Survival Rate Influences the Type of Web Communities Used by Cancer Patients

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Cancer patients are using online support communities more than ever before. These sites offer both emotional and informational support, and empower patients by enabling them to talk with other patients who are facing similar issues. According to a new study, online support communities for cancers with a high survival rate contain a greater amount of emotional support than do online communities for cancers with a low survival rate [1]. Researchers at the University of Michigan Health System and VA Ann Arbor Healthcare System also found that online support communities for cancers with a low survival rate contained more informational support than did communities for cancers with a high survival rate.

online-help-and-supportThe new study, presented last month at the 2008 annual meeting of the North American Primary Care Research Group [2], evaluated the differences in emotional and informational social support content in online communities for cancers with low and high survival rates.

The researchers analyzed over 3,500 messages from 587 individuals in eight online support communities located within Yahoo!Groups and the Association of Cancer Online Resources (ACOR) websites for four different types of cancer with low or high five-year survival rates. Across all communities, there was a greater amount of emotional support than informational support.

High survival rate communities contained a greater proportion of emotional support than low survival rate communities (65% vs. 55%). In contrast, low survival rate communities contained a greater proportion of informational support than high survival rate communities (33% vs. 25%).

High survival rate community support
Emotional support: 65%
Informational support: 25%

Low survival rate community support
Emotional support: 55%
Information support: 33%

Participants in the study were members of support communities for four different types of cancer: lung cancer, melanoma, pancreatic cancer and thyroid cancer. They participated in eight different online communities and were all reviewed under the same time period.

According to the primary author of the study, Lorraine Buis, Ph.D. [3]:

When primary care providers refer individuals to online communities for support, they should be aware that there might be differing amounts of support based on the survival rate of a particular cancer.

According to the National Cancer Institute’s Surveillance Epidemiology and End Results (SEER) Cancer Statistics Review, which reports the most recent cancer incidence, mortality, survival, prevalence and lifetime risk statistics, from 1975 to 2005 cancer patients with thyroid cancer and melanoma of the skin had high survival rates (96.6% and 91.2% respectively) while lung cancer and pancreatic cancer had low survival rates (12.1% and 5.1% respectively) [4].

In addition to helping patients, online support communities help family and friends cope with the struggles that cancer presents. This is the first study to assess the influence of cancer patient survival rates on social support content in online communities for cancer.

A number of patient social networks are listed in the Highlight HEALTH Web Directory.

References

  1. Buis et al. Relationship between cancer survival rate and social support within online communities for cancer. 2008 Annual Meeting of the North American Primary Care Research Group, Rio Grande, Puerto Rico.
  2. 2008 Annual Meeting of the North American Primary Care Research Group (NAPCRG) Program. 2008 Nov 15 — 18.
  3. Cancer survival rates impact type of Web communities used by patients. University of Michigan Health System press release. 2008 Nov 18.
  4. SEER Cancer Statistics Review, 1975-2005, Age-adjusted SEER Incidence and U.S. Death Rates and 5-year Relative Survival Rates, National Cancer Institute. Bethesda, MD, based on November 2007 SEER data submission, posted to the SEER web site, 2008.
About the Author

Walter Jessen, Ph.D. is a Data Scientist, Digital Biologist, and Knowledge Engineer. His primary focus is to build and support expert systems, including AI (artificial intelligence) and user-generated platforms, and to identify and develop methods to capture, organize, integrate, and make accessible company knowledge. His research interests include disease biology modeling and biomarker identification. He is also a Principal at Highlight Health Media, which publishes Highlight HEALTH, and lead writer at Highlight HEALTH.