Atlas of cancer mortality in the United Arab Emirates aided by application of geographic information system methods

The aim of this study was to determine cancer mortality rates for the United Arab Emirates (UAE) and to create an atlas of cancer mortality for the UAE. This atlas is the first of its kind in the Gulf country and the Middle East. Death certificates were reviewed for a period from January 1, 1990 to December 31, 1999 and cancer deaths were identified. Cancer mortality cases were verified by comparing with medical records. Age-adjusted cancer mortality rates were calculated by gender, emirate/medical district and nationality (UAE nationals and overall UAE population). Individual rates for each emirate were compared to the overall rate of the corresponding population for the same cancer site and gender. Age-adjusted rates were mapped using MapInfo software. High rates for liver, lung and stomach cancer were observed in Abu Dhabi, Dubai and the northern emirates, respectively. Rates for UAE nationals were greater compared to the overall UAE population. Several factors were suggested that may account for high rates of specific cancers observed in certain emirates. It is hoped that this atlas will provide leads that will guide further epidemiologic and public health activities aimed at preventing cancer. ^

A community-based assessment of the spatiotemporal distribution of influenza in a U.S.-Mexico border county during the spring 2009 novel H1N1 swine-origin influenza A pandemic

This study retrospectively evaluated the spatial and temporal disease patterns associated with influenza-like illness (ILI), positive rapid influenza antigen detection tests (RIDT), and confirmed H1N1 S-OIV cases reported to the Cameron County Department of Health and Human Services between April 26 and May 13, 2009 using the space-time permutation scan statistic software SaTScan in conjunction with geographical information system (GIS) software ArcGIS 9.3. The rate and age-adjusted relative risk of each influenza measure was calculated and a cluster analysis was conducted to determine the geographic regions with statistically higher incidence of disease. A Poisson distribution model was developed to identify the effect that socioeconomic status, population density, and certain population attributes of a census block-group had on that area's frequency of S-OIV confirmed cases over the entire outbreak. Predominant among the spatiotemporal analyses of ILI, RIDT and S-OIV cases in Cameron County is the consistent pattern of a high concentration of cases along the southern border with Mexico. These findings in conjunction with the slight northward space-time shifts of ILI and RIDT cluster centers highlight the southern border as the primary site for public health interventions. Finally, the community-based multiple regression model revealed that three factors—percentage of the population under age 15, average household size, and the number of high school graduates over age 25—were significantly associated with laboratory-confirmed S-OIV in the Lower Rio Grande Valley. Together, these findings underscore the need for community-based surveillance, improve our understanding of the distribution of the burden of influenza within the community, and have implications for vaccination and community outreach initiatives.^