RESUMO
To prevent and control foodborne diseases, there is a fundamental need to identify the foods that are most likely to cause illness. The goal of this study was to rank 25 commonly consumed food products associated with Salmonella enterica contamination in the Central Region of Mexico. A multicriteria decision analysis (MCDA) framework was developed to obtain an S. enterica risk score for each food product based on four criteria: probability of exposure to S. enterica through domestic food consumption (Se); S. enterica growth potential during home storage (Sg); per capita consumption (Pcc); and food attribution of S. enterica outbreak (So). Risk scores were calculated by the equation Se*W1 +Sg*W2 +Pcc*W3 +So*W4 , where each criterion was assigned a normalized value (1-5) and the relative weights (W) were defined by 22 experts' opinion. Se had the largest effect on the risk score being the criterion with the highest weight (35%; IC95% 20%-60%), followed by So (24%; 5%-50%), Sg (23%; 10%-40%), and Pcc (18%; 10%-35%). The results identified chicken (4.4 ± 0.6), pork (4.2 ± 0.6), and beef (4.2 ± 0.5) as the highest risk foods, followed by seed fruits (3.6 ± 0.5), tropical fruits (3.4 ± 0.4), and dried fruits and nuts (3.4 ± 0.5), while the food products with the lowest risk were yogurt (2.1 ± 0.3), chorizo (2.1 ± 0.4), and cream (2.0 ± 0.3). Approaches with expert-based weighting and equal weighting showed good correlation (R2 = 0.96) and did not show significant differences among the ranking order in the top 20 tier. This study can help risk managers select interventions and develop targeted surveillance programs against S. enterica in high-risk food products.
Assuntos
Frutas , Sementes , Bovinos , Animais , México , Galinhas , Fatores de RiscoRESUMO
Chicken meat is often associated withSalmonella entericacontamination worldwide. This study proposes a risk assessment model for human salmonellosis linked to the domestic consumption of chicken meat in the central region of Mexico, incorporating genotypic and phenotypic data. SixS. entericagroups were used, considering the presence of specific virulence genes and multidrug resistance (MDR). Sixteen exposure scenarios were established considering retail point (RP1 = fresh market/butcher shop; RP2 = mini-super/supermarket), transportation, home storage, cooking, and cross-contamination. The model predicted a mean annual salmonellosis cases of 66,754 due to chicken consumption (CI95% 10775-231606). The mean probability of illness (Pill) among the exposure scenarios ranged from 2.5 × 10-9 to 3.7 × 10-6, 7.7 × 10-8 to 1.1 × 10-4, and 6.7 × 10-4 to 7.8 × 10-2 for low, moderate, and high virulence groups. Exposure scenarios with the highest Pill were not responsible for most cases due to their low frequency of occurrence. The high virulence/ MDR group was responsible for most cases (66.5 %), despite the low S. enterica prevalence (RP1 0.5 % and RP2 5.0 %). The years lost due to disability (YLD) value for MDR was 2.6 × higher than for non-MDR. Spearman rank showed that the inputs with higher influence on the variability of salmonellosis depended on the type of exposure scenario. For example, the cooking temperature and time had the most significant influence in the scenarios where S. enterica can survive after cooking. Including the microbial genotypic and phenotypic characteristics in risk assessment modeling highlights the importance of focusing on high-virulent and MDR strains, which are not the most frequent but represent the highest public health risk.