Intensified food production and correlated risks to human health in the Greater Mekong Subregion: a systematic review
Bibliographic record
Abstract
BACKGROUND: Intensified food production, i.e. agricultural intensification and industrialized livestock operations may have adverse effects on human health and promote disease emergence via numerous mechanisms resulting in either direct impacts on humans or indirect impacts related to animal and environmental health. For example, while biodiversity is intentionally decreased in intensive food production systems, the consequential decrease in resilience in these systems may in turn bear increased health risks. However, quantifying these risks remains challenging, even if individual intensification measures are examined separately. Yet, this is an urgent task, especially in rapidly developing areas of the world with few regulations on intensification measures, such as in the Greater Mekong Subregion (GMS). METHODS: We systematically searched the databases PubMed and Scopus for recent studies conducted on the association between agricultural (irrigation, fertilization, pesticide application) and livestock (feed additives, animal crowding) intensification measures and human health risks in the GMS. The search terms used were iteratively modified to maximize the number of retrieved studies with relevant quantitative data. RESULTS: We found that alarmingly little research has been done in this regard, considering the level of environmental contamination with pesticides, livestock infection with antibiotic resistant pathogens and disease vector proliferation in irrigated agroecosystems reported in the retrieved studies. In addition, each of the studies identified focused on specific aspects of intensified food production and there have been no efforts to consolidate the health risks from the simultaneous exposures to the range of hazardous chemicals utilized. CONCLUSIONS: While some of the studies identified already reported environmental contamination bearing considerable health risks for local people, at the current state of research the actual consolidated risk from regional intensification measures cannot be estimated. Efforts in this area of research need to be rapidly and considerably scaled up, keeping pace with the current level of regional intensification and the speed of pesticide and drug distribution to facilitate the development of agriculture related policies for regional health promotion.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
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 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".