Rita Roquette

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

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. Cancers spatial epidemiology represents an important concern, mainly for public health policies design aimed to minimise the impact of chronic disease in specific populations.

The relevance of spatial aggregation level and of applied methods in the analysis of geographical distribution of cancer mortality in mainland Portugal (2009–2013)

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.

The 1918–1919 Influenza Pandemic in Portugal: A Regional Analysis of Death Impact

Abstract 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.