Ambient air pollution (AAP) is recognized a cardiovascular risk factor and lipid profile dysregulation seems to be one of the potential mediators involved. However, results from epidemiologic research on the association between exposure to AAP and altered lipid profile have been inconsistent. This study aims to systematically review and meta-analyse epidemiologic evidence on the association between exposure to ambient air pollutants (particulate matter, nitrogen oxides, sulphur dioxide, ozone, carbon monoxide, back carbon) and lipid profile parameters (Total cholesterol; High-Density Lipoprotein Cholesterol; Low-Density Lipoprotein Cholesterol; TG-Triglycerides) or dyslipidaemia.Systematic electronic literature search was performed in PubMed, Web of Science and Scopus databases (last search on 24th May 2019) using keywords related to the exposure (ambient air pollutants) and to the outcomes (lipid profile parameters/dyslipidaemia). Qualitative and quantitative information of the studies were extracted and fixed or random-effects models were used to obtain a pooled effect estimate per each pollutant/outcome combination.22 studies were qualitatively analysed and, from those, 3 studies were quantitatively analysed. Particulate matters were the most studied pollutants and a considerable heterogeneity in air pollution assessment methods and outcomes definitions was detected. Age, obesity related measures, tobacco consumption, sex and socioeconomic factors were the most frequent considered variables for confounding adjustment in the models. In a long-term exposure scenario, we found a 3.14% (1.36%e4.95%) increase in TG levels per 10 mg/m 3 PM 10 increment and a 4.24% (1.37%e7.19%) increase in TG levels per 10 mg/m 3 NO 2 increment. No significant associations were detected for the remaining pollutant/outcome combinations.Despite the few studies included in the meta-analysis, our study suggests some epidemiologic evidence supporting the association between PM 10 and NO 2 exposures and increased TG levels. Due to the very low level of evidence, more studies are needed to clarify the role of lipid profile dysregulation as a mediator on the AAP adverse cardiovascular effects.

Cancer is a major concern among chronic diseases today. Spatial epidemiology plays a relevant role in this matter and we present here a review of this subject, including a discussion of the literature in terms of the level of geographic data aggregation, risk factors and methods used to analyse the spatial distribution of patterns and spatial clusters. For this purpose, we performed a websearch in the Pubmed and Web of Science databases including studies published between 1979 and 2015. We found 180 papers from 63 journals and noted that spatial epidemiology of cancer has been addressed with more emphasis during the last decade with research based on data mostly extracted from cancer registries and official mortality statistics. In general, the research questions present in the reviewed papers can be classified into three different sets: i) analysis of spatial distribution of cancer and/or its temporal evolution; ii) risk factors; iii) development of data analysis methods and/or evaluation of results obtained from application of existing methods. This review is expected to help promote research in this area through the identification of relevant knowledge gaps. Cancer's spatial epidemiology represents an important concern, mainly for public health policies design aimed to minimise the impact of chronic disease in specific populations. IntroductionGiven the relevance of spatial epidemiology in health research and the emphasis of cancer among chronic diseases, as well as the growing amount of studies in this area, it is important to know what the literature says about spatial epidemiology of cancer as well as provide its structured description. According to the World Health Organization (WHO), cancer is a leading cause of death in the world (WHO, 2015). It is also the cause of various morbidities and co-morbidities and can be responsible for loss of years of life years as well as loss of years without disability. Considering the aging population, it is predicted that the number of new cases of cancer will increase by more than 12% over the next decade in the European Union (EU) (DGS, 2013). The fight against cancer is a major challenge in public health. This challenge is due in part to the inequalities in terms of incidence, mortality, and survival. Therefore, a multidisciplinary approach is needed (Bastos et al., 2010). Among the various fields that can contribute to the development of knowledge about this disease, spatial epidemiology plays an important role. It can promote the understanding of spatial and temporal distribution patterns, helping to better identify the risk factors that influence them.Three types of approach can be established in spatial epidemiology: i) mapping; ii) geographic correlation; and iii) clustering (Elliott and Wartenberg, 2004). Mapping or map design regarding health and disease situations is the most often mentioned and used of these three approaches. Further, geographic correlation studies have the goal to spatially compare the health with several types of factors such as e...

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Background Cancer is a leading cause of morbidity and mortality in the world. In Portugal, colorectal cancer is one of the most incident cancers; thus, it is crucial to act to fight it. Knowledge of the geographical distribution of the incidence and mortality of colorectal cancer can facilitate the execution of these actions and make them more effective. Methods Our paper aims to describe and discuss the geographical patterns of colorectal cancer incidence and mortality in mainland Portugal municipalities (2007–2011). We used the Besag, York and Mollié (BYM) model to compute the relative risk (RR) and posterior probability (PP). We performed a cluster analysis with Global Moran’s Index and Local Moran’s Index (LISA). We ran a geographically weighted regression (GWR) to compare incidence and mortality patterns. Results Incidence and mortality have different distributions of RR values. The interval of RR concerning incidence was higher than the interval of RR concerning mortality. PP values reinforce the finding of higher heterogeneity of the incidence of colorectal cancer. The comparison of the cluster maps for incidence and mortality shows a few municipalities classified with the same cluster type in both maps. Additionally, the GWR results show that the percentage of RR mortality explained by RR incidence differs throughout mainland Portugal. From the comparison of our results with the prevalence of risk factors (at NUTS II level), the need to be aware of smoking habits, alcohol consumption and the unhealthy diet of the Portuguese population stands out. Conclusions There are differences in the geographical distribution of the RR incidence and RR mortality of colorectal cancer in mainland Portugal municipalities. Likewise, it is relevant to highlight the cluster of two municipalities with high RR values concerning colorectal cancer’s incidence and mortality. Future research is necessary to explain the geographical differences in the distribution of colorectal cancer in mainland Portugal municipalities. Based on our findings, it may be interesting to examine the influence of smoking, alcohol consumption, diet and screening on colorectal cancer in greater detail. Additionally, it may be relevant to develop an analysis focused on municipalities where the incidence values explain the mortality values poorly (or well). Electronic supplementary material The online version of this article (10.1186/s12885-019-5719-9) contains supplementary material, which is available to authorized users.

Aedes albopictus is an invasive mosquito that has colonized several European countries as well as Portugal, where it was detected for the first time in 2017. To increase the knowledge of Ae. albopictus population dynamics, a survey was carried out in the municipality of Loulé, Algarve, a Southern temperate region of Portugal, throughout 2019, with Biogents Sentinel traps (BGS traps) and ovitraps. More than 19,000 eggs and 400 adults were identified from May 9 (week 19) and December 16 (week 50). A positive correlation between the number of females captured in the BGS traps and the number of eggs collected in ovitraps was found. The start of activity of A. albopictus in May corresponded to an average minimum temperature above 13.0 °C and an average maximum temperature of 26.2 °C. The abundance peak of this A. albopictus population was identified from September to November. The positive effect of temperature on the seasonal activity of the adult population observed highlight the importance of climate change in affecting the occurrence, abundance, and distribution patterns of this species. The continuously monitoring activities currently ongoing point to an established population of A. albopictus in Loulé, Algarve, in a dispersion process to other regions of Portugal and raises concern for future outbreaks of mosquito-borne diseases associated with this invasive mosquito species.

Although the impact of deaths occurring during the 1918-1919 influenza pandemic has been assessed in many archeo-epidemiologic studies, detailed estimates are not available for Portugal. We applied negative binomial models to monthly data on respiratory-related and all-cause deaths at the national and district levels from Portugal for 1916-1922. Influenza-related excess mortality was computed as the difference between observed and expected deaths. Poisson regression was used to estimate the association of geographic and sociodemographic factors with excess mortality. Two waves of pandemic influenza-July 1918 to January 1919 and April to May 1919-were identified, for which the excess all-cause death rate was 195.7 per 10,000 persons. All districts of Portugal were affected. The pandemic hit earlier in southeastern districts and the main cities, but excess mortality was highest in the northeast, in line with the high death burden experienced by northern Spanish provinces. During the period of intense excess mortality (fall/winter 1918-1919), population density was negatively associated with pandemic impact. This pattern changed during the March 1919 to June 1920 wave, when excess mortality increased with population density and in northern and western directions. Portuguese islands were less and later affected. Given the geographic heterogeneity evidenced in our study, subnational sociodemographic characteristics and connectivity should be integrated in pandemic preparedness plans.

BackgroundKnowledge regarding the geographical distribution of diseases is essential in public health in order to define strategies to improve the health of populations and quality of life.The present study aims to establish a methodology to choose a suitable geographic aggregation level of data and an appropriated method which allow us to analyze disease spatial patterns in mainland Portugal, avoiding the “small numbers problem.” Malignant cancer mortality data for 2009–2013 was used as a case study.MethodsTo achieve our aims, we used official data regarding the mortality by all malignant cancer, between 2009 and 2013, and the mainland Portuguese resident population in 2011. Three different spatial aggregation levels were applied: Nomenclature of Territorial Units for Statistics, level III (28 areas), municipalities (278 areas), and parishes (4050 areas).Standardized Mortality Ratio (SMR) and relative risk (RR) were computed with Besag, York and Mollié model (BYM) for the evaluation of geographic patterns of mortality data. We also estimated Global Moran’s I, Local Moran’s I, and posterior probability (PP) for the spatial cluster analysis.ResultsOur results show that the occurrence of lower and higher extreme values of the standardized mortality ratio tend to increase with the decrease of data spatial aggregation. In addition, the number of local clusters is higher at small spatial aggregation levels, although the area of each cluster is generally smaller. Regarding global clustering, data forms clusters at all considered levels.Relative risk (RR) computed by Besag, York and Mollié model, in turn, also shows different results at the municipalities and parishes levels. However, the difference is smaller than the difference obtained by SMR computation. This statement is supported by the coefficient variation values.ConclusionsOur findings show that the choice of spatial data aggregation level has high importance in the research results, as different aggregation levels can lead to distinct results.In terms of the case study, we conclude that for the period of 2009–2013, cancer mortality in mainland Portugal formed clusters. The most suitable applicable spatial scale and method seemed to be at the municipalities level and Besag, York and Mollié model, respectively. However, further studies should be conducted in order to provide greater support to these results.