The Effect of Hazard Analysis Critical Control Point Programs on Microbial Contamination of Carcasses in Abattoirs: A Systematic Review of Published Data
Bibliographic record
Abstract
Hazard analysis critical control point (HACCP) programs have been endorsed and implemented globally to enhance food safety. Our objective was to identify, assess, and summarize or synthesize the published research investigating the effect of HACCP programs on microbial prevalence and concentration on food animal carcasses in abattoirs through primary processing. The results of microbial testing pre- and post-HACCP implementation were reported in only 19 studies, mostly investigating beef (n=13 studies) and pork (n=8 studies) carcasses. In 12 of 13 studies measuring aerobic bacterial counts, reductions were reported on beef (7/8 studies), pork (3/3), poultry (1/1), and sheep (1/1). Significant (p<0.05) reductions in prevalence of Salmonella spp. were reported in studies on pork (2/3 studies) and poultry carcasses (3/3); no significant reductions were reported on beef carcasses (0/8 studies). These trends were confirmed through meta-analysis of these data; however, powerful meta-analysis was precluded because of an overall scarcity of individual studies and significant heterogeneity across studies. Australia reported extensive national data spanning the period from 4 years prior to HACCP implementation to 4 years post-HACCP, indicating reduction in microbial prevalence and concentration on beef carcasses in abattoirs slaughtering beef for export; however, the effect of abattoir changes initiated independent of HACCP could not be excluded. More primary research and access to relevant proprietary data are needed to properly evaluate HACCP program effectiveness using modeling techniques capable of differentiating the effects of HACCP from other concurrent factors.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".