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The methodological choice of aggregated estimates for meta-analysis may be notable for some common drawbacks, including variations in the cut-off values of depression, and lower statistical power for analyzing the associated factors. The study aimed to refine the precision of previous findings on the prevalence of depressive symptoms among medical students, through gathering individual participant data (IPD) as identified from our previous reviews. In the present study, we searched MEDLINE, EMBASE, PsycINFO, WanFang, Scielo and LILACS to identify published systematic reviews and meta-analyses up to March 2018, then individual data was requested for further analysis (PROSPERO registration: CRD42018091917). The participants' age, sex, year of study, scores for depressive symptoms, and other predictor variables were requested. To pool the prevalence from the included studies, random-effects model (two-step method) was used. Multiple linear regression was used to examine the associated factors on the depression z-scores (one-step method). Of the 249 studies, the datasets of 34 studies were included. The crude prevalence was 19.4% (95% CI: 18.8%, 19.9%) by one-step method and the pooled prevalence was 18.1% (95% CI: 14.1%, 22.1%) by two-step method. Multiple linear regression revealed that being a female, older age, and senior year of study were significantly associated with the z-score. The pooled prevalence of depressive symptoms from the Individual Participant Data (IPD) meta-analysis was lower than the previous meta-analyses using aggregated data. Age, sex, and year of study were significantly associated with the depression z-score. IPD meta-analysis may provide a more accurate estimation of disease burden, and allow verification of associated factors.

Abstract Background The methodological choice of aggregated estimates for meta-analysis may be notable for some common drawbacks, including variations in the cut-off values of depression, and lower statistical power for analyzing the associated factors. The study aimed to refine the precision of previous findings on the prevalence of depressive symptoms among medical students, through gathering individual participant data (IPD) as identified from our previous reviews. Material and methods In the present study, we searched MEDLINE, EMBASE, PsycINFO, WanFang, Scielo and LILACS to identify published systematic reviews and meta-analyses up to March 2018, then individual data was requested for further analysis (PROSPERO registration: CRD42018091917). The participants’ age, sex, year of study, scores for depressive symptoms, and other predictor variables were requested. To pool the prevalence from the included studies, random-effects model (two-step method) was used. Multiple linear regression was used to examine the associated factors on the depression z-scores (one-step method). Results Of the 249 studies, the datasets of 34 studies were included. The crude prevalence was 19.4% (95% CI: 18.8%, 19.9%) by one-step method and the pooled prevalence was 18.1% (95% CI: 14.1%, 22.1%) by two-step method. Multiple linear regression revealed that being a female, older age, and senior year of study were significantly associated with the z-score. Conclusion The pooled prevalence of depressive symptoms from the Individual Participant Data (IPD) meta-analysis was lower than the previous meta-analyses using aggregated data. Age, sex, and year of study were significantly associated with the depression z-score. IPD meta-analysis may provide a more accurate estimation of disease burden, and allow verification of associated factors.

Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that use recent history data and periodic trends to accurately estimate time series data, such as glucose concentration in diabetes. These models have been proven to be a good option to provide accurate blood glucose predictions under free-living conditions. These models can cope with patient variability under variable-length time-stamped daily events in supervision and control applications. However, the seasonal-models-based framework usually needs of several months of data per patient to be fed into the system to adequately train a personalized glucose predictor for each patient. In this work, an in silico analysis of the accuracy of prediction is presented, considering the effect of training a glucose predictor with data from a cohort of patients (population) instead of data from a single patient (individual). Feasibility of population data as an input to the model is asserted, and the effect of the dataset size in the determination of the minimum amount of data for a valid training of the models is studied. Results show that glucose predictors trained with population data can provide predictions of similar magnitude as those trained with individualized data. Overall median root mean squared error (RMSE) (including 25% and 75% percentiles) for the predictor trained with population data are { 6.96 [ 4.87 , 8.67 ] , 12.49 [ 7.96 , 14.23 ] , 19.52 [ 10.62 , 23.37 ] , 28.79 [ 12.96 , 34.57 ] , 32.3 [ 16.20 , 41.59 ] , 28.8 [ 15.13 , 37.18 ] } mg/dL, for prediction horizons (PH) of { 15 , 30 , 60 , 120 , 180 , 240 } min, respectively, while the baseline of the individually trained RMSE results are { 6.37 [ 5.07 , 6.70 ] , 11.27 [ 8.35 , 12.65 ] , 17.44 [ 11.08 , 20.93 ] , 22.72 [ 14.29 , 28.19 ] , 28.45 [ 14.79 , 34.38 ] , 25.58 [ 13.10 , 36.60 ] } mg/dL, both training with 16 weeks of data. Results also show that the use of the population approach reduces the required training data by half, without losing any prediction capability. [ABSTRACT FROM AUTHOR]

Background: The methodological choice of aggregated estimates for meta-analysis may be notable for some common drawbacks, including variations in the cut-off values of depression, and lower statistical power for analyzing the associated factors. The study aimed to refine the precision of previous findings on the prevalence of depressive symptoms among medical students, through gathering individual participant data (IPD) as identified from our previous reviews. Material and methods: In the present study, we searched MEDLINE, EMBASE, PsycINFO, WanFang, Scielo and LILACS to identify published systematic reviews and meta-analyses up to March 2018, then individual data was requested for further analysis (PROSPERO registration: CRD42018091917). The participants' age, sex, year of study, scores for depressive symptoms, and other predictor variables were requested. To pool the prevalence from the included studies, random-effects model (two-step method) was used. Multiple linear regression was used to examine the associated factors on the depression z-scores (one-step method). Results: Of the 249 studies, the datasets of 34 studies were included. The crude prevalence was 19.4% (95% CI: 18.8%, 19.9%) by one-step method and the pooled prevalence was 18.1% (95% CI: 14.1%, 22.1%) by two-step method. Multiple linear regression revealed that being a female, older age, and senior year of study were significantly associated with the z-score. Conclusion: The pooled prevalence of depressive symptoms from the Individual Participant Data (IPD) meta-analysis was lower than the previous meta-analyses using aggregated data. Age, sex, and year of study were significantly associated with the depression z-score. IPD meta-analysis may provide a more accurate estimation of disease burden, and allow verification of associated factors. [ABSTRACT FROM AUTHOR]

The role of maternal nutrition in affecting offspring fertility, through alteration of foetal programming, has been demonstrated in animal-based experiments. However, results from human populations appear inconsistent and sometimes contradictory, likely because they have been based on single famine events. In this paper, we adopt a different approach. We combine official annual time series of daily nutrient availability with a sample of women's reproductive histories from the 1961 Italian Census to investigate the role of maternal nutritional status in pregnancy on offspring childlessness. The analysis therefore covers cohorts of females born between 1861 and 1939. Our results show a negative association between calorie availability in pregnancy and the odds of offspring childlessness, whereas no association is found between protein availability and offspring childlessness. The consequences of poor calorie intake were aggravated during the summer, likely due to the participation of pregnant women in physically demanding work. [ABSTRACT FROM AUTHOR]

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The aim of this study is to contribute to the decision-makers in terms of creating awareness to prevent and reduce the harmful consumption of alcohol by revealing sociodemographic, health-related, social and environmental indicators-related factors affecting the consumption of alcohol of individuals in Turkey, and sizes of the effect. We believe that the results will be useful these days in which the COVID 19 pandemic has helped us better understand the importance of our health. The data set obtained from Turkish Statistical Institute, was used by weighting. Factors affecting individuals' alcohol consumption were determined using gompit regression analysis. It was determined that age, education level, marital status, employment status, health status, and psychosocial status indicators are important factors affecting alcohol use. As a result of the study, it is recommended that decision-makers and policymakers develop alcohol consumption prevention policies for individuals between the ages of 25 and 64, men, university graduates, divorced-widowed-single individuals, individuals with psychosocial problems and receiving support. [ABSTRACT FROM AUTHOR]

Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that use recent history data and periodic trends to accurately estimate time series data, such as glucose concentration in diabetes. These models have been proven to be a good option to provide accurate blood glucose predictions under free-living conditions. These models can cope with patient variability under variable-length time-stamped daily events in supervision and control applications. However, the seasonal-models-based framework usually needs of several months of data per patient to be fed into the system to adequately train a personalized glucose predictor for each patient. In this work, an in silico analysis of the accuracy of prediction is presented, considering the effect of training a glucose predictor with data from a cohort of patients (population) instead of data from a single patient (individual). Feasibility of population data as an input to the model is asserted, and the effect of the dataset size in the determination of the minimum amount of data for a valid training of the models is studied. Results show that glucose predictors trained with population data can provide predictions of similar magnitude as those trained with individualized data. Overall median root mean squared error (RMSE) (including 25% and 75% percentiles) for the predictor trained with population data are {6.96[4.87,8.67], 12.49[7.96,14.23], 19.52[10.62,23.37], 28.79[12.96,34.57], 32.3[16.20,41.59], 28.8[15.13,37.18]} mg/dL, for prediction horizons (PH) of {15,30,60,120,180,240} min, respectively, while the baseline of the individually trained RMSE results are {6.37[5.07,6.70], 11.27[8.35,12.65], 17.44[11.08,20.93], 22.72[14.29,28.19], 28.45[14.79,34.38], 25.58[13.10,36.60]} mg/dL, both training with 16 weeks of data. Results also show that the use of the population approach reduces the required training data by half, without losing any prediction capability.

Objectives: The purpose of this study was to determine the socio-demographic and economic factors affecting the frequency of alcohol use by employees aged 15 and above in Turkey. Method: In this study, the micro data set obtained from the Turkey Health Survey, which was conducted by the Turkey Statistical Institute between 2008 and 2016, was employed. The factors affecting the frequency of alcohol use by employees were determined by employing ordered logistic regression, ordered probit regression, and generalized ordered probit regression analyses. Results: According to the analysis results, it was concluded that the best model was the generalized ordered probit regression. The results of this model showed that, gender, age, education level, occupational status, marital status, income level, psycho-social support status and survey year were the variables affecting the frequency of alcohol use. Conclusion: In the development of preventive strategies in regarding alcohol use, male individuals, individuals between the ages of 25–55, individuals with university degrees, divorced/widowed individuals, administrators, professionals, individuals from low-income segments, and depressed individuals, who are receiving psycho-social support, should be considered as priorities and more attention should be paid to these groups. [ABSTRACT FROM AUTHOR]

This study analyzed individual water and feed consumption related to weight of weaned piglets and their link to diarrhea. Data were collected from 15 batches of 102 piglets each, using specific automata (connected feeders, connected drinkers, automatic weighing stations, RFID ear tags). Analyses were carried out every week on the 138 healthy animals compared by weight category. The average feed consumption had no significant difference between weight categories (light, medium, heavy pigs) whatever the week and was close to 4% of the live weight. For the average water consumption according to weight, it was close to 10%. There was no significant difference between weight groups, except at the end of the period, where the variability of one heavy pig was so high that its own water consumption caused significant difference when compared with the light group. But these overall stable averages do not highlight the high intra-individual variabilities, which was around 40% for both water and feed data at the beginning of trial. At the end, it was almost 16% for feed consumption and 25% for water. The comparison between healthy and diarrheic piglets showed no statistical difference for average water consumption on the day of the first clinical signs and even 1 and 2 days before. In contrast, the average feed consumption had a very significant difference (P ≤ 0.001) for days 5–7 after the weaning and a significant difference for day 8 (P ≤ 0.05). Differences were also significant for data 24 and 48 h before first clinical signs. This means either that diarrheic piglets decrease their feed consumption the first days after weaning or that it is because they eat less that they become diarrheic. So, the hypothesis was that feed consumption could be an interesting indicator to detect early sick animals. Nevertheless, despite this difference, machine learning methods failed in detecting individually diarrheic animals from water and feed consumption related to weight, because of considerable individual variability. To improve these results, one solution could be to collect other data from new sensors like automatic measurement of body temperature or location of piglets in the pen by image analysis.

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download. [ABSTRACT FROM AUTHOR]

Background: The role of smoking in nasopharyngeal carcinoma (NPC) remains uncertain, especially in endemic regions. We conducted an individual participant data (IPD) meta-analysis of prospective cohort studies to investigate the associations between smoking exposure and risk of NPC.Methods: We obtained individual participant data of 334 935 male participants from six eligible population-based cohorts in NPC-endemic regions, including two each in Guangzhou and Taiwan, and one each in Hong Kong and Singapore. We used one- and two-stage approaches IPD meta-analysis and Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of NPC for smoking exposure adjusting for age and drinking status.Results: During 2 961 315 person-years of follow-up, 399 NPC evens were ascertained. Risks of NPC were higher in ever versus never smokers (HRone-stage = 1.32, 95% CI = 1.07-1.63, P = 0.0088; HRtwo-stage = 1.27, 1.01-1.60, 0.04). These positive associations appeared to be stronger in ever smokers who consumed 16+ cigarettes/day (HRone-stage = 1.67, 95% CI = 1.29-2.16, P = 0.0001), and in those who started smoking at age younger than 16 (2.16, 1.33-3.50, 0.0103), with dose-response relationships (P-values for trend = 0.0028 and 0.0103, respectively). Quitting (versus daily smoking) showed a small reduced risk (stopped for 5+ years: HRone-stage = 0.91, 95% CI = 0.60-1.39, P = 0.66; for former smokers: HRtwo-stage = 0.84, 0.61-1.14, 0.26).Conclusions: This first IPD meta-analysis from six prospective cohorts in endemic regions has provided robust observational evidence that smoking increased NPC risk in men. NPC should be added to the 12-16 cancer sites known to be tobacco-related cancers. Strong tobacco control policies, preventing young individuals from smoking, would reduce NPC risk in endemic regions. [ABSTRACT FROM AUTHOR]

• Decrease in voluntary admission rates in regional GHPU during Covid-19 pandemic. • Five out of 82 psychiatric patients tested, were positive to Covid-19 (6%). • All MHDs complied with the operative indicators for Covid-19 emergency. • Community-based mental health system might be better placed to respond to Covid-19. • Integration between elements of healthcare enhance rapid services' reorganisation. to assess the changes in prevalence, incidence and hospitalisation rates during the first four months of 2020, compared to the same period of 2019, in Friuli Venezia Giulia Mental Health Departments (MHDs); to analyse the features of MHDs patients tested for Sars-Cov-2, and to monitor whether MHDs applied and adhered to regional recommendations. Observational study using MHDs' administrative data and individual data on suspected and positive cases of Sars-Cov-2. Adherence to recommentations was assessed using 21 indicators. Changes in rates were calculated by Poisson regression analysis, while the Fisher exact test was used for assessing differences between suspected and positive cases. The decrease in voluntary admission rates on 100,000 inhabitants in hospital services was significantly larger from January to April 2020, compared to the same period of 2019 (P <0.001), while no other data showed a significant decrease. Among the 82 cases tested for Sars-Cov-2, five were positive, and they significantly differ from suspected cases only in that they were at home or in supported housing facilities prior to the test. The MHDs mostly complied with the indicators in the month after the publication of recommendations. Outpatient services continued to work normally during the emergency, while hospital services decreased their activities. A low number of positive cases was found among MHDs' users, which might be linked to a rapid reconversion of services, with an extensive use of home visits and telepsychiatry. These preliminary data should be interpreted with caution, due to the small size and the limited period of observation. [ABSTRACT FROM AUTHOR]

Objective To investigate the death time of patients with coronavirus disease 2019 (COVID-19). Methods The death time was calculated and analyzed using individual data and aggregated data through the daily notification of the epidemic situation and the death cases published on the website of the Heath Commission of China and provinces. Results In the 153 patients who died of COVID-19. the shortest time from onset to death was 4 days and the longest time was 50 days with the mean± standard deviation of (16.7±9.2) days. The median was 14 days and the 95 % confidence interval was 4.6-42.9. The shortest time from admission to death was 1 day and the longest time was 50 days with the mean ± standard deviation of 02.1±7.81 days. The median was 11 days and the 95% confidence interval was 2-32.8. The time curve from diagnosis to death was skewed. The death time from diagnosis to death was 0 to 48 days with the mean ± standard deviation of (11.1±8.9) days. The median was 9 days. the interquartile interval was 10.5 days, and the 95% confidence interval was 0-35.4. It took 3 days from onset to admission and 1 day from admission to diagnosis. Aggregated data showed tha t the time from diagnosis to death of COVID-19 patients in China. China (except Hubei Province), Hubei Province and Wuhan City was 8.9, 6 and 6 days. respectively. Conclusion The time from diagnosis to death of COVID-19 patients varied significantly. with the median time of 6-9 days in different regions. [ABSTRACT FROM AUTHOR]

Although dramatically reduced in Western and developed countries, maternal mortality is still today one of the most relevant social and health scourges in developing countries. This is the reason why high levels of maternal mortality are always interpreted as a sign of low living standards, ignorance, poverty and woman discrimination. Maternal mortality represents, therefore, a very peculiar characteristic of demographic systems of ancien regime. Despite this important role in demographic systems, no systematic study has been addressed to investigate the impact of maternal mortality in historical Italy. The aim of this article is to shed some light on such a phenomenon by investigating its trend over time and the determinants in some Italian populations between the 18th and the early 20th centuries. The analysis will make use of civil and parish registers linked together by means of nominative techniques, and it will be, therefore, carried out at the micro level. [ABSTRACT FROM AUTHOR]

We employ the support vector machine (SVM) classifier, over different types of kernels, to investigate whether observable variables of individuals and their household information are able to describe their consumption decision of film at theaters in Brazil. Using a very big dataset of 340,000 individuals living in metropolitan areas of a whole large developing economy, we performed a Knowledge Discovery in Databases to classify the film consumers, which results in 80% instances correctly classified. To reduce the degrees of freedom for SVM and to learn the more important determinants of film consumption, we apply the Linear Discriminant Analysis that allows us to identify the key determinants of this consumption. The main individual characteristics are age, education (that merges to be a student), income, and preferences for cultural goods. Regarding the main geographic characteristics, these are the timing of sample, population concentration, and supply of movie theaters. The results point to an ineffective policy for the sector at the time investigated. [ABSTRACT FROM AUTHOR]

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.

• Data on fetal weights and skeletal findings from rats on three different days around parturition. • Analysis of co-occurrence of ossification status in different bones. We present a comprehensive description of spontaneous skeletal development of Wistar rat fetuses close to parturition. Fetuses were collected on the day of expected parturition and one and two days before (Gestation Days 21–23) and skeletal development state was examined. Data is presented on an individual fetal basis to allow insights in developmental patterns. The distal parts of the limbs and the cervical and caudal vertebral column were identified as areas of the greatest developmental difference in the examined time frame. Extensive knowledge of the ossification process in individual fetuses allows a better resolution of deviations from normal and gives valuable information for the grading of delayed maturation and structural deviations. The data base is helpful to overcome the bottleneck of coincident occurrence of low incidences of skeletal abnormalities in the dose groups and the control group complicating risk assessment of test articles. [ABSTRACT FROM AUTHOR]

Objective: Various indicators and assessment tools exist to measure diets and nutrition. Most studies eventually rely on one approach. Relatively little is known about how closely results match when different tools are used in the same context. The present study compares and correlates different indicators for the same households and individuals to better understand which indicators can be used as proxies for others.Design: A survey of households and individuals was carried out in Kenya in 2015. Seven-day food consumption and 24 h dietary recalls were administered at household and individual level, respectively. Individual height and weight measures were taken. Different indicators of food access (energy consumption, household dietary diversity scores), dietary quality (individual dietary diversity scores, micronutrient intakes) and nutrition (anthropometric indicators) were calculated and correlated to evaluate associations.Setting: Rural farm households in western Kenya.ParticipantsData collected from 809 households and 1556 individuals living in these households (782 female adults, 479 male adults, 295 children aged 6-59 months).Results: All measures of food access and dietary quality were positively correlated at individual level. Household-level and individual-level dietary indicators were also positively correlated. Correlations between dietary indicators and anthropometric measures were small and mostly statistically insignificant.Conclusions: Dietary indicators from 7d food consumption recalls at the household level can be used as proxies of individual dietary quality of children and male and female adults. Individual dietary diversity scores are good proxies of micronutrient intakes. However, neither household-level nor individual-level dietary indicators are good proxies of individual nutritional status in this setting. [ABSTRACT FROM AUTHOR]

Data on the role of circadian related factors in the etiology of endometrial cancer are scarce. We collected individual data on night shift work or daily sleep duration from 7,207 cases and 22,027 controls participating in 11 studies from the Epidemiology of Endometrial Cancer Consortium (E2C2). Main analyses were performed among postmenopausal women: 6,335 endometrial cancer cases and 18,453 controls. Using individual data, study-specific odd ratios (ORs) and their corresponding 95% confidence intervals (CIs) were estimated with logistic regression and pooled analyses were conducted using random-effects meta-analyses. A non-significant inverse association was observed between endometrial cancer and night shift work (OR=0.89, 95%CI=0.72-1.09; I 2 =0.0%, P heterogeneity= 0.676). Associations did not vary by shift type (permanent or rotating), or duration of night work. Categorizations of short (<7h) or long (≥9h) sleep duration were not associated with endometrial cancer risk (OR short =1.02, 95%CI=0.95-1.10; I 2 =55.3%, P heterogeneity =0.022; OR long =0.93, 95%CI=0.81-1.06; I 2 =11.5%, P heterogeneity =0.339). No associations were observed per 1-h increment of sleep (OR=0.98, 95%CI=0.95-1.01; I 2 =46.1%, P heterogeneity =0.063), but an inverse association was identified among obese women (OR=0.93, 95%CI=0.89-0.98 per 1-h increment; I 2 =12.7%, P heterogeneity =0.329). Overall, these pooled analyses provide evidence that night shift work and sleep duration are not strong risk factors for endometrial cancer in postmenopausal women., Competing Interests: Declaration of competing interest The authors of this paper have no conflicts of interest to disclose relevant to the present work., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: The prevalence of post-stroke fatigue differs widely across studies, and reasons for such divergence are unclear. We aimed to collate individual data on post-stroke fatigue from multiple studies to facilitate high-powered meta-analysis, thus increasing our understanding of this complex phenomenon.Methods: We conducted an Individual Participant Data (IPD) meta-analysis on post-stroke fatigue and its associated factors. The starting point was our 2016 systematic review and meta-analysis of post-stroke fatigue prevalence, which included 24 studies that used the Fatigue Severity Scale (FSS). Study authors were asked to provide anonymised raw data on the following pre-identified variables: (i) FSS score, (ii) age, (iii) sex, (iv) time post-stroke, (v) depressive symptoms, (vi) stroke severity, (vii) disability, and (viii) stroke type. Linear regression analyses with FSS total score as the dependent variable, clustered by study, were conducted.Results: We obtained data from 14 of the 24 studies, and 12 datasets were suitable for IPD meta-analysis (total n = 2102). Higher levels of fatigue were independently associated with female sex (coeff. = 2.13, 95% CI 0.44-3.82, p = 0.023), depressive symptoms (coeff. = 7.90, 95% CI 1.76-14.04, p = 0.021), longer time since stroke (coeff. = 10.38, 95% CI 4.35-16.41, p = 0.007) and greater disability (coeff. = 4.16, 95% CI 1.52-6.81, p = 0.010). While there was no linear association between fatigue and age, a cubic relationship was identified (p < 0.001), with fatigue peaks in mid-life and the oldest old.Conclusion: Use of IPD meta-analysis gave us the power to identify novel factors associated with fatigue, such as longer time since stroke, as well as a non-linear relationship with age. [ABSTRACT FROM AUTHOR]

Our study focuses on the carbon emission and mitigation potential in the domain of daily travel in Beijing. Using a sample with the focus on working class, the average individual carbon emissions from daily travel are computed as 1.46 kg/day•person and 2.40 kg/day•person for weekday and weekend. Except for residents who need to conduct more long-distance trips, the main contributor to the emissions of the rest high emitters are not the need of longer daily travel distances or the need to conduct more long distance trips, but the need to use cars more intensively for trips with similar distances compared with low emitters. High emitters are associated with the characteristics of being male, having higher income, owning cars, and being in the age between 30s and 40s. On the other hand, living within fifth ring and having good accessibility to public transport are associated with lower emissions. We innovatively use trip-based information to obtain a more relevant and realistic assessment of mitigation potential through mode shift under the current transport system. The mitigation percentage can be as high as 20%–25% if only travel time is considered, but will be substantially constrained by practical barriers. Therefore mitigation policy for daily transport should not only focus on improving travel time of low emission modes to comparable levels with cars but also tackle practical barriers for car drivers to use low emission modes. [ABSTRACT FROM AUTHOR]