The epidemiology of diabetes in Manitoba : an exploration through time and space
Bibliographic record
Abstract
Diabetes mellitus (DM) is becoming epidemic world wide, with the number of cases projected to increase from 171 million cases in the year 2000 to 366 million cases in the year 2030. The number of cases of DM in Manitoba is also rapidly increasing. In 1998, it was estimated that there were 54,926 adults living with diagnosed DM in Manitoba, a significant increase from 30,104 cases in 1986. The purpose of this study was to describe the temporal and geographic variability of DM in the province of Manitoba and to assess the degree to which this variability is associated with underlying population characteristics. This was accomplished in four separate studies. The first study focused on describing and modeling the spatial distribution of DM prevalence in the City of Winnipeg, Manitoba in 1998. The second study compared the demographic, temporal and geographic patterns of DM incidence and prevalence in the Manitoba First Nation population to the non-First Nation population from 1989 to 1998. The third study used a number of diverse spatial techniques to visualize, explore and model the incidence of DM in the province of Manitoba between 1989 and 1998. The fourth study used a component cohort projection model to back-cast and forecast the prevalence of DM in Manitoba from 1950 to 2050. All analyses were based on DM incidence and prevalence data derived from the Manitoba diabetes database. The results of these four studies suggest that the number of DM cases will continue to rapidly increase into the foreseeable future in Manitoba. They also suggest that despite an observed gradient in DM risk by age, socioeconomic and First Nation status, all population groups are at significant risk for developing the disease. It is concluded that prevention programs focusing only on groups at highest risk for developing DM will have little chance of success and that population based prevention approaches which tackle the fabric of everyday life will be required to stem the epidemic. It is also concluded that population based prevention efforts will need to be informed by further research utilizing diverse historical, anthropological and geographical research methods which can identify the range of forces affecting DM risk, and the types and scales of interventions which may be required simultaneously at individual, local, regional, national, and even international levels to deal effectively with the DM epidemic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".