3 QUESTIONS ON... How to Better Understand How the Environments We Live in Affect Cancer Risk
Bibliographic record
Abstract
Alexander James David Wray: Alexander James David WrayLeia M. Minaker, PhD: Leia M. Minaker, PhDWhat types of communities have the lowest cancer risk? What aspects of the environments we live in contribute most to our cancer risk? These are some of the questions researchers set out to answer in a multidisciplinary scoping review published last year in the journal Cancer (2019; doi.org/10.1002/cncr.32376). They looked at data across cancer epidemiology, urban planning, public policy, engineering, and more. It examines studies on land use, transportation, housing, greenspace, accessibility, and more. In an interview with Oncology Times, the study's coauthors—Alexander James David Wray, a master's candidate in the Human Environments Analysis Lab at Western University in Canada, and Leia M. Minaker, PhD, Assistant Professor in the School of Planning at University of Waterloo—explained what they hope this review will accomplish, how they went about investigating complex questions, and what cancer care providers should know about it. 1 What were the key findings from this research and what's new about them that we didn't previously know from other research? MINAKER: “The overarching goal of this project was to describe and synthesize the pathways by which features of our environment ‘get under the skin’ of residents. In other words, how do features of the built environment create risks for cancer? Alternatively, how do features of the built environment prevent cancer? “This research wasn't so much about finding new discoveries as it was about synthesizing what exists to point to new directions for research on this topic. We wanted to work at the intersection of urban planning and medicine—because the health care system has a vested interest in planning healthy, cancer-preventing communities.” 2 That's a fairly onerous task to try to sort out how all the features of the environments around us may affect cancer risk. How did you go about reviewing the existing data on this topic? WRAY: “We identified 13 subject-specific databases based on previous reviews in this area, and discipline-specific expertise we were looking to incorporate in the review (including HeinOnline and LexisNexis for law-related studies, or JSTOR for social science and humanities-based papers). We developed a pathway model to frame our research findings based on theories in urban planning and geography about the interconnections between health and the built/social environment, guidance from the Institute of Medicine on the largest lifestyle and environmental risk factors of cancer, and narrowing in on the top 10 cancers in our countries of interest. “On another note, we elected to just examine evidence from Australia, Canada, New Zealand, the United Kingdom, and the United States of America given they share similar governmental systems and division of powers in managing urban development and policy. “The pathway model thus flows from an element of the built environment (such as land use and greenspace) to a risk factor (diet, physical activity) to one of the top 10 cancer sites. We analyzed the literature by coding each study across these three domains, and then drawing the links between the three of them to show how researchers are linking elements of the built environment through specific risk factors to particular cancer sites. We included all types of evidence, even including theoretical commentaries, to ensure we cast a wide enough net. “While a lot of work (it took a full year of reading and systematically coding the literature), we think it paid off well by providing a broad overview of the past 20 years of research related to this topic. “There have been a couple of other reviews on this topic. Ours was the first to incorporate several different databases and look from several different disciplinary perspectives, since we were interested in not only the health care/public health research, but also in the urban planning and social science literature. We found that over 20 percent of the literature base in the social and natural sciences was being missed in many of the reviews already on the topic. Thus, our approach broadened the viewpoint that is taken to cancer research by uncovering many interesting studies that use novel methods (including social network analysis and spatio-temporal exposure modeling based on residential histories) and assess common problems (such as screening adherence, lifestyle risk factors, etc.) with a new perspective.” 3 What is the bottom line that practicing oncologists and cancer care providers should know about this work and the next steps of your research? WRAY: “We definitely think there is a need for more discussion from social scientists on urban policy and planning as it relates to cancer, and on the flip side for more work from the medical field to incorporate aspects of the built, social, and natural environment into cancer research.” MINAKER: “Oncologists and cancer care providers are used to dealing with patients one at a time. But they can legitimately take a role in advocating for policies that help to create cities where the default choice is the healthy choice that helps people prevent cancer throughout their entire life course. These people are trusted members of society and have a powerful voice that should be used to push for positive changes. The earlier we can build healthy habits among people, and the more we can push our cities to making healthy and sustainable lifestyle choices easy and convenient, the greater chance we have of reducing the number of cases.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".