Salmonella Paratyphi B and Salmonella Heidelberg DISSERTATION

Prevalence, Resistance Patterns and Risk Factors for Antimicrobial Resistance in Poultry Farms

and Retail Chicken Meat in Colombia and Molecular Characterization of Salmonella Paratyphi B and Salmonella Heidelberg

by

Maria del Pilar Donado Godoy DVM (Universidad Nacional de Colombia) 1988

M.Sc. (University of Guelph, Ontario Veterinary College) 1992 DISSERTATION

Submitted in partial satisfaction of the requirements for the degree of

DOCTOR OF PHILOSOPHY

in

Epidemiology

in the

OFFICE OF GRADUATE STUDIES

of the

UNIVERSITY OF CALIFORNIA

DAVIS

Approved:

_____________________________________ Ian Gardner, Chair

_____________________________________ Barbara Byrne

_____________________________________ Woutrina Miller

Committee in Charge

2010

ii

Prevalence, Resistance Patterns and Risk Factors for Antimicrobial Resistance in Poultry Farms and Retail Chicken Meat in Colombia and Molecular Characterization of Salmonella Paratyphi

B and Salmonella Heidelberg

Maria del Pilar Donado Godoy

2010

iii

Dedicated to

My parents, Guillermo and Bertha and,

Mi amor, Xavier

iv

ABSTRACT

The development of antimicrobial resistance among bacteria (AMR) is currently one of the world’s

most pressing public health problems. Misuse of antimicrobial agents both in humans and

animals has narrowed the potential use of antibiotics for the treatment of infections in humans. To

monitor the evolution of AMR and to develop control measures, some countries, such as the

USA, Canada and Denmark have set up national integrated monitoring systems. Surveillance

components in these programs help monitor changes in susceptibility/resistance of selected

zoonotic bacterial pathogens and commensal organisms recovered from animals, some retail

meats, and humans to antimicrobial agents. The growth of Colombia as an emerging economy

has resulted in the rapid development of its poultry industry and consequently new challenges in

food safety with the increased use of antibiotics. Since Colombia presents scenarios that can

potentially foster the emergence of antibiotic resistance and also does not have a monitoring

system, we conducted this pilot research to establish baseline data and to adapt processes for

the establishment of a Colombian Integrated Program for Antimicrobial Resistance Surveillance

(COIPARS).

We assessed the prevalence, risk factors and antimicrobial resistance profiles of a single food-

borne pathogen of importance for human health, Salmonella sp., and two commensal bacteria

Escherichia coli and Enterococcus, in commercial broiler farms of the two largest poultry

producing departments of Colombia and in retail meat chicken collected in Bogota, Capital District.

We further characterized isolates of the most prevalent Salmonella serovars, Salmonella

Paratyphi B dT+ and Heidelberg, using pulsed-field gel electrophoresis (PFGE) to evaluate their

genetic relatedness and to determine potential geographically predominant clones.

Our study found a high prevalence of Salmonella sp. in commercial broiler farms (41%) and

poultry houses (65%) and almost 26% in retail meat samples. Salmonella Paratyphi B dT+

represented 76% of Salmonella sp. isolated on farms and 51% of those isolated from retail meat

samples. Salmonella Heidelberg represented 23% and 16% of Salmonella isolates from farms

v

and retail meat samples, respectively. This is the first time that Salmonella Paratyphi B dT+ has

been reported in the poultry chain in Colombia.

Salmonella isolated from farms and retail meat as well as Escherichia coli and enterococci

isolated from chicken meat showed extensive resistance to antimicrobial agents. Ninety-eight

percent of Salmonella sp. isolates were reported as multi-drug resistant. Ceftiofur, enrofloxacin,

nalidixic acid and tetracycline were the antimicrobials that showed the highest frequency of

resistance among Salmonella and E. coli isolates. For enterococci, we found that 62% of E.

faecium isolates were resistant to quinupristin/dalfopristin, which is used to treat nosocomial

human infections when vancomycin resistance is present.

The DNA fingerprinting of S. Paratyphi B dT+ and S. Heidelberg isolates revealed marked

heterogeneity. However, similar genotypes of both serovars were demonstrated to be present in

farms and in retail outlets as well as among isolates coming from different farms within each

region and from farms located in the two geographically distant departments. A possible

dissemination of similar genotypes of both serovars along the poultry chain was hypothesized.

Lack of rigorous biosecurity and some high risk management practices of the poultry industry in

Colombia were possible explanations.

In conclusion, we consider that the findings of our pilot project, the first of its kind conducted in

Colombia, indicate potential risks for human health related to food-borne pathogens present in

the poultry chain. Despite the fact that longitudinal studies are required to further characterize

these risks, we believe that these results confirm the utility of establishing the COIPARS. Our

findings also suggest that it is possible to follow the processes required for establishing a

surveillance system similar to the ones in the USA or Canada while adapting the protocols for

local conditions. Finally, a program like COIPARS can also provide scientific evidence for

implementation of the new Colombian biosecurity legislation (Resolution 001183 issued by the

Colombian Agricultural Institute on March 25, 2010) to decrease the prevalence of Salmonella in

poultry farms.

Approved: ________________________________________________________________

vi

ACKNOWLEDGEMENTS

“Tarda en llegar y al final, al final, siempre hay recompensa…en la zona de promesas”. Gustavo

Cerati

“It takes time to arrive and in the end, in the end, there is always a reward… in the zone of

promises”. Gustavo Cerati

Before entering the ”zone of promises”, I want to thank a thousand of times over, to each and

every one of the people who were alongside in one way or another. Today it is hard for me to

write… please forgive me because the words are not flowing as they could. I have been

submerged in this journey for so long now that some of the words have slowly gotten lost…

“Life also gives you people with beautiful hearts” …

and for all those nice individuals, I am filled with gratitude; even though you may not hear the

melody, there is music accompanying me in my thanks to each one of you.

I would like to thank Ian Gardner for his marvelous patience and support even though “all the

roads seemed to get farther and farther away from Rome.” Thank you for being there and

believing that it was possible to begin in Kenya and end up in Colombia. Thank you for

supporting me in each of the many episodes of “magical realism” that can only be enjoyed or

suffered in countries like mine. I would also like to thank Barbara Byrne and Woutrina Miller for

their involving in this bodywork that came from such a different, distant, and unknown place. To

all three of you, I offer my most profound appreciation and gratitude for providing me the

guidance and support that culminated in this dissertation.

My enormous gratitude to Enrique Pérez, one of those “winged” human beings who saved me

twice from deep abysses and took me by the hand until I was free from all harm. My special

thanks to Enrique Perez from PAHO-WHO and Richard Reid-Smith from CIPARS for

brainstorming and helped me to setting up the Colombian Integrated Program for Antimicrobial

Resistance Surveillance (COIPARS) and for making it possible by giving me scientific, logistical

and financial support to make this initiative working. A special acknowledgment to you, Enrique,

Richard and to Ole Heuer from DANMAP, for coming to Colombia and believing that was the

vii

appropriate country to launch the pilot integrated program for the antimicrobial resistance

surveillance in Latin America. Also, special thanks to Ezra Barzilay and Jane Wichard from CDC

for participating in this initiative and for the scientific support that you have provided to me.

Thanks also to the Instituto Colombiano Agropecuario (ICA) for funding most of the research

activities related to this PhD Project. Thank you for providing me with the reagents, tools,

equipment, two laboratories, and related infrastructure that facilitated my research so successfully

in Colombia. Special thanks to each of my friends and colleagues at ICA for believing in the

importance of this project and for helping me build the support network with the official and

private Colombian entities that participated in making this project possible: Deyanira Barrero,

McAllister Tafur, Fabiola Rodriguez, Miryam Gallego,Mariluz Villamil, Paula Castaneda, Alex

Barbosa, Alvaro Pedraza, Anita Puentes, Aida Rojas, Ivonne Hernandez, Claudia Calderon,

Myriam Wilches, Dilmer, Jhon Leon, David Urdaneta, all the personnel of the LNDV, and finally to

Yesid Gonzales, Yenny and Jaime and the personnel of the Centro de Diagnostico de

Bucaramanga, Santander.

To the Instituto Nacional para la Vigilancia de Medicamentos y Alimentos (INVIMA) and

especially thak you to Mercedes Vanegas for the serotypification of Salmonella sp. that originated

in retail stores. I would also like to thank Adriana Coral and her staff (Claudia Carrillo and Fabián

Torres) from the Grupo Éxito as well as to Consuelo Vanegas, the director of the LEMA,

Universidad de Los Andes and her students Ricardo Castellanos, and Andrés Valderrama for

participating in the COIPARS network and providing technical and laboratory support for the

retail store meat analyses.

Thanks to the Instituto Colombiano de Salud (INS) and specially to Jaime Moreno, Sandra

Escobar and Caterin Rodriguez, for participating in the COIPARS network and giving me the

basic training and ongoing support necessary for characterizating Salmonella sp. using PFGE.

Special thanks to Martha Vives, Angelita Holguin, and Vivi Clavijo from Los Andes University, for

launching the molecular characterization of Salmonella using PFGE in the Salmonella laboratory

of the ICA. And finally, thank you Michael Hume, from the USDA, for the support and guidance in

the analysis of the salmonella isolates and for assisting with the scientific advice you graciously

viii

provided in development of the dendograms. Michael, I promise you a wonderful night of salsa in

Bogota.

Special thanks to Adriana León and Alex Garcia for introducing me to the Colombian poultry

industry who opened doors for me to be able to work on this project, and who open the doors of

the poultry industry to work in this area. I also thank the companies of Cundinamarca and

Santander.

Remerciements chaleureux à Alba Marina Cotes, director of CBB, at Corpoica in Colombia, for

rescuing me and believing in the goals of this research and the impact that my findings may have

for Colombia. It was refreshing that you were always sure that I was going to finish. Merci Alba

Marina! My special gratitude to Patricia Diaz, the best secretary assistant ever, for facilitating the

things…I always trusted you. I hope we can work together again in the future. Thank to my other

working colleagues at Corpoica.

To Colciencias and Fulbright for funding me with a scholarship the first three years of the PhD,

and specially to Jimmy Quintero for being patient and understanding with challenges that I faced

while developing this research.

Special thanks to Phil Kass, my winged professor, for being a wonderful human being and a

professor that unconditionally supported me. You shared your amazing epidemiology knowledge,

your family and your life. For me you are a paradigm of how a professor should be for graduate

students.

My gratitude to Maria Victoria Ovalle, my colleague and friend, part of the staff of the new

Salmonella Laboratory of the ICA, and participating fully for standardizing the antimicrobial

susceptibility automated system and the Salmonella serotyping procedures in the laboratory.

Working with you was fabulous and I look forward to continue working in the near future. Also,

thanks to Aura Lucia Leal from GREBO, for being part of the COIPARS initiative since the

beginning.

My overwhelming gratitude to Maribel León, DVM, for having working as my assistant through all

the activities of this project, for pushing me to continue, for her unwavering energy in the face of

numerous challenges, for travelling with me though farewell places to sample and for accepting

ix

all financial limitations of this project. It is now my turn to help you with your dream of pursuing a

PhD.

On Friday, I went to my last day of school, hand in hand with Tina, who also brought me to school

at UCDavis my first day of class, when I had so many expectations, fears, so much excitement to

begin my studies again after a twenty year hiatus. Tina and Jose, thank you for being my home

in Davis, my parents’ home, the home of my brothers and sisters, the home where there has

always been a place for me, where I have laughed and cried, the home where I began everything

anew, and where I am beginning everything again. To Monica, thank you for taking me to the

home of the “Moras” family and the home of my love from which I never want to leave…

To my close friends and roommates in Davis with whom I shared my language, table, sunrises

and sunsets, laughter and tears, complicity, scoldings, little fights, thank you; you always knew

what to do or say to help me along this road, you will always be in my heart. Thank you Montse

Canedo, for accompanying me in everything, both the good and the bad, for talking and talking,

for analyzing, for being my companion and partner; Ricardo Ertze, the “man of the house”, for

your sweetness, your support, and for being there from the very first day; Rossio Motta for

making me laugh when you thought I was crying and for sharing profoundly my sorrows and joys;

and Miryam Gallego for your friendship and solidarity that is still going strong.

Enormous thank you’s to my gang, my friends, my companions in the Epi-group, Poh-Sin Yap,

Naila Baig-Ansari, Sophia Papageorgiou , Banafsheh Sadeghi and Anup Srivastav (Anupi-rat) for

sharing with me wonderful working times and laughs. Special thanks to Poh-Sin for your immense

generosity and your never-ending friendship, you have shown so much about the meaning of

friendship. I am so glad that your life is now painted by many beautiful colors, Bogota is waiting

for you. My warm thanks to Naila, my Pakistani connection, and my sister in this journey. I thank

life for putting you in my way and allowing us to work together side by side until the very end. Our

Pakistani-Colombian connection cannot be broken. Sophie, my deepest gratitude for listening to

me, for working closely with me day and night and for giving me your lovely friendship--we have

to be sure that we also share, in the future, a space to rest our minds and bodies. Bani,, special

gratitude for being close to me in the first and the last times of my PhD, for your generosity and

x

for sharing with me the warmest and most courageous part of your heart. Even though I will be far

away, you know you can count on me. To Locksley, a marvelous human being, warm thanks for

listening to me and give me his wise advice the first years of the PhD. Anupi-rat, thank you for

everything and more, for making me feel like a child enjoying the classes I took with you. You

know where to find me if you ever need me. In the final part of this road, Syrukh appeared and

she was my mouse companion in the office of Tupper Hall, that she called “the dungeon”. Thank

you for the company and being here in the long nights of working.

I want to express my giant gratitude to the rest all my friends for standing with me in this long

voyage where I appeared and disappeared without reason and for waiting patiently for better

times to come. To write the acknowledgments to every one of you would take days and I want to

get back home to celebrate with all of you--now it is time to party!!! Los quiero mucho: Sonia,

Anny, Fredy, Alberto, Pilar, Nohora, Fernando, Richard, Fanny, Loly, Pepa, Coco, Sara y Rober.

Thank you to my parents, Bertha y Guillermo, for your understanding and for your whole life

strong support “pase lo que pase”. I love you deeply and now the times I will spend with you will

be more and more. Thank you to my sister Rocio for being in my side always with her wonderful

smile: I am so proud of you, of your way to face the life and I am sorry for not being in Rome

when the times where so difficult. Thank you to the rest of my family for the all the understanding.

I will be back soon.

And finally, my heart to you Xavi, my quotidian support, my companion in life, thank you for

sharing one wing with me, for being the best “razon de vivir mi vida”. This song by Victor Heredia

is for you completamente Mon Amour de ma vie:

Para decidir si sigo poniendo

Esta sangre en tierra

Este corazón que bate su parche

Sol y tinieblas.

Para continuar caminando al sol

Por estos desiertos

xi

Para recalcar que estoy vivo

En medio de tantos muertos;

Para decidir

Para continuar

Para recalcar y considerar

Solo me hace falta que estés aquí

Con tus ojos claros

Ay! fogata de amor y guía

Razón de vivir mi vida

Para aligerar este duro peso

De nuestros días

Esta soledad que llevamos todos

Islas perdidas

Para descartar esta sensación

De perderlo todo;

Para analizar por donde seguir

Y elegir el modo

Para aligerar

Para descartar

Para analizar y considerar


Con tus ojos claros


Razón de vivir mi vida

Para combinar lo bello y la luz

Sin perder distancia

Para estar con vos sin perder el ángel

De la nostalgia

xii

Para descubrir que la vida va

Sin pedirnos nada

Y considerar que todo es hermoso

Y no cuesta nada,

Para combinar

Para estar con vos

Para descubrir y considerar,


Con tus ojos claros.


Razón de vivir mi vida.

xiii

TABLE OF CONTENTS

ABSTRACT ........................................................................................................................IV

ACKNOWLEDGEMENTS ..................................................................................................VI

LIST OF TABLES......................................................................................................... XIV LIST OF FIGURES........................................................................................................ XV

CHAPTER 1 : INTRODUCTION AND LITERATURE REVIEW.........................................1

THE ISSUE: STEADY INCREASE IN ANTIMICROBIAL RESISTANCE AND THE LACK OF DEVELOPMENT OF NEW ANTIMICROBIAL DRUGS...................................................1 AN APPROPRIATE RESPONSE: INTEGRATED AMR SURVEILLANCE SYSTEMS ..3 THE COLOMBIAN SITUATION ......................................................................................4 DESIGN OF THE PILOT PROGRAM OF COIPARS.......................................................6 REFERENCES ...............................................................................................................9

CHAPTER 2 PREVALENCE, RISK FACTORS, AND ANTIMICROBIAL RESISTANCE (AMR) PROFILES OF SALMONELLA SP. IN COMMERCIAL BROILER FARMS IN TWO IMPORTANT POULTRY-PRODUCING REGIONS OF COLOMBIA ......................................................13

ABSTRACT...................................................................................................................13 INTRODUCTION ..........................................................................................................13 METHODS....................................................................................................................15 RESULTS .....................................................................................................................18 DISCUSSION ...............................................................................................................23 REFERENCES .............................................................................................................39

CHAPTER 3 PREVALENCE, RESISTANCE PATTERNS AND RISK FACTORS FOR ANTIMICROBIAL RESISTANCE (AMR) BACTERIA IN RETAIL CHICKEN MEAT IN COLOMBIA..........................................................................................................................................42

ABSTRACT...................................................................................................................42 INTRODUCTION ..........................................................................................................43 METHODS....................................................................................................................44 RESULTS .....................................................................................................................47 DISCUSSION ...............................................................................................................50 REFERENCES .............................................................................................................63

CHAPTER 4 MOLECULAR CHARACTERIZATION OF SALMONELLA PARATYPHI B DT+ (S. JAVA) AND SALMONELLA HEIDELBERG FROM POULTRY AND RETAIL CHICKEN MEAT IN COLOMBIA. ......................................................................................................................82

ABSTRACT...................................................................................................................82 INTRODUCTION ..........................................................................................................82 MATERIALS AND METHODS ......................................................................................84 RESULTS .....................................................................................................................85 DISCUSSION ...............................................................................................................86 REFERENCES .............................................................................................................89

CHAPTER 5 CONCLUSION.............................................................................................94

xiv

LIST OF TABLES

Chapter 2 TABLE 2-1. MINIMUM INHIBITORY CONCENTRATION (MIC) RANGE AND INTERPRETATIONS FOR

ANTIMICROBIALS USED IN THE PANEL............................................................................27 TABLE 2-2.MINIMUM INHIBITORY CONCENTRATIONS (MIC) AND ZONE DIAMETER FOR THE

INTERPRETATION OF ANTIMICROBIALS NOT INCLUDED IN THE PANEL. ..............................28 TABLE 2-3. REPORTED USE OF ANTIMICROBIALS IN THE FARMS FROM TWO POULTRY PRODUCTION

DEPARTMENTS OF COLOMBIA ......................................................................................29 TABLE 2-4. DISTRIBUTION OF SALMONELLA SEROVARS BY DEPARTMENT ................................30 TABLE 2-5. PERCENTAGE OF ANTIMICROBIAL SENSITIVE AND RESISTANT SALMONELLA SP. ISOLATES

..................................................................................................................................31 TABLE 2-6. COMPARISON OF RESISTANCE (%) AMONG SALMONELLA ISOLATES FROM THE TWO

DEPARTMENTS............................................................................................................32 TABLE 2-7. NUMBER OF ANTIMICROBIALS PER RESISTANCE PATTERN CLASSIFIED PER SEROVAR33 TABLE 2-8. NUMBER OF RESISTANT ISOLATES IN PATTERNS BY SEROVARS AND BY DEPARTMENT34 TABLE 2-9. AMR PATTERN DISTRIBUTION BY SEROVAR..........................................................35 TABLE 2-10: MULTIPLE LOGISTIC REGRESSION MODEL OF FACTORS ASSOCIATED WITH THE PREVALENCE

OF SALMONELLA AT THE HOUSE LEVEL (N=296) AND INCLUDING FARM AS A RANDOM EFFECT...................................................................................................................................36

Chapter 3 TABLE 3-1 DISTRIBUTION OF SALMONELLA SEROVARS BY TYPE OF STORE ..............................55 TABLE 3-2 PREVALENCE OF ANTIMICROBIAL RESISTANT BACTERIAL ISOLATES.........................56 TABLE 3-3 ANTIMICROBIAL RESISTANCE PATTERN DISTRIBUTION FOR THE MOST PREVALENT SALMONELLA

SEROVARS..................................................................................................................57 TABLE 3-4 PREVALENCE OF ANTIMICROBIAL RESISTANT SALMONELLA SP., ESCHERICHIA COLI AND

ENTEROCOCCUS ISOLATES OF VERY HIGH IMPORTANCE IN HUMAN MEDICINE..................58 TABLE 3-5 PREVALENCE OF RESISTANT SALMONELLA, E.COLI , AND ENTEROCOCCUS OF HIGH AND

MEDIUM IMPORTANCE IN HUMAN MEDICINE....................................................................59 TABLE 3-6 DISTRIBUTION SALMONELLA SP., E.COLI AND ENTEROCOCCUS BY THE NUMBER OF

ANTIMICROBIAL DRUGS TO WHICH THEY WERE RESISTANT. ............................................60 TABLE 3-7 RESISTANCE MARKERS PRESENT IN ENTEROCOCCUS SP. ......................................61

xv

LIST OF FIGURES Chapter 3 FIGURE 1 PREVALENCE OF RESISTANCE TO ANTIMICROBIALS IN SALMONELLA SP. ISOLATED FROM

CHICKENS: FARM VS RETAIL STORES IN BOGOTA, COLOMBIA, 2009. ..............................62 Chapter 4 FIGURE 1 SIMILARITY DENDOGRAM OF S. PARATYPHI B DT+ MACRORESTRICTION PATTERNS GENERATED

USING XBAL. CLONE COLUMN INDICATES THE NUMBER OF INDISTINGUISHABLE ISOLATES PRESENTING THE SAME PFGE PATTERN. THE CLUSTERS ARE MENTIONED AS CX (PERCENTAGE SIMILARITY COEFFICIENT). THE ORIGINS OF THE ISOLATES ARE REPORTED AS SANTANDER FARMS, CUNDINAMARCA FARMS, INDEPENDENT RETAIL SHOPS OR RETAIL CHAIN IN BOGOTA DC. THE DIFFERENT INTEGRATED COMPANIES ARE IDENTIFIED AS SAOX OR RCOX. THE SCALE BAR INDICATES PERCENT SIMILARITY COEFFICIENT...............................................................92

FIGURE 2 SIMILARITY DENDOGRAM OF S. HEIDLEBERG MACRO RESTRICTION PATTERNS GENERATED USING XBAL. CLONE COLUMN INDICATES THE NUMBER OF INDISTINGUISHABLE ISOLATES PRESENTING THE SAME PFGE PATTERN. THE CLUSTER IS MENTIONED AS CX (PERCENTAGE OF SIMILARITY COEFFICIENT). THE ORIGIN OF THE ISOLATES ARE REPORTED AS SANTANDER FARMS, CUNDINAMARCA FARMS, INDEPENDENT RETAIL SHOPS OR RETAIL CHAIN IN BOGOTA, DC. THE DIFFERENT INTEGRATED COMPANIES ARE IDENTIFIED AS SAOX OR RCOX. THE SCALE BAR INDICATES PERCENT SIMILARITY...................................................................................93

1

Chapter 1 : Introduction and Literature Review THE ISSUE: STEADY INCREASE IN ANTIMICROBIAL RESISTANCE AND THE LACK OF DEVELOPMENT OF NEW ANTIMICROBIAL DRUGS

The use of antimicrobials combined with improvements in sanitation, nutrition and immunization

has lead to a dramatic decrease in deaths and a major gain in human life expectancy 1. However,

with the increased use of antimicrobials, antimicrobial resistance (AMR) has emerged as one of

the greatest threats to human health security 2 and a most pressing public health problem of

serious concern to public health, animal health and also food safety authorities 3-7.

The increase in AMR has narrowed the potential uses of antibiotics for the treatment of infections

in humans and animals 8. As a striking example, the Centers for Disease Control and Prevention

(CDC), estimated that the total of Methicillin Resistant Staphylococcus Infections (MRSI) in US

hospitals and communities has increased from 2 % in 1974 to almost 63% in 2004 9.

Similarly, with Salmonella being an important cause of food-borne diarrheal disease in human

beings 10-12, the reduction in the number of antibiotics available for effective treatment of

Salmonella related infectious diseases in humans and animals has become a serious concern 8.

The frequency and extent of the resistance to antimicrobials by Salmonella vary based on the

antimicrobial usage in humans and animals and the ecological differences in the epidemiology of

Salmonella infections 13. Globally, Salmonella exhibits extensive resistance profiles which have

been associated with higher rates of morbidity and mortality 13-15 and the use of antimicrobials in

food producing animals 16.

Similarly the important commensal bacteria, Escherichia coli (Gram-negative) and Enteroccus

(Gram-positive) also result in infectious diseases in man. With their ability to transfer resistance

genes to human pathogens 17, 18, these bacteria pose a more serious global threat for human

health than selection pressure 19. Furthermore, the level and degree of resistance that occur in

commensal bacteria is linked to the amount and class of antimicrobial agents used to produce a

2

kilogram of meat 20 which varies from country to country 21. Finally, since E. coli and enterococci

can be easily obtained from healthy animals, they are good indicator bacteria to monitor AMR.

The causes of AMR are hypothesized to include the abuse and misuse of antibiotics in both

human and animals (Aarestrup, 2005). These practices include: over-prescription of broad-

spectrum antimicrobials when a narrow-spectrum oral agent would be more appropriate 22; self-

medication with antimicrobials 23; low patient compliance 24; and the combination of highly

susceptible patients in hospital settings, with the intensive and prolonged antimicrobial use

resulting in nosocomial infections with highly resistant bacterial pathogens 25.

In humans, WHO has identified several underlying causes for the increased need of

antimicrobials and consequently their misuse stemming from an increase in infections due to a)

urbanization with overcrowding and poor sanitation, b) pollution, environmental degradation and

changing weather patterns, c) increased exposure of the elderly to nosocomial infections, d) the

spread of infectious diseases and resistant microorganism between continents due to booming

global trade and travel, and e) massive use of antibiotics in food-producing animals and poultry

flocks 1.

The presence of AMR bacteria in primary animal production represents a high risk for humans

since AMR bacteria of animal origin can be transmitted from animals to humans through the food

supply (food-borne pathogens), water or direct contact with animals 26-28.

In farms, factors that can influence bacterial resistance vary depending on herd or flock health

status, farm management and environment 29. These practices include over-prescription of broad-

spectrum drugs by veterinarians instead of narrow-spectrum drugs 30, feeding of low doses of

antibiotics for growth promotion 31-34 and use of non-approved drugs or drugs used in extra-label

manner are believed to contribute to the development of antimicrobial resistance 35, 36. Although

widespread use of antimicrobials in the primary sector has benefits for producers, it also

contributes to the increasing emergence of AMR bacteria 37.

One of the solutions to minimize the effects of AMR could be the production of new antimicrobials

but unfortunately, the development of new antimicrobials has not been able to keep up with the

3

increased demand for new antibiotics due to AMR 38. After the discovery of penicillin by Fleming

in 1929, the sulphonamides, the first class of antibiotics was launched in 1937 followed in 1944

by streptomycin, the first aminoglycoside 39. Since then, several other classes of antibiotics have

followed: chloramphenicol, tetracyclines, macrolides, vancomycins, methicillins, cephalosporins,

quinolones, lipopeptides, and glycopeptides. However, almost at the same time that these

antibiotics were first used, bacteria responded by showing diverse forms of resistance 3. The

main classes of currently-used antibiotics were introduced between 1940 and 1962 38 and after

nearly 38 years of research and development a new class of oxazolidinones (linezolid) was

introduced in 2000 40. In 2009, the reports of the Infectious Disease Society of America (IDSA) 41,

the European Centre for Disease Prevention and Control (ECDC) and the European Medicines

Agency (EMA) 42 pointed out that the antibiotic pipeline was nearly bare with only 15 antibiotics in

development that may provide benefit over existing drugs. Further development is jeopardized by

the lack of financial incentives, regulatory uncertainty, insufficient federal supported research,

greater public/private collaborations and the lack of an antibiotic innovation and conservation fee.

AN APPROPRIATE RESPONSE: INTEGRATED AMR SURVEILLANCE SYSTEMS

In order to monitor the evolution of antimicrobial resistance, some countries have set-up

integrated monitoring systems such as the National Antimicrobial Resistance Monitoring System

(NARMS) in the USA, the Canadian Integrated Program for Antimicrobial Resistance Surveillance

(CIPARS) or the Danish Integrated Antimicrobial Resistance Monitoring and Research

Programme (DANMAP).

Following WHO recommendations, these systems have adopted a tripartite approach to include

human clinical cases, food animals and retail meats. The CIPARS, for example, integrates data

from the national microbiology laboratories on human cases, data from the animal production

sector at farm, abattoirs and retail levels and finally data on antimicrobial usage both in human

and animal populations 43. Similarly, NARMS, which started in 1996, is a national collaborative

network involving the FDA, CDC, and USDA (United States Department of Agriculture). The

system was developed to monitor changes in susceptibility/resistance of select zoonotic bacterial

4

pathogens and commensal organisms recovered from animals, some retail meats, and humans

to antimicrobial agents of public health and animal health significance 44.

The goals of these integrated surveillance programs could be summarized in the goals of NARMS

which include providing descriptive data and trends on antimicrobial susceptibility/resistance

patterns in zoonotic, food-borne bacterial pathogens and select commensal organisms in order to

identify unusual or high levels of antibacterial drug resistance in humans, animals, and retail

meats as well as to contain it. The goals also include conducting epidemiological studies to

better understand the phenomenon of resistance, promote the prudent use of antimicrobials as

well as assist the FDA in decision making for approving safe and effective drugs for humans and

animals (NARMS, program review 2007).

The efficacy of such systems at curbing the development of AMR has been demonstrated on

numerous occasions. As an example, DANMAP reported drastic reductions of vancomycin

resistant Enterococcus faecium (VRE) in boiler and human isolates in Denmark as a result of the

ban of avoparcin as a growth promoter 18. A reduction of VRE prevalence from 80 % to less than

5 % was achieved in 3 years among broiler isolates 18 and from 13% to 3 % among human

isolates in 3 years.

THE COLOMBIAN SITUATION

In Colombia there is no formal integrated system at the national level for surveillance of AMR.

Nevertheless, the Instituto National de Salud (INS) has been doing passive surveillance and

research in serotypes and trends of antimicrobial susceptibility, in pathogens of importance in

human health such as Streptococcus pneumoniae, Haemophilus influenzae, Neisseria

meningitidis, Neisseria gonorrhoeae, Salmonella sp. and Shigella sp. 45-49.

For AMR in humans, four groups of researchers reported results of passive surveillance in human

hospitals and clinical cases 50-53. The research of all these groups showed an increasing trend in

the occurrence of antimicrobial resistance in several microorganisms that cause disease in

human populations.

5

However, in the case of food animals and food of animal origin, there are no surveillance reports

for Colombia but the Instituto Colombiano Agropecuario (ICA) has information on AMR bacterial

isolates from clinical cases in different animal species and regions of the country. The Instituto

Nacional de Vigilancia de Medicamentos y Alimentos (INVIMA) has recently begun analyses of

AMR in some bacteria in food of animal origin.

Thus there is a need for collaboration between the human, agricultural and food sectors since the

only data that can be found in Colombia are on AMR in clinically-significant human and animal

pathogens. An integrated AMR surveillance program that could yield information on trends in

AMR and at the same time, monitor the consumption of antimicrobial would be highly beneficial to

many stakeholders given the rapid increase in animal protein consumption in Colombia and the

potential for food exports.

However, there are still limiting factors including inadequate knowledge of the baseline

prevalence of food-borne pathogens like Salmonella, and their antimicrobial resistance profiles in

retail meat of different origins in the country to fully benefit from international commerce. In

Colombia, there has not been a comprehensive prevalence survey of Salmonella in poultry in the

last 10 years. Only fragmented reports in localized areas with limited sample sizes are available

49. Furthermore, there is a lack of knowledge regarding AMR in farms, in abattoirs and in retail

stores for any of the animal production systems. The Colombian poultry industry has shown in

improving the quality of the poultry-derived products, and in implementing Hazard Analysis

Critical Control Point (HACCP) systems on farms and at abattoirs. In both cases, knowledge of

the frequency, risk factors and resistance patterns of AMR in the country is fundamental to the

development of HACCP programs.

To mitigate the risks of AMR development associated with the particular characteristics of

Colombian’s situation both in human medicine and in animal production, initiation of a Colombian

Integrated Program for Antimicrobial Resistance Surveillance (namely COIPARS) is an

appropriate response. The COIPARS could meet the animal health and welfare requirements of

the animal industries and address the public health concerns about resistance that originate from

6

the use of antimicrobials. Also, an integrated AMR surveillance program would support the

participation of Colombia in the World Trade Organization where the goal is to have animal origin

products that are free of AMR bacteria.

DESIGN OF THE PILOT PROGRAM OF COIPARS

Since a comprehensive survey on the AMR situation in Colombia did not exist, this research

project was designed as a pilot program before implementing a fully integrated system at the

national level. The objective of this pilot program was twofold: 1) to establish baseline data, and 2)

to adapt working processes between national institutes and future stakeholders of the COIPARS.

Constitution of stakeholder consortium

A consortium of Colombian private and public organisation was firstly assembled to facilitate

access to the sites of sampling and to adequate laboratory capacities. This consortium included:

a) CORPOICA, Corporacion Colombiana de Investigacion Agropecuaria, the National Institute of

Agricultural Research; b) ICA, the Instituto Colombiano Agropecuario , National Institute for

Agriculture; c) INVIMA, Instituto para la Vigilancia de Medicamentos y Alimentos, the Colombian

Food and Drug Administration; d) INS, Instituto Nacional de Salud, the National Health Institute; e)

LEMA, Laboratory of Microbiology and Food Ecology of Los Andes University; f) GREBO, Grupo

de Research group in Human AMR of the National University of Colombia; g) Carulla Vivero S.A.,

the largest retail chain in Colombia which is linked to the French group Casino; and h) poultry

companies.

International Advisory Team

In order to incorporate the existing international expertise in AMR integrated surveillance systems,

an international advisory board was formed consisting of experts from PAHO/WHO (Enrique

Perez), CIPARS (Richard Reid-Smith), DANMAP (Ole Heuer), CDC-NARMS (Ezra Barzilay) and

UC Davis (Ian Gardner).

Standard Procedures

7

Following the recommendation of the advisory team, the standard procedures developed by

CIPARS were established as reference approaches and were adapted to the Colombian situation

as needed 54, 55

Selection of the poultry chain for the pilot program

To establish a baseline in the animal production sector, the poultry industry was selected

because it is by far the most integrated and standardized animal production system in Colombia,

and offers an excellent tracking system of production from the farm level through to the retail

sales outlets. Poultry production is a strategic sector in the emerging economy of Colombia and is

the fastest growing animal production sector (11% of growth in 2007). In 2007, a nationwide

initiative was launched about the health and safety status in the poultry chain by 5 ministries,

namely COMPES Avicola, resulting in favourable conditions to obtain collaboration of the various

stakeholders of the poultry sector towards the COIPARS initiative.

Selection of bacteria to be included in the pilot program

One food-borne pathogen of importance for human health, Salmonella sp, was included in the

pilot program, in addition to two commensal bacteria, Escherichia coli and Enterococcus sp.

Sampling geographical regions

At the farm level, the study was done in the two departments where poultry farming is the main

economic activity: Santander and Cundinamarca. The department of Santander is situated in

eastern Colombia and contributes about 32% of the total chicken production with 134 million birds

per year. Cundinamarca is situated in the oriental branch of Andes Cordillera and produces 137

million chickens/year and contributes about 33% of the country’s total chicken production. At the

retail level, the sampling was focused in the Capital District of Bogota (Bogota, DC). This is the

largest urban concentration in Colombia with 8 million inhabitants and represents the main

commercialization area for poultry products from the Santander and Cundinamarca regions.

Overall design

In order to establish baseline data in the poultry chain of Colombia, 3 studies were conducted:

8

1. Prevalence, risk factors, and antimicrobial resistance (AMR) profiles of Salmonella sp. in

commercial broiler farms in the two important poultry-producing regions of Colombia.

2. Prevalence, resistance patterns and risk factors for antimicrobial resistance (AMR) of

Salmonella, Escherichia coli and Enterococcus in retail chicken meat in Colombia.

3. Molecular characterization of Salmonella Paratyphi B dT+ and S. Heidelberg isolates

from poultry and retail chicken meat in Colombia.

The objectives of this pilot project were to establish baseline data and to validate processes for

the implementation of an integrated surveillance program for antimicrobial resistance (AMR) in

Colombia.

9

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[18] van den Bogaard AE, Stobberingh EE. Epidemiology of resistance to antibiotics. Links between animals and humans. Int J Antimicrob Agents. 2000 14: 327-35.

[19] Boerlin P, Reid-Smith RJ. Antimicrobial resistance: its emergence and transmission. Anim Health Res Rev. 2008 9: 115-26.

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[21] Aarestrup FM. Veterinary drug usage and antimicrobial resistance in bacteria of animal origin. Basic Clin Pharmacol Toxicol. 2005 96: 271-81.

[22] Louie JP, Bell LM. Appropriate use of antibiotics for common infections in an era of increasing resistance. Emerg Med Clin North Am. 2002 20: 69-91.

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[24] Kardas P. Patient compliance with antibiotic treatment for respiratory tract infections. J Antimicrob Chemother. 2002 49: 897-903.

[25] Levin PD, Fowler RA, Guest C, Sibbald WJ, Kiss A, Simor AE. Risk factors associated with resistance to ciprofloxacin in clinical bacterial isolates from intensive care unit patients. Infect Control Hosp Epidemiol. 2007 28: 331-6.

[26] Ramchandani M, Manges AR, DebRoy C, Smith SP, Johnson JR, Riley LW. Possible animal origin of human-associated, multidrug-resistant, uropathogenic Escherichia coli. Clin Infect Dis. 2005 40: 251-7.

[27] Swartz MN. Human diseases caused by foodborne pathogens of animal origin. Clin Infect Dis. 2002 34 Suppl 3: S111-22.

[28] Funk JA, Lejeune JT, Wittum TE, Rajala-Schultz PJ. The effect of subtherapeutic chlortetracycline on antimicrobial resistance in the fecal flora of swine. Microb Drug Resist. 2006 12: 210-8.

[29] Acar JF, Moulin G. Antimicrobial resistance at farm level. Rev Sci Tech. 2006 25: 775-92.

[30] Sarkar P, Gould IM. Antimicrobial agents are societal drugs: how should this influence prescribing? Drugs. 2006 66: 893-901.

[31] Prescott JF. Antimicrobial use in food and companion animals. Anim Health Res Rev. 2008 9: 127-33.

[32] Mathew AG, Cissell R, Liamthong S. Antibiotic resistance in bacteria associated with food animals: a United States perspective of livestock production. Foodborne Pathog Dis. 2007 4: 115-33.

[33] Hammerum AM, Heuer OE, Emborg HD, Bagger-Skjot L, Jensen VF, Rogues AM, et al. Danish integrated antimicrobial resistance monitoring and research program. Emerg Infect Dis. 2007 13: 1632-9.

[34] Hammerum AM, Heuer OE, Lester CH, Agerso Y, Seyfarth AM, Emborg HD, et al. Comment on: withdrawal of growth-promoting antibiotics in Europe and its effects in relation to human health. Int J Antimicrob Agents. 2007 30: 466-8.

[35] Sharma R, Sharma CL, Kapoor B. Antibacterial resistance: current problems and possible solutions. Indian J Med Sci. 2005 59: 120-9.

[36] Weese JS. Prudent use of antimicrobials. In: Giguère S, ed. Antimicrobial therapy in veterinary medicine. Ames, Iowa: Blackwell Pub., 4th ed, 2006:xvii, 626 p.

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[37] Aarestrup FM, Pires SM. Comment on: Causal regulations vs. political will: why human zoonotic infections increase despite precautionary bans on animal antibiotics. Environ Int. 2009 35: 760-1.

[38] Coates A, Hu Y, Bax R, Page C. The future challenges facing the development of new antimicrobial drugs. Nat Rev Drug Discov. 2002 1: 895-910.

[39] Giguère S. Antimicrobial therapy in veterinary medicine. 4th ed. Ames, Iowa: Blackwell Pub., 2006.

[40] Diekema DI, Jones RN. Oxazolidinones: a review. Drugs. 2000 59: 7-16.

[41] Spellberg B. Antibiotic resistance:promoting critically needed antibiotic research and developement and the apropiate use ("stewardship") of these precious drug. Before the House Committee on Energy and Commerce Subcommittee on Health: Infectious Disease Society of America (IDSA). 2010.

[42] ECDC/EMEA. The bacterial challenge: time to react. A call to narrow the gap between multidrug-resistant bacteria in the EU and the development of new antibacterial agents. Stockholm: European Centre for Disease Prevention and Control/European Medicines Agency, 2009.

[43] CIPARS. Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS), 2007. Guelph, ON: Government of Canada. Public Health Agency of Canada.ed, 2010.

[44] CDC. National Antimicrobial Resistance Monitoring System (NARMS). ed, 2009.

[45] Wiesner M, Hidalgo M, Castaneda E, Agudelo CI. Molecular analysis of Salmonella enteritidis and Typhimurium clinical and food isolates by pulsed-field gel electrophoresis in Bogota, Colombia. Microb Drug Resist. 2006 12: 68-73.

[46] Agudelo CI, Moreno J, Sanabria OM, Ovalle MV, Di Fabio JL, Castaneda E. [Streptococcus pneumoniae: serotype evolution and patterns of antimicrobial susceptibility in invasive isolates from 11 years surveillance (1994 -2004) in Colombia]. Biomedica. 2006 26: 234-49.

[47] Moreno J, Phandanouvong V, Castaneda E. [Molecular surveillance of invasive penicillin-resistant Streptococcus pneumoniae Colombian isolates recovered from children less than 5 years of age]. Biomedica. 2004 24: 296-301.

[48] Ovalle MV, Agudelo CI, Munoz N, Castaneda E, Gallego CR, Nunez S, et al. [Surveillance of Haemophilus influenzae serotypes and antimicrobial resistance in Colombia, 1994-2002]. Biomedica. 2003 23: 194-201.

[49] INS. Serotipos y Patrones de Susceptibilidad Antimicrobiana de Patógenos de Importancia en Salud Pública. Estadisticas de la Vigilancia en Salud Publicaed, 2010.

[50] Leal AL, Eslava-Schmalbach J, Alvarez C, Buitrago G, Mendez M. [Endemic tendencies and bacterial resistance markers in third-level hospitals in Bogota, Colombia]. Rev Salud Publica (Bogota). 2006 8 Suppl 1: 59-70.

[51] Miranda MC, Perez F, Zuluaga T, Olivera Mdel R, Correa A, Reyes SL, et al. [Antimicrobial resistance in gram negative bacteria isolated from intensive care units of Colombian hospitals, WHONET 2003, 2004 and 2005]. Biomedica. 2006 26: 424-33.

[52] Arias CA, Reyes J, Zuniga M, Cortes L, Cruz C, Rico CL, et al. Multicentre surveillance of antimicrobial resistance in enterococci and staphylococci from Colombian hospitals, 2001-2002. J Antimicrob Chemother. 2003 51: 59-68.

[53] Pfaller MA, Jones RN, Doern GV, Salazar JC. Multicenter evaluation of antimicrobial resistance to six broad-spectrum beta-lactams in Colombia: comparison of data from 1997 and 1998 using the Etest method. The Colombian Antimicrobial Resistance Study Group. Diagn Microbiol Infect Dis. 1999 35: 235-41.

12

[54] CIPARS. CIPARS: Retail Field Staff Manual - Ontario. In: Public Health Agency of Canada. 2007.

[55] Public Health Agency of Canada. Canadian Integrated Program for Antimicrobial Resistance Survaival (CIPARS). Methodology fo the isolation of Salmonella sp. from meat and poultry samples. LLZA-Unite de Saint-Hyacintheed, 2007:5.

13

Chapter 2 Prevalence, Risk Factors, and Antimicrobial Resistance (AMR) Profiles of Salmonella sp. in Commercial Broiler Farms in Two Important Poultry-Producing Regions of Colombia

ABSTRACT

Salmonella sp. is the most common food-borne pathogen associated with diarrheal disease in

humans. Food animals, especially poultry, are important direct and indirect sources of human

Salmonella sp. infections. The use of antimicrobials in the primary sector benefits producers but

also contributes to the increasing emergence of AMR bacteria. As a first step towards

implementing the Colombian Integrated Program for Antimicrobial Resistance Surveillance

(COIPARS), this study aimed to establish the prevalence, distribution of serovars, antimicrobial

resistance profiles and risk factors for Salmonella sp. isolated from poultry farms in the two

largest areas of poultry production in Colombia. Salmonella was isolated from 29 farms (41.4%)

with a Salmonella sp. positive prevalence of 65% in the 315 houses sampled. S. Paratyphi B was

the most prevalent serovar (76.4%), followed by S. Heidelberg (22.7%). None of the Salmonella

isolates were susceptible to all the antimicrobials tested in this study and the number of

antimicrobials to which an isolate was resistant ranged from 2 to 15. The resistance pattern

TCY-XNL-NAL was found in over 40% of the isolates of S. Heidelberg. For S. Paratyphi B, many

patterns (34) were present even though the predominant resistance pattern was CIP-NIT-TCY-

SXT-XNL-STR-ENR-NAL (15%). Of all the biosecurity practices, the two factors that were

significantly (P < 0.05) associated with the presence of Salmonella on a farm were related to

hygiene: the cleaning of fixed equipment and the composting of dead birds within the farm.

Findings from the present study provide scientific evidence for implementation of official policies

that support new biosecurity legislation to decrease the prevalence of Salmonella in poultry farms.

INTRODUCTION

14

Worldwide, Salmonella is an important cause of food-borne diarrheal disease in human beings

and often is associated with the consumption of poultry products 1-3. In recent years, an added

public health concern has been the development of antimicrobial resistance (AMR) that markedly

reduces the number of antibiotics available for effective treatment of infectious diseases in

humans and animals 4.

Modern food animal production systems often use antimicrobial agents to prevent, control and

treat bacterial infections and these agents are commonly used as growth promoters in poultry and

swine production systems 5,6. Widespread use of antimicrobials in the primary sector has benefits

for producers but also contributes to the increasing emergence of AMR bacteria 5. Factors that

can influence bacterial resistance in farms are numerous and vary depending on flock health

status, farm management and environment 7. Practices such as the prescription of broad-

spectrum drugs by veterinarians instead of narrow-spectrum drugs 8, feeding of low doses of

antibiotics for growth promotion 9-12, and use of non-approved drugs or drugs used in extra-label

manner are believed to contribute to the development of antimicrobial resistance 13,14.

The presence of AMR bacteria in primary production represents a high risk for humans since

AMR bacteria of animal origin can be transmitted from animals to humans through the food

supply, water or by direct contact with animals. Sometimes resistance genes can even be

transferred from animals through human pathogens that are normally human-specific 15.

Awareness of the prevalence of AMR in food animals provides baseline data in order to

implement an integrated AMR surveillance system and also facilitates the evaluation of

interventions used to control the AMR.

Over the past decade, the Colombian poultry sector production has expanded and is attempting

to meet international food safety and animal health standards. The Colombian poultry industry is

free of avian influenza, which allows it access to international markets. However, there are

limiting factors such as endemic Newcastle Disease as well as insufficient knowledge of the

baseline prevalence of chemical residues and pathogens such as Salmonella sp.

15

In Colombia, there has not been a comprehensive prevalence survey of Salmonella in poultry in

the last 10 years. Only fragmented reports in localized areas with limited sample sizes are

available. Furthermore, there is a lack of knowledge regarding AMR in farms, abattoirs and retail

stores for any of the predominant animal production systems. The Colombian poultry industry has

been showing interest in improving the quality of the poultry-derived products, and in

implementing Hazard Analysis Critical Control Point HACCP systems on farms and at abattoirs.

In both cases, knowledge of the frequency, risk factors and resistance patterns of AMR in the

country is fundamental to the development of HACCP programs.

The objective of the present study was to determine the prevalence, distribution of serovars,

antimicrobial resistance profiles and risk factors for Salmonella sp. isolated from poultry farms in

the two largest areas of poultry production in Colombia. This was the first step to provide

baseline data for an integrated and unified antimicrobial resistance surveillance system in the

country.

METHODS

Study Site: The study was done in the two departments (states) where poultry farming is the

main economic activity: Santander and Cundinamarca. The department of Santander is situated

in eastern Colombia and contributes 32.4% of the total chicken production with 134 million birds

per year. Cundinamarca is situated in the oriental branch of Andes Cordillera and produces 137

million chickens/year, contributing approximately 33.1% of the total chicken production.

Farms

The sampling unit was the farm. A convenience sample of 70 farms from 12 of the largest 20

poultry companies in these two regions was selected based on the willingness of the enterprise

managers to participate. The numbers of farms per enterprise was selected proportional to the

chicken population of the operation. In each farm, two to five houses that were in production were

selected randomly.

Questionnaire

16

A questionnaire was completed for each farm and included general characteristics of the farm,

biosecurity practices and house-specific information. The questionnaire was conducted by the

senior author and was answered by the veterinarian of each farm. Questions related to farm type,

altitude of the farm, size, biosecurity management, type of ventilation, antimicrobial treatments of

chickens, type of housing, cleaning of housing, feeding management, use of prophylactic and

therapeutic treatments, and use of growth promoters were considered. A pre-test of the

questionnaire was done in five operations.

Sampling Procedure

Two drag swabs and a pooled fecal sample were collected from each selected house. The pooled

sample was taken from fecal samples collected in five different places of the house at random.

Then, the samples were placed in sterile Nasco bags and transported on ice in insulated

containers to the Instituto Colombiano Agropecuario Animal (ICA)- Diagnostic Laboratories

(LNDV) laboratories in Bogota within 12 hours of collection. All the samples were processed at

the ICA-LNDV Salmonella Laboratory.

Salmonella culture and serotyping of isolates

Culture for Salmonella followed World Organization of Animal Health recommendations 16. Briefly,

following pre-enrichment on brain heart infusion (BHI) broth at 37 and 42°C for 24 hours, drag

swabs and fecal samples were subcultured to tetrathionate broth for 18-24 hours at 37°C and

Rappaport-Vassiliadis broth at 43°C for 18-24 hours. Salmonella growth was assessed by the

use of MacConkey agar and selective plating media, xylose lysine desoxycholate (XLD), xylose

lactose tergitol 4 (XLT-4) and CHROMagar Salmonella. Three presumptive Salmonella

colonies per plate were screened by biochemical tests such as lysine iron agar, triple sugar iron

agar, citrate agar, Sulfur Indol Motility (SIM) media and urease. Identification was confirmed by

the use of direct slide agglutination with Salmonella O antiserum Poly A & Vi (Difco, Franklin

Lakes, NJ). Three to five suspect colonies from each Salmonella-positive plate were streaked on

nutrient agar and incubated for 18-24 hours at 37°C, with re-confirmation of Salmonella sp. done

on one of these colonies by the automated PhoenixTM system (Carrol et al., 2006). Serovar

17

identification was performed based on the Kauffman-White scheme. Salmonella Paratyphi B was

differentiated by the fermentation of d-tartrate based on lead acetate test following the protocol

described in a prior study 17. Serotyping was done in the ICA-LNDV Salmonella laboratory.

Three colonies of all Salmonella strains were frozen at -70° C in a strain collection for further

characterization. Detailed records of their origin, dates, farm, flock, houses and type of sample

were registered for each isolate.

Antimicrobial susceptibility testing methods

The PhoenixTM automated microbiological system was used to determine the antimicrobial

minimum inhibitory concentration (MIC) for isolates. The PhoenixTM is an automated microbroth

dilution system for Gram-positive and Gram-negative bacteria (Carrol et al., 2006). The selection

of antimicrobials was based on their use in animal and human health and included amoxicillin-

clavulanic acid (AMC), amikacin (AMK), ampicillin (AMP), aztreonam (ATM), cefazolin (CZO),

cefepime (FEP), cefotaxime (CTX), cefoxitin (FOX), ceftazidime (CAZ), ceftriazone (CRO),

ciprofloxacin (CIP), ertapenem (ETP), gentamicin (GEN), imipenem (IPM), levofloxacin (LVX),

meropenem (MEM), nitrofurantoin (NIT), piperacillin/tazobactam (TZP), tetracycline (TCY),

tobramycin (TCY), and trimethoprim-sulfamethoxazole (SXT). Specifications for the concentration

range of each antibiotic in BD PhoenixTM NMIC/ID-121 panel are in Table 2-1. Interpretative

criteria was based on CLSI standards18. Ceftiofur (XNL), enrofloxacin (ENR), streptomycin (STR),

Chloramphenicol (CHL) and nalidixic acid (NAL) were evaluated by use of Kirby-Bauer disk

diffusion and results were interpreted based on criteria stated by the Clinical Laboratory

Standards Institute (CLSI) 19(Table 2-2).

Escherichia coli ATCC 25922 was used as a control organism on each run of the assay.

Data Management and Statistical Analysis

Data entry and error checking was done using Microsoft Access 2007. The statistical package

SPSS 16 was used for the description of antimicrobial patterns and SAS version 9.2 was used for

the univariate and multivariate analysis.

18

Descriptive Analysis: Chi-square or Fisher’s exact test was used for describing categorical

variables and independent t-tests for continuous variables between departments and companies.

Correlation between variables of interest was done using Pearson’s correlation coefficients.

Isolate level description: An isolate was defined as resistant if it was not susceptible to one or

more of the antimicrobials that were tested. Isolates with intermediate results were classified as

susceptible. The frequency and percentage distribution of antibiotic resistance was calculated by

serovar and by department. Antimicrobial resistance pattern for each isolate was determined,

categorized on the basis of its antimicrobial susceptibility status, and was classified by serovar

and farm.

Farm level analysis: A positive farm was defined as one in which Salmonella sp. was isolated

from one or more houses. Univariable logistic regression was used to evaluate the association

between the potential risk/protective factors and the presence of Salmonella sp. The selection of

the potential risk factors was based on the Akaike’s information criteria (AIC). The AIC was also

used to determine the goodness-of-fit of the farm-level multivariable model. For this procedure,

each covariate was added into the null model (model with intercept only) one by one and the

AICs produced by each covariate were noted. The covariates with the smallest AIC were added

into the model individually. The selected model was the one that minimized the AIC. This

process was ended when the addition of a covariate did not reduce the AICs at P ≤ 0.05.

House level analysis: A positive house was any house in which Salmonella sp. was isolated from

one or more samples. Proc Logistic was also used for univariable analysis at the house level. For

the multivariable analysis, a generalized linear mixed model with a random effect for house within

farm was used. Risk factors from the logistic model at farm level were forced in and variables

measured at house level were evaluated for significance.

Odds ratios and 95% confidence intervals (CI) were obtained for risk factors in the final model for

both the farm and house level analysis.

RESULTS

Poultry Farms

19

A total of 917 drag swab and fecal samples from 70 farms (315 houses) samples were tested for

Salmonella.

Of the 70 farms, 28 were located in the department of Cundinamarca and 42 in the department of

Santander. In both departments, poultry population and number of houses were similar, with a

mean population of 76,428 birds per farm in Cundinamarca (4.5 houses per farm) and 64,916 in

Santander (4.2 houses per farm). The mean number of birds per house in both regions was

similar at approximately 12,580 birds.

The mean house size in the two departments was 1,043 m2 (range: 250-3,600) with

Cundinamarca being slightly smaller (956 m2) than that of Santander (1,192) (P≤ 0.05). The bird

density of Cundinamarca was 13 birds/m2 and for Santander 12 birds/m2.

General characteristics of the farms

The majority of farms (98.6%) used all-in all-out production and raised Ross broilers (97.1%).

Chicks are 1 day old when they enter the broiler farm system and are approximately 40-42 days

when they exit. Almost one-third of the farms in Cundinamarca (32.1%) and almost one-half in

Santander (45.3%) were mixed poultry-cattle operations. Almost one-third of the farms in

Cundinamarca recycled the barn litter for future use whereas in Santander this percentage was

14.3%. Only one-third of the farms in both regions had concrete floors. Twenty-nine percent of

the farms in Cundinamarca drained onto land/fields whereas in Santander 77.1% did the same.

The rest of the farms had an open drainage which sometimes led to a large body of water. Water

was supplied by the municipal aqueducts to only 25% of the farms in Cundinamarca and 11.9% in

Santander; while the other farms received their water from wells, artificial lakes and body water.

As a common practice, the producers always added chlorine to disinfect the water for the birds. In

the majority of the farms (94%), the drinking water source for humans and animals was shared.

None of the farms reported using growth promoters in their poultry feed. The use of anti-

mycoplasma agents was reported frequently (65.7%). Overall, the most common antimicrobials

reportedly used for treatments were calcium phosphomycin (44.3%), enrofloxacin (44.3%),

20

ciprofloxacin (31.4%), norfloxacin (12.9%) and trimethoprim-sulfamethoxazole (18.6%). (Table

2-3)

Biosecurity practices

Biosecurity practices were grouped into four categories following an adaptation of a standard

survey for poultry farms 20: isolation, hygiene and apparel, flock health care and monitoring, and

good management practices. Results are presented by region if significant differences existed

between departments.

Isolation: In order to maintain a safe distance between the birds and prevent possible disease

incursion, about one-third of the farms (31.4%) were located at least one mile from another

poultry operation. All farms (100%) had feed delivered by trucks that also transported feed to

other poultry operations; however in 70% of the farms, all vehicles had to pass under a

disinfection arc before entry. In Cundinamarca, 10.7% of the farms lent or borrowed equipment

from other poultry operations while in Santander this percentage was 59.5%. The main entrance

of all the farms in Cundinamarca and 78.6% of the farms in Santander was always closed. The

presence of unauthorized visitors on the premises was forbidden in 70% of the farms. In 89.3% of

the farms in Cundinamarca and 69% of those in Santander, authorized visitors were asked about

prior places they have been before coming into the farms. Restricted access signs existed on

62.9% of the farms.

Rodent pest management was done in almost all the farms (97.1%). Other livestock, dogs and

cats were allowed in 100% of the Cundinamarca farms and 66.7% of Santander farms. No other

birds, including poultry, game birds or waterfowl were allowed in the premises of any of the farms

in both regions.

Hygienic practices: In order to prevent transmission of pathogens, litter was not spread on fields

adjacent to 96.4% of the farms of Cundinamarca and 73.8 % of Santander farms. Cleanout and

disinfection of the entire facility (walls, floor, roof, curtains, divisions) was done in 100% of the

houses after a production cycle in both regions. However, cleaning of fixed equipment such as

21

rafters, sills, lighting fixtures, fan blades, motors, louvers and heaters was reportedly done by only

84.4% of the farms.

Dead birds were disposed of using compost by over 80% of the farms in both regions. Poultry

houses were kept empty of birds for at least 3 weeks in 46.4 % of the facilities of Cundinamarca

and only 4.8% in Santander. Approved insecticide to control Alphitobius diaperinus on top of the

new litter was used in 71.4% of the farms in Cundinamarca and 47.6% in Santander.

A single person managed each house in 85.7% of the farms of Cundinamarca and in 54.8% of

Santander. Cleaning and disinfection of hands between units was done in 39.3% of the farms of

Cundinamarca and in 11.9% of Santander.

Flock healthcare and monitoring: Each farm had a veterinarian who was responsible for

routine blood collections and necropsies. Birds were routinely bled to screen for infectious agents

in 75% of the farms of Cundinamarca and 50% of the farms of Santander. Necropsies of sick or

dead birds were done in 85.7% of the farms in both regions. Antibiotics were used as a

prophylactic measure on the first day the birds arrived at the farms in 85.7% of the farms in

Cundinamarca and in 28.6 % of the farms in Santander.

Good management practices such as cleaning and disinfection of water several times a week

were done in 67.9% of the farms of Cundinamarca and in 97.6 % of the farms of Santander.

Biosecurity company official programs were implemented in 85.7% of the farms.

Distribution of Salmonella

Of the 70 farms visited, Salmonella was isolated from 29 farms (41.4%) with a Salmonella sp.

positive prevalence of 65% in the 315 houses sampled. Among the 29 Salmonella positive farms,

more than half the houses were sampled (55%). The mean number of Salmonella positive

samples within positive farms was 29.3%. S. Paratyphi B was the most prevalent serovar

(76.4%), followed by S. Heidelberg (22.7%) (Table 2-4). In 20 (69%) farms, S. Paratyphi B was

the only serovar isolated while S. Heidelberg was the only serovar in four (13.8%). In the

remaining farms (4/29), both serovars were isolated.

22

Resistance Patterns of Salmonella

The percentage of susceptible, intermediate and resistant isolates for each antimicrobial agent

and the percentage of resistant isolates by department is shown in Table 2-5 and Table 2-6.

Antimicrobials that showed significant differences (p-value < 0.05) in the percentage of resistance

between Cundinamarca and Santander were AMC, AMP, ATM, CZO, FOX, CAZ, TOB, ENR and

NAL.

None of the Salmonella isolates were susceptible to all the antimicrobials used in this study

(Table 2-7) and the number of antimicrobials to which an isolate was resistant ranged from 2 to

15. Most isolates of S. Heidelberg (16/25) were resistant to 2 to 4 antimicrobials agents whereas

most of S. Paratyphi isolates (53/84) were resistant to 9 to 15 antimicrobial agents. In

Cundinamarca, the greatest number of antimicrobials per isolate of S. Paratyphi B ranged from 5

to 8 while in Santander for the same serotype, the range was 9 to 15 (Table 2-8). For S.

Heidelberg, we found no differences in the mean number of antibiotics in the pattern in the 2 to 4

category between regions. However, in the 9-15 category, for the same serovar, a greater

number of antibiotics presented resistance in Cundinamarca (13) as opposed to Santander (9.8).

The mean number of antimicrobials to which resistance was seen was 10.1 for S. Paratyphi B,

5.3 for S. Heidelberg and 7 for the sole S. group 56-61 isolate. The specific type of antimicrobial

resistance patterns grouped according serovar is presented in Table 2-9. The resistance pattern

TCY-XNL-NAL was found in over 40% of the isolates of S. Heidelberg. For S. Paratyphi B, many

patterns (34) were present even though the predominant resistance pattern was CIP-NIT-TCY-

SXT-XNL-STR-ENR-NAL (15%).

Risk factors for Salmonella

At the farm level, the only factor associated with a reduced odds of being Salmonella positive was

the cleaning of fixed equipment by air blowing or wiping down (OR=0.2; 95% CI= 0.06 to 0.8).

Disposal of dead birds by composting was marginally protective (OR=0.3; 95% CI= 0.01 to 1.13).

(Table not shown/See Appendix 2-1)

23

At the house level, with the model including the farm as a random effect, the cleaning of all

equipment and the use of compost for the disposal of dead birds were again protective against a

farm being positive for Salmonella (Table 2-10). House density per square meter was also

associated with a farm being Salmonella positive. Farms that used blowers or wiped down all

their fixed equipment for cleaning had an almost 5% lower risk of being Salmonella positive as

opposed to farms that did not. The use of compost for the disposal of dead birds was protective

against a farm being Salmonella positive, albeit not significantly. Increased bird density (defined

as per 1000 birds/m2) was marginally associated with a farm being Salmonella positive (Table

2-10). We found that a 1-unit increase in the number of birds/m2 was associated with a 20%

increase in the predicted odds of being a Salmonella positive farm.

An additional analysis was done at sample level. In this case, disposal of dead birds by

composting and the blown off or wiped clean of all fixed equipment were also protective factors.

The type of sample (drag swab) was associated with being positive for Salmonella as well as the

number of birds per square meter. (Table not shown/See Appendix 2-2)

DISCUSSION

Our study found the Salmonella prevalence in commercial broiler farms in Colombia of the two

selected regions to be more than 40%. To our knowledge, our study is the first published to

provide data on the prevalence of Salmonella sp. in the poultry industry in Colombia. This

prevalence is comparatively higher than that reported in other parts of the world, for example the

prevalence in laying hen flocks varied from 25% in Mexico 21 to 33% in Brazil 22. In other regions

of the world, the prevalence of Salmonella in broiler farms ranged from 35% in Senegal 23, 37% in

Algeria 24, to as high as 50% in Quebec, Canada 25.

Similar to findings in Venezuela 26, our study found S. Paratyphi B variant Java as the most

common serovar present in broilers which is contrary to findings from most parts of the world

where S. Paratyphi variant Java has a prevalence less than 3% 27-29. However, according to the

Dutch National Salmonella Centre surveillance, in the Netherlands, S. Paratyphi B variant Java

increased from less than 2% of all isolates in 1996 to 60% in 2002, which may be indicative of a

widespread increase in the prevalence in this particular serovar 30,31.

24

This is also the first study done in Colombia to provide comprehensive baseline data regarding

antimicrobial resistance as well as resistance patterns that may be of importance to animal and

public health entities. The information collected by our study is a necessary first step towards the

implementation of a Colombian integrated program for antimicrobial resistance surveillance.

More than 96% of the isolates were resistant to tetracycline, which is greater than in the more

industrialized countries such as USA (33%-48%) 27,29 Denmark (8%) 32 and Canada (3-22%) 33.

In addition, we frequently found resistance to ciprofloxacin (47%), enrofloxacin (74%) and

nalidixic acid (86%). These figures contrasted with the Venezuelan study 26, i.e. resistance to

ciprofloxacin was much lower at 4% in Venezuela as compared to Colombia but higher for

nalidixic acid (96%). Consistent high resistance to the quinolone family of drugs poses a serious

public health problem for Colombia due to its importance in human medicine and broad spectrum

of activity. Resistance to antimicrobials in the cephalosporin class of antibiotics varied widely

(3%-97%). Cephalosporins, (e.g. cefazolin and cefoxitin) that are commonly used to treat human

infections had greater than 45% resistance in our study. Ceftiofur resistance was also extremely

prevalent in Colombia (97%) compared with findings in other countries 34. Thus, with the high

resistance in three of the most important antimicrobial types, our findings could indicate a

problem of overuse of these antimicrobials by Colombian poultry farmers for prophylaxis and

therapy of bacterial infection.

An unusual finding of our study was that all isolates were classified as multidrug resistant, with

none of the 109 isolates susceptible to all of the 26 antimicrobials tested. These findings raise

concern regarding the management practices in poultry production systems of Colombia. Over

90% of Colombia’s poultry meat is produced by 43 companies. Even though we selected 12

companies, we speculate that our findings are reflective of the remaining enterprises because

there was minimal variation between management practices 35. Even in the 12 poultry farms

studied in the two most important departments for poultry production, we did not find any major

differences in their production characteristics and biosecurity practices.

A limitation of our study is that our sampling strategy for farm selection was not random.

25

However, we do not believe that this impacted the findings of our study because according to our

census, the general characteristics of premises namely its size, bird density, number of houses,

strains and management practices were similar throughout the 5 departments which are most

responsible for poultry production. Also, this study has the limitation of possible reporting bias

regarding the use of antibiotics in broilers. Veterinarians in charge of the farms do not know the

type and levels of antibiotics used in feed, the type of antibiotics used in the first day of the chicks

before leaving the hatchery to the farm. Also, they are not authorized by the companies to give

this information. A possible way to minimize it in future studies is getting this information by

supervising the process in the hatchery with the representatives of the official sector (ICA).

Second, official authorities could also supervise the whole process in feed mills. Finally, proxies

such as the records of the importation of generic antibiotics by the companies could be used.

Of all the biosecurity practices, the two factors we found most significantly associated with the

presence of Salmonella on a farm were related to hygiene. The most significant practice in our

study was related to the cleaning of fixed equipment such as rafters, sills, lighting fixtures, fan

blades, motors, etc, i.e. cleaning of equipment that is usually not perceived as being directly

involved with the production of poultry or requiring careful cleaning because its nature (e.g. fixed

equipment with electrical components that require special care not to be exposed to liquid). As

early as 1980, Salmonella was isolated from dust collected from the chick’s environment in two

broiler houses in the UK 36. In a review chapter by Murray, the author cites a study in Denmark

which also isolated Salmonella from dust, insulation materials and wood in broiler units 37.

Another factor associated with a farm being Salmonella negative was composting of dead birds

within the farm. This practice is new in Colombia and is being adopted gradually by poultry

operation managers. At the time of our study, this practice was not officially mandated by the

National Institute of Agriculture (ICA) and the general practice on some farms was to dispose of

dead poultry by either burying them on their own facility or elsewhere. However, the burial

process was insufficient to prevent other animals from easily digging the carcasses out and thus

facilitating the spread of Salmonella and other pathogens.

26

In conclusion, we consider that our findings can contribute to providing scientific evidence for

implementation of new official policies that support the new biosecurity legislation to decrease the

prevalence of Salmonella in poultry farms. The resolution 001183 issued by the Colombian

Agricultural Institute on March 25, 2010, established the conditions of biosecurity that all the

poultry farms in Colombia should meet in order to be certified by the government.

27

Table 2-1. Minimum Inhibitory concentration (MIC) range and interpretations for antimicrobials used in the panel

Panel Contents MIC Interpretive Standard (ug/mL)

Antimicrobial Abbreviation

Conc. Range

(ug/mL) S I R

Amikacin AMK 8-32 ≤16 32 ≥64

Amoxicillin/Clavulanate AMC 4/2-16/8 ≤8/4 16/8 ≥32/16

Ampicillin AM 4-16 ≤8 16 ≥32

Aztreonam ATM 2-16 ≤8 16 ≥32

Cefazolin CAZ 2-16 ≤8 16 ≥32

Cefepime FEP 1-16 ≤8 16 ≥32

Cefotaxime CTX 4-32 ≤8 16-32 ≥64

Cefoxitin FOX 4-16 ≤8 16 ≥32

Ceftazidime CAZ 0.5-2 ≤8 16 ≥32

Ceftriaxone CRO 2-32 ≤8 16-32 ≥64

Ciprofloxacin CIP 0.5-2 ≤1 2 ≥4

Ertapenem ETP 0.5-4 ≤2 4 ≥8

Gentamicin GM 2-8 ≤4 8 ≥16

Imipenem IPM 1-8 ≤4 8 ≥16

Levofloxacin LVX 1-4 ≤2 4 ≥8

Meropenem MEM 1-8 ≤4 8 ≥16

Nitrofurantoin FM 16-64 ≤32 64 ≥128

Piperacilin/Tazobactam TZP 2/4-64/4 ≤16/4 32/4-64/4 ≥128/4

Tetracycline TE 2-8 ≤4 8 ≥16

Tobramycin NN 2-8 ≤4 8 ≥16

Trimethoprim/ Sulfamethoxazole SXT 0.5/9.5-2/38 ≤2/38 - ≥4/76

28

Table 2-2.Minimum inhibitory concentrations (MIC) and zone diameter for the interpretation of antimicrobials not included in the panel.

Zone Diameter Interpretive Standards (mm)

Antimicrobial

Code

Control Zone Diameter Limits

(mm) S I R

Ceftiofur XNL 24-30 ≥23 - - Enrofloxacin ENO 32-40 ≥23 17-22 ≤16 Tilmicosin TIL 7-21 ≥14 11-13 ≤10 Streptomycin STR 12-20 ≥15 12-14 ≤11 Chloramphenicol CHL - ≥18 13-17 ≤12 Nalidixic Acid NA - ≥19 14-18 ≤13

29

Table 2-3. Reported use of antimicrobials in the farms from two poultry production departments of Colombia Antimicrobial

Cundinamarca (%) (Number of farms)

Santander (%) (Number of farms)

Anti-mycoplasma agents Tylosin tartrate 10.7 (3) 35.7 (15) Tilmicosin phosphate 46.4 (13) 11.9 (5) Tiamulin hydrogen fumarate 14.3 (4) 2.4 (1) Lincomycin-Spectinomycin 14.3 (4) 0 (0) Tiamulin and chlortetracycline 0(0) 2.4 (1) Therapeutic AMR agents Calcium phosphomycin 42.9 (12) 45.9 (19) Florfenicol 0 (0) 11.9 (5) Amoxicillin 0 (0) 2.4 (1) Sulphadiazine 3.6 (1) 0 (0) Sulfamethazine 3.6 (1) Trimethoprim-sulfamethoxazole 7.1 (2) 26.2 (11) Enrofloxacin 64.3 (18) 31 (13) Ciprofloxacin 25 (7) 35.7 (15) Norfloxacin 3.6 (1) 19 (8)

30

Table 2-4. Distribution of Salmonella serovars by department

Serovars

Department Heidelberg Paratyphi B S. Group 56-61 Total

Cundinamarca 8 (18.2%) 35 (79.5%) 1 (2.3%) 44 (100.0%)

Santander 17 (25.8%) 49 (74.2%) 0 (0.0%) 66 (100.0%)

Total 25 (22.7%) 84 (76.4%) 1 (0.9%) 110 (100.0%)

31

Table 2-5. Percentage of antimicrobial sensitive and resistant Salmonella sp. isolates

Salmonella sp.

Sensitive +Intermediate Resistant

Antimicrobial Abbreviation (%) (%)

Amikacin AMK 100 0

Amoxicillin-Clavulanate AMC 52.8 47.2

Ampicillin AMP 50 50

Aztreonam AZT 91.8 8.2

Cefazolin CZO 50.5 49.5

Cefepime FEP 100 0

Cefotaxime CTX 100 0

Cefoxitin FOX 52.3 47.7

Ceftazidime CAZ 50.9 49.1

Ceftiofur XNL 2.7 97.3

Ceftriaxone CRO 97.3 2.7

Chloramphenicol CHL 86.4 13.6

Ciprofloxacin CIP 52.7 47.3

Enrofloxacin ENR 26.4 73.6

Ertapenem ETP 100 0

Gentamicin GEN 95.5 4.5

Imipenem IPM 100 0

Levofloxacin LVX 99.1 0.9

Meropenem MEM 100 0

Nalidixic acid NAL 13.6 86.4

Nitrofurantoin NIT 26.4 73.6

Piperacillin/Tazobactam TZP 100 0

Streptomycin STR 24.5 75.5

Tetracycline TCY 4.5. 95.5

Tobramycin TOB 95.5 4.5

Trimethoprim/Sulfamethoxazole STX 30 70

32

Table 2-6. Comparison of resistance (%) among Salmonella isolates from the two departments

Salmonella sp.

Cundinamarca Resistant

Santander Resistant


(%) (%)

Amikacina AMK 0 0

Amoxicillin-Clavulanatea AMC 18.2 66.7

Ampicillina AMP 18.2 71.7

Aztreonama AZT 0 13.6

Cefazolina CZO 18.6 69.7

Cefepime FEP 0 0

Cefotaxime CTX 0 0

Cefoxitina FOX 18.6 66.7

Ceftazidimea CAZ 18.2 69.7

Ceftiofur XNL 100 95.5

Ceftriaxone CRO 0 4.5

Chloramphenicol CHL 13.6 13.6

Ciprofloxacin CIP 56.8 40.9

Enrofloxacina ENR 84.1 66.7

Ertapenem ETP 0 0

Gentamicin GEN 9.1 1.5

Imipinem IPM 0 0

Levofloxacin LVX 2.3 0

Meropenem MEM 0 0

Nalidixic acida NAL 95.2 80.3

Nitrofurantoin NIT 81.8 71.2

Piperacillin/Tazobactam TZP 0 0

Streptomycin STR 81.8 71.2

Tetracycline TCY 97.7 93.9

Tobramycina TOB 11.4 0

Trimethoprim/Sulfamethozole STX 68.2 71.2 a Significant differences (p<= 0.05)

33

Table 2-7. Number of antimicrobials per resistance pattern classified by serovar

No. of antimicrobials in resistant

pattern per serovar (%)

Serovar 1-4 5-8 9-15

Heidelberg 16 (94.1) 3 (8.8) 6 (10.2)

Paratyphi B 1 (5.9) 30 (88.2) 53 (89.8)

S. group 56-61 0 (0) 1 (2.9) 0 (0)

Total 17 (100) 34 (100) 59 (100)

34

Table 2-8. Number of resistant isolates in patterns by serovars and by department

No of resistant isolates (%)

Department and serovar 1-4 5-8 9-15 Total

Heidelberg 7 (87.5) 0 (.0) 1 (12.5) 8 (100)

Paratyphi B 1 (2.9) 20 (57.1) 14 (40.0) 35 (100)

Total 8 (18.2) 20 (5.7) 15 (34.1) 44 (100)

Cundinamarca

Heidelberg 9 (52.9) 3 (17.6) 5 (29.4) 17 (100)

Paratyphi B 0 (0) 10 (20.4) 39 (79.6) 49 (100) Santander

Total 9 (13.6) 13 (19.7) 44 (66.7) 66 (100)

35

Table 2-9. AMR pattern distribution by serovar

Serotype 1 Pattern Frequency Percent

TCY- NAL 2 8.0 TCY- XNL -NAL 10 40.0 TCY-XNL-ENR-NAL 4 16.0 TCY-XNL-STR-ENR-NAL 1 4.0 AMP-ATM-CZO-CAZ-TCY- XNL- NAL 1 4.0 AMP-ATM-CZO- CAZ-CRO-TCY-XNL-NAL 1 4.0 AMC-AMP-CZO-FOX-CAZ-TCY-XNL-ENR-NAL 1 4.0 AMC-AMP-CZO-FOX-CAZ- CIP- TCY-XNL-ENR- NAL 3 12.0 AMC-AMP-CZO-FOX-CAZ-CIP-TCY-XNL-STR-NAL 1 4.0

Heidelberg

AMC-AMP- CZO-FOX-CAZ-CIP-NIT-TCY-SXT-XNL-STR-ENR-NAL 1 4.0 Total 25 100.0

TCY-XNL-ENR-NAL 1 1.2 NIT-TCY-SXT-STR-ENR-NAL 1 1.2 NIT-TCY-SXT-XNL-STR-NAL 1 1.2 NIT-TCY- SXT-XNL-STR-ENR-NAL 4 4.8 NIT-TCY-TOB- XNL-STR-ENR-NAL 1 1.2 GEN-TCY-SXT-XNL-STR-NAL 1 1.2 GEN-NIT-TCY-TOB-SXT-XNL-STR-ENR-NAL 3 3.6 GEN-NIT-TCY-TOB-SXT-XNL-STR-ENR-CHL-NAL 1 1.2 CIP-NIT- SXT-XNL-STR-ENR- NAL 1 1.2 CIP-NIT-TCY-XNL-STR-ENR- NAL 2 2.4 CIP-NIT-TCY-XNL-STR-ENR-CHL-NAL 1 1.2 CIP-NIT-TCY- SXT-XNL-STR-ENR- NAL 16 19.0 CIP-NIT-TCY- SXT-XNL-STR-ENR-CHL-NAL 3 3.6 CIP-LVX-NIT-TCY- XNL-STR-ENR-NAL 1 1.2 AMP-NIT-TCY-SXT-XNL-STR-ENR-NAL 1 1.2 AMC-AMP-CZO-FOX-CAZ-NIT-SXT-XNL-STR- 2 2.4 AMC-AMP-CZO-FOX-CAZ-NIT-SXT-XNL-STR-ENR-CHL-NAL 1 1.2 AMC-AMP-CZO-FOX-CAZ-NIT-TCY-XNL-STR-NAL 1 1.2 AMC-AMP-CZO-FOX-CAZ- NIT-TCY-SXT-XNL 1 1.2 AMC-AMP-CZO-FOX-CAZ- NIT-TCY-SXT-XNL-ENR-CHL 1 1.2 AMC-AMP-CZO-FOX-CAZ-NIT-TCY-SXT-XNL-STR 6 7.1 AMC-AMP-CZO-FOX-CAZ-NIT-TCY-SXT-XNL-STR-ENR-NAL 6 7.1 AMC-AMP-CZO-FOX-CAZ-NIT-TCY-SXT-XNL-STR-ENR-CHL 2 2.4 AMC-AMP-CZO-FOX-CAZ-CIP-TCY-XNL-ENR-NAL 1 1.2 AMC-AMP-CZO-FOX-CAZ-CIP-NIT-SXT-XNL-STR-ENR-NAL 1 1.2 AMC-AMP-CZO-FOX-CAZ-CIP-NIT-TCY-SXT-XNL-STR 1 1.2 AMC-AMP-CZO-FOX-CAZ-CIP-NIT-TCY-SXT-XNL-STR-ENR-NAL 13 15.5 AMC-AMP-CZO-FOX-CAZ-CIP-NIT-TCY-SXT-XNL-STR-ENR-CHL 1 1.2 AMC-AMP-CZO-FOX-CAZ-CRO-TCY-XNL-ENR-NAL 1 1.2 AMC-AMP-CZO-FOX-CAZ-CRO-CIP-NIT-TCY-SXT-XNL-STR-ENR-NAL 1 1.2

AMC-AMP-ATM-CZO-FOX-CAZ-TCY-SXT-XNL-STR-CHL 1 1.2 AMC-AMP-ATM-CZO-FOX-CAZ-NIT-TCY-SXT-XNL-STR-ENR-NAL 1 1.2 AMC-AMP-ATM-CZO- FOX-CAZ-CIP-NIT-TCY-SXT-XNL-STR-ENR-NAL 1 1.2

Paratyphi B

AMC-AMP-ATM-CZO-FOX-CAZ-CIP-NIT-TCY-SXT-XNL-STR-ENR-CHL-NAL 4 4.8

Total 84 100.0

S. group 56-61

NIT-TCY-SXT-XNL-STR-ENR-NAL 1 100.0

36

Table 2-10: Multiple logistic regression model of factors associated with the prevalence of Salmonella at the house level (n=296) and including farm as a random effect.

Variable

Positive Salmonella

n (%)

Negative Salmonella

n (%)

Odds ratio

95% confidence

interval

Use of compost for the disposal of dead birds Yes No

48 (19.8) 12 (22.6)

195 (80.2) 41 (77.4)

0.44 1

0.1, 1.5

Cleaning of fixed equipment (blower/wiping down) Yes No

33 (13.2) 27 (58.7)

217 (86.8) 19 (41.3)

0.05 1

0.02, 0.2

Bird density per square meter

1.2 1.0, 1.43

Hosmer and Lemeshow Goodness-of-Fit test probability= 0.93

37

Appendix 2-1 Multiple logistic regression model of factors associated with the prevalence of Salmonella at the farm level (n=70)

Variable

Positive Salmonella

n (%)

Negative Salmonella

n (%) Odds ratio

95% confidence

interval

Disposal of dead birds by composting Yes No

22 (37.9) 7 (58.3)

36 (62.1) 5 (41.7)

0.3 1

0.01, 1.3

Cleaning of fixed equipment (blower/wiping down) Yes No

22 (37.3) 4 (36.4)

37 (62.7) 7 (63.6)

0.2 1

0.06, 0.8

38

Appendix 2-2 Multiple logistic regression model of factors associated with the prevalence of Salmonella at sample level (n=886) and adjusting for farm as a random effect.

Variable

Positive Salmonella

n (%)

Negative Salmonella

n (%)

Prevalence odds ratio

95% confidence

interval

Disposal of dead birds by composting Yes No

88 (12.1) 22 (14.0)

641 (87.9) 135 (86)

0.12 1

0.04, 0.37

Blown off or wiped clean of all equipment Yes No

65 (8.7) 45 (33.1)

685 (91.3) 91 (66.9)

0.45 1

0.14, 1.38

Bird density per square meter

1.14

0.97, 1.34

Sample type Drag swab Feces

93 (16.0) 17 (5.6)

487 (84.0) 289 (94.4)

5.95

1

2.98, 11.9

39

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4. Angulo FJ, Nargund VN, Chiller TC. Evidence of an association between use of anti-microbial agents in food animals and anti-microbial resistance among bacteria isolated from humans and the human health consequences of such resistance. J Vet Med B Infect Dis Vet Public Health 2004;51(8-9):374-9.

5. Aarestrup FM, Pires SM. Comment on: Causal regulations vs. political will: why human zoonotic infections increase despite precautionary bans on animal antibiotics. Environ Int 2009;35(4):760-1.

6. Dibner JJ, Richards JD. Antibiotic growth promoters in agriculture: history and mode of action. Poult Sci 2005;84(4):634-43.

7. Acar JF, Moulin G. Antimicrobial resistance at farm level. Rev Sci Tech 2006;25(2):775-92.

8. Sarkar P, Gould IM. Antimicrobial agents are societal drugs: how should this influence prescribing? Drugs 2006;66(7):893-901.

9. Prescott JF. Antimicrobial use in food and companion animals. Anim Health Res Rev 2008;9(2):127-33.

10. Mathew AG, Cissell R, Liamthong S. Antibiotic resistance in bacteria associated with food animals: a United States perspective of livestock production. Foodborne Pathog Dis 2007;4(2):115-33.

11. Hammerum AM, Heuer OE, Emborg HD, Bagger-Skjot L, Jensen VF, Rogues AM, Skov RL, Agerso Y, Brandt CT, Seyfarth AM, Muller A, Hovgaard K, Ajufo J, Bager F, Aarestrup FM, Frimodt-Moller N, Wegener HC, Monnet DL. Danish integrated antimicrobial resistance monitoring and research program. Emerg Infect Dis 2007;13(11):1632-9.

12. Hammerum AM, Heuer OE, Lester CH, Agerso Y, Seyfarth AM, Emborg HD, Frimodt-Moller N, Monnet DL. Comment on: withdrawal of growth-promoting antibiotics in Europe and its effects in relation to human health. Int J Antimicrob Agents 2007;30(5):466-8.

13. Sharma R, Sharma CL, Kapoor B. Antibacterial resistance: current problems and possible solutions. Indian J Med Sci 2005;59(3):120-9.

14. Weese JS. Prudent use of antimicrobials. In: Giguère S, ed. Antimicrobial therapy in veterinary medicine. 4th ed. Ames, Iowa: Blackwell Pub., 2006;xvii, 626 p.

15. Odensvik K, Grave K, Greko C. Antibacterial drugs prescribed for dogs and cats in Sweden and Norway 1990-1998. Acta Vet Scand 2001;42(1):189-98.

16. OIE. . Terrestrial Animal Health Code. World Organization for Animal Health, 2009.

17. Malorny B, Bunge C, Helmuth R. Discrimination of d-tartrate-fermenting and -nonfermenting Salmonella enterica subsp. enterica isolates by genotypic and phenotypic methods. J Clin Microbiol 2003;41(9):4292-7.

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18. Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing. 19th. Informational Supplement M100-S19. Wayne, PA 19087-1898: CLSI/NCCLS, 2007.

19. Clinical and Laboratory Standars Institute. Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated from animals. Approved Standards. M31-A3. 3th ed. PA: Wayne, 2008.

20. Pierson FW. Biosecurity: Principles and Practices in the Commercial Poultry Industry.Available at: nsgd.gso.uri.edu/vsgcp/vsgcpc98001/vsgcpc98001_part1.pdf Accessed on April 2010.

21. Salles RPR, Teixeira RSdC, Moura J, Siqueira AAd, Silva EEd, Moraes TGVd, Nogueira GC, Andrade JDMd, Castro SBd, Cardoso WM. Salmonella sp. bacteriology monitoring in laying hens at different growing and laying periods from poultry farms in Metropolitan Region of Fortaleza, CE, Brazil. Ciencia Animal Brasileira 2008;9(2):427-432.

22. Gama NMSQ, Berchieri Junior A, Fernandes SA. Occurrence of Salmonella sp. in laying hens. Revista Brasileira de Ciencia Avicola 2003;5(1):15-21.

23. Dione MM, Ieven M, Garin B, Marcotty T, Geerts S. Prevalence and antimicrobial resistance of Salmonella isolated from broiler farms, chicken carcasses, and street-vended restaurants in Casamance, Senegal. J Food Prot 2009;72(11):2423-7.

24. Elgroud R, Zerdoumi F, Benazzouz M, Bouzitouna-Bentchouala C, Granier SA, Fremy S, Brisabois A, Dufour B, Millemann Y. Characteristics of Salmonella contamination of broilers and slaughterhouses in the region of Constantine (Algeria). Zoonoses and Public Health 2009;56(2):84-93.

25. Arsenault J, Letellier A, Quessy S, Normand V, Boulianne M. Prevalence and risk factors for Salmonella sp. and Campylobacter sp. caecal colonization in broiler chicken and turkey flocks slaughtered in Quebec, Canada. Prev Vet Med 2007;81(4):250-64.

26. Boscan-Duque LA, Arzalluz-Fisher AM, Ugarte C, Sanchez D, Wittum TE, Hoet AE. Reduced susceptibility to quinolones among Salmonella serotypes isolated from poultry at slaughter in Venezuela. J Food Prot 2007;70(9):2030-5.

27. Oloya J, Doetkott D, Khaitsa ML. Antimicrobial drug resistance and molecular characterization of Salmonella isolated from domestic animals, humans, and meat products. Foodborne Pathog Dis 2009;6(3):273-84.

28. Zewdu E, Cornelius P. Antimicrobial resistance pattern of Salmonella serotypes isolated from food items and personnel in Addis Ababa, Ethiopia. Trop Anim Health Prod 2009;41(2):241-9.

29. Lestari SI, Han F, Wang F, Ge B. Prevalence and antimicrobial resistance of Salmonella serovars in conventional and organic chickens from Louisiana retail stores. J Food Prot 2009;72(6):1165-72.

30. van Pelt W, van der Zee H, Wannet WJ, van de Giessen AW, Mevius DJ, Bolder NM, Komijn RE, van Duynhoven YT. Explosive increase of Salmonella Java in poultry in the Netherlands: consequences for public health. Euro Surveill 2003;8(2):31-5.

31. Denny J, Threlfall J, Takkinen J, Lofdahl S, Westrell T, Varela C, Adak B, Boxall N, Ethelberg S, Torpdahl M, Straetemans M, van Pelt W. Multinational Salmonella Paratyphi B variant Java (Salmonella Java) outbreak, August - December 2007. Euro Surveill 2007;12(12):E071220 2.

32. DANMAP 2005. Use of antimicrobial agens and occurrence of antimicrobial resistance in bacteria from food animals, foods and humans in Denmark. Denmark: Danish Institute for Food and Veterinary Research, 2005.

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33. Government of Canada. Canadian Integrated Progam for Antimicrobial Resistance Surveillance (CIPARS) 2004. Guelph, ON: Public Health Agency of Canada, 2006.

34. Dutil L, Irwin R, Finley R, Ng LK, Avery B, Boerlin P, Bourgault AM, Cole L, Daignault D, Desruisseau A, Demczuk W, Hoang L, Horsman GB, Ismail J, Jamieson F, Maki A, Pacagnella A, Pillai DR. Ceftiofur resistance in Salmonella enterica serovar Heidelberg from chicken meat and humans, Canada. Emerg Infect Dis;16(1):48-54.

35. I Censo Nacional de Avicultura Industrial. Bogota: Ministero de Agricultura Y Desarrollo Rural, Departamento Adminstrativo Nacional De Estacdistica-Dane, Federacion Nacional de Avicultores de Colombia-Fenavi, 2002.

36. Morgan-Jones SC. The occurrence of Salmonellae during the rearing of broiler birds. Br Poult Sci 1980;21(6):463-70.

37. Murray CJ. Environmental Aspects of Salmonella. In: Wray C, Wray A, eds. Salmonella in domestic animals. Wallingford, Oxon, UK ; New York, NY, USA: CABI Pub., 2000;x, 463 p.

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Chapter 3 Prevalence, Resistance Patterns and Risk Factors for Antimicrobial Resistance (AMR) bacteria in Retail Chicken Meat in Colombia

ABSTRACT

Antimicrobial resistance can be transferred from zoonotic bacteria in food animals and from food

of animal origin to human populations, narrowing the potential uses of antimicrobials for the

treatment of severe infections. As a step towards implementing the Colombian Integrated

Program for Antimicrobial Resistance Surveillance (COIPARS), this study aimed to establish the

baseline antimicrobial resistance patterns of Salmonella sp., generic Escherichia coli and

Enterococcus sp. isolates in retail poultry meat from independent stores and from a main chain

distributor center. Several antimicrobials used in humans and animals were tested using an

automated system. Salmonella sp. was isolated from 26% of the samples while E. coli and

Enterococcus were detected in 82.5% and 93.5% of the samples, respectively. The principal

finding of concern in this study was that almost 98% of isolates tested were multidrug resistant

(MDR) and only one isolate of Salmonella was susceptible to all of the antimicrobials tested (2%).

Ceftiofur, enrofloxacin, nalidixic acid and tetracycline were the antimicrobials that showed the

highest frequency of resistance among Salmonella and E. coli isolates. For enterococci, we found

that 62% E. faecium isolates were resistant to quinupristin/dalfopristin, which is significant

because it is used to treat nosocomial infections when vancomycin resistance is present.

Vancomycin resistance was detected in 3% of the E. faecalis isolates. The results of our study

highlight the need for the rapid implementation of an integrated program for surveillance of

antimicrobial resistance by the Colombian authorities in order to monitor trends, raise

awarenesss, and help promote practices to safeguard higher generation antimicrobial agents.

43

INTRODUCTION

In recent years, antimicrobial resistance (AMR) has emerged as a global public health problem by

threatening to narrow the potential uses of antibiotics for the treatment of infectious diseases(17).

The implications of AMR raise concerns not only among public health authorities but also among

animal and food safety authorities(17, 32, 48, 49) because AMR bacteria can be transmitted from

food animals to humans through the food chain(45).

Modern food animal production systems commonly use antimicrobial agents to prevent, control

and treat bacterial infections(45) and some of these agents are also used as growth promoters in

poultry and swine production systems (4, 43). Although widespread use of antimicrobials in the

primary sector has benefits for producers, it also contributes to the increasing emergence of AMR

bacteria (4).

Meat products are sources of human infection for known food-borne AMR pathogens such as

Salmonella sp. and Campylobacter (25, 29, 34, 36). Salmonella sp. can carry resistance genes

that can be transmitted to humans(9). Similarly, because commensal bacteria, such as

Escherichia coli and enterococci, can transfer resistance genes to pathogens (38, 50), it makes

them a more serious global threat to human health than antibiotic selection pressure (10) .

Furthermore, the frequency and extent of resistance that occur in commensal bacteria is linked to

the amount and class of antimicrobial agents used to produce a kilogram of meat (34), which

varies from country to country (2).

Animal protein consumption is rapidly increasing in Colombia, similar to the trend in other

developing countries. It is estimated that a person on minimum wage in Colombia purchases

almost three times more chicken meat and twice as much swine meat annually than bovine meat.

In such dynamic conditions, the food safety considerations, particularly in the poultry chain, are

an increasing challenge.

Because Colombia is free of avian influenza, the poultry industry has attempted to reach

international food safety and animal health standards in order to take advantage of access to

international markets. However, there are still limiting factors including inadequate knowledge of

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the baseline prevalence of food-borne pathogens like Salmonella and their antimicrobial

resistance profiles in retail meat of different origins that prevent Colombia from fully benefiting

from international commerce. There is also limited data about the antimicrobial resistance of

commensal bacteria such as Escherichia coli and enterococci in retail chicken meat in Colombia.

To properly assess the risk linked to food-borne pathogens and to the development of AMR,

several countries including Canada (11), USA (26), and Denmark (8) have initiated integrated and

unified programs for the surveillance of antimicrobial resistance. These programs integrate data

along the meat chain from farm production, abattoirs, and retail sectors with data from human

populations. Based on the surveillance data, a risk assessment is performed and policies are

developed. These programs have been shown to be effective at slowing the development of

AMR(18, 28).

As part of a pilot initiative to set-up such integrated systems in Colombia, namely COIPARS

(Colombian integrated Program for Antimicrobial Resistance Surveillance), we conducted a

survey with the objective of determining the prevalence, resistance patterns and risk factors for

AMR Salmonella sp., generic Escherichia coli and Enterococcus sp. in retail poultry meat.

METHODS

Study site and sample size. Samples were collected in Bogota between March and October

2009 from two different retail facilities. The sample size (n = 200) was determined by financial

constraints. One hundred samples were collected from independent retail stores and 100 were

taken from the distribution center of the main retail chain market group of the country. The

selection of independent retail stores was done based on the political division of Bogota

(localities).

Sample collection and transporting. For the independent stores, samples were placed in

insulated containers with cold gel packs following the methodology of the Canadian Integrated

Program for Antimicrobial Resistance Surveillance (CIPARS) (13) and then transported in less

than four hours to the laboratory for processing. In the main distribution center, samples were

selected at arrival and transported immediately to the laboratory for processing.

45

Data collection. Date and time of sample collection, ambient temperature, company, store type,

store name, location, type of sample, origin, if coming from organic or antibiotic free farm, sell-by

date and the price per kilogram were recorded, as well as the date and sample temperature on

arrival at the laboratory for processing. The socio-economic status of each locality where the

sample was collected was also noted in order to allow us to represent the AMR situation through

the political and economic divisions of the city. Bogota is stratified based on the location of the

owner's residence and income into 6 socio-economic strata(44): 1 (lowest), 2 (low), 3 (mid-low),

4 (mid-high), 5 (high) and 6 (highest).

Sampling procedure. Sampling of chicken meat was done following the CIPARS protocols

(2007) (13) which consists of one pack of thighs with skin on styrofoam trays per sample. This

sample type was chosen because it is the most popular chicken product in supermarkets.

Microbiological isolation and identification. Isolation and identification were done at the

Laboratory of Microbiological and Food Ecology, (LEMA), Universidad de los Andes, Bogota and

at the Laboratory for Quality Control of the main chain market. Serotyping of Salmonella was

done at the Microbiological Laboratory of National Institute for Drug and Food Surveillance

(INVIMA) which is the national reference laboratory for bacteria isolated from food.

Briefly, the microbiological isolation was done following the protocols of the CIPARS, 2007:

Isolation methods for Salmonella sp., generic E.coli, and Enterococcus sp. from meat and poultry

samples (39-41) (See Appendices 1-4). The confirmation of the bacterial genus of the isolates

was done using the automated antimicrobial susceptibility system (PhoenixTM) for AMR testing.

The serovar identification was done based on the Kauffman-White scheme for classification of

somatic O and flagellar H antigen type. All the isolates were frozen at -70°C for further study.

Antimicrobial susceptibility testing methods. The PhoenixTM automated microbiological

system was used to determine the antimicrobial minimum inhibitory concentration (MIC) for

isolates. The PhoenixTM is an automated microbroth dilution system for Gram-positive and

Gram-negative bacteria (12). The selection of antimicrobials was based on their use in animal

and human health. For Gram-negative bacteria, the panel included amoxicillin-clavulanic acid

46

(AMC), amikacin (AMK), ampicillin (AMP), aztreonam (ATM), cefazolin (CZO), cefepime (FEP),

cefotaxime (CTX), cefoxitin (FOX), ceftazidime (CAZ), ceftriazone (CRO), ciprofloxacin (CIP),

ertapenem (ETP), gentamicin (GEN), imipenem (IPM), levofloxacin (LVX), meropenem (MEM),

nitrofurantoin (NIT), piperacillin/tazobactam (TZP), tetracycline (TCY), tobramycin (TCY), and

trimethoprim-sulfamethoxazole (SXT). Interpretative criteria were based on Clinical and

Laboratory Standards Institute (CLSI) standards (15). Specifications for the concentration range

of each antibiotic in BD PhoenixTM NMIC/ID-121 panel are in Appendix 5.

For Enterococcus sp., the panel included amoxicillin/clavulanic acid (AMC), ampicillin (AMP),

cefazolin (CZO), cefoxitin (FOX), chloramphenicol (CHL), ciprofloxacin (CIP), clindamycin (CLI),

erythromycin (ERY), fosfomycin (FOS), fusidic acid (FUS), gentamicin (GEN), gentamicin high

(GEH), levofloxacin (LVX), linezolid (LNZ), mupirocin (MUP), nitrofurantoin (NIT), oxacillin (OXA),

penicillin G (PEN), quinupristin/dalfopristin (QDA), rifampin (RA), streptomycin high (STH),

teicoplanin (TEC), trimethoprim-sulfamethoxazole (SXT) and vancomycin (VAN). Interpretative

criteria were based on CLSI standards (15). Specifications for the concentration range of each

antibiotic in BD Phoenix TM NMIC/ID-53 panel are in Appendix 5.

Ceftiofur (XNL), enrofloxacin (ENR), streptomycin (STR), chloramphenicol (CHL), nalidixic acid

(NAL) and tilmicosin (TIL) were evaluated by use of Kirby-Bauer disk diffusion and results were

interpreted based on criteria stated by the CLSI (16) shown in Appendix 6.

Categorization of antimicrobials. Following the methodology of CIPARS(14), the categorization

of antimicrobials was done based on the critical importance to human health. Antimicrobials were

classified as category I (very high importance in human medicine), category II (high importance),

category III (medium importance) and category IV (low importance). Antibiotics in Category IV

were not included in this study.

Resistance Markers. Extended- spectrum-β-lactamase (ESBL) was tested for E. coli using the

Phoenix ESBL test (47). For the enterococci, resistant markers included high-level gentamicin

resistance (HLGR), high-level streptomycin resistance (HLSR) and vancomycin resistance (VRE)

47

in accordance with the Becton Dickinson description of the detection of resistance markers

protocol (1)

Control Organisms

Escherichia coli ATCC 25922 and Enterococcus faecalis ATCC 29212 were used as control

organisms weekly for the Phoenix and for each run of the antibiogram.

Statistical Analysis

Data management and statistical analysis. Data entry and error checking were done using

Microsoft Access 2007(30). The statistical package SPSS 16 (7) was used for comparing

antimicrobial patterns and for univariable and multivariable analysis.

Descriptive analysis: Chi-square or Fisher’s exact test were used for comparing categorical

variables and independent t-tests for continuous variables between store types and socio-

economic category. Correlation between variables of interest was done using Pearson’s

correlation coefficients. A difference was considered statistically significant if the P < 0.05.

Isolate level description: An isolate was defined as resistant if it was not susceptible to one or

more of the antimicrobials that were tested. Isolates with intermediate results were classified as

susceptible. The frequency and percentage distribution of antibiotic resistance was calculated by

bacterium and by type of store, and in the case of Salmonella sp. also for serovar. The

antimicrobial resistance pattern for each isolate was determined, categorized on the basis of its

antimicrobial susceptibility status, and was classified by serovar and store type.

RESULTS

Samples and retail stores. The samples from independent stores came from 85% (17/20) of the

localities and covered five of Bogota’s six socio-economic strata (SES). Among the independent

stores, 30% of the samples were from butchers, 10% were from supermarkets and the remaining

60% were from a combination of stores including company stores, wet markets (open food

market) and small neighborhood stores.

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Mean sample temperature at stores was 4 ºC (range, 0 to 12ºC) and at arrival in the laboratory

was 7ºC (0 to 14ºC). Mean sample weight was 530 g (range, 210 to 1100 g). The mean price per

kilogram of meat ranged from 2.3 to 5.7 US dollars.

None of the samples were air-chilled. After slaughtered, chicken were immersed in iced,

chlorinated water. All samples from the main chain distribution center were processed following

HACCP methodology in the center. Only 8 (3.9%) samples were classified as antibiotic-free

samples that came from organic production systems that did not use antimicrobials as growth

promoters in the feed or as disease treatments.

Prevalence of Salmonella sp. The prevalence of Salmonella was 25.5% (51/200). There were no

statistically significant difference in the prevalence in independent samples (23%) and chain

distribution center (28%). S. Paratyphi B was the most prevalent serovar (51%) followed by S.

Heidelberg (15.7%), S. Enteritidis (15.7%) and S. Typhimuirum (5.9%). S. Lome and S. Muenster

were each found in a single sample (4%). The remaining Salmonella were classified as Rough

(7.9%). Prevalence of serovar by type of stores is shown in Table 3-1. No association was found

between the types of stores or SES and the prevalence of bacteria isolated as well as their

resistance patterns (P > 0.1).

Resistance patterns of Salmonella sp. The percentages of resistant Salmonella isolates for each

antimicrobial agent are presented in Table 3-2. All Salmonella isolates were resistant to TIL.

Antimicrobials that showed significant differences (P ≤ 0.05) in the percentage of resistance

among independent and main chain distributor center were CIP, NIT and NAL. Resistance

percentages were also significantly (P < 0.01) different among serovars for CIP, NIT, STX, ENR,

STR and NAL, and specifically for S. Paratyphi B, the percentage of resistant isolates for these

antimicrobials were higher.

Only one of the Salmonella isolates was susceptible to all antimicrobials used in this study and

the number of antimicrobials in the patterns ranged from 1 to 14. Among the Salmonella isolates,

31.4% (16/51) were resistant to 1 to 4 antimicrobials, 33.3% (17/51) to 5 to 8 and 35.3% (18/51)

to 9 to14 antimicrobials. The median number of antimicrobials to which Salmonella had

49

resistance was 7. Ceftiofur and nalidixic acid were the antimicrobials that showed the highest

frequency of resistance. Antimicrobial resistance patterns grouped according serovar are

presented in Table 3-3.

XNL, NAL, TCY were the most prevalent multidrug resistant antimicrobials found in four core

patterns as follows: pattern 1: XNL-NAL-TCY (51%); pattern 2: XNL-NAL-TCY-ENR-NIT-STR

(35.5%); pattern 3: XNL-NAL-CIP-ENR-NIT-STR-STX (31.4%) and, pattern 4: XNL-NAL-TCY-

CIP-ENR-NIT-STR-STX (23.5%). These patterns were only found in S. Paratyphi B and S. Rough.

Significant differences (P < 0.05) in the percentage of pattern 3 (XNL-NAL-CIP-ENR-NIT-STR-

STX) were found between the two origin sources of the isolates. Seventy-five percent isolates

from the chain distribution center showed pattern 3 compared with 25% from the independent

stores.

The resistance of Salmonella sp. to antimicrobials based on the human importance categories is

shown in Table 3-4 and Table 3-5. We also compared the resistance to antimicrobials for

Salmonella sp. between farms and retail stores and found that nearly all the antimicrobials in

category II and category III had 40% or more resistance, irrespective of farm or retail source

(Figure 1).

Escherichia coli. E. coli was recovered from 82.5% (165/200) of samples, and was present in

69% of independent store samples compared with 96% of samples from the chain distributor

center. The percentage of resistant E. coli isolates for each antimicrobial agent is presented in

Table 3-2. The specific type of antimicrobial resistance patterns is presented in Appendix 7. Since

all Enterobacteriacea were resistant to tilmicosin (21), this antimicrobial was not included the

pattern profile. Resistance of E. coli to antimicrobials based on the human importance categories

is presented in Table 3-4 and Table 3-5. Extended- spectrum-β-lactamase marker was present in

26 (15.8%) of the isolates.

Enterococcus sp. Enterococci were recovered from 93.5% (187/200) of the samples. Three

species of enterococci were found in this study: Enterococcus faecalis was the most common

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Enterococcus (85.6%), followed by Enterococcus faecium (13.9%). There was a single isolate of

Enterococcus rafinosus (0.6%).

The percentage of recovery for E. faecalis was higher in the samples that came from chain

distributor center (55.6%) compared with the independent store (P = 0.01) while the percentage

of recovery of E. faecium, was higher in samples from independent stores (73.1%) (P<0.01).

Enterococci were resistant to many antimicrobials. The highest percentage of resistance was

showed by tilmicosin (97.9%) followed by tetracycline (96.8%), quinupristin/dalfopristin (94.1%),

erythromycin (81.3%) and enrofloxacin (80.2%) (Table 3-2). E. faecalis showed a significally

higher resistance percentage (P< 0.05) for ciprofloxacin, gentamicin, levofloxacin,

quinupristin/dalfopristin and tetracycline while E. faecium presented higher significant resistance

(P< 0.05) for nitrofurantoin and penicillin (Table 3-2). The percentage of resistant E. faecalis and

E. faecium for vancomycin were 3.1% and 3.8%, respectively.

The distribution of resistance based on the importance in human medicine is presented in Table

3-4 and Table 3-5. The most common AMR pattern observed for E. faecalis was CLI-ERY-ENR-

XNL-OXA-QDA-STH-STR-TIL-TCY (23/160) for E. faecium was CLI- ERY-ENR- NIT-OXA-STH-

STR-TIL-TCY-XNL (3/26). The median number of antimicrobials in the resistance pattern for all

the enterococci was 11. (Appendix 8) Overall 80% of E. faecium and 96.2% of E. faecalis were

resistant to more than 9 antimicrobials (Table 3-6).

Resistance markers were present in 68.8% (110/160) of the isolates of E. faecalis, and

61.5%(16/26) of E. faecium (Table 3-7). The resistance markers presented by the enterococci

isolates were high-level gentamicin resistance, high-level streptomycin resistance, combination of

high-level gentamicin resistance and high-level streptomycin resistance, vancomycin resistance

and the combination of high- level streptomycin resistance and vancomycin resistance.

DISCUSSION

This study was done as the second step towards implementation of an integrated antimicrobial

surveillance program for AMR in Colombia (COIPARS). The initial step was a study to determine

the prevalence, risk factors and AMR profiles of Salmonella in broiler farms. Most of the previous

51

studies in Colombia (6, 24, 31) evaluated Salmonella sp., E. coli or enterococci isolated from

either food-borne diseases or from nosocomial infections in hospital settings, whereas our study

presents the first set of baseline data for AMR in retail market chicken for Salmonella sp. and two

commensal bacteria.

One of the strengths of the study was that it adapted methodologies from CIPARS which were not

only easy to implement but also allowed us to utilize standardized procedures to assess the

prevalence of bacteria of interest. It also allowed us to make comparisons with other integrated

surveillance programs that utilized the same methods (14).

As in any other country, the occurrence of Salmonella sp. in food products in Colombia is a risk

for human health. The prevalence of Salmonella (25.5%) in this study was different from the ones

reported in retail market surveys in other countries which ranged from 3% in New Zealand (51) to

as high as 39.3% in Brazil (42) and 42% in Australia (37). However, the methodology used by

these studies differed from ours and thus it is difficult to make conclusive inferences. Therefore,

we recommend that more studies take place following an easy-to-use protocol in order for valid

global comparisons to be made. Our study demonstrated the ease with which the Canadian

CIPARS protocol can be adapted by official as well as private laboratories in a developing country

such as Colombia.

We had expected to find differences in prevalence of AMR between independent stores and main

distributor center. However, despite different processing plants as well as diverse handling

environments, we did not find any differences between these two types of establishments. In

comparison with our first study at the farm level, we found an increase in the number of serovars

in retail stores than on broiler farms. Specifically, we found only two serovars (S. Paratyphi B and

S. Heidelberg) at broiler farms whereas in the current study four additional serovars were found.

This increase in serovars indicates that additional contamination may be taking place anywhere

between the farms and the retail stores and should be addressed (34). Alternatively, these

serovars could have arisen from farms not sampled in our previous study. Our recommendation

is that follow-up studies take place to identify the source of contamination in the food chain.

52

The presence of antimicrobial resistance to several antimicrobials in almost all the isolates of the

three bacteria evaluated in this study poses a risk to the human and animal population in

Colombia. This is an important finding considering the ability of these bacteria to cause food-

borne diseases and disseminate resistance genes (3, 23, 35).

The principal finding of concern in this study was that almost 98% of all the isolates tested were

multidrug resistant (MDR) and only one isolate of Salmonella was susceptible to all of the

antimicrobials tested (2%). Again, these results are in concordance with the results in a previous

study of ours in poultry farms where 100% of the Salmonella isolates were MDR and none of the

isolates were susceptible to all drugs tested. These are one of the highest reported prevalence of

AMR resistant Salmonella sp. in retail market meat (25, 27). Knowing that MDR is multi-causal,

the importance of initiating the integrated AMR surveillance program is even greater.

Regarding resistance to first and second-generation quinolones which are the principal agents

used in the treatment of human salmonellosis, our study found the prevalence of resistance to

ciprofloxacin (41.2%), enrofloxacin (56.9%) and nalidixic acid (64.7%) to be much higher than that

reported in Canada (14), USA (25, 34), and Denmark (18). In order to control this increase in

resistance, the Colombian Ministry of Agriculture should consider mandating the use of

prescriptions as a requirement for purchase of antimicrobials for use in the animal production

sector.

Another important finding in this study was the high percentage of resistance to ceftiofur (94.1%),

which was similar to the one we found in a previous study in broiler farms (97.3%). This suggests

the possibility of an association between the use of antimicrobials like ceftiofur at primary

production system and the presence of resistance in retail market bacteria. Extended–spectrum

cephalosporins are used to treat many human infections included septicemia in pregnant women

and children and the use of ceftiofur in primary production system could lead to resistance to

other cephalosporins (19). To diminish the risk of AMR to public health, our recommendation is

that actions should be taken by the official sector in Colombia in order to control the use of

ceftiofur as was done in Canada based on the results of the reports of CIPARS(14).

53

Escherichia coli is abundant in the gastrointestinal tract, can cause disease in its own right under

some circumstances, and was used in this study as an indicator of antimicrobial resistance of

Gram-negative bacteria. Given that the prevalence of resistant isolates found for most of the

antimicrobials was significantly different in E. coli (P<0.05) compared to Salmonella sp., we

recommend including both bacteria in an integrated AMR surveillance program.

Ninety-nine percent of the isolates of Escherichia coli were resistant to at least one antimicrobial

drug. This is higher than prior studies (20) and one source could possibly be at the farm level.

The highest percentage of resistance was to tetracycline (92.7%) and could be associated with

the use of chlortetracycline (5) in feed as growth a promoter which is allowed in broiler farms in

Colombia contrasting with other European countries like Denmark, where it has been well

documented that a substantial decrease of AMR occurred after growth promoter bans (22).

Based on the AMR patterns found in our study, more research should be done to establish the

types of growth promoters used in animal feed in Colombia and their impact on acquiring

antimicrobial resistance. Furthermore, the use of antimicrobials in animal feed should also be

closely evaluated.

Similar to findings in the USA (23, 46), we also found a high prevalence of resistance in E.

faecium isolates to the important antimicrobial, quinupristin/dalfopristin, which is used when

vancomycin resistance is present. This finding is especially puzzling since this streptogramin is

not yet available in Colombia and the use of virginamycin, a suspected contributor to human

carriage of E. faecium (23), is prohibited in Colombian poultry farms. The detection of vancomycin

resistance (VR) E. facecalis isolates poses a risk to humans due to the potential spread of VR

genes among enterococci and to staphylococci, which are major causes of nosocomial

infections(33).

One limitation of our study was that due to financial constraints, we were unable to evaluate

abattoirs which are the link between the farm and the retail stores. This prevented us evaluating

the quantitative contribution of the primary sector in the presence of AMR bacteria in retail stores.

Future studies should take into account the entire poultry food chain using a similar study protocol.

54

Another limitation was that we did not demonstrate an association between types of stores or

SES and the prevalence of bacteria isolated as well as their resistance patterns due to the limited

sample size. However that was not the main objective of our study and we would recommend

future studies to evaluate this aspect as predictors of AMR.

In conclusion, our findings suggest the importance of assessing the classes of antimicrobials, the

amounts and the type of uses in the poultry industry in Colombia as a final step for the

implementation of COIPARS. Furthermore, with the high prevalence of MDR reported in our

study, Colombian authorities should endeavor to facilitate the implementation of an integrated

AMR surveillance program.

55

Table 3-1 Distribution of Salmonella serovars by type of store

Independent (n=23)

Main chain distributor center

(n=28)

Serovars Frequency Prevalence

(%) Frequency Prevalence (%)

Paratyphi B 9 39.1 17 60.7 Heidelberg 4 17.4 4 14.3 Enteritidis 3 13.0 5 17.9 Typhimurium 3 13.0 -- --

Muenster 1 4.3 1 3.6 Lome -- -- 1 3.6 Rough (b:1,2) 2 8.7 -- --

Rough (g, m:-) 1 4.3 -- --

56

Table 3-2 Prevalence of antimicrobial resistant bacterial isolates

Salmonella

sp. (n=51)

E. coli

(n=165)

Enterococcus sp.

(n=187)

E. faecalis (n=160)

E. faecium (n=26)

Antimicrobial Abbreviation (%) (%) (%) (%) (%)

Amikacin AMK 0 0.6 -- -- -- Amoxicillin-Clavulanate AMC 31.4 8.5 0 0 0 Ampicillin AMP 33.3 41.2 0 0 0 Aztreonam AZT 0 15.2 -- -- -- Cefazolin CZO 33.3 25.3 0 100 96.2 Cefepime FEP 2.0 4.5 -- -- -- Cefotaxime CTX 0 7.9 -- -- -- Cefoxitin FOX 31.4 9.1 -- -- -- Ceftazidime CAZ 0 9.5 -- -- -- Ceftiofur XNL 94.1 64.2 100 100 100 Ceftriaxone CRO 2.0 7.9 -- -- -- Cephalotin CEP -- -- -- -- -- Chloramphenicol CHL 7.8 36.4 21.9 23.1 15.4 Ciprofloxacin CIP 41.2 32.1 30.5 33.1 11.5 Clindamycin CLI -- -- 100 100 100 Enrofloxacin ENR 56.9 50.3 80.5 80.6 76.9 Erythromycin ERY -- -- 81.3 82.5 73.1 Ertapenem ETP 2.0 2.3 -- -- -- Fosfomycin FOS -- -- 0 0 0 Gentamicin GEN 0 8.5 31.6 34.4 15.4 Gentamicin- combination GEH -- -- 20.3 21.2 15.4 Imipenem IPM 0 0 -- -- -- Levofloxacin LVX 2.0 29.1 16.1 18.1 3.8 Linezolid LNZ -- -- 1.1 1.2 0 Meropenem MEM 0 0 -- -- -- Nalidixic acid NAL 64.7 64.0 -- -- -- Nitrofurantoin NIT 51.0 7.9 6.4 1.2 34.6 Oxacillin OXA -- -- 0.5 100 100 Penicillin PEN -- -- 2.2 0.6 12.0 Piperacillin/Tazobactam TZP 2.0 0 -- -- -- Quinupristin/Dalfopristin QDA -- -- 94.1 100 61.5 Streptomycin STR 56.9 71.5 100 100 100 Streptomycin-combination STH -- -- 56.7 56.2 57.7

Teicoplanin TEC -- -- 1.6 1.9 0 Tetracycline TCY 60.8 92.7 96.8 98.1 88.5 Tilmicosin TIL 100.0 94.5 97.9 98.1 96.2 Tobramycin TOB 2.0 7.3 -- -- -- Trimethoprim/ Sulfamethoxazole STX 47.1 32.7 2.7 2.5 3.8

Vancomycin VAN -- -- 2.7 3.1 3.8

57

Table 3-3 Antimicrobial resistance pattern distribution for the most prevalent Salmonella

serovars

Serovar Pattern Frequency Prevalen

ce (%)

XNL 2 7.7

NIT-ENR-XNL-NAL 1 3.8

NIT-TCY-SXT-ENR-XNL-STR-NAL 2 7.7

CIP-NIT-SXT-ENR-STR-NAL 1 3.8

CIP-NIT-SXT-ENR-XNL-STR -NAL 4 15.4

CIP-NIT-TCY-ENR-XNL-STR-NAL 1 3.8

CIP-NIT-TCY-SXT-ENR-XNL-STR-NAL 1 3.8

CIP-NIT-TCY-SXT-ENR-XNL-STR-CHL-NAL 1 3.8

CIP-NIT-TOB-TCY-SXT-ENR-XNL-STR-CHL-NAL 1 3.8

CIP-LVX-NIT -TCY-SXT-ENR-XNL-STR -NAL 1 3.8

CZO-FEP-CRO-ETP-XNL 1 3.8

AMP-CIP-NIT-TCY-SXT-ENR-XNL-STR-NAL 1 3.8

AMC-AMP-CZO-FOX-TCY-XNL-NAL 1 3.8

AMC-AMP -CZO-FOX- NIT-TCY-SXT-XNL-STR- 1 3.8

AMC-AMP-CZO-FOX-NIT-TCY-SXT-ENR-XNL-STR-NAL 1 3.8

AMC-AMP-CZO-FOX-CIP-NIT-TCY-SXT-ENR-XNL-STR-

NAL 4 15.4

Paratyphi B

AMC-AMP-CZO-FOX-CIP-NIT-TCY-SXT-ENR-XNL-STR-

CHL-NAL 2 7.7

Total 26 100.0

TCY-NAL 1 12.5

TCY-ENR-XNL-STR-NAL 1 12.5

CIP-TCY-ENR-XNL-NAL 1 12.5

AMC-AMP-CZO-FOX-TCY-XNL-NAL 3 37.5

AMC-AMP-CZO-FOX-CIP-TCY -ENR-XNL-NAL 2 25.0

Heidelberg

Total 8 100.0

- 1 12.5

XNL 6 75.0

NIT-XNL 1 12.5 Enteritidis

Total 8 100.0

ENR-XNL-STR 1 33.3

TCY-XNL-STR 1 33.3

TCY-SXT-XNL-STR 1 33.3 Typhimurium

Total 3 100.0

58

Table 3-4 Prevalence of antimicrobial resistant Salmonella sp., Escherichia coli and Enterococcus isolates of very high importance in human medicine.

Percentage Resistant

Category Antimicrobial Abbreviation

Salmonella sp.

(n=51)

E. coli (n=165)

E. faecalis (n=160)

E. faecium (n=26)

Carbapenems Ertapenem ETP 2.0 2.3 -- -- Imipenem IPM 0 0 -- -- Meropenem MEM 0 0 -- -- Cephalosporins 3

rd & 4

th

gen

Cefepime FEP 2.0 -- -- -- Cefotaxime CTX 0 7.9 -- -- Cefoxitin FOX 31.4 9.1 -- -- Ceftazidime CAZ 0 9.5 -- -- Ceftiofur XNL 94.1 64.2 100 100 Ceftriaxone CRO 2.0 9.5 -- -- Fluoroquinolones Ciprofloxacin CIP 41.2 32.1 33.1 11.5 Enrofloxacin ENR 56.9 50.3 80.6 76.9 Levofloxacin LVX 2.0 29.1 18.1 3.8 Glycopeptides Teicoplanin TEC -- -- 1.9 0 Vancomycin VAN -- -- 3.1 3.8 Monobactams Aztreonam AZT 0 15.2 -- -- Ozazolidinones Linezolid LZD -- -- 1.2 0 Penicillin β-lactamase inhibitor combinations

Amoxicillin-Clavulanate AMC 31.4 8.5 0 0 Ampicillin AMP 33.3 41.2 0 0 Piperacillin/Tazobactam TZP 2.0 0 -- -- Streptogramins

I – Very High Importance

Quinupristin/Dalfopristin SYN 100 61.5

59

Table 3-5 Prevalence of resistant Salmonella sp., E.coli , and Enterococcus of high and medium importance in human medicine

Percentage Resistant


Salmonella sp.

(n=51)

E. coli (n=165)

E. faecalis (n=160)

E. faecium (n=26)

Aminoglycosides Amikacin Gentamicin

AMK GEN

0 0

0.6 8.5

-- 34.4

-- 15.4

Gentamicin-Synergy1 GEH -- -- 21.2 15.4 Tobramycin TOB 2.0 7.3 -- -- Streptomycin STR 56.9 71.5 100 100 Streptomycin-Synergy STH -- -- 56.2 57.7 Cephalosporins 1

st & 2

nd gen.

Cefazolin CZO 33.3 25.3 100 96.2 Cephalothin CEP -- 60.5 -- -- Lincosamides Clindamycin CLI -- -- 100 100 Macrolides Erythromycin ERY -- -- 82.5 73.1 Tilmicosin TIL 100.0 94.5 -- -- Penicillins Penicillin PEN -- -- 0.6 12 Oxacillin OXA -- -- 100 100 Quinolones

Nalidixic acid NAL 64.7 64.0 -- --

II-

Hig

h I

mp

ort

an

ce

Trimethoprim/Sulfamethoxazole

STX 47.1 32.7 2.5 3.8

Fosfomycin FOS -- -- 0 0 Nitrofurans Nitrofurantoin NIT 51.0 7.9 1.2 34.6

Tetracycline TCY 60.8 92.7 98.1 88.5

III

- M

ed

ium

Im

po

rta

nce

Chloramphenicol CHL 7.8 36.4 23.1 15.4

1 Gentamicin- Streptomycin (500µg/ml-1000µg/ml)

60

Table 3-6 Distribution Salmonella sp., E.coli and Enterococcus by the number of antimicrobial drugs to which they were resistant.

Prevalence of resistance (%)

Number of Antimicrobials

Salmonella sp.(n=51)

E. coli (n=165)

E. faecalis (n=160)

E. faecium (n=26)

0 2.0 1.2 0 0 1-4 33.3 20.6 0 0 5-8 33.4 40.0 3.8 19.2 9-16 31.3 38.2 96.2 80.8

61

Table 3-7 Resistance markers present in Enterococcus sp.

Number (Percentage resistant) Markers E. faecalis (n=110) E. faecium (n=26) HLGRI 17 (10.6%) 1(3.8%) HLSRII

70 (63.6%) 12 (46.2%) HLSR-HLGRIII

17 (15.4%) 3(11.5%) HLSR-VREIV

2 (1.8%) 1(3.8%) VREV

3(2.7%) 1(3.8%) I High Level Gentamicin Resistance IIHigh Level Streptomycin Resistance III High Level Gentamicin Resistance - High Level Streptomycin Resistance IV High Level Streptomycin Resistance - Vancomycin Resistance VVancomycin Resistance

62

Figure 1.Prevalence of resistance to antimicrobials in Salmonella sp. isolated from chickens: farm vs retail stores in Bogota, Colombia, 2009.

0 10 20 30 40 50 60 70 80 90 100

ETPIPM

MEMFEPCTXFOXCAZXNL

CROCIP

ENRLVXAZT

AMKAMCAMPTZP

GENTOBSTRCZONALSTXNIT

TCYCHL

Cat

egor

izat

ion

of a

ntim

icro

bial

s ba

sed

on im

port

ance

in h

uman

med

icin

e

Resistant isolates (%)

Farm Retail

I

II

III

63

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Appendices

Appendix 1 Salmonella sp., E. coli and Enterococccus sp. isolation scheme

HANDLING PROCEDURE

Clean the bag with gauze sterile and 70 % alcohol. Cut the bag with sterile scissors and take out the sample with the sterile forceps.

Put 25 g of chicken in the bag for stomacher with 225 ml of buffered peptone water and mix for 15 minutes at 230 RPM in the stomacher.

Pour out the content in a sterile SCHOTT bottle of 500 ml for transport of the processed sample.

Salmonella sp. Use a sterile test-tube to transfer 50 ml to a 200 ml sterile bottle with lid. Incubate at 35°C for 24 h.

E. coli Use a sterile test-tube to transfer 50 ml to a sterile bottle with lid of 200 ml, that contains 50 ml of E. coli (EC) broth at double concentration. Incubate at 45°C for 24 h.

Enterococcus sp. Use a sterile test-tube to transfer 50 ml to a sterile bottle with lid of 200 ml, that contains 50 ml of Enterococcosel broth at double concentration. Incubate at 35°C for 24 h.

SAMPLE ALLOCATION

68

Appendix 2 Salmonella sp. isolation scheme from 50 ml of Buffered Peptone Water (BPW) incubated at 35°C for 24 hours

Check the purity of MCK plate. If the colonies are not pure repeat the procedure transferring the isolated colonies from the same MCK plates.

Transfer 0.1 ml from the 50 ml of BPW incubated to the Modified Semi-Solid Rappaport Vassiliadis (MSRV) plate using a pipette sterile. Incubate at 42°C for 24 to 72 h

Inspect the MSRV plate at 24 h. Take the distance measure from the inoculation point to the limit of migration.

If migration is not evident or <20 mm, the plate should be re-incubated up to 72 h, and then can be classified as migration negative.

If the limit of migration is >20 mm, transfer a loopful of growth from the migration edge on MRSV onto Chromoagar -Salmonella. Incubate at 35°C for 24 h.

Inspect the CHROMagar Salmonella plate to confirm light mauve to mauve-colored colonies.

Transfer a typical colony onto a MacConkey (MCK) plate for isolation. Repeat this procedure for three typical colonies. Incubate at 35°C for 24 h.

If the MCK plate shows typical growth lactose fermenting colonies, the sample will be classified as negative.

69

Appendix 2 (ctd) Salmonella sp. isolation scheme from 50 ml of BPW incubated at 35°C for 24 hours

Kliger

Azucares K/A UREA (-)

H2S variable INDOL (-)

Gas (+)

Check the purity of MCK plate. If the colonies are not pure repeat the procedure transferring the isolated colonies from the same MCK plates.

For each one of the isolates on the MCK plates, inoculate the isolated colonies in an Urea tube, SIM (Sulfur-Indol-Motility) tube and a tube of inclined Kliger Iron Agar. Incubate all of them at 35°C for 24 h.

Interpret the results of the biochemical tests.

If the results are typical for Salmonella sp., inoculate a BHI tube for each of the isolates beginning from a Trypticase soy agar (TSA) colony. Incubate at 35°C for 24 h.

Put 0.5 ml of turbid broth BHI in 4 cryovials with 0.5 ml of sterile glycerol. Mix in vortex and mark the cryo vials. Store frozen at -70°C

If the results are not typical the isolate will be classified as negative.

Typical results for Salmonella sp.; to read the test indol add 2 or 3 drops of Kovac’s reagent to the SIM tube.

70

Appendix 3 E. coli isolation scheme from 50 ml of BPW incubated at 35°C for 24 hours and from 50 ml of double concentration EC broth incubated et 45°C for 24 hours

For each isolate, transfer a loopful of the incubated mix onto a Eosin Methylene Blue (EMB) plate. Incubate at 35°C for 24 hours

If the EMB plate has typical growth for E. coli, select 3 typical colonies and inoculate each one onto a new EMB plate. Incubate at 35°C for 24 hours

If the plate of EMB does not have a metallic shine, typical of E. coli, the sample will be classified as negative.

Inspect the TSA plates for purity; if they are not pure, repeat the procedure transferring the isolated colonies from the same MCK plates.

For each isolate on TSA plates, inoculate beginning from the isolated colonies; one SIM tube and one inclined citrate tube. Incubate all of them at 35°C for 24 h.

The citrate should be incubated with the lid loose because its utilization requires oxygen.

If the EMB isolates are pure and confirm the growth of E.coli with metallic sheen, choose one well isolated colony and inoculate a TSA plate.

71

Appendix 3 (ctd): E. coli isolation scheme from 50 ml of BPW incubated at 35°C for 24 hours and from 50 ml of double concentration EC broth incubated et 45°C for 24 hours

INDOL CITRATE

(+) (-)

(-) (-)

Interpret the results of the biochemical tests.

If the results are not typical, the isolate will be classified as negative.

Typical results for E .coli; read the indole test by adding 2 or 3 drops of Kovac’s reagent to the SIM tube.

If the results are typical for E. coli, inoculate a BHI tube for each one of the isolates beginning from one colony from TSA. Incubate at 35°C for 24 h.

Put 0.5 ml of the turbid BHI broth in 4 cryovials with 0.5 ml of sterile glycerol. Mix in the vortex and mark the cryovials. Store frozen at -70°C

72

Appendix 4 Enterococcus faecalis and E. faecium isolation scheme from 50 ml of BPW incubated at 35°C for 24 hours and 50 ml of double concentration Enterococcosel incubated at 35°C for 24 hours

For each isolate, transfer a loopful of the incubated mix, onto an Enterococcosel plate. Incubate at 35°C for 24 to 48 h

If the Enterococcosel plate has typical growth for Enterococcus, select 3 typical colonies and inoculate them onto a new Enterococcus plates. Incubate at 35°C for 48 h

If at 24 h. there are not typical colonies for Enterococcus sp. (reduction of esculin), the sample will be re-incubated for 48h and then, could be classified as negative.

Check the Enterococcosel plates for purity; if they are not pure, repeat the procedure transferring the isolated colonies from the same Enterococcosel plates.

Take one typical colony from each plate and transfer a onto a Slanetz and Bartley (SB) agar plates. Incubate at 35°C for 48 h.

If 24 h later, there is growth or the broth turns brown.

If at 24 hours there is not growth or broth does not turn brown, the sample is classified as negative.

If at 24 hours there are not typical colonies of Enterococcus sp, the sample will be re-incubated up to 48 h and then can be classified as negative

73

Appendix 4. (ctd): Enterococcus faecalis and faecium isolation scheme from 50 ml of BPW incubated at 35°C for 24 hours and 50 ml of double concentration Enterococcosel incubated at 35°C for 24 hours From each plate of SB agar,

transfer a typical colony onto a TSA-Blood agar plate. Incubate at 35°C for 48 h.

Pick one colony from each pure plate of TSA-Blood and put it in a microscope slide and add a drop of hydrogen peroxide at 3%. If there is presence of bubbles,

the catalase test is positive (+) and the isolate is negative for Enterococcus sp.

Also, inoculate colonies from TSA-Blood agar onto tubes phenol red with 0.25% L-arabinosa and with 1% d-mannitol. Incubate at 35°C for 24 to 48 h.

Interpret the results of the biochemical tests for sugar fermentation (if the fermentation is positive the tube turns the color to yellow, if this is negative stays red), and check the TSA-Blood and SB agar plates. Compare with the next table.

For the test of fermentation you need to take a high quantity of inoculum because if the sample is insufficient, it is possible to obtain a false-negative. If at 24 h, the tube shows an orange coloring, the tube should be re-incubated up to 48 h.

74

Appendix 4 (ctd). Enterococcus faecalis and faecium isolation scheme from 50 ml of BPW incubated at 35°C for 24 hours and 50 ml of double concentration Enterococcus incubated at 35°C for 24 hours

Blood Agar SB agar l-Arabinose D-Mannitol

E. faecalis Medium Colonies Red colonies (-) (+)

No gamma-hemolisis

With or without metallic shine

E. faecium Small Colonies Red colonies (+) (+)

Partial α-hemolysis With or without metallic shine

If the results are typical for Enterococcus sp., from one colony on TSA, inoculate a BHI tube for each one of the isolates. Incubate at 35°C for 24 h.

Put 0.5 ml of the BHI turbid broth in 4 cryovials with 0.5 ml of sterile glycerol. Mix in vortex and mark the cryo vials. Store frozen at -70°C

75

Appendix 5 Minimum Inhibitory concentration (MIC) range and interpretations for antimicrobials used in the gram-negative panel for Salmonella sp, E.coli and Enterococcus sp.

Panel Contents MIC Interpretive Standard

(ug/mL)


Conc. Range (ug/mL)

S I R

Amikacin AMK 8-32 ≤16 32 ≥64 Amoxicillin/Clavulanate AMC 4/2-16/8 ≤8/4 16/8 ≥32/16 Ampicillin (Gram-negative) AMP 4-16 ≤8 16 ≥32 Ampicillin (Enterococcus) AMP 0.25-8 ≤8 16 ≥32 Aztreonam ATM 2-16 ≤8 16 ≥32 Cefazolin CAZ 2-16 ≤8 16 ≥32 Cefepime FEP 1-16 ≤8 16 ≥32 Cefotaxime CTX 4-32 ≤8 16-32 ≥64 Cefoxitin FOX 4-16 ≤8 16 ≥32 Ceftazidime CAZ 0.5-2 ≤8 16 ≥32 Ceftriaxone CRO 2-32 ≤8 16-32 ≥64 Ciprofloxacin CIP 0.5-2 ≤1 2 ≥4 Clindamycin) CC 0.5-2 ≤0.5 1-2 ≥4 Ertapenem ETP 0.5-4 ≤2 4 ≥8 Erythromycin ERY 0.25-4 ≤0.5 1-4 ≥8 Fusidic Acid FOS 2-8 Gentamicin GEN 2-8 ≤4 8 ≥16 Gentamicin-Sinergy GMS 500 Imipenem IPM 1-8 ≤4 8 ≥16 Levofloxacin LVX 1-4 ≤2 4 ≥8 Linezolid LZD 1-4 ≤2 4 ≥8 Meropenem MEM 1-8 ≤4 8 ≥16 Nitrofurantoin NIT 16-64 ≤32 64 ≥128 Oxacillin OXA 0.25-2 ≤0.25 - ≥0.5 Penicillin PEN 0.125-4 ≤8 - ≥16

Piperacilin/Tazobactam TZP 2/4-64/4 ≤16/4 32/4-64/4 ≥128/4

Streptomycin-Synergy STS 1000 Tetracycline TCY 2-8 ≤4 8 ≥16 Teicoplanin TEC 1-16 ≤8 16 ≥32 Tobramycin TOB 2-8 ≤4 8 ≥16 Trimethoprim/Sulfamethoxazole SXT 0.5/9.5-2/38 ≤2/38 - ≥4/76 Vancomycin VAN 1-16 ≤4 8-16 ≥32

76

Appendix 6 Zone diameter for the interpretation of antimicrobials not included in the panel.

Zone Diameter Interpretive Standards (mm)

Antimicrobial

Code Control

Zone Diameter Limits (mm) S I R

Ceftiofur XNL 24-30 ≥23 - - Enrofloxacin ENO 32-40 ≥23 17-22 ≤16 Tilmicosin TIL 7-21 ≥14 11-13 ≤10 Streptomycin STR 12-20 ≥15 12-14 ≤11 Chloramphenicol CHL - ≥18 13-17 ≤12 Nalidixic Acid NA - ≥19 14-18 ≤13

77

Appendix 7 E.coli AMR pattern distribution

Pattern Frequency Percent

None 2 1.2

NAL- 1 .6

STR- 1 .6

XNL- -CEP 1 .6

XNL--NAL-CEP 1 .6

XNL--STR-CEP 1 .6

TCY-STR- 2 1.2

TCY-STR -NAL-CEP 1 .6

TCY - -CEP 2 1.2

TCY - -CHL -CEP 1 .6

TCY - -CHL-NAL- 1 .6

TCY --STR- 4 2.4

TCY --STR-CEP 1 .6

TCY --STR -NAL- 3 1.8

TCY --STR -NAL-CEP 1 .6

TCY-XNL- - 3 1.8

TCY-XNL- -CEP 1 .6

TCY-XNL--NAL- 1 .6

TCY-XNL--NAL-CEP 1 .6

TCY-XNL- -CHL - 1 .6

TCY-XNL--STR- 2 1.2

TCY-XNL--STR-CEP 2 1.2

TCY-XNL--STR -NAL- 1 .6

TCY-XNL--STR -NAL-CEP 2 1.2

TCY-XNL--STR-CHL - 1 .6

TCY-XNL--STR-CHL-NAL- 1 .6

TCY-XNL--STR-CHL-NAL-CEP 1 .6

TCY -ENR-STR -NAL-CEP 1 .6

TCY -ENR - - 2 1.2

TCY -ENR --NAL-CEP 1 .6

TCY -ENR --STR -NAL- 1 .6

TCY -ENR --STR -NAL-CEP 2 1.2

TCY -ENR-XNL--NAL- 1 .6

TCY -ENR-XNL--STR -NAL- 2 1.2

TCY -ENR-XNL--STR -NAL-CEP 1 .6

TCY -ENR-XNL--STR-CHL -CEP 1 .6

TCY -ENR-XNL--STR-CHL-NAL-CEP 4 2.4

TCY-SXT- -CHL -CEP 1 .6

TCY-SXT--STR-CHL - 1 .6

TCY-SXT -XNL- -CHL - 1 .6

TCY-SXT -XNL- -CHL -CEP 1 .6

TCY-SXT -XNL--STR-CHL - 1 .6

TCY-SXT-ENR --STR- 1 .6

TCY-SXT-ENR --STR -NAL- 3 1.8

TCY-SXT-ENR-XNL--STR -NAL-CEP 1 .6

TCY-SXT-ENR-XNL--STR-CHL - 1 .6

TCY-SXT-ENR-XNL--STR-CHL-NAL-CEP 1 .6

NIT-TCY --STR-CHL-NAL- 1 .6

NIT-TCY -ENR --STR -NAL- 1 .6

NIT-TCY -ENR-XNL- -CHL-NAL- 1 .6

GEN-TCY --STR- 1 .6

GEN-TCY-SXT -XNL--STR- 1 .6

GEN-TOB -TCY-SXT--STR-CHL - 1 .6

CIP-TCY-SXT-ENR-XNL--STR-CHL-NAL-CEP 2 1.2

78


Pattern Frequency Percent CIP -NIT-TCY -ENR-XNL--STR -NAL-CEP 1 .6

CIP -LVX-TCY -ENR --STR -NAL- 1 .6

CIP -LVX-TCY -ENR --STR -NAL-CEP 1 .6

CIP -LVX-TCY -ENR --STR-CHL-NAL-CEP 1 .6

CIP -LVX-TCY -ENR-XNL--STR -NAL-CEP 1 .6

CIP -LVX-TCY-SXT-ENR --STR -NAL- 1 .6

CIP -LVX-TCY-SXT-ENR --STR-CHL-NAL-CEP 1 .6

CIP -LVX-TCY-SXT-ENR-XNL--NAL-CEP 1 .6

CIP -LVX-TCY-SXT-ENR-XNL- -CHL-NAL-CEP 1 .6

CIP -LVX-TCY-SXT-ENR-XNL--STR -NAL-CEP 2 1.2

CIP -LVX-TCY-SXT-ENR-XNL--STR-CHL-NAL-CEP 1 .6

CIP -LVX -NIT-TCY -ENR --NAL-CEP 1 .6

CIP -LVX -NIT-TCY -ENR --STR -NAL- 1 .6

CIP -LVX -NIT-TCY-SXT-ENR --STR-CHL-NAL- 1 .6

CIP -LVX -NIT-TCY-SXT-ENR-XNL--STR -NAL- 1 .6

CIP -LVX -NIT-TCY-SXT-ENR-XNL--STR-CHL-NAL- 1 .6

CIP -GEN-TCY -ENR-XNL- -CHL-NAL-CEP 1 .6

CIP -GEN -LVX-TOB -TCY -ENR --STR- 1 .6

CIP-ETP-LVX-TCY-SXT-ENR-XNL- -CHL-NAL-CEP 1 .6

CZO -CIP -LVX -XNL--NAL- 1 .6

AMP-TCY - - 1 .6

AMP-TCY --STR- 1 .6

AMP-TCY --STR-CEP 1 .6

AMP-TCY --STR-CHL-NAL- 1 .6

AMP-TCY-XNL- -CHL-NAL-CEP 1 .6

AMP-TCY-XNL--STR- 2 1.2

AMP-TCY -ENR - -CHL-NAL-CEP 1 .6

AMP-TCY -ENR --STR-CHL-NAL- 1 .6

AMP-TCY -ENR-XNL--STR -NAL-CEP 1 .6

AMP-TCY -ENR-XNL--STR-CHL-NAL-CEP 1 .6

AMP-TCY-SXT--STR- 1 .6

AMP-TOB -TCY -ENR-XNL--STR -NAL-CEP 1 .6

AMP -GEN-TCY-XNL--STR-CHL-NAL- 1 .6

AMP -GEN-TOB -TCY-SXT-ENR --STR-CHL-NAL-CEP 1 .6

AMP -CIP -LVX -SXT-ENR-XNL--STR-CHL-NAL-CEP 1 .6

AMP -CIP -LVX-TCY-XNL--STR- 1 .6

AMP -CIP -LVX-TCY -ENR-XNL--STR-CHL-NAL-CEP 1 .6

AMP -CIP -LVX-TCY-SXT -STR-CHL - 1 .6

AMP -CIP -LVX-TCY-SXT-ENR --STR-CHL-NAL-CEP 1 .6

AMP -CIP -LVX-TCY-SXT-ENR-XNL- -NAL-CEP 1 .6

AMP -CIP -LVX-TCY-SXT-ENR-XNL- -STR- 1 .6

AMP -CIP -LVX-TCY-SXT-ENR-XNL- -STR -NAL-CEP 1 .6

AMP -CIP -LVX-TCY-SXT-ENR-XNL--STR-CHL-NAL-CEP 1 .6

AMP -CIP -GEN -LVX-TOB -TCY-SXT -XNL--STR-CHL-NAL- 1 .6

AMP -CZO-TCY-XNL- -CEP 1 .6

AMP -CZO -CIP -LVX-TCY -ENR-XNL -STR -NAL-CEP 1 .6

AMP -CZO -CIP -LVX-TCY-SXT-ENR-XNL- -CHL-NAL-CEP 1 .6

AMP -CZO -CIP -LVX-TCY-SXT-ENR-XNL--STR-CHL-NAL- 1 .6

AMP -CZO -CIP -LVX-TCY-SXT-ENR-XNL--STR-CHL-NAL-CEP 2 1.2

AMP -CZO -CIP -GEN -LVX-TOB -TCY -ENR-XNL--STR -NAL-CEP 1 .6

AMP -CZO-FEP-CTX -CAZ-CRO-CIP -LVX -NIT-TCY -ENR-XNL--STR-CHL-NAL-CEP 1 .6

AMP-ATM-CIP -LVX-TCY-SXT-ENR-XNL--STR -NAL- 1 .6

AMP-ATM-CZO -XNL- -CHL -CEP 1 .6

AMP-ATM-CZO-TCY-XNL--NAL-CEP 1 .6

AMP-ATM-CZO-TCY-XNL- -CHL -CEP 1 .6

79


Pattern Frequency Percent AMP-ATM-CZO-TCY -ENR --STR-CHL-NAL-CEP 1 .6

AMP-ATM-CZO-TCY-SXT -XNL--STR-CHL-NAL-CEP 1 .6

AMP-ATM-CZO-TOB-XNL- -CEP 1 .6

AMP-ATM-CZO -NIT-TCY -ENR-XNL--STR -NAL-CEP 1 .6

AMP-ATM-CZO -GEN-TCY-XNL--STR -NAL-CEP 1 .6

AMP-ATM-CZO -CIP -LVX-TCY -ENR-XNL--STR -NAL-CEP 2 1.2

AMP-ATM-CZO -CIP -GEN -LVX-TOB -TCY-SXT-ENR-XNL--STR-CHL-NAL-CEP 1 .6

AMP-ATM-CZO -CTX -CAZ-CRO -TCY-XNL--STR-CEP 1 .6

AMP-ATM-CZO -CTX -CAZ-CRO -TCY-SXT -XNL- -CHL-NAL-CEP 1 .6

AMP-ATM-CZO -CTX -CAZ-CRO -TCY-SXT -XNL--STR -NAL-CEP 1 .6

AMP-ATM-CZO-FEP-CTX -CAZ-CRO -TCY-SXT -XNL--STR-CEP 1 .6

AMP-ATM-CZO-FEP-CTX -CAZ-CRO -ETP - -CEP 1 .6

AMP-ATM-CZO-FEP-CTX -CAZ-CRO -ETP -TCY-XNL--STR-CHL-NAL- 1 .6

AMP-ATM-CZO-FEP-CTX -CAZ-CRO-CIP -LVX-TCY -ENR-XNL- -CHL-NAL-CEP 1 .6

AMP-ATM-CZO-FEP-CTX -CAZ-CRO-CIP -GEN -LVX-TOB -TCY-SXT-ENR-XNL--STR -NAL-CEP 1 .6

AMC-AMP-FOX-XNL- -CEP 1 .6

AMC-AMP -CZO-FOX-XNL -NAL-CEP 1 .6

AMC-AMP -CZO-FOX -ENR-XNL--STR -NAL-CEP 1 .6

AMC-AMP -CZO-FOX -TCY-XNL--STR-CEP 1 .6

AMC-AMP -CZO-FOX -TCY-XNL--STR-CHL -CEP 1 .6

AMC-AMP -CZO-FOX -TCY -ENR-XNL--STR -NAL-CEP 1 .6

AMC-AMP -CZO-FOX -TCY-SXT -XNL--STR-CEP 1 .6

AMC-AMP -CZO-FOX-CIP -LVX-TCY -ENR-XNL--NAL-CEP 1 .6

AMC-AMP -CZO-FOX-CIP -LVX-TCY-SXT-ENR-XNL -STR-CHL-NAL-CEP 1 .6

AMC-AMP -CZO-FOX-CIP -GEN -LVX-TOB -TCY-SXT-ENR-XNL--STR -NAL-CEP 1 .6

AMC-AMP-ATM-CTX-FOX-CAZ-CRO-CIP -NIT-TCY-SXT-ENR --NAL-CEP 1 .6

AMC-AMP-ATM-CTX-FOX-CAZ-CRO-CIP -LVX-TCY -ENR-XNL--STR -NAL-CEP 1 .6

AMC-AMP-ATM-CZO-FOX-CIP -GEN -LVX-TOB -TCY-SXT -XNL--STR-CHL-NAL-CEP 1 .6

AMC-AMP-ATM-CZO -CTX-FOX-CAZ-CRO -TCY -ENR-XNL--STR-CEP 1 .6

AMK -AMP-ATM-CZO -CTX-FOX-CAZ-CRO-CIP -LVX -NIT-TOB -TCY-SXT -XNL--STR -NAL-CEP 1 .6

Total 165 100.0

80

Appendix 8 Enterococcus AMR pattern by species

Susceptible Organism Frequency Percent

None 13 100.0

E faecalis

CZO -ENR-XNL-TIL-STR -CLI -OXA -QDA- 2 1.2

CZO -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 1 .6

CZO -TCY-XNL -STR -CLI-ERY-OXA -QDA-STH - 1 .6

CZO -TCY-XNL-TIL-STR -CLI -OXA -QDA- 4 2.5

CZO -TCY-XNL-TIL-STR -CLI-ERY-OXA -QDA- 2 1.2

CZO -TCY-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 7 4.4

CZO -TCY-XNL-TIL-STR-CHL-CLI -OXA -QDA- 1 .6

CZO -TCY-XNL-TIL-STR-CHL-CLI -OXA -QDA-STH - 1 .6

CZO -TCY-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH - 3 1.9

CZO -TCY -ENR-XNL -STR -CLI-ERY-OXA -QDA- 1 .6

CZO -TCY -ENR-XNL-TIL-STR -CLI -OXA -QDA- 7 4.4

CZO -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA- 16 10.0

CZO -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA -TEC-VAN 1 .6

CZO -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 23 14.4

CZO -TCY -ENR-XNL-TIL-STR -CLI-ERY -LNZ-OXA -QDA -TEC-VAN 1 .6

CZO -TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA- 1 .6

CZO -TCY -ENR-XNL-TIL-STR-CHL-CLI -OXA -QDA- 1 .6

CZO -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-QDA-STH - 1 .6

CZO -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA- 2 1.2

CZO -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH 4 2.5

CZO -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY -LNZ-OXA-PEN-QDA -TEC-VAN 1 .6

CZO -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA-STH 1 .6

CZO -TCY-SXT-ENR-XNL-TIL-STR -CLI -OXA -QDA-STH - 1 .6

CZO -TCY-SXT-ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH 2 1.2

CZO-NIT-TCY-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 1 .6

CZO-GEN-TCY-XNL-TIL-STR -CLI -GEH -OXA -QDA- 1 .6

CZO-GEN-TCY-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 1 .6

CZO-GEN-TCY-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA- 1 .6

CZO-GEN-TCY-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA-STH - 1 .6

CZO-GEN-TCY -ENR-XNL-TIL-STR -CLI -OXA -QDA- 1 .6

CZO-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA- 2 1.2

CZO-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH 2 1.2

CZO-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA- 1 .6

CZO-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA-STH 3 1.9

CZO-GEN-TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA- 1 .6

CZO-GEN-TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH - 6 3.8

CZO-GEN-TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA-STH - 1 .6

CZO -CIP -TCY-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH - 1 .6

CZO -CIP -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA- 1 .6

CZO -CIP -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH 1 .6

CZO -CIP -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH - 2 1.2

CZO -CIP -LVX -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA- 3 1.9

CZO -CIP -LVX -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 7 4.4

CZO -CIP -LVX -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH -VAN 1 .6

CZO -CIP -LVX -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH - 2 1.2

CZO -CIP -LVX -TCY-SXT-ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 1 .6

81

Appendix 8 Enterococcus AMR pattern by species

Susceptible Organism Frequency Percent

CZO -CIP-GEN-TCY-XNL-TIL-STR -CLI -OXA -QDA- 1 .6

CZO -CIP-GEN-TCY-XNL-TIL-STR -CLI-ERY-OXA -QDA- 1 .6

CZO -CIP-GEN-TCY-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 1 .6

CZO -CIP-GEN-TCY-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA-STH - 1 .6

CZO -CIP-GEN-TCY-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH - 1 .6

CZO -CIP-GEN-TCY-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA-STH - 1 .6

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR -CLI -GEH -OXA -QDA- 2 1.2

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA- 1 .6

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 1 .6

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH -VAN 1 .6

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA- 4 2.5

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA-STH - 2 1.2

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA- 1 .6

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA-STH 1 .6

CZO -CIP-GEN-LVX -TCY -ENR-XNL -STR -CLI -GEH -OXA -QDA- 1 .6

CZO -CIP-GEN-LVX -TCY -ENR-XNL-TIL-STR -CLI -GEH -OXA -QDA- 3 1.9

CZO -CIP-GEN-LVX -TCY -ENR-XNL-TIL-STR -CLI -GEH -OXA -QDA-STH - 1 .6

CZO -CIP-GEN-LVX -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA- 1 .6

CZO -CIP-GEN-LVX -TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA- 3 1.9

CZO -CIP-GEN-LVX -TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA-STH -

2 1.2

CZO -CIP-GEN-LVX -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA-STH

3 1.9

CZO -CIP-GEN-LVX-NIT-TCY -ENR-XNL-TIL-STR -CLI -GEH -OXA -QDA- 1 .6

Total 160 100.0

E. faecium

-NIT-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA-STH - 1 3.8

CZO-XNL-TIL-STR -CLI -OXA -QDA- 1 3.8

CZO-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 1 3.8

CZO -ENR-XNL-TIL-STR -CLI -OXA -QDA- 1 3.8

CZO -TCY-XNL -STR -CLI -OXA- 1 3.8

CZO -TCY-XNL-TIL-STR -CLI -OXA- 1 3.8

CZO -TCY-XNL-TIL-STR -CLI-ERY-OXA -QDA- 1 3.8

CZO -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA- 2 7.7

CZO -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH - 2 7.7

CZO -TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-OXA -QDA-STH 1 3.8

CZO-NIT-TCY -ENR-XNL-TIL-STR -CLI -OXA- 1 3.8

CZO-NIT-TCY -ENR-XNL-TIL-STR -CLI -OXA -QDA- 1 3.8

CZO-NIT-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA-STH - 3 11.5

CZO-NIT-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA-PEN -STH - 2 7.7

CZO-NIT-TCY-SXT-ENR-XNL-TIL-STR -CLI-ERY-OXA-PEN-QDA-STH - 1 3.8

CZO -LVX -TCY-XNL-TIL-STR -CLI -OXA- 1 3.8

CZO-GEN-TCY -ENR-XNL-TIL-STR -CLI-ERY-GEH -OXA -QDA-STH - 1 3.8

CZO-GEN-TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA- 1 3.8

CZO -CIP -TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA -QDA-STH 1 3.8

CZO -CIP-GEN-TCY -ENR-XNL-TIL-STR-CHL-CLI-ERY-GEH -OXA -QDA-STH 2 7.7

Total 26 100.0

E. raffinosus

CZO -CIP-NIT-TCY -ENR-XNL-TIL-STR -CLI-ERY-OXA-STH - 1 100.0

82

Chapter 4 Molecular Characterization of Salmonella Paratyphi B Dt+ (S. Java) and Salmonella Heidelberg from Poultry and Retail Chicken Meat in Colombia. ABSTRACT

Salmonella Paratyphi B dT+ variant (also termed Salmonella Java) and Salmonella Heidelberg

are human pathogens that are frequently isolated from poultry. As part of the steps towards

implementing the Colombian Integrated Program for Antimicrobial Resistantence Surveillance

(COIPARS), this study characterized molecular patterns of S. Paratyphi B dT+ and Salmonella

Heidelberg isolated from poultry and retail chicken meat using pulsed-field gel electrophoresis

(PFGE). The objective of this study was to analyze the genetic relationship among isolates and to

determine potential geographically-predominant genotypes. Both serovars exhibited marked

heterogeneity. The DNA fingerprinting of 82 isolates of S. Paratyphi B dT+ revealed 42 patterns,

while the PFGE of the 21isolates of S. Heidelberg revealed 10 patterns. Similar genotypes of both

serovars were demonstrated to be present in farms and in retail outlets. For S. Paratyphi B dT+,

closely genetically related isolates were found among isolates coming from different farms and

different integrated poultry companies within both departments (Santander and Cundinamarca)

and from farms located in the two geographically distant departments. For S. Heidelberg, the

number of farms with genetically related isolates was less than for S.Paratyphi B dT+. A possible

dissemination of similar genotypes of both serovars along the poultry chain is hypothesized and

some facilitating factors existing in Colombia are reviewed. To further characterize the potential

risks for human outbreaks related to the presence of these two Salmonella serovars in the

Colombian poultry chain, and to monitor the dynamic evolution of their clonage lineages in

Colombia, longitudinal studies using pulsed-field gel electrophoresis combined with other

molecular sub-typing methods are needed.

INTRODUCTION

Salmonella Paratyphi B d-tartrate-fermenting (dT+) variant (also termed Salmonella Java) and

Salmonella Heidelberg are food-borne pathogens associated worldwide with human

83

salmonellosis (16, 20). Risks associated with these two human pathogens are two-fold: their

specific pathogenicity in humans and their multidrug resistance (MDR) profiles to antimicrobials.

Salmonella Paratyphi B dT+ is an emerging public health pathogen that causes enteric fever and

self-limiting gastroenteritis (7, 36). Salmonella Paratyphi B dT+ has animal reservoirs and has

caused significant outbreaks in several countries as a result of the contamination of food products

(36). The organism has been reported in poultry worldwide (4, 17, 33) and is thought to become

the most important pathogen at the end of the broiler growing period (33). Strains of S. Paratyphi

B dT+ are generally multidrug resistant (MDR) to antimicrobials such as ampicillin,

chloramphenicol, streptomycin, spectomycin, sulphonamides and tetracyclines (11). Salmonella

Heidelberg was among the top four serotypes isolated in 2006 in the US from human

salmonellosis cases (6) and was associated with approximately 7% of Salmonella-related deaths.

Salmonella Heidelberg is mainly derived from poultry, is notable for MDR and is of great concern,

because it can cause septicemia and myocarditis (22). Antimicrobial resistant (AMR) S. Paratyphi

B as well as AMR S. Heidelberg can spread through the food chain from the primary production

to the retail chicken meat resulting in significant risks to human health.

To monitor food-borne pathogens and to conduct AMR surveillance, the World Health

Organization has recommended a three-fold approach to include human clinical cases, food

animals and retail meats(37). Countries like Canada (8), USA (21) and Denmark (1) have

established integrated and unified programs for the surveillance of antimicrobial resistance.

These programs integrate the data along the food chain in order to monitor food-borne pathogens

and to prevent the spread of AMR bacteria from animals to humans.

The current study is part of a pilot initiative to implement an integrated system in Colombia,

namely COIPARS (Colombian Integrated Program for Antimicrobial Resistance Surveillance). In

the first phase, the prevalence of Salmonella isolates and Salmonella serovars and their

antimicrobial resistance patterns were defined in chickens on 70 farms including 350 houses from

the two main poultry production departments of Colombia: Santander and Cundinamarca. That

study was followed by a second in retail poultry meat of 100 independent stores and the main

84

chain distributor center in Bogota District Capital (DC). Results from this preliminary study

showed a prevalence of 41.4% of Salmonella isolates on farms (65% at house level), and 25.5%

in retail meat samples. Salmonella Paratyphi B dT+ was the most prevalent serovar (76.4%) on

farms and in retail meat samples (51%), followed by S. Heidelberg, 22.7% and 15.7% on farms

and retail meat samples, respectively. Both serovars exhibited MDR profiles to antibiotics.

In this study we characterized the molecular patterns of isolates of S. Paratyphi B dT+ and S.

Heidelberg obtained from poultry farms from Santander and Cundinamarca, and from

independent stores and the main retailer distribution center of Bogota DC. We used Pulsed-field

gel electrophoresis (PFGE) since it is among the most reliable, discriminatory, and reproducible

typing methods to detect high degrees of DNA polymorphism for epidemiological purposes (12,

25, 38). The objectives of this study were to assess the genetic relatedness of these isolates and

to determine whether there were geographically predominant clones.

MATERIALS AND METHODS

Salmonella strain sources

A total of 114 serotyped (Kauffman-White scheme) Salmonella isolates were included in this

study. Salmonella were collected during 2008 and 2009 on poultry farms from the departments of

Santander and Cundinamarca, and from independent retail stores and a main chain meat

distributor center in Bogota DC and were kept frozen at -70C. Salmonella Paratyphi B dT+

isolates (n = 85) were obtained from three different sources: 38 drag swabs samples and 16 fecal

samples from 29 poultry farms; 6 cecal content samples obtained in processing plants and from

25 retail chicken meat samples from 10 independent stores and one main chain center of Bogota

DC. Twenty-nine isolates of S. Heidelberg came from 16 drag swabs and 5 feces from poultry

farms and, from 8 samples from retail stores.

Pulsed-field gel electrophoresis analysis (PFGE)

PFGE was used to assess the genetic relatedness following the protocol of the Centers for

Disease Control and Prevention (27) with the following modifications. Cell suspensions were

made using the procedure of the National Health Institute of Colombia (INS), which consisted of

85

suspending the cells in 2 ml of cell suspension buffer (100 mM Tris:100 mM EDTA, pH 8.0; CSB)

and then a dilution of 1:10 of the initial suspension (1900 µl CSB plus 100 µl of the initial

suspension) was made. Next, the concentration of the cell suspension was adjusted to an optical

density (OD) of 1.35 at 610 nm in the spectrophotometer. Finally, the concentration was adjusted

to a final volume of 200 µl with cell suspension using the following formula 200 /(OD x 10) where

200 is the final volume, OD is the optical density and 10 is the dilution factor. Ten microliters of

proteinase K (Sigma-Aldrich, Baltimore MD, USA) solution (20 mg/ml) was added to the

suspension. Casting plugs were made with 1% Seakem Gold agarose (Cambrex, Cambridge MD,

USA). The isolates were processed by adding 200 µl of 1% Seakem Gold agarose to 200 µl of

the cell suspension. Digestion of DNA was performed by incubating a quarter of each casting

plug with Xbal (Fermentas, Burlington ON, Canada). Gel electrophoresis was carried out using a

CHEF DR–III unit (Bio-Rad, Hercules, CA) with an initial and final switch times of 2.2 and 63.8

seconds, respectively. The included angle was 120º at 14ºC and the run time was 18 hours.

Salmonella Braenderup H9812 was used as reference strain on each gel. Gels were stained with

ethidium bromide (10mg/ml) and photographed in Gel Doc. Genotypes were compared

(Molecular Analysis Fingerprinting Software, version 1.6, Bio-Rad Laboratories, Hercules, CA) by

the use of the Dice coefficient of similarity with the unweighted pair group technique using

arithmetic averages (UPGMA) to prepare the dendograms. To evaluate different patterns, a

demarcation system based on the concepts of Tenover (32) were used: ≥ 93%, probably the

same isolate; 85 to 92%, very similar; 80 to 84%, similar: 75 to 79%, somewhat similar; and ≤

74%, not similar. This criteria states that if an isolate varies from a main type by just three or

fewer bands, it will be considered as a subtype.

RESULTS

The genotypes obtained by PFGE were analyzed separately by Salmonella serovar category and

similarity dendograms were constructed for S. Paratyphi B dT+ (Figure 1) and S. Heidelberg

(Figure 2). Both serovars exhibited extensive heterogeneity. Of the 114 isolates, 103 were

differentiated with the use of PFGE. Eleven isolates were not include in the analysis because they

either were untypable due to DNA degradation (29) or had insufficient DNA loading.

86

Salmonella Paratyphi B dT+.

The DNA fingerprinting by PFGE of the 82 isolates revealed 42 patterns: 19 patterns among the

37 isolates from farms of the department of Santander (SA), 9 patterns among the 20 isolates

from farms of Cundinamarca (RC) and 20 patterns among the 25 isolates from retail stores from

Bogota DC.

The similarity dendogram of S. Paratyphi B dT+ genotypes showed three clusters of 30 (C1), 27

(C2) and 5 (C3) isolates with a percentage similarity coefficient (% SC) of > 85 % within each

cluster. For C1, 50 % of isolates were from farms of 3 different integrated companies operating in

Santander department; 27% were isolates from farms of a single integrated company operating in

Cundinamarca and 23% of the isolates were isolates from retail stores and the chain center in

Bogota DC. For C2 (n=27), 56, 26 and 18 % of isolates were isolates from farms in Santander

department (belonging to four different integrated companies), farms in Cundinamarca (belonging

to three integrated companies) and retail stores and main chain center of Bogota DC, respectively.

For C3, all the isolates were from retail market chicken collected in Bogota DC.

Two groups of 9 and 22 indistinguishable isolates were identified within C1 and C2 clusters

respectively. In both groups, isolates were isolates from farms of Santander and of Cundinamarca

as well as from retail chicken meat from Bogota.

Salmonella Heidelberg

The DNA fingerprinting by PFGE of the 21 isolates revealed 10 patterns: 8 patterns among the 13

isolates from farms of the department of Santander, 3 patterns from farms of Cundinamarca and

4 patterns among the isolates of retail from Bogota DC (See Figure 2)

The similarity dendogram of Salmonella Heidelberg genotypes showed one cluster of 8 isolates

with a %SC of > 85 %. All the isolates were isolates from farms of Santander belonging to 2

integrated companies. Within the cluster, 5 isolates coming from farms of one integrated

company were indistinguishable.

DISCUSSION

87

The present study showed that S.Paratyphi B dT+ and S. Heidelberg isolates obtained from the

two most important poultry producing departments, and the retail meat of the area with the

greatest chicken consumption in Colombia, exhibited high genetic heterogeneity. The

demonstrated presence of S. Paratyphi B dT+, which had not been reported in Colombia to date,

and S. Heidelberg, with hypothesized dissemination along the Colombian poultry chain may

create a risky situation for possible human outbreaks as has been shown in Europe and USA (6,

36).

For S. Paratyphi B dT+, the finding of high genetic heterogeneity appears to be different from the

one reported in Germany where one clonage lineage of S. Paratyphi B dT+ successfully

displaced the others after a period of time (23). As far as S. Heidelberg is concerned, the genetic

diversity found in this study is consistent with results reported by Linne et al. (20), in a study of S.

Heidelberg isolates from food animals in the USA that showed 30 patterns among 58 S.

Heidelberg isolates tested using XbaI.

The presence of closely genetically related S. Paratyphi B dT+ isolates in this study, some of

them with indistinguishable PFGE patterns, between poultry farms of the two departments and

the retail stores, between farms within the same department, and between farms located in the

two distant departments led us to hypothesize about possible dissemination of clones along the

poultry chain in Colombia. The possible causes of the dissemination of S. Paratyphi B dT+ could

be related to the contamination from the environment (10), to the findings that hygiene measures

seem to be not as effective for S. Paratyphi B dT+ as they are for other Salmonella (S. Enteritidis,

S. Infantis and S. Virchow)(35) and to the ability of this serovar to colonize and to rapidly spread

within a group of chickens and persist until slaughter (34).

Some specific poultry industry practices in Colombia could facilitate dissemination of S. Paratyphi

B dT+. The transportation of birds to the abattoirs is done in open trucks using the same plastic

boxes for different farms that could facilitate farm-to-farm contamination(24). Use of the same

feed trucks for many farms in the same company should be considered as a possible cause of

cross-contamination between farms (28). Also, most of the farms have soil floors which readily

88

allow for the development and persistence of rodent and beetle populations that can transmit

Salmonella between adjoining farms (30). Similarly, farms belonging to different integrated

companies are geographically mixed and the reduced distances between farms, between the

sources of water and the poor isolation measures makes an effective Salmonella control program

difficult (3). Finally, poultry manure is transported and commercialized across the country in order

to use it as fertilizer or feed components for other animal productions(26).

For S. Heidelberg, the presence of closely genetically related isolates, some of them with

indistinguishable PFGE patterns, has been shown to exist on poultry farms of the two

departments and the retail stores and mainly between farms within Santander department. The

possible dissemination within a department appears to be less than for S. Paratyphi B dT+.

Almost 90% of the isolates of the identified S. Heidelberg cluster came from the same integrated

company. This result could be correlated to the demonstrated S. Heidelberg vertical transmission

from layers to the eggs (13-15) making the hatchery of an integrated company a potential source

of contamination(18). Contamination of the integrated feedmill (9, 19) and the Salmonella status

of the previous flock also could play a role (5).

The demonstrated dynamic evolution of a clonal population of these serovars such as the

reported case for S. Paratyphi B dT+ in Europe (23) suggests the need for implementing

longitudinal studies in the poultry chain in Colombia including hatchery, farms, slaughter houses

and retail stores. Even though PFGE is considered the “gold standard” of the molecular typing

techniques and is used worldwide for outbreak investigations, is a technically challenging

technique with some limitations as reported by Barret et al. (2). Therefore, we recommend that

for these longitudinal studies the PFGE analysis should be complemented by other molecular

tools such DNA microarrays that are more discriminative than PFGE. (31).

89

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38. Xia, X., S. Zhao, A. Smith, J. McEvoy, J. Meng, and A. A. Bhagwat. 2009. Characterization of Salmonella isolates from retail foods based on serotyping, pulse field gel electrophoresis, antibiotic resistance and other phenotypic properties. Int J Food Microbiol. 129:93-8.

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Figure 1 Similarity dendogram of S. Paratyphi B dT+ macrorestriction patterns generated using Xbal. Clone column indicates the number of indistinguishable isolates presenting the same PFGE pattern. The clusters are mentioned as Cx (percentage similarity coefficient). The origins of the isolates are reported as Santander farms, Cundinamarca farms, Independent retail shops or retail chain in Bogota DC. The different integrated companies are identified as SaOx or RcOx. The scale bar indicates percent similarity coefficient.

92

11

A

I

158

133

137

169

30

69

159

39

25

22

87

121

136

146

81

G

140

74

128

61

18

55

120

8

149

23

160

161

125

162

127

1

29

46

J

4

27

164

100908070605040 Ref. Clones Santander

Farm

Cundinamarca

Farm

Independent

Retail

Chain

Retail

92 Sa03

11 Sa01

A A

I Sa03

158 2 Sa01 1

133 Rc04

137 Sa01

169 Rc04

30 2 2 Rc04

69 Rc04

159 1

39 Sa01

25 Rc04

22 Rc04

87 Sa01

121 2 A Rc04

136 Sa01

146 9 Sa01,Sa02 ,3 Sa03 3Rc04 Rc04

81 Sa03

G 3 2 Sa01, Sa02

140 Sa01

74 Rc04

128 Sa02

61 Rc04

18 Sa02

55 Rc04

120 3 Sa01 E, Rc04

8 22 6 Sa01, 2 Sa02, 3Sa03, Sa05 3 Rc04, Rc01, 2 Rc03, Rc07 A O, Rc04

149 2 2 Sa03

23 3 Rc01, Rc03, Rc05

160 1

161 1

125 Rc04

162 1

127 O

1 Sa03

29 Rc05

46 Sa02

J 2 Sa01 1

4 Sa03

27 Rc04

164 1

c1

(88.1)

c2

(87.2)

c3

(86.5)

93

Ref Clones Santander

Farm

Cundina-marca

Farm Independent

Retail Chain Retail

Sa03

1

Sa01 A

2 Sa01, Sa03

5 5 Sa01

Sa01

2 Rc08 Rc08

Sa03

Sa01 2 Rc04

3 Sa01 Rc05 Rc09

Figure 2 Similarity dendogram of S. Heidelberg macro restriction patterns generated using Xbal. Clone column indicates the number of indistinguishable isolates presenting the same PFGE pattern. The cluster is mentioned as Cx (percentage of similarity coefficient). The origin of the isolates are reported as Santander farms, Cundinamarca farms, independent retail shops or retail chain in Bogota, DC. The different integrated companies are identified as SaOx or RcOx. The scale bar indicates percent similarity.

41

C

E

F

14

6

68

178

H

B

1009080706050

c (87/6%)

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Chapter 5 Conclusion Our research provided four main indications about the current situation in the Colombian poultry

chain concerning one food-borne pathogen, Salmonella sp., and two commensal bacteria:

Escherichia coli and enterococci. First, we found a high prevalence of Salmonella sp. in

commercial broiler farms (41%) and in retail meat samples (26%). Second, Salmonella Paratyphi

B dT+ and Salmonella Heidelberg were the most frequent serovars among Salmonella isolates

on farms and in retail meat samples. This is the first time that Salmonella Paratyphi B dT+ has

been reported in the poultry chain in Colombia. Third, Salmonella isolated from farms and retail

meat, as well as Escherichia coli and enterococci isolated from chicken meat, showed extensive

resistance to antimicrobial agents. Ninety-eight percent of isolates were reported as multi-drug

resistant. Ceftiofur, enrofloxacin, nalidixic acid and tetracycline were the antimicrobials that

showed the highest frequency of resistance among Salmonella and E. coli isolates. For

enterococci, we found that 62% of E. faecium isolates were resistant to quinupristin/dalfopristin,

which is used to treat nosocomial human infections when vancomycin resistance is present.

Fourth, the DNA fingerprinting of S. Paratyphi B dT+ and S. Heidelberg isolates revealed that

similar genotypes of both serovars were present in farms and in retail outlets, as well as among

isolates coming from different farms within each region and from farms located in the two

geographically distant departments.

These findings indicate potential risks for human health in Colombia related to food-borne

infections with bacteria of poultry origin. These risks are related to the high prevalence of

Salmonella, the extensive antimicrobial resistance (AMR) of the food-borne pathogen and two

commensal bacteria surveyed, and to the possible dissemination of these pathogens along the

poultry chain. Because the potential impact on human health can be significant, further

characterization of these risks and development of appropriate measures to mitigate them are

needed. We recommend that further studies be done to determine if the profiles of Salmonella,

Escherichia coli and enterococci isolates from humans show any similarity with the profiles of the

isolates from the poultry chain. Furthermore, studies should also take place to confirm the

hypothesized dissemination of Salmonella along the poultry chain, which could be followed by a

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review of the efficiency of biosecurity measures currently implemented in Colombia’s poultry

sector. Finally, the extensive AMR profiles we found trigger the need for an in-depth assessment

of the antimicrobial drug usage in the production sector.

The future research agenda should therefore include a comparison of phenotypic AMR and

genotypic profiles of Salmonella, Escherichia coli and enterococci of human isolates with those of

the poultry chain. The dynamic evolution of a clonal population of Salmonella sp. and their

potential circulation in the poultry chain should be investigated through longitudinal studies in the

poultry chain including hatchery, farms, slaughter houses and retail stores with phenotypic AMR

and genetic profiles characterization. In order to complement the results, new research on

consumption of antimicrobials through various stages of production in the poultry chain in

Colombia is also necessary. These data could be gathered by cross-sectional studies and also

by using the records gathered by the National Institute of Agriculture (ICA) on the kilograms of

active ingredient imported by pharmaceutical companies and by poultry companies. Once the use

of antimicrobials in the poultry primary chain is established, it would be necessary to adapt

existing software to monitor the antimicrobial use in the primary production. Finally, studies are

also needed to compare different practices of consumption of antimicrobial agents in animals (the

use of antimicrobials in low doses as growth promoters and the use of antimicrobials in-feed and

in-water for metaphylaxis and prophylaxis) along with the development of resistance.

In Colombia, the poultry production sector is facing a double challenge, i.e. increasing the volume

of production to address a rapidly growing national demand, and at the same time improving the

quality and food safety aspects of retail products. As in other emerging economies, the

Colombian poultry sector grows at a much higher rate than those of established economies such

as the USA. Due to the demographic and economic growth of the country and to the fact that the

buying capacity of a person on minimum wage is 124 kg of chicken meat, per capita consumption

of broiler meat increased substantially from 14.2 kg/year in 2000 to 22.7 kg/year in 2009. This

was nearly a 58% increase as compared to 16% during the same period for the USA. Between

2005 and 2009, the Colombian poultry production grew by 34%, whereas the US production

remained almost flat with less than 1% growth. In these dynamic conditions in Colombia, the

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food safety aspects along the poultry chain face many challenges. Over the past decade, both

the Colombian poultry sector production and the authorities have attempted to reach international

food safety and animal health standards. Significant progress has been made through

implementation of Good Manufacturing Practices (GMPs) and Hazard Analysis Critical Control

Point (HACCP), which was recently made mandatory in slaughterhouses. However, our research

indicates that these efforts in the biosecurity area including the usage of antimicrobials need to be

strengthened, especially at the primary production level. Specifically for antimicrobial usage, it

would be interesting to monitor the efficiency of pilot measures that some countries have already

implemented. These include measures such as mandatory veterinary prescription for the use of

antimicrobials for any purpose in the animal industry, limitations on the veterinarian’s prescription,

registration of use and delivery of drugs by a veterinarian, or the prohibition of pharmacies and

pharmaceutical industries from offering economic incentives to the veterinarians for antimicrobial

sales.

All the stakeholders, poultry producers, retailers, agriculture and health authorities are conscious

about the challenges faced in Colombia. Fortunately, the conditions and the timing have been

conducive to strengthening the network of stakeholders that was initiated by our research project.

Indeed, it is the first time that most of the stakeholders who have a potential role in minimizing

AMR development have jointly participated. The technical procedures that our project used to

create this network and conduct surveillance should be published and adapted for the use in

other animal production systems such as bovine milk and meat, swine and aquaculture.

Finally, with the support of PAHO-WHO and the cooperation of stakeholders, we are looking

forward to the progressive implementation of COIPARS. Our recommendation is to continue the

expansion of the surveillance program by inviting other animal production sectors, such as the

swine and cattle, to participate in the process and develop similar plans that will help to improve

the food safety and biosecurity of the animal production chains in Colombia.

Salmonella Paratyphi B and Salmonella Heidelberg DISSERTATION

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