Citation
Tuberculosis Acquisition and Transmission among Persons of Haitian Descent Living in Florida, USA

Material Information

Title:
Tuberculosis Acquisition and Transmission among Persons of Haitian Descent Living in Florida, USA
Creator:
Seraphin, Marie N
Publisher:
University of Florida
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Epidemiology
Committee Chair:
MORRIS,JOHN GLENN
Committee Co-Chair:
LAUZARDO,MICHAEL
Committee Members:
COOK,ROBERT L
SALEMI,MARCO
YANG,YANG
Graduation Date:
8/6/2016

Subjects

Subjects / Keywords:
haiti
immigration
mobility
transmission
tuberculosis
Miami-Dade County ( local )
Tuberculosis ( jstor )
Diseases ( jstor )
Immigration ( jstor )
Genre:
Unknown ( sobekcm )

Notes

General Note:
Persons of Haitian descent make up about two percent of the population of the State of Florida but account for a quarter of the tuberculosis (TB) among the foreign-born population. TB among Haitians immigrants in Florida is characterized by high levels of genetic clustering, which supports the conclusion that there may be foci of TB transmission in Haitian communities. There is, however, limited literature on factors driving TB incidence and contributing to TB transmission among Haitians. It is also not clear whether transmission from Haitians-born persons to U.S.-born persons is occurring. Haitians living in the United States (U.S.) maintain strong ties to Haiti through remittance and travel. Post-immigration return trips to Haiti may account for elevated TB disease as well as facilitate the importation of diverse Mycobacterium tuberculosis strains in Haitian communities in Florida. This dissertation explores tuberculosis transmission and acquisition, with an emphasis on the role of Haitians and post-immigration return migration on the molecular epidemiology and transmission dynamics of M. tuberculosis in Florida. Our findings show that transmission events in Florida over the past six years involved M. tuberculosis Haarlem strains. Transmission in the community for the most part was segregated by country of birth; however, high level of HIV infection and the prison setting contributed to transmission between U.S.-born and Haitian-born persons. We also report a modest association between post-immigration return trips and TB disease in the Haitian population living in Florida. Overall, our findings support conclusions that both immigration and local transmission affect TB epidemiology in Florida. In our conclusion chapter, we discuss the public health implications of our results as they pertain to TB control among high-risk groups in Florida, including the Haitian subpopulation. We also provide a discussion on the application of novel methods to inform decisions on TB outbreak investigation and control.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Seraphin, Marie N. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
8/31/2017

Downloads

This item has the following downloads:


Full Text

PAGE 1

TUBERCULOSIS ACQUISITION AND TRANSMISSION AMONG PERSONS OF HAITIAN DESCENT LIVING IN FLORIDA, USA By MARIE NANCY SERAPHIN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2016

PAGE 2

2016 Marie Nancy Sraphin

PAGE 3

To my father

PAGE 4

4 ACKNOWLEDGMENTS First I want to thank my father, who has encouraged me throughout this journey. I am infinitely grateful to my dissertation committee chair, Dr. J. Glenn Morris, Jr. and co chair, Dr. Michael Lauzardo for believing in me and giving me the opportunity to work on this project. I am particularly grateful to have benefitted from the guidance and support of the members of committee, who have challenged and advised me as I execute the work. I would like to acknowledge the Florida Department of Health Bureau of Tuberculosis Control and the countless County Health Dep artment staff who contributed to the collection of the data used in this dissertation. Particularly, I would like to acknowledge Drs. Jean, Fils Aime and Parkes from the Florida Department of Health in Miami Dade and Palm Beach counties, respecti vely, wit hout whom, my study on post immigration mobility and TB disease among the Haitian population living in South Florida would not have been possible. I am grateful to the nursing staff at these county h ealth d epartment clinics ; your warm welcome and support d uring data collection made an impossible task possible. Finally, to my SNTC family, thank you for all you do; particularly thank you for creating a work environment that made the long hours fun.

PAGE 5

5 TABLE OF CONTENTS Page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LI ST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 BACKGROUND ................................ ................................ ................................ ...... 14 Introduction ................................ ................................ ................................ ............. 14 Origin and Spread of Myc obacterium tuberculosis Complex ................................ .. 14 Global Epidemiology of Tuberculosis ................................ ................................ ...... 16 The Synergistic Influence of HIV on the Global Tuberculo sis Burden .............. 17 Drug Resistance and Tuberculosis Control ................................ ...................... 18 Tuberculosis Pathophysiology and the Quantification of Transmission .................. 19 Tuberculosis Acquisition and Transmission among Haitian Immigrants ................. 20 Tuberculosis Epidemiology in Haiti ................................ ................................ ... 20 Tuberculosis in the U.S. ................................ ................................ ................... 21 Tuberculosis among Haitian Immigrants in the U.S ................................ .......... 22 Contributi on of Dissertation ................................ ................................ .............. 23 2 SPATIOTEMPORAL CLUSTERING OF MYCOBACTERIUM tuberculosis COMPLEX GENOTYPES IN FLORIDA: GENETIC DIVERSITY SEGREGATED BY COUNTRY OF BIRTH ................................ ................................ ....................... 27 Introduction ................................ ................................ ................................ ............. 27 Materials and Methods ................................ ................................ ............................ 29 Study Population ................................ ................................ .............................. 29 Strain Classification and Spatial Mapping ................................ ........................ 29 Spatial Descriptive Statistics, Genotyping Coverage and Genotype Diversity .. 30 MTBC Spatiotemporal Clusters and TB Transmission ................................ ..... 31 Multinomial Spatiotemporal Clusters ................................ ................................ 33 Results ................................ ................................ ................................ .................... 34 Genotyping Coverage and MTBC Genetic Diversity ................................ ........ 35 Space time Genotyping Clustering ................................ ................................ ... 36 Discussion ................................ ................................ ................................ .............. 38

PAGE 6

6 3 WHOLE GENOME SEQUENCING FOR THE INVESTIGATION OF A TUBERCULOSIS OUTBREAK INVOLVING PRISON AND COMMUNITY CASES IN FLORIDA, U.S.A ................................ ................................ ................... 53 Introduction ................................ ................................ ................................ ............. 53 Data Collection and Sequencing ................................ ................................ ............. 54 Epidemiologic Data ................................ ................................ .......................... 54 Whole Genome Sequencing and SNP Detection ................................ ............. 54 Data Analyses ................................ ................................ ................................ .. 55 Comparison between sequenced and rep orted cases ............................... 55 Phylogenetic analyses ................................ ................................ ............... 56 Results ................................ ................................ ................................ .................... 57 Clinical and Dem ographics Characteristics of the Cases ................................ 57 Spatial descriptors ................................ ................................ ............................ 58 Phylogenetic Reconstruction ................................ ................................ ............ 58 Origin of the outbreak ................................ ................................ ................ 60 Source of the outbreak in the prison system ................................ .............. 60 Discussion ................................ ................................ ................................ .............. 61 4 POST IMMIGRATION RETURN TRIPS AND RISK OF TUBERCULOSIS DISEASE AMONG PERSONS OF HAITIAN DESCENT LIVING IN FLORIDA ...... 75 Introduction ................................ ................................ ................................ ............. 75 Methods ................................ ................................ ................................ .................. 76 Subjects ................................ ................................ ................................ ............ 76 Case definition ................................ ................................ ........................... 76 Control definition ................................ ................................ ........................ 77 Data Collection Procedures ................................ ................................ .............. 78 Measures ................................ ................................ ................................ ................ 78 Frequency and Duration of Post Immigration Mobility ................................ ...... 78 Demographic and Socio economic information ................................ ................ 79 Hea lth Access and History ................................ ................................ ............... 79 Acculturation ................................ ................................ ................................ ..... 79 Tuberculosis Knowledge ................................ ................................ .................. 80 Data Analyses ................................ ................................ ................................ .. 80 Results ................................ ................................ ................................ .................... 81 Demographic and Socio Economic Characteristics ................................ .......... 81 Post immigration Return Trips and Tuberculosis Disease ................................ 82 Discussion ................................ ................................ ................................ .............. 83 5 CONCLUSIONS, IMPLICATIONS AND FUTURE RESEAR CH ............................. 93 Contributions of the Dissertation ................................ ................................ ............. 9 3 Future Directions ................................ ................................ ................................ .... 95 Tes ting TB Control Interventions Targeted at Persons of Haitian Descent ...... 95 Investigation into Bacterial Virulence ................................ ................................ 95 Applying Genomic s to Inform TB Outbreak Investigations ............................... 97

PAGE 7

7 Conclusion ................................ ................................ ................................ 98 APPENDIX A MYCOBACTERIUM TUBERCULOSIS COMPLEX SUBLINEAGE ALLELIC DIVERSITY FLORIDA 2009 2013 ................................ ................................ ......... 99 B SUPPLEMENTAL OBSERVATIONS FROM WHOLE GENOME SEQUENCE ANALYSES ................................ ................................ ................................ ........... 103 C LIST OF QUESTIONS USED TO TEST PARTI CIPANT TUBERCULOSIS KNOWLEDGE ................................ ................................ ................................ ...... 105 LIST OF REFERENCES ................................ ................................ ............................. 106 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 121

PAGE 8

8 LIST OF TABLES Table page 2 1 Characteristics of Genotyped Tuberculosis Cases in Florida by Year of Isolation, 2009 2013 ................................ ................................ ........................... 45 2 2 Allel ic Diversity of Clinical M. tuberculosis Complex Isolates in Florida, 2009 2013 ................................ ................................ ................................ ................... 48 2 3 Characteristics of Cases inside the Multinomial Haarlem Clusters ..................... 51 3 1 Characteristics of the Cases Involved in the Outbreak by Whole Genome Sequencing Status ................................ ................................ ............................. 67 4 1 Characteristics of Haitians with Tuberculosis Disease (Cases) and wit hout TB Disease (Controls) ................................ ................................ ........................ 87 4 2 Post Immigration Travel and Association with Tuberculosis Disease among Persons of Haitian Descent Living in Florida ................................ ...................... 91 A 1 Allelic Diversity of Difference M. tuberculosis Complex Sublineages Isolated in Florida, 2009 2013 ................................ ................................ .......................... 99 B 1 Mycobacterium tuberculosis Reference Genomes used to comp lement the Whole Genome Sequence Analyses. ................................ ............................... 103

PAGE 9

9 LIST OF FIGURES Figure page 1 1 Conceptual Model of Tuberculosis Burden in Haitian Communities in Fl orida, U.S. ................................ ................................ ................................ .................... 25 1 2 Reported Tuberculosis Cases in the State of Florida b y Key Subpopulations, 1993 2014 ................................ ................................ ................................ ......... 26 2 1 Genotyping Coverag e and Mycobacterium tuberculosis complex Allelic Diversity in Florida. ................................ ................................ ............................. 46 2 2 The spatial means, 1 standard deviation ellipses and standard distances of the culture confirmed and genotyped case s for each of the five years (2009 2013) ................................ ................................ ................................ ................. 47 2 3 Spatiotemporal Clustering of Mycobacterium tuberculosis Beijing (Panel A) and Haarlem (Panel B) Lineages in Florida, 2009 2013 ................................ .... 49 2 4 Multinomial space time cluster of the Mycobacterium tuberculosis Haarlem Lineage in Florida, 2009 2013. ................................ ................................ .......... 50 2 5 Spatiotemporal Genotype Clus ters in Areas of Low and High Genetic Diversity in Florida, 2009 2013 ................................ ................................ ........... 52 3 1 Epidemic Curve of the Spoligotyping and 24 locus MIRU VNTR Defined M. tuberculosis Outbreak. ................................ ................................ ........................ 66 3 2 Spatiotemporal Descriptive Statistics Comparing Outbreak and Sequenced Cases. ................................ ................................ ................................ ................ 68 3 3 Estimates of the Evolutionary Divergence between the Spoligot yping and MIRU VNTR Defined FL0117 Outbreak Cases ................................ .................. 69 3 4 Minimum Spanning Tree (MST) of FL0117 Cases. Nodes represent each of the sequenced cases (n=21). ................................ ................................ ............. 70 3 5 Likelihood Mapping Analysis Testing the Phylogenetic Signal of the Sequence Alignment. ................................ ................................ ......................... 71 3 6 Midpoint rooted Maximum Likelihood Phylogeny of the 21 FL0117 Isolat es and One Reference Isolate. ................................ ................................ ................ 72 3 7 Maximum Clade Credibility Phylogeny of the FL0117 Cluster illustrating the relationship between U.S. born (Cyan) and Foreign born cases (Red) cases. ... 73

PAGE 10

10 3 8 Centers in the Florida Prison System prior to their Diagnosis with TB Disease .. 74 4 1 Scatterplot with B splines of the relationship between frequency and duration of trips to Haiti among persons of Haitian descent ................................ ............ 89 4 2 Scatterplot with B splines of the relationship between number of years in Florida, number of trips made to Haiti (A) and average trip duration in weeks (B) among persons of Haitian descent. ................................ .............................. 90 4 3 Final Destinations of Per sons of Haitian Descent Who Reported to have Visited Haiti in the Past Two Year ................................ ................................ ...... 92 B 1 Midpoint rooted Maximum Likelihood Phylogeny of the 21 FL0117 Iso lates and 12 Reference Isolates ................................ ................................ ................ 104

PAGE 11

11 LIST OF ABBREVIATIONS AIC BEAST Akaike Information Criterion Bayesian Evolutionary Analysis Sampling Trees EBSP Extended Bayesian Skyline Plot ESS GIMS HPD LTBI MCC MCMC MDR TB MIRU ML MRCA MST MTBC NGS SNP Spoligotyping TB TIMS VNTR WGS Effective Sample Size Genotype Information Management System Highest Posterior Density Interval Latent Tuberculosis Infection Maximum Clade Credibility Markov Chain Monte Carlo Multidrug resistant tuberculosis Mycobacterial Interspersed Repet itive Unit Maximum Likelihood Most Recent Common Ancestor Minimum Spanning Tree Mycobacterium tuberculosis Complex Next Generation Sequencing Single Nucleotide Polymorphism Spacer Oligonucleotide Typing Tuberculosis Tuberculosis Information Management Syst em Variable Number Tandem Repeat Whole Genome Sequence

PAGE 12

12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TUBERCULOSI S ACQUISITION AND TRANSMISSION AMONG PERSONS OF HAITIAN DESCENT LIVING IN FLORIDA, USA By Marie Nancy Sraphin August 2016 Chair: John Glenn Morris, Jr Cochair: Michael Lauzardo Major: Epidemiology Persons of Haitian descent make up about two percent of the population of the State of Florida but account for a quarter of the tuberculosis (TB) among the foreign born population. TB among Haitians immigrants in Florida is characterized by high levels of genetic clustering, which supports the conclusion tha t there may be foci of TB transmission in Haitian communities. There is, however, limited literature on factors driving TB incidence and contributing to TB transmission among Haitians. It is also not clear whether transmission from Haitians born persons to U.S. born persons is occurring. Haitians living in the United States (U.S.) maintain strong ties to Haiti through remittance and travel. Post immigration return trips to Haiti may account for elevated TB disease as well as facilitate the importation of di verse Mycobacterium tuberculosis strains in Haitian communities in Florida. This dissertation explores tuberculos is transmission and acquisition with an emphasis on the role of Haitians and post immigration return migration on the molecular epidemiology and transmission dynamics of M. tuberculosis in Florida. Our findings show that transmission events in Florida over the past six years involved M. tuberculosis Haarlem strains. T ransmission in the

PAGE 13

13 community for the most part was segregated by country of bi rth; however, high level of HIV infection and the prison setting contributed to transmission between U.S. born and Haitian born persons. We also report a modest association between post immigration return trips and TB disease in the Haitian population livi ng in Florida. Overall, our findings support conclusions that both immigration and local transmission affect TB epidemiology in Florida. In our conclusion chapter, we discuss the public health implications of our results as they pertain to TB control amon g high risk groups in Florida, including the Haitian sub population. We also provide a discussion on the application of novel methods to inform decisions on TB outbreak investigation and control.

PAGE 14

14 CHAPTER 1 BACKGROUND Introduction Tuberculosis (TB) is a n airborne infectious disease caused for the most part by the organism Mycobacterium t uberculosis 1 TB is an old and debilitating disease affecting people in their prime age with grave socio e conomic consequences 2 4 Between the 17 th and 18 th century, TB killed as many as twenty percent of the European and North American population 5,6 Improvements in standards of living, the discovery of the tubercl e bacillus in 1882 and the recognition that the disease is contagious largely contributed to TB decline by the 19 th century 5 Nevertheless, due to population movement and migration, the HIV/AIDs epidemic and the development of drug resistance, TB has gained t cause of death d ue to an infectious disease in the developing world and affects more people than never before 7,8 This introduction reviews the epidemiology of TB from a population and molecular perspective and presents my investigations into the acquisition and transm ission of disease in the Haitian immigrant population in Florida. Origin and Spread of Mycobacterium t uberculosis Complex M. tuberculosis the causative agent of TB, is a member of the Mycobacterium tuberculosis Complex (MTBC), a group of seven closely re lated organisms that can cause disease in mammal 6,9 The MTBC is composed of the animal adapted bacteria Mycobacterium caprae (sheep and goa ts), Mycobacterium microti (voles), and Mycobacterium pinnipedii (seals and sea lions) 9 Apart from M. tuberculosis the human adapted members of the complex from here one referred to simply as MTBC, also includes Mycobacterium africanum limited exclusively to West Africa 10 Before the

PAGE 15

15 pasteurization process was widely applied, M. bovis was an important pathogen among persons wh o consumed raw milk, especially children 9 For a long time, we knew very little about the MTBC. The full genome sequencing of the laboratory strain H37Rv has allowed great i nsights into the bacterial genome and led to the development of the molecular typing tools necessary to investigate the molecular epidemiology of the MTBC 11,12 Today we know that the MTBC is composed of six lineages associated with particular geographical regions and population groups 9 At the lineage level, further sublineage structures have been defined, with the most important ones being Haarlem, Latin American and Mediterranean (LAM), and the ill defined T subline ages within the Euro American Lineage and the Beijing sublineage within East Asian 13,14 The MTBC is a highly clonal group, nevertheless, random insertion and deletion events contribut e to between and within lineage diversity that may be of grave conseque nces to global TB control clinical management, drug and vaccine development 10,15 19 The MTBC began and expanded out of Africa, following the first human migration, with global dispersal estimated to have occurred in the last 10,000 years 6,20,21 Several studies support the conclusion that the MTBC pre date modern humans; recent findings also suggest that humans and animal adapted MTB C share a common ancestor, suggesting co evolution in lieu of descent by transmission 21 The population and the early trade migrations 6,22 Rising evidence suggests that the long co existence with human has resulted in a symbiotic relationship whereby the bacterium uses the host immune response to foster its survival and transmission potential 9,23,24 The MTBC has no known environmental reservoi r, thus human demography and host

PAGE 16

16 immune response have greatly shaped the MTBC population structure as we know it today 10,24 its success to es tablish initial infection and generate secondary cases 10,2 3 Evidence from the literature suggests that latency, a hallmark of the MTBC, was adapted as a survival mechanism against limited susceptible host population 6,22 Rising global population may have contributed to the emergence of more virulent MTBC lineages, th markedly shorter latency period 10 Global Epidemiology of Tuberculosis In 2014, globally, there were 9.6 million cases of TB and 1.5 million died as a direct result of the disease 8 Two billion of the world population is estimated to already be latently infected and over their lifetime, five to ten percent will p rogress to active disease 8,25 TB is a debilitating disease with important social and economic implications for the patient, his family and community, as it is often adults who are economically active and women of childbearing age (15 49 years old) who are most often affected 3,7 The bulk of TB cases, 95% of cases and 98% of deaths, occur in middle and low income countries, where poverty and limited resources contribu te to the continued spread of the disease 7,8 Nevertheless, aided by population movement and migration, TB presents a challenge in high income countries, where, despite overall low population incidence, TB foci thrive in the foreign born population, man y of whom emigrated from high TB burden countries 26 28 I mmigration may also serve as a conduit for the introduction of diverse MTBC strains into low incidence settings likely to complicate control efforts.

PAGE 17

17 The Synergistic Influence of HIV on the Global Tuberculosis Burden HIV is in itself a major public health problem of global importance. Unfortunately, HIV also acts synergistically with tuberculosis to cause substantial morbidity and mortality worldwide. Countries with high tuberculosis burden also have high HIV prevalence and often for HIV infected patients tuberculosis is the primary opportunistic infection leading to death 29 Of the 33.4 million people l iving with HIV worldwid e in 2008 30% were either latently or actively infected with tuberculosis 29 Thirteen percent of the 8.8 million new cases of tuberculosis were co infected with HIV in 2010 30 In addition, 12 percent of the 1.5 million tuberculosis related mortality were att ributed to HIV infection in 2014 30 TB and HIV share some of the same social and economic determinants such as poverty, access to testing and treatment 29 Risk taking behaviors such as injecting drugs drive HIV transmission while also being socio economic determinants of tuberculosis 29 TB known risk factor for the reactivation of latent infection leading to active tub erculosis disease 29,31 People latent ly infected with tub erculosis hav e 21 to 34 times higher odds of developing active tuberculosis disease if they are also co infected with HIV 29 On the other hand, TB speeds the progression of HIV infection, where HIV infected individual, especially those with low CD4 cell counts tend to have more severe disseminated TB disease and increased susceptibility to other opportunistic infections 29,30 Unlike other opportunistic infection s in HIV infected individuals, TB is contagious and readily transmissible and has contributed to a number of nosocomial outbreaks in HIV treatment centers 30,32

PAGE 18

18 Drug Resistance and Tuberculosis Control Multidrug resistant tuberculosis (MDR TB) defined as resistance to the first line antibiotics Isoniazid and Rifampicin, poses a serious threat to the possible elimination of tuberculosis by 20 50 33 Primary MDR TB which is MDR TB in people who had not previously been treated for TB is of particular concern as it infers spread and transmission of drug resistant strains of the TB germ 31,34 The first cases of MDR TB were reported shortly after the introduction of rifampicin in 1966 35 Large outbreaks of hospital acquired MDR TB in both patients and healthcare workers in New York City in 1992 and later in Russia, Spain, Chile and Italy precipitated MDR TB to the status of global pub lic health problem, and a threat to the effective control and possible elimination of tuberculosis 35 Primary resistance hints at transmission of drug resistant strains in communities 36 Secondary MDR TB results from inadequate treatment regimen and/or non adherence 3,35,36 Globally, 3 .7% (95% CI: 2.1 to 5.2%) of treatment nave and 20% (95% CI: 13 to 26%) of previously treated tuberculosis cases have MDR TB 3 Nevertheless, t hese estimates may just be the lower end of a problem of greater magnitude 3 Many of the countries hardest hit by TB do not have the diagnostic capacity or the surveillance systems in place to capture MDR TB cases 3 MDR TB treatment tends to last longer, is more toxic, costly and less effective 3,35 Drug susceptible TB requires an average of 6 month treatment, have a cure rate of 86% and cost $250 per patient 35,37 It requires an average of 20 months of treatment with more toxic second line drugs at a cost ranging from $5,930 to $14, 348 per patient to treat MDR TB for a cure rate ranging from 48% to 70% 32,35,37 Death rates for MDR TB have been estimated to be between 3.5% to 14% 35 In the deve loping world, TB treatment and control is done under the World Health Organization strategy: Directly Observed

PAGE 19

19 Therapy Short course or DOTS which relies on five components: political and financial commitment, rapid case detection, standardized treatment r egimen, supervision and patient support, adequate drug supply and stock management, and a monitoring an evaluation system to measure impact 35 Several studies have identified poor DOTS as the underlying cause of MDR TB 32,35,38 40 Tuberculosis Path ophysiology and the Quantification of Transmission Tuberculosis is mainly transmitted through aerosolized droplets containing MTBC and successful infection that is latent or inactive (LTBI) establishes in 30% to 50% of exposed individuals 41 LTBI cases have a 10.0% lifetime risk of progression to active disease, with 5.0% of individuals doing so within the first two years post infection; those who are HIV infected have a 10.0% annual risk of progression to active disease 41 Recent infection i s more likely to transition to active disease that is infectious and as such is epidemiologically important for TB surveillance 42 Rapid detection and treatment of these cases is imperative to the control and potential elimination of TB in low incide nce countries 42,43 Insertion Sequence (IS) 6110 based restriction fragment length polymorphism (RFLP) was the first molecular fingerprinting method used to quantify MTBC transmission 44 46 Although IS6110 RFLP remains more discriminatory, it has been supplanted by less labor intensive and more standardized metho ds such as spacer oligonucleotide typing (Spoligotyping) and mycobacterial interspersed repetitive units (MIRU VNTR ) 46,47 Spoligotyping and MIRU VNTR used toge ther provide acceptable discriminatory power to differentiate disease due to reactivation of latent infection from transmission. The availability of large databases of MTBC strain Spoligotyping and MIRU VNTR patterns have facilitated TB molecular investiga tion by allowing for strain

PAGE 20

20 comparison and identification 14,48 Nevertheless, these markers are unable to resolve timing and directionality of transmission events 44 Next generation whole genome sequencing (WGS) is rapidly becom ing the method of choice to study the evolution and transmission dynamics of monomorphic bacteria such as the MTBC 49 54 WGS allows researchers to screen a larger proportion of the M TBC geno me for sing le nucleotide polymorphism (SNP). SNP differences between strains allows for the unequivocal delineation of previously unrecognized outbreaks 49,50,54 In addi tion, the analysis of these SNP wi thin a Bayesian coalescent framework allows for the investigation of the evolutionary history and transmission dynamics of specific strains 55 59 Tuberculosis Acquisition and Transmission among Haitian Immigrants Tuberculosis Epidemiology in Haiti An estimated 11 million people inhabit t he Republic of Haiti 60 The island nation occupies the western half of Hispaniola which it shares with the Dominican Republic. A stunted economy and political instability have severely affec ted effective DOTS implementation and TB control in Haiti. In 2014, 25.0% of the National TB control program was unfunded and 75% of the $ 8.6 million budge t came from international donors 60 Haiti is estimated to have the highest TB burden in Latin America and the Caribbean region, with prevalence and incidence of 244 and 200 per 100,000 respectively in 201 4 60 Outside of Sub Saharan Africa, the island nation of Haiti has the highest HIV burden in the Latin America and Caribbean Region, with an estimated HIV prevalence of 2.2% among adult population 61 In addition, several studies have reported increasing rates of multidrug resistant tuberculosis 62 65 ; one study reported a 6% prevalence of MDR TB in treatment nave persons in Port au Prince, the capital

PAGE 21

21 and most populou s city 62 This prevalence characterizes Port au Prince as a MDR TB possible 66 At the national level, MDR TB estim ates range from 2.5% (95% CI: 1.2 3.8) in treatment nave patients and 11% (95% CI: 6.1 17) in retreated cases. Following the devastating January 12, 2010 earthquake, routine surveillance identified an MDR TB prevalence of 5.0% in the crowded tent citi es around Port au Prince 65 In the G ressier/Leogane region, our group has detected a MDR TB prevalence of 10.0%. Haitians have a long history of rural urban and international migration. As of 2012, 54.8% of the Haitian population was urbanized, with a projected growth of 2.5% from 2012 to 2 030 67 It is also estimated that three in four Haitians live outside of Haiti, notably, in the U.S. 68, 69 Due to their provenance from a high TB incidence country, Haitian immigrants represent an important population group for TB control in their adopted countr ies Tuberculosis in the U.S. Immigration has had a substantial impact on TB epidemiology in the U.S. TB incidence has drastically decreased over the past 20 years in the general U.S. born population; however, the same rate of decline has not been observed among persons born outside of the U.S. 70 An estimated sixty six percent of the annual in cident TB cases were reported among the foreign born population in 2015 71 TB control in the U.S. is largely attributed to efforts to rapidly diagnose and treat infectious cases, aggressive contact tracing and treatment of latently infected individu als at risk of progressing to active disease 72 Currently, less than a quarter of all reported TB cases in the U.S. results from recent transmission. Among the foreign born, less than 10.0% of recently transmitted cases can be traced to transmission that occurred in the U.S. The available

PAGE 22

22 literature shows that at the national level, TB disease risk in the foreign born population is largely due to reactivation of disease acquired prior to emigration and often peaks two to fiv e years post immigration and stay s elevated throughout the lifetime 73,74 However, there may exist regional var iations in these estimates, especially in states where the annual TB incidence is higher than the national average of 3.0 per 100,000 and large proportion of the population originates from high TB burden countries 75 Tuberculosis among Haitian Immigrants in the U.S Haitian immigrants represent one of the fastest growing ethnic groups in the U.S. Due to their provenance from a high HIV and TB burden country Haitians also represent an important population group for the epidemiology of TB in their adopted country. At th e national level, Haitians represent about 1.3% of the almost 43 million foreign born persons in the U.S. but account for 3.0% of the annual TB burden 71 The annual case rate is 189 per 100,000 among recent Haitian immigrants (<2 years) and 40 pe r 100,000 among those in the U.S. two years or more 73 Almost half of legal Haitian immigrants in total population 20 Haitians account for about a quarter of all reported TB cases among the foreign born population in the state (Figure 1 1 ). TB genotyping surveillance data show that TB disease in the Haitian population in Florida is characterized by high levels of genetic clustering, indicative of recent transmission. In addition, a TB cluster involving a strain historically isolated among Haitians in the State may be involved in an outbreak in the Florida prison system. These observations support conclusions that there may be foci of TB transmission in Haitian communities with transm ission risk to non Haitians. There is, however, limited literature on the factors driving TB incidence and potentially

PAGE 23

23 contributing to TB transmission within the Haitian population and, due to limitations to current genotyping methods, we cannot unequivoca lly measure recent transmission 78 Haitians maintain strong ties to Haiti through remittance and travel. It is estimated that 90% of Haitian living in the US send money to Haiti; an average of $163 monthly 79 Substantial numbers of Hai tians travel to Haiti each year Research shows that people who travel to visit friends and family have the same TB risk as the local population in the country they visit 80 82 Increased frequency and duration of trips may put Haitians at elevated risk of TB acquisition while in Haiti. The combination of repeated exposure to M. tuberculosis high HIV prevalence, and low level of LTBI screening and treatment in the Haitian population living in Florida may account for the elevated TB burden in Haitian communities. Contribution of Dissertation This thesis seeks to understand the factors driving TB acquisition and transmission among persons of Hait ian descent living in Florida. Persons of Haitian descent have a much higher TB case rates in the State of Florida compared to all other immigrant groups (Figure 1 1). In addition, surveillance data suggests local transmission within Haitian communities w ith spillover risk to non Haitians. We posit that post immigration mobility between Florida and Haiti contributes to elevated TB infection risk among Haitians living in Florida. Coupled with the high community level HIV infection, newly acquired infection progresses quickly to active disease resulting in an elevated overall community level risk of TB acquisition (Figure 1 2) To test this hypothesis, we first explore the influence of immigration on TB genetic diversity and transmission in Florida with a fo cus on Haitian immigrants These data provide an understanding of the role of Haitians on TB transmission in Florida and identifies M. tuberculosis Haarlem

PAGE 24

24 sublineage as the strain responsible for putative transmission events in the state over the past si x years. In chapter three we apply WGS and Bayesian phylogenetic methods to investigate a Spoligotyping and 24 locus MIRU VNTR defined Haarlem cluster involving U.S. born persons in the Florida Prison System and Haitian born persons in the community. SNP analyses suggest ed two concurrent outbreaks separated by birth country. In chapter four we investigate d the frequency and duration of post immigration return trips to Haiti and its association with TB disease among Haitians living in Florida. Irrespectiv e of the number of years surveyed participants reported to have lived in the U.S., on average Haitians made at least 7.13 trips to Haiti and each trip lasts an average of two weeks. In additions, the number of trips increased the longer people reported to have lived in the U.S The data presented have important impact for tuberculosis control in Haitian immigrants in Florida. To our knowledge, we are the first to measure the occurrence of return trips to Haiti and its impact on TB disease among Haitians imm igrants in Florida. In addition, our analysis of the MTBC genetic diversity in the state of Florida followed by our use of WGS to investigate a Spoligotyping and 24 locus MIRU VNTR TB outbreak has the potential to pave the road for the integration of WGS i nto TB control activities in Florida, leading to more targeted contact investigation. Together all three studies contribute to the understanding of TB transmission dynamics in Florida among two high risk groups and lay important groundwork for future studi es looking at interventions to control and prevent TB disease in these subpopulations.

PAGE 25

25 Figure 1 1. Conceptual Model of Tuberculosis Burden in Haitian Communities in Florida, U.S

PAGE 26

26 Figure 1 2 Reported Tuberculosis Cases in the State of Florid a by Key Subpopulations 1993 2014 We drew the f igure in Excel, using 22 years of TB surveillance from the State of Florida Population data for the State of Florida pulled from FloridaCharts We used the proportion of persons 5 years and above who speak English less than perfect at home as a proxy measure for foreign birth. Estimates for the Haitian population were pulled from census bureau and was only available for the years 2007 2014.

PAGE 27

27 C HAPTER 2 SPATIOTEMPORAL CLUSTERING OF MYCOBACTERIUM tuberculosis COMPLEX GENOTYPES IN FLORIDA: GENETIC DIVERSITY SEGREGATED BY COUNTRY OF BIRTH 1 Introduction Tuberculosis (TB) continues to present a major global public health challenge. In 2014, 9.6 million people developed the disease and 1.5 million died 8 An estimated two billion of the world population is latently infected, and over their lifetime, five to ten percent wi ll progress to active disease 25 The bulk of TB morbidity and mortality is concentrated in low and middle income countries 8 Nevertheless, aided by population movement and migration, TB also presents a challenge in high income countries 26 28,83 The United States (U.S.) is a low incidence country where immigration has had a substantial impact on TB epidemiology 2 6,71,84 Public health efforts such as contact tracing and prophylactic treatment of latent infection have led to a substantial decrease in TB incidence over the past 20 years; from 10.4 per 100,000 in 1992 to 3.0 per 100,000 in 2014 71 This ste ady decline, however, obscures the substantial burden among foreign born persons whom continue to account for a larger proportion of the incident cases in the U.S. 71 As incidence is decreasing overall in the general U.S. population, TB is incre asingly concentrated within high risk U.S. born individuals 50,85,86 and immigrant subgroups in large urban centers 87 In such settings prompt identifications of local transmission is important to the continued efforts towards TB elimination. 1 This chapter is published: Sraphin MN, Lauzardo M, Doggett RT, Zabala J, Morris JG Jr, Blackburn JK (2016) Spatiotemporal Clustering of Mycobacterium tuberculosis Complex Genotypes in Florida: Genetic Diversity Segregated by Country of Birth. PLoS ONE 11(4): e0153575. d oi:10.1371/j ournal.pone.0153575

PAGE 28

28 Spacer oligonucleotide typing (Spoligotyping) and mycobacterial interspersed repetitive units variable number tandem repeats (MIRU VNTR) used together have been instrumental to the detection of Mycobacterium tuberculosis complex (MTBC) outbreaks in the community by classifying strains into clusters of isolates with identical genotype patterns, with clustering serving as a proxy measure for transmission 88,89 In the U.S., universal genotyping of cul ture confirmed TB cases by both Spoligotyping and MIRU VNTR has been available since 2004 90 Evidence from a number of studies, however, suggest that w ithin immigrant groups from high TB burden countries and local born populations from enclosed rural regions, MTBC genotype clusters may result 89,91,92 A number of studies have used a combination of genotyping and spatial scan statistics to quantify MTBC transmission in heterogeneous set tings 93 95 Indeed, tracking genotype clusters in space and time may provide a better picture of MTBC transmission dynamics. In this study we used space time scan statistics in SaTScan 96 to identify foci of M. tuberculosis genotype clusters due to recent transmission in Florida, U.S. and assess the influence of foreign birth on clustering. Spatiotemporal clusters were evaluated in relation to the spatial distribution of M. tuberculosis genetic diversity in the State. In 2014, almost 20.0% of the more than 19.8 millio n residents in Florida were foreign born 97 During the same year, TB incidence was 3.0 per 100,000 and over half of the cases occurred in the foreign born population 71 The foreign born population in Florida is localized in the Southern regions of the State, including the largely urban co unties of Miami Dade, Broward and Palm Beach 98 However, the spatial distribution and genetic diversity of M. tuberculosis in Florida is unknown. As TB incidence is

PAGE 29

29 decreasing in the U S so too is funding allocated to TB control. State TB control programs have to focus limited resources to targeting high risk groups and areas of active TB transmission 99,100 Studies combining geospatial scan statistics with molecular markers of TB transmission and epidemiological data will help public health officials focus limited resources to areas where they are most needed. Materials and Methods Study Population As part of the MTBC genotyping surveillance program, at least one isolate from every culture confirmed TB case in the U S is genotyped by Spoligotyping and 24 locus MIRU VNTR using standardized methods 90 The genotyping data is linked to the National Tuberculosis Information Man agement System (TIMS) using a unique patient identifier so that initial drug susceptibility profile, clinical, socio demographic and risk factor data are available for each genotyped isolate 90 From January 1, 2009 to December 31, 2013, Spoligotyping patterns in octal designation, 24 loci MIRU VNTR, patient residential zip code and age at time of diagnosis, pre treatment drug resistance profile, HIV status, history of incarceration, and country of birth were available for 2,531 culture confirmed TB cases. Strain Classification and Spatial Mapping The 2,531 isolates were classified into 74 strain families and sublineages using the web application MIRU VNTR plus on the Spoligotyping and 24 locus MIRU VNTR data 48 Loci were ordered as reported in Mazars et al., 2001 101 and CDC notations used for ambiguous and indeterminate sites Strain matching was allowed within up to four locus difference, using the categorical genetic distance measure. We downloaded

PAGE 30

30 a polygon of the five digit Florida ZIP Code Areas from the Florida Geographic Data Library (FGDL), current as of 2012 102 Using the geographical coordinates at the centroids of each polygon, we created a GIS database by geocoding each reported and digit residential zip code at the time of diagnosis. We assumed no substantial chang e in zip code shapefile over the study period. Spatial Descriptive Statistics, Genotyping Coverage and Genotype Diversity We evaluated bias in genotyping compared to culture confirmed cases by computing the yearly unweighted spatial mean centers (the ave rage X and Y coordinates) for the reported and genotyped data We examined yearly directional trends within one standard deviation of the means by computing the standard deviation ellipses and standard distances for zip code centroids using the directional distribution tool in ArcGIS Spatial Statistics Tools (ESRI) 103 To compare the mean centers and dire ctional distribution measures for the two GIS databases, we overlaid them on a map of Florida. A shift in the yearly spatial mean centers would represent a bias in reported culture confirmed TB cases compared to cases that were eventually genotyped. We ca lculated the genotyping coverage as the proportion of the culture confirmed TB cases that were genotyped for each zip code location using Geospatial Modelling Environment v7.2.1 104 We measured the overall 24 Locus MIRU VNTR discriminatory power for the sample using the Hunter Gaston discriminatory index (HGDI) 105 calculated using the equation:

PAGE 31

31 (2 1) where N is the total number of strains in the sample, s is the total number of different MIRU VNTR patterns, and n j is the number of strains sharing the same j th pattern. We measured the MIRU VNTR allelic diversity ( h ) at each of the 24 different loci according to the equation: (2 2) where x i is the frequency of the ith allele at the locus 106 In computing the allelic diversity, we removed indeterminate and ambiguous sites; thus, the total number of isolates analyzed was 2,414. To assess the spatial distribution of MTBC genetic diversity, we computed an average allel ic diversity at the zip code centroid using the same formula with x i representing the frequency of the ith allele at that location. MTBC Spatiotemporal Clusters and TB Transmission We grouped the strains into major lineages and sublineages: Beijing, East African Indian (EAI), Haarlem, Latin American Mediterranean (LAM), T, X, S, U, and Central Asian (CAS) and M. Bovis Strains of low frequencies (Africanum, Manu1, Manu2, H37Rv, and Zero ) strains were analyzed separately. Within each sublineage category, we defined state based genotype clusters as two or more cases with identical Spoligotyping and 24 locus

PAGE 32

32 MIRU VNTR profile. We used the discrete retrospective space time permutation model implemented in SaTScan v9.4 to test for high rates of genotype clusters that also cluster in space and time 96 The Bernoulli space time model would have been most appropriate; however, it does not allow for covariate adjustment 96 As we lacked information on the at risk population at a resolution comparable to the genotyped case data, we were unable to implement the Poisson space time stati stics as previously used to identify clusters of recent TB transmission in the U.S. 93 The space time permutation model is ideal when the information on the population at risk is not available and is described in detail elsewh ere 107 Briefly, the space time permutation model derives case expectations under the assumption of independent spatial inte raction between case dates and locations. To operationalize the space time permutation model, the study area is scanned using cylinders of varying sizes, with the base representing space and the height representing time. The number of cases inside the cyl inders is compared to the case expectations outside by Poisson generalized linear ratio (GLR), where the cylinder that maximizes the GLR is considered the most likely cluster and additional significant clusters are defined as secondary 107 We identified space time clusters at a maximum temporal and spatial window of 20.0% and 50.0% of the study period and case data, respectivel y after testing different temporal and spatial window combinations with no significant changes in cluster size, location or test statistics. We made no distinction between primary or secondary clusters. We only allowed one set of geographical coordinates p er location and no overlapping clusters. Statistically significant clusters were evaluated at a p permutations under the null assumption of complete spatial randomness.

PAGE 33

33 To account for differences in the spatial di stribution of foreign born individuals and HIV rates in Florida, we first ran unadjusted space time permutation models and then models adjusted for the binary indicators of county level risk of HIV and foreign born population density 108 Covariates were considered to affect the unadjusted clusters if they reduced the test statistic, which would suggest that part of the excess cases within the unadjusted cluster could be explained by th e covariate 108 Single year HIV rates and estimates of the proportion of individuals 5 years and older who speak a language other than English at home, as a proxy measure of foreign birth covering the period 2009 to 2013 were obtained from FloridaCharts 98 We computed the rate change for each county and the state, comparing 2009 rates to rates reported for 2013. We created binary indicators of county level HIV risk and foreign born po pulation to that of the state. A county was coded as having an elevated HIV risk and/or a high density of foreign born individuals, if the rate change for these two indic ators increased in comparison to the S Multinomial Spatiotemporal Clusters In cases where we detected statistically significant space time permutation clusters, we ran multinomial space time models to assess the influence of country of birth on MTBC genotype clustering using the same settings as for the space time permutation models 96 Self reported country of birth and number of years since immigration were available for all foreign born cases. Based on the frequency of observations, we created a five level indicator that grouped report ed country of birth into US/Canada, Haiti, Latin America, South America and Other. Timing of immigration was

PAGE 34

34 an increased occurrence of MTBC genotype clusters from particular populations based on birth country 109 Ethical Clearance The data were collected as part of public health practice and thus participants were not consented. The Institutional Review Boards (IRB) of the University of Florida and the Florida Department of Health, respectively approved the use of the data for this study and all data were de identified before analyses. Results Between 2009 and 2013, 3,739 TB cases were reported to the Florida Department of Health (FDOH). We excluded 879 observations : 808 culture negative cases and 71 cases witho ut zip code information. Of the remaining culture confirmed TB cases, 2, 531 (88.5 %) were genotyped. The period January 1, 2009 to December 31, 2013 represented the period during which genotyping coverage was ideal in Florida due to improvement to the sur veillance system. We observed the lowest number of genotyped cases in 2009 and 2013, while a similar number of reported cases were genotyped during 2010, 2011 and 2012 ( Table 2 1 ). Overall, 46.3% of genotyped cases clustered i.e. two or more cases shared identical Spoligotyping and 24 locus MIRU VNTR patterns. Across the five year period, genotyped cases differed significantly by the number of clustered cases, history of incarceration and country of birth. Among those born outside the U.S., genotyping did not differ significantly by timing since immigration to the U.S. In addition, there was no significant difference by gender, age at diagnosis, HIV status or history of homelessness.

PAGE 35

35 Genotyping Coverage and MTBC Genetic Diversity The 2,531 isolates were ca tegorized into 1,644 distinct 24 locus MIRU VNTR patterns consisting of 291 genotype clusters of two or more isolates. The discriminatory index for the sample was very high (HGDI=0.997). The final sample size for the genetic diversity analyses consisted of 2,510 observations; 21 genotyped observations were excluded as they could not be geocoded. Genotyping coverage over the five year study period was low in some locations where we observed a percentage of culture confirmed cases genotyped ranging from 0.0% to 25.0% ( Fig ure 2 1A ). Overall, genotyping coverage reached above 80.0% for most parts of the study region. In addition, the spatial distribution of genotyping efforts, based on spatial descriptors, showed little difference from the distribution of repor ted TB cases in the study region over the five year study period when comparing the reported culture confirmed cases to the genotyped cases ( Figure 2 2 ). In Table 2 2 we show the allelic diversity ( h ) for each of 24 MIRU VNTR loci. The diversity ranged fro m 0.09 for the MIRU 27 locus to 0.824 for the MIRU 4156 locus. Four loci (MIRU 02, MIRU 20, MIRU 24, MIRU 27) exhibited low level of diversity ( h <0.30 ) We have provided a supplemental table showing the sublineage specific allelic diversity for isolates i ncluded in this study ( Table A 1). Some of the sublineages were highly diverse at the loci, while most of the others were completely monomorphic (0.00 > h <0.30). In Figure 2 1B we show the spatial distribution of average allelic diversity ( h ) of the MTBC strain families isolated during the study period. Although a number of locations did not report cases during the study period, allelic diversity ranged from low to moderate (0.00 > h < 0.30) in most locations. Diversity was especially high ( h > 0.40) in t he Southeast, East Central and Northeast

PAGE 36

36 Regions, with the highest level of genetic diversity observed in known foreign born population hubs in Central and South Florida. Space time Genotyping Clustering We ran twelve space time permutation models inclu ding only genotype clustered strains: 107 Beijing strains, 17 M. Bovis strains, 6 CAS strains, 32 EAI strains, 250 Haarlem strains, 179 LAM strains, 31 S strains, 178 T strains, 32 U strains, 282 X strains, 4 others and 43 undefined, respectively. We iden tified one significant space time cluster of the Beijing family in the Southeast Region of the state, which persisted from March 2009 to October 2012 ( Fig ure 2 3 A ). However, the cluster was no longer significant once we adjusted for county level HIV risk a nd foreign born population density. We detected two significant clusters of the Haarlem sublineage in North Central and East Central Florida ( Fig ure 2 3 B ) These clusters persisted even after adjusting for both county level HIV risk and foreign born popul ation density. In addition, cluster sizes, location or test statistics did not vary with varying combination of spatial or temporal windows. Of the 250 cases involved in the Haarlem genotype cluster, 163 were U.S. born, 29 were recent immigrants (<5 years) and 58 had lived in the U.S. at least five years. Based on the adjusted space time permutation models, potential recent TB transmission in Florida involved Haarlem strain families and was estimated at 15.60% overall; 21.47% (35/250) among U.S. born cases, 5.17% (3/58) among foreign The multinomial models showed three areas of high occurrence of the Haarlem sublineage; one that expanded over the Northern region of the state and the other two localized in the Southeast Region ( Fig ure 4 ). The characteristics of the cases included

PAGE 37

37 inside each of these clusters are presented in Table 3 The Northern cluster included a total of 50 cases all born in the U.S./Canada (RR=1.75). Fift y percent of the strains were of the H2 clade, 48.0% of the H1, and 2.0% were of the H3 clade. Four percent of the strains were resistant to at least one first line anti tuberculosis drugs (Isoniazid, Rifampin, and Ethambutol), a quarter of the cases were HIV positive and 56.0% were incarcerated at the time of TB diagnosis. Among persons with a history of incarceration, infection with the H2 sublineage was significantly higher (89.3%) compared to the other sublineages (p<.0001), while there was no significa nt difference in HIV and drug resistance prevalence between the three clades. The two Southern clusters (A and B) included a total of 66 cases. Over 90.0% of the 35 cases in Cluster A were born in Haiti (RR=5.05); 60.0% had immigrated to the U.S. more than five years prior to diagnosis (RR=2.51) and 29% had immigrated less than five years prior to diagnosis (RR=3.48). Thirty percent of cases within cluster A were HIV positive and 5.7% were incarcerated at the time of diagnosis. Over 34% of the cases were i nfected with an H1 clade, 17.1% H2 and 48.6% H3. About 14.0% of the strains were resistant to at least one first line TB drugs. Within Cluster B, there were greater than expected occurrence of foreign born individuals from Latin America (RR= 6.53), South America (RR= 4.48) and Haiti (RR= 2.00) as compared to outside the cluster. In addition, the relative risk of cluster membership was 2.3 among recent immigrants and 3.16 among immigrants who had been in Florida at least five years prior to diagnosis. About 16% of the strains were of the H1 clade while 25.8% and 48.4% were of the H2 and H3 clades respectively. Resistance to at least one anti tuberculosis drug was 9.7% and 19.4% of the cases were HIV positive. Prevalence of HIV, drug resistance or history of incarceration did not

PAGE 38

38 differ by infection with either one of the three Haarlem clades in either of the two Southern clusters. Based on the multinomial scan results, recent transmission was estimated at 46.40% (116/250) overall; 36.81% (60/163) among U.S bo rn cases, 62.07% (18/29) among recent immigrants and 65.52% (38/58) among immigrants who have lived in Florida at least five years. Discussion The MTBC demography in its diversity. Indeed, we identified 74 different sublineages using Spoligotyping and 24 Locus MIRU VNTR in this study. Nevertheless, many of the strain families were monomorphic at most of the loci tested while some were quite diverse, illustrating the contrasting evolutionary hi story of these loci within the foreign born and U.S. born populations in the State. Florida is a popular immigration destination for nations in Latin America, South America and the Caribbean, many with a national TB incidence several magnitude higher than that of Florida 110 The spatial distribution of allelic diversity observed in this study is consistent with prior reports that have shown little contribution of Foreign born population to TB transmission in the U.S. 73,74 We estimated that close to five percent of TB cases reported in Florida during 2009 2013 was potentially due to recent transmission. In addition, among foreign born persons, the relative risk of cluster membership was equally likely between recent (< 5 years) America. We identified potential transmission clusters of the Haarlem family spanning different regions of the state. In addition, these space time clusters remained significant even after adjusting for both foreign born population density and county level HIV risk.

PAGE 39

39 These observations support conclusions that these clusters are not a result of common MTBC genotype reactivation due to HIV infection in Foreign born population hubs in Flo rida and thus strengthen the evidence for M. tuberculosis Haarlem recent transmission in the state. Our findings are consistent with studies that have documented the circulation of the Haarlem sublineage in high TB incidence settings 111 113 as well as among immigrant groups in the U S 15,26,84 Clustered TB cases among recent immigrants are often interpreted as disease acquired prior to immigration 74 which does not negate the possibility of active TB transmiss ion within recent immigrant groups in the U.S 114 Of the 18 recent immigrants with genotype and space time clustered dise ase in our study, five had lived in the US at least two years prior to diagnosis, which falls well within the estimated time for clinical presentation of recently acquired disease 41 The dichotomy between recent and long term immigrant highlights missed opportunities for effective TB control among foreign born persons in the U.S. eages, and along with the ubiquitously successful Beijing strain, is associated with increased virulence and 23 Interestingly, we identified space time clusters of Haarlem genotypes clusters in regions of Florida where genetic diversity is also relatively high ( Figure 2 5 ). The results could signify that among the pool of circulating MTBC genotypes in the State, these Haarlem clades are more successful. As an obligate pathogen, MTBC success is intrinsically tied to its ability to establish initial infection and generate secondary cases 23 As this study was cross sectional and the clusters identified were small, we lacked the statistica l power to investigate factors driving the selection of Haarlem clades in Florida. Future

PAGE 40

40 studies comparing the secondary infection rates of these genotypes and their capacity to cause lung cavitation as proxy measures for virulence are warranted. Compar ing the clinical characteristics of the multinomial Haarlem clusters identified in this study, we observed that resistance to first line anti tuberculosis drugs was significantly higher in the Southern clusters predominated by Foreign born persons compared to the Northern cluster, while HIV prevalence was similar in both clusters. Our findings are not surprising as initial drug resistance to first line anti tuberculosis drugs is expected to be higher in individuals born in countries where TB treatment is at first empirical and infrastructure for drug susceptibility testing is lacking [45] HIV and anti tuberculosis drug resistance have been cited as two contemporary drivers of MTBC geneti c diversity, as they may act together to increase patient infectious period when drugs to not work and increase transmissibility of strains to immunocompromised HIV infected persons 24 However, there is inconclusive evidence as to whether HIV co infected persons transmit more TB than HIV negative individuals or whether drug resistance mutations confer a fitness advantage or disadvantage 24 In our study we did not observed a significant difference in HIV and drug resistance prevalence between the different Haarlem sublin eages, indicating that transmission was independent of HIV infection and not affected by drug resistance mutations. Prior studies that have documented the increased transmission of multidrug resistant Haarlem strains in South African children and HIV nega tive Tunisians 116,117 Nevertheless, as our study was a cross sectional analysis, we lack the evidence to conclusively comment on the possible transmission of multidrug resistant Haarlem strains in Florida.

PAGE 41

41 The Haarlem sublineage is among the most geographi cally widespread modern MTBC sublineages and is found ubiquitously throughout North America and the Caribbean; nevertheless, the geographical distribution of certain clades may be more restricted 118 In the US, the H2 and H3 clades are consistently reported in states where high proportion of the popu lation is foreign born 14,26 The striking difference in H2 clade distribution and patient clinical characteristics observed between the multinomial Haarl em clusters in this study may be a result of host pathogen co evolution fostering more efficient MTBC transmission within population groups from the same region of birth 9,119 MTBC transmission in urban centers where foreign and local populations i nteract tends to occur predominantly among high risk individuals or those with impaired immune systems 119 A significantly higher proportion of individuals infected with an H2 strain in the Northern cluster had a history of incarceration and HIV prevalence within the three clusters was twenty percent and higher. Haarlem subline age is among the predominant MTBC genotypes described in Haiti and among Haitian immigrants 63,120 Prior molecular investigations in our laboratory had identified the H3 clade as emergent, i.e. sp reading faster than background transmission rate, in the Haitian population living in Florida, with history of incarceration as the primary risk factor for transmission (unpublished data). It is impossible to postulate as to the transmission link between t hese three clusters using the current molecular markers. Nevertheless, it is likely that a combination of poor TB screening practices in Florida jails and HIV induced immunosuppression played a role in the spread of the Haarlem clades in Florida. The stren gth of these analyses is that we used a combination of two biomarkers to track MTBC genotype clusters in space and time over a period of 5 years in Florida.

PAGE 42

42 Combining Spoligotyping and 24 locus MIRU VNTR increased our discriminatory power to detect true MT BC genotype clusters, indicative of recent transmission. In addition, the five year time period allowed for enough time to capture recent transmission that resulted in active disease reported to FDOH. It is nevertheless important to point out some importan t limitations of the analyses. First, we used surveillance data, which may suffer from reporting bias. Although by statute, TB is a reportable disease in Florida, it is possible that not all cases are captured. In particular, some cases diagnosed by privat e laboratories in 2009 were not genotyped and not included in these analyses. In addition, only culture confirmed cases are genotyped; thus, cases diagnosed based on clinical criteria alone and culture negative cases do not figure in these analyses. We obs erved a statistically significant difference in genotyped cases compared to reported cases among persons with a history of incarceration and country of birth. These findings may reflect a bias in genotyping due to outbreak investigations in the State of Fl orida. Indeed there have been a number of high profile TB outbreaks in the State that may have resulted in increased genotyping surveillance 121,122 It is also li kely that the difference reflects the decreasing trend in TB incidence in the general U.S. population but the higher burden among high risk groups, such as those with a history of incarceration, homelessness, or drug use 123 125 We were concerned that the genotyping coverage for the State of Florida may not be representative of the reported culture confirmed cases. This bias was formally evaluated using spatial descr iptive statistics ( Figure 2 2 ). Although shift in the data could be observed for 2012, overall, there was little difference in dispersion (standard distance) or directionality (deviational ellipse), as evidenced by the high level of overlap in the circles and ellipses. In fact, we think the shift observed in

PAGE 43

43 2012 could be attributed to decreasing TB incidence and ensuring change in TB epidemiology in Florida, whereby more and more of TB cases are reported within foreign born individuals who tend to live in urban centers in Central and South Miami. In areas of low TB transmission and high level of foreign born population, it is expected that MTBC genetic diversity will be high and representative of the population distribution. As genotyping coverage was low i n some areas of the state, genetic diversity may be higher than estimated in this study. However, the spatial distribution of the reported and genotyped cases for each of the five years spanning the study period showed that genotyping efforts were equally distributed throughout the state and followed a similar spatial pattern as the case report data. Thus, the MTBC genetic diversity reported in this study is representative of the reported TB cases in Florida during 2009 and 2013. An inherent limitation of the SaTScan methods is that it assumes a constant radius for each candidate spatial temporal window, which effectively limits clusters in space and time. In reality, TB outbreaks tend to disperse in space as they progress, thus a conical rather than a cyl indrical window would be more appropriate to simulate this natural TB outbreak progression. SaTScan, nevertheless, has been shown to be very sensitive to low disease incidence, as is the case in this study and tend to overestimate rather than underestimate cluster sizes, as compared to other spatial cluster detection methods 126 Finally, as the multinomial space time models were not adjusted for foreign born population density or county level HIV risk, we cannot refute the alternative explanation that the Haarlem clades are widespread throughout North America and the Caribbean and that cluster membership reflects the spatial distribution of the US and Foreign born populations in Florida. Future studies

PAGE 44

44 involving more discriminatory molecular methods such as whole genome sequencing should help elucidate the link between these Haarlem clusters by identifying true transmission links and directionality 50,83 Substantial progress has been made in controlling TB in the US; however, public health officials should not be complacent. Potential transmission clusters of Haarlem sublineages in areas of high genetic diversity raise concern over the clonal expansion of these globally successful strain families in Florida. In particular, renewed efforts to contro l TB in the prison population is warranted, as the prison system seems to be a common ground where Foreign and U.S. born persons interact and high levels of HIV infection render persons susceptible to TB infection. In these settings, transmission bottlene cks can easily facilitate the predominance of these potentially more virulent Haarlem clades. Our findings can be used to inform TB control in Florida prisons and target interventions to Foreign born populations in the community. In particular, renewed eff orts to screen for TB infection in the jails prior to prison transfers are warranted to prevent outbreak in this vulnerable population.

PAGE 45

45 Table 2 1. Characteristics of Genotyped Tuberculosis Cases in Florida by Year of Isolation 2009 2013 Characteristics 2009 (n=382) 2010 (n=573) 2011 (n=560) 2012 (n=518) 2013 (n=498) Total (n=2531) P value Clustered Cases No Yes 183 (47.9) 199 (52.1) 333 (58.1) 240 (41.9) 291 (52.0) 269 (48.0) 279 (53.9) 239 (46.1) 274 (55.0) 224 (45.0) 1360 (53.7) 1171 (46.3) 0.0304 Gender Male Female 257 (67.3) 125 (32.7) 376 (65.6) 197 (34.4) 368 (65.7) 192 (34.3) 331 (63.9) 187 (36.1) 308 (61.9) 190 (38.2) 1640 (64.8) 891 (35.2) 0.4795 Age 25 44 Years 45 64 Years 42 (11.0) 138 (36.1) 134 (35.1) 68 (17.8) 67 (11.7) 188 (32.8) 210 (36.7) 108 (18.9) 60 (10.7) 162 (28.9) 226 (40.4) 112 (20.0) 52 (10.0) 155 (30.0) 203 (39.2) 108 (20.9) 53 (10.6) 153 (30.7) 195 (39.2) 97 (19.5) 274 (10.8) 796 (31.5) 968 (38.3) 493 (19.5) 0.7173 Origin US/Canada Haiti Latin America South America Other 199 (52.1) 54 (14.1) 52 (13.6) 47 (12.3) 30 (7.9) 286 (45.7) 71 (12.4) 75 (13.1) 65 (11.3) 76 (13.3) 256 (45.7) 78 (13.9) 53 (9.5) 78 (13.9) 95 (17.0) 272 (52.5) 55 (10.6) 54 (10.4) 67 (12.9) 70 (13.5) 216 (43.4) 65 (13.1) 56 (11.2) 69 (13.9) 92 (18.5) 1229 (48.6) 323 (12.8) 290 (11.5) 326 (12.9) 363 (14.3) 0.0018 Time in the US* < 5 Years 66 (37.7) 109 (62.7) 95 (34.4) 181 (65.6) 86 (29.7) 204 (70.3) 74 (31.1) 164 (68.9) 91 (32.9) 186 (67 .2) 412 (32.7) 844 (67.0) 0.4250 HIV Status Negative Positive Refused Missing 249 (65.2) 63 (16.5) 35 (9.2) 35 (9.2) 410 (71.6) 76 (13.3) 43 (7.5) 44 (7.7) 393 (70.2) 76 (13.3) 43 (7.5) 44 (7.7) 371 (71.6) 56 (10.8) 53 (10.2) 38 (7.3) 382 (76.7) 65 (13.1) 37 (7.4) 14 (2.8) 1805 (71.3) 343 (13.6) 211 (8.3) 172 (6.8) 0.1123 History of Incarceration No Yes 355 (92.9) 27 (7.1) 552 (96.3) 21 (3.7) 545 (97.3) 15 (2.7) 496 (95.8) 22 (4.3) 477 (95.8) 21 (4.2) 2425 (95.8) 106 (4.2) 0.0217 History of Homelessness No Yes Missing 353 (92.4) 29 (7.6) 0 (0.0) 514 (89.7) 49 (8.6) 10 (1.8) 502 (89.6) 53 (9.5) 5 (0.9) 455 (87.8) 57 (11.0) 6 (1.2) 447 (89.8) 48 (9.6) 3 (0.6) 2271 (89.7) 236 (9.3) 24 (1.0) 0.1473 Notes : *Among Foreign born cases. We lacked data on timing of immigration for three (3) cases; P value for ch i square test of equal proportion across the five year study period; among cases with known HIV status and those who refused the test. Descriptive Analyses were conducted using SAS v9.4 (Cary, NC, USA).

PAGE 46

46 Figure 2 1. Genotyping Coverage and Mycobacterium tuberculosis complex Allelic Diversity in Florida. Maps show genotyping coverage as a percent of reported cases that were genotyped (Panel A) and the allelic diversity of the different sublineages (Panel B) isolated in different study locations from 2009 to 2013. Allelic diversity sh own is an average for the 24 Locus MIRU VNTR loci isolated at a local. The smaller the number the less diverse the M. tuberculosis complex population diversity is at that location.

PAGE 47

47 Figure 2 2 The spatial means, 1 standard deviation ellipses and standa rd distances of the culture confirmed and genotyped cases for each of the five years (2009 2013). Descriptive statistics were calculated using zip code centroid for each database TIMS (Tuberculosis Information Management System) and GIMS (Genotype Informat ion Management System) for each year. Overall, little difference in dispersion (standard distance) or directionality (deviational ellipse) was observed between the two datasets. Shifts in the spatial mean for the year 2012 can be observed, likely driven by decreasing incidence and changes in TB epidemiology in the State of Florida.

PAGE 48

48 Table 2 2. Allelic Diversity of Clinical M. tuberculosis Complex Isolates in Florida, 2009 2013 MIRU VNTR Locus Number of Isolates with Indicated MIRU Allele Allelic Diversity Index 0 1 2 3 4 5 6 7 8 9 10 MIRU 02 218 2146 49 1 0.20 MIRU 04 72 24 2026 101 27 122 23 9 4 6 0.29 MIRU 10 5 9 201 714 967 449 44 6 19 0.71 MIRU 16 5 116 412 1713 165 3 0.46 MIRU20 19 242 2148 5 0.20 MIRU 23 9 22 8 201 45 1 438 664 19 8 0.56 MIRU24 3 2194 215 2 0.17 MIRU 26 2 13 215 160 374 1276 179 170 22 3 0.67 MIRU 27 6 22 51 2298 33 3 1 0.09 MIRU 31 1 318 1608 218 238 30 1 0.52 MIRU 39 6 44 1985 320 46 1 12 0.31 MIRU 40 16 333 387 1179 329 99 4 4 19 6 2 0.70 MIRU 424 25 213 1232 309 572 57 6 0.66 MIRU 577 5 1 150 804 1373 70 9 2 0.56 MIRU 1955 94 128 574 896 497 140 46 10 15 14 0.76 MIRU 2163b 28 626 450 484 431 243 57 46 47 2 0.82 MIRU 2165 1 33 681 1208 394 22 34 29 1 7 4 0.64 M IRU 2347 3 3 229 212 1944 22 1 0.34 MIRU 2401 3 391 874 8 1120 7 11 0.63 MIRU 2461 4 214 1951 49 62 37 88 9 0.34 MIRU 3171 4 132 135 2034 41 67 1 0.28 MIRU 3690 25 173 563 1313 210 77 17 26 6 4 0.64 MIRU 4052 57 246 1119 900 87 3 2 0.63 MIRU 4156 116 6 26 103 247 389 410 725 292 100 0.82 Notes: ) were removed from the calculations for genetic diversity; the total sample used is n=2,414.

PAGE 49

49 Figure 2 3 Spatiotemporal Clustering of Mycob acterium tuberculosis Beijing (Panel A) and Haarlem (Panel B) Lineages in Florida, 2009 2013 Maps show the locations of significant space time clusters of M. tuberculosis Beijing (Panel A), and Haarlem (Panel B) sublineages in Florida, 2009 2013. Clusters were adjusted for county level foreign born population density and HIV risk. Adjusted Beijing Clusters were non significant and are not shown).

PAGE 50

50 Figure 2 4 Multinomial space time cluster of the M ycobacterium tuberculosis Haarlem Lineage in Florida, 2009 2013. Maps show the location of significant multinomial space time clusters of M. tuberculosis Haarlem in Florida, 2009 2013. Categories refer to case country/region of birth and timing of immigration among foreign born cases. Rela tive Risk (RR) estimates indicate whether or not greater than expected numbers occurred in a given category. RR>1 represents a greater than expected number of individuals of certain category inside the spatial cluster compared to outside.

PAGE 51

51 Table 2 3. Cha racteristics of Cases inside the Multinomial Haarlem Clusters Characteristics Northern Cluster (N=50) Southern Cluster A (N=35) Southern Cluster B (N=31) P value Haarlem Clades H1 H2 H3 Other 24 (48.0) 25 (50.0) 1 (2.0) 12 (34.3) 6 (17.1) 17 (48. 6) 5 (16.1) 8 (25.8) 15 (48.4) 3 (9.7) <.0001 Any Drug Resistance No Yes 48 (96.0) 2 (4.0) 30 (85.7) 5 (14.3) 28 (90.3) 3 (9.7) 0.2097 Patient HIV Status Negative Positive Refused Testing 36 (72.0) 12 (24.0) 2 (4.0) 24 (68.6) 7 (20.0) 4 (11.4 ) 25 (80.7) 6 (19.4) 0.3637 History of Incarceration No Yes 22 (44.0) 28 (56.0) 33 (94.3) 2 (5.7) 31 (100.0) <.0001 Birth Origin U.S. born Immigrant (<5 years) 50 (100.0) 4 (11.4) 10 (28.6) 21 (60.0) 6 (19.4) 8 (25.8) 17 (54.8) <.0001 Notes: tailed test; analyses were conducted using SAS v9.4 (Cary, NC, USA).

PAGE 52

52 Figure 2 5 Spatiotemporal Genotype Clusters in A reas of Low and High Genetic Diversity in Florida, 2009 2013. Map shows the Space time and multinomial genotype clusters of the Haarlem sublineage projected on to the allelic diversity map for Florida. Significant transmission clusters can be observed in areas of medium to high allelic diversity.

PAGE 53

53 CHAPTER 3 W HOLE GENOME SEQUENCING FOR THE INVESTIGATION OF A TUBERCULOSIS OUTBREAK INVOLVING PRISON AND COMMUNITY CASES IN FLORIDA, U.S.A Introduction Following 20 years of sustained annual decline in incidence, progress toward TB elimination in the United States (U. S.) seemed to have stalled 127 The factors that contributed to the 1985 1992 resurgence, mainly the HIV epidemic, immigration from high TB burden countries and increased transmission within the U.S. continue to fuel TB outbreak in contemporary times 127,128 At the natio nal level, incident TB cases arise from well identified high risk populations, such as prisoners, the homeless and the foreign born 122,129 In 2015, there were almost 10,000 reported TB cases, 66.2% of which occurred among persons born outside the U.S. 71,127 Among the U.S. born population, there continue to exist wide racial and socia l disparities in TB incidence 122,127,129 In March 2009, a pulmonary TB case was reported to the Florida Department of Health from an inmate at the Lake City Jail in Columbia County, Florida, which triggered an outbreak investigation Twenty seven more case all U.S. born, were identified among the prison population between March 2009 and Octob er 2013. Genotyping by Spoligotyping and 24 locus MIRU VNTR on cultured cases showed that they all shared identical genotype profile and were assigned to the genotype cluster FL0117. A r eview of the Florida TB genotyping information management system (TB G IMS) 90 identified 44 more cases with the same genotype profile reported in the community ; the majority (29 /44) were Haitian born. Contact investigation, among the prison cases could not find a source for the putative outbreak. Cases in the community span 13 different counties

PAGE 54

54 throughout the State In these analyses, we use WGS combine with the available epidemiological data to investigate the putative out break Data Collection and S equencing Epidemiologic Data State TB control staff collects extensive clinical, socio demographic and risk factor data on each reported TB case to guide TB case management and control efforts as part of the National Tuberculosi s Surveillance System (NTSS) 130 The Florida Bureau of Public Health Laboratories (BPL) maintains a repository of culture confirmed TB isolates for the s tate. Cases diagnosed by commercial or private laboratories are required to forward specimen t o BPL. A report of Verified Case of TB (RVCT) was available for all putative outbreak cases 131 Data from these reports were used to supplement the molecular investigation. Whole Genome Sequencing and SNP Detection BPL forwarded a ll available frozen isolates associated with the FL0117 cluster to our group for subculture on liquid media and genomic DNA (gDNA) extracted according to in house protocol. The gDNA quality and quantity was determined using the Agilent Genomic S creenTape assay (Agilent Technologies, San Diego, CA). Five microgram of gDNA per isolate was used to create libraries for next generation sequencing on the instructions and in house protocol. The next generation sequencing methods used are described in detail elsewhere 132 Using Sickle 133 short paired end reads of low quality de novo assembled into contigs using Velvet Optimizer 134 We used Mauve and

PAGE 55

55 ProgressiveMauve to order and align the genomic contigs with the M. tuberculosis strain CDC1551 (accession no. AE00516) as a guide 135,136 We converted the resulting .XMFA file to FASTA and opened it in Mega v1.7 to call the variable sites. The variant file was exported in FASTA format for further analysis. Data Analyses Comparison between s e quenced and reported c ases We recovered less than half of the FL0117 cluster isolates from BPL. In addition, we were not able to successfully subculture some of the isolates we received. As one of our goals was to evaluate how cases are related, we wanted to assure that sequenced isolates were representative of the putative outbreak. We compared the clinical and socio demographic characteristics of sequenced and non s equenced FL0117 strains using chi (ESRI, Redlands, CA) to geocode all FL0117 cases to the centroid of the reported patient residential zip code at the time of diagnosis. We obtained th e geographical coordinates from a polygon of the five digit Florida ZIP Code Areas downloaded from the Florida Geographic Data Library (FGDL) and current as of 2012 102 We computed unweighted spatial means the corresponding distributional ellipse and deviational ellipse within one standard deviation of the spatial mean for both reporte d and sequenced isolates in order to evaluate that sequenced isolates were similar in spatial distribution to the reported cases. We were interested in assessing that the spatial mean for the sequenced isolates were within one standard deviation of the spa tial mean for the reported cases, despite obvious deviation in the two mean centers We used the retrospective space time permutation model in SaTScan v9.4 to compare the spatiotemporal centroids of the sequenced and reported cases. We wanted to evaluate

PAGE 56

56 that the sequenced isolates did not cluster in space and time, indicat ing that sequenced cases were representative of the reported cases throughout the State. Phylogenetic a nalyses We evaluated the evolutionary history between the sequences by computing the SNP difference within and between the outbreak isolates and the references using Mega 137 and minimum spanning tree constructed in PHYLOViZ 138 In addition, we compared the SNP difference between prison and community cases and between Forei gn born and U.S. born cases. We tested the phylogenetic signal by likeli hood mapping using the program IQTree v1.4.2 139 The results of the analysis are presented in the form of a triangle where each corner represents the pos terior probability of fully resolved phylogenies. The middle triangle represents the posterior probability where possible phylogenies are equally likely and are thus unresolved 140 We also inferred a maximum likelihood phylogenetic tree with 1,000 ultra fast bootstrap support and ascertainment bias corr ection 141 Both likelihood mapping and the maximum likelihood tree were inferred usin g the Kimura 3 parameter model of evolution, selected as the best fit model based on Bayesian Information Criterion (BIC) using the program IQtree 139 T h e date of isolation for all genomes was used to calibrate a molecular clock and infer the timescale of the outbreak in order to estimate the date of the most recent common ancestor. In practice we used the Bayesian coalescence based phylogenetic framework implemented in Beast v 2.4.1, using the General Time Reversible (GTR) model with four gamma categories and a strict clock rate 142 The mutation rate, 3.0310 10 as estimated by Ford et al. for reactivated disease, was used as a strong prior for the clock rate 19 A posterior distribution of trees and model parameters was generated by

PAGE 57

57 running 510 7 Markov Chain Monte Carlo (MCMC) generations with sampling ever y 5,000 steps. We used Tracer v1.5 to analyze the output and assess mixing, using an effective sample size (ESS) value above 200 as the cutoff value for proper mixing of the MCMC. To ensure the reproducibility of the posterior distribution, we ran the ana lysis three (3) times using different random seed numbers. We used TreeAnnotator v2.4.1 to select a maximum clade credibility (MCC) tree from the trees posteriors distribution. Contact tracing on the community cases was incomplete. Among the prison cases, we had data on their data of incarceration and movements to different detention centers throughout the Florida prison system in the times leading to their diagnosis with TB. These data were linked to the Florida TB registry to extract dates of tuberculin skin test (TST) conversion, as an indication of TB infection. The pool data was used to reconstruction a timeline of the movements, linking all other cases to the three inmates for whom we had WGS data. Results Clinical and Demographics Charact eristics of the Cases Between March 2009 and December 2015, 76 cases of TB were reported to the FDOH with identical Spoligotyping and 24 locus MIRU VNTR profiles. The first two cases were diagnosed in March 2009, followed by nine more cases by June. Cases were continuously reported throughout the rest of 2009, peaking in 2010; a second peak was observed by late 2011 (Figure 3 1). A review of GIMS identified five cases reported between 2003 and 2008 with the same genotype cluster name. The clinical and dem ographic characteristics of a ll cases are reported in Table 3 1. Over sixty percent of the cases were reported among U.S. born persons incarcerated at the time of diagnosis and a third was among foreign born persons in the community; 35.8% of whom were

PAGE 58

58 bor n in Haiti. Among the foreign born cases, 54.8% had lived in the U.S. at least five years. The mean age of the cases was 41.6 (SD=16.6) years; 13.6% were aged younger than 25 years old, including three pediatric cases. Over 28% of the cases were HIV positi ve, 4.9% were homeless in the year prior to diagnosis, 17.3% reported injection/non infection drug use, and 7.4% reported past year alcohol abuse. Spatial descriptors Twenty one of the 80 FL0117 cases were sequenced, including one historical isolate from 2007; however, cases in the first p eak were not sequenced (Figure 3 1). Many of the frozen isolates could not be located, could not be subculture or were contaminated. Comparison between sequenced and non sequenced isolated by characteristics of the patien ts, showed no significant difference, except for age and h istory of incarceration (Table 3 1). As expected, the spatial mean center of the outbreak cases deviated substantially from the mean center of the sequenced isolates; however, there was no differenc e in dispersion or directionality between the sequenced and reported sample as evidenced by the complete overlap in th e circles and ellipses (Figure 3 2). In addition, while the outbreak cases clustered in space and time, the sequenced cases did not, indic ating that selected isolates for sequencing were not biased toward a specific geographical location or time. Phylogenetic Reconstruction Figure 4 3 shows the evolutionary relationship between the FL0117 cluster in terms of the pairwise genetic difference. Overall, there was a mean difference of 119 (SD=3) SNP between the FL0117 isolates. Strains isolated from U.S. born cases were separated by a mean of 81 (SD=4) SNP, while strains from foreign born cases showed more diversity with 15 3 SNP (SD=4) difference. A t test showed the within group SNP

PAGE 59

59 difference were significant at a p<0.0001. U.S. and Foreign born cases overall were separated by 125 (SD=4) SNP difference. The three s trains isolated among individuals while incarcerated showed 56 (SD=6) SNP differe nce between them, compared to 128 (SD=3) SNP difference between strains isolated in the community. Figure 3 4 shows the minimum spanning tree (MST) illustrating the evolutionary relationship between the putative outbreak cases. The numbers on the lines rep resent the SNP distance to the nearest neighbor. Each node represents a case and they are colored according to place of isolation. Yellow nodes are cases diagnosed while incarcerated and cyan nodes are cases identified in the community. The MST is split in two with U.S born and foreign born cases grouping closer together, respectively. Cases TB10 and TB28 seemed to play the central role in connecting the two groups. Based our evaluation of the SNP differences and the MST, we evaluated the maximum likelihoo d (ML) and Bayesian phylogenies testing the hypothesis that the FL0117 cases represented two distinct strain populations that shared a recent common ancestor. Supplementary ML analysis including an additional 11 reference genomes was conducted to better ev aluate the phylogenetic relationship between our strains. A description of the selected reference genomes i s available in Appendix, Table B 1. The a priori likel ihood mapping analysis (Figure 3 5) confirmed that the SNPs alignment would reliably resolve t he evolutionary relationships among outbreak strains. The ML tree shows the putative outbreak cases clustering within a monophyletic group with 100% bootstrap support (Figure 3 6). The supplementary analyses, which included 12 diverse reference genomes, su pported our hypothesis that the outbreak strains were closely related to each other (Figure B 1). The U.S. born cases clustered closely

PAGE 60

60 together and included the prison cases, indicating that the M. tuberculosis strain involved in the outbreak among U.S. b orn cases was genetically distinct from the one isolated among the fo reign born population. Figure 3 7 shows the Bayesian reconstruction of the FL0117 cluster applying coalescent theory. Visualization of the MCC tree revealed two concomitant TB outbreaks involving two M. tuberculosis lineages with TB49 case as the likely source for both outbreaks. One hundred percent of the tree posterior distribution supported the phylogenetic relatedness between the two strain populations. The time labeled MCC tree showe d that the two strains diverged from a common ancestor with TB49 at around mid 2009 (95% HPD, 2004 to 2015). Origin of the o utbreak Both the ML and time labeled phylogenetic trees support conclusion that TB49 is the most likely source for the outbreak in both groups. The two outbreak strains diverged from a shared common ancestor with TB49 in 2009, about a year prior to the identification of the outbreak among the inmates. TB49, a 23 year old HIV negative woman from Haiti had lived in Florida two years pri or to her diagnosis with cavitary TB disease. Interestingly, TB11, a Haitian born 42 year old male who had lived in the U.S. 21 years prior to diagnosis clustered together with the U.S. born cases. TB11 did not have a history of incarceration but reported injection drug use. Based on the time labeled phylogenetic tree, it is likely that the source strain circulated in the Haitian community first at least 5 years prior to introduction in the prison system. Source of the outbreak in the prison s ystem In Flori da, there are 143 correctional facilities divided into three regions, representing specific geographical areas of the State. The Receiving and Medical Center or RMC serves double duty as the receiving center for inmates from the central

PAGE 61

61 region and the sole hospital deserving every inmate in the state. Within a region, prisoners are frequently moved between correctional facilities to offset overcrowding. We had prison term and movement information on 25 of the 30 inmates involved in the outbreak. Historical TST results were available for five inmates; two were positive prior to incarceration, including one who was HIV positive and the other three tested positive while incarcerated and several years prior to the first case in 2009. From the re constructed time line in Figure 3 8, we can see that prison terms overlap for several years prior to diagnosis with TB disease between 2009 and 2010. More importantly, all inmates spend at least a year at the Columbia correctional institution prior to diagnosis. Inmate ni ne (9) died of causes unrelated to TB disease in 2011, and was diagnosed post mortem. He moved between several facilities and spent a year at the Receiving Medical Center (RMC) before expiring at the South Florida Receiving Center (SFRC) in 2011. All but o ne inmate spent at least one year at Columbia C.I. prior to being evaluated as a contact of infectious TB case in 2009 and subsequent TB diagnosis. Discussion Due to the clonal evolution and the limited genomic diversity in the MTBC it is difficult to c haracterize transmission. Currently, genotype clusters of two or more isola tes, as defined by spoligotyping and 24 locus MIRU VNTR is used as a proxy measure of transmission 143 However, these molecular markers are unable to resolve the timing and directionality of transmission in an outbreak 54,144 WGS is rapidly become the method of choice to study the evolution and transmission dynamics of monomorphic bacteria such as M. tuberculosis 49 54 WGS allows a larger pr oportion of the MTBC genome to be screened for single nucleotide polymorphism (SNPs). SNPs differences between strains allows for the unequivocal delineation of previously unrecognized

PAGE 62

62 outbreaks 49,50,54 In addition, the analysis of these SNPs within a Bayesian and phylogenetic framework allows for the investigation of the evolutionary history and transmission dynamics of specific strains 55 59 We applied these methods to investigate an extensive TB outbreak involving foreign born persons in the community and U.S. born persons incarcerated at the time of diagnosis. Using SNP and Bayesian phylogenetic analyses, we were able to break down the large cluster of 80 putative cases defined by Spoligotyping and 24 locus MIRU VNTR into two independent outbreaks, one in the Haitian community, and the other in the Florida Pris on system. Traditional genotyping methods had identified the outbreak as clonal and prior analysis in our lab had shown separate space time cluster centroids for the outbreak but was unable to identify a link 145 Using WGS, we were able to show, that the two strains diverged recently, but shared a recent common ancestor with an historical isolate from a woman who emigrated from Haiti two years prior to her diagnosis with cavitary TB. High HIV prevalence and risky behaviors such as injection drug use contributed to transmission in both subpopulations; however, bacterial evolution was likely due to transmission bottlenecks brought about by t he social segregation between the foreign and local population. The literature suggests that TB transmission between foreign and local populations are not efficient, likely because the two populations are segregated culturally rather than because of less e fficient transmission between heterogeneous population groups 119,146 Our data suggests that the Haitian strain circulated in the community for some times as indicated by the larger pairwise genetic distance between Haitian cases compared to U .S. cases.

PAGE 63

63 The primary goal of these analyses was to guide FDOH staff in focusing efforts and resources to investigate the outbreak. As of December 2015, cases with the same genotype profile were being reported with no known source. We identified one pos sible link between the two outbreaks, information FDOH can use to seed a more focused outbreak investigation by retracing the contacts of the two cases diagnosed in 2015. Considering the divergent evolution of the two strains, contact investigation should be tailored to the population group of interest. In the community, FDOH should focus on retracing the contacts of the two cases diagnosed in 2015. Case TB10 diagnosed in the community in 2014 is a U.S. born HIV positive male and non IDU user who reported t o have been homeless in the year prior to diagnosis. These social characteristics have been shown to complicate traditional contact tracing but are also risk factors for tuberculosis transmission 50,122 FDOH will need to employ novel investigative k methods along with WGS have been shown to be efficient in investigating TB outbreaks in transient populations 50,147,148 However, the method is time consuming and requires screening people that may not directly be involved in the outbreak. The integration of WGS and mathematical models promise to revolutionize TB outbreak investigation by tracking transmission events in time, thus narrowing down the pool of potential contacts to investigate 149 In either case WGS data need to be matched to epidemiologic data to conclusive rule in transmission events. Pooling together the available epidemiologic data, we were able to show that the strain responsible for the outbreak in the p rison population originated from the community. As we had sequence data for just three out of 30 of the inmate population

PAGE 64

64 affected, we reconstructed a timeline of their prison time to tie the sequenced cases to the reported cases. We were able to show that the diseased inmates all have overlapping prison terms and spent time at least one year at one correctional facility before their diagnosis with TB. The reconstructed timeline also highlights several missed opportunities for TB control. Several of the inm ates had short stays at RMC between 2006 and 2008 but where not evaluated for TB disease until 2009. When the outbreak was discovered in 2009, most of these inmates were evaluated as a contact of an infectious case. These visits to RMC present several oppo rtunities to evaluate the inmates for TB and institute early infection control, begin with the first case that was diagnosed with TB at death. Our results should be interpreted in light of the following limitations. First, we only sequenced about a quart er of the cases identified as part of the outbreak. The bacterial diversity could be larger than observed, adding more branches to the phylogenetic reconstruction proposed in this study. Second, we observed large pairwise SNP difference between our sample that renders interpretation of transmission clusters while others have use SNP difference <50 150 SNP difference is likely not a good measure of transmission in our sample as we sampled a small proportion of the putative ou tbreak for sequencing 150 SNP difference may become smaller when we evaluate the evolutionary history between all 80 reported cases. Finally, our outbreak was sparsely sampled for sequencing as indicated by the long branches in the phylogenetic trees. Some of the frozen isolates could not be recovered, while others could not be sub cultured, limiting the final sample available for sequencing. The transmission events

PAGE 65

65 described based on these data may not have occurred in the sequence observed. There may be unobserved transmission events along the phylogenetic branch es that may contradict our understanding of the outbreak. Nevertheless, twenty one out of a possible 80 strains were available for sequencing, which assuming random sampling would be sufficient to capture most of the genetic diversity in the population. W e formally evaluated bias in sampling by comparing the spatial and temporal distribution of sequenced and reported strains As sequenced isolates were evenly distributed in space and time, we judge d the sample random enough to capture the genetic diver sity in the population (Figure 3 2). As a comparison, twelve diverse M. tuberculosis genomes were processed and analyzed together with our sample, and it was clear that all sequences involved in the putative outb reak were monophyletic (Figure B 1 ). In conclusi on, using WGS coupled with epidemiologic data, we were able to confirm an extensive TB outbreak and inform public health decisions for contact tracing and TB control in a prison and community population. More importantly, WGS allowed us to observe the bact erial diversity in the outbreak likely driven by the socio demographic separation between the U.S. born and foreign born subpopulations studied.

PAGE 66

66 Figure 3 1. Epidemic Curve of the Spoligotyping and 24 locus MIRU VNTR Defined M. tubercu losis Outbreak Red bars indicate sequenced isolates as a proportion of all outbreak isolates reported in that quarter. 0 2 4 6 8 10 12 14 16 Number of Cases Reported Sequenced

PAGE 67

67 Table 3 1. Characteristics of the Cases Involved in the Out break by Whole Genome Sequencing Status Characteristics Sample N (%) Seque nced n (%) Not Sequenced n (%) P value Total Sample 80 21 ( 26.3 ) 59 (73.8 ) Age (SD) 41.6 (16.6) 41.0 (21.6) 41.8 (14.8) 0.8714 Gender Male Females 54 (66.5 ) 2 6 (32.5 ) 14 ( 25.9 ) 7 ( 26 .9 ) 40 ( 74.1 ) 19 ( 73 .1 ) 1.0000 Race Black White Other 59 (7 3.8 ) 17 (21.3 ) 4 ( 5.0 ) 16 ( 27.1 ) 5 ( 29.4 ) 4 3 ( 7 2 9 ) 12 ( 70.6 ) 4 (100.0) 0.7241 Birth Place U.S. Haiti Other Missing 47 (58.8 ) 28 (35.0 ) 3 (3.8 ) 2 (2.5) 11 (23.4 ) 8 ( 28.6 ) 2 (66.7) 3 6 ( 76.6 ) 20 ( 71 .4 ) 1 (33.3) 2 (100.0) 0. 34 21 Tuberculosis Type Pulmonary Pleural Other 70 (87.5) 2 (2.5) 8 (10.0) 18 (25.7) 1 (50.0) 2 (25.0) 52 (74.3) 1 (50.0) 6 (75.0) 0.8240 Treatment Outcome Completed Refused D ied Other 67 (83.8) 1 (1.3) 4 (5.0) 2 (2.5) 16 (23.9) 2 (50.0) 1 (50.0) 51 (76.1) 1 (100.0) 2 (50.0) 1 (50.0) 0.5163 Risk Factor Diagnosed while incarcerated Past Year Alcohol Abuse Past Year Drug Use ** Past Year Homelessness HIV Positive 30 (37.5 ) 6 (7.5 ) 14 (17.5 ) 4 ( 5.0 ) 23 (28.8 ) 3 (10.0) 1 ( 16.7 ) 4 ( 2 8.6 ) 1 (25.0) 4 ( 17.4 ) 27 ( 90.0 ) 5 ( 83.3 ) 10 ( 71.4 ) 3 ( 75.0 ) 19 ( 82.6 ) 0.01 68 0.6358 0.6302 1.0000 0.6164 Number of Years in the U.S. *** <5 Years 5 Years 14 (46.7 ) 16 (53.3 ) 5 ( 35.7 ) 5 ( 29.4 ) 12 ( 70.6 ) 9 ( 64.3 ) 1.000 Live outside the U.S. 7 ( 8.8 ) 3 (42.9) 4 (57.1) 0.5698 Notes : Chi square comparison between sequenced and non sequenced isolates; indicates mean age and standard deviation (SD); *Includes one (1 ) case born in the Dominican Republic, one (1) born in Vietnam and one (1) born in Grenada; * Includes injection and non injection drug use; ** Among Foreign born cases.

PAGE 68

68 Figure 3 2 Spatiotemporal Descriptive Statistics Comparing Outbreak and Sequenced Cases. Shifts in the mean centers of the two populations can be observe d; however, there is complete overlap in dispersion and directionality. In addition, we did not observe a bias towards a specific geographical location or time, as sequenced isolates did not cluster spatiotemporally, unlike reported cases (purple).

PAGE 69

69 Fi gure 3 3 Estimates of the Evolutionary Divergence between the Spoligotyping and MIRU VNTR Defined FL0117 Outbreak Cases. The number of base differences per sequence from between sequences is shown. The analysis involved 21 nucleotide sequences. Bolded tex t indicates cases diagnosed while incarcerated. Evolutionary analyses were conducted in MEGA7 151

PAGE 70

70 Figure 3 4 Minimum Spanning Tree (MST) of FL0117 Cases. Nodes represent each of the sequenced cases (n=21). Cyan identifies cases diagnosed in the community and Yellow identifies the three cases diagnosed while incarcerated. Yellow outline identifies central nodes. The numbers on the branches represent single nucleotide variant (SNV) between each pairs of isolates.

PAGE 71

71 Figure 3 5 Likelihood Mapping Analysis Testing th e Phylogenetic Signal of the Sequence Alignment. Frequencies indicate the posterior probabilities for a set of possible unrooted phylogenies (n= 550 ). The central triangle represents the posterior probability where all possible phylogenies are equally suppo rted and are thus unresolved while the rectangles represent areas where t he data support conflicting t ree topologies. The results indicate all sequence alignments included in our study will reliably reconstruct the outbreak phylogeny.

PAGE 72

72 Fi gure 3 6 : Midpoint rooted M aximum Likelihood Phylogen y of the 21 FL0117 Isolates and One Reference Isolate Tip labels colors indicate patient birth origin: Blue indicates U.S. born cases, Red indicates Foreign born cases and the reference strain is in Bla ck; @ indicate pediatric case with Haitian guardian; **denotes cases diagnosed while incarcerated; all others were diagnosed in the community. The analysis assumed the sequences are evolving according to the Kimura 3 parameter model.

PAGE 73

73 Figure 3 7 Maximum Clade Credibility Phylogeny of the FL0117 Cluster illustrating the relationship between U.S. born ( Cyan ) and Foreign born cases (Red) cases; t he two letter Birth country abbreviation is identified in the taxon name: HT Haiti; VT Vietnam; GR Grenada; US U. S. ; ** indicates cases diagnosed while incarcerated; all others were diagnosed in the community; @ indicated pediatric case born to Haitian parents. Blue horizontal bars indicate 95% Highest Posterior Density Interval ( HPD ) Branches colors represent the p osterior distribution of trees. The analysis assumed the sequences are evolving according to the General Time Reversible (GTR) model.

PAGE 74

74 Figure 3 8. Syste m prior to their Diagnosis with TB D isease. TST: Tuberculin Skin Test as an indication of TB infection status. All inmates involved in the outbreak had overlapping prison terms and had spent time at the Columbia Correctional Institution at least a year pr ior to TB diagnosis ; cases 4 and 2 were sequenced.

PAGE 75

75 CHAPTER 4 POST IMMIGRATION RETURN TRIPS AND RISK OF TUBERCULOSIS DISEASE AMONG PERSONS OF HAITIAN DESCENT LIVING IN FLORIDA Introduction Individuals of Haitian descent represent one of the fastest gro wing Ethnic immigrant groups in the United States (U.S.). In 2013, Haitians made up 1.3% of the almost 42 million foreign born individuals in the U.S. Almost half of legal Haitian immigrants live in the State of Florida, notably South Florida, where they represent 2.2% incidence in the U.S. annually and 26.0% of the TB case load in Florida 76,77 The high TB burden among Haitians could be explained by the fact that Ha iti has a high TB burden and Haitian immigrants are reactivating TB infections acquired before emigrating. However, s tate based M. tuberculosis genotyping surveillance data suggest on going TB transmission in Haitian communities with risk of transmission t o non Haitians. National and s such, there are limited data on the health challenges specific to this population. This broad classification may mask cultural, social and behavioral p ractices which may put Haitians at increased risk of TB 152,153 Haitians living in the U.S. maintain strong ties to Haiti through remittance and travel. It is estimated that 90% of Haitians living in the U.S. send money to Haiti; an average of $163 monthly 79 In addition substantial numbers of Haitians travel to Haiti e ach year, although accurate estimates are unavailable. Research shows that people who travel to TB endemic countries have the same M. tuberculosis infection risk as the local population 80 82 Increased frequency and duration of trips may put Haitians immigrants at elevated risk of TB acquisition while in Haiti. However, there have not

PAGE 76

76 been prior studies that have measured instances of return migration and its association with TB disease among Haitians. We hypothesize that post immigration return trips made to Haiti since acquiri ng legal residency in the U.S. is a risk factor for TB disease among Haitians immigrants To test this hypothesis, we conducted a clinic based study in South Florida. To our knowledge, this hypothesis has not been tested before in this population. Methods Subjects We conducted an unmatched case control study, with two controls to each case to describe the prevalence of post immigration mobility and its association with TB disease and TB knowledge among persons of Haitian descent living in Florida. Study ac tivities were based out of the Florida Department of Health in Palm Beach and Miami Dade Counties, where an estimated 52% of legal Haitian immigrants in the State of Florida live. In Miami Dade, participants were recruited at the County Health Department D owntown Miami Offices and annex in Little Haiti. In Palm Beach, we recruited participants at the Delray Beach Heath Center. Eligibility criteria for the study included being at least 18 years of age, of Haitian descent and able to travel outside of the U.S without legal repercussions. Case d efinition Cases were persons of Haitian descent who met laboratory or clinical definition for tuberculosis disease and undergoing treatment under the supervision of the Florida Department of Health in Palm Beach and Mi ami Dade Counties. The national TB surveillance case definition is based on laboratory findings or clinical evidence of active

PAGE 77

77 disease due to M. tuberculosis infection. Laboratory criteria for diagnosis requires that M. tuberculosis be isolated from a clin ical specimen either by culture or by nucleic acid amplification test (NAAT) or demonstration of acid fast bacilli in a clinical specimen when a culture has not been done or cannot be obtained. A clinical case is defined based on all of the following crite ria: (1) a positive Mantoux tuberculin skin test or positive interferon gamma release assay for M. tuberculosis and (2) other signs and symptoms compatible with TB i.e. abnormal chest radiograph, abnormal chest computerized tomography scan or other chest i maging study, or clinical evidence of current disease, and (3) treatment with two or more anti TB medications and (4) a completed diagnostic evaluation. Pre existing cases were identified by clinic staff and referred for study participation. Newly diagnose d cases were only referred after completion of their diagnostic evaluation and where deemed no longer infectious by the providers. Control d efinition Controls were persons of Haitian descent without TB disease accessing Florida Department of Health clinic s in Palm Beach or Miami Dade Counties. Currently controls are being enrolled at the HIV and TB clinics in Delray Beach and at the Latent TB infection and Immunization Clinics i n Miami Dade County Little Haiti Clinic. Persons accessing the TB clinics are e ligible as control if they are evaluated for latent infection, i.e. they do not meet laboratory or clinical criteria for TB disease. Family members of these participants who met inclusion criteria were also eligible to participate.

PAGE 78

78 Data Collection Procedu res We initiated participant enrollment in November 2015. Providers at the different clinics referred persons of Haitian descent aged 18 years and older to research staff who explain study goals and procedures. Clients who consented to participate in the study were administered a brief computer assisted interview about their post immigration mobility, TB knowledge, risk perception and risk behaviors. Interviews were be tween 5 to 10 minutes. Responses were captured on the open source software EpiInfo 154 Compensation and Ethical Clearance The University of Florida and the Florida Department of Health Institutional Review Boards resp ectively approved the study To protect patient confidentiality, a waiver of documentation of informed consent was obtained for the study. Participants did not receive monetary compensation but a copy You Can Prevent Tuberculosis : a Patient Educational Measures Frequency and Duration of Post Immigration Mobility Participants were asked how long they have lived in Florida and if they had made any trips to Haiti during that time. Among participants who reported to have ev er made a return trip to Haiti, we asked if they traveled in the past two years. Among all participants who reported to have made at least one trip to Haiti, we collect information on their final destination, the total number of trips made since living in Florida, and how long trip s last.

PAGE 79

79 Demographic and Socio economic information reported age ( <45, 45+) the number of years they have lived in Florida (<5 years, 5 Participants also reported their employment status (unemployed, employed part time, employed full time), education (None, Elementary, High school, College or more), their relationship status (Single, Married, Other), income bracket, number of people living in the same household as them and the number of rooms in the house used for sleeping. We created a proxy measure of crowding operationalized as a ratio of the available bedrooms and the total occupants in the household, with ratios closer to one (1) indicating a lower level of crowding. Participants were also asked to report lack of access to a vehicle, history of home eviction and past year food insecurity. We created a composite measure of socio economic status (SES), where persons who responded Health Access and History We collected information on self reported health status (Excellent, Very Good, Good, Fa ir, or poor), HIV, Diabetes, and LTBI diagnosis, access to health insurance (None, Private/Employer Bought, Government), smoking, alcohol and drug use. Among persons who reported to have ever smoked or drink alcohol, we collected information on past 30 day cigarette smoking and binge drinking, defined as at least four alcoholic drinks for men and three for women in one occasion. Acculturation Participants were asked to report what language they speak most often at home, what language they watch television, whether they have an email account and use of

PAGE 80

80 social media such as Twitter, Facebook or Instagram. Initial assessment of responses to these questionnaire items showed that email and social media us were strongly ger participants more likely to report these items. Thus, language spoken at home was used as the sole measure of acculturation for this study. Tuberculosis Knowledge We measured TB knowledge among both cases and controls who reported to have ever heard o f the disease called tuberculosis (TB). Participants are asked to enumerate all the signs and symptoms that they know would tell them that a person has TB disease. Interviewers probed participants to name as many signs as possible until participant said th quizzed participants on TB pathology and modes of transmission, using the Centers for quiz. Among participants who responded to these questio ns, we created a TB knowledge score ranging from 0 15. We considered participants to have a knowledge score of zero (0) if they reported to have never heard of TB before the interview. The complete list of questions used is presented in the appendix. Data Analyses We compared the socio demographic and behavioral risk factor characteristics of of low cell counts (<5) to compare categorical variables and non parametric Wilcoxon rank sum test to compare the distribution of numerical variables. We detected statistically significant differences at a p

PAGE 81

81 evaluated graphically the relationship between the number of years participants rep orted to have lived in the U.S. and the number and duration (in weeks) of return trips to Haiti using scatterplots with penalized B splined We also requested a scatterplot with b splined to visualize the relationship between frequency and duration of trip s. We measured the unadjusted and adjusted odds (OR) and 95% confidence interval (CI) for the association between ever and past two year travel and TB disease using unconditional logistic regression. Models were adjusted for known confounders using backgro und elimination and Akaike Information Criteria (AIC) for model selection 155 All analyses were conducted in SAS v9.4 (Cary, NC, USA). Results Demographic and Socio Economic Characteristics We recruited 199 people (26 cases and 173 controls) for the study of which we enrolled and interviewed 118 (18 cas es and 100 controls). Among those not enrolled, 12 could not travel for legal reasons and the others refused to participate for lack of time, interest or trust. Three participants dropped out of the study. We present the data for the questions they answer e d before dropping out. Table 4 1 describes the characteristics of the sample. Overall, there were no statistically significant differences in age, gender; number of years lived in the U.S., education and socio economic characteristics between cases and con trols. Cases significantly differed from controls by relationship status, with 58.2% of the controls reporting to be married compared to 23.5% of the cases (p=0.0135). Cases also tended to be significantly of lower income, with 58.8% (10/18) of the cases reporting no income for the previous year compared to 16.7% of cases reported income of $35,000 or more (p=0.0715). Almost 18.0% of cases reported to have received no formal education compared to 56.1% of controls

PAGE 82

82 having a High School diploma and 32.7% wi th college education or more (p=0.0997). Cases were also less acculturated than controls with 18.2% of controls reporting to speak mainly English at home, compared to 100.0% of controls reporting speaking Haitian Creole at home (p=0.0702). Cases also rated their general health status lower than controls; 23.5% of the cases reported their health as fair compare to 35.0% of controls reporting their general health as excellent (p=0.0068). In general, cases were more knowledgeable about TB compared to controls; 82.4% of controls (14/18) had general knowledge and were able to correctly identify signs and symptoms of TB compared to 57.0% of controls. The median TB knowledge score among cases was 11.0 ( 2 =4.9) compared to 9.0 ( 2 =10.9) among controls p=0.0329. Pos t immigration Return Trips and Tuberculosis Disease Overall, 64.4% of the sample reported to have ever traveled to Haiti since establishing residence in Florida and 58.4% of them made at least one trip in the past two years. Among cases, 55.6% reported to have ever traveled to Haiti and 70.0% traveled in the past two years. Sixty six percent of the controls reported to have ever traveled to Haiti and 56.7% traveled in the past two years. Figure 4 1 shows the scatterplots with B splines for the number and duration of trips in weeks O verall, there were wide variations in how long trips lasted on average; however, the average duration was around three weeks. Figure 4 2 shows the relationship between frequency and duration of trips by how long participants re ported to have lived in the U.S. The number of return trips seemed to increase with the number of years Haitians reported to have lived in Florida (Panel A). Trip duration did not seem to vary by length of residence in Florida (Pane B).

PAGE 83

83 Table 4 2 shows the association between tuberculosis disease status and post immigration return trips to Haiti in the past two years. For the analyses, people who reported to have never traveled to Haiti were recorded to have not made a trip either in the past two years. In unadjusted analyses, travel in the past two years was not associated with TB disease. In a model adjusted for number of trips and duration of individual trips in addition to other risk factors travel in the past two years was a significant predictor of TB disease (AOR= 6.88 95% CI: 1.23 38.67 ). In addition, we observed that length of stay in Florida was associated with a significantly lower odd of TB disease among people immigrated 5 9 years ago (AOR=0.14 ; CI: 0. 02 0.95 ) and those who ars ago (AOR=0.13 ; CI: 0.02 0. 90 ) compared to those in Florida less than five years Discussion In this study, we sought to describe post immigration mobility and its association with tuberculosis disease among persons of Haitian descent living in Flor ida. Prior literature has reported a TB infection risk comparable to local population among foreign travelers to TB endemic countries 80,81,156,157 However, this is the first study to document the frequency and duration of post immigration return trips and its association to TB disease among an immigrant population. We found that over sixty percent of the sample interviewed reported to have made at least one trip to Haiti since acquiring legal residence in the U.S. In addition, among people who reported to have traveled to Hai ti, length of stay was on average three weeks. This length of stay was independent of the number of years people rep orted to have lived in the U.S.; however, the number of return trips increased with the number years since immigration Although we did not

PAGE 84

84 collect information on the environment Haitians who travel to Haiti stay when they visit with family, it is likely that their home environments in Haiti reflects to some degree their environment in Florida, high level of household crowding and low SES. The se environments and the three week average stay combine to increase the risk of TB infection while in Haiti. In addition, majority of the participants reported Port au Prince and its suburbs as their final destination when visiting Haiti. Post the January 12 th 2010, Port au Prince faced a lot of challenging with TB control. Regional estimates put the rate of multidrug resistance in the region at 6.0%, a rate at which mathematical model estimates person to person transmission of resistant M. tuberculosis st rains is possible 66 In the U.S., public health efforts focus on screening of LTBI cases to prevent progression to active disease in the future 72,158 Recent immigrants, i.e. thos e who moved to the U.S. in the previous five years, are targeted for LTBI screening as their risk of progressing to active disease is higher; past five years studies show their TB risk is comparable to the general U.S. population 43,73,74 However, these estimates do not take into consideration repeated exposure due to return migration to the country of origin, especially among immigrants from high TB burden countries. The TB incidence in Haiti is several hundred times greater than that of the U.S. Haitian immigrants who make f requent extended return trips to Haiti are at increased risk of exposure to M. tuberculosis In our study, we measured the odds of TB disease increased six fold among Haitians who reported to have traveled to Haiti in the past two years. Our results sugges t that LTBI screening should be extended to Haitians who have lived in the U.S. long term and make at least one trip to Haiti annually. Over a third of this group

PAGE 85

85 currently is not captured under the Centers for Disease Control (CDC) LTBI screening recommen dations as they are under 45 years old 159 Currentl y, LTBI treatment uptake is low in Haitian immigrants in Florida ; about half of all diagnosed cases initiate treatment and less than 40.0% complete therapy 160,161 We need more research to evaluate the effectiveness and cost benefits of such intervention in the Haitia n immigrant population. As to be expected, cases were much more educated about TB disease than controls. Although only four of the 100 controls interviewed reported to have never heard of TB disease before the interview, most harbored common misconception about TB disease, 91.4% agreed that one might have to take medicine for TB infection even if they do not feel sick. Thus, it is possible that through education, LTBI screening and treatment may be encouraged in Haitian communities. There are some limitati ons to our study. First, our findings are limited by our sample size. We had initially plan to recruit 100 cases and 200 controls. However, there are currently not 100 Haitians undergoing treatment under the supervision of the FDOH in South Florida. In add ition, we had a low response rate for the study; between Palm Beach and Miami Dade, we had an overall participation rate of 69.2% for the cas es and 57.8% for the controls. We continue recruitment and hope to reach our target enrollment soon. We recruited c ases and controls at clinics offered by the Florida Department of Health in Palm Beach and Miami Dade. Second, although DOH manages almost 100% of TB cases, controls have many options for health care. It is likely that those of lower SES were more likely t o accessed services at the health department and are thus not representative of Haitians living in Florida. Nevertheless, Health departments offer a wide range of services accessible to people of different SES level. At both enrollment

PAGE 86

86 sites, we interviewe d doctors, nurses, college students and graduates undergoing latent tuberculosis screening for employment purposes. We do not believe our sample is drastically different from the general Haitian population living in South Florida. Finally, although post i mmigration travel was associated with TB disease in adjusted analysis, controls made more trips to Haiti than cases, contrary to our hypotheses. Although some cases were recently diagnosed with TB diseases, the majority were prevalent cases. Healthy worker s bias may account for the fact that post immigration mobility was more prevalent among controls than cases. However, taking into account the cumulative number and duration of trips, we observed that the risk was significantly greater among cases than cont rols. In conclusion, this study shows that post immigration mobility between Haiti and Florida is a risk factor for TB disease among Haitians living in Florida. Public Health efforts to educate the population on the benefits of LTBI screening and dispel mi sconceptions about TB are needed. Future studies to evaluate the potential impact of interventions targeted at Haitians who make extended return trips to Haiti should also be explored.

PAGE 87

87 Table 4 1. Characteristics of Haitians with Tuberculosis Disease (C ases) and without TB Disease (Controls) Characteristics Cases N =18 Controls N =100 P value Age <40 Years 40 Years 9 (50.0) 9 (50.0) 46 (46.0) 54 (54.0) 0. 7541 Gender Female Male 11 (61.1) 7 (38.9) 64 (64.0) 36 (36.0) 0.7969 Number of Years Lived in Florida < 5 Years 5 9 Years 8 (44.4) 2 (11.1) 8 (44.4) 29 (29.0) 23 (23.0) 48 (48.0) 0.3563 Language Spoken at Home Haitian Creole English 17 (100.0) 81 (81.8) 18 (18.2) 0.0702 General Health Self a ssessment Excellent Very Good Good Fair Poor 2 (11.8) 2 (11.8) 6 (35.3) 3 (17.7) 4 (23.5) 35 (35.0) 17 (17.0) 36 (36.0) 10 (10.0) 2 (2.0) 0.0068 Access to Health Insurance None Private/Employer Bought Government 7 (41.2) 3 (17.7) 7 (41.2) 36 (36.0) 32 (32.0) 32 (32.0) 0.5139 Diabetic 3 (16.7) 16 (16.0) 0.2157 HIV Positive 3 (16.7) 12 (12.0) 0.6993 Latent TB Infection n/a 20 ( 20.0) Ever Smoked 2 (11.1) 12 (12.0) 0.1867 Past 30 Days Smoking 4 (33.3) 1.0000 Ever Consumed Alcohol 4 (22.2) 31 (31.0) 0.1634 Past 30 days binge drinking 5 (16.1) 1.0000 Ever Used Drugs 2 (11.1) 5 (5.0) 0.0499 Employment Status Unempl oyed Employed Part time Employed Full time 10 (58.8) 5 (29.4) 2 (11.8) 35 (35.0) 48 (48.0) 17 (17.0) 0.2092 Education None Elementary High School College or more 3 (17.7) 9 (52.9) 5 (29.4) 3 (3.1) 8 (8.2) 55 (56.1) 32 (32.7) 0.0997 Marital Stat us Single Married Other 10 (58.8) 4 (23.5) 3 (17.7) 26 (26.5) 57 (58.2) 15 (15.3) 0.0135 Past Year Household Income

PAGE 88

88 Table 4 1. Continued Characteristics Cases N =18 Controls N =100 P value No Income Less than $15,000 $15,000 to $24,999 $25,0 00 to $34,999 $35,000 or more 10 (58.8) 2 (11.8) 4 (23.5) 1 (5.9) 28 (29.2) 26 (27.1) 17 (17.7) 9 (9.4) 16 (16.7) 0.0715 Household Crowding ** 0.76 (0.33) 0.71 (0.42) 0. 5602*** Ever Incarcerated 4 (4.0) 1.0000 Ever Homeless at least 60 Days 2 (2 .0) 0.4890 TB Knowledge Score 72.9 56.6 0.0653** Notes: *indicates two sided p value; **indicates mean with standard deviation in parentheses; ** indicates Satterthwaite test for unequal variance

PAGE 89

89 Figure 4 1. Scatterplot with B splines of the relationship between frequency and duration of trips to Haiti among persons of Haitian descent

PAGE 90

90 Figure 4 2. Scatterplot with B splines of the r elationship between number o f years in Florida, number of trips made to Haiti (A) and average trip duration in weeks (B) among persons of Haitian d escent. A B

PAGE 91

91 Table 4 2. Post Immigration Travel and Association with Tuberculosis Disease among Persons of Haitian Descent Living in Florid a Unadjusted OR (95% CI) Adjusted OR (95% CI) Past two Year Travel No Yes 1.00 1.04 (0.37, 2.91) 1.00 6. 88 ( 1. 23 38.67 ) Age <40 Years 40 Years 1.17 (0.43, 3.20) 1.00 4.0 1 ( 0. 76 21.04 ) 1.0 0 Gender Male Female 1.00 0.88 (0.32, 2.48) 1.00 0. 26 (0. 0 4 1. 47 ) Number of Years in U.S. <5 Years 5 9 Years 10 Years 1.00 0.32 (0.06, 1.63) 0.60 (0.21, 1.78) 1.00 0.1 4 (0.02, 0. 95 ) 0. 13 (0.0 2 0.90 ) HIV Status Negative Positive 1.00 1.47 (0.37, 5.82) 1.00 1. 79 (0. 21 15.0 ) Self reported diabetes No Yes 1.00 1.13 (0.29, 4.37) 1.00 5.13 (0.71, 37.29) Alcohol use No Yes 1.00 0.67 (0.21, 2.27) 1.00 0.08 (0.01, 1.02) Drug Use N o Yes 1.00 2.53 (0.45, 14.26) 1.00 5.69 (0.34, 94.58) Stay in weeks 0.93 (0.78, 1.11) 0.86 (0.7 0 1.0 6 ) Number of Trips 0.93 (0.82, 1.06) 0. 91 (0. 74 1. 11 ) Socio Economic Status 0.54 (0.24, 1.19) 0.3 5 (0.1 2 1.06 ) Household Crowding 1.35 (0.42, 4.35 ) 2. 11 (0. 41 10.95 )

PAGE 92

92 Figure 4 3. Final Destinations of Persons of Haitian Descent Who Reported to have Visited Haiti in the Past Two Year 0 5 10 15 20 25 Artibonite Centre Grand-Anse Nippes Nord Nord-Est Nord-Ouest Ouest Sud Sud-Est Number of Travelers Haitian Departments Final Destination when Visiting Haiti in the Past Two Years

PAGE 93

93 CHAPTER 5 CONCLUSIONS, IMPLICATIONS AND FUTURE RESEARCH Contributions of the Dissertation Tuberculosis i ncidence in the general U.S. population has decreased substantially over the past 20 years. However, cases continue to arise from well defined risk groups, such as foreign born persons from high TB burden countries the homeless and prisone rs. The U.S. is focused on elimination; however, to make meaningful progress towards this goal, public health officials need to stop transmission and disease reactivation in high risk groups. We provide evidence for recent TB transmission intersecting two high risk sub pop ulations i n the State of Florida: Haitian born persons in the community and U.S. born persons in the prison system. Importantly, we show that post immigration mobility between Haiti and Florida is a contributor to TB acquisition among persons of Haitian de scent living in Florida. Persons of Haitian descent present a unique challenge to TB control efforts as they originate from a high TB and HIV burden country. We discuss the implications of our results for TB control efforts in the U.S. and among the Haiti an population in Florida. We also provide a discussion on the application of novel methods such as next generation sequencing to inform control efforts and plans to apply these tools to study TB molecular epidemiology among Haitians. At a time when progres s towards TB elimination seemed to have stalled the risk factors for TB in Florida are reminiscent of the factors that contributed to TB resurgence in the la t e 1980s, mainly the HIV epidemic, immigration from high burden countries and increased transmissi on in the U.S. 127 In the State of Florida, persons born in Haiti account for over a quarter of all incident TB cases reported among the foreign born

PAGE 94

94 population. The literature shows that TB among immigrants is due to reactivation of disease acquired before emigrati ng and past the first five years of emigration, TB risk levels off, albeit higher than the rates in the local population 73,74 This dissertation provides evidence that TB transmission is just as likely in recent and non recent Haitian immigrants in Florida. In addition, elevated TB risk can be observed independent of how long Haitians have lived in the U.S., potent ially because of repeated TB exposure during post immigration trips between Haiti and Florida In the U.S., public health efforts focus on screening of LTBI cases to prevent progression to active disease in the future 72,158 Recent immigrants, i.e. those who moved to the U.S. in the previous five years, are targeted for LTBI screening as their risk of progressing to active disease is higher 43,73,74 At the national level, TB among the foreign born is due to reactivation of disease acquired before emigrating and local transmission is low 73,74 However, these estimates do not take into consideration repeated ex posure due to return migration to the country of origin, especially among immigrants from high TB burden countries. We have shown that post immigration mobility is a risk factor for TB disease in the Haitian immigrant population living in Florida. We have also shown that recent immigrants are just as likely as non recent immigrants to be part of transmission clusters in the U.S. Thus, the recent and non recent dichotomy highlights miss ed opportunities for effective TB control among Haitian immigrants in the U.S. that would effectively have an impact on local transmission. The TB incidence in Haiti is several hundred times greater than that of the U.S. Haitian immigrants who make frequent extended return trips to Haiti are at increased risk of exposure to M. tuberculosis Over a third of this group currently is not captured under

PAGE 95

95 the Centers for Disease Control (CDC) LTBI screening recommendations as they are under 45 years old 159 LTBI screening should be extended to Haitians who have lived in the U.S. long term and make at least one trip to Haiti annually. Future Directions Testing TB Control Interventions Targeted at Persons of Haitian Descent Our results suggest that Haitian immigrants in Florida could benefit from extended LTBI screening and treatment. However, it is not known how such services will be r eceived and whether they will be cost effective. Currently, LTBI treatment uptake is low in Haitian immigrants in Florida ; about half of all diagnosed cases initiate treatment and less than 40.0% complete therapy 160,161 TB related stigma is well documented in the Hai tian population in the U.S. and Haiti 160,161 We found that Haitians may be reticent to initiate LTBI treatment due to misunderstanding between the difference between latent and active disease. Most Haitian immigrants harbor common misconception about TB disease, al though the majority agreed that one might have to take medicine for TB infection even if they do not feel sick. Thus, it is possible that through education, LTBI screening and treatment may be encouraged in Haitian communities. We need more research to eva luate the effectiveness and cost benefits of such intervention in the Haitian immigrant population. Investigation into Bacterial Virulence Immigration presents unique challenges to TB elimination in low incidence countries, as it facilitates importation o f new disease and TB strains capable of increased transmission. The introduction of these strains into congregate settings such as jails and prisons can prove disastrous for TB control efforts In our first study we show greater bacterial diversity in regi ons of Florida where the foreign born population

PAGE 96

9 6 density is also high. The number of secondary cases generated by two of the cases involved in the FL0117 outbreak we investigated in study two suggest s that the causative strain may have a fitness advantage Future molecular investigation comparing the FL0117 strains to strains isolated in Florida and Haiti may help answer questions as to the virulence of this specific strain and test hypotheses for its long term implications for TB control in Florida. Post i mmigration between Haiti and Florida in the Haitian immigrant population is very prevalent and a significant risk factor for TB disease. This mobility may present important implication for TB control in the U.S. The three studies in my dissertation provide evidence for a predominance of TB disease caused by Haarlem strains in the Haitian population and identify both reactivation and re infection, potentially during post immigration return trips to Haiti, as important contributor to disease. The idea that tr avel contributes to the spread of infectious pathogens is not novel 162 Population movement has been one of the greatest contributors to the current global distribution of the MTBC 6,20 M. tuberculosis Haarlem lineage is among the most geographically widespread modern MTBC sublineages 118 Prior studies have documented increased transmission of multidrug resistant Haarlem strains not associated with HIV infection 116,117 We need further research into the population structure of M. tuberculosis strains is olated among Haitians to monitor their contribution to TB epidemiology in the U.S. Particularly, we need research into the association of these strains with disease presentation and the development of drug resistance.

PAGE 97

97 Applying Genomics to Inform TB Outbr eak Investigations We have shown that despite the breath of M. tuberculosis diversity isolated in the State of Florida, strains are monomorphic at most of the 24 different MIRU loci currently used for genotyping 145 Consequently, in outbreak investigation s 24 locus MIRU VNTR typing will not be able to differentiate between these strains. Whole genome sequencing has become the gold standar d for molecular research into M. tuberculosis However, its use to inform public health decision is less practiced. We used WGS to inform the Florida Department of Health that an outbreak previously defined as clonal in fact involv ed two distinct bacteria l strains that diversified from a common source in recent times. That information will be instrumental to how FDOH proceeds with the outbreak investigation. Nevertheless, there remain some challenges before WGS can fully be integrated into TB control progr ams as a routine genotyping tool. Unfortunately, while genomics can help to rule in outbreaks, it does not confirm them. We still need rigorous epidemiologic d ata to guide the genomic analyse s and conclusions One particular challenge to routine genotyping by WGS, and an emerging area of research is arriving at a SNP cut off to define transmission cluster s In fact, with current genotyping methods, clusters are defined based on identical genotyping patterns. However, compared to traditional methods, WGS sc reens the full genome for regions of difference and, due to the long latency period in TB, substantial within host diversity may occur that renders this comparison difficult 45,150 Ideally, if two cases are clonal there will be no SNP difference between them, as the two genomes will be identical. P rior research had shown that the M. tuberculosis mutation rate in latency and reactivation is the same 19 However, emerging evidence suggests substantial variation occur in latency, in reacti vation and during transmission 163 W ithin transmission clusters with well

PAGE 98

98 documented epidemiological links, as many as five SNP can be expected between and within cases 164 We are of the opinion that t his variability may be lineage specific, further complicating cluster definitions using SNPs. Research into the mechanisms of bacterial variability is an exciting and emerging field with basic science and programmatic applications. We will need c areful and long term evaluation of WGS results before full integrated into TB control programs as a routine genotyping tool Conclusion In conclusion, this dissertation contributes to an understanding of the broad impact of immigration on TB epidemiology in Florida as it pertains to bacterial strain importation and diversification. We also provide evidence for the impact of persons of Haitian descent on TB epidemiology in Florida and highlight several areas for future research to help control TB in this population.

PAGE 99

99 APPENDIX A MYCOBACTERIUM TUBERCULOSIS COMPLEX SUBLINEAGE ALLELIC DIVERSITY FLORIDA 2009 2013 Table A 1. Allelic Diversity of Difference M. tuberculosis Complex Sublineages Isolated in Florida, 2009 2013 Subli neage s / Locus 2 4 10 16 20 23 24 26 27 31 39 40 424 577 1955 2163 b 2165 2347 2401 2461 3171 3690 4052 4156 BEIJI NG 0.07 0.05 0.31 0.20 0.01 0.25 0.00 0.46 0.08 0.31 0.45 0.18 0.39 0.04 0.57 0.71 0.39 0.07 0.30 0.15 0.03 0.35 0.47 0.66 BEIJI NG LIKE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.50 0 .00 0.50 0.50 0.00 0.00 0.50 0.00 0.50 0.00 0.00 0.00 0.50 0.50 0.00 BOV 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 BOVI S1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 BOVI S1_B CG 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 BOVI S3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 CAS 0.00 0.28 0.28 0.00 0.00 0.50 0.00 0.61 0.00 0.28 0.00 0.44 0.67 0.00 0.00 0.00 0.28 0.00 0.28 0.00 0.00 0.44 0.44 0.50 CAS1 _DEL HI 0.00 0.00 0.57 0.29 0.00 0.13 0.00 0.66 0.00 0.40 0.35 0.19 0.72 0.00 0.13 0.13 0.07 0.07 0.13 0.00 0.13 0.35 0.25 0.67 CAS2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 EAI 0.00 0.00 0.00 0.44 0.44 0.00 0.00 0.00 0.00 0.44 0.00 0.44 0.00 0.00 0.00 0.44 1.00 0.00 0.00 0.00 0.00 0.67 0.00 0.44 EAI1_ SOM 0.00 0.53 0.00 0.47 0.00 0.47 0.00 0.00 0.22 0.53 0.59 0.22 0.00 0.38 0.72 0.56 0.59 0.00 0.00 0.00 0.00 0.66 0.00 0.75 EAI1_ SOM EAI2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 EAI1_ SOM EAI4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 EAI2_ MANI LLA 0.00 0.28 0.26 0.04 0.02 0.06 0. 00 0.02 0.02 0.13 0.34 0.04 0.02 0.18 0.50 0.72 0.18 0.00 0.00 0.41 0.02 0.00 0.00 0.50 EAI2_ NTB 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 EAI3_ IND 0.22 0.69 0.38 0.22 0.00 0.0 0 0.00 0.00 0.00 0.63 0.38 0.59 0.22 0.00 0.22 0.00 0.22 0.00 0.00 0.00 0.22 0.53 0.00 0.66 EAI4_ VNM 0.54 0.58 0.15 0.00 0.00 0.29 0.00 0.00 0.00 0.15 0.15 0.15 0.00 0.00 0.51 0.00 0.15 0.15 0.00 0.15 0.15 0.29 0.00 0.50 EAI5 0.22 0.73 0.00 0.50 0.23 0.5 1 0.00 0.00 0.00 0.55 0.48 0.58 0.30 0.00 0.76 0.63 0.63 0.00 0.47 0.63 0.22 0.55 0.00 0.70 EAI5 or EAI3 0.00 0.50 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.28 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 EAI6_ BGD1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 H1 0.46 0.10 0.32 0.24 0.19 0.23 0.00 0.21 0.04 0.32 0.01 0.17 0.10 0.01 0.23 0.62 0.15 0.23 0.10 0.14 0.35 0.29 0.25 0.63 H2 0.00 0.00 0.05 0.07 0.05 0. 00 0.00 0.05 0.03 0.20 0.00 0.05 0.03 0.00 0.22 0.19 0.03 0.03 0.03 0.05 0.00 0.12 0.22 0.27

PAGE 100

100 Table A 1. Continued Subli neage s / Locus 2 4 10 16 20 23 24 26 27 31 39 40 424 577 1955 2163 b 2165 2347 2401 2461 3171 3690 4052 4156 H3 0.00 0.03 0.49 0.11 0.44 0.52 0.00 0.17 0.02 0.14 0.04 0.40 0.13 0.15 0.50 0.78 0.16 0.50 0.15 0.03 0.09 0.15 0.50 0.69 H3 LAM 0.00 0.00 0.24 0.24 0.12 0.00 0.00 0.00 0.00 0.12 0.00 0.48 0.12 0.12 0.39 0.42 0.32 0.00 0.12 0.00 0.00 0.12 0.12 0.23 H3 T3 0.00 0.00 0.00 0.00 0.00 0 .44 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00 0.44 0.00 0.00 0.44 0.44 0.00 0.44 H37R v 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 LAM1 0.17 0.00 0.42 0.17 0.00 0.11 0.00 0.65 0.00 0.04 0.04 0.17 0.63 0.15 0.14 0.55 0.07 0.43 0.00 0.04 0.27 0.00 0.36 0.74 LAM1 0_CA M 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 LAM1 2_MA D1 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LAM2 0.00 0.05 0.13 0.17 0.05 0.46 0.00 0.50 0.00 0.00 0.00 0.00 0.67 0.47 0.05 0.58 0.09 0.00 0.00 0.00 0.05 0.00 0.00 0.49 LAM2 LAM4 0.00 0.00 0.48 0.00 0 .32 0.18 0.00 0.34 0.00 0.00 0.00 0.00 0.58 0.18 0.00 0.70 0.00 0.48 0.00 0.48 0.00 0.54 0.00 0.34 LAM3 0.04 0.06 0.24 0.47 0.02 0.15 0.00 0.47 0.04 0.34 0.04 0.49 0.31 0.44 0.20 0.52 0.06 0.04 0.00 0.10 0.00 0.00 0.04 0.64 LAM3 and S /conv ergen t 0.49 0. 41 0.41 0.24 0.00 0.41 0.00 0.61 0.49 0.41 0.00 0.73 0.41 0.24 0.41 0.73 0.61 0.00 0.41 0.00 0.00 0.65 0.00 0.78 LAM3 LAM6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LAM4 0.23 0 .00 0.00 0.34 0.12 0.00 0.00 0.60 0.12 0.12 0.12 0.23 0.52 0.50 0.32 0.50 0.23 0.00 0.00 0.44 0.60 0.12 0.12 0.72 LAM5 0.50 0.05 0.37 0.50 0.09 0.05 0.00 0.32 0.13 0.24 0.00 0.57 0.56 0.45 0.00 0.60 0.00 0.00 0.00 0.49 0.51 0.05 0.48 0.72 LAM5 LAM6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LAM6 0.24 0.00 0.41 0.41 0.00 0.41 0.00 0.24 0.00 0.24 0.00 0.78 0.24 0.41 0.45 0.24 0.41 0.00 0.00 0.24 0.45 0.00 0.00 0.57 LAM7 _TUR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LAM8 0.00 0.00 0.38 0.00 0.00 0.00 0.00 0.38 0.00 0.00 0.38 0.38 0.38 0.00 0.00 0.38 0.38 0.00 0.00 0.38 0.00 0.38 0.00 0.38 LAM9 0.48 0.14 0.43 0.61 0.14 0.15 0.00 0.47 0.23 0.45 0.04 0.62 0.71 0.29 0.20 0.64 0.10 0.00 0.09 0.49 0.57 0.14 0.41 0.81 LAM9 S 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 MAN U1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 MAN U2 0.38 0.38 0.63 0.00 0.38 0.63 0.00 0.63 0.00 0.63 0.38 0.50 0.63 0.38 0.63 0.75 0.38 0.38 0.63 0.00 0.00 0.63 0.00 0.75 ORPH AN 0.29 0.45 0 .70 0.55 0.23 0.61 0.00 0.68 0.13 0.50 0.30 0.78 0.69 0.56 0.75 0.81 0.72 0.42 0.62 0.38 0.30 0.74 0.64 0.83

PAGE 101

101 Table A 1. Continued Subli neage s / Locus 2 4 10 16 20 23 24 26 27 31 39 40 424 577 1955 2163 b 2165 2347 2401 2461 3171 3690 4052 4156 S 0.42 0.26 0.20 0.26 0.12 0.12 0.00 0.53 0.03 0.53 0.03 0.58 0.51 0.06 0.00 0.53 0.35 0.03 0.00 0.06 0.35 0.06 0.15 0.77 T1 0.20 0.21 0.44 0.65 0.10 0.42 0.00 0.56 0.17 0.42 0.08 0.76 0.43 0.43 0.48 0.74 0.58 0.01 0.27 0.04 0.13 0.73 0.27 0.68 T1 (H0) 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.50 T1 (T4 CE1 ancest or0) 0.00 0.00 0.36 0.00 0.00 0.00 0.00 0.66 0.00 0.24 0.00 0.24 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.41 0.41 0.00 0.74 T1 T 2 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.50 0.00 0.00 0.00 0.50 0.50 0.00 0.00 0.00 0.00 0.50 0.00 0.50 T1_R US2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.42 0.00 0.00 0.00 0.42 0.00 0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 T2 0 .17 0.41 0.64 0.52 0.09 0.33 0.00 0.47 0.41 0.32 0.24 0.64 0.65 0.59 0.71 0.75 0.65 0.09 0.25 0.00 0.25 0.69 0.17 0.72 T2 T3 0.00 0.44 0.57 0.40 0.15 0.15 0.00 0.65 0.38 0.29 0.00 0.29 0.40 0.38 0.44 0.72 0.00 0.00 0.15 0.57 0.15 0.15 0.15 0.68 T3 0.00 0 .10 0.00 0.60 0.00 0.18 0.00 0.27 0.00 0.34 0.00 0.65 0.00 0.00 0.10 0.45 0.42 0.00 0.00 0.00 0.00 0.63 0.00 0.41 T4 0.00 0.00 0.14 0.27 0.14 0.00 0.00 0.14 0.14 0.27 0.00 0.14 0.27 0.27 0.00 0.26 0.14 0.00 0.00 0.00 0.00 0.14 0.26 0.26 T4 CEU1 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 T5 0.00 0.50 0.50 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.50 0.50 0.00 0.50 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 T5_M AD2 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 T5_R US1 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.28 0.00 0.00 0.00 0.28 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.50 0.00 0.28 U 0.04 0.19 0.72 0 .66 0.04 0.50 0.00 0.64 0.08 0.40 0.19 0.70 0.63 0.57 0.55 0.73 0.52 0.26 0.55 0.19 0.08 0.47 0.60 0.76 U (LAM 30) 0.00 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.44 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 U (likely H) 0.00 0. 00 0.32 0.00 0.48 0.48 0.00 0.00 0.00 0.00 0.00 0.32 0.00 0.00 0.48 0.48 0.00 0.48 0.00 0.00 0.00 0.00 0.48 0.64 U (likely H3) 0.00 0.00 0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.18 U (like ly LAM) 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.50 0.00 0.00 0.00 0.50 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.50 U (likely S) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 U (likely T3) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 X1 0.18 0.00 0.62 0.22 0.03 0.05 0.00 0.59 0.16 0.48 0.20 0.77 0.59 0.03 0.50 0.65 0.13 0.11 0.29 0.18 0.38 0.44 0 .55 0.76 X1 LAM9 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 X2 0.02 0.02 0.06 0.06 0.06 0.05 0.00 0.50 0.02 0.05 0.00 0.20 0.40 0.10 0.17 0.25 0.06 0.03 0.02 0.01 0.01 0.26 0.09 0.48

PAGE 102

102 Table A 1. Continued Subli neage s / Locus 2 4 10 16 20 23 24 26 27 31 39 40 424 577 1955 2163 b 2165 2347 2401 2461 3171 3690 4052 4156 X3 0.00 0.07 0.53 0.03 0.03 0.05 0.00 0.52 0.12 0.48 0.03 0.58 0.54 0.00 0.51 0.68 0.02 0.08 0.03 0.46 0.25 0.22 0. 05 0.56 ZERO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.44 0.00 0.00

PAGE 103

103 APPENDIX B SUPPLEMENTAL OBSERVATIONS FROM WHOLE GENOME SEQUENCE ANALYSES Table B 1 Mycobacterium tuberculosis Refere nce Genomes used to complement the Whole Genome Sequence Analyses. Reference Sequence Accession Number Strain ID Year of Isolation Place of Isolation Kato Maeda et al. 2012 SRP025757 E5360000 2008 San Francisco, U.S. Casali et al. 2012 ERP000192 M0814443 2005 Samara Oblast, Russia Clark et al. 2013 ERP000520 A70620 A70555 2005 2004 Kampala, Uganda Luo et al. 201 SRP029424 HLJ090004 HLJ090071 2009 2009 Heilongjiang, China Guerra Assuno et al. 2015 ERP000436 1026522 1995 Karonga District, Malawi Gardy et al. 2011 SRA020129 MT0009 MT0006 MT0003 MT0001 2006 2000 2000 2005 British Columbia, Canada Winglee et al. 2016 SRP030046 MAL020147 2007 Mali Ocheretina et al. 2015 SRX750472 SRS741137 2011 Port au Prince, Haiti Valway et al. 1998 165 NC_002755.2 CDC1551 1995 Tennessee, U.S.

PAGE 104

104 Figure B 1. Midpoint rooted M aximum Likelihood Phylogeny of the 21 FL0117 Isolates and 12 Reference Isolates. Tip labels colors indicate patient birth origin : Blue indicates U.S, Red indicates foreign. The two letter Birth country abbreviation is identified in the taxon name: HT Haiti; VT Vietnam; GR Grenada; US U.S. R eference strains are in Black; @ind icate pediatric case with Haitian guardian; ** denotes cases diagnosed while incarcerated; all others were diagnosed in the community.

PAGE 105

105 APPENDIX C LIST OF QUESTIONS USED TO TEST PARTICIPANT TUBERCULOSIS K NOWLEDGE What symptoms can show that a person has TB ? Answer Choices Coughing with sputum 0 1 Coughing for over 3 weeks 0 1 Blood in sputum 0 1 Loss of appetite 0 1 Night sweats 0 1 Weight loss without dieting/ exercising 0 1 I do not know 0 1 I am going to read you a few statements about TB, can you tell me whether you agree or do not agree: TB is cause by germs called bacteria TB is spread from one person to another through the air Everyone sho uld get tested for TB Everyone who gets infected with TB bacteria will get sick Some people can get TB disease easier than others TB disease can be cured TB can affect other parts of the body besides the lungs TB infection and TB disease are the same TB ba cteria have a hard time living in fresh air and sunlight If you have TB infection you may have to take medicine, even Answer Choices 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree 0=Disagree 1=Agree

PAGE 106

106 LIST OF R EFERENCES 1. CDC | TB | Core Curriculum on Tuberculosis: What the Clinician Should Know. http://www.cdc.gov/tb/education/corecurr/. Accessed June 14, 2013. 2. Daniel TM. The history of tuberculosis. Respir Med 2006;100(11):1862 1870. doi:10.1016/j.rme d.2006.08.006. 3. Glaziou P, Falzon D, Floyd K, Raviglione M. Global epidemiology of tuberculosis. Semin Respir Crit Care Med 2013;34(1):3 16. doi:10.1055/s 0032 1333467. 4. Miller TL, McNabb SJN, Hilsenrath P, Pasipanodya J, Weis SE. Personal and Socie tal Health Quality Lost to Tuberculosis. PLoS ONE 2009;4(4):e5080. doi:10.1371/journal.pone.0005080. 5. Wilson LG. Commentary: Medicine, population, and tuberculosis. Int J Epidemiol 2005;34(3):521 524. doi:10.1093/ije/dyh196. 6. Comas I, Coscolla M, L uo T, et al. Out of Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat Genet 2013;45(10):1176 1182. doi:10.1038/ng.2744. 7. Grange JM, Zumla A. The global emergency of tuberculosis: what is the cause? J R Soc Promot Health 2002;122(2):78 81. doi:10.1177/146642400212200206. 8. World Health Organization. Global Tuberculosis Report 2015 .; 2015:204. http://www.who.int/tb/publications/global_report/en/. Accessed January 10, 2016. 9. Gagneux S. Host pathogen coev olution in human tuberculosis. Philos Trans R Soc Lond B Biol Sci 2012;367(1590):850 859. doi:10.1098/rstb.2011.0316. 10. Gagneux S, Small PM. Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development. Lanc et Infect Dis 2007;7(5):328 337. doi:10.1016/S1473 3099(07)70108 1. 11. Cole ST, Brosch R, Parkhill J, et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998;393(6685):537 544. doi:10.1038/31159. 12. Cole ST. Comparative and functional genomics of the Mycobacterium tuberculosis complex. Microbiology 2002;148(10):2919 2928. 13. Ozcaglar C, Shabbeer A, Vandenberg S, Yener B, Bennett KP. Examining the sublineage structure of Mycobacterium tuberculosis complex strains with multiple biomarker tensors. Proc IEEE Int Conf Bioinforma Biomed 2010:543 548. doi:10.1109/BIBM.2010.5706625.

PAGE 107

107 14. Demay C, Liens B, Burguire T, et al. SITVITWEB -a publicly available international multimarker database for studying M ycobacterium tuberculosis genetic diversity and molecular epidemiology. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis 2012;12(4):755 766. doi:10.1016/j.meegid.2012.02.004. 15. Click ES, Moonan PK, Winston CA, Cowan LS, Oeltmann JE. Relationship between Mycobacterium tuberculosis phylogenetic lineage and clinical site of tuberculosis. Clin Infect Dis Off Publ Infect Dis Soc Am 2012;54(2):211 219. doi:10.1093/cid/cir788. 16. Borrell S, Gagneux S. Strain diversity, epistasis and the evolution of drug resistance in Mycobacterium tuberculosis. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis 2011;17(6):815 820. doi:10.1111/j.1469 0691.2011.03556.x. 17. Malik AN, Godfrey Faussett P. Effects of genetic variability of Mycobacterium tu berculosis strains on the presentation of disease. Lancet Infect Dis 2005;5(3):174 183. doi:10.1016/S1473 3099(05)01310 1. 18. Kser CU, Feuerriegel S, Summers DK, Archer JAC, Niemann S. Importance of the genetic diversity within the Mycobacterium tuberc ulosis complex for the development of novel antibiotics and diagnostic tests of drug resistance. Antimicrob Agents Chemother 2012;56(12):6080 6087. doi:10.1128/AAC.01641 12. 19. Ford CB, Shah RR, Maeda MK, et al. Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug resistant tuberculosis. Nat Genet 2013;45(7):784 790. doi:10.1038/ng.2656. 20. Wirth T, Hildebrand F, Allix Bguec C, et al. Origin, Spread and Demography of the M ycobacterium tuberculosis Complex. PLoS Pathog 2008;4(9):e1000160. doi:10.1371/journal.ppat.1000160. 21. Donoghue HD, Spigelman M, Greenblatt CL, et al. Tuberculosis: from prehistory to Robert Koch, as revealed by ancient DNA. Lancet Infect Dis 2004;4(9 ):584 592. doi:10.1016/S1473 3099(04)01133 8. 22. Hershberg R, Lipatov M, Small PM, et al. High functional diversity in Mycobacterium tuberculosis driven by genetic drift and human demography. PLoS Biol 2008;6(12):e311. doi:10.1371/journal.pbio.0060311. 23. Coscolla M, Gagneux S. Consequences of genomic diversity in Mycobacterium tuberculosis. Semin Immunol 2014;26(6):431 444. doi:10.1016/j.smim.2014.09.012.

PAGE 108

108 24. Brites D, Gagneux S. Old and new selective pressures on Mycobacterium tuberculosis. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis 2012;12(4):678 685. doi:10.1016/j.meegid.2011.08.010. 25. Jasmer RM, Nahid P, Hopewell PC. Latent Tuberculosis Infection. N Engl J Med 2002;347(23):1860 1866. doi:10.1056/NEJMcp021045. 26. Vanhomwegen J, Kwara A, Martin M, et al. Impact of immigration on the molecular epidemiology of tuberculosis in Rhode Island. J Clin Microbiol 2011;49(3):834 844. doi:10.1128/JCM.01952 10. 27. Garzelli C, Lari N, Cuccu B, Tortoli E, Rindi L. Impact of immigration on t uberculosis in a low incidence area of Italy: a molecular epidemiological approach. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis 2010;16(11):1691 1697. doi:10.1111/j.1469 0691.2009.03149.x. 28. Fenner L, Gagneux S, Helbling P, et al. Mycobacterium tuberculosis transmission in a country with low tuberculosis incidence: role of immigration and HIV infection. J Clin Microbiol 2012;50(2):388 395. doi:10.1128/JCM.05392 11. 29. Kwan CK, Ernst JD. HIV and Tuberculosis: a Deadly Human Syndem ic. Clin Microbiol Rev 2011;24(2):351 376. doi:10.1128/CMR.00042 10. 30. Gray JM, Cohn DL. Tuberculosis and HIV coinfection. Semin Respir Crit Care Med 2013;34(1):32 43. doi:10.1055/s 0032 1333469. 31. WHO | Tuberculosis. WHO. http://www.who.int/mediac entre/factsheets/fs104/en/. Accessed June 20, 2013. 32. Nardell E, Dharmadhikari A. Turning off the spigot: reducing drug resistant tuberculosis transmission in resource limited settings. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis 2010;14(10): 1233 1243. 33. Dye C, Williams BG. The Population Dynamics and Control of Tuberculosis. Science 2010;328(5980):856 861. doi:10.1126/science.1185449. 34. Ormerod LP. Multidrug resistant tuberculosis (MDR TB): epidemiology, prevention and treatment. Br Me d Bull 2005;73 74:17 24. doi:10.1093/bmb/ldh047. 35. Marahatta SB. Multi drug resistant tuberculosis burden and risk factors: an update. Kathmandu Univ Med J KUMJ 2010;8(29):116 125. 36. Chiang C Y, Centis R, Migliori GB. Drug resistant tuberculosis: p ast, present, future. Respirol Carlton Vic 2010;15(3):413 432. doi:10.1111/j.1440 1843.2010.01738.x.

PAGE 109

109 37. Pooran A, Pieterson E, Davids M, Theron G, Dheda K. What is the cost of diagnosis and management of drug resistant tuberculosis in South Africa? PloS One 2013;8(1):e54587. doi:10.1371/journal.pone.0054587. 38. Pablos Mendez A, Gowda DK, Frieden TR. Controlling multidrug resistant tuberculosis and access to expensive drugs: a rational framework. Bull World Health Organ 2002;80(6):489 500. 39. Liang L, Wu Q, Gao L, et al. Factors contributing to the high prevalence of multidrug resistant tuberculosis: a study from China. Thorax 2012;67(7):632 638. doi:10.1136/thoraxjnl 2011 200018. 40. Zhao P, Li XJ, Zhang SF, Wang XS, Liu CY. Social behaviour risk factors for drug resistant tuberculosis in mainland China: a meta analysis. J Int Med Res 2012;40(2):436 445. 41. Millet J P, Moreno A, Fina L, et al. Factors that influence current tuberculosis epidemiology. Eur Spine J 2013;22(Suppl 4):539 548. doi:10 .1007/s00586 012 2334 8. 42. LoBue PA, Enarson DA, Thoen TC. Tuberculosis in humans and its epidemiology, diagnosis and treatment in the United States. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis 2010;14(10):1226 1232. 43. Cain KP, Kenzie WRM. Overcoming the Limits of Tuberculosis Prevention among Foreign Born Individuals: Next Steps toward Eliminating Tuberculosis. Clin Infect Dis 2008;46(1):107 109. doi:10.1086/523732. 44. Kanduma E, McHugh T d., Gillespie S h. Molecular methods for Mycobac terium tuberculosis strain typing: a users guide. J Appl Microbiol 2003;94(5):781 791. doi:10.1046/j.1365 2672.2003.01918.x. 45. Mathema B, Kurepina NE, Bifani PJ, Kreiswirth BN. Molecular Epidemiology of Tuberculosis: Current Insights. Clin Microbiol Re v 2006;19(4):658 685. doi:10.1128/CMR.00061 05. 46. Achtman M. Evolution, population structure, and phylogeography of genetically monomorphic bacterial pathogens. Annu Rev Microbiol 2008;62:53 70. doi:10.1146/annurev.micro.62.081307.162832. 47. Driscol l JR. Spoligotyping for molecular epidemiology of the Mycobacterium tuberculosis complex. Methods Mol Biol Clifton NJ 2009;551:117 128. doi:10.1007/978 1 60327 999 4_10. 48. Weniger T, Krawczyk J, Supply P, Niemann S, Harmsen D. MIRU VNTRplus: a web tool for polyphasic genotyping of Mycobacterium tuberculosis complex bacteria. Nucleic Acids Res 2010;38(Web Server issue):W326 W331. doi:10.1093/nar/gkq351.

PAGE 110

110 49. Walker TM, Ip CLC, Harrell RH, et al. Whole genome sequencing to delineate Mycobacterium tubercu losis outbreaks: a retrospective observational study. Lancet Infect Dis 2013;13(2):137 146. doi:10.1016/S1473 3099(12)70277 3. 50. Gardy JL, Johnston JC, Sui SJH, et al. Whole Genome Sequencing and Social Network Analysis of a Tuberculosis Outbreak. N En gl J Med 2011;364(8):730 739. doi:10.1056/NEJMoa1003176. 51. Coker OO, Regmi SM, Suriyaphol P, Chininmanu K, Prammananan T, Chaiprasert A. Whole Genome Sequence of a Multidrug Resistant Mycobacterium tuberculosis Beijing Sequence Type 10 Isolate from an Outbreak in Thailand. Genome Announc 2014;2(4). doi:10.1128/genomeA.00803 14. 52. Fleischmann RD, Alland D, Eisen JA, et al. Whole Genome Comparison of Mycobacterium tuberculosis Clinical and Laboratory Strains. J Bacteriol 2002;184(19):5479 5490. doi:1 0.1128/JB.184.19.5479 5490.2002. 53. Merker M, Kohl TA, Roetzer A, et al. Whole genome sequencing reveals complex evolution patterns of multidrug resistant Mycobacterium tuberculosis Beijing strains in patients. PloS One 2013;8(12):e82551. doi:10.1371/jo urnal.pone.0082551. 54. Roetzer A, Diel R, Kohl TA, et al. Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiological study. PLoS Med 2013;10(2):e1001387. doi :10.1371/journal.pmed.1001387. 55. Grenfell BT, Pybus OG, Gog JR, et al. Unifying the Epidemiological and Evolutionary Dynamics of Pathogens. http://www.sciencemag.org. Accessed April 1, 2014. 56. Drummond AJ, Pybus OG, Rambaut A, Forsberg R, Rodrigo AG. Measurably evolving populations. Trends Ecol Evol 2003;18(9):481 488. doi:10.1016/S0169 5347(03)00216 7. 57. Drummond AJ, Rambaut A, Shapiro B, Pybus OG. Bayesian Coalescent Inference of Past Population Dynamics from Molecular Sequences. Mol Biol Evol 2005;22(5):1185 1192. doi:10.1093/molbev/msi103. 58. Drummond AJ, Ho SYW, Phillips MJ, Rambaut A. Relaxed phylogenetics and dating with confidence. PLoS Biol 2006;4(5):e88. doi:10.1371/journal.pbio.0040088. 59. Bollback JP. Bayesian model adequacy and c hoice in phylogenetics. Mol Biol Evol 2002;19(7):1171 1180. 60. World Health Organization. Haiti Tuberculosis Profile .; 2016. http://www.who.int/tb/country/data/profiles/en/. Accessed May 25, 2016.

PAGE 111

111 61. UNAIDS. UNAIDS Report on the Global AIDS Epidemic .; 2013. http://www.unaids.org/en/media/unaids/contentassets/documents/epidemiology/2 013/gr2013/UNAIDS_Global_Report_2013_en.pdf. Accessed May 6, 2014. 62. Joseph P, Severe P, Ferdinand S, et al. Multidrug resistant tuberculosis at an HIV testing center in Haiti. AIDS Lond Engl 2006;20(3):415 418. doi:10.1097/01.aids.0000206505.09159.9a. 63. Ferdinand S, Sola C, Verdol B, et al. Molecular characterization and drug resistance patterns of strains of Mycobacterium tuberculosis isolated from patients in an AID S counseling center in Port au Prince, Haiti: a 1 year study. J Clin Microbiol 2003;41(2):694 702. 64. Ocheretina O, Morose W, Gauthier M, et al. Multidrug resistant tuberculosis in Port au Prince, Haiti. Rev Panam Salud Pblica Pan Am J Public Health 2 012;31(3):221 224. 65. Joseph P. Tuberculosis among Displaced Haitians: The Aftermath of the Haiti Earthquake. 2011. http://www.bc.lung.ca/association_and_services/documents/03Joseph TBAmongDisplacedPopulation_AftermathofHaitisEarthquakeFeb2011.pdf. 66. the amplification dynamics of drug resistance. Nat Med 2004;10(10):1111 1116. doi:10.1038/nm1102. 67. At a glance: Haiti Statistics. UNICEF. http://www.unicef.org/infobycountr y/haiti_statistics.html#118. Accessed February 6, 2015. 68. Groupe URD Haiti Migration and urban governance: two fundamental and related issues in the reconstruction of the city of Port au Prince. http://www.urd.org/Migration and urban governance two. A ccessed February 13, 2015. 69. Martin P, Midgley E, Teitelbaum MS. Migration and Development: Whither the Dominican Republic and Haiti? Int Migr Rev 2002;36(2):570 592. doi:10.1111/j.1747 7379.2002.tb00093.x. 70. CDC | TB | Fact Sheets | Trends in Tuber culosis United States. http://www.cdc.gov/tb/publications/factsheets/statistics/TBTrends.htm. Accessed August 13, 2014. 71. CDC. Reported Tuberculosis in the United States, 2014 .; 2015. http://www.cdc.gov/tb/statistics/reports/2014/default.htm. Accessed April 1, 2016.

PAGE 112

112 72. CDC. Essential Components of a Tuberculosis Prevention and Control Program. Recommendations of the Advisory Council for the Elimination of Tuberculosis. MMWR 1995;44(RR 11):1 16. 73. Cain KP, Benoit SR, Winston CA, Mac Kenzie WR. Tub erculosis among foreign born persons in the united states. JAMA 2008;300(4):405 412. doi:10.1001/jama.300.4.405. 74. Ricks PM, Cain KP, Oeltmann JE, Kammerer JS, Moonan PK. Estimating the Burden of Tuberculosis among Foreign Born Persons Acquired Prior t o Entering the U.S., 2005 2009. PLoS ONE 2011;6(11):e27405. doi:10.1371/journal.pone.0027405. 75. Sraphin MN, Lauzardo M. Mycobacterium tuberculosis complex transmission is Infect Genet Evol 2015;36:547 551. doi:10.1016/j.meegid.2015.08.038. 76. CDC Reported Tuberculosis in the United States, 2012 TB. http://www.cdc.gov/tb/statistics/reports/2012/default.htm. Accessed March 26, 2014. 77. U. S. Census Bureau. 2013 American Community Sur vey 1 Year Estimates. http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?fpt=ta ble. Accessed January 30, 2015. 78. Scott AN, Menzies D, Tannenbaum T N, et al. Sensitivities and specificities of spoligotyping and mycobacterial int erspersed repetitive unit variable number tandem repeat typing methods for studying molecular epidemiology of tuberculosis. J Clin Microbiol 2005;43(1):89 94. doi:10.1128/JCM.43.1.89 94.2005. 79. Sohmer R, Jackson D, Katz B, Warren D. The Haitian Communi ty in Miami Dade: A Growing the Middle Class Supplement Brookings Institution Metropolitan Policy Program; 2005:12. http://www.brookings.edu/~/media/research/files/reports/2005/9/miami%20dade% 20sohmer/20050901_haiti.pdf. Accessed January 30, 2015. 80. Ri eder HL. Risk of Travel Associated Tuberculosis. Clin Infect Dis 2001;33(8):1393 1396. doi:10.1086/323127. 81. Cobelens FG, van Deutekom H, Draayer Jansen IW, et al. Risk of infection with Mycobacterium tuberculosis in travellers to areas of high tubercu losis endemicity. Lancet 2000;356(9228):461 465. doi:10.1016/S0140 6736(00)02554 X. 82. Wilder Smith A, Foo W, Earnest A, Paton NI. High risk of Mycobacterium tuberculosis infection during the Hajj pilgrimage. Trop Med Int Health TM IH 2005;10(4):336 33 9. doi:10.1111/j.1365 3156.2005.01395.x.

PAGE 113

113 83. Walker TM, Lalor MK, Broda A, et al. Assessment of Mycobacterium tuberculosis transmission in Oxfordshire, UK, 2007 12, with whole pathogen genome sequences: an observational study. Lancet Respir Med 2014;2(4) :285 292. doi:10.1016/S2213 2600(14)70027 X. 84. Rodwell TC, Kapasi AJ, Barnes RFW, Moser KS. Factors associated with genotype clustering of Mycobacterium tuberculosis isolates in an ethnically diverse region of southern California, United States. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis 2012;12(8):1917 1925. doi:10.1016/j.meegid.2012.08.022. 85. Malakmadze N, Gonzlez IM, Oemig T, et al. Unsuspected recent transmission of tuberculosis among high risk groups: implications of universal tube rculosis genotyping in its detection. Clin Infect Dis Off Publ Infect Dis Soc Am 2005;40(3):366 373. doi:10.1086/427112. 86. Sterling TR. Drug resistant tuberculosis in New York City: lessons to remember. Clin Infect Dis 2006;42(12):1711 1712. 87. Oren E, Winston CA, Pratt R, Robison VA, Narita M. Epidemiology of Urban Tuberculosis in the United States, 2000 2007. Am J Public Health 2011;101(7):1256 1263. doi:10.2105/AJPH.2010.300030. 88. Chaoui I, Zozio T, Lahlou O, et al. Contribution of spoligotypi ng and MIRU VNTRs to characterize prevalent Mycobacterium tuberculosis genotypes infecting tuberculosis patients in Morocco. Infect Genet Evol 2014;21:463 471. doi:10.1016/j.meegid.2013.05.023. 89. Teeter LD, Ha NP, Ma X, et al. Evaluation of large genot ypic Mycobacterium tuberculosis clusters: contributions from remote and recent transmission. Tuberculosis 2013;93:S38 S46. doi:10.1016/S1472 9792(13)70009 X. 90. Ghosh S, Moonan PK, Cowan L, Grant J, Kammerer S, Navin TR. Tuberculosis genotyping informat ion management system: enhancing tuberculosis surveillance in the United States. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis 2012;12(4):782 788. doi:10.1016/j.meegid.2011.10.013. 91. Braden CR, Templeton GL, Cave MD, et al. Interpretation of Restriction Fragment Length Polymorphism Analysis of Mycobacterium tuberculosis Isolates from a State with a Large Rural Population. J Infect Dis 1997;175(6):1446 1452. 92. Kulaga S, Behr M, Nguyen D, et al. Diversity of Mycobacterium tuberculosis Isolat es in an Immigrant Population: Evidence against a Founder Effect. Am J Epidemiol 2004;159(5):507 513. doi:10.1093/aje/kwh065.

PAGE 114

114 93. Moonan PK, Ghosh S, Oeltmann JE, Kammerer JS, Cowan LS, Navin TR. Using Genotyping and Geospatial Scanning to Estimate Recen t Mycobacterium tuberculosis Transmission, United States. Emerg Infect Dis 2012;18(3):458 465. doi:10.3201/eid1803.111107. 94. Haase I, Olson S, Behr MA, et al. Use of geographic and genotyping tools to characterise tuberculosis transmission in Montreal. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis 2007;11(6):632 638. 95. Tuite AR, Guthrie JL, Alexander DC, et al. Epidemiological evaluation of spatiotemporal and genotypic clustering of Mycobacterium tuberculosis in Ontario, Canada. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis 2013;17(10):1322 1327. doi:10.5588/ijtld.13.0145. 96. Kulldorff M. SaTScan User Guide for Version 9.2. http://www.satscan.org/cgi bin/satscan/register.pl/SaTScan_Users_Guide.pdf?todo=process_userguide_dow nload. P ublished 2014. Accessed February 7, 2015. 97. United States Census Bureau. Population estimates, July 1, 2014, (V2014). http://www.census.gov/quickfacts/. Published 2015. Accessed June 24, 2015. 98. Florida Department of Health. Florida Health Statistics and Community Health Data. Division of Public Health Statistics & Performance Management. http://www.floridacharts.com/charts/default.aspx. Published 2015. Accessed April 19, 2015. 99. Centers for Disease Control and Prevention (CDC). From the Centers fo r Disease Control and Prevention. Tuberculosis morbidity among U.S. born and foreign born populations -United States, 2000. MMWR Morb Mortal Wkly Rep 2002;51(5):101 104. 100. Tuberculosis elimination revisited: obstacles, opportunities, and a renewed com mitment. Advisory Council for the Elimination of Tuberculosis (ACET). MMWR Recomm Rep Morb Mortal Wkly Rep Recomm Rep Cent Dis Control 1999;48(RR 9):1 13. 101. Mazars E, Lesjean S, Banuls AL, et al. High resolution minisatellite based typing as a portabl e approach to global analysis of Mycobacterium tuberculosis molecular epidemiology. Proc Natl Acad Sci U S A 2001;98(4):1901 1906. doi:10.1073/pnas.98.4.1901. 102. University of Florida Zip Code Areas (Five Digit) in Florida 2012 (FGDC) / U.S. ZIP Code Areas (Five Digit)(ISO). http://www.fgdl.org/metadataexplorer/explorer.jsp. Accessed April 19, 2015. 103. Svensson K, Bck E, Eliasson H, et al. Landscape Epidemiology of Tularemia Out breaks in Sweden. Emerg Infect Dis 2009;15(12):1937 1947. doi:10.3201/eid1512.090487.

PAGE 115

115 104. Hawthorne B. Geospatial Modelling Environment .; 2009. http://www.spatialecology.com/gme/. Accessed June 16, 2015. 105. Hunter PR, Gaston MA. Numerical index of th e discriminatory ability of typing J Clin Microbiol 1988;26(11):2465 2466. 106. Selander RK, Caugant DA, Ochman H, Musser JM, Gilmour MN, Whittam TS. Methods of multilocus enzyme electrophoresis fo r bacterial population genetics and systematics. Appl Environ Microbiol 1986;51(5):873 884. 107. Kulldorff M, Heffernan R, Hartman J, Assuno R, Mostashari F. A space time permutation scan statistic for disease outbreak detection. PLoS Med 2005;2(3):e5 9. doi:10.1371/journal.pmed.0020059. 108. Root ED, Meyer RE, Emch ME. Evidence of localized clustering of gastroschisis births in North Carolina, 1999 2004. Soc Sci Med 2009;68(8):1361 1367. doi:10.1016/j.socscimed.2009.01.034. 109. Hightower J, Kracali k IT, Vydayko N, Goodin D, Glass G, Blackburn JK. Historical distribution and host vector diversity of Francisella tularensis, the causative agent of tularemia, in Ukraine. Parasit Vectors 2014;7(1). doi:10.1186/s13071 014 0453 2. 110. World Health Organ ization. Global tuberculosis Control 2014: WHO Report. WHO. http://www.who.int/tb/publications/global_report/en/. Published 2014. Accessed June 12, 2015. 111. Yimer SA, Norheim G, Namouchi A, et al. Mycobacterium tuberculosis Lineage 7 Strains Are Associa ted with Prolonged Patient Delay in Seeking Treatment for Pulmonary Tuberculosis in Amhara Region, Ethiopia. J Clin Microbiol 2015;53(4):1301 1309. doi:10.1128/JCM.03566 14. 112. Martins MC, Saraiva Giampaglia CM, Oliveira RS, et al. Population structure and circulating genotypes of drug sensitive and drug resistant Mycobacterium tuberculosis clinical isolates in So Paulo state, Brazil. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis 2013;14:39 45. doi:10.1016/j.meegid.2012.10.015. 113. Farnia P, Masjedi MR, Mirsaeidi M, et al. Prevalence of Haarlem I and Beijing types of Mycobacterium tuberculosis strains in Iranian and Afghan MDR TB patients. J Infect 2006;53(5):331 336. doi:10.1016/j.jinf.2005.12.020. 114. Lillebaek T, Andersen AB, Bauer J, et al. Risk of Mycobacterium tuberculosis transmission in a low incidence country due to immigration from high incidence areas. J Clin Microbiol 2001;39(3):855 861. doi:10.1128/JCM.39.3.855 861.2001.

PAGE 116

116 115. Keshavjee S, Farmer PE. Tuberculosis, Drug Resis tance, and the History of Modern Medicine. N Engl J Med 2012;367(10):931 936. doi:10.1056/NEJMra1205429. 116. Marais BJ, Victor TC, Hesseling AC, et al. Beijing and Haarlem Genotypes Are Overrepresented among Children with Drug Resistant Tuberculosis in the Western Cape Province of South Africa. J Clin Microbiol 2006;44(10):3539 3543. doi:10.1128/JCM.01291 06. 117. Mardassi H, Namouchi A, Haltiti R, et al. Tuberculosis due to Resistant Haarlem Strain, Tunisia. Emerg Infect Dis 2005;11(6):957 961. doi:1 0.3201/eid1106.041365. 118. Brudey K, Driscoll JR, Rigouts L, et al. Mycobacterium tuberculosis complex genetic diversity: mining the fourth international spoligotyping database (SpolDB4) for classification, population genetics and epidemiology. BMC Micro biol 2006;6:23. doi:10.1186/1471 2180 6 23. 119. Gagneux S, DeRiemer K, Van T, et al. Variable host pathogen compatibility in Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 2006;103(8):2869 2873. doi:10.1073/pnas.0511240103. 120. Brudey K, Fillio l I, Ferdinand S, et al. Long term population based genotyping study of Mycobacterium tuberculosis complex isolates in the French departments of the Americas. J Clin Microbiol 2006;44(1):183 191. doi:10.1128/JCM.44.1.183 191.2006. 121. Cavanaugh JS, Powe ll K, Renwick OJ, et al. An Outbreak of Tuberculosis Among Adults With Mental Illness. Am J Psychiatry 2012;169(6):569 575. doi:10.1176/appi.ajp.2011.11081311. 122. Centers for Disease Control and Prevention (CDC). Notes from the field: tuberculosis clus ter associated with homelessness -Duval County, Florida, 2004 2012. MMWR Morb Mortal Wkly Rep 2012;61(28):539 540. 123. The PLoS Medicine Editors. The Health Crisis of Tuberculosis in Prisons Extends beyond the Prison Walls. PLoS Med 2010;7(12):e1000383 doi:10.1371/journal.pmed.1000383. 124. Bamrah S, Yelk Woodruff RS, Powell K, Ghosh S, Kammerer JS, Haddad MB. Tuberculosis among the homeless, United States, 1994 2010. Int J Tuberc Lung Dis 2013;17(11):1414 1419. doi:10.5588/ijtld.13.0270. 125. Asgha r RJ, Patlan DE, Miner MC, et al. Limited Utility of Name Based Tuberculosis Contact Investigations among Persons Using Illicit Drugs: Results of an Outbreak Investigation. J Urban Health 2009;86(5):776 780. doi:10.1007/s11524 009 9378 z.

PAGE 117

117 126. Barro AS, Kracalik IT, Malania L, et al. Identifying hotspots of human anthrax transmission using three local clustering techniques. Appl Geogr 2015;60:29 36. doi:10.1016/j.apgeog.2015.02.014. 127. Salinas JL, Mindra G, Haddad MB, Pratt R, Price SF, Langer AJ. Lev eling of Tuberculosis Incidence United States, 2013 2015. MMWR Morb Mortal Wkly Rep 2016;65(11):273 278. doi:10.15585/mmwr.mm6511a2. 128. Cantwell MF, Snider DE, Cauthen GM, Onorato IM. Epidemiology of tuberculosis in the United States, 1985 through 19 92. JAMA 1994;272(7):535 539. 129. MacNeil JR, Lobato MN, Moore M. An Unanswered Health Disparity: Tuberculosis Among Correctional Inmates, 1993 Through 2003. Am J Public Health 2005;95(10):1800 1805. doi:10.2105/AJPH.2004.055442. 130. Centers for Dise ase Control and Prevention (CDC). Expanded tuberculosis surveillance and tuberculosis morbidity -United States, 1993. MMWR Morb Mortal Wkly Rep 1994;43(20):361 366. 131. Centers for Disease Control and Prevention. Report of Verified Case of Tuberculosis (RVCT): Instruction Manual USA; 2009:162. http://www.cdc.gov/tb/programs/rvct/InstructionManual.pdf. Accessed May 23, 2016. 132. Prosperi M, Veras N, Azarian T, et al. Molecular epidemiology of community associated methicillin resistant Staphylococcus au reus in the genomic era: a cross sectional study. Sci Rep 2013;3:1902. doi:10.1038/srep01902. 133. Joshi NA, Fass JN. Sickle: A Sliding Window, Adaptive, Quality Based Trimming Tool for FastQ Files .; 2011. https://github.com/najoshi/sickle. Accessed Apri l 26, 2016. 134. Zerbino DR. Using the Velvet de novo assembler for short read sequencing technologies. Curr Protoc Bioinforma Ed Board Andreas Baxevanis Al 2010;CHAPTER:Unit 11.5. doi:10.1002/0471250953.bi1105s31. 135. Rissman AI, Mau B, Biehl BS, Da rling AE, Glasner JD, Perna NT. Reordering contigs of draft genomes using the Mauve Aligner. Bioinformatics 2009;25(16):2071 2073. doi:10.1093/bioinformatics/btp356. 136. Darling AE, Mau B, Perna NT. progressiveMauve: Multiple Genome Alignment with Gene Gain, Loss and Rearrangement. PLOS ONE 2010;5(6):e11147. doi:10.1371/journal.pone.0011147. 137. Hall BG. Building phylogenetic trees from molecular data with MEGA. Mol Biol Evol 2013;30(5):1229 1235. doi:10.1093/molbev/mst012.

PAGE 118

118 138. Francisco AP, Vaz C, Monteiro PT, Melo Cristino J, Ramirez M, Carrio JA. PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods. BMC Bioinformatics 2012;13:87. doi:10.1186/1471 2105 13 87. 139. Nguyen L T, Schmidt HA, von Haeseler A, Minh BQ. IQ TREE: a fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol Biol Evol 2015;32(1):268 274. doi:10.1093/molbev/msu300. 140. Strimmer K, von Haeseler A. Likelihood mapping: A simple method to visualize phylogen Proc Natl Acad Sci U S A 1997;94(13):6815 6819. 141. Minh BQ, Nguyen MAT, von Haeseler A. Ultrafast Approximation for Phylogenetic Bootstrap. Mol Biol Evol 2013;30(5):1188 1195. doi:10.1093/molbev/mst024. 142. Bouc kaert R, Heled J, Khnert D, et al. BEAST 2: A Software Platform for Bayesian Evolutionary Analysis. PLOS Comput Biol 2014;10(4):e1003537. doi:10.1371/journal.pcbi.1003537. 143. Ferdinand S, Millet J, Accipe A, et al. Use of genotyping based clustering t o quantify recent tuberculosis transmission in Guadeloupe during a seven years period: analysis of risk factors and access to health care. BMC Infect Dis 2013;13(1):364. doi:10.1186/1471 2334 13 364. 144. Kato Maeda M, Ho C, Passarelli B, et al. Use of W hole Genome Sequencing to Determine the Microevolution of Mycobacterium tuberculosis during an Outbreak. PLOS ONE 2013;8(3):e58235. doi:10.1371/journal.pone.0058235. 145. Sraphin MN, Lauzardo M, Doggett RT, Zabala J, Jr JGM, Blackburn JK. Spatiotemporal Clustering of Mycobacterium tuberculosis Complex Genotypes in Florida: Genetic Diversity Segregated by Country of Birth. PLOS ONE 2016;11(4):e0153575. doi:10.1371/journal.pone.0153575. 146. Fenner L, Egger M, Bodmer T, et al. HIV Infection Disrupts the Sympatric Host Pathogen Relationship in Human Tuberculosis. PLoS Genet 2013;9(3). doi:10.1371/journal.pgen.1003318. 147. Social network analysis methods to characterize tuberculosis transmission patterns | BC Medical Journal. http://www.bcmj.org/bc centr e disease control/social network analysis methods characterize tuberculosis transmission pat. Accessed September 7, 2014. 148. Klovdahl AS, Graviss EA, Yaganehdoost A, et al. Networks and tuberculosis: an undetected community outbreak involving public pla ces. Soc Sci Med 1982 2001;52(5):681 694.

PAGE 119

119 149. Didelot X, Gardy J, Colijn C. Bayesian inference of infectious disease transmission from whole genome sequence data. Mol Biol Evol 2014;31(7):1869 1879. doi:10.1093/molbev/msu121. 150. Hatherell H A, Colij n C, Stagg HR, Jackson C, Winter JR, Abubakar I. Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review. BMC Med 2016;14:21. doi:10.1186/s12916 016 0566 x. 151. Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol Biol Evol March 2016:msw054. doi:10.1093/molbev/msw054. 152. Saint Jean G, Crandall LA. Sources and barriers to health care coverage for Haitian immigrants in Miami Dade county, Florida J Health Care Poor Underserved 2005;16(1):29 41. doi:10.1353/hpu.2005.0016. 153. Rahill GJ, Dawkins MP, De La Rosa M. Haitian Picuristes /Injectionists as Alternatives to Conventional Health Care Providers in South Florida. Soc Work Public Health 2011 ;26(6):577 593. doi:10.1080/19371918.2010.513649. 154. CDC Epi Info TM http://wwwn.cdc.gov/epiinfo/. Accessed June 18, 2015. 155. general theory and its analytical extensions. Ps ychometrika 1987;52(3):345 370. doi:10.1007/BF02294361. 156. Al Jahdali H, Memish ZA, Menzies D. Tuberculosis in association with travel. Int J Antimicrob Agents 2003;21(2):125 130. 157. Elfrink F, van den Hoek A, Mensen ME, Sonder GJ. Screening travel lers to high endemic countries for infection with Mycobacterium tuberculosis using interferon gamma release assay; a prospective study. BMC Infect Dis 2014;14. doi:10.1186/1471 2334 14 515. 158. White RG, Houben RMGJ. Towards elimination in industrialise d countries: expanding diagnosis and treatment of LTBI among immigrants [Editorial]. Int J Tuberc Lung Dis 2014;18(4):380 380. doi:10.5588/ijtld.14.0156. 159. Linas BP, Wong AY, Freedberg KA, Horsburgh CR. Priorities for Screening and Treatment of Latent Tuberculosis Infection in the United States. Am J Respir Crit Care Med 2011;184(5):590 601. doi:10.1164/rccm.201101 0181OC. 160. Coreil J, Lauzardo M, Heurtelou M. Anticipated tuberculosis stigma among health professionals and Haitian patients in South Florida. J Health Care Poor Underserved 2012;23(2):636 650. doi:10.1353/hpu.2012.0046.

PAGE 120

120 161. Coreil J, Mayard G, Simpson KM, Lauzardo M, Zhu Y, Weiss M. Structural Forces and the Production of TB related Stigma among Haitians in Two Contexts. Soc Sci Med 1982 2010;71(8):1409 1417. doi:10.1016/j.socscimed.2010.07.017. 162. Arguin PM, Marano N, Freedman DO. Globally Mobile Populations and the Spread of Emerging Pathogens. Emerg Infect Dis 2009;15(11):1713 1714. doi:10.3201/eid1511.091426. 163. Prez Lago L, Herranz M, Lirola MM, Bouza E, Viedma DG de. Characterization of Microevolution Events in Mycobacterium tuberculosis Strains Involved in Recent Transmission Clusters. J Clin Microbiol 2011;49(11):3771 3776. doi:10.1128/JCM.01285 11. 164. Prez Lago L Comas I, Navarro Y, et al. Whole genome sequencing analysis of intrapatient microevolution in Mycobacterium tuberculosis: potential impact on the inference of tuberculosis transmission. J Infect Dis 2014;209(1):98 108. doi:10.1093/infdis/jit439. 165. V alway SE, Sanchez MPC, Shinnick TF, et al. An Outbreak Involving Extensive Transmission of a Virulent Strain of Mycobacterium tuberculosis. N Engl J Med 1998;338(10):633 639. doi:10.1056/NEJM199803053381001.

PAGE 121

121 BIOGRAPHICAL SKETCH Marie Nancy Sraphin gr aduated from the Department of Epidemiology, housed jointly in the College of Public Health and Health Professions and the College of Medicine. She is a member of the Emerging Pathogens Institute and the Division of Infectious Disease and Global Medicine Nancy holds undergraduate degrees in biology and s ociology from the University of Miami and a Master in Public Health from Boston University. Prior to her doctoral studies, she worked as a Monitoring and Evaluation (M&E) officer for a non profit organizati research interests include the design and evaluation of interventions for the control of infectious diseases. In August 2016, she will start a post doctoral position with the Division of Infectious Diseases and Global Medicine at the University of Florida Her work will focus on understanding the evolution and transmission dynamics of Mycobacterium tuberculosis and its implications for tuberculosis control.