1,720,983 research outputs found
Dietary Intake of Meat Cooking-Related Mutagens (HCAs) and Risk of Colorectal Adenoma and Cancer: A Systematic Review and Meta-Analysis
No full textMuch evidence suggests that the positive association between meat intake and
colorectal adenoma (CRA) and cancer (CRC) risk is mediated by mutagenic compounds generated
during cooking at high temperature. A number of epidemiological studies have estimated the
effect of meat-related mutagens intake on CRC/CRA risk with contradictory and sometimes
inconsistent results. A literature search was carried out (PubMed, Web of Science and Scopus)
to identify articles reporting the relationship between the intake of meat-related mutagens
(2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), 2-amino-3,8-dimethylimidazo[4,5-f]
quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo[4,5-f] quinoxaline: DiMeIQx, benzo(a) pyrene
(B(a)P) and “meat derived mutagenic activity” (MDM)) and CRC/CRA risk. A random-effect model
was used to calculate the risk association. Thirty-nine studies were included in the systematic review
and meta-analysis. Polled CRA risk (15229 cases) was significantly increased by intake of PhIP
(OR = 1.20; 95% CI: 1.13,1.28; p < 0.001), MeIQx (OR = 1.14; 95% CI: 1.05,1.23; p = 0.001), DiMeIQx
(OR = 1.13; 95% CI: 1.05,1.21; p = 0.001), B(a)P (OR = 1.10; 95% CI: 1.02,1.19; p = 0.017) and MDM
(OR = 1.17; 95% CI: 1.07,1.28; p = 0.001). A linear and curvilinear trend was observed in dose–response
meta-analysis between CRA risk in association with PhIP, MDM, and MeIQx. CRC risk (21,344 cases)
was increased by uptake of MeIQx (OR = 1.14; 95% CI: 1.04,1.25; p = 0.004), DiMeIQx (OR = 1.12;
95% CI: 1.02,1.22; p = 0.014) and MDM (OR = 1.12; 95% CI: 1.06,1.19; p < 0.001). No publication bias
could be detected, whereas heterogeneity was in some cases rather high. Mutagenic compounds
formed during cooking of meat at high temperature may be responsible of its carcinogenicit
Outlier robust small domain estimation via bias correction and robust bootstrapping
Full text linkSeveral methods have been devised to mitigate the effects of outlier values on survey estimates. If outliers are a concern for estimation of population quantities, it is even more necessary to pay attention to them in a small area estimation (SAE) context,where sample size is usually very small and the estimation in often model based. In this paper we set two goals: The first is to review recent developments in outlier robust SAE. In particular, we focus on the use of partial bias corrections when outlier robust fitted values under a working model generate biased predictions from sample data containing representative outliers.Then we propose an outlier robust bootstrap MSE estimator for M-quantile based small area predictors which considers a bounded-block-bootstrap approach. We illustrate these methods through model based and design based simulations and in the context of a particular survey data set that has many of the outlier characteristics that are observed in business surveys
A spatio-temporal approach to latent variables: modelling gender (I'm)balance in the Big Data era
No full textThe pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to reflect women’s and men’s lives. In pursuing the 2030 Sustainability Development Goals (SDGs), the availability of high- quality gender-sensitive data has generated the current informative outburst. In the process, gender-sensitive data collection has departed from a mere dis- aggregation between men and women towards an unprecedented multifaceted informational spectrum. Methods for full exploiting gender-sensitive statis- tics, both standard and big data, though, faces some levels of criticality. The traditional descriptive linear combinations of a collection of simple indica- tors yields contradictory order results, whereas inference has so far privileged latent modelling only, holding several constraints. A novel statistical perspec- tive stems from recent developments of Multivariate Latent Markov Models (MLMMs), suitable to express a latent characteristic both in time and space. In addition to introducing covariates, on any measurement scales, not only in the structural model bul also the measurement one, MLMMs are innovative in so that they can handle a vast mass of data from very different sources. Thus they lead the way to an extensive investigation of the gender gap, account- ing for apparent contradictions in rankings and hence highlighting different paths, or “transition”, toward a more equitable society
Spatial Distribution of Multidimensional Educational Poverty in Italy using Small Area Estimation
No full textInclusive and equitable education and the promotion of lifelong learning opportunities for all are important targets in the 2030 Agenda for Sustainable Development. Deprivation in education, read also as deprivation of opportunities and rights i.e. health, culture, participation, social relations, referred to as educational poverty (EP), has attracted interest of researchers, which highlighted its complexities and consequences, such as being excluded from acquiring the skills needed to live in a world characterized by knowledge-based economy, rapidity and innovation. In the last few years, the Italian National Statistical Institute started to measure it by a multidimensional index, the composite educational poverty index (EPI). The index is based on survey direct estimates, which are reliable only at regional (NUTS 2) level, while to monitor and contrast the phenomenon it is important to obtain information at a finer geographical level. In this paper small area estimation models are applied to the unidimensional indicators which compose the multidimensional EPI. The aim is to enhance the knowledge of the spatial distribution of EP at local level in Italy, separating urban and non urban areas and focusing on peripheries in Italian Regions, using DEGURBA classification in order to help the policy maker to address resources towards the areas where the phenomenon is strongly present
A western dietary pattern increases prostate cancer risk: A systematic review and meta-analysis
No full textDietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with the glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by “a posteriori” methods. A literature search was carried out (PubMed, Web of Science) to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risks estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs), for a first-percentile increase in dietary pattern score, were combined by a dose-response meta-analysis. Twelve observational studies were included in the meta-analysis which identified a “Healthy pattern” and a “Western pattern”. The Healthy pattern was not related to PC risk (OR = 0.96; 95% confidence interval (CI): 0.88–1.04) while the Western pattern significantly increased it (OR = 1.34; 95% CI: 1.08–1.65). In addition, the “Carbohydrate pattern”, which was analyzed in four articles, was positively associated with a higher PC risk (OR = 1.64; 95% CI: 1.35–2.00). A significant linear trend between the Western (p = 0.011) pattern, the Carbohydrate (p = 0.005) pattern, and the increment of PC risk was observed. The small number of studies included in the meta-analysis suggests that further investigation is necessary to support these findings
The local distribution of in-work poverty and sectoral employment: an analysis of local dynamics in Italy
No full textIn-work poverty has risen to become a key feature of European societies. In 2017, the percentage of workers at risk of low pay in Italy reached an estimated 25% and the issue rose to the forefront of the public and political debate. Yet, due to data limitations, few studies analysed the local distribution of this phenomenon and investigated the macro-determinants associated with its rise. By applying Small Area Estimates (SAE) to EU-SILC data we obtain a novel map of the distribution of in-work poverty in Italy, defined as the share of workers at risk of low pay (AROLP) between 2008 and 2017. The unit of analysis of Local Labour Systems, a non-administrative unit based on commuter flows, highlights the deepening of Italian dualism between Northern and Southern areas, as well as rising within-region wage inequality. By means of a panel fixed-effects model linking estimates of AROLP with data on local sectoral employment, we observe that growth in low-skill sectors such as agriculture is associated with increases in AROLP incidence. On the contrary trends of low pay are negatively associated with the growth of manufacturing and construction sectors, and jobs in non-market services, such as public sector jobs. In addition, variations in overall employment represent the strongest predictor for dynamics of low-pay incidence
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Small area estimation of agricultural data
No full textImproving social inclusion and life conditions is the object of many international project (“InGRID-2: supporting expertise in inclusive growth”, “MAKSWELL: Making Sustainable development and WELL-being frameworks works for policy analysis”). In the evidence-based policy making to combat poverty, agricultural data assume a key role. In fact, mainly in developing countries, it is out of doubt that monitoring agricultural indicators at local level allows for an appropriate policy intervention. In general, local/small area level is the geographical level to which data are requested in order to planning sub-regional policies and/or evaluate the results of policy. At a local/small area level direct estimates of agricultural and rural statistics are not accurate because sample surveys are usually designed so that direct estimators lead to reliable estimates only for larger domains (states, regions)
- …
