1. Food as a possible fomite for transmission of antibiotic resistant bacteria in an urban environment
Sylvia Omulo1, Robert Mugoh2, Jennifer Zambriski1, Margaret Davis1 and Douglas R. Call1
1Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, Kenya Medical Research Institute/Centers for Disease Control (KEMRI/CDC) Public Health Collaboration, Nairobi.
High prevalence of antibiotic resistance is continuously reported in sub-Saharan Africa. However, factors that influence antibiotic resistance remain poorly understood. Consequently, we are investigating the role of sanitation in the spread of antibiotic resistance in Kenya. As a first step, we conducted a small-scale study in Kibera, a large informal settlement in Nairobi, to estimate the load of antibiotic-resistant bacteria in the environment. We collected 104 soil and 55 food samples around Kibera households and employed the spread-plating technique to quantify antibiotic-resistant Escherichia coli CFU in soil slurry and food rinsates. Of the three antibiotics we tested (ampicillin, chloramphenicol and tetracycline), E. coli resistance was highest against ampicillin both in soil (23%) and food (16%). Interestingly, we found a high load of resistant bacteria (>2x106 CFU/ml of rinsate) on a local kale (sukumawiki) eaten in many Kenyan households. This suggests the potential that food can transmit antimicrobial-resistant bacteria. Identifying specific drivers of antibiotic resistance is critical where demand for health resources out-compete supply.
Abstract
Table 2: E. coli CFU counts from rinsates of food items
Figure 1. Outline of lab procedures used to process and test soil and food samples 1
We confirmed the presence of E. coli in soil and food samples from Kibera, Ampicillin resistance was the most common phenotype for E. coli, both in soil and food samples. Of all food samples, the local kale (sukumawiki) seemed most contaminated with E. coli, including with resistant forms of E. coli. One kale sample had E. coli resistant to all antibiotics tested.
This preliminary study confirmed the feasibility of quantifying E. coli and total counts of resistant bacteria using the specific methods for this project. We recovered fewer E. coli-positive soil samples than expected, although this likely varies with the rainy/dry seasons. While food samples were sampled less intensively, kales had the highest CFU counts, consistent with a previous reports showing relatively high coliform counts with these greens (Kutto 2011; Gallagher, 2013).
Discussion
To identify drivers of antibiotic resistance in an urban slum in Kenya
Objective
Food samples
10 ml of sterile distilled water (dH2O) was added into Ziploc bags containing food samples for rinsing. 1 ml of the rinsate was then preserved in glycerol until tested as below
Lab methods
Significance
Resistance to antibiotics is a global problem
It is thought to be perpetuated by antibiotic misuse in human and animals
Antibiotic selection pressure is not the only driver of resistance
Factors that influence antibiotic resistance remain poorly understood
The impact of household sanitation on antibiotic resistance is unknown
Hypothesis
Household sanitation has a greater impact on the emergence of antibiotic resistance than antibiotic use.
Approach
We conducted a small-scale study in Kibera, an urban slum in Nairobi, to estimate the load of antibiotic-resistant bacteria in the environment
Sampling
Collection of soil samples began at any “cluster” (defined neighborhood) border that allowed sampling in two divergent directions. Dry top-soil was collected after every 50 m from the cluster border in two directions (~10 samples per cluster). ~5 food samples were bought from food vendors within each cluster. All samples were collected in sterile Ziploc bags.
Results
Table 1. Overall E. coli distribution in soil and food samples; characteristic E. coli colonies on MacConkey agar shown in the picture on the left of the table.
Conclusion
Food, such as kale, should be included as a potential means for disseminating antibiotic resistant and pathogenic bacteria.
Future steps
Survey households in Kibera to investigate links between sanitation and E. coli resistance
Employ repeated sampling of human and environmental samples over rainy and dry seasons to identify shared resistance traits between E. coli in humans and in food and water
Figure 2. Proportion of total food items sampled which contained E. coli. (Kachumbari is a local vegetable accompaniment of raw onions, tomatoes, coriander, chillis etc)
Acknowledgements
We thank the Paul Allen School for Global Animal Health (WSU) for providing funding and technical support for this work. We also acknowledge the support we received from our collaborators in Kenya including CDC-Kenya, Kenya Medical Research Institute (KEMRI) and the KEMRI/CDC Public Health Collaboration
Contact Information
Please any questions or comments to
H4H generations

2. (Neat slurry or rinsate)
Food as a possible fomite for transmission of antibiotic resistant bacteria in an urban environment
Sylvia Omulo1, Jennifer Zambriski1, Margaret Davis1 and Douglas R. Call1
1Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA
High prevalence of antibiotic resistance is continuously reported in sub-Saharan Africa. However, factors that influence antibiotic resistance remain poorly understood. Consequently, we are investigating the role of sanitation in the spread of antibiotic resistance in Kenya. As a first step, we conducted a small-scale study in Kibera, a large informal settlement in Nairobi, to estimate the load of antibiotic-resistant bacteria in the environment. We collected 104 soil and 55 food samples around Kibera households and employed the spread-plating technique to quantify antibiotic-resistant Escherichia coli CFU in soil slurry and food rinsates. Of the three antibiotics we tested (ampicillin, chloramphenicol and tetracycline), E. coli resistance was highest against ampicillin both in soil (23%) and food (16%). Interestingly, we found a high load of resistant bacteria (>2x106 CFU/ml of rinsate) on a local kale (sukumawiki) eaten in many Kenyan households. This suggests the potential that food can transmit antimicrobial-resistant bacteria. Identifying specific drivers of antibiotic resistance is critical where demand for health resources out-compete supply.
Abstract
Table 2: E. coli CFU counts from rinsates of food items
900 uL
100uL 100uL 100uL 100uL 100uL 100uL 100uL
1 g soil mL dH2O
Incubate 37oC, h
Count total number of colonies in the plate with the lowest dilution showing distinct colonies expressing this per gm of soil sample (thus 20 CFU at a dilution of 10-6 = 2.0 x 108/gm; ‘neat’ sample is at 1:10 dilution)
Interpretation of results
E. coli present
E. coli absent
AmpR E. coli present
AmpS E. coli (if present in 1)
ChlR E. coli present
ChlS E. coli (if present in 1)
TetR E. coli present
TetS E. coli (if present in 1)
(R-resistant; S-susceptible)
Add 50uL of diluted sample, spread using glass rod
Plain Mac Mac + Amp Mac + Chl Mac + Tet
(Neat slurry or rinsate)
Food sample rinsed in 10 mL dH2O 2
We confirmed the presence of E. coli in soil and food samples from Kibera, Ampicillin resistance was the most common phenotype for E. coli, both in soil and food samples. Of all food samples, the local kale (sukumawiki) seemed most contaminated with E. coli, including with resistant forms of E. coli. One kale sample had E. coli resistant to all antibiotics tested.
This preliminary study confirmed the feasibility of quantifying E. coli and total counts of resistant bacteria using the specific methods for this project. We recovered fewer E. coli-positive soil samples than expected, although this likely varies with the rainy/dry seasons. While food samples were sampled less intensively, kales had the highest CFU counts, consistent with a previous reports showing relatively high coliform counts with these greens (Kutto 2011; Gallagher, 2013).
Discussion
To identify drivers of antibiotic resistance in an urban slum in Kenya
Objective
Sampling
Collection of soil samples began at any “cluster” (defined neighborhood) border that allowed sampling in two divergent directions. Dry top-soil was collected after every 50 m from the cluster border in two directions (~10 samples per cluster). ~5 food samples were bought from food vendors within each cluster. All samples were collected in sterile Ziploc bags.
Sample handling and testing illustrated in Figure 1.
Methods
Significance
Resistance to antibiotics is a global problem
It is thought to be perpetuated by antibiotic misuse in human and animals
Antibiotic selection pressure is not the only driver of resistance
Factors that influence antibiotic resistance remain poorly understood
The impact of household sanitation on antibiotic resistance is unknown
Hypothesis
Household sanitation has a greater impact on the emergence of antibiotic resistance than antibiotic use.
Approach
We conducted a small-scale study in Kibera, an urban slum in Nairobi, to estimate the load of antibiotic-resistant bacteria in the environment
Figure 1. Outline of lab procedures used to process and test soil and food samples
Results
Table 1. Overall E. coli distribution in soil and food samples; characteristic E. coli colonies on MacConkey agar shown in the picture on the left of the table.
Soil
% of Total
Food
No E. coli 68
65% 45
82%
Susc. E. coli 36
35% 11
20%
AmpR E. coli 24
23% 9
16%
ChlR E. coli 2 2% 1
TetR E. coli 9%
Conclusion
Food, such as kale, should be included as a potential means for disseminating antibiotic resistant and pathogenic bacteria.
Future steps
Survey households in Kibera to investigate links between sanitation and E. coli resistance
Employ repeated sampling of human and environmental samples over rainy and dry seasons to identify shared resistance traits between E. coli in humans and in food and water
Figure 2. Proportion of total food items sampled which contained E. coli. (Kachumbari is a local vegetable accompaniment of raw onions, tomatoes, coriander, chillis etc)
Acknowledgements
We thank the Paul Allen School for Global Animal Health (WSU) for providing funding and technical support for this work. We also acknowledge the support we received from our collaborators in Kenya including CDC-Kenya, Kenya Medical Research Institute (KEMRI) and the KEMRI/CDC Public Health Collaboration
Contact Information
Please any questions or comments to
H4H generations