RESUMO
BACKGROUND: Chronic kidney disease of unknown origin (CKDu) is an epidemic that disproportionately affects young agriculture workers in hot regions. It has been hypothesized that repeated acute kidney injury (AKI) may play a role in the development of disease. METHODS: Latent class mixed models were used to identify groups of Guatemalan sugarcane harvesters based on their daily changes in creatinine over 6 consecutive days in 2018. Exponential smoothing state space models were used to forecast end-of-season creatinine between the identified groups. Percent change in estimated glomerular filtration rate (eGFR) across the harvest was compared between groups. RESULTS: Twenty-nine percent (n = 30) of the 103 workers experienced repeated severe fluctuations in creatinine across shift. The model with multiplicative error, multiplicative trend, and multiplicative seasonality was able to accurately forecast end-of-season creatinine in the severe group (mean percentage error [MPE]: -4.7%). eGFR of workers in the severe group on average decreased 20% across season compared to 11% decline for those in the moderate group (95% confidence interval for difference: -17% to 0%). CONCLUSIONS: Daily fluctuations in creatinine can be used to forecast end-of-season creatinine in sugarcane harvesters. Workers who experience repeat severe daily fluctuations in creatinine, on average, experience a greater reduction in kidney function across the season.
RESUMO
To improve food safety on farms, it is critical to quantify the impact of environmental microbial contamination sources on fresh produce. However, studies are hampered by difficulties achieving study designs with powered sample sizes to elucidate relationships between environmental and produce contamination. Our goal was to quantify, in the agricultural production environment, the relationship between microbial contamination on hands, soil, and water and contamination on fresh produce. In 11 farms and packing facilities in northern Mexico, we applied a matched study design: composite samples (n = 636, equivalent to 11,046 units) of produce rinses were matched to water, soil, and worker hand rinses during two growing seasons. Microbial indicators (coliforms, Escherichia coli, Enterococcus spp., and somatic coliphage) were quantified from composite samples. Statistical measures of association and correlations were calculated through Spearman's correlation, linear regression, and logistic regression models. The concentrations of all microbial indicators were positively correlated between produce and hands (ρ range, 0.41 to 0.75; P < 0.01). When E. coli was present on hands, the handled produce was nine times more likely to contain E. coli (P < 0.05). Similarly, when coliphage was present on hands, the handled produce was eight times more likely to contain coliphage (P < 0.05). There were relatively low concentrations of indicators in soil and water samples, and a few sporadic significant associations were observed between contamination of soil and water and contamination of produce. This methodology provides a foundation for future field studies, and results highlight the need for interventions surrounding farmworker hygiene and sanitation to reduce microbial contamination of farmworkers' hands.IMPORTANCE This study of the relationships between microbes on produce and in the farm environment can be used to support the design of targeted interventions to prevent or reduce microbial contamination of fresh produce with associated reductions in foodborne illness.
Assuntos
Bactérias/isolamento & purificação , Contaminação de Alimentos/análise , Embalagem de Alimentos/instrumentação , Bactérias/genética , Fazendas , Microbiologia de Alimentos/instrumentação , Mãos/microbiologia , Humanos , México , Recursos HumanosRESUMO
Several produce-associated outbreaks have been linked to the packing facility. Equipment surfaces may be an important source of contamination. The goal was to assess whether the microbial load of packing facility surfaces is associated with the microbial load of produce. From November 2000 to December 2003, 487 matched produce (14 types) and equipment surfaces (six production steps) were sampled from eight packing facilities in the United States near the border with Mexico and enumerated for aerobic plate counts (APC), Escherichia coli , Enterococcus, and coliforms. Bivariate correlations were assessed by Spearman's ρ, and adjusted associations were assessed by multilevel mixed linear regression models. In general, the microbial load both increased and decreased on produce (0.2 to 1.0 log CFU/g) and equipment surfaces (0.5 to 3.0 log CFU/cm2) across production steps. Equipment surface and produce microbial loads were correlated, but correlations varied from none to high depending on the equipment surface. For example, significant correlations (P < 0.01) included APC (ρ = 0.386) and Enterococcus (ρ = 0.562) with the harvest bin, E. coli (ρ = 0.372) and Enterococcus (ρ = 0.355) with the merry-go-round, Enterococcus (ρ = 0.679) with rinse cycle equipment, APC (ρ = 0.542) with the conveyer belt, and for all indicators with the packing box (ρ = 0.310 to 0.657). After controlling for crop type, sample replicate group, and sample location, there were significant positive associations between the log concentration of Enterococcus on produce and the harvest bin (ß = 0.259, P < 0.01) and the rinse cycle (ß = 0.010, P = 0.01), and between the log concentration of all indicators on produce and the packing box (ß = 0.155 to 0.500, all P < 0.01). These statistically significant associations between microbial loads on packing facility surfaces and fresh produce confirm the importance of packing facility sanitation to protect produce quality and safety.