Preventive oncology & epidemiology · 2026
Spatial Analysis of Lung Cancer Mortality and Its Potential Risk Factors: A Regional Study in Kansas.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Sarker Indrani, Asante Derrick, Colwell Stephanie, Mubarak Sauda, Bristy Piali Dey, McGuirk Matthew, Pepper Sam, Jayawardhana Ananda, Lambart Leah, Ratnayake Isuru, Mudaranthakam Dinesh Pal
Аннотация
INTRODUCTION: Lung cancer is a leading cause of cancer deaths in the U.S., with significant geographic disparities in incidence and mortality. Understanding the relationship between spatial variations and other risk factors to lung cancer mortality counts (LCMC) is critical for guiding targeted public health interventions.
OBJECTIVE: This study examines how spatial variations, demographic, socioeconomic, behavioral, health, and environmental risk factors are associated with LCMC in Kansas.
METHODS: LCMC data from 105 Kansas counties were analyzed using Poisson and Negative Binomial models incorporating Conditional Autoregressive (CAR) and Besag-York-Mollié 2 (BYM2) spatial effects models. Predictors included elderly population (%), rurality, poverty, housing, smoking, obesity, pollution, and proximity to coal power plants. Model performance was assessed using Deviance Information Criterion and the Mean Absolute Percentage Error.
RESULTS: The Poisson BYM2 model with correlated heterogeneity provided the best overall performance. In this model, the elderly population (%) and rurality were significantly and positively associated while PM2.5 showed an unexpected negative association.
CONCLUSION: Spatial models, particularly BYM2, provide valuable insights into LCMC hotspots and risk factors. Public health strategies should focus on equity in high-risk clusters through targeted interventions and improved access to healthcare.
OBJECTIVE: This study examines how spatial variations, demographic, socioeconomic, behavioral, health, and environmental risk factors are associated with LCMC in Kansas.
METHODS: LCMC data from 105 Kansas counties were analyzed using Poisson and Negative Binomial models incorporating Conditional Autoregressive (CAR) and Besag-York-Mollié 2 (BYM2) spatial effects models. Predictors included elderly population (%), rurality, poverty, housing, smoking, obesity, pollution, and proximity to coal power plants. Model performance was assessed using Deviance Information Criterion and the Mean Absolute Percentage Error.
RESULTS: The Poisson BYM2 model with correlated heterogeneity provided the best overall performance. In this model, the elderly population (%) and rurality were significantly and positively associated while PM2.5 showed an unexpected negative association.
CONCLUSION: Spatial models, particularly BYM2, provide valuable insights into LCMC hotspots and risk factors. Public health strategies should focus on equity in high-risk clusters through targeted interventions and improved access to healthcare.