Abstract
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.
Highlights
Knowledge 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
Maps showed the absence of extreme classes in Nomenclature of Territorial Units for Statistics (NUTS) Nomenclature of Territorial Units for Statistics (III)
The higher values of Standardized Mortality Ratio (SMR) were greater than 130%, in two municipalities located in the southeast, on the border with Spain
Summary
Knowledge 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. In the analysis of geographical distribution of any type of phenomena, in the field of health, the spatial and temporal level of data aggregation is determinant. The aggregation of disease occurrence data affects the patterns of geographical distribution, as well as the analysis of potential factors that could promote its development. In the analysis of health data, both spatial and temporal aggregated numbers are usually considered in order to preserve the individual’s confidentiality [1]. Like cancer, the aggregation level tends to be higher, due to the needs of data confidentiality, and to increase the rates’ statistical robustness. As a result of the spatial aggregation of data, the number of geographical areas under analysis decreases and a reduction of geographical variation of disease patterns occurs. The aggregation of data is responsible for a decreased possibility of detecting clusters [2]
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