis were done in R 4.4.1. Results: Based on the results obtained using the multivariable COPY method for hypertension, the effects of age, education, physical activity, diabetes, obesity, smoking, anxiety, and depression were significant. It was also found that people with a family history of hypertension and a history of diabetes, obesity, and smoking are at higher risk for hypertension. Conclusion: The present study showed that the results of this study can be effective in designing and implementing intervention programs for the control and prevention of hypertension in this area. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

BackgroundTo date, several studies have demonstrated that erroneous labeling of Penicillin allergy (PAL) can significantly impact treatment options and result in adverse clinical outcomes, while other studies have reported no negative effects. Therefore, to systematically evaluate these effects and investigate the association between adverse clinical outcomes and the Penicillin label, we conducted this meta-analysis.MethodSearches were conducted in the PubMed, Embase, Cochrane Library, and Web of Science databases from inception to 13 July 2024. The search strategy utilized terms (“antibiotic allergy label,” “penicillin allergy label,” and “allergy label”) and (“death,” “readmission,” “adverse outcome,” and “clinical adverse outcome”). In the study selection process, the PICOS framework and stringent inclusion/exclusion criteria were applied. The quality of the initially included studies was independently assessed using the Newcastle-Ottawa Scale (NOS). Data from the included studies, including relative risk (RR) and 95% confidence intervals (CI), were extracted and analyzed using Stata 16.0. Sensitivity analyses were conducted to validate the results. Heterogeneity was assessed using the I2 and Cochrane Q tests, and publication bias was evaluated using Egger’s test and funnel plot analysis.ResultsA total of 497 relevant studies were identified through four databases. Following a thorough screening process, 11 studies encompassing 1,200,785 participants were ultimately included. The combined evidence suggests that penicillin allergy labeling is associated with increased mortality RR = 1.06 (95% CI 1.06–1.07, I2 = 0.00%), acute heart failure (RR = 1.19, 95% CI 1.09–1.30, τ2 = 0.00, I2 = 92.39%), ICU events (RR = 1.10, 95% CI 1.01–1.19, τ2 = 0.00, I2 = 57.09%), and mechanical ventilation events (RR = 1.16, 95% CI 1.09–1.24, τ2 = 0.00, I2 = 23.11%). Additionally, there was no significant association with readmissions (RR = 1.05, 95% CI 0.95–1.16, I2 = 0.00%).ConclusionOur findings indicate that penicillin allergy labels are associated with an increased risk of mortality in patients, as well as being linked to acute heart failure, heightened ICU requirements, and mechanical ventilation.Systematic Review Registration:PROSPERO, identifier CRD42024571535. Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD4202457153.

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

We study causal inference under case-control and case-population sampling. Specifically, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risks defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We offer algorithms for inference on the causal parameters that are aggregated over the true population distribution of the covariates. We show the usefulness of our approach by studying three empirical examples: the benefit of attending private school for entering a prestigious university in Pakistan; the relationship between staying in school and getting involved with drug-trafficking gangs in Brazil; and the link between physicians' hours and size of the group practice in the United States. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Post-graduate students and faculty usually conduct case-control studies. However, sometimes they conduct cohort studies that are short-duration. Most investigators enroll the participants in two groups according to the exposure. Then, follow the participants for some duration. At the end of the study, relative risk is calculated, and the work is published in some journal. The exposure may be one time, which may or may not be quantified. The follow-up duration may not be participant-specific, and differential follow-up does not exist. The author has given three examples: the first example of consanguineous marriages and congenital disabilities, the second example of the ABO blood group system and childhood asthma, and the third example of insecticide spraying and stillbirth. In the given examples, cumulative or density incidence cannot be calculated in a true sense and, therefore, risk ratio. Even estimating the incidence of outcome variables in some studies is not appropriate. Risk ratio calculation in such scenarios is questionable because exposure quantification, follow-up period, and combination are the limiting factors. In case-control studies, the prevalence ratio is calculated, which is analogous to relative risk. The author suggests that, in such circumstances, prevalence ratio calculation will be more appropriate.

Abstract 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.

Asymptotic inferences about the difference, ratio or odds‐ratio of two independent proportions are very common in diverse fields. This article defines for each parameter eight conditional inference methods. These methods depend on: (1) using a chi‐squared type statistic or a z type one; (2) using the classic Yates continuity correction or the less well‐known Conover one; and (3) whether the p‐value of the test is determined by doubling the one‐tailed p‐value or by the Mantel method (asymmetrical approach). In all cases, the conclusions are: (i) the methods based on the chi‐squared statistic should not be used, as they are too liberal; (ii) for those in favor of using the criterion of doubling the p‐value, the best method is using the z statistic with Conover continuity correction; and (iii) for those in favor of the asymmetrical approach, the best method is based on the z statistic with Conover continuity correction and the Mantel p‐value. [ABSTRACT FROM AUTHOR]

In sport medicine, epidemiology of sport-related injury provides the foundation for understanding what types of injuries and illnesses occur and who is at the highest risk. Thus, accurate and transparent reporting and interpretation of risk metrics is essential to sports epidemiology. Odds ratios (OR) and risk ratios (RR) are two statistics used to quantify the association between exposure and outcome and are frequently seen in sports medicine literature. While similarities exist, there are optimal ways to use and interpret OR and RR based on the study design and outcome incidence in the target population. In this short communication, we will present common study designs (e.g. prospective cohort, case-control, cross-sectional) along with recommendations for the use and interpretation of OR and RR. This will ultimately assist practitioners in choosing and interpreting these frequently confused measures of association and also help journal reviewers better understand the appropriate use of these measures when evaluating a manuscript. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Observational research suggests higher red and processed meat intakes predict greater risks of developing or dying from cardiovascular diseases (CVD) and type 2 diabetes mellitus (T2DM), but this research limits causal inference. This systematic review of reviews utilizes both observational and experimental research findings to infer causality of these relations. Reviews from four databases were screened by two researchers. Reviews included unprocessed red meat (URM), processed meat (PM), or mixed URM + PM intake, and reported CVD or T2DM outcomes. Twenty-nine reviews were included, and causality was inferred using Bradford Hill's Criteria. Observational assessments of CVD outcomes and all meat types consistently reported weak associations while, T2DM outcomes and PM and Mixed URM + PM assessments consistently reported strong associations. Experimental assessments of Mixed URM + PM on CVD and T2DM risk factors were predominately not significant which lacked coherence with observational findings. For all meat types and outcomes, temporality and plausible mechanisms were established, but specificity and analogous relationships do not support causality. Evidence was insufficient for URM and T2DM. More experimental research is needed to strengthen these inferences. These results suggest that red and processed meat intakes are not likely causally related to CVD but there is potential for a causal relationship with T2DM. [ABSTRACT FROM AUTHOR]