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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: 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]

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.

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.

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.

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]

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]

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]

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]

Summary: The binomial proportion is a classic parameter with many applications and has also been extensively studied in the literature. By contrast, the reciprocal of the binomial proportion, or the inverse proportion, is often overlooked, even though it also plays an important role in various fields. To estimate the inverse proportion, the maximum likelihood method fails to yield a valid estimate when there is no successful event in the Bernoulli trials. To overcome this zero‐event problem, several methods have been introduced in the previous literature. Yet to the best of our knowledge, there is little work on a theoretical comparison of the existing estimators. In this paper, we first review some commonly used estimators for the inverse proportion, study their asymptotic properties, and then develop a new estimator that aims to eliminate the estimation bias. We further conduct Monte Carlo simulations to compare the finite sample performance of the existing and new estimators, and also apply them to handle the zero‐event problem in a meta‐analysis of COVID‐19 data for assessing the relative risks of physical distancing on the infection of coronavirus. [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]

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. 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 (P non-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). 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. PROSPERO CRD42022372666. • The shapes of the associations between VAI and T2DM were identified by almost all available studies. • VAI had a strong positive linear association with T2DM whether analysis of cohort studies or cross-sectional studies. • Management of weight loss among the people characterized by the higher VAI may decrease the risk of T2DM. [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 FROM AUTHOR]

Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.

Abstract This study investigated the risk of heatwaves for people with disabilities and other socioeconomic attributes using Health Care Bigdata in South Korea. The Health Care Bigdata provides detailed information on heat-related illness (HRI) patients in 2011–2020 from seven major cities. We employed the Distributed Lag Nonlinear Model (DLNM) to measure heat waves' relative risk. Our findings are four-fold. First, the relative risk (RR) of disabled people was 5.075 (95% confidence interval 4.476–5.674), significantly surpassing that of non-disabled people, 3.296 (2.517–4.075). Second, among various personal characteristics studied, disability influenced RR the most, exceeding impacts from elderly (4.457: 3.748–5.166), low-income (3.909: 3.004–4.813), and outdoor (4.052: 2.940–5.164). Third, the disabled young group (5.305: 4.414–6.195) was more vulnerable than the non-disabled elderly group (4.287: 3.576–4.999). Lastly, no significant difference in relative risk was observed between the mild (4.413: 3.855–4.971) and severe disabled groups (4.013: 3.121–4.905).

Introduction: The Coronavirus Disease-2019 pandemic is considered the most catastrophic disease of the last decades, with evolving knowledge and improved management over time. Pregnant women are known to be particularly vulnerable, and COVID-19 has been shown to damage the endothelium due to its proinflammatory nature. Aim: To determine the occurrence of oligohydramnios in pregnant women who tested positive for COVID-19. Materials and Methods: A retrospective observational study was conducted at the Department of Obstetrics and Gynaecology, Koru Health Groups, Ankara, Çankaya, Turkey from January 2023 to June 2023. The study included a total of 459 women who were followed during pregnancy and delivered in the same hospital between April 2020 and October 2022. The patients were divided into two groups: COVID-19 positive group (n=101) and negative group (n=358). Various parameters such as age, parity, gestational week at birth, Body Mass Index (BMI), 1st and 5th-minute Appearance, Pulse, Grimace, Activity and Respiration (APGAR) scores, weight of the newborn, Amniotic Fluid Index (AFI), and the risk of developing oligohydramnios were assessed between the positive and negative groups. Parametric variables were analysed using independent t-tests and non parametric ones using Mann-Whitney U tests. Chi-square test was used to compare categorical variables. A p-value of

[Introduction] Accurate and efficient short-term electricity load forecasting is a prerequisite for ensuring the safe and reliable operation of power system, and it is also the basis for the rational arrangement of power generation plans in the power grid. Therefore, studying the relationship between meteorology and electricity load is of great significance for load forecasting. [Method] Based on the electricity load data at 15 min intervals during the period between January 1 of 2013 and December 31 of 2021 provided by the State Grid Hebei Electric Power Co., Ltd. as well as the corresponding meteorological observation data of Shijiazhuang station, this paper analyzed the temporal variation characteristics of daily peak electricity load in Shijiazhuang, and in particular, the meteorological conditions corresponding to the samples with a daily peak electricity load that was 10% higher than that of the previous day were analyzed. The Spearman's rank correlation method was used to analyze the correlation between daily peak electricity load in Shijiazhuang and the meteorological factors of the previous day, and significantly correlated meteorological factors were identified. The response curves of the significantly correlated meteorological factors to the next day's peak electricity load were drawn using the smooth curve fitting method, and the analysis revealed the changing trend of daily peak electricity load with the variations of meteorological factors, as well as the response thresholds. For different threshold ranges, the relative risk of meteorological factors to the changes of the daily peak electricity load was calculated based on the Poisson distribution. On this basis, the variation magnitudes of daily peak electricity load caused by per unit change in each meteorological factor within different threshold ranges in Shijiazhuang were calculated, that is, the quantitative impacts of the changes in different meteorological factors on the variation of daily peak electricity load were revealed. [Result] Taking temperature as an example, when the daily average, maximum and minimum temperatures are higher (lower) than the thresholds, the relative risk to the next day′s peak electricity load increases (decreases) by 2.25% (0.62%), 1.92% (0.57%) and 2.07% (0.60%) respectively for every 1 °C increase in temperature. [Conclusion] Based on the relative risk of different meteorological factors to daily peak electricity load in Shijiazhuang, a method for predicting the next day′s peak electricity load is proposed. The test performed using the daily electricity load and meteorological data of Shijiazhuang in 2022 reveals that the prediction effect can meet the needs of daily electricity meteorological service.

The study investigates the effect of emissions of particulate matter arising out of different construction activities from five construction sites in Kanpur city, India. Limited research studies on exposure assessments related to particulate matter (PM) emissions from construction activities in the Indian context contribute to the uniqueness of this study. It was observed that for all study areas the concentrations of particulate matter (PM2.5 and PM10) exceeded the National Ambient Air Quality Standard (NAAQS) by a factor of 18–20. The highest concentration of PM10 (1308; 1267 µg/m3) and PM2.5 (1056; 1123 µg/m3) was associated with construction activity related to cutting and laying of tiles observed over the two monitoring campaigns conducted during November–December 2019 and January–February 2020. The human health risk exposure assessment assessed on relative risk (RR) determined on WHO methodology showed that the highest RR values of 2.669 and 4.13 were obtained for emissions of PM10 and PM2.5, respectively, from laying and cutting of tiles. The attributable factor (AF) from both PM10 (AF = 0.6253) and PM2.5 (AF = 0.757) also showed higher possibility of respiratory diseases for workers associated with laying and cutting of tiles. Health impact assessment conducted using the International Council for Radiological Protection (ICRP) model also confirmed that maximum and minimum deposition rates of particulates occurred for construction activities associated with cutting and laying of tiles and painting and washing, respectively. This suggests potential occupational health hazards for workers involved in construction activities being exposed to high levels of PM concentrations. [ABSTRACT FROM AUTHOR]

The aim of the present study was to assess the risk of primary central nervous system (CNS) tumour incidence in a cohort of 22,377 Mayak Production Association workers chronically exposed to ionising radiation. There were 96 primary CNS tumours, including 42 cases of glioma and 44 cases of meningioma, registered during the whole follow-up period (1948–2018). The study demonstrated that the risk of primary CNS tumour incidence was associated with sex, attained age, calendar period, tall body height, age at the beginning of exposure, and facility type. There was no association found between risk of CNS tumour incidence and body mass index, smoking (males) and alcohol consumption status. The study did not find an effect of the total external gamma radiation dose absorbed in the brain on risk of CNS tumour incidence irrespective of whether an adjustment for the total external neutron dose absorbed in the brain was included or not. Excess relative risk per 1 Gy of external gamma brain dose was 0.05 (95% confidence interval (CI) −0.30; 0.70) for all CNS tumours, −0.18 (95% CI –; 0.44) for gliomas, and 0.38 (95% CI −0.32; 2.08) for meningiomas without adjustment for total neutron brain dose. There was no effect modification by sex, attained age, age at hire or facility. [ABSTRACT FROM AUTHOR]

This study investigated the risk of heatwaves for people with disabilities and other socioeconomic attributes using Health Care Bigdata in South Korea. The Health Care Bigdata provides detailed information on heat-related illness (HRI) patients in 2011–2020 from seven major cities. We employed the Distributed Lag Nonlinear Model (DLNM) to measure heat waves' relative risk. Our findings are four-fold. First, the relative risk (RR) of disabled people was 5.075 (95% confidence interval 4.476–5.674), significantly surpassing that of non-disabled people, 3.296 (2.517–4.075). Second, among various personal characteristics studied, disability influenced RR the most, exceeding impacts from elderly (4.457: 3.748–5.166), low-income (3.909: 3.004–4.813), and outdoor (4.052: 2.940–5.164). Third, the disabled young group (5.305: 4.414–6.195) was more vulnerable than the non-disabled elderly group (4.287: 3.576–4.999). Lastly, no significant difference in relative risk was observed between the mild (4.413: 3.855–4.971) and severe disabled groups (4.013: 3.121–4.905). [ABSTRACT FROM AUTHOR]

It is commonly necessary to perform inferences on the difference, ratio, and odds ratio of two proportions p1 and p2 based on two independent samples. For this purpose, the most common asymptotic statistics are based on the score statistics (S-type statistics). As these do not correct the bias of the estimator of the product pi (1–pi), Miettinen and Nurminen proposed the MN-type statistics, which consist of multiplying the statistics S by (N–1)/N, where N is the sum of the two sample sizes. This paper demonstrates that the factor (N–1)/N is only correct in the case of the test of equality of two proportions, providing the estimation of the correct factor (AU-type statistics) and the minimum value of the same (AUM-type statistics). Moreover, this paper assesses the performance of the four-type statistics mentioned (S, MN, AU and AUM) in one and two-tailed tests, and for each of the three parameters cited (d, R and OR). We found that the AUM-type statistics are the best, followed by the MN type (whose performance was most similar to that of AU-type). Finally, this paper also provides the correct factors when the data are from a multinomial distribution, with the novelty that the MN and AU statistics are similar in the case of the test for the odds ratio. [ABSTRACT FROM AUTHOR]

Introduction: The Coronavirus Disease-2019 pandemic is considered the most catastrophic disease of the last decades, with evolving knowledge and improved management over time. Pregnant women are known to be particularly vulnerable, and COVID-19 has been shown to damage the endothelium due to its proinflammatory nature. Aim: To determine the occurrence of oligohydramnios in pregnant women who tested positive for COVID-19. Materials and Methods: A retrospective observational study was conducted at the Department of Obstetrics and Gynaecology, Koru Health Groups, Ankara, Çankaya, Turkey from January 2023 to June 2023. The study included a total of 459 women who were followed during pregnancy and delivered in the same hospital between April 2020 and October 2022. The patients were divided into two groups: COVID-19 positive group (n=101) and negative group (n=358). Various parameters such as age, parity, gestational week at birth, Body Mass Index (BMI), 1st and 5th-minute Appearance, Pulse, Grimace, Activity and Respiration (APGAR) scores, weight of the newborn, Amniotic Fluid Index (AFI), and the risk of developing oligohydramnios were assessed between the positive and negative groups. Parametric variables were analysed using independent t-tests and non parametric ones using Mann-Whitney U tests. Chi-square test was used to compare categorical variables. A p-value of <0.05 was considered statistically significant. Results: The mean age of the pregnant women was 29.2±4.4 years, ranging from 20 to 43 years. Oligohydramnios was detected in 95 women, with 64 of them testing positive for COVID-19. There were no statistically significant differences in terms of age, parity, gestational week of birth, weight at birth, and 1st and 5th-minute APGAR scores between the two groups. However, BMI was significantly higher in COVID-19 positive patients compared to negative ones (p=0.003). Oligohydramnios was found to be significantly higher in COVID-19-positive pregnant women (n=64) compared to negative ones (n=31) (p<0.0001). Conclusion: The COVID-19 pandemic has caused an unprecedented level of global health problems. The virus's prothrombotic tendency leads to placental insufficiency, predisposing to oligohydramnios and intrauterine growth retardation. [ABSTRACT FROM AUTHOR]

Climate change has been linked to the escalating frequency, duration, and intensity of heat waves in the Mediterranean region, intensifying health concerns for the general populace. Urban environments face elevated health risks due to concentrated populations and the urban heat island effect, further amplifying nighttime heat conditions. This study aims to project changes in heat wave characteristics and the associated population exposure risk in a large Mediterranean city, Thessaloniki, Greece. High-resolution climate simulations, using the WRF model, were conducted for three 5-year periods (2006–2010, 2046–2050, 2096–2100) under the RCP8.5 emission scenario, covering Thessaloniki with a 2 km grid. By the end of the century, Thessaloniki is projected to experience over 60 annual heat wave days, compared to ~8 in the present climate, while some episodes were found to persist beyond 30 days. The relative risk during heat wave days is expected to rise, which is primarily due to nighttime heat stress. Interestingly, the results indicate that minimum apparent temperature might be a more reliable indicator in predicting heat-related mortality compared to maximum apparent temperature. These findings emphasize the growing importance of informed heat mitigation and adaptation strategies and healthcare preparedness in urban areas facing escalating heat-related health challenges. [ABSTRACT FROM AUTHOR]

AbstractGradient boosting methods become popular in recent years to analyze right-censored survival data where Cox’s proportional hazards model is the widely used statistical model. However, there are very limited studies on the differences between the two approaches for right-censored survival data. We compare two boosting methods with Cox’s proportional hazards model in this paper: one is the gradient boosting decision tree and the other is gradient boosting with component-wise linear models. The differences between the two boosting methods are presented. A simulation study is conducted to investigate the performance of the three methods in practice where only the main effects of covariates are included. The results show that the boosting methods outperform Cox’s proportional hazards model in both the relative and absolute risk estimation in the proportional hazards model except when Cox’s proportional hazards model is fully specified with nonlinear and interaction covariates effects. It indicates that the boosting methods, particularly the gradient boosting decision tree, is a very competitive method for right-censored survival data if complicated covariate effects may exist but are unknown to the investigator. We illustrate an application of the boosting methods with real data analysis. [ABSTRACT FROM AUTHOR]

There has been a gap regarding current knowledge of the effect of PM on pulmonary TB, such as the exposure-time-response between them. This study aimed to explore the distributed lag effects of particulate matter (PM) on active pulmonary tuberculosis (TB) and identify the vulnerable groups. A generalized additive mixed model combined with a distributed lag non-linear model was applied to quantify the association between PM and active pulmonary TB with adjustment for potential confounders. Relative risk (RR) and cumulative RR with 95% confidence interval (CI) were calculated to quantify the exposure-time-response. A total of 16,486 cases of active pulmonary TB were notified. Results suggested that a unit 10 µg/m3 increase of daily PM2.5 concentration was positively associated with active pulmonary TB morbidity at 36–115 lag day and RR reached maximum at 66 lag day (1.0076; 95%CI, 1.0031–1.0122), and the cumulative RR was 2.1940 (95%CI, 1.2292–3.9161). For PM10, this association was significantly positive at 73–117 lag day, and RR reached maximum at 100 lag day (1.0036; 95%CI, 1.0003–1.0067), and the cumulative RR was not significant. This study provides evidence that PM significantly associate with active pulmonary TB. Vulnerability to PM2.5 was identified in male, female, 0–18 ages, 19–64 ages, workers, and students. Our findings have significant implications for developing local strategies to prevent and reduce health impact in PM polluted areas. [ABSTRACT FROM AUTHOR]

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The Swiss Blood Stem Cell Transplantation and Cellular Therapy Group (SBST) leads a mandatory national registry for all hematopoietic stem cell transplants (HCT) and cellular therapies. After 25 years, information was available for 11,226 patients receiving an HCT (4031 allogeneic and 7195 autologous), including 925 pediatric patients. We compared patient characteristics and outcome by quinquennia 1997–2001, 2002–2006, 2007–2011, 2012–2016, and 2017–2021. There were numerous changes over time. Allogeneic transplant recipients became older (median age 33.7 vs. 54.3) and had more frequently unrelated donors and reduced intensity conditioning in later quinquennia. Similarly, age increased for recipients of autologous HCT (median 48.3 vs. 59.9). We did not see a significant drop in transplant activity during the SARS‐CoV‐2 pandemic. Analysis of outcome showed overall survival (relative risk (RR) of death 0.664 (0.529–0.832) and progression free survival (RR 0.708 (0.577–0.870) being improved over time comparing the latest to the first quinquennium adjusting for risk factors. Non‐relapse mortality decreased in recipients of allogeneic HCT (RR: 0.371 (0.270–0.509)) over time but relapse risks did not. Outcome of autologous HCT improved as well across quinquennia, this improvement was mainly due to decreased relapse risks (RR 0.681 (0.597–0.777)), possibly related to maintenance treatment or rescue treatment for relapse mainly in myeloma patients. Cellular therapies other than allogeneic or autologous HCT, particularly chimeric antigen receptor T‐cells (CAR‐T) treatment have started to increase after 2019, year of approval of the first commercial CAR‐T product in Switzerland. Data on chimeric antigen receptor T‐cell treatment are too early for comparative analyses. Detailed analyses of changes over time are presented. This study includes all HCTs, and cellular therapies, data useful for quality assurance programs, health care cost estimation and benchmarking. Between 50% and 60% of patients are long‐term survivors after both types of HCT, indicating growing populations of surviving patients requiring long‐term care. [ABSTRACT FROM AUTHOR]

Summary: 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.

BackgroundPsychiatric diseases are a serious threat to human health. Studies have shown an association between ambient air pollution and psychiatric diseases, yet the results are inconsistent. ObjectiveTo explore the potential effects of air pollution exposure on psychiatric diseases using Zhengzhou City as an example. MethodsOutpatient consultation data of the Clinical Psychology Clinic at The First Affiliated Hospital of Henan University of Traditional Chinese Medicine as well as air quality data between 28 October 2013 and 28 August 2019 were collected. The exposure-lag effect between air pollutant exposure and daily outpatient visits was assessed using a distribution lag nonlinear model (DLNM), and subgroup analyses were conducted including sex (male and female), age (59 years), season (spring, summer, autumn, and winter), and types of diseases (depression and anxiety). ResultsA total of 112986 valid confirmed outpatient visit records for psychiatric diseases were collected. The largest proportions of visits in each subgroup were females, 18-59 years old, spring, and depression. The results of one-day lagged risk of total outpatient visits showed that PM2.5, PM10, SO2, and NO2 all significantly associated with an increased risk of psychiatric outpatient visits. The one-day lagged risk peaks were: PM2.5 (RR=1.0016, 95%CI: 1.0011, 1.0022), PM10 (RR=1.0012, 95%CI: 1.0001, 1.0022), SO2 (RR=1.0060, 95%CI: 1.0002, 1.0117), and NO2 (RR=1.0144, 95%CI: 1.0100, 1.0187). The cumulative lagged risk peaks were: PM2.5 (CumRR=1.0274, 95%CI: 1.0158, 1.0392), PM10 (CumRR=1.0124, 95%CI: 1.0044, 1.0205), SO2 (CumRR=1.0214, 95%CI: 1.0009, 1.0423), and NO2 (CumRR=1.0613, 95%CI: 1.0460, 1.0770). The results of subgroup analysis showed more significant impacts of air pollution exposure on females and people aged 18-59 years, and in winter. To be specific, PM2.5 (RR=1.0022, 95%CI: 1.0006, 1.0037), PM10 (RR=1.0015, 95%CI: 1.0003, 1.0027), SO2 (RR=1.0082, 95%CI: 1.0016, 1.0148), and NO2 (RR=1.0156, 95%CI: 1.0106, 1.0206) exposures associated with an increased risk of outpatient visits for psychiatric disorders in women; PM2.5 (RR=1.0018, 95%CI: 1.0011, 1.0024), PM10 (RR=1.0012, 95%CI: 1.0001, 1.0023), and NO2 (RR=1.0142, 95%CI: 1.0095, 1.0189) associated with an increased risk of visits in those 18-59 years of age; PM2.5 (RR= 1.0043, 95%CI: 1.0001, 1.0085), PM10 (RR= 1.0038, 95%CI: 1.0007, 1.0069), and NO2 (RR= 1.0215, 95%CI: 1.0117, 1.0313) in winter months significantly associated with an increased risk of psychiatric outpatient visits. In addition, the risk of depression and anxiety visits varied depending on the concentration of air pollutants and the duration of exposure. The risk of visits for depression were more affected by CO, NO2, and SO2, and the risk of visits for anxiety were more affected by PM2.5, PM10, and O3. ConclusionExposure to air pollution is associated with an increased risk of psychiatric outpatient visits. The effects vary by air pollutants as well as by sex, age, type of psychiatric illness, and season.

Background. Prostate cancer maintains a relatively high standardized uptake value and share of patients followed up for 5 or more years. Accordingly, distant outcomes in these patients appear to be influenced by factors other than the underlying disease.Objective. To analyze the morbidity in prostate cancer patients with additional malignancies potentially linked with the decrease in the survival rate in the Mari El Republic.Methods. The present study involved 1434 prostate cancer patients firstly enrolled in the period from 2012 to 2021. A group of patients in this sample was identified with additional malignancies (other than prostate cancer) diagnosed within the period from 6 months prior to prostate cancer diagnosis to the end of 2021. Comparison of the incidence of malignancies among prostate cancer patients and the general population was performed via a 2 × 2 crosstab analysis by calculating the relative risk and its 95% confidence interval. The difference was considered significant when 95% confidence interval did not include 1. In addition, chi-square values and corresponding p-values were calculated. Statistical analyses were performed using SPSS 13.0 (SPSS Inc., USA) and Microsoft Excel 2007 (Microsoft Corporation, USA).Results. 31 (32.29%) additional malignancies were identified (prostate cancer was diagnosed within 6 months before prostate cancer diagnosis and up to 6 months thereafter), 7 additional malignancies (7.29%) were registered 6 months to 1 year after prostate cancer diagnosis, and 61 additional malignancies (63.54%) during the later period. The most common primary malignancies among all patients included: bladder cancer (relative risk = 15.23 [95% confidence interval: 10.42–22.26]), nonmelanoma skin cancer (relative risk = 3.77 [2.34–6.07]), colorectal cancer (relative risk = 2.10 [1.24–3.54]), gastric cancer (relative risk = 2.01 [1.08–3.73]), and kidney cancer (relative risk = 4.69 [2.51–8.75]).Conclusion. Within 7.1 years (median) of follow-up, additional malignancies develop in 6.70% of prostate cancer patients. These patients reveal the higher risk than the population average value, thereby constituting a risk group.

AbstractBacteriophages and their potential contribution to antimicrobial resistance (AMR) have attracted growing attention in the context of medicine and pharmaceutics. A major objective of the CAMDA challenge is to acquire more knowledge about the relationship between viruses, their hosts and AMR genes in determining if AMR indeed can spread through phages. This study is focused on exploring the relationship and possible dependencies between bacteriophages and AMR based on the data collected from urban environments all over the world. The samples in the data are classified into two categories: high and low, according to the observed levels of AMR genes. The approach used in our analyses consists of several different methods which assess the differential abundance of phages, their diversity across samples, the impact on AMR categories and associations with AMR genes. The relationship between phages, their hosts and AMR is also explored by a Bayesian spatial model, using the AMR category (low vs high) as a factor. We found a higher relative risk for phages known to infect Staphylococcus aureus alone or both S. aureus and Acinetobacter baumannii in the high AMR group, which implies that these phages may have a role in the dissemination of antimicrobial genes.

Background Heatwaves represent the main indices of climate change, while mortality is one of the established markers of their human effects. For unknown reasons populations adapt to temperature variations/challenges differently. Thus, to allow better precision and prediction, heatwave evaluations should be enriched by historical context and local data. Methods The mortality data for 1987 were collected from the Piraeus municipality registry, whereas data for Athens were obtained from literature retrieved from PUBMED. Ambient characteristics were extracted from the Geronikolou’s 1991 BSc thesis and the reports of national organizations. From the death events, the odds ratio and relative risk in Piraeus compared to the Athens were calculated. Finally, a simple neural network proposed the dominant ambient parameter of the heatwave effects in the city residents of each location. Results The 1987 heatwave was more lethal (seven-fold) in Athens than in Piraeus and dependent on atmospheric nitric oxide (NO) concentration (with probability 0.999). In the case of Piraeus in 1987, ozone characterized the phenomenon (with probability 0.993). Conclusions The odds of dying due to a heatwave are highly dependent on lifestyle, population sensitivity to preventive measures and public health policy, while the phenomenon was mainly moderated by ozone in Piraeus in 1987, and NO in Athens irrespective of year.

We develop a proportional incidence model that estimates vaccine effectiveness (VE) at the population level using conditional likelihood for aggregated data. Our model assumes that the population counts of clinical outcomes for an infectious disease arise from a superposition of Poisson processes with different vaccination statuses. The intensity function in the model is calculated as the product of per capita incidence rate and the at‐risk population size, both of which are time‐dependent. We formulate a log‐linear regression model with respect to the relative risk, defined as the ratio between the per capita incidence rates of vaccinated and unvaccinated individuals. In the regression analysis, we treat the baseline incidence rate as a nuisance parameter, similar to the Cox proportional hazard model in survival analysis. We then apply the proposed models and methods to age‐stratified weekly counts of COVID‐19–related hospital and ICU admissions among adults in Ontario, Canada. The data spanned from 2021 to February 2022, encompassing the Omicron era and the rollout of booster vaccine doses. We also discuss the limitations and confounding effects while advocating for the necessity of more comprehensive and up‐to‐date individual‐level data that document the clinical outcomes and measure potential confounders. [ABSTRACT FROM AUTHOR]

Background: To evaluate the correlation between elevated maternal serum alpha-fetoprotein (AFP) in the second trimester and ischemic placental disease (IPD). Methods: A retrospective cohort study was conducted to analyze the data of 22,574 pregnant women who delivered in the Department of Obstetrics at Hangzhou Women's Hospital from 2018 to 2020, and were screened for maternal serum AFP and free beta-human chorionic gonadotropin (free ß-hCG) in the second trimester. The pregnant women were divided into two groups: elevated maternal serum AFP group (n = 334, 1.48%); and normal group (n = 22,240, 98.52%). Mann-Whitney U-test or Chi-square test was used for continuous or categorical data. Modified Poisson regression analysis was used to calculate the relative risk (RR) and 95% confidence interval (CI) of the two groups. Results: The AFP MoM and free ß-hCG MoM in the elevated maternal serum AFP group were higher than the normal group (2.25 vs. 0.98, 1.38 vs. 1.04) and the differences were all statistically significant (all P < .001). Placenta previa, hepatitis B virus carrying status of pregnant women, premature rupture of membranes (PROM), advanced maternal age (=35 years), increased free ß-hCG MoM, female infants, and low birth weight (RR: 2.722, 2.247, 1.769, 1.766, 1.272, 0.624, 2.554 respectively) were the risk factors for adverse maternal pregnancy outcomes in the elevated maternal serum AFP group. Conclusions: Maternal serum AFP levels during the second trimester can monitor IPD, such as IUGR, PROM, and placenta previa. Maternal women with high serum AFP levels are more likely to deliver male fetuses and low birth weight infants. Finally, the maternal age (=35 years) and hepatitis B carriers also increased maternal serum AFP significantly. [ABSTRACT FROM AUTHOR]

Background Recent work in the UK estimated the prevalence of current cannabinoid-based vaping to be higher than in the USA, a factor previously associated with e-cigarette or vaping-associated lung injury (EVALI). Research in the USA has demonstrated that attendances to emergency departments relating to e-cigarettes began to rise before the EVALI outbreak, suggesting that vapers also experience milder forms of vaping-related illness. Aim Quantify symptom prevalence and healthcare utilization amongst current UK vapers. Design Voluntary online survey of individuals aged 16 and over within the UK. Methods Anonymized data were collected on demographics, vaping/smoking status and vaping substances used. Current vapers were asked about the presence of 10 prevalent symptoms from previous US EVALI case series, healthcare attendances and diagnoses given. Risk-ratios were calculated to compare the likelihood of symptoms and attendances between substances. Results A total of 2477 complete responses were analysed. In all, 397 respondents were current vapers. Symptom prevalence within the previous 12 months ranged from 3.8% to 30.5% (bloody sputum, cough). Healthcare attendances per symptomatic respondent ranged from 0.1 to 1.4 (bloody sputum, shortness of breath). Current vapers of cannabinoid-based products (alone/in combination) had the most attendances per symptomatic respondent for 9/10 symptoms and were more likely to report symptoms aside from 'cough' (nicotine-free e-liquids [risk ratio = 1.7]). Clinicians reportedly never diagnosed vaping-related illness. Conclusions UK vapers experience symptoms previously reported in EVALI cases for which they also seek healthcare. Users of cannabinoid-based products were more likely to report symptoms and accounted for a higher healthcare burden. UK vapers may also experience vaping-related illness that does not meet EVALI case criteria. [ABSTRACT FROM AUTHOR]

Purpose: The association between second-hand smoke (SHS) exposure and cervical cancer (CC) risk is still unclear. The aim of this study is to provide an accurate and updated estimate of this association. Methods: Through an original methodology to identify original publications, we conducted a systematic review and meta-analysis of all epidemiological studies published up to October 2022 evaluating the association between SHS exposure and CC risk among female non-smokers. Meta-analytic estimates were obtained using random-effects models and dose–response relationships were derived using log-linear functions. Results: Out of 25 eligible studies, 21 were included in the meta-analysis, providing a pooled relative risk (RR) of cervical intraepithelial neoplasia (CIN) of grade 2 or higher of 1.52 (95% confidence interval, CI 1.30–1.78, 21 studies) for overall SHS exposure versus non-exposure. When restricting the analysis to invasive CC, the pooled RR was 1.42 (95% CI 1.17–1.71, 13 studies), whereas the pooled RR for CIN was 1.50 (95% CI 1.22–1.84, 6 studies). Analyzing RR by setting or source of SHS exposure resulted in significant associations with CC risk for SHS exposure at home (RR for CIN2+ 1.49, 95% CI 1.21–1.84, 14 studies), in non-specified settings (RR for CIN2+ 1.64, 95% CI 1.20–2.23, 8 studies) and from partner (RR for CIN2+ 1.55, 95% CI 1.25–1.94, 10 studies). The risk of CIN2+ significantly increased linearly with the intensity and pack-years of SHS exposure. Conclusion: This comprehensive review and meta-analysis confirmed the association of SHS exposure with CC, further suggesting the need to raise concern about SHS exposure in the population. [ABSTRACT FROM AUTHOR]

In this work we analyze different aspects regarding the terminology, understanding concepts and approaches in modeling and measuring components and variables related to safety and risk. This discussion conversation is open for further interpretations and suggestions. We study it with the help of models (images) artificial, descriptions, scientific approaches, discussions, etc., and using the help of poly-semantic languages. Various kinds of risks arise precisely because of the uncertainty of the situation. Mathematical models use uncertainties in several ways: randomness, which is explained and measured by objective probability and estimated using statistical methods. Uncertainty, measured by subjective probabilities, is estimated by expert methods, or by fuzzy uncertainty methods. Each of these approaches has its advantages and disadvantages. Despite the difference in approaches to measuring uncertainties, the development of a risk situation are described in an appropriate way using an event tree. We show the construction of an event tree, that allows us to see details and specify in the development of a risk situation. We also discuss how this approach can be used to evaluate the sensitivity of the output characteristics of the process to the parameters of the initial information. [ABSTRACT FROM AUTHOR]

The effectiveness of a vaccine is measured by means of protective vaccine efficacy, defined by V E = 1 − A R V A R U , where A R V and A R U are, respectively, the disease attack rates in the vaccinated and the unvaccinated population. For each of the cohoret and case-control designs, methods have been presented in the literature for calculating the required sample size when the desired width of the confidence interval and the probability of coverage are pre-specified, where an equal number of individuals were assumed to be allocated to the vaccine and placebo group. In this article, we present a method for calculating the required sample size with a specified degree of precision when there is an unequal allocation of individuals across the two groups. The sample size required to achieve a desired power for the relevant level α test has also been explored, keeping the unequal allocation proportion in mind. The fraction of individuals allocated to the placebo group (ρ) can be so chosen that the total sample size or the expected number of people developing the disease or some other criteria of interest is minimized. [ABSTRACT FROM AUTHOR]

This study aimed to identify and evaluate the short-term and lag effects of environmental factors on asthma hospitalizations in different regions. The ecological study on asthma is performed in three regions of Guangxi, China, that are distinctly different in geography and climate. We used distributed lag non-linear models to investigate the exposure-response-lag relationship between meteorological factors, air pollutants, and asthma hospital admissions across the three regions during 2015 (January 1 to December 31). Cold was an important meteorological factor affecting asthma. At lag 0, the relative risk (RR; 23°C as reference) of cold in the Northwest, Northeast, and South was 1.10 (10°C), 1.14 (8°C), and 1.30 (11°C), respectively. NO 2 was identified as the most important air pollutant affecting asthma. The RR of asthma hospitalization increased by 10.9% (at lag 4), 8.1% (at lag 0), and 4.2% (at lag 2) for every 10 μg/m3 increase in NO 2 concentration in the Northwest, Northeast, and South, respectively. In the three regions of Guangxi, there were differences in the dominant factors affecting asthma hospitalizations. Differences in geography can inform governments as to how to prepare the healthcare system to meet the expected peaks. [Display omitted] [ABSTRACT FROM AUTHOR]

In the context of recent climate change, temperature-attributable mortality has become an important public health threat worldwide. A large number of studies in Europe have identified a relationship between temperature and mortality, while only a limited number of scholars provided evidence for Serbia. In order to provide more evidence for better management of health resources at the regional and local level, this study aims to assess the impact of summer temperature on the population in Serbia, using daily average temperature (Ta) and mortality (CDR (crude death rate) per 100,000). The analysis was done for five areas (Belgrade, Novi Sad, Niš, Loznica, and Vranje), covering the summer (June–August) period of 2001–2015. In order to quantify the Ta-related CDR, a generalized additive model (GAM) assuming a quasi-Poisson distribution with log as the link function was used. Five regression models were constructed, for each area, revealing a statistically significant positive relationship between Ta and CDR in four areas. The effect of Ta on CDR was defined as the relative risk (RR), which was obtained as the exponential regression coefficient of the models. RR indicates that a 1 °C increase in Ta at lag0 was associated with an increase in CDR of 1.7% for Belgrade, Novi Sad, and Niš and 2% for Loznica. The model for Vranje did not quantify a statistically significant increase in CDR due to Ta (RR=1.006, 95% CI 0.991–1.020). Similar results were confirmed for gender, with a slightly higher risk for women. Analysis across lag structure showed different exposure, but the highest effect of Ta mainly occurs over the short term and persists for 3 days. [ABSTRACT FROM AUTHOR]

Background Heatwaves represent the main indices of climate change, while mortality is one of the established markers of their human effects. For unknown reasons populations adapt to temperature variations/challenges differently. Thus, to allow better precision and prediction, heatwave evaluations should be enriched by historical context and local data. Methods The mortality data for 1987 were collected from the Piraeus municipality registry, whereas data for Athens were obtained from literature retrieved from PUBMED. Ambient characteristics were extracted from the Geronikolou’s 1991 BSc thesis and the reports of national organizations. From the death events, the odds ratio and relative risk in Piraeus compared to the Athens were calculated. Finally, a simple neural network proposed the dominant ambient parameter of the heatwave effects in the city residents of each location. Results The 1987 heatwave was more lethal (seven-fold) in Athens than in Piraeus and dependent on atmospheric nitric oxide (NO) concentration (with probability 0.999). In the case of Piraeus in 1987, ozone characterized the phenomenon (with probability 0.993). Conclusions The odds of dying due to a heatwave are highly dependent on lifestyle, population sensitivity to preventive measures and public health policy, while the phenomenon was mainly moderated by ozone in Piraeus in 1987, and NO in Athens irrespective of year.

Background Meteorological factors play an important role in human health. Clarifying the occurrence of dog and cat bites (DCBs) under different meteorological conditions can provide key insights into the prevention of DCBs. Therefore, the objective of the study was to explore the relationship between meteorological factors and DCBs and to provide caution to avoid the incidents that may occur by DCBs. Methods In this study, data on meteorological factors and cases of DCBs were retrospectively collected at the Shanghai Climate Center and Jinshan Hospital of Fudan University, respectively, in 2016–2020. The distributed lag non-linear and time series model (DLNM) were used to examine the effect of meteorological elements on daily hospital visits due to DCBs. Results A total of 26,857 DCBs were collected ranging from 1 to 39 cases per day. The relationship between ambient temperature and DCBs was J-shaped. DCBs were positively correlated with daily mean temperature (rs = 0.588, P