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1 June 2022
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The cover image is adapted from Figure 2 in the article “Mapping the Lay of the Land: Using Interactive Network Analytic Tools for Collaboration in Rural Cancer Prevention and Control,” by Carothers and colleagues. The original figure shows a visualization of a resource sharing network with agencies sized by degree and betweenness centrality. Cancer mortality rates in the U.S. are higher in rural than urban areas, especially for colorectal cancer. Modifiable cancer risks (e.g., tobacco use, obesity) are more prevalent among U.S. rural than urban residents. Social network analyses are common, yet rural informal collaborative networks for cancer prevention and control and practitioner uses of network findings are less well understood. Informal networks can benefit from network visualizations and analyses that demonstrate network structures, strengths, and gaps, yet the authors found little in the literature on how best to disseminate social network analysis findings to optimize usefulness to collaborating agencies. The study team developed and conducted a network survey informed by key informant interviews and prior work, then examined the data with social network analysis (SNA) and visualization methods. Service areas had more connections for exchanging information than for more time-intensive collaborative activities of co-developing and sustaining ongoing services and programs, and co-developing and sharing resources. On average, collaborative activities were not dependent on just a few agencies to bridge gaps to hold networks together. Rural informal cancer prevention and control networks in this study are highly connected and largely decentralized. Disseminating network findings help ensure usefulness to rural health and social service practitioners who address cancer risks. For more information, see the article beginning on page 1159. - PDF Icon PDF LinkTable of Contents
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ISSN 1055-9965
EISSN 1538-7755
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Research Articles
Long-Term Outcomes among Adolescent and Young Adult Survivors of Acute Leukemia: A Surveillance, Epidemiology, and End Results Analysis
Amy M. Berkman; Clark R. Andersen; Branko Cuglievan; David C. McCall; Philip J. Lupo; Susan K. Parsons; Courtney D. DiNardo; Nicholas J. Short; Nitin Jain; Tapan M. Kadia; J.A. Livingston; Michael E. Roth
Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk
Xiang Shu; Zhishan Chen; Jirong Long; Xingyi Guo; Yaohua Yang; Conghui Qu; Yoon-Ok Ahn; Qiuyin Cai; Graham Casey; Stephen B. Gruber; Jeroen R. Huyghe; Sun Ha Jee; Mark A. Jenkins; Wei-Hua Jia; Keum Ji Jung; Yoichiro Kamatani; Dong-Hyun Kim; Jeongseon Kim; Sun-Seog Kweon; Loic Le Marchand; Koichi Matsuda; Keitaro Matsuo; Polly A. Newcomb; Jae Hwan Oh; Jennifer Ose; Isao Oze; Rish K. Pai; Zhi-Zhong Pan; Paul D.P. Pharoah; Mary C. Playdon; Ze-Fang Ren; Robert E. Schoen; Aesun Shin; Min-Ho Shin; Xiao-ou Shu; Xiaohui Sun; Catherine M. Tangen; Chizu Tanikawa; Cornelia M. Ulrich; Franzel J.B. van Duijnhoven; Bethany Van Guelpen; Alicja Wolk; Michael O. Woods; Anna H. Wu; Ulrike Peters; Wei Zheng
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