Whose burden? A comprehensive approach to describing burden of disease by synthesizing evidence from diverse perspectives
Bibliographic record
Abstract
Epidemiologists tasked with addressing public concerns about a specific health issue and developing effective public health strategies aimed at reducing related health risks must begin by describing the extent of the health threat in the target population. Typical approaches use quantitative measurement of pertinent epidemiologic indicators to assess the impact of the health threat. To ensure that public health strategies developed through the investigative process are relevant to the target population, however, public health researchers can gain valuable insights by also ascertaining how affected members of the target population view the health issue and related risks. While existing literature espouses the benefits of building collective knowledge to capture the depth and complexity of health and disease, there is limited information on the most effective ways to synthesize different forms of evidence to construct a comprehensive assessment of the burden of disease. Indigenous communities in the Northwest Territories and Yukon concerned about their high prevalence of Helicobacter pylori infection and the associated risk of stomach cancer are currently guiding research that addresses their concerns. In this dissertation, I report research I conducted as part of this community-driven research program to characterize the self-described impact of disease by individuals at risk and describe disease burden using more conventional epidemiologic approaches. I then describe the similarities and differences in disease burden assessed using diverse forms of evidence and synthesize this information to provide a comprehensive description of disease burden among those impacted. My synthesis confirmed a disproportionate impact of H. pylori infection and associated diseases among northern Indigenous populations compared to other groups. However, academic, healthcare, and community research partners did not appear to have a shared understanding of H. pylori and its impacts; furthermore, the burden attributed to H. pylori infection by northern community members may be broader than the burden as described by academic scientists and healthcare providers. In addition to these contributions, this dissertation provides examples of best practices when using epidemiologic approaches to describe disease burden, particularly when community concerns about a specific health issue trigger investigations. Using examples of community-academic collaborations that identify and implement reciprocal learning initiatives and incorporate the target population’s characterizations of the health problem, this work highlights the importance of building shared knowledge about a health issue of interest. Ultimately, this dissertation illustrates the value of incorporating diverse perspectives when seeking effective public health solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.231 | 0.388 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.061 | 0.035 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.035 | 0.051 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".