J. Glenn Morris

Animal antibiotic use has an early but important impact on the emergence of antibiotic resistance in human commensal bacteria

Antibiotic use is known to promote the development of antibiotic resistance, but substantial controversy exists about the impact of agricultural antibiotic use (AAU) on the subsequent emergence of antibiotic-resistant bacteria among humans. AAU for animal growth promotion or for treatment or control of animal diseases generates reservoirs of antibiotic-resistant (AR) bacteria that contaminate animal food products. Mathematical models are an important tool for understanding the potential medical consequences of this increased exposure. We have developed a mathematical model to evaluate factors affecting the prevalence of human commensal AR bacteria that cause opportunistic infections (e.g., enterococci). Our analysis suggests that AAU hastens the appearance of AR bacteria in humans. Our model indicates that the greatest impact occurs very early in the emergence of resistance, when AR bacteria are rare, possibly below the detection limits of current surveillance methods.

Annual Cost of Illness and Quality-Adjusted Life Year Losses in the United States Due to 14 Foodborne Pathogens†

In this article we estimate the annual cost of illness and quality-adjusted life year (QALY) loss in the United States caused by 14 of the 31 major foodborne pathogens reported on by Scallan et al. (Emerg. Infect. Dis. 17:7-15, 2011), based on their incidence estimates of foodborne illness in the United States. These 14 pathogens account for 95 % of illnesses and hospitalizations and 98 % of deaths due to identifiable pathogens estimated by Scallan et al. We estimate that these 14 pathogens cause

Cholera transmission: the host, pathogen and bacteriophage dynamic

14.0 billion (ranging from

Critical Factors Influencing the Occurrence of Vibrio cholerae in the Environment of Bangladesh

4.4 billion to

Hyperinfectivity: A Critical Element in the Ability of V. cholerae to Cause Epidemics?

33.0 billion) in cost of illness and a loss of 61,000 QALYs (ranging from 19,000 to 145,000 QALYs) per year. Roughly 90 % of this loss is caused by five pathogens: nontyphoidal Salmonella enterica (

Attributing illness to food.

3.3 billion; 17,000 QALYs), Campylobacter spp. (

Multilocus Sequence Typing for Characterization of Clinical and Environmental Salmonella Strains

1.7 billion; 13,300 QALYs), Listeria monocytogenes (

Diarrheagenic Escherichia coli Infection in Baltimore, Maryland, and New Haven, Connecticut

2.6 billion; 9,400 QALYs), Toxoplasma gondii (

Multilocus Sequence Typing Reveals a Lack of Diversity among Escherichia coli O157:H7 Isolates That Are Distinct by Pulsed-Field Gel Electrophoresis

3 billion; 11,000 QALYs), and norovirus (

A 4-Year Study of the Epidemiology of Vibrio cholerae in Four Rural Areas of Bangladesh

2 billion; 5,000 QALYs). A companion article attributes losses estimated in this study to the consumption of specific categories of foods. To arrive at these estimates, for each pathogen we create disease outcome trees that characterize the symptoms, severities, durations, outcomes, and likelihoods of health states associated with that pathogen. We then estimate the cost of illness (medical costs, productivity loss, and valuation of premature mortality) for each pathogen. We also estimate QALY loss for each health state associated with a given pathogen, using the EuroQol 5D scale. Construction of disease outcome trees, outcome-specific cost of illness, and EuroQol 5D scoring are described in greater detail in a second companion article.