How Statistics Track Foodborne Illnesses

Today we take a look at how statistics track foodborne illnesses and the role statistics play.  Why are statistics so important when it comes to foodborne illnesses? Statistics plays an essential role in public health, providing the methods needed to collect, analyze, and interpret data on diseases, including foodborne illnesses. In the context of foodborne outbreaks, statistical analyses help to identify patterns, such as the geographic distribution of cases, the demographics of affected populations, and the timing of illness onset. These patterns provide clues about the source of the outbreak and the pathways of contamination.

In outbreak investigations, statistical methods help determine whether observed cases of illness are part of a true outbreak or are merely coincidental. By comparing the number of cases during a specific period to the expected number of cases (based on historical data), statisticians can detect deviations from the norm, signaling the possibility of an outbreak. This process, known as outbreak detection, relies heavily on statistical tools to assess whether the increase in cases is significant enough to warrant further investigation.

Once an outbreak is detected, statistical analysis can be used to identify the likely source of contamination, evaluate the risk associated with specific foods, and guide public health responses. In this context, statistics not only helps identify the origin of an outbreak but also informs decision-making on control measures, such as food recalls, public warnings, and changes in food production practices.

The outbreak must be analyzed properly to summarize the data and describe the characteristics of the outbreak. This includes geographic information about the population affected as well as sources of the exposure. Here are some metrics that are often included in the summary:

• Incidence rates: The number of new cases of illness during a specific time period, usually expressed as a rate per 100,000 people. Incidence rates help quantify the extent of the outbreak and allow comparisons between different regions or demographic groups.
• Attack rates: The proportion of people exposed to a suspected source who develop illness. This measure is particularly useful in determining the likelihood that a particular food item or venue is associated with the outbreak.
• Case fatality rates: The proportion of people who die as a result of the illness. This statistic helps assess the severity of the outbreak and the potential need for more aggressive public health interventions.

These statistics help determine if a foodborne illness can be confined to one local area or if there needs to be a wider description because of the source.

Once descriptive statistics have helped to identify patterns in the outbreak data, statistical models can be used to test hypotheses about the source of contamination. Hypothesis generation is a key step in outbreak investigations, as it guides the collection of additional data and informs the direction of further statistical analysis.

One of the most common statistical methods used in foodborne outbreak investigations is case-control studies. In a case-control study, investigators compare individuals who have become ill (cases) with individuals who have not (controls) to identify differences in exposures to specific food items or environmental factors. Statistical tests, such as the chi-square test or Fisher’s exact test, are then used to determine whether there is a statistically significant association between exposure to a particular food item and the likelihood of illness.

An Example:

For example, during the investigation of a multistate outbreak of Escherichia coli O157linked to spinach in 2006, case-control studies were used to identify spinach consumption as a significant risk factor for illness. By comparing the dietary habits of those who became ill with those who did not, investigators were able to trace the source of contamination to spinach from a particular region, leading to a nationwide recall and subsequent changes in agricultural practices.

Statistical Methods for Detection:

Detecting foodborne illness outbreaks in real time is a critical public health function, and statistical methods play a key role in this process. Surveillance systems, such as the Foodborne Diseases Active Surveillance Network (FoodNet) in the United States, collect data on foodborne illnesses from hospitals, laboratories, and public health departments. Statistical algorithms are applied to this data to identify clusters of cases that may indicate an outbreak.

A common statistical method for outbreak detection is the use of control charts, which monitor the number of reported cases over time. A control chart plots the number of cases against a baseline level of expected cases, which is based on historical data. If the number of cases exceeds a certain threshold (typically set at two or three standard deviations above the mean), this signals a potential outbreak. Control charts are particularly useful for detecting outbreaks of known pathogens, such as Salmonella or E. coli, where historical data is available to establish a baseline.

Another statistical method used in outbreak detection is the scan statistic, which searches for clusters of cases in both time and space. The scan statistic divides the study area into overlapping windows of time and space and then compares the number of observed cases within each window to the expected number of cases. If a significant cluster is detected, this suggests that the outbreak may be localized to a particular region or time period. The scan statistic is particularly useful in identifying outbreaks that may not follow a predictable pattern, such as those associated with contaminated produce or imported foods.

The Challenges:

While statistics provides invaluable tools for identifying the source of foodborne outbreaks, there are several challenges and limitations to consider. One major challenge is the availability and quality of data. Outbreak investigations rely on accurate and timely data from multiple sources, including patient interviews, laboratory tests, and food production records. Incomplete or inaccurate data can hinder the ability of statisticians to draw meaningful conclusions.

Another limitation is the potential for bias in statistical analyses. For example, case-control studies can be affected by recall bias, where individuals who have become ill are more likely to remember certain food exposures than those who have not. Similarly, selection bias can occur if the cases or controls in a study are not representative of the broader population.

Despite these challenges, the application of statistical methods in foodborne outbreak investigations continues to improve with advancements in data collection, analytical techniques, and computational power.

At the end of the day statistics provide a huge role in identifying the sources of foodborne illnesses. For more information on food safety and fun food facts please keep an eye on Make Food Safe as we update the blog daily.