Citation
Effect of Mixed Strain Infections on Clinical and Epidemiological Features of Tuberculosis in Florida

Material Information

Title:
Effect of Mixed Strain Infections on Clinical and Epidemiological Features of Tuberculosis in Florida
Creator:
Asare-Baah, Michael
Publisher:
University of Florida
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Epidemiology
Committee Chair:
Rasmussen,Sonja A
Committee Co-Chair:
Lauzardo,Michael
Committee Members:
Morris,John Glenn
Striley,Catherine L
Graduation Date:
5/1/2020

Subjects

Subjects / Keywords:
mdr-tb
miru-vntr
tuberculosis
Genre:
Unknown ( sobekcm )

Notes

General Note:
Mixed infections with distinct Mycobacterium tuberculosis (MTB) strains within a single host have been documented in different settings; however, this phenomenon is rarely considered in the management and care of new and re-infected tuberculosis (TB) cases. This study aims to establish the epidemiological and clinical features of mixed infections among culture-confirmed TB patients enrolled in tuberculosis care at the Florida Department of Health (FDOH) as well as to assess the association between mixed infections and death. We deployed a cross-sectional study design to analyze de-identified surveillance data of TB cases enrolled from April 2008 to January 2018. Assignment of MTB lineages and molecular profiling of patients were done using spoligotyping and 24-locus Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR). The prevalence of mixed infections among the 3,599 culture-confirmed TB cases used in this analysis was 3% (107). Increased odds of mixed infections were observed among older patients, 45-64 years (AOR = 2.73; 95% CI: 1.05, 7.07; p= 0.0387), those who were 65 years and above (AOR = 3.83; 95% CI: 1.42, 10.29; p= 0.0079) and patients with diabetes (OR = 1.8; 95% CI: 1.09, 2.94; p= 0.0221). There was no association between mixed infections and death. The study affirms the low prevalence of mixed infections in a setting with low TB incidence and provides an insight on the epidemiological and clinical characteristics of patients with mixed infections which is essential in the management of TB patients. Future investigations involving the use of Whole Genome Sequencing (WGS) will be instrumental in providing a higher discriminatory power for mixed infections.

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Source Institution:
University of Florida
Holding Location:
University of Florida
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All applicable rights reserved by the source institution and holding location.
Embargo Date:
5/31/2021

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EFFECT OF MIXED STRAIN INFECTIONS ON CLINICAL AND EPIDEMIOLOGICAL FEATURES OF TUBERCULOSIS IN FLORIDA By MICHAEL ASARE BAAH A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2020

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© 2020 M ichae l A sare B aah

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To my family

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4 ACKNOWLEDG E MENTS I thank the chair, Dr. Sonja A. Rasmussen, co chair, Dr. Michael Lauzardo and members of my supervisory committee Dr. Catherine L. Striley and Dr. John Glenn Morris Jr. for their mentoring, support and guidance throughout this project. I am particula rly grateful and indebted to Dr. Michael Lauzardo and Dr. Awewura Kwara for their immense assistance, believing in me and granting me the opportunity to execute this thesis work successfully. I thank Dr. Nancy Seraphin for all her inputs and making time to listen whenever I had concerns. I also extend my gratitude to Dr. Varma, my academic adviser, for her counse l ling and coaching. To the faculty and staff at the department of epidemiology, I say a big thank you for your encouragement and the conducive lear ning environment you provided. Finally, to the team at the Florida Department of Health and the Southeastern National Tuberculosis Center, I say thank you for the warm reception and embracing me as one of your own.

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5 TABLE OF CONTENTS page ACKNOWLEDGEMENTS ................................ ................................ ............................... 4 LIST OF TABLES ................................ ................................ ................................ ............ 6 LIST OF FIGURES ................................ ................................ ................................ .......... 7 LIST OF ABBREVIATIONS ................................ ................................ ............................. 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 11 Rationale ................................ ................................ ................................ ................. 15 Objective ................................ ................................ ................................ ................. 16 Hypothesis ................................ ................................ ................................ .............. 16 2 METHODS ................................ ................................ ................................ .............. 17 Study Population ................................ ................................ ................................ ..... 17 Ethical Consideration ................................ ................................ .............................. 18 Measures ................................ ................................ ................................ .......... 18 Exposure variable ................................ ................................ ...................... 18 Outcome variables ................................ ................................ ..................... 19 Covariates/confounders ................................ ................................ ............. 19 Effect mod ifier ................................ ................................ ............................ 20 Statistical Analysis ................................ ................................ ............................ 20 3 RESULTS ................................ ................................ ................................ ............... 22 Sample Characteristics ................................ ................................ ........................... 22 Associatio n between Mixed Infections and TB Related Epidemiological Characteristics ................................ ................................ ................................ ..... 22 Association between Mixed Infections and Clinical TB Rel ated Characteristics ..... 24 4 DISCUSSION ................................ ................................ ................................ ......... 29 5 CONCLUSION ................................ ................................ ................................ ........ 33 APPENDIX: SUPPLEMENTAL INFORMATION ON THE POPULATION USED IN THIS ANALYSIS ................................ ................................ ................................ ..... 34 LIST OF REFERENCES ................................ ................................ ............................... 35 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 40

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6 LIST OF TABLES Table page 3 1 Epidemiological Characteristics of the Study Population by Infection Type and Death ................................ ................................ ................................ ........... 25 3 2 Clinical and TB Related Features of the Study Population by Infection Type and Death ................................ ................................ ................................ ........... 26 3 3 Unadjusted and Adjusted Association between Mixed Infection Type and Demographic Characteristics of the Study Population ................................ ........ 27 3 4 Unadjusted and Adjusted Association between Mixed Infection Type and Clinical TB related Characteristics ................................ ................................ ...... 28 A 1 Socio demographic Characteristics of Included vs Excluded Sample ................ 34

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7 LIST OF FIGURES Figure page 2 1 Flow Diagram of Sample Selection. ................................ ................................ .... 21

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8 LIST OF ABBREVIATIONS AIC FHMS Akaike Information Criterion Florida Health Management System CI Confidence Interval CNV Copy Number Variant DST Drug Susceptibility Testing FDOH GIMS IRB Florida Department of Health Genotype Information Management System Institutional Review Board HIV Human Immunodeficiency Virus LSP Large Sequence Polymorphism LTBI Latent Tuberculosis Infection MDR Multidrug Resistant MIRU Mycobacterial Interspersed Repetitive Unit MTBC Mycobacterium tuberculosis Complex RFLP Restriction Fragment Length Polymorphism SNP Single Nucleotide Polymorphism Spoligotyping Spacer Oligonucleotide Typing TB Tuberculosis TIMS NTGSN Tuberculosis Information Management System National Tuberculosis Genotyping and Surveillance Network VNTR Variable Number Tandem Repeat WGS Whole Genome Sequenc ing

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9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EFFECT OF MIXED STRAIN INFECTIONS ON CLINICAL AND EPIDEMIOLOGICAL FEATURES OF TUBERCULOSIS IN FLORIDA By Michael Asare Baah May 2020 Chair: Sonja A. Ras mussen Cochair: Michael Lauzardo Major: Epide miology Mixed infections with distinct Mycobacterium tuberculosis (M TB ) strains within a single host have been documented in different settings; however, this phenomenon is rarely considered in the management and care of new and re infected tuberculosis (TB) cases . This study aims to establish the epidemiological and clinical features of mixed infections among culture confirmed TB patients enrolled in tuberculosis care at the Florida Department of Health (FDOH) as well as to assess the association between mixed infections and death. We de ployed a cross sectional study design to analyz e de identified surveillance data of TB cases enrolled from April 2008 to January 2018. Assignment of M TB lineages and molecular profiling of patients were done using spoligotyping and 24 locus Mycobacterial Interspersed Repetitive Unit Variable Number Tand em Repeat ( MIRU VNTR ) . T he prevalence of mixed infections among the 3,599 culture confirmed TB cases used in this analysis was 3% (107 ) . Increased od d s of mixed infections were observed among older patients, 45 64 years (AOR = 2.73; 95% CI: 1.05 , 7.07; p= 0.0387) ,

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10 those who we re 65 years and above (AOR = 3.83; 95% CI: 1.42 , 10.29; p= 0.0079) and patients with diabetes (OR = 1.8; 95% CI: 1.09 , 2.94; p= 0.0221). There was no association between mixed infections and death . The study affirms the low prevalence of mixed infections in a setting with low TB incidence and provides an insight on the epidemiological and clinical characteristics of patients with mixed infections which is essential in the management of TB patients . Future investigations involving the use of Whole Genome Sequencing (WGS) will be instrumental in providing a higher discriminatory power for mixed infections.

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11 CHAPTER 1 INTRODUCTION Tuberculosis (TB) remains a disease of global public health concern with an estimated annual incidence of approximately 1 0 million cases . 1 , 2 The 2018 Global Health TB Report, describes TB as the number one cause of death from a single inf ectious agent, causing over 1. 2 million deaths . 1 It is estimated that a round 1.7 billion people ( 23% of the global population ) are pro jected to have latent TB infection and hence are at risk of developing active TB disease in their lifetime . 1 I n the United States (US ), t he burden of TB is relatively low , with an incidence of 2.8 per 100 000 population . 3 , 4 Over the past twenty years, there has been a consistent decline in the annual trend of TB incidence in the US . H owever, the rate of d ecline has slowed in the last couple of years , raising concerns about stalled progress towards TB elimination. 5 T he situation is similar in the state of Florida which account s for 6 .5 % (5 91 ) of the total notified TB cases in t he US and is among the top four states with the highest number of cases in 201 8. 6 The emergence of HIV/AIDS, the increasing rate of antimicrobial resistance in recent years , and the lack of a universally effective vaccine against the pulmonary form of the disease have been the critical driving forces of the global TB pandemic. 2 , 7 TB left untreated will kill approximately 50% of individuals wi th active TB disease with the risk much higher among HIV co infected patients . T his highlights the importance of early detection, diagnosis , and effective treatment as crucial strategies in the control and elimination of TB. 7 In humans , TB is generally caused by agents of the Mycobacterium tuberculosis complex ( MTB C) known as Mycobacterium tuberculosis sensu stricto and

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12 Mycobacterium a fricanum ( this group also includes Mycobacterium b ovis ) . 8 MTB C s are gram positive acid fast bacteria that are obligate human pathogens transmitted via aerosols generated when a patient with the pulmonary form of the disease coughs, sneezes, talks, sings or laughs. 1 , 9 In t he case of Mycobacterium b ovis , the path ogen is tr ansmitted from livestock and other wild mammals to hu mans th rough the ingestion of infected animal products , in ha lation and to some extent by direct contact with body f luids . 10 The outcome of exposure to the Mycobacterium tuberculosis (M TB ) bacilli can take a varying range of forms , which include the rapid clearing of the bacteria th rough the action of an innate immune response, the progression of the infection to active disease , or a lifetime containment of the latent form without any clinical signs of the disease. 2 , 8 The v ariabilit y observed in the presentation and outcome of TB infection and disease ha s been linked traditionally to differences in host immune responses and environmental variables . H owever, there is evidence to suggest that genetic variations found among the M TB strains play a vital role in the clinical phenotypes of TB . 8 , 9 The MTBC display a clonal population structure with little genetic differences. 11 The low genetic diversity and virulence observed with in the M TB strains could be attributed to the lack of established virulence factors such as toxins as compared to other bacteria l genera . 12 , 13 Despite the limited genetic diversity within the M TB strains, various s tudies have shown remarkable differences in virulence and patho genesis including the presence of cavitation, the occurrence of drug resistance, and high rates of transmissibility in different hosts among specific strains and lineages of the MTB C . 12 , 14 , 15

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13 T raditionally, TB disease is believed to be caused by infection from a single M TB strain with recurrence assumed to be due to reactivation of the initial strain that resulted in the first episode. 16 , 17 This concept was disputed when more than one M TB strain was isolated from a single patient using phage typing. 18 Subsequently, evidence from various molecular studies using different genotyping methods have been able to distinguish infection from multiple distinct M TB strains within a single host. 19 , 20 , 21 This phenomenon of mixed infections may occur as a result of either within host strain modification (microevolution) after a single infection event or reinfection by sequential or simultaneous exposure to more than one strain . 21 , 22 These mixed infections may explain the underl yi ng mechanisms that cause changes in drug resistance patterns during treatment or retreatment , and maybe seen as either acquired or transmitted resistanc e . 21 , 23 However, since an individual ca n be infected at one point by different strains with different phenotypic characteristics like growth rates and drug resistance thresholds, within host competition between the different strains may adversely affect the clinical outcomes of patients with th ese infections. 24 The phenomenon may also alter the population dynamics of the pathogen in the community resulting in the transmission of resistant s trains . 22 Different MTB strains can als o infect the same or different anatomical parts of a single host resulting in compartmentalization due to mixed infection or clonal heterogeneity. 22 , 25 Evidence from various studies has demonstrated the occurrence of mixed infections in different geographical settings and among both HIV positive and HIV negative TB patients. 16 , 21 , 22 , 25 , 26 The incidence of these mixed infections depends the transmission

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14 pres sure of the pathogen , and the overall incidence of TB within the given population. 22 Host immunity counts as a major contributing factor to the occurrence of within host genetic diversity of the MTB strain at the individual level , making the phenomenon more like l y among TB/HIV co infected patients. 25 , 27 The introduction of numerous molecular genotyping methods in clinical and experimental settings for the differential identification of the MTB strains ha s provided a good understanding of the global phylogenetic diversity of MTB as well as an insight into factors influencing TB clinical outcomes. 8 , 9 Mycobacterial interspersed repetitive unit varia ble number tandem repeat (MIRU VNTR) and other PCR based genotyping techniques have been critical tools in providing superior resolution in the mapping of genetic diversity among MTB strains including the presence of multiple strains within a single host a s well as identifying genomic signature patterns associated with pathogenesis, drug resistance and disease transmission. 28 , 18 Compared to other DNA genotyping techniques, the MIRU VNTR method is the most widely used in the detection of mixed infections of MTB strains. 29 In the United States (US), the use of spacer oligonucleoti de sequencing ( spoligotyping ) and 24 locus MIRU VNTR for genotyping of MTB cases as part of routine service delivery has been influential in providing a deeper understanding of the molecular epidemiology of TB as well as in the prevention and control of TB . 14 The MIRU VNTR method relies on variations in the number of cop ies of repeats in the highly variable regions of the MTB genome to show changes in the genom ic structure and has the advantage of enhancing the detection of within host heterogeneity as compared with spoligotyping and restriction fragment length polymorphism (RFLP)

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15 analysis . 8 , 30 The MIRU VNTR technique works as a microsatellit e typing system that classifies MTB strains by the number of copies of repeats at different loci . 31 , 30 These copy number variants (CNVs) are used in distinguishing MTB strains from other similarly typed strains. 30 The occurrence of mixed infections is usually exhibited by the presence of two or three CNVs at a single locus resulting from either clonally heterogeneous infection wh ere multiple CNVs are found in only one locus or mixed infection where two or more loci have multiple CNVs. 31 E vidence of mixed MTB infections has been documented in many geographical settings including high density populated communities, hospitals and less crowded locations . 21 , 22 , 25 , 32 However, the epidemiological and clinical features of cases with these infections , which are critica l in providing insight into the disease dynamics and essential to TB control efforts , have not been thoroughly examined. Additionally , the prevalence and factors associated with these mixed infections regarding disease transmission, and response to treatme nt remain unclear. Rationale Mixed infections involving both drug susceptible and drug resistance strains within a single host has the potential of rendering standard treatment regimen ineffective, complicating laboratory diagnosis , and affecting the trans mission dynamics of the disease. 16 , 26 , 30 Drug susceptibility testing (DST) in such instances may fail to detect th e minority drug resistant strains , which may result in adverse treatment outcome due to the presence of underlying resistance as well as an increase d risk of acquired resistance with the st andard treatment regimen. 29 , 33 The phenomenon may alter the immune responses of patients with ongoing TB infections and render them more susceptible to reinfection. 34 This occurrence has

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16 implications for s to clinical therapy and optimal anti m icrobial dosing strategies . 30 , 35 or absence of in host genetic diversity to consider factors like comorbidity and malnutrit ion, 20 a good understanding of the molecular epidemiology of mixed MTB infections and its associated features is critical in the TB control effort. An appreciation of this mechanism is vital in the quest f or a suitable TB vaccine, to evaluat e treatment regimen s and to predict disease trajectories. 36 Objective This study aims to describe the epidemiological and clinical features of mixed infections among culture confirmed TB patient s enrolled in tuberculosis care at the Florida Department of Health (FD OH) . Additionally, the study seeks to determine the association between mixed infections and death among th is cohort of patients. Hypothesis We hypothesize that mixed infections among TB patients can adversely impact disease presentation and the risk of death .

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17 CHAPTER 2 METHODS A cross sectional study design was deployed to analyz e de identified surveillance data to assess the ep idemiological and clinical features of mixed infections on culture confirmed TB patients enrolled in the Florida Department of Health (FDOH) TB registry from April 2008 to January 2018. The dataset used in this analysis consisted of TB cases diagnosed and managed at all the county health department s in Florida with their clinical and socio demographic information. Th is dataset also includes data on routinely determined genotype informatio n of all culture confirmed TB cases using spoligotyping and 24 locus MIRU VNTR methods as per the national ly standardized procedure. 37 The dataset was extracted from the Florida Health Management System (FHMS) and the Tuberculosis Information Management System (TI M S) databases . G enotyping inform ation was obtained through the National Tuberculosis Genotyping and Surveillance Network (NT GS N) . These two databases we re linked using a unique identifier to capture the socio demographic, clinical , and TB related risk factors for each genotyped TB case . 37 Study P opulation The source population consisted of 6,393 registered TB cases from April 2008 to January 2018 , of which 3 , 669 (57.4%) were culture positive. Data used in this analysis before 2009 were based on a convenie nce sample of available c ultured isolates because samples with non viable isolates at the time were not captured and are thus inc omplete. D ata from 20 09 onwards captured all sequential culture confirmed cases used in this analysis. The study included all TB c ases who wer e culture positive at baseline and ha d assigned lineages using 24 locus MIRU VNTR. A total 2, 724 cases

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18 who were cu lture negat ive, had pendin g results, had no culture done , or with out available cu lture re sults , were excluded from the initial source population (Figure 2 1) . Addi tional ly, 70 cases who were culture positive but had no MIRU VNTR genotyping results were also ex cluded. Overall , a sample size of 3,599 cases representing 98.1% of the culture confirmed TB cases w as used in thi s analysis. Significant differences were observed when c omparing the st udy po pulation with the excluded population for all socio demographic features except ethnicity (Table A 1). No table among th ese dif ferences is the which is consistent with the fact that majority of children under 15 years are culture negative and are usually diagnosed clinically using radiographic imaging . The higher propo rtion of patients 24 years (54 .2 %) in the exclude d population may account for some of the other differences observed between the two groups. Ethical Consideration The data was obtained as part of public health surveillance; hence signed informed consent was not re quired . The study was approved by the Institutional Review Board s (IRB s ) of both the University of Florida and the Florida Department of Health (FDOH) , with all data used having been de identified. Measures Exposure v ariable For each cul ture confirmed TB case, the 24 locus MIRU VNTR profile pattern based on genotyped results of at least one isolate was linked to the TB registry data by a unique patient identifier. The primary exposure variable was the presence of mixed infections based o n the MIRU VNTR pattern , which was defined as having more than

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19 one copy number variant (CNV) in at least one locus w ith all other profile patterns defined as simple infections. Mixed infection was measured as a dichotomous variable indicating the presence or absence of mixed strain infection s . Outcome variables The occurrence of death was the outcome of interest and was defined as death due to TB infection among the st udy population . Death was ev aluated as a cate gori cal va riable and dichotomized as Yes / No. Covariates/ c onfounders The study controlled for the following demographic variables; age at diagnosis ( 0 24, 25 44, 45 64 and ) , which was captured in years and analyzed as a categorical variable , sex (male/female), race (Hispan ic/non Hispanic), ethnicity (White, Black and Asians/Others) , and country of birth (US Born/ Non US Born). The category Asian/Others in the ethnic groupings consisted of Asians, Pacific Islander s , American Indian s , and Alaskan Native s. Other covariates which a re known TB related environmental ris k factors obtained from patient s r eport s included history of homeless ness in the past year ( Y es/ N o), history of residential correctional facility use (Yes/No), history of alcohol use in the past year ( Yes/No) , and history of s ubstance us e (Yes/No) which was measured as a combination of either intravenous drug use and non intravenous drug use . The presenting disease site (pulmonary/extrapulmonary), evidence of cavitation (Yes/No), miliary disease (Yes/No), history of previous TB disease (Yes/No), multi drug resistant TB (Yes/No) defined as resistant to both the first line drugs isoniazid and rifampicin, comorbidity with diabetes (Yes/No) , and HIV status

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20 (positive/negative/unknown) were adjusted for as clinical related risk factors associated with both the exposure and outcome of interest. Effect m odifier Lineage was assessed as an effect modifier and categorized into four main distinct groups, namely: Euro American, Indo Oceanic, East Asian, and Oth ers . 7 The Others category consisted of the East African Indian, M. b ovis and M. a fricanum strains. The full lineage names were assigned based on the spoligotyping and 24 locus MIRU VNTR patterns. The nomenclature system used was consistent with both large sequence polymorphism (LSP) and single nucleotide polymorphism (SNP). 31 , 38 Statistical Analysis T he descriptive statistics of the categorical va riables was determ ine d using Chi where appropriate . A c onfounding effect was assessed by determining the association between the potential confounder and both the exposure variable (mixed infections) and the outcome variable ( death ). Missing information on covariates seen in less than 5% of observations w as excluded from the analys is . 39 L ogistic regression was used to obtain the crude and adjusted odds ratios (AOR s ) for the association between mixed infection s and death . The full multivariate model was adjusted for all covariates of interest as listed above. T he backwards elimination technique was used in the selection of the final model . 40 The following covariates : HIV status, evidence of MDR TB , and history of previous TB disease were forced into the final model based on evidence from the literature. 41 , 33 T he influence of confounding variables on the association between mixed infection and death was assessed using changes in model estimates as compared to the full adjusted model. As part of the backward elimination technique, c ovariates with less than a 10% change in

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21 the parameter estimates for mixed infection s when dropped and did not significantly affect the model fit were regarded as non c onfounding variables and removed from the final model. 42 , 40 Test for ultimate model fit was done based on the change in Akaike Information Criteria (AIC). 43 T he effect of MTB lineage as a modifier in the association between mixed infection and death was assessed . All d ata analysis w ere done using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) with significance determin ed at a 0.05 alpha level and 95% confidence intervals (CIs) . Figure 2 1. Flow Diagram of Sample Selection. Registered TB Cases (n= 6393) Culture Positive (n =3669) Cases with MIRU VNTR profile (n=3599) Cases without MIRU VNTR profile excluded (n=70) Culture Negative (n= 670) Not Done (n=674) Not Available/Unknown (n= 1370) Pending (n=10) Total excluded (n=2724)

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22 CHAPTER 3 RESULTS Sample Characteristics Table 3 1 and 3 2 provide the descriptive statistics of the epidemiological and TB related clinical features of the sample population by both infection type and death , respectively . The prev alence of mixed infec tion s among the 3, 599 culture pos i tive cases used in this analysis was 3% (107) , w h ile the incidence of death w as 12.8% (461). The study p opulation were predominantly male (67.8%) , w hite (51.2%), of non Hispanic ethnicity (72.9%) , and non US born (53.2%). Although the age d istr ibution of the population was almost even among the various age ca tegories, the majority of the cases (4 1.4%) were with in the 45 64 year group . A high er proportion of patients reported no history of homelessness ( 87 .8%), no history of correctional facility use ( 96%) , no history of alcohol use ( 78.9%), and no history of substance use ( 89 . 4%). Most of the patients in th e study sample had the pulmonary form of TB (97.9%) and were infected with the Euro American strain (82%) . The proportion of p atients with multi drug resistant TB was 1.9% , while those having comorbidit y with diabet es and HIV infection were 11.6 % and 13 .2 % , re spectively. A p ower ana lysis conducted o n the study sample size using the PS: Power and Sample Size Calculation version 3.1.6 , revealed a power of o ne , indicating an ad equate power to detect statistical si gnifica nce . Association between M ixed I nfections and T B R elated Epidemiological Characteristics Table 3 1 describes the epide mi ologi cal characteristics by infection type and death . C ompared to those with simple infections, cases with mixed infections were more like ly to be male (73.8% vs 67.6%), above 65 years of age (26.2% vs 15.7%), Hispanic

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23 (33.6% vs 26.9%), white (58.9% vs 51.0%) and non US born (60.8% vs 53.0%). A higher proportion of cases reporting a history of alcohol use in the past year (23.4% vs 21.0%) were more like ly to have mixed infections as compared to simple infection s. Contra ry to expec ta tio n, patients with a history of homelessness ( 9.2 vs 11. 0%) , substance use ( 9.4% vs 10.7 %) , and th ose previously incar cerated in a correctional facility ( 1.9 vs 4.1) were less like ly to have mixed infections . The trend for the inc i dence of death was consistent with that of mixed infections w ith ma les ( 70.9 % vs 67.4%), pat ients above 65 years ( 33.6% vs 1 3.5%) , non US born (54 .5% vs 53.1%) , a nd those with a history of alc ohol use (23.2% vs 20.8%) more at risk of death while th ose with a history of homelessness ( 10.9 % vs 11.0%) and substance use ( 6 .1% vs 11.3 ) were less like ly to e xperience death . Un like those with mixed infections, the occurrence of death was more like ly among th ose with n on Hispanic ethnicity ( 75.5% vs 72.5%) , Asian/Other race ( 15.4% vs 11.6%) and those with a previous history of correctional facility use ( 6.5 % vs 3.6%) . In the unadjusted analysis (Table 3 3 ), age of 65 years and above (OR = 3. 80 ; 95% CI: 1.47 , 9.94; p = 0 .0064) was associated with increased risk of mixed infection s while being black (OR= 0.52; 95% CI: 0.32, 0.84; p= 0.0073) was a significant protective predictor of mixed infections. Demographic and environmental risk factors like sex, race, country of birt h, history of alcohol use in the past y ear, history of substance use in the past year, history homelessness , and history of correctional facility use, were not associated with the risk of mixed infection s . T he adjusted analysis showed older age groups ; 45 64 years ( AOR = 2. 73 ; 95% CI: 1.0 5 , 7.07 ; p= 0.0387 ) , and 65 years (AOR = 3.83 ; 95% CI: 1.42 , 1 0 . 29; p=

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24 0.0079 ) as being associated with mixed infections , as compared with the younger age Association between M ixed I nfections and C linical TB R elated Characteristics Table 3 2 shows the distribution of the clinical TB related features by infection type and death. A higher proportion of pat ients with mixed infections were more like ly to experience death (17.8% vs 12.7%) , have evidence of disease c avitation (37.4% vs 32.8), have comorbi dity with diabetes (18.7 vs 11.4), present as HIV negative (93.9% vs 86.5%) , and have extra pulmonary disease presentation ( 3.7% vs 2.0%). Cases with mixed infections were more like ly to be infected with either the I ndo Oceanic ( 15.1% vs 7.9%) or East Asian strain (11.0% vs 8.1%). In the unadjusted analysis of the association between mixe d infections and clinical TB related risk factors shown in Table 3 4 , Patients with diabetes had a n 8 0% increased odds of mixed infection s as compared to those with only TB infection (OR = 1. 8 ; 95% CI: 1. 09 , 2.94 ; p= 0.0221). Co infection with HIV was a protective predictor of mixed infections with patients coinfected with HIV having a significantly lower odds of mixed infections in both the unadjusted (OR = 0.42 95% CI: 0.18, 0.95; p= 0.0381) and adjusted (AOR=0.42; 95% CI: 0 .18, 0.99; p=0.0488) models. Higher odds of m ixed infection s were associated with death (OR = 1.49 95% CI: 0.90 , 2.47) although not statistically significant .

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25 Table 3 1 . E pidemiol ogical C haracteristics of the S tudy P opulation by I nfection T ype a nd Death Characteristic Sample (%) Simple Infections (%) Mixed Infections (%) P value Death (No) (%) Death (Yes) (%) P Value Total 3599 (100) 3492 (97) 107 (3) 3138 (87.2) 461(12.8) Sex Male 2441 (67.8) 2362 (67.6) 79 (73.8) 0.1769 2114 (67.4) 327 (70.9) 0.1260 Female 1158 (32.2) 1130 (32.4) 28 (26.2) 1024 (32.6) 134 (29.1) Age (years) 378 (10.5) 373 (10.7) 5 (4.7) 0.0077 354 (11.3) 24 (5.2) 0.0001 25 44 1155 (32.1) 1127 (32.3) 28 (26.2) 1046 (33.3) 109 (23.6) 45 64 1489 (41.4) 1443 (41.3) 46 (43.0) 1316 (41.9) 173 (37.5) 577 (16.0) 549 (15.7) 28 (26.2) 422 (13.5) 155 (33.6) Ethnicity Hispanic 975 (27.1) 939 (26.9) 36 (33.6) 0.1215 862 (27.5) 113 (24.5) 0.1821 Non Hispanic 2624 (72.9) 2553 (73.1) 71 (66.4) 2276 (72.5) 348 (75.5) Race White 1842 (51.2) 1779 (51.0) 63 (58.9) 0.0032 1620 (51.6) 222 (48.2) 0.0535 Black 1323 (36.8) 1299 (37.2) 24 (22.4) 1155 (36.8) 168 (36.4) Asian/Other 434 (12.1) 414 (11.9) 20 (18.7) 363 (11.6) 71 (15.4) Country of Origin US Born 1683 (46.8) 1641 (47.0) 42 (39.2) 0.1139 1473 (46.9) 210 (45.5) 0.5771 Non US Born 1916 (53.2) 1851 (53.0) 65 (60.8) 1665 (53.1) 251 (54.5) History of homelessness Yes 395 (11) 385 (11.0) 10 (9.4) 0.2526 354 (11) 50 (10.9) 0.5228 No 3159 (87.8) 3065 (87.8) 94 (87.9) 2751 (87.7) 408 (88.5) Unknown 45 (1.3) 42 (1.2) 3 (2.8) 42 (1.3) 3 (0.7) Resident Correctional Facility Yes 144 (4.0) 142 (4.1) 2 (1.9) 0.4453 114 (3.6) 30 (6.5) 0.0033 No 3455 (96.0) 3350 (95.9) 105 (98.1) 3024 (96.4) 431 (93.5) History of Alcohol Use Yes 758 (21.1) 733 (21.0) 25 (23.4) 0.5531 651 (20.8) 107 (23.2) 0.2256 No 2841 (78.9) 2759 (79.0) 82 (76.6) 2487 (79.2) 354 (76.8) History of Substance Use Yes 383 (10.6) 373 (10.7) 10 (9.4) 0.6590 355 (11.3) 28 (6.1) 0.0007 No 3216 (89.4) 3119 (89.3) 97 (90.7) 2783 (88.7) 433 (93.9)

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26 Table 3 2 . Clinical and TB R elated F eatures of the S tudy P opulation by I nfection T ype and Death Characteristic Sample (%) Simple Infections (%) Mixed Infections (%) P value Death (No) (%) Death (Yes) (%) P Value Death Yes 461 (12.8) 442 (12.7) 19 (17.8) 0.1200 No 3138 (87.2) 3050 (87.3) 88 (82.2) Previous TB Disease Yes 103 (2.9) 101 (2.9) 2 (1.9) 0.7695 89 (2.8) 14 (3.0) 0.8093 No 3496 (97.1) 3391 (97.1) 105 (98.1) 3049 (97.2) 447 (97) Evidence of Cavitation Yes 1185 (32.9) 1145 (32.8) 40 (37.4) 0.1944 1040 (33.1) 145 (31.5) 0.0048 No 662 (18.4) 638 (18.3) 24 (22.4) 552 (17.6) 110 (23.9) Not Available 1752 (48.7) 1709 (48.9) 43 (40.2) 1546 (49.3) 206 (44.7) Miliary TB Disease Yes 124 (7.0) 120 (7.0) 4 (6.5) 1.000 103 (6.8) 21 (8.5) 0.3250 No 1650 (93.0) 1592 (93.0) 58 (93.5) 1423 (93.3) 227 (91.5) MTB lineages Euro American 2761 (82.0) 2709 (82.2) 52 (71.2) 0.0608 2433 (82.3) 328 (79.4) 0.5315 Indo Oceanic 271 (8.1) 260 (7.9) 11 (15.1) 232 (7.9) 39 (9.4) East Asian 274 (8.1) 266 (8.1) 8 (11.0) 237 (8.0) 37 (9.0) Other 62 (1.8) 60 (1.8) 2 (2.7) 53 (1.8) 9 (2.2) HIV Status Negative 2850 (86.8) 2757 (86.5) 93 (93.9) 0.0323 2555 (87.6) 295 (80.4) 0.0001 Positive 435 (13.2) 429 (13.5) 6 (6.1) 363 (12.4) 72 (19.6) Diabetes Yes 418 (11.6) 398 (11.4) 20 (18.7) 0.0204 356 (11.3) 62 (13.5) 0.1879 No 3181 (88.4) 3094 (88.6) 87 (81.3) 2782 (88.7) 399 (86.5) MDR TB Yes 69 (1.9) 68 (2.0) 1 (0.9) 0.0001 57 (1.8) 12 (2.6) 0.0498 No 3395 (95.1) 3301 (95.3) 94 (87.9) 2970 (95.4) 425 (92.8) Not Available 107 (3.0) 95 (2.7) 12 (11.2) 86 (2.8) 21 (4.6) Disease Site Pulmonary 3524 (97.9) 3421 (98) 103 (96.3) 0.2840 3081 (98.2) 443 (96.1) 0.0034 Extra Pulmonary 75 (2.1) 71 (2) 4 (3.7) 57 (1.8) 18 (3.9)

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27 Table 3 3 . Unadjusted and A djusted A ssociation b etween M ixed I nfection T ype and D emographic C haracteristics of the S tudy P opulation Characteristics Unadjusted OR (95% CI) Unadjusted P Value Adjusted OR (95% CI) Adjusted P Value Sex Male 1.35 (0.87, 2.09) 0.1786 Female 1.00 Age (years) 24 1.00 1.00 25 44 1.85 (0.71, 4.83) 0.2074 1.91 (0.73, 5.04) 0.1901 45 64 2.38 (0.94, 6.02) 0.0680 2.73 (1.05, 7.07) 0.0387 65 3.80 (1.47, 9.94) 0.0064 3.83 (1.42, 10.29) 0.0079 Ethnicity Non Hispanic 1.00 1.00 Hispanic 1.38 (0.92, 2.07) 0.1226 1.31 (0.79, 2.17) 0.2986 Race White 1.00 Black 0.52 (0.32, 0.84) 0.0073 Asian/Other 1.36 (0.82, 2.28) 0.2366 Country of Origin US Born 1.00 1.00 Non US Born 1.37 (0.93, 2.03) 0.1154 1.32 (0.79, 2.19) 0.2899 History of homelessness No 1.00 Yes 0.85 (0.44, 1.64) 0.6220 Missing 2.33 (0.71, 7.65) 0.1635 Resident Correctional Facility No 1.00 Yes 0.45 (0.11, 1.84) 0.2660 History of Alcohol Use No 1.00 1.00 Yes 1.15 (0.73, 1.81) 0.5533 1.22 (0.74, 2.01) 0.4457 History of Substance Use No 1.00 Yes 0.86 (0.45, 1.67) 0.6593

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28 Table 3 4 . Unadjusted and A djusted A ssociation between M ixed I nfection T ype and C linical TB related C haracteristics Characteristics Unadjusted OR (95% CI) Unadjusted P Value Adjusted OR (95% CI) Adjusted P Value Death No 1.00 1.00 Yes 1.49 (0.90, 2.47) 0.1223 1.15 (0.63, 2.10) 0.6406 Previous TB Disease No 1.00 1.00 Yes 0.64 (0.16, 2.63) 0.5352 0.001 (0.001, 999.9) 0.9826 Evidence of Cavitation No 1.00 Yes 0.93 (0.56, 1.55) 0.7778 Missing 0.67 (0.40, 1.11) 0.1204 Miliary TB Disease No 1.00 Yes 0.92 (0.33, 2.56) 0.8657 Missing 0.69 (0.47, 1.03) MTB lineages Euro American 0.57 (0.14, 2.42) 0.4511 Indo Oceanic 1.27 (0.27, 5.88) 0.7602 East Asian 0.90 (0.19, 4.36) 0.8981 Other 1.00 HIV Status Negative 1.00 1.00 Positive 0.42 (0.18, 0.95) 0.0381 0.42 (0.18, 0.99) 0.0488 Diabetes No 1.00 Yes 1.8 (1.09, 2.94) 0.0221 MDR TB No 1.00 1.00 Yes 0.52 (0.07, 3.76) 0.5141 0.53 (0.07, 3.86) 0.5273 Not Available 3.43 (1.83, 6.42) <.0001 4.83 (2.44, 9.57) <.0001 Disease Site Extra Pulmonary 1.00 1.00 Pulmonary 0.53 (0.19, 1.49) 0.2312 0.50 (0.17, 1.48) 0.2135

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29 CHAPTER 4 DISCUSSION Many studies have associated high prevalence of mixed infections with setting s with high TB incidence and high transmission potential . 25 , 36 , 44 , 45 , This is evident in the study by Cohen et al. (2016) that reported a 21.1% prevalence of heterogeneous infections among TB case in KwaZulu Natal , South Africa and that by Shamputa et al. (2 006) that found a prevalence of 13.1% among male inmates in a penitentiary hospital in Georgia , USA. 24 , 36 Considering the low incidence of TB in Florida (3.2/100 000 population), a 3% prevalence of mixed infection is consisten t with this hypothesis. Our finding s , however, are contra dictory to results f rom recent studies in India and China that found a prevalence of <0.4% and 3. 5 % , respectively , in settings with high TB incidence and high population density. 46 , 29 This suggest s that the occurrence of mixed infection in moderate and low in cidence TB settings may not be entirely due to clinical and socio epidemiological factors but may be influenced by bacterial factors like the infectivity of the strain. 47 Additionally, factors such as the sensitivity of the genotyping te chnique used, 25 handling and processing of samples which may involve lapses in the decontamination procedures , and different culturing methods may significantly contribute to the differences in the proportion of mixed infection obse rved in different environments. 29 Variati ons in the prevalence of mixed i nfection s can also be att ributed to difference s in its detection rate , which is main ly dependent on the study desi gn, sample si ze , and the method used in the genotyping of isolates. 41 The MIRU VNTR genotyping technique assesses heterogeneity f rom a restricted set of loci lowering its sensitivity and may also be limited in the differentiation of mixed infection from allele evolution. 25 , 21 Whole Genome Sequencing (WGS) provides the

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30 ability to distinguish between highly related but genetically distinct strains of MTB and stands as the method of choice in providing a more accurate estimation of the prevalence of mixed infection s . There were no c ases of treatment failure, which is cultu re positivity at four months of therapy , hence adverse outcome was assessed using the occurrence of death. M ixed i nfection was not significantly associated with death in our study population , a lthough t h e exist ence of mixed infections , as demonstrated by o ther investigations , have the potenti al to adver sely affect disease outcome especially among cases infected with multiple strains of different dru g susceptib ility thresholds . 25 , 48 This may be due to the a b ility of the resistant strain s to thrive under st andard treatment re gimen and the possible reacti vation of the susceptible strain s in a switch to M DR treatment . The presence o f underlying resistance may also increase the risk of acquired resistan ce under antibiotic pressure d uring standard trea tment. The phenomenon is more common in places with high TB burden and high prevalence of MDR TB 48 , which explains the lac k of association obser ved in our st udy population. T he occurrence of mixed infection s was more like ly amo ng males although the association did not reach sta tistical sign ificance, this is consistent with findings by Pang and his colleagues, suggesting male sex as having increas ed susceptibility to MTB infection s . 29 Males are more like ly to engage in risk related behaviors and social interacti ons that may compromise their immunity and predispose them to multiple infection s . Older age was significant l y associated with a high risk of mixed infections which was expected due to the altered im munity associated with o ld age. There is also the

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31 possi bility of activa tion of latent strains that might have sur vived over years of accumulation from numerous exposures to different MTB strains. In theory, we expect patients with immunocompromis ing conditions to have a higher risk of harboring multiple infections, which indeed has been confirmed in several studies linking high rates of mixed infections with populations with high HIV prevalence. 45 , 49 , 47 Pa radoxically , in our study, co infection with HIV was a protective predictor of mixed infection s with lower odds (AOR=0 .42; 95% CI: 0.18 , 0.99; p=0.0488) observed in our study population . This observation m ight be an indic ation of recent transmission s of the MTB pathogens among the HIV infec ted population with a shorter lap time between the time of infection and the development of active disease since people with HIV are more frequently screened for TB . The presence of underly ing morbidity among TB patients such as d iabete s is associated with immunosuppression 50 , p atients with diabetes may experience a delay in the clearance of the MTB pathogen after treatment and may have a high risk of reinfection. 51 T he increased odds observed in this study is consistent with findings from other st udies reporting significant associati o n s between diabetes and both MTB in fection and increased risk of adverse treatment outcome s . 50 , 52 Patients with diabetes , simi lar to those with HIV infection , may b e prone to multiple infection s under the s ame mechanism. Although s ome MTB lineages are known to induce various forms of immune responses and may create an environment to co exi s t with other strain s , 53 most stud ies like our s , have not been able to establish a clear association between mixed infection s a nd lineage. T he East Asian (Beijing) lineage ha s been credited with its unique ability to

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32 generate a ability to fight infection s and is known to cause a more aggressive form of the diseas e with a high proportion of adverse treatment outcomes , 54 however , other studies have failed to establish th e s e finding s . 35 , 53 Further investigation may be required to determine the ass ociation bet ween mixed infections and MTB li neages. T he study finding s are based on a c ross s ectional analysis of routinely collected data and should not be interpreted as cau sal re lationship s . The lack of a prospective component to this study limited our ability to examine possible residual confounders , such as other co morbi dities that patient s may present with and the influence of socioeconomic sta tus . The us e of country of origin may not necess ari ly be a good proxy for ass essing pa based on places visited . The sample si ze used in the study was sufficiently large to give ac curate results and provide a smaller ma rgin of error with a satisfactory statistical power of one . T he use of 24 locus MIRU VNTR was an advantage that increased the sensitivity and the discriminative power in the detection of mixed infections in our study population as compared with studies 20,48 that used less sensitive techni ques like spoligotyping and RFLP .

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33 CHAPTER 5 CONCLUSION Th e study demonstrates a low prevalence of mixed TB infection s ( 3%) in a low inci dence TB setti ng. Mixed TB infection was not significantly associated with death . H o wever , high er risk of mixed infections was associated with o lder individ ua ls and patients with diabetes . The findi ngs p rovide a comprehensive understanding o f the epidemiological and clinical features of mixed infections in Flo rida , which is essential in TB case man a gement and control strategies . Future investigations should include prospective st udies examining mixed infect ions and prevalent risk factors among TB pati ents , as well as the use of Whole Genome Sequencing (WGS) , which pr ovides a hig her discriminatory power in differentiating between clonal he t e roge ne ity due to mi cro evolution of the MTB strain and m ixed infection s due to sequential or si multane ous exposure to mult iple MTB strains.

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34 APPENDIX SUPPLEMENTAL INFORMATION ON THE PO PULATION USED IN THIS ANALYSIS Table A 1 Socio demographic C haracteristics of Incl uded vs E xcluded S ample Characteristic Sample Included (%) Excluded (%) P Value Total 6393 3599 ( 56.3 ) 27 94 ( 43.7 ) Sex Male 3296 (65.2) 2441 (67.8) 8 55 (58. 7 ) <.0001 Female 1759 (34.8) 1158 (32.2) 601 (41. 3 ) Age (years) 1857 (29.1) 378 (10.5) 147 9 ( 52.9 ) <.0001 25 44 1561(24.2) 1155 (32.1) 406 ( 14.5 ) 45 64 1974 (30.9) 1489 (41.4) 485 ( 17.4 ) 1001 (15.7) 577 (16.0) 577 ( 16.0 ) Ethnicity Hispanic 1349 (26.7) 975 (27.1) 3 74 ( 25. 7) 0. 3068 Non Hispanic 3706 (73.3) 2624 (72.9) 10 82 ( 74. 3) Race White 2514 (39.3) 1842 (51.2) 672 (24.0) <.0001 Black 1876 (29.3) 1323 (36.8) 5 53 (19.8) Asian /Other 2003 (31.3) 434 (12.1) 1569 (56.2) Country of Origin US Born 2312 (45.7) 1683 (46.8) 6 29 ( 43. 2) 0.0 213 Non US Born 2743 (54.3) 1916 (53.2) 827 ( 56. 8) History of Homelessness Yes 465 (9.2) 395 (11 .0 ) 70 ( 4. 8) <.0001 No 4528 (89.6) 3159 (87.8) 13 69 ( 94. 0) Unknown 62 (1.2) 45 (1.3) 1 7 (1.2) Resident Correctional Facility Yes 186 (3.7) 144 (4.0) 42 ( 2. 9) 0.0 562 No 4869 (96.3) 3455 (96.0) 1414 ( 97. 1) History of Alcohol Use Yes 908 (14.5) 758 (21.1) 15 0 ( 5. 6) <.0001 No 5345 (85.5) 2841 (78.9) 25 04 ( 94. 4) History of Substance Yes 546 (8.5) 383 (10.6) 1 63 ( 5. 8) <.0001 No 5847 (91.5) 3216 (89.4) 2 631 ( 94. 2)

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40 BIOGRAPHICAL SKETCH Michael Asare Baah received his M S in e pidemiology degree f rom the University of F lorida in May 2020 . D uring his graduate stud ies , he worked as a D43 F oga r ty trainee un der the tutorship of Dr. Awewura Kwara and Dr. Michael Lauzardo . His research interest s a re in the area of infectious disease of poverty , specifically tuberculosis and H IV .