The research team, led by L. Marcela Montilla Rodríguez and colleagues, mined a retrospective dataset of 1,868 food samples collected by official surveillance programs between 2017 and 2023. Of these, 1,846 samples carried enough information to be included in the inferential statistical analyses. The outcome of interest was deliberately strict: a sample was classified as microbiologically non-compliant if it failed to meet at least one regulatory microbiological criterion, regardless of whether the failing indicator was a harmless marker of poor hygiene or a confirmed foodborne pathogen. By the end of the analysis, 39.5 percent of all evaluated samples had crossed that threshold — a figure that means nearly two in five ready-to-eat products tested by regulators in the region over seven years violated at least one microbiological standard.

The identity of the failing indicators is where the story becomes genuinely instructive. Hygiene indicators, rather than virulent pathogens, dominated the non-compliance landscape. Total coliforms were the single most common reason for failure, appearing in 29.0 percent of samples, followed by aerobic mesophilic bacteria at 12.7 percent and Escherichia coli at 12.3 percent. Genuine foodborne pathogens — the organisms most likely to trigger outbreaks — were detected only infrequently. This pattern matters because it reframes what surveillance in tropical LMIC settings is actually catching: not dramatic contamination events with exotic killers, but chronic, systemic lapses in basic hygiene and process control that show up in indicator organisms long before a pathogen necessarily appears.

To move beyond simple counts, the researchers applied multivariable logistic regression, a statistical technique that estimates the independent effect of each factor on the odds of non-compliance while holding all other measured variables constant. The results were striking in their magnitude. Raw or highly handled foods — products that receive extensive manual manipulation after any initial processing — had 8.40 times higher odds of microbiological non-compliance than thermally processed foods, with a 95 percent confidence interval of 6.59 to 10.72. In practical terms, a ready-to-eat salad, cut fruit, or hand-assembled sandwich in this dataset was more than eight times as likely to fail a microbiological criterion as a thermally processed equivalent. Heat, it turns out, remains the most reliable intervention in the entire food safety toolkit, and its absence defines the highest-risk category.

Geography and time also left measurable fingerprints. Samples collected in rural or dispersed municipalities — settlements scattered across the vast Orinoquía plains, far from dense urban infrastructure — showed 1.31 times higher odds of non-compliance than samples from other areas, with a confidence interval of 1.01 to 1.70. The effect is modest compared with the food-type effect, but it is statistically real, and it points toward structural disadvantages: longer and less controlled cold chains, fewer certified processing facilities, and thinner regulatory coverage per square kilometer. Rural dispersed territories in tropical LMICs are precisely the environments where electricity reliability, refrigeration capacity, and inspection frequency tend to be weakest, and the data suggest those weaknesses translate into measurably dirtier food.

Time told its own story. Samples collected in 2020 — the first year of the COVID-19 pandemic — carried 1.78 times higher odds of non-compliance than samples from other years, with a 95 percent confidence interval of 1.16 to 2.74. The pandemic disrupted nearly every link in the food chain: labor shortages in processing plants, strained cold chains, redirected inspection resources, and shifts toward takeaway and delivery formats that increased handling. The 2020 signal in this dataset is consistent with the hypothesis that public health emergencies degrade food safety systems in ways that show up in routine surveillance, even when the surveillance itself continues to function.

Beyond the regression models, the team deployed exploratory tools that are increasingly common in modern food safety analytics: heatmaps and principal component analysis. These methods compress thousands of sample-level observations into visual and mathematical summaries that reveal structure invisible to simple tabulation. What emerged was a picture of pronounced heterogeneity — microbiological profiles differed across temporal, territorial, operational, and food-related dimensions, meaning that the microbial signature of a failed sample depended on when it was collected, where it came from, how it was handled, and what kind of food it was. Yet cutting across all that heterogeneity was one consistent thread: hygiene indicators predominated everywhere, in every stratum the researchers examined.

The authors draw a consequential policy conclusion from this consistency. If the dominant failure mode in ready-to-eat foods is poor hygienic quality rather than pathogen presence, then surveillance systems built exclusively around pathogen detection are measuring the wrong thing most of the time. The study argues for incorporating hygienic quality indicators and process control metrics alongside pathogen testing into microbiological surveillance programs. Indicator organisms such as total coliforms and E. coli are cheaper to test, faster to quantify, and act as early-warning signals of the conditions under which pathogens could proliferate. A surveillance system that tracks them systematically can identify deteriorating hygiene practices before they culminate in an outbreak, rather than documenting one after the fact.

There is also a resource-allocation argument embedded in the findings. Food safety agencies in LMICs operate under chronic budget constraints and cannot inspect and test everything. The regression results provide an evidence base for risk-based prioritization: raw and highly handled ready-to-eat foods deserve the most intensive scrutiny, followed by products from rural and dispersed municipalities, with heightened vigilance during periods of systemic disruption such as pandemics. This is the essence of risk-based surveillance — directing finite laboratory and inspection capacity toward the combinations of food type, territory, and circumstance where the statistical probability of failure is highest, rather than spreading it uniformly across a landscape where most samples will pass.

The study’s setting gives its findings particular weight. The Colombian Orinoquía is a tropical, geographically dispersed region that exemplifies the surveillance challenges facing much of the low- and middle-income world: long distances, heterogeneous municipalities, informal food sectors, and limited laboratory infrastructure. Seven years of official data from such a context is a rare and valuable asset, and the analysis demonstrates what that asset can yield when subjected to rigorous statistical treatment. For food safety authorities across tropical LMICs, the message is twofold: the microbial enemy in ready-to-eat foods is most often poor hygiene rather than exotic pathogens, and the tools to find it — indicator testing, multivariable risk modeling, and geographically aware surveillance design — already exist. What remains is the institutional commitment to deploy them where the odds of failure are greatest, so that the nearly 40 percent non-compliance rate documented here becomes a baseline to be beaten rather than a permanent feature of the food supply.

Subject of Research: Factors associated with microbiological non-compliance in ready-to-eat foods based on seven years of food safety surveillance data in Colombia

Article Title: Factors associated with microbiological non-compliance in ready-to-eat foods: A seven-year analysis of official food safety surveillance data in a tropical low- and middle-income country

Article References: Montilla Rodríguez, L. M., Obando Bastidas, J. A., Rodríguez Fajardo, H. A., Mora López, A. M., Aragón, S. V. T., Rozo Cruz, D. P., Vega, E. C., Pavas Escobar, N. C., Obando Vargas, L. N., Farfán, J. P., & Gonzalez Robayo, M. S. (2026). Factors associated with microbiological non-compliance in ready-to-eat foods: A seven-year analysis of official food safety surveillance data in a tropical low- and middle-income country. PLOS Global Public Health, 6(10), e0006492. https://doi.org/10.1371/journal.pgph.0006492

Image Credits: AI Generated

DOI: 10.1371/journal.pgph.0006492

Keywords: food safety, ready-to-eat foods, microbiological surveillance, coliforms, Escherichia coli, Colombia, low- and middle-income countries, logistic regression, hygiene indicators, risk-based surveillance, foodborne pathogens, Orinoquía