Disparities in cervical and breast cancer mortality in Brazil

OBJETIVO: Analisar a evolução da mortalidade por câncer do colo uterino e de mama no Brasil, segundo indicadores socioeconômicos e assistenciais. MÉTODOS: Foram analisados dados agregados de 30 anos (1980- 2010) de mortalidade por câncer de mama e colo uterino. Os dados de óbitos foram extraídos do Sistema de Informações sobre Mortalidade, os denominadores populacionais, do Instituto Brasileiro de Geografia e Estatística, e os indicadores socioeconômicos e assistenciais do Instituto de Pesquisa Econômica e Aplicada. Foram calculadas as médias móveis desagregadas por capitais e municípios do interior dos estados. O percentual de mudança anual das taxas foi estimado a partir da regressão linear segmentada por joinpoint. Foi feita correlação de Pearson entre as taxas médias trienais do final do período e os indicadores selecionados das capitais e de cada estado brasileiro. RESULTADOS: Houve queda da mortalidade por câncer do colo uterino em todo o período, exceto em municípios das regiões Norte e Nordeste fora das capitais. Houve declínio na mortalidade por câncer de mama nas capitais a partir do final da década de 1990. Os indicadores socioeconômicos positivos correlacionaram-se inversamente com a mortalidade de câncer do colo uterino. Observou-se forte correlação direta entre indicadores positivos e inversa com a taxa de fecundidade e a mortalidade por câncer de mama nos municípios do interior dos estados. CONCLUSÕES: Encontra-se em curso um mecanismo dinâmico entre aumento de risco por câncer de mama e do colo uterino com atenuação da mortalidade em função da expansão de oferta e acesso ao rastreamento, diagnóstico e tratamento, porém de forma desigual.OBJECTIVE: To analyze cervical and breast cancer mortality in Brazil according to socioeconomic and welfare indicators. METHODS: Data on breast and cervical cancer mortality covering a 30-year period (1980-2010) were analyzed. The data were obtained from the National Mortality Database, population data from the Brazilian Institute of Geography and Statistics database, and socioeconomic and welfare information from the Institute of Applied Economic Research. Moving averages were calculated, disaggregated by capital city and municipality. The annual percent change in mortality rates was estimated by segmented linear regression using the joinpoint method. Pearson’s correlation coefficients were conducted between average mortality rate at the end of the three-year period and selected indicators in the state capital and each Brazilian state. RESULTS: There was a decline in cervical cancer mortality rates throughout the period studied, except in municipalities outside of the capitals in the North and Northeast. There was a decrease in breast cancer mortality in the capitals from the end of the 1990s onwards. Favorable socioeconomic indicators were inversely correlated with cervical cancer mortality. A strong direct correlation was found with favorable indicators and an inverse correlation with fertility rate and breast cancer mortality in inner cities. CONCLUSIONS: There is an ongoing dynamic process of increased risk of cervical and breast cancer and attenuation of mortality because of increased, albeit unequal, access to and provision of screening, diagnosis and treatment

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

A preprint version of the article is available at: arXiv:2402.15366v2 [physics.ins-det], https://arxiv.org/abs/2402.15366 . Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/MLG-23-001 (CMS Public Pages). Report numbers: CMS-MLG-23-001, CERN-EP-2023-303.Data Availability: No datasets were generated or analyzed during the current study.Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.SCOAP3. Open access funding provided by CERN (European Organization for Nuclear Research