Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package

Cervical cancer incidence rates considering migration status in mainland China using Bayesian model-Estimation based on 2016 cancer registry data

In mainland China, cancer registration relies on household-registered populations, overlooking migrant populations. Estimating cervical cancer incidence among permanent residents, including migrants, offers a more accurate representation of the true burden. The data from 487 cancer registries across China in 2016 were analyzed using a Bayesian spatial regression model with the integrated nested Laplace approximation-stochastic partial differential equation method. The study estimated cervical cancer incidence among household-registered populations and adjusted for migrant populations using a weighting method based on interprovincial distribution and age stratification to derive the incidence of cervical cancer in the permanent residents. Data from the China Population Census, the China Migrants Dynamic Survey, and the Urban Statistical Yearbook were incorporated. The estimated crude incidence rate of cervical cancer among permanent residents was 17.4/100,000 in mainland China, with an age-standardized incidence rate (ASIR) of 17.2/100,000. The largest disparities in cervical cancer crude incidence rate between permanent residents and household-registered populations were observed in Guizhou (2.4/100,000, 95% CI 1.9-2.9/100,000), Zhejiang (-1.2/100,000, 95% CI -1.8 to -0.6/100,000) and Tianjin (-1.1/100,000, 95% CI -1.5 to -0.7/100,000). The number of the estimated cervical cancer incident cases was 8948. Guangdong saw an increase of 887 cases, while Henan had a decrease of 1430 cases. Guizhou had the highest ASIR (28.1/100,000), and Beijing had the lowest ASIR (11.0/100,000). The significance of this study is that it improves the accuracy of cervical cancer data in China. These findings provide evidence for developing cervical cancer prevention and control strategies, and offer insights for other countries and regions facing migration challenges.

Addressing Inequity in Spatial Access to Lung Cancer Screening

BACKGROUND

The successful implementation of an equitable lung cancer screening program requires consideration of factors that influence accessibility to screening services.

METHODS

Using lung cancer cases in British Columbia (BC), Canada, as a proxy for a screen-eligible population, spatial access to 36 screening sites was examined using geospatial mapping and vehicle travel time from residential postal code at diagnosis to the nearest site. The impact of urbanization and Statistics Canada's Canadian Index of Multiple Deprivation were examined.

RESULTS

Median travel time to the nearest screening site was 11.7 min (interquartile range 6.2-23.2 min). Urbanization was significantly associated with shorter drive time (p < 0.001). Ninety-nine percent of patients with ≥60 min drive times lived in rural areas. Drive times were associated with sex, ethnocultural composition, situational vulnerability, economic dependency, and residential instability. For example, the percentage of cases with drive times ≥60 min among the least deprived situational vulnerability group was 4.7% versus 44.4% in the most deprived group.

CONCLUSIONS

Populations at risk in rural and remote regions may face more challenges accessing screening services due to increased travel times. Drive times increased with increasing sociodemographic and economic deprivations highlighting groups that may require support to ensure equitable access to lung cancer screening.

The Nova Scotia Community Cancer Matrix: A geospatial tool to support cancer prevention

Globally, cancer is a leading cause of death and morbidity and its burden is increasing worldwide. It is established that medical approaches alone will not solve this cancer crisis. Moreover, while cancer treatment can be effective, it is costly and access to treatment and health care is vastly inequitable. However, almost 50% of cancers are caused by potentially avoidable risk factors and are thus preventable. Cancer prevention represents the most cost-effective, feasible and sustainable pathway towards global cancer control. While much is known about cancer risk factors, prevention programs often lack consideration of how place impacts cancer risk over time. Maximizing cancer prevention investment requires an understanding of the geographic context for why some people develop cancer while others do not. Data on how community and individual level risk factors interact is therefore required. The Nova Scotia Community Cancer Matrix (NS-Matrix) study was established in Nova Scotia (NS), a small province in Eastern Canada with a population of 1 million. The study integrates small-area profiles of cancer incidence with cancer risk factors and socioeconomic conditions, to inform locally relevant and equitable cancer prevention strategies. The NS-Matrix Study includes over 99,000 incident cancers diagnosed in NS between 2001 and 2017, georeferenced to small-area communities. In this analysis we used Bayesian inference to identify communities with high and low risk for lung and bladder cancer: two highly preventable cancers with rates in NS exceeding the Canadian average, and for which key risk factors are high. We report significant spatial heterogeneity in lung and bladder cancer risk. The identification of spatial disparities relating to a community's socioeconomic profile and other spatially varying factors, such as environmental exposures, can inform prevention. Adopting Bayesian spatial analysis methods and utilizing high quality cancer registry data provides a model to support geographically-focused cancer prevention efforts, tailored to local community needs.