Can we understand and improve poorer cancer survival in rural-dwellers?

Research output: Contribution to journalArticle

Abstract

In this article, the concept of geographical cancer equality is described and strong evidence that rural populations have poorer cancer outcomes is highlighted. Currently, hard evidence is lacking for what causes poorer outcomes in rural populations with cancer, and this hinders efforts to address this striking health inequality. Current trends in ‘big data’ science and systems to assess the quality of universities’ research output could discourage the very kind of research that will truly discover why rural communities fare worse with cancer. Smaller local studies, employing mixed research methods and using technology in innovative ways, offer the way forward and will signpost policies and interventions with real potential to redress urban–rural cancer inequality. Rural-dwellers have poorer cancer survival than those living in cities. In a recent systematic review, we identified 39 studies from seven countries, the majority reporting poorer survival in rural areas.1 Place of residence is a major source of inequity worldwide, but compared to other sociodemographic factors affecting cancer outcomes, geographical cancer inequalities are under-researched and barely understood. One problem is that current insights into geographical inequity derive from population-based studies and big data research. It is accepted practice for data scientists to offer explanations for significant associations in their data. In the case of rural residence and increased cancer mortality, the usual explanation is almost too obvious: rural-dwellers have poorer access to health services. In reality, geographical inequality is likely multi-factorial, driven by patient, topographical, and cultural factors, as well as just service organisation. Potentially modifiable local factors are obscured in big data. Here, we argue that collected insights from robust local and primary care-based studies …
Original languageEnglish
Article numberbjgpopen19X101646
Number of pages4
JournalBJGP Open
Volume3
Issue number2
Early online date15 May 2019
DOIs
Publication statusPublished - Jul 2019

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Neoplasms
Rural Population
Research
Survival
Information Systems
Health Services
Primary Health Care
Organizations
Technology
Mortality
Health
Population

Keywords

  • Cancer
  • geographic factors
  • rurality
  • mortality
  • socioeconomic factors
  • general practice

Cite this

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title = "Can we understand and improve poorer cancer survival in rural-dwellers?",
abstract = "In this article, the concept of geographical cancer equality is described and strong evidence that rural populations have poorer cancer outcomes is highlighted. Currently, hard evidence is lacking for what causes poorer outcomes in rural populations with cancer, and this hinders efforts to address this striking health inequality. Current trends in ‘big data’ science and systems to assess the quality of universities’ research output could discourage the very kind of research that will truly discover why rural communities fare worse with cancer. Smaller local studies, employing mixed research methods and using technology in innovative ways, offer the way forward and will signpost policies and interventions with real potential to redress urban–rural cancer inequality. Rural-dwellers have poorer cancer survival than those living in cities. In a recent systematic review, we identified 39 studies from seven countries, the majority reporting poorer survival in rural areas.1 Place of residence is a major source of inequity worldwide, but compared to other sociodemographic factors affecting cancer outcomes, geographical cancer inequalities are under-researched and barely understood. One problem is that current insights into geographical inequity derive from population-based studies and big data research. It is accepted practice for data scientists to offer explanations for significant associations in their data. In the case of rural residence and increased cancer mortality, the usual explanation is almost too obvious: rural-dwellers have poorer access to health services. In reality, geographical inequality is likely multi-factorial, driven by patient, topographical, and cultural factors, as well as just service organisation. Potentially modifiable local factors are obscured in big data. Here, we argue that collected insights from robust local and primary care-based studies …",
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N2 - In this article, the concept of geographical cancer equality is described and strong evidence that rural populations have poorer cancer outcomes is highlighted. Currently, hard evidence is lacking for what causes poorer outcomes in rural populations with cancer, and this hinders efforts to address this striking health inequality. Current trends in ‘big data’ science and systems to assess the quality of universities’ research output could discourage the very kind of research that will truly discover why rural communities fare worse with cancer. Smaller local studies, employing mixed research methods and using technology in innovative ways, offer the way forward and will signpost policies and interventions with real potential to redress urban–rural cancer inequality. Rural-dwellers have poorer cancer survival than those living in cities. In a recent systematic review, we identified 39 studies from seven countries, the majority reporting poorer survival in rural areas.1 Place of residence is a major source of inequity worldwide, but compared to other sociodemographic factors affecting cancer outcomes, geographical cancer inequalities are under-researched and barely understood. One problem is that current insights into geographical inequity derive from population-based studies and big data research. It is accepted practice for data scientists to offer explanations for significant associations in their data. In the case of rural residence and increased cancer mortality, the usual explanation is almost too obvious: rural-dwellers have poorer access to health services. In reality, geographical inequality is likely multi-factorial, driven by patient, topographical, and cultural factors, as well as just service organisation. Potentially modifiable local factors are obscured in big data. Here, we argue that collected insights from robust local and primary care-based studies …

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