This data product contains estimates of the cost of U.S. illnesses caused by foodborne pathogens. The cost estimates in this data product are drawn from the 2025 article, Hoffmann et al., Economic burden of foodborne illnesses acquired in the United States (a full citation is provided in the Resources section below).
The Economic Research Service (ERS) 2025 cost estimates are reported in 2023 U.S. dollars. This data product includes instructions and data that can be used to adjust the estimates for inflation and income growth to any year between 2000 and the most recent year available. Further information is provided below.
The estimates in this data product expand on and update those reported in the 2014 and 2021 estimates. The 2025 estimates expand coverage beyond 15 major pathogens to include all foodborne illnesses, and update disease modeling and cost estimates. All three versions of the cost of foodborne illness estimates rely on the same foodborne illness case, hospitalizations, and deaths estimates published by the U.S. Centers for Disease Control and Prevention (CDC) in 2011 (Scallan et al. 2011a, 2011b). Because of the expansion in coverage and improvements in the coverage of chronic outcomes, a comparison of the current cost estimates with previous estimates of total cost of illness or for those pathogens that cause chronic health outcomes should not be interpreted as solely a change in their cost. The 2014 and 2021 versions of the Economic Research Service’s Cost of Foodborne Illness data product are available in files archived on the data product Overview page.
This page provides the following information:
- Scope/coverage
- Estimation methods
- Adjusting cost-of-illness estimates over time
- Strengths and limitations
- Resources
- Recommended citation
- Acknowledgments
Scope/coverage
This data product provides annual cost estimates for all foodborne illnesses estimated by the CDC to be contracted in the United States (Scallan et al., 2011a, 2011b). These CDC foodborne disease-incidence estimates and the ERS cost of foodborne illness estimates include gastroenteritis caused by 31 major foodborne pathogens and other unspecified foodborne agents. The ERS estimates represent costs for people who do not seek medical care, those who seek outpatient care only, those who are hospitalized, the cost of associated long-term health problems, and willingness to pay to prevent deaths. The cost estimates build on 2011 CDC estimates of the incidence of U.S. foodborne illness because these estimates were the most recent available for foodborne illnesses from all 31 pathogens and unspecified agents at the time the data product was developed.
Total hospitalization cost estimates in this data product include the cost of treating sepsis (blood poisoning), a serious complication that can result from infections, but do not report the cost of sepsis treatment separately. Because of the importance of this complication, a separate paper does report the cost of foodborne illness hospitalizations that develop sepsis (Ahn et al., 2022).
Estimation methods
Cost of foodborne illness estimates synthesize a variety of data and results from peer-reviewed scientific literature. Estimating the costs of foodborne illnesses involves two basic steps:
- Development of pathogen-specific disease-outcome tree models that include health outcomes from infection to recovery or death, estimating the likelihood an illness resulted in each health outcome.
- Estimation of the economic costs of each health outcome, the total cost for each pathogen, and total cost for foodborne illnesses without a specified pathogen cause.
A more detailed discussion of conceptual issues involved in measuring the cost of illness is provided in the ERS report, Making Sense of Recent Cost-of-Foodborne-Illness Estimates.
Estimating physical outcomes
The health outcomes and their severity vary by pathogen. Health outcomes may include:
- Non-hospitalized cases who do not seek medical care;
- Non-hospitalized cases who seek medical care;
- Hospitalized cases of varying severity;
- Chronic illnesses (where research adequately supports including chronic conditions); and
- Deaths.
Estimates of the incidence of illness, hospitalization, and deaths for each pathogen are taken from CDC research, as are rates of care seeking (Scallan et al., 2011a, 2011b). Outpatient cases are calculated as cases seeking care minus hospitalizations.
Foodborne illnesses can cause chronic conditions and long-term disabilities in addition to more common short-term illnesses. These long-term outcomes are not included in CDC’s 2011 incidence estimates but are included in ERS’s cost of foodborne illness estimates to the extent supported by review of the latest scientific literature for Hoffmann et al. (2025). The following long-term outcomes are included in the ERS cost of illness estimates:
| Chronic health outcome | Pathogen cause(s) |
|---|---|
| Guillain-Barré Syndrome (GBS) | Campylobacter |
| Irritable Bowel Syndrome (IBS) | Campylobacter, Salmonella, Shigella |
| Reactive Arthritis | Campylobacter, Salmonella, Shigella, Yersinia |
| Chronic and End-Stage Renal (Kidney) Disease | STEC O157 and non-O157 |
| Neurological disorders | Listeria monocytogenes (in newborns) |
| Vision impairment | Toxoplasma (in adults and newborns) |
| Cognitive and hearing impairment | Toxoplasma (in newborns) |
The average length of a hospital stay is estimated from the National Inpatient Sample (NIS) hospitalization data for 2016–19, using International Classification of Diseases, Tenth Revision (ICD-10) codes in any diagnostic position to identify pathogen-specific diagnosis. The NIS database is developed through a Federal-State-industry partnership, sponsored by the U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality (AHRQ). The duration of non-hospitalized cases and long-term outcomes are drawn from prior scientific research.
A detailed description of the data and assumptions used in developing the disease-outcome trees and cost estimates is provided in the appendices of Hoffmann et al. (2025).
Estimating the cost of physical outcomes
The methods used by ERS to develop estimates of the cost of nonfatal illnesses are designed to be consistent with methods used in prior ERS cost-of-illness estimates. Estimates of the cost of non-fatal outcomes are the sum of medical treatment costs and lost wages. Deaths are valued using a value of statistical life (VSL) estimate developed by the U.S. Environmental Protection Agency, National Center for Environmental Economics. The data product provides a conservative estimate of society’s willingness to pay to prevent these illnesses because the estimates do not capture the value of time in non-labor market activities or individuals’ willingness to pay to reduce their risk of pain and suffering and other non-financial impacts of these illnesses.
Medical Treatment Costs
Hospitalized Cases
The National Inpatient Sample (NIS) provides national estimates of the cost and length of all hospitalizations in the United States, regardless of the source of health-care insurance coverage or lack of insurance. Relevant hospitalizations in the NIS were identified using pathogen-specific International Classification of Disease, Tenth Revision (ICD-10) codes in any diagnostic position (Hoffmann et al., 2025).
For all pathogens except M. bovis, cost of hospitalization is the mean hospitalization cost from the NIS for 2016–19. Mean values (rather than median) are used because the purpose of the estimates is to measure the total cost of foodborne illness in the country rather than the most typical costs for an individual hospitalization. Multiple years were used to smooth annual cost variations. Due to a shift between editions of the International Classification of Disease codes (ICD-9 to ICD-10) in late 2015, 2016 was used as the starting year. To avoid the influence of the Coronavirus (COVID-19) pandemic on foodborne-illness hospitalization costs, 2019 was used as the final year. Estimates of the hospitalization cost of tuberculosis (M. bovis) were taken from prior tuberculosis studies (Hoffmann et al., 2025).
Non-hospitalized Medical Treatment Cost
The mean outpatient treatment cost of acute gastroenteritis was used to estimate the cost of non-hospitalized cases seeking outpatient care for all pathogens except M. bovis. These cost estimates were developed using MarketScan Commercial Claims and Encounters data. MarketScan Claims and Encounters data include more than 90 million people in the United States covered by employer-sponsored health insurance (Whitham et al., 2022). Outpatient cost estimates for M. bovis were taken from prior literature on the cost of treating tuberculosis (Hoffmann et al., 2025).
Productivity Loss
Cost of illness estimates need to reflect the opportunity cost of time spent being ill. Lost wages provide a conservative measure of this opportunity cost. Lost wages were estimated for each health outcome as the product of workdays lost per case and daily wage rate, adjusted for the mean U.S. employment rate. Workdays lost were based on estimates of the duration of the health outcome, adjusted based on an assumed 5-day work week.
Cost of Chronic Illness and Disabilities
Medical costs and lost wages from chronic and congenital health outcomes that result from foodborne infections are based on prior cost-of-illness studies in the peer-reviewed scientific literature (Hoffmann et al., 2025).
Deaths
Deaths were valued using a VSL of $13.5 million (2023 U.S. dollars), based on a U.S. Environmental Protection Agency (EPA) meta-analysis of existing estimates (U.S. EPA, 2024). The data product discounts estimates of the value of deaths that occur in the future (as a result of chronic disease) back to present value.
The White House Office of Management and Budget’s Circular A-4 directs Federal agencies to value children’s deaths at least as high as adults’, unless there is "specific and compelling evidence to suggest otherwise." At the time these cost of foodborne illness estimates were developed, there were no known willingness-to-pay (WTP) studies for miscarriages, stillbirths, and neonatal deaths. The estimates therefore value these outcomes using the same VSL used to value all other deaths. An appendix to Hoffmann et al. (2025) provides a sensitivity analysis of what the cost of illness by pathogen and the total cost of foodborne illnesses would be if miscarriages and stillbirths were not included as an outcome. Together these two sets of estimates provide the range within which societal willingness to pay to prevent these outcomes likely falls.
Adjusting cost-of-illness estimates over time
Adjusting non-fatal illness cost estimates over time
This data product presents cost-of-illness estimates in 2023 U.S. dollars. The value of medical treatment costs and lost wages can be adjusted over time by adjusting for inflation (or deflation). The prices of different goods and services purchased do not all change at the same rate over time. For this reason, ERS recommends using item-specific consumer price indexes (CPIs). The spreadsheet with CPI time-series data also includes instructions on which price index to use with specific elements of the cost-of-illness estimates. Updated CPIs will be provided as they become available, allowing users to express the ERS cost of foodborne illness estimates in future-dollar values or to adjust them to a prior reference year.
When adjusting costs in this data set over time, 2023 should be viewed as the baseline year. One way to remember how to adjust a cost over time is to compare two ratios. The ratio of costs in year Y to costs in 2023 is equal to the ratio of the CPI in year Y to the CPI in 2023:
cost (year Y)/cost (2023) = CPI (year Y)/CPI (2023)
The relationship between these two ratios can be rearranged to solve for the cost-of-illness estimates in year Y:
cost (year Y) = cost (2023) x CPI (year Y)/CPI (2023)
Adjusting the VSL over time
The value of statistical life (VSL) is calculated from a point on the demand curve for reductions in risk of death. The demand curve for reductions in risk of death reflects the amount that people are willing to pay to lower their risk of death. While it may seem odd to talk about choosing whether to pay to reduce risk of death, people do this all the time in very mundane ways. For example, people regularly think about the tradeoff between the purchase price and safety performance when buying an automobile.
Demand for a good or service varies with its price and with people’s real income. For example, the demand for luxury sports cars is higher among very high-income groups than it is among low-income groups. Demand for safety generally increases as real incomes rise. As a result, over time, VSL values need to be adjusted for both price inflation (or deflation) and changes in real income.
ERS has calculated adjustments to the VSL over time and provides a spreadsheet of annual VSL values from 2000 through the most recent year for which data on price indexes and real income are available. These VSL estimates are reported in nominal dollars for the year indicated. This allows the estimates to be used to adjust the cost-of-illness estimates to a year of interest. ERS uses EPA’s estimate of the income elasticity of the VSL of 0.40 and the “all-items” CPI in adjusting the VSL over time (U.S. EPA, 2024). This spreadsheet will also be updated as new data become available.
Strengths and limitations
The 2025 estimates expand on the coverage of prior ERS estimates of the cost of foodborne illness and include all U.S. foodborne illnesses, as measured by CDC, up from 15 in the previous estimates. In addition, the disease-outcome modeling needed to measure the impact of these illnesses is based on an updated review of the scientific literature. An important advance made in the 2025 estimates was an update of information on the rate of chronic health problems caused by foodborne illnesses.
The updated review of the scientific literature on chronic health outcomes identified likely additional impacts, such as schizophrenia from toxoplasmosis. However, it was determined that more disease surveillance and research are needed to support the inclusion of these impacts in the cost of foodborne illness estimates. Furthermore, while the medical treatment costs in this research reflect the impacts of antibiotic resistance, the researchers were unable to separate out the impacts of antibiotic resistance on cost of foodborne illness due to a lack of primary scientific research.
While these estimates are an approximation of willingness to pay to prevent foodborne illness in the United States, they are a conservative approximation. A full estimate of willingness to pay to prevent an illness must include individuals’ willingness to pay to prevent the non-financial impacts of the illness, e.g., pain and suffering. Due to a lack of research these impacts could not be included. There is also a lack of research on willingness to pay to prevent miscarriages and stillbirths. Additional primary research is needed to fill these knowledge gaps.
Uncertainty Analysis
As with any modeling exercise, the estimates are measured with uncertainty and uncertainty bounds are provided. Most elements of the model are measured with uncertainty or represent a distribution of observed outcomes. These elements include: CDC’s case, hospitalization, and death estimates (Scallan et al., 2011a, 2011b), outpatient care-seeking estimates, duration of illness, medical care costs, and estimates of the incidence and cost of chronic outcomes resulting from acute foodborne illnesses. Hoffmann et al. (2025) provides data and documentation on each source of uncertainty. The estimates were developed using a Monte Carlo simulation model to simultaneously account for all sources of uncertainty. Mean results and uncertainty bounds (credibility intervals) presented in this data product are the result of this simulation modeling. A detailed description of this modeling can be found in Hoffmann et al. (2025).
Pathogen-specific Monte Carlo simulation model Excel workbooks are available on request.
Resources
Ahn, J.W., Scallan Walter, E., White, A.E., McQueen, R.B. & Hoffmann, S. (2022). Identifying sepsis from foodborne hospitalization: Incidence and hospitalization cost by pathogen. Clinical Infectious Diseases, 75(5), pp.857–866.
Hoffmann, S., White, A.E., McQueen, R.B., Ahn, J.W., Gunn-Sandell, L.B. & Scallan Walter, E.J. (2025). Economic burden of foodborne illnesses acquired in the United States. Foodborne Pathogens and Disease, 22(1), p.4‒14.
Scallan, E., Hoekstra, R. M., Angulo, F. J., Tauxe, R. V., Widdowson, M. A., Roy, S. L., ... & Griffin, P. M. (2011). Foodborne illness acquired in the United States—major pathogens. Emerging Infectious Diseases, 17(1), 7.
Scallan, E., Griffin, P. M., Angulo, F. J., Tauxe, R. V., & Hoekstra, R. M. (2011). Foodborne illness acquired in the United States—unspecified agents. Emerging Infectious Diseases, 17(1), 16.
Whitham, H.K., Gilliland, A.E., Collier, S.A., Scallan Walter, E. & Hoffmann, S. (2022). Direct outpatient health care costs among commercially insured persons for common foodborne pathogens and acute gastroenteritis, 2012–15. Foodborne Pathogens and Disease, 19(8), pp.558–568.
U.S. Environmental Protection Agency (EPA). (2024). Guidelines for Preparing Economic Analyses (3rd edition). Report number EPA-240-R-24-001. Washington, DC.
U.S. Office of Management and Budget. (2003, September 17). Regulatory Analysis. Circular A-4:31.
A list of research related to the cost of foodborne illness is provided on the Background & History page of this data product.
Recommended citation
U.S. Department of Agriculture, Economic Research Service. (2025). Cost Estimates of Foodborne Illnesses [data product].
Acknowledgments
ERS thanks the U.S. Agency for Health Care Quality and their State collaborators for use of National Inpatient Sample data. The researchers also thank the U.S. Centers for Disease Control and Prevention for their collaboration on estimation of outpatient treatment costs. ERS also wishes to acknowledge the contribution of scientists across the Federal Government and in academia in reviewing disease modeling decisions.