Sustainable home environments : proactively addressing sickness-related damp and moldy environments to reduce the impact on the BC health care system
Bibliographic record
Abstract
In developed countries nearly half of a person’s life is spend in their home environment. Worldwide, up to 30% of new and remodelled buildings may have indoor air quality issues with the quality of housing in general playing a decisive role in occupant health. Upwards of 50% of homes in North America contain damp or moldy environments with microbial debris being a key element in indoor air pollution. Asthma prevalence has been deemed a by-product of unhealthy home environments, exacerbated by exposure to high levels of mold and dampness. Asthma impacts 28 million people in North America and accounts for over $62 billion in health care costs and economic impact from lost productivity, lost work days, and early death. This thesis presents literature that demonstrates the link and extent of impact among damp and moldy indoor environments and respiratory disease using asthma as a case study. To quantify the effects, a reliable empirical tool that ranks residential indoor environment condition and predicts associated respiratory health-effect risk in homes has been developed and validated. To support the delivery of potentially significant health benefits and public health care system cost savings, this thesis considers a method, based on economics and risk analysis, to reduce respiratory health impact and validates the basis for a proactive sustainable health care prevention program based on residential mold and dampness remediation. The financial assessment conducted in this thesis utilizing social cost-benefit risk analysis suggests the direct economic burden on the public health care system (PHCS) from high-use (severe and persistent) mold and dampness-affected asthmatics is $5.4 billion annually in North America with an estimated $2.8 billion in savings from a prevention program available for reallocation purposes and the freeing of system capacity for the over-burdened health care system. A patient-centric component costing analysis was conducted to supplement and support literature data. A proposed prevention program implementation strategy consists of identifying the mold and dampness affected high-use asthmatics, treating their environment, administering the prevention program, monitoring progress, and maintaining an ongoing record of the continuing reduction in cost impact to the public health care system to ensure program sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".