Advertisement

Reduced transmission of Mycobacterium africanum compared to Mycobacterium tuberculosis in urban West Africa

Open AccessPublished:June 04, 2018DOI:https://doi.org/10.1016/j.ijid.2018.05.014

      Highlights

      • The estimated recent tuberculosis (TB) transmission rate (clustering rate of 41.2%) was found to be high in Ghana.
      • There is a need for increased TB awareness by the national tuberculosis control program.
      • Mycobacterium africanum (MAF) transmits at a lower rate compared to Mycobacterium tuberculosis in Ghana.
      • The incidence of MAF remained fairly constant over the study years.
      • Other factors may likely be responsible for maintaining MAF in West Africa.

      Abstract

      Objective

      Understanding transmission dynamics is useful for tuberculosis (TB) control. A population-based molecular epidemiological study was conducted to determine TB transmission in Ghana.

      Methods

      Mycobacterium tuberculosis complex (MTBC) isolates obtained from prospectively sampled pulmonary TB patients between July 2012 and December 2015 were characterized using spoligotyping and standard 15-locus mycobacterial interspersed repetitive unit variable number tandem repeat (MIRU-VNTR) typing for transmission studies.

      Results

      Out of 2309 MTBC isolates, 1082 (46.9%) unique cases were identified, with 1227 (53.1%) isolates belonging to one of 276 clusters. The recent TB transmission rate was estimated to be 41.2%. Whereas TB strains of lineage 4 belonging to M. tuberculosis showed a high recent transmission rate (44.9%), reduced recent transmission rates were found for lineages of Mycobacterium africanum (lineage 5, 31.8%; lineage 6, 24.7%).

      Conclusions

      The study findings indicate high recent TB transmission, suggesting the occurrence of unsuspected outbreaks in Ghana. The observed reduced transmission rate of M. africanum suggests other factor(s) (host/environmental) may be responsible for its continuous presence in West Africa.

      Keywords

      Introduction

      Tuberculosis (TB) is a global health emergency; in 2016 an estimated 10.4 million people got sick, while 1.7 million died of TB (
      • WHO
      Global tuberculosis report.
      ). In 1993, the World Health Organization (WHO) declared TB a global health emergency and called for more efforts and resources to fight TB. Due largely to the inefficacy of the bacillus Calmette–Guérin (BCG) vaccine against pulmonary TB in adults, the current TB control strategy relies on case detection and treatment under the directly observed therapy short course (DOTs) strategy. The conventional indicators used to assess national control programs under this strategy focus on the proportion of cases that are cured at the end of treatment or whose sputum microscopy becomes negative after the first 2 months of treatment. Such indicators ignore equally important aspects of TB control, which include the duration of infectivity, the frequency of reactivation, and the risk of progression among the infected contacts, as well as the proportion of TB due to recent transmission.
      Understanding transmission dynamics will contribute to knowledge on factors that enhance the spread of the disease, which is useful for developing preventive interventions. Molecular epidemiological studies have been very useful in a number of countries, identifying populations at risk and areas of high transmission, as well as providing much understanding on the prevalence of different Mycobacterium tuberculosis complex (MTBC) strains with varied virulence and drug resistance rates (
      • Anderson L.F.
      • Tamne S.
      • Brown T.
      • Watson J.P.
      • Mullarkey C.
      • Zenner D.
      • et al.
      Transmission of multidrug-resistant tuberculosis in the UK: a cross-sectional molecular and epidemiological study of clustering and contact tracing.
      ,
      • Malm S.
      • Linguissi L.S.
      • Tekwu E.M.
      • Vouvoungui J.C.
      • Kohl T.A.
      • Beckert P.
      • et al.
      New Mycobacterium tuberculosis complex sublineage, Brazzaville, Congo.
      ,
      • Seto J.
      • Wada T.
      • Suzuki Y.
      • Ikeda T.
      • Mizuta K.
      • Yamamoto T.
      • et al.
      Mycobacterium tuberculosis transmission among elderly persons, Yamagata Prefecture, Japan, 2009-2015.
      ,
      • Varghese B.
      • Al-Omari R.
      • Grimshaw C.
      • Al-Hajoj S.
      Endogenous reactivation followed by exogenous re-infection with drug resistant strains, a new challenge for tuberculosis control in Saudi Arabia.
      ,
      • Walker T.M.
      • Lalor M.K.
      • Broda A.
      • Saldana Ortega L.
      • Morgan M.
      • Parker L.
      • et al.
      Assessment of Mycobacterium tuberculosis transmission in Oxfordshire, UK, 2007-12, with whole pathogen genome sequences: an observational study.
      ,
      • Yang C.
      • Luo T.
      • Shen X.
      • Wu J.
      • Gan M.
      • Xu P.
      • et al.
      Transmission of multidrug-resistant Mycobacterium tuberculosis in Shanghai, China: a retrospective observational study using whole-genome sequencing and epidemiological investigation.
      ). These studies have shown that the dynamics of TB transmission vary greatly geographically. Even though Africa harbors a large proportion of the global TB cases, with a current incidence of 254 per 100 000 population (
      • WHO
      Global tuberculosis report.
      ), population-based molecular epidemiological studies needed to understand transmission patterns are rare. The few studies conducted have not been population-based and have lacked an in-depth analysis of the transmission dynamics of MTBC strains belonging to different lineages (
      • Asante-Poku A.
      • Otchere I.D.
      • Osei-Wusu S.
      • Sarpong E.
      • Baddoo A.
      • Forson A.
      • et al.
      Molecular epidemiology of Mycobacterium africanum in Ghana.
      ,
      • Glynn J.R.
      • Alghamdi S.
      • Mallard K.
      • McNerney R.
      • Ndlovu R.
      • Munthali L.
      • et al.
      Changes in Mycobacterium tuberculosis genotype families over 20 years in a population-based study in Northern Malawi.
      ,
      • Mulenga C.
      • Shamputa I.C.
      • Mwakazanga D.
      • Kapata N.
      • Portaels F.
      • Rigouts L.
      Diversity of Mycobacterium tuberculosis genotypes circulating in Ndola, Zambia.
      ).
      The molecular typing tools – spacer oligonucleotide typing (spoligotyping) and mycobacterial interspersed repetitive unit variable number tandem repeat (MIRU-VNTR) typing – have been used successfully for strain differentiation in TB transmission studies due to their combined high discriminatory power and reproducibility; furthermore, in combination with epidemiological data, they have been used for the detection of recent TB transmission and outbreaks (
      • Anderson L.F.
      • Tamne S.
      • Brown T.
      • Watson J.P.
      • Mullarkey C.
      • Zenner D.
      • et al.
      Transmission of multidrug-resistant tuberculosis in the UK: a cross-sectional molecular and epidemiological study of clustering and contact tracing.
      ,
      • Barnes P.F.
      • Cave M.D.
      Molecular epidemiology of tuberculosis.
      ,
      • Maguire H.
      • Dale J.W.
      • McHugh T.D.
      • Butcher P.D.
      • Gillespie S.H.
      • Costetsos A.
      • et al.
      Molecular epidemiology of tuberculosis in London 1995-7 showing low rate of active transmission.
      ,
      • Surie D.
      • Fane O.
      • Finlay A.
      • Ogopotse M.
      • Tobias J.L.
      • Click E.S.
      • et al.
      Molecular, spatial, and field epidemiology suggesting TB transmission in community, not hospital, Gaborone, Botswana.
      ,
      • Varghese B.
      • Al-Omari R.
      • Grimshaw C.
      • Al-Hajoj S.
      Endogenous reactivation followed by exogenous re-infection with drug resistant strains, a new challenge for tuberculosis control in Saudi Arabia.
      ). Currently, the high cost and expertise needed for whole genome sequencing and analysis have precluded its use in population-based studies, and considering capacity building in a low-resource setting like Ghana, spoligotyping and MIRU-VNTR typing remain good alternatives.
      TB in humans is caused mainly by Mycobacterium tuberculosis sensu stricto (MTBss) and Mycobacterium africanum (MAF), which are further divided into seven lineages: MTBss lineages 1–4 and 7 (L1–L4 and L7); MAF lineages 5 and 6 (L5 and L6) (
      • Blouin Y.
      • Hauck Y.
      • Soler C.
      • Fabre M.
      • Vong R.
      • Dehan C.
      • et al.
      Significance of the identification in the Horn of Africa of an exceptionally deep branching Mycobacterium tuberculosis clade.
      ,
      • de Jong B.C.
      • Antonio M.
      • Gagneux S.
      Mycobacterium africanum–review of an important cause of human tuberculosis in West Africa.
      ). While MTBss is distributed globally, MAF is restricted to West Africa, where it is responsible for up to 50% of TB cases (
      • Gagneux S.
      • Small P.M.
      Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development.
      ). Nevertheless, reports mainly from the Gambia where L6 is prevalent, suggest MAF is attenuated compared to MTBss, hence could be outcompeted by MTBss (
      • de Jong B.C.
      • Antonio M.
      • Gagneux S.
      Mycobacterium africanum–review of an important cause of human tuberculosis in West Africa.
      ,
      • de Jong B.C.
      • Hill P.C.
      • Aiken A.
      • Awine T.
      • Antonio M.
      • Adetifa I.M.
      • et al.
      Progression to active tuberculosis, but not transmission, varies by Mycobacterium tuberculosis lineage in The Gambia.
      ,
      • Kallenius G.
      • Koivula T.
      • Ghebremichael S.
      • Hoffner S.E.
      • Norberg R.
      • Svensson E.
      • et al.
      Evolution and clonal traits of Mycobacterium tuberculosis complex in Guinea-Bissau.
      ). However, an 8-year study recently conducted in Ghana found the prevalence of MAF to be fairly constant at approximately 20%, indicating that MAF and MTBss may be transmitted equally (
      • Yeboah-Manu D.
      • Asare P.
      • Asante-Poku A.
      • Otchere I.D.
      • Osei-Wusu S.
      • Danso E.
      • et al.
      Spatio-temporal distribution of Mycobacterium tuberculosis complex strains in Ghana.
      ). The objective of this study was to determine the transmission dynamics of TB caused by MTBss and MAF in Ghana.

      Methods

      Study design and population

      This study was a population-based prospective study in which sputum samples were collected from consecutive clinically diagnosed pulmonary TB patients reporting to 12 selected health facilities within an urban setting (Accra Metropolitan Assembly (AMA)) and the rural setting of East Mamprusi District (MamE) (Supplementary material, Figure S1). The study was conducted from July 2012 to December 2015. A pulmonary TB case was defined as an individual with a case of TB that was confirmed both clinically and bacteriologically. Detailed demographic and epidemiological data were obtained from consented participants.

      Mycobacterial isolation, species identification, and drug susceptibility testing

      The sputum samples were decontaminated and cultured on Lowenstein–Jensen medium to obtain mycobacterial isolates. These isolates were confirmed as MTBC by detecting the MTBC-specific insertion sequence IS6110 using PCR (
      • Yeboah-Manu D.
      • Yates M.D.
      • Wilson S.M.
      Application of a simple multiplex PCR to aid in routine work of the mycobacterium reference laboratory.
      ). In vitro drug susceptibility to isoniazid and rifampicin were determined using either the microplate Alamar Blue cell viability assay, as described elsewhere (
      • Otchere I.D.
      • Asante-Poku A.
      • Osei-Wusu S.
      • Baddoo A.
      • Sarpong E.
      • Ganiyu A.H.
      • et al.
      Detection and characterization of drug-resistant conferring genes in Mycobacterium tuberculosis complex strains: a prospective study in two distant regions of Ghana.
      ), and/or the GenoType MTBDRplus assay (Hain Lifescience), following the manufacturer’s protocol (
      • Barnard M.
      • Albert H.
      • Coetzee G.
      • O’Brien R.
      • Bosman M.E.
      Rapid molecular screening for multidrug-resistant tuberculosis in a high-volume public health laboratory in South Africa.
      ).

      Lineage and strain classification

      Lineage and strain classification of the MTBC was achieved in a stepwise manner using large sequence polymorphism typing identifying regions of difference 4, 9, 12, 702, and 711 (
      • de Jong B.C.
      • Antonio M.
      • Gagneux S.
      Mycobacterium africanum–review of an important cause of human tuberculosis in West Africa.
      ,
      • Gagneux S.
      • Small P.M.
      Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development.
      ), single nucleotide polymorphism typing, spoligotyping (
      • Kamerbeek J.
      • Schouls L.
      • Kolk A.
      • van Agterveld M.
      • van Soolingen D.
      • Kuijper S.
      • et al.
      Simultaneous detection and strain differentiation of Mycobacterium tuberculosis for diagnosis and epidemiology.
      ), and MIRU-VNTR typing (
      • Supply P.
      • Allix C.
      • Lesjean S.
      • Cardoso-Oelemann M.
      • Rusch-Gerdes S.
      • Willery E.
      • et al.
      Proposal for standardization of optimized mycobacterial interspersed repetitive unit-variable-number tandem repeat typing of Mycobacterium tuberculosis.
      ). For MIRU-VNTR typing, a customized set of 8 MIRU loci was first used, as described by
      • Asante-Poku A.
      • Nyaho M.S.
      • Borrell S.
      • Comas I.
      • Gagneux S.
      • Yeboah-Manu D.
      Evaluation of customised lineage-specific sets of MIRU-VNTR loci for genotyping Mycobacterium tuberculosis complex isolates in Ghana.
      , and clustered cases were resolved by analyzing the remaining 7 loci of the standard MIRU-15 loci set (
      • Supply P.
      • Allix C.
      • Lesjean S.
      • Cardoso-Oelemann M.
      • Rusch-Gerdes S.
      • Willery E.
      • et al.
      Proposal for standardization of optimized mycobacterial interspersed repetitive unit-variable-number tandem repeat typing of Mycobacterium tuberculosis.
      ). All assays were well controlled with PCR amplifications and pre-PCR procedures conducted in physically separated compartments to avoid laboratory cross-contamination. The presence of more than one allelic repeat number (multiple allele) for any given locus is suggestive of laboratory cross-contamination, multiple strain infection, or microevolution of a single strain. To prevent bias resulting from cross-contamination and multiple strain infection, isolates with multiple alleles at more than one MIRU locus (described as ‘untypeable’) were excluded from further analysis. Isolates with only one multiple allele at any given locus were, however, included due to the possibility of microevolution.
      The spoligotyping patterns and assigned shared type numbers obtained were defined according to the SITVITWEB database (http://www.pasteur-guadeloupe.fr: 8081/SITVIT_ONLINE/), while sub-lineages were assigned based on the MIRU-VNTRplus database (http://www.miru-vntrplus.org) (
      • Allix-Beguec C.
      • Harmsen D.
      • Weniger T.
      • Supply P.
      • Niemann S.
      Evaluation and strategy for use of MIRU-VNTRplus, a multifunctional database for online analysis of genotyping data and phylogenetic identification of Mycobacterium tuberculosis complex isolates.
      ). Strains with no lineage nomenclature data were further identified using the TB lineage database (
      • Shabbeer A.
      • Cowan L.S.
      • Ozcaglar C.
      • Rastogi N.
      • Vandenberg S.L.
      • Yener B.
      • et al.
      TB-Lineage: an online tool for classification and analysis of strains of Mycobacterium tuberculosis complex.
      ) or otherwise regarded as orphan strains. A strain was defined as an MTBC isolate with a unique molecular signature, and thus a unique spoligotype pattern and/or a unique MIRU-VNTR allelic pattern for the number of investigated MIRU loci.

      Clustering analysis and risk factor assessment

      Clustering analysis was performed using the categorical parameter and the unweighted pair group method with arithmetic mean (UPGMA) coefficient from a constructed phylogenetic tree using the online MIRU-VNTR tool. Clustering analysis was based on the assumption that strains with the same DNA fingerprint may be epidemiologically linked and associated with recent TB transmission (
      • Hall A.
      What is molecular epidemiology?.
      ). A cluster was defined as two or more isolates (same strain) that share an indistinguishable spoligotype and 15-locus MIRU-VNTR allelic pattern, but allowing for one missing allelic data at any one of the difficult-to-amplify MIRU loci (VNTR 2163, 3690, and 4156). The size of a cluster was also defined using the total number of isolates in the cluster classified into categories of small (2 isolates), medium (3–5 isolates), large (6–20 isolates), and very large (>20 isolates).
      The recent transmission rate was estimated using the n− 1 formula (
      • Glynn J.R.
      • Vynnycky E.
      • Fine P.E.
      Influence of sampling on estimates of clustering and recent transmission of Mycobacterium tuberculosis derived from DNA fingerprinting techniques.
      ): (ncc)n, where nc is the total number of clustered cases, c is the number of clusters, and n is the total number of cases in the sample.
      Only one strain per participant was included in the analysis, and follow-up cases were excluded. The clustering analysis was stratified first by location and then by MTBC lineage. The spatial distribution and clustering among all of the observed Spoligo/MIRU strain types were studied by constructing a minimum spanning tree (MST) with Bionumerics software (Applied Maths, Sint-Marteen-Latem, Belgium).

      Data management and analysis

      Both molecular and epidemiological data were analyzed. Epidemiological data retrieved from all participants with positive MTBC cultures were included in the analysis while excluding data from those with no growth, contaminated cultures, and isolated non-tuberculous mycobacterial species. All statistical analyses were conducted using the Stata statistical package version 14.2 (Stata Corp., College Station, TX, USA). The association of specific lineages and/or sub-lineages of the MTBC with time and/or geographical locations were explored using the Chi-square test and a logistic regression model. For the determination of independent predictive factors for recent TB transmission, a multivariate analysis (forward stepwise approach with a probability entry of 0.1) was conducted using a logistic regression model while estimating the odds ratios (OR). p-Values of <0.05 were considered significant.
      The study is reported according to the Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID) guidelines (
      • Field N.
      • Cohen T.
      • Struelens M.J.
      • Palm D.
      • Cookson B.
      • Glynn J.R.
      • et al.
      Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID): an extension of the STROBE statement.
      ).

      Results

      Characteristics of study participants

      A total 3303 sputum smear-positive pulmonary TB cases were recruited, 382 (11.6%) from the rural setting and 2921 (88.4%) from the urban setting; 2604 (78.8%) MTBC isolates were obtained from these cases (Supplementary material, Table S1). After excluding 13 Mycobacterium bovis and isolates that were untypeable (described in the Methods section), 2309 of 2604 isolates (88.7%) were included for clustering analysis. The participants comprised 1631 (71%) males and 663 (29%) females (there was no record of sex for 15 participants) with a median age of 39 years (range 3–91 years) and 33 years (range 4–90 years), respectively (Figure 1; Supplementary material, Table S1). The male-to-female ratio observed was comparable to the national average of approximately 2:1.
      Figure 1
      Figure 1Pipeline for recruited participants and culture-positive TB cases included in the clustering analysis.
      *Category described as untypeable for MIRU-VNTR includes isolates with ≥2 MIRU loci unamplified (n = 164, 71.3%) and isolates with a double allele at ≥2 MIRU loci (n = 66, 28.7%). These isolates were described as suspected mixed infection or laboratory contamination and hence were excluded from further analysis.
      #Frequency was expressed as the total number of Mycobacterium tuberculosis complex (MTBC) isolates obtained.
      Of the 2309 participants with MTBC genotyping results, 201 (8.7%) were from the rural setting and 2108 (91.3%) from the urban setting. Among this study cohort, 7.4% (184/2482) of participants were previously treated cases including relapse, which is similar to the national value of 7.0% (
      • WHO
      Global tuberculosis report 2015.
      ). Seventy-one percent (1561/2208) presented with a sputum smear microscopy bacterial burden result of at least 2+ and 33% (544/1665) admitted having contact with at least one TB patient. In a multivariate logistic regression analysis, it was found that male patients were less likely to be infected with a L5 strain (adjusted OR 0.7, 95% confidence interval (CI) 0.5–0.9) and individuals living in villages were more likely to be infected with a L6 strain (OR 6.6, 95% CI 1.2–36.1) (Supplementary material, Table S2).

      Population structure and recent transmission rate estimation

      Among the 2309 MTBC isolates analyzed for clustering, 1870 (81.0%) were MTBss and 439 (19.0%) were MAF. Six of the seven human-adapted MTBC lineages were found, with L4, L5, and L6 being most frequent: 1741 (75.4%), 289 (12.5%), and 150 (6.5%) isolates, respectively (Table 1). The relative proportions of the most frequent MTBC lineages remained constant over the entire 3.5-year study period (ptrend: L4 p = 0.72, L5 p = 0.84, L6 p = 0.25; Figure 2).
      Table 1Geographical distribution and population structure of MTBC in Ghana by spoligotyping.
      Rural, n (%)Urban, n (%)Combined, n (%)
      Proportions stated here are column-wise distributions with respect to the categories of species, lineages or sub-lineages.
      MTBC isolates204 (8.8)2118 (91.2)2322
      Species distribution
      M. tuberculosis172 (9.2)1698 (90.8)1870 (80.5)
      M. africanum29 (6.6)410 (93.4)439 (18.9)
       Animal3 (23.1)10 (76.9)13 (0.6)
      Human adapted MTBC lineage distribution
       Lineage_14 (10.5)34 (89.5)38 (1.6)
       Lineage_214 (21.5)51 (78.5)65 (2.8)
       Lineage_31 (3.8)25 (96.2)26 (1.1)
       Lineage_4153 (8.8)1588 (91.2)1741 (75.4)
       Lineage_515 (5.2)274 (94.8)289 (12.5)
       Lineage_614 (9.3)136 (90.7)150 (6.5)
      Lineage_4 sub-lineage distribution
       Cameroon77 (7.4)969 (92.6)1046 (60.1)
       Ghana50 (13.3)326 (86.7)376 (21.6)
       Haarlem12 (7.7)144 (92.3)156 (9.0)
       LAM7 (14.0)43 (86.0)50 (2.9)
       Uganda1 (2.5)39 (97.5)40 (2.3)
       Other (S, U, X, NEW-1)5 (9.8)46 (90.2)51 (2.9)
       Not determined1 (4.5)21 (95.5)22 (1.3)
      MTBC, Mycobacterium tuberculosis complex.
      a Proportions stated here are column-wise distributions with respect to the categories of species, lineages or sub-lineages.
      Figure 2
      Figure 2Temporal distribution of 2309 Mycobacterium tuberculosis complex (MTBC) isolates stratified by lineage. Lineages are color-coded with the universally accepted color codes for the main MTBC lineages.
      Of the 2309 isolates included for clustering analysis, 1227 (53.1%) isolates clustered in 276 different clusters with a mean cluster size of 4 (range 2–35) and 1082 (46.9%) unique isolates were identified, giving a total of at least 1358 different MTBC strains circulating within the study population (Table 2a). Using the n − 1 method, the overall clustering rate (reflecting the recent transmission rate) was estimated to be 41.2%. Lineages 2, 4, and 5 contributed high clustering rates of 53.8%, 44.9%, and 31.8%, respectively (Table 2a). The Cameroon, Ghana, and Haarlem sub-lineages of L4 were the most abundant sub-lineages and, compared to the LAM sub-lineage, contributed significantly to the observed high L4 clustering rate (p< 0.05) (Figure 3). There was no significant difference in the clustering rate between the Cameroon and Ghana sub-lineages (p = 0.57) (Figure 3). While no significant difference in the recent transmission rates was seen between members of MAF (L5 and L6, p = 0.118), it was found that L4 was transmitted significantly more (p < 0.001), with seven of its clusters having very large cluster sizes (>20 isolates per cluster) made up of the Ghana sub-lineage (four very large clusters) and Cameroon sub-lineage (three very large clusters) (Figure 3; Supplementary material, Figure S2). Notwithstanding the lower transmissibility of L5 and L6 compared to L4, four large clusters were also observed for each of these lineages. The urban and rural settings had estimated recent transmission rates of 41.7% and 9.0%, respectively.
      Table 2aClustering analysis stratified by lineages and major sub-lineage populations of MTBC.
      LineageIsolates (n)Clustered cases (c)Clustered strains (nc)Single cases (s)Total strain types (s + c)Clustering rate
      The clustering rate was used to estimate the recent transmission rate.
      (%)
      Lineage 13837313410.5
      Lineage 265843223053.8
      Lineage 3262422247.7
      Lineage 4174120198275996044.9
      Cameroon
      Major lineage 4 sub-population.
      104612361443255546.9
      Ghana
      Major lineage 4 sub-population.
      3763620617020645.2
      Haarlem
      Major lineage 4 sub-population.
      1562391658843.6
      LAM
      Major lineage 4 sub-population.
      50625253138.0
      Uganda
      Major lineage 4 sub-population.
      40516242927.5
      Lineage 52895114314619731.8
      Lineage 6150114810211324.7
      Summary
      The summary was calculated using only the items in cells corresponding to the six main lineages.
      230927612271082135841.2
      MTBC, Mycobacterium tuberculosis complex.
      a The clustering rate was used to estimate the recent transmission rate.
      b Major lineage 4 sub-population.
      c The summary was calculated using only the items in cells corresponding to the six main lineages.
      Figure 3
      Figure 3Cluster distribution and size stratified by lineage (panel A and C) and sub-lineage (panel B and D). *p < 0.001, #p = 0.118, ¤p = 0.565.

      Exploring the diversity and clustering within the MTBC lineages

      Very large molecular clusters (clusters with >20 isolates; defined in the Methods section) were observed for L4, in addition to one strikingly large cluster belonging to the Beijing family of lineage 2 (Figure 4; Supplementary material, Figure S3). Generally, only a few multidrug-resistant MTBC strains were observed across all the major lineages (Supplementary material, Figures S4–S6). There was no single large cluster with all isolates being multidrug-resistant (Supplementary material, Figure S4). The spatial distributions of the isolates constituting each cluster stratified by study setting are shown in the Supplementary material, Figures S7–S9.
      Figure 4
      Figure 4Minimum spanning tree (MST) representation of the clustering of 2322 Mycobacterium tuberculosis complex (MTBC) isolates from Accra Metropolitan Assembly and East Mamprusi District built with Bionumerics software. The color code reflects the main MTBC lineages 1 to 6 with the size depicting the number of clustered isolates with an identical strain type.

      Molecular epidemiology and factors associated with clustering: logistic regression modeling

      Risk factors associated with recent TB transmission were sought. A total of 675 individuals belonging to either large (6–20 isolates) or very large (>20 isolates) molecular clusters were identified, with a combined median cluster size of 14 (range 6–35). The majority of the individuals belonging to very large clusters were male, with a male-to-female ratio of approximately 3:1, significantly higher than the 2:1 ratio observed in the general TB patient population (p = 0.022). Three large clusters – cluster ID MSC4193, MSC5003.X, and MSC4107, with cluster sizes of 9, 7, and 7 respectively – involved only male subjects (Table 3).
      Table 2bClustering analysis stratified by study setting and lineages/major sub-lineage populations of MTBC.
      LineageIsolates (n)Clustered cases (c)Clustered strains (nc)Single cases (s)Total strain types (s + c)Clustering rate
      The clustering rate was used to estimate the recent transmission rate.
      (%)
      UrbanRuralUrbanRuralUrbanRuralUrbanRuralUrbanRuralUrbanRural
      Lineage 1344307027430411.80
      Lineage 25114513341810231154.921.4
      Lineage 3251204021123180
      Lineage 41588153183109072568112886413845.69.8
      Cameroon
      Major lineage 4 sub-population.
      96977112557510394675067247.86.5
      Ghana
      Major lineage 4 sub-population.
      326503241821214438176424616
      Haarlem
      Major lineage 4 sub-population.
      14412201813639831042.416.7
      LAM
      Major lineage 4 sub-population.
      4376025018724744.20
      Uganda
      Major lineage 4 sub-population.
      3915016023128128.20
      Lineage 5274154901370137151861532.10
      Lineage 61361410043093141031424.30
      Summary
      The summary was calculated using only the items in cells corresponding to the six main lineages.
      210820125211113129977172122918341.79
      MTBC, Mycobacterium tuberculosis complex.
      a The clustering rate was used to estimate the recent transmission rate.
      b Major lineage 4 sub-population.
      c The summary was calculated using only the items in cells corresponding to the six main lineages.
      Table 3Characteristics of large molecular clusters resulting from combined 15-MIRU and spoligotyping cluster analysis.
      NumberCluster code
      Cluster codes in bold font involved evidence of household transmission.
      Number of cases in clusterSex, male: femaleMedian age (IQR)Diagnosis lapse
      Time lapse (in months) between first diagnosed case and last diagnosed case.
      (months)
      Same residential district
      Number of participants with the same district of residence. Only >2 individuals in the same residential district are indicated. ‘/’ is used to separate individuals from different districts.
      Known risk factor (number)
      ‘Other’ in this category refers to alcohol or substance abuse.
      Lineage (sub-lineage)Drug resistance
      Number of participants carrying strains with drug resistance to either isoniazid or rifampicin.
      1MSC4063.X3531:434 (26–44)407/5/5/4/4/5Smoking (6)

      Other (8)
      L4 (Cameroon)3
      2MSC4060.X3424:1034 (25–45)416/4/3/3/3/3Smoking (6)

      Other (5)
      L4 (Cameroon)4
      3MSC4045.X3026:440 (29–48)397/3/3/3/3Smoking (5)

      HIV (4)

      Other (3)
      L4 (Cameroon)2
      4MSC20012722:535 (27–48)378/5Smoking (8)

      HIV (4)

      Other (2)
      L2 (Beijing)1
      5MSC40312619:741 (33–52)366/4/3Smoking (6)

      HIV (3)

      Other (1)
      L4 (Ghana)11
      6MSC41102621:538 (28–51)396/3Smoking (5)

      HIV (1)

      Other (4)
      L4 (Ghana)ND
      7MSC40952416:835 (24–45)397/6Smoking (5)

      Other (2)
      L4 (Ghana)9
      8MSC40272116:527 (25–45)406/3Smoking (3)L4 (Ghana)3
      9MSC4063.31918:128 (21–45)415/5Smoking (7)L4 (Cameroon)ND
      10MSC4063.181810:735 (24–41)366/3Smoking (4)

      HIV (1)
      L4 (Cameroon)ND
      11MSC40131513:242 (32–55)324/3Smoking (3)

      HIV (2)

      Other (2)
      L4 (Haarlem)2
      12MSC41361513:236 (28–44)346Smoking (2)

      HIV (1)

      Other (3)
      L4 (Haarlem)ND
      13MSC4040148:631 (27–45)333/3HIV (1)

      Other (4)
      L4 (Cameroon)1
      14MSC4069.X1411:327 (23–38)346/3Smoking (2)

      HIV (2)

      Other (1)
      L4 (Cameroon)ND
      15MSC4073149:540 (29–47)245/4Smoking (3)L4 (Cameroon)3
      16MSC5002.X147:740 (38–53)285HIV (2)

      Smoking (1)
      L5 (West African I)2
      17MSC4063.2138:437 (27–44)384Smoking (2)

      Other (3)
      L4 (Cameroon)ND
      18MSC4068.X139:435 (30–44)275/3Smoking (6)

      HIV (2)

      Other (2)
      L4 (Cameroon)ND
      19MSC4024126:528 (26–42)373Smoking (2)

      Other (1)
      L4 (X3)4
      20MSC4060.18127:535 (32–40)363/3Smoking (2)

      HIV (2)

      Other (3)
      L4 (Cameroon)1
      21MSC4063.17127:526 (24–51)39NDSmoking (4)

      HIV (1)

      Other (3)
      L4 (Cameroon)2
      22MSC4138117:441 (30–48)284Smoking (4)L4 (LAM)ND
      23MSC4069.3105:532 (24–39)313Smoking (1)

      HIV (1)
      L4 (Cameroon)2
      24MSC4104107:235 (25–54)345Smoking (3)

      Other (1)
      L4 (Ghana)6
      25MSC6006104:641 (35–47)335/3Smoking (1)

      Other (2)
      L6 (West African II)ND
      26MSC4045.397:243 (32–50)333Smoking (1)

      Other (1)
      L4 (Cameroon)1
      27MSC4060.2196:332 (26–43)22NDHIV (1)

      Other (2)
      L4 (Cameroon)ND
      28MSC4060.395:432 (25–53)343Smoking (1)

      Other (2)
      L4 (Cameroon)ND
      29MSC419399:036 (30–41)28NDSmoking (6)

      HIV (1)

      Other (2)
      L4 (Cameroon)ND
      30MSC4068.386:245 (34–54)272Smoking (2)

      HIV (1)

      Other (1)
      L4 (Cameroon)ND
      31MSC402276:150 (46–62)364Smoking (1)

      Other (1)
      L4 (Haarlem)ND
      32MSC4060.473:434 (30–49)225Smoking (1)L4 (Cameroon)ND
      33MSC4080.1374:324 (17–50)203Smoking (1)L4 (Cameroon)1
      34MSC408276:135 (28–40)333Smoking (1)L4 (Ghana)ND
      35MSC410777:038 (29–53)283/3Smoking (2)

      Other (1)
      L4 (Ghana)1
      36MSC5003.274:335 (26–57)33NDHIV (1)L5 (West African I)1
      37MSC5003.X77:043 (26–66)343Smoking (2)

      Other (1)
      L5 (West African I)ND
      38MSC600475:244 (36–50)31NDHIV (1)

      Other (3)
      L6 (West African II)3
      MIRU, mycobacterial interspersed repetitive unit; L2, lineage 2; L4, lineage 4; L5, lineage 5; L6, lineage 6; ND, none determined; IQR, interquartile range.
      a Cluster codes in bold font involved evidence of household transmission.
      b Time lapse (in months) between first diagnosed case and last diagnosed case.
      c Number of participants with the same district of residence. Only >2 individuals in the same residential district are indicated. ‘/’ is used to separate individuals from different districts.
      d ‘Other’ in this category refers to alcohol or substance abuse.
      e Number of participants carrying strains with drug resistance to either isoniazid or rifampicin.
      Epidemiological investigations revealed both localized and dispersed recent transmission among the clustered cases, with suggested evidence of household transmission in at least six large clusters (MSC4063.X, MSC2001, MSC4095, MSC4063.18, MSC4069.X, and MSC4104). Specifically, the same L4 strain (part of cluster MSC4069.X) was found among three individuals belonging to the same household, with the oldest person (age 49 years) reporting having contact with his son who had TB 4 months prior to his episode (suggestive of household transmission). The majority of the large clusters involved TB strains circulating over almost the entire study period (Supplementary material, Figure S10). Apart from three Ghana sub-lineage clusters (MSC4104, MSC4031, and MSC4095) and one L6 cluster (MSC6004), with respectively 60% (6/10), 42% (11/26), 38% (9/24), and 43% (3/7) of isolates showing resistance to rifampicin and/or isoniazid (Table 3), such high levels of drug resistance were not observed in the other large and very large clusters. Only 2% of the isolates belonging to large and very large clusters were multidrug-resistant TB strains and this was significantly lower than that for small (2 isolates) and medium (3–5 isolates) (4%) clusters (p = 0.031).
      For the determination of possible factors associated with recent TB transmission, a general logistic regression model including all MTBC lineages was first performed, using the event of belonging to a clustered case as the outcome variable and participant variables as possible predictors (Table 4). In a separate logistic regression model, risk factors associated with recent TB transmission were tested stratified independently by L4 and L5 (Table 5), excluding L6 due to the limited sample size. In the multivariable analysis for the general logistic regression model, it was found that harboring either an isoniazid- or rifampicin-resistant MTBC strain (adjusted OR 0.7, 95% CI 0.5–0.9) was associated with a lower odds of belonging to a clustered case (Table 4). All other factors such as education status, occupation, income level, ethnicity, religion, and HIV status had no association with recent TB transmission.
      Table 4Logistic regression analysis of risk factors associated with TB clustering (recent TB transmission).
      VariableMTBC (N = 2309)UnivariateMultivariate
      For the multivariate model, only variables with p<0.1 and with at least 90% of available data were included. However ‘locality’ was excluded due to the small sample size from the rural setting. Residence classification, marital status, isoniazid mono-resistance, and MDR were excluded due to collinearity with other variables in the model.
      Total TB cases, n (%)Clustered cases
      A cluster was defined as two or more isolates (same strain) that share an indistinguishable spoligotype and 15-locus MIRU-VNTR allelic pattern, but allowing for one missing allelic data at any one of the difficult-to-amplify MIRU loci.
      , n (%)
      OR (95% CI)p-ValueAdjusted OR (95% CI)p-Value
      Year diagnosed2309 (100)1229 (53·2)
       2012244 (10·6)147 (60·3)1·4 (1·0–1·8)0·0431·3 (0·9–1·7)0·113
       2013776 (33·6)410 (52·8)Reference
       2014707 (30·6)365 (51·6)1·0 (0·8–1·2)0·6420·9 (0·7–1·1)0·203
       2015582 (25·2)307(52·8)1·0 (0·8–1·2)0·9751·0 (0·8–1·2)0·703
      Sex2294 (99·4)
       Male1631 (71·1)863 (52·9)1·0 (0·8–1·2)0·685
       Female663 (28·9)357 (53·8)Reference
      Age (years)
      A significant decreasing trend in the probability of belonging to a clustered case was found with increasing age category (p=0.004).
      2224 (96·3)
       <1537 (1·7)25 (67·6)1·6 (0·8–3·3)0·1831·6 (0·8–3·2)0·221
       15–29639 (28·7)360 (56·3)Reference
       30–39570 (25·6)307 (53·9)0·9 (0·7–1·1)0·3870·9 (0·7–1·2)0·688
       40–59778 (35·0)398 (51·2)0·8 (0·7–1·0)0·0520·9 (0·7–1·1)0·241
       >59200 (9·0)97 (48·5)0·7 (0·5–1·0)0·0530·9 (0·6–1·1)0·211
      Nationality1781 (77·1)
       Ghanaian1714 (96·2)932 (54·4)Reference
       Other67 (3·8)38 (56·7)1·1 (0·7–1·8)0·706
      Locality2309 (100)1229 (53·2)
       Rural201 (8·7)74 (36·8)Reference
       Urban2108 (91·3)1155 (54·8)2·1 (1·5–2·8)<0·001
      Residence classification1642 (71·1)
       Village69 (4·2)27 (39·1)0·5 (0·3–0·8)0·007
       Town182 (11·1)96 (52·7)0·9 (0·6–1·2)0·415
      City residential area52 (3·2)27 (51·9)0·8 (0·5–1·5)0·564
       City suburb1136 (69·2)636 (56·0)Reference
       City slum203 (12·4)112 (55·2)1·0 (0·7–1·3)0·83
      Residential district1538 (66·6)
       Ablekuma545 (35·4)298 (54·7)Reference
       Ashiedu Keteke170 (11·1)100 (58·8)1·2 (0·8–1·7)0·343
       Ayawaso220 (14·3)124 (56·4)1·1 (0·8 to 1·5)0·672
       Kpeshie224(14·6)121 (54·0)1·0 (0·7–1·3)0·867
       Mamprusi East70 (4·6)22 (31·4)0·4 (0·2–0·6)<0·001
       Okaikoi176 (11·4)98 (55·7)1·0 (0·7 to 1·5)0·816
       Osu Klottey133 (8·6)78 (58·7)1·2 (0·8–1·7)0·409
      Household type1624 (70·3)
       Self-contained412 (25·4)221 (53·6)1·0 (0·8–1·2)0·797
       Compound house1212 (74·6)659 (54·4)Reference
      Education1748 (75·7)
       Primary222 (12·7)125 (56·3)1·1 (0·8–1·5)0·637
       Middle/JHS637 (36·4)347 (54·5)Reference
       Secondary429 (24·5)232 (54·1)1·0 (0·8–1·3)0·899
       Tertiary190 (10·9)110 (57·9)1·1 (0·8–1·6)0·405
       No education270 (15·4)141 (52·2)0·9 (0·7–1·2)0·534
      Occupation1722 (74·6)
       Unemployed390 (22·6)208 (53·3)0·9 (0·7–1·1)0·423
       Unskilled951 (55·2)530 (55·7)Reference
       Skilled381 (22·1)198 (52·0)0·9 (0·7–1·1)0·213
      Monthly income (GH¢)1622 (70·2)
       None371 (22·9)213 (57·4)Reference
       <301807 (49·7)438 (54·3)0·9 (0·7–1·1)0·315
       301–1000407 (25·1)218 (53·6)0·8 (0·6–1·1)0·281
       >100037 (2·3)15 (40·5)0·5 (0·3–1·0)0·052
      Religion1771 (76·7)
       Christian1361 (76·9)739 (54·3)Reference
       Islam302 (17·0)161 (53·3)1·0 (0·7–1·2)0·755
       Other26 (1·5)14 (53·9)1·0 (0·4–2·1)0·963
       Not religious82 (4·6)49 (59·7)1·2 (0·8–2·00·366
      Ethnicity1760 (76·4)
       Akan570 (32·3)309 (54·2)Reference
       Ewe259 (14·7)143 (55·2)1·0 (0·8–1·4)0·788
       Ga/Adangbe544 (30·8)310 (57·0)1·1 (0·9–1·4)0·352
       Other392 (22·2)196 (50·0)0·8 (0·6–1·1)0·199
      Marital status1758 (76·1)
       Single766 (43·6)431 (56·3)Reference
       Married742 (42·2)395 (53·2)0·9 (0·7–1·1)0·237
       Divorced167 (9·5)99 (59·3)1·1 (0·8–1·6)0·476
       Widowed83 (4·7)35 (42·2)0·6 (0·3–0·9)0·015
      Smear positivity2208 (95·6)
       Scanty 1–9173 (7·8)96 (55·5)1·1 (0·8–1·5)0·714
       1+474 (21·5)237 (50·0)0·9 (0·7–1·1)0·151
       2+546 (24·7)294 (53·9)1·0 (0·8–1·2)0·957
       3+1015 (46·0)548 (54·0)Reference
      Previous TB treatment1737 (75·2)
       Yes291 (16·8)153 (52·6)0·9 (0·7–1·2)0·535
       No1446 (83·2)789 (54·6)Reference
      Risk of TB contact
       Close friend/household1665 (72·1)
       No contact1121 (67·3)594 (53·0)Reference
       1 contact212 (12·7)118 (55·7)1·1 (0·8–1·5)0·475
       2–5 contacts309 (18·6)179 (57·9)1·2 (0·9–1·6)0·123
       6–10 contacts23 (1·4)15 (65·2)1·7 (0·7–4·0)0·249
      Imprisonment1660 (71·9)
       Yes97 (5·8)56 (57·7)1·1 (0·8–1·7)0·513
       No1563 (94·2)849 (54·3)Reference
      Health/laboratory worker1661 (71·9)
       Yes47 (2·8)25 (53·2)0·9 (0·5–1·7)0·85
       No1614 (97·2)881 (54·6)Reference
      Immunosuppressive condition1695 (73·4)
       Any893 (52·7)488 (54·6)1·0 (0·9–1·2)0·747
       None802 (47·3)432 (53·9)Reference
      Diabetes mellitus534 (23·1)
       Yes104 (19·5)54 (51·9)1·0 (0·7–1·5)0·957
       No430 (80·5)222 (51·6)Reference
      HIV status1166 (50·5)
      Positive144 (12·3)82 (56·9)1·1 (0·8–1·6)0·481
       Negative1022 (87·7)550 (53·8)Reference
      Smoking1518 (65·7)
       Yes434 (28·6)237 (54·6)1·0 (0·8–1·2)0·949
       No1084 (71·4)590 (54·4)Reference
      Substance abuse (excluding alcohol)1401 (60·7)
       Yes140 (10·0)84 (60·0)1·3 (0·9–1·8)0·172
       No1261 (90·0)680 (53·9)Reference
      Substance abuse (including alcohol)1474 (63·8)
       Yes460 (31·2)250 (54·3)1·0 (0·8–1·3)0·858
       No1014 (68·8)546 (53·8)Reference
      Lineage2309 (100)
       Lineage 138 (1·7)7 (18·4)0·2 (0·08–0·4)<0·0010·13 (0·05–0·36)<0·001
       Lineage 265 (2·8)43 (66·2)1·5 (0·9–2·5)0·1261·5 (0·9–2·5)0·155
       Lineage 326 (1·1)4 (15·4)0·1 (0·05–0·4)<0·0010·15 (0·05–0·45)0·001
       Lineage 41741 (75·4)984 (56·5)Reference
       Lineage 5289 (12·5)143 (49·5)0·8 (0·6–1·0)0·0260·7 (0·6–0·9)0·032
       Lineage 6150 (6·5)48 (32·0)0·4 (0·3–0·5)<0·0010·3 (0·2–0·5)<0·001
      Lineage 4 sub-lineage
       Cameroon1046 (60·1)616 (58·9)Reference
       Ghana376 (21·6)206 (54·8)0·8 (0·7–1·1)0·167
       Haarlem156 (9·0)91(58·3)1·0 (0·7–1·4)0·895
       LAM50 (2·9)25 (50·0)0·7 (0·4–1·2)0·215
       Uganda40 (2·3)16 (40·0)0·5 (0·2–0·9)0·02
       Other51 (2·9)26 (51·0)0·7 (0·4–1·3)0·265
       Not determined22 (1·3)4 (18·2)0·2 (0·1–0·5)0·001
      Drug resistance2300 (99·6)
       Any313 (13·6)138 (44·1)0·6 (0·5–0·8)<0·0010·7 (0·5–0·9)0·002
       None1987 (86·4)1090 (54·9)Reference
      Isoniazid mono-resistant2300 (99·6)
       Yes295 (12·8)129 (43·7)0·6 (0·5–0·8)<0·001
       No2005 (87·2)1099 (54·8)Reference
      Multidrug resistant (MDR)2300 (99·6)
       Yes81 (3·5)35 (43·2)0·7 (0·4–1·0)0·063
       No2219 (96·5)1193 (53·8)Reference
      Cluster size (n)1227 (53·1)
       Small (2)290 (23·6)
       Medium (3–5)262 (21·4)
       Large (6–20)452 (36·8)
       Very large (>20)223 (18·2)
      MTBC, Mycobacterium tuberculosis complex; TB, tuberculosis; OR, odds ratio; CI, confidence interval; JHS, junior high school; GH¢, Ghanaian cedi.
      a For the multivariate model, only variables with p< 0.1 and with at least 90% of available data were included. However ‘locality’ was excluded due to the small sample size from the rural setting. Residence classification, marital status, isoniazid mono-resistance, and MDR were excluded due to collinearity with other variables in the model.
      b A cluster was defined as two or more isolates (same strain) that share an indistinguishable spoligotype and 15-locus MIRU-VNTR allelic pattern, but allowing for one missing allelic data at any one of the difficult-to-amplify MIRU loci.
      c A significant decreasing trend in the probability of belonging to a clustered case was found with increasing age category (p = 0.004).
      Table 5Risk factors associated with TB clustering: logistic regression analysis stratified by lineage.
      Only variables with p<0.1 from the general logistic regression model in Table 4 were included in this analysis. *p<0.05; **p<0.001.
      VariablesLineage 4 (n = 1741)UnivariateMultivariate
      For the multivariate model, only variables with p<0.1 and with at least 90% of available data were included.
      Lineage 5 (n = 289)Univariate
      TB cases, n (%)Clustered cases
      A cluster was defined as two or more isolates (same strain) that share an indistinguishable spoligotype and 15-locus MIRU-VNTR allelic pattern, but allowing for one missing allelic data at any one of the difficult-to-amplify MIRU loci.
      , n (%)
      OR (95% CI)Adjusted OR (95% CI)p-ValueTB cases, n (%)Clustered cases
      A cluster was defined as two or more isolates (same strain) that share an indistinguishable spoligotype and 15-locus MIRU-VNTR allelic pattern, but allowing for one missing allelic data at any one of the difficult-to-amplify MIRU loci.
      , n (%)
      OR (95% CI)p-Value
      Year diagnosed1741 (100)289 (100)
       2012183 (10·5)120 (65·6)1·5 (1·1–2·1)*1·4 (1·0–2·1)0·06226 (9·0)14 (53·8)1·2 (0·5–2·9)0·659
       2013568 (32·6)318 (56·0)Reference98 (33·9)48 (49·0)Reference
       2014548 (31·5)300 (54·7)1·0 (0·8–1·2)1·0 (0·7–1·3)0·84792 (31·8)43 (46·7)0·9 (0·5–1·6)0·757
       2015442 (25·4)244 (55·2)1·0 (0·8–1·2)1·0 (0·7–1·3)0·95573 (25·3)38 (52·1)1·2 (0·6–2·1)0·691
      Age (years)1672283
       <1527 (1·6)20 (74·1)2·1 (0·9–5·0)5 (1·8)3 (60·0)
       15–29497 (29·7)289 (58·2)Reference78 (27·6)42 (53·8)
       30–39432 (25·8)252 (58·3)1·0 (0·8–1·3)68 (24·0)31 (45·6)
       40–59580 (34·7)315 (54·3)0·9 (0·7–1·1)94 (33·2)48 (51·1)
       >59136 (8·1)71 (52·2)0·8 (0·5–1·2)38 (13·4)16 (42·1)
      Locality1741 (100)289 (100)
       Rural153 (8·8)59 (38·6)Reference15 (5·2)4 (26·7)Reference
       Urban1588 (91·2)923 (58·1)2·2 (1·6–3·1)**274 (94·8)139 (50·7)2·8 (0·9–9·1)0·081
      Residential district1165189
       Ablekuma412 (35·4)237 (57·5)Reference77 (40·7)39 (50·7)Reference
       Ashiedu Keteke132 (11·3)81 (61·4)1·2 (0·8–1·8)13 (6·9)5 (38·5)0·6 (0·2–2·0)0·419
       Ayawaso178 (15·3)111 (62·4)1·2 (0·8–1·8)21 (11·1)7 (33·3)0·5 (0·2–1·4)0·163
       Kpeshie166 (14·2)88 (53·0)0·8 (0·6–1·2)37 (19·6)25 (67·6)2·0 (0·9–4·6)0·091
       Mamprusi East56 (4·8)19 (33·9)0·4 (0·2–0·7)*4 (2·1)1 (25·0)0·32 (0·03–3·26)0·339
       Okaikoi134 (11·5)80 (59·7)1·1 (0·7–1·6)24 (12·7)12 (50·0)1·0 (0·4–2·4)0·956
       Osu Klottey87 (7·5)58 (66·7)1·5 (0·9–2·4)13 (6·9)5 (38·5)0·6 (0·2–2·0)0·419
      Monthly income (GH¢)1222
       None275 (22·5)167 (60·7)Reference
       <301605 (49·5)351 (58·0)0·9 (0·7–1·2)
       301–1000314 (25·7)184 (58·6)0·9 (0·7–1·3)
       >100028 (2·3)11 (39·3)0·4 (0·2–0·9)*
      Marital status1322
       Single591 (44·7)355 (60·1)Reference
       Married549 (41·5)312 (56·8)0·9 (0·7–1·1)0·9 (0·7–1·2)0·589
       Divorced124 (9·4)78 (62·9)1·1 (0·8–1·7)1·1 (0·7–1·7)0·543
       Widowed58 (4·4)24 (41·4)0·5 (0·3–0·8)*0·5 (0·3–0·8)0·011
      Lineage 4 sub-lineage
       Cameroon1046 (60·1)614 (58·7)
       Ghana376 (21·6)206 (54·8)0·9 (0·7–1·1)0·9 (0·7–1·2)0·403
       Haarlem156 (9·0)91 (58·3)1·0 (0·7–1·4)1·0 (0·7–1·5)0·87
       LAM50 (2·9)25 (50·0)0·7 (0·4–1·2)0·7 (0·4–1·4)0·354
       Uganda40 (2·3)16 (40·0)0·5 (0·2–0·9)*0·4 (0·2–0·8)0·013
       Other51 (2·9)26 (51·0)0·7 (0·4–1·3)0·8 (0·4–1·6)0·558
       Not determined22 (1·3)4 (18·2)0·2 (0·1–0·5)*0·10 (0·03–0·35)<0·001
      Drug resistance1736
       Any241 (13·9)114 (47·3)0·7 (0·5–0·9)*0·7 (0·5–1·0)0·059
       None1495 (86·1)867 (58·0)Reference
      TB, tuberculosis; OR, odds ratio; CI, confidence interval; GH¢, Ghanaian cedi.
      a Only variables with p< 0.1 from the general logistic regression model in Table 4 were included in this analysis. *p< 0.05; **p< 0.001.
      b For the multivariate model, only variables with p< 0.1 and with at least 90% of available data were included.
      c A cluster was defined as two or more isolates (same strain) that share an indistinguishable spoligotype and 15-locus MIRU-VNTR allelic pattern, but allowing for one missing allelic data at any one of the difficult-to-amplify MIRU loci.
      Finally, using adjusted predictions, it was found that the probability of belonging to a clustered case decreased with age and increased with the number of TB contacts (Figure 5). In a separate logistic regression analysis, including age as a continuous variable with belonging to a clustered case as the outcome variable, it was found that each year increase in age was significantly associated with an approximately 1% (95% CI 0.13–2.00%) decrease in the odds of a TB patient being part of a recent transmission event (p = 0.007).
      Figure 5
      Figure 5Adjusted predictions of the probability of belonging to a clustered case with 95% confidence interval: (A) at the year of diagnosis, (B) while ageing, (C) considering the number of close TB contact(s), and (D) considering the number of circulating Mycobacterium tuberculosis complex (MTBC) lineages.

      Discussion

      The aims of this study were to conduct a population-based prospective molecular epidemiological study to analyze the transmission dynamics of MTBC strains circulating in Ghana and to identify risk factors associated with recent TB transmission.
      A high MTBC isolate recovery rate of 78.8% was obtained, higher than that reported in similar studies (
      • Hamblion E.L.
      • Le Menach A.
      • Anderson L.F.
      • Lalor M.K.
      • Brown T.
      • Abubakar I.
      • et al.
      Recent TB transmission, clustering and predictors of large clusters in London, 2010-2012: results from first 3 years of universal MIRU-VNTR strain typing.
      ,
      • Mears J.
      • Abubakar I.
      • Cohen T.
      • McHugh T.D.
      • Sonnenberg P.
      Effect of study design and setting on tuberculosis clustering estimates using Mycobacterial Interspersed Repetitive Units-Variable Number Tandem Repeats (MIRU-VNTR): a systematic review.
      ) and this strengthens the power of the sample size to make assessments of the TB transmission rate in Ghana. This study identified a high TB clustering (recent TB transmission) rate of 41.2%, which is quite alarming, with the urban and rural areas having estimated rates of 41.7% and 9.0%, respectively (Table 2b). These findings call for intensifying community outreach programs to encourage early case reporting and infection control. Moreover, the analysis predicted the probability of clustering to generally increase with the increase in the number of TB contacts (Figure 5). This means that a susceptible individual is likely to have TB and be involved in a recently transmitted event as the number of TB contacts increases.
      Within the study population, no association of recent TB transmission was found with education status, occupation, income level, ethnicity, religion, or HIV status. However, it was observed that individuals below the age of 30 years were associated with recent TB transmission, and this is similar to observations made elsewhere (
      • Hamblion E.L.
      • Le Menach A.
      • Anderson L.F.
      • Lalor M.K.
      • Brown T.
      • Abubakar I.
      • et al.
      Recent TB transmission, clustering and predictors of large clusters in London, 2010-2012: results from first 3 years of universal MIRU-VNTR strain typing.
      ,
      • Vluggen C.
      • Soetaert K.
      • Groenen G.
      • Wanlin M.
      • Spitaels M.
      • Arrazola de Onate W.
      • et al.
      Molecular epidemiology of Mycobacterium tuberculosis complex in Brussels, 2010-2013.
      ). Also in this study, it was observed that each year increase in age was associated with an approximately 1% (95% CI 0.13–2.00; p = 0.007) decrease in the odds of a TB patient being part of a recent transmission event, implying that compared to younger individuals, older individuals are more likely to get active TB disease by reactivation of latent TB infection rather than through a recent transmission event (
      • Hamblion E.L.
      • Le Menach A.
      • Anderson L.F.
      • Lalor M.K.
      • Brown T.
      • Abubakar I.
      • et al.
      Recent TB transmission, clustering and predictors of large clusters in London, 2010-2012: results from first 3 years of universal MIRU-VNTR strain typing.
      ). This finding puts age as a risk factor for recent TB transmission in Ghana. However, this finding was largely driven by L4 and L5, since separate analysis was not valid for L6 due to the small sample size. Furthermore, it was found that the male-to-female ratio among very large clusters was significantly higher than that observed in the general TB patient population (p = 0.022). This finding, together with the observation that some large clusters involved only male subjects, also indicates that males have a higher risk of recent TB transmission compared to females, suggesting that males may engage in certain social activities that predispose them to belonging to a recent transmission event.
      A lower rate of multidrug-resistant TB was seen among large clustered cases compared to the general population (2% vs. 4%, p = 0.031), indicating a low multidrug-resistant TB transmissibility within the study population. This finding further suggests that the majority of drug-resistant TB cases in Ghana acquired the drug resistance during treatment, which indicates poor patient compliance (
      • Danso E.
      • Addo I.Y.
      • Ampomah I.G.
      Patients’ compliance with tuberculosis medication in Ghana: evidence from a Periurban community.
      ). Moreover, it was also found that compared to drug (isoniazid and/or rifampicin)-sensitive MTBC strains, it was unlikely to find MTBC strains with isoniazid and/or rifampicin resistance involved in a recent transmission event (adjusted OR 0.7, 95% CI 0.5–0.9).
      Within the study setting, a reduced transmission of MAF (L5: 31.8%, L6: 24.7%) compared to MTBss L4 (44.9%) was observed. The high recent transmission rate observed for L4 was driven by both the Cameroon and Ghana sub-lineages, with no difference in their transmissibility, hence identifying these sub-lineages as very important pathogens. The high recent transmission of the Ghana sub-lineage coupled with recently reported association with drug resistance (
      • Otchere I.D.
      • Asante-Poku A.
      • Osei-Wusu S.
      • Baddoo A.
      • Sarpong E.
      • Ganiyu A.H.
      • et al.
      Detection and characterization of drug-resistant conferring genes in Mycobacterium tuberculosis complex strains: a prospective study in two distant regions of Ghana.
      ) is of public health importance and hence calls for the national tuberculosis control program to support peripheral diagnostic laboratories with facilities to accurately detect and help control the spread of the Ghana sub-lineage.
      The higher recent transmission rate for L4 compared to L5 and L6 may not necessarily imply the outcompeting of L5 and L6 by L4, as their relative proportions remained constant over the entire study period (Figure 2) and also based on previous reports (
      • Yeboah-Manu D.
      • Asare P.
      • Asante-Poku A.
      • Otchere I.D.
      • Osei-Wusu S.
      • Danso E.
      • et al.
      Spatio-temporal distribution of Mycobacterium tuberculosis complex strains in Ghana.
      ). Despite the low transmissibility of MAF, the observed stable relative proportion over the entire study period may be because the pathogen has adapted to infecting specific host populations (possibly due to unidentified host genetic or environmental factors peculiar to some West African inhabitants), hence enabling the maintenance of a stable prevalence over time. Using adjusted predictions for the probability of clustering, it was found that MAF L5 may still have the propensity to transmit equally to lineage 4 (Figure 5), not forgetting the confounding effect of a higher diversity in spoligotype pattern of L5 compared to L4 and hence reduced clustering of the former (
      • Asante-Poku A.
      • Otchere I.D.
      • Osei-Wusu S.
      • Sarpong E.
      • Baddoo A.
      • Forson A.
      • et al.
      Molecular epidemiology of Mycobacterium africanum in Ghana.
      ). Compared to L4, a significant association of L6 with individuals living in villages was found (OR 6.6, p< 0.05; Supplementary material, Table S2). The low recent TB transmission in the villages coupled with an association of L6 could be the reason why low frequencies of L6 strains were observed within the study setting.
      This report could be limited by the possibility of an underestimation of the recent transmission rate resulting from the misclassification of strains as unique if they were actually clustered outside of the restricted geographic sampling site and sampling period. However, measures were taken to address the underestimation of recent TB transmission by recruiting up to 90% of the diagnosed TB cases spanning a 3.5-year period. In addition, the possibility of overestimating recent TB transmission rates is also possible considering that the basis of the clustering analysis was done using combined 15-locus MIRU-VNTR typing and spoligotyping, whereas whole genome sequencing could have offered a better resolution of strains.
      Overall, the findings indicate high recent TB transmission, suggesting the occurrence of unsuspected outbreaks. The intensification of community education is recommended to improve early case reporting and infection control.

      Funding

      This research was funded by a Wellcome Trust Intermediate Fellowship Grant ( 097134/Z/11/Z ) to Dorothy Yeboah-Manu. The funding source had no role in the study design, collection, analysis, and interpretation of the data, in the writing of the report, or in the decision to submit the paper for publication.

      Ethical approval

      The Scientific and Technical Committee and then the Institutional Review Board at NMIMR, University of Ghana (FWA00001824) reviewed and approved the study.

      Conflict of interest

      We declare that we have no competing interest.

      Acknowledgements

      The authors are grateful for the administrative support of Frank Bonsu, National Tuberculosis Control Program, Ghana, and to the laboratory heads and nurses at various facilities who recruited cases and all study participants. We thank the national service personnel for providing great help in completing the questionnaires and making sputum samples available for laboratory investigations. We thank Vida Yirenkyiwaa Adjei and Portia Abena Morgan of Noguchi Memorial Institute for Medical Research for their assistance with some of the laboratory procedures.
      Prince Asare was supported by a West African Centre for Cell Biology of Infectious Pathogens (WACCBIP)–World Bank ACE PhD Studentship.

      Appendix A. Supplementary data

      The following is Supplementary data to this article:

      References

        • Allix-Beguec C.
        • Harmsen D.
        • Weniger T.
        • Supply P.
        • Niemann S.
        Evaluation and strategy for use of MIRU-VNTRplus, a multifunctional database for online analysis of genotyping data and phylogenetic identification of Mycobacterium tuberculosis complex isolates.
        J Clin Microbiol. 2008; 46: 2692-2699
        • Anderson L.F.
        • Tamne S.
        • Brown T.
        • Watson J.P.
        • Mullarkey C.
        • Zenner D.
        • et al.
        Transmission of multidrug-resistant tuberculosis in the UK: a cross-sectional molecular and epidemiological study of clustering and contact tracing.
        Lancet Infect Dis. 2014; 14: 406-415
        • Asante-Poku A.
        • Nyaho M.S.
        • Borrell S.
        • Comas I.
        • Gagneux S.
        • Yeboah-Manu D.
        Evaluation of customised lineage-specific sets of MIRU-VNTR loci for genotyping Mycobacterium tuberculosis complex isolates in Ghana.
        PLoS One. 2014; 9e92675
        • Asante-Poku A.
        • Otchere I.D.
        • Osei-Wusu S.
        • Sarpong E.
        • Baddoo A.
        • Forson A.
        • et al.
        Molecular epidemiology of Mycobacterium africanum in Ghana.
        BMC Infect Dis. 2016; 16: 385
        • Barnard M.
        • Albert H.
        • Coetzee G.
        • O’Brien R.
        • Bosman M.E.
        Rapid molecular screening for multidrug-resistant tuberculosis in a high-volume public health laboratory in South Africa.
        Am J Respir Crit Care Med. 2008; 177: 787-792
        • Barnes P.F.
        • Cave M.D.
        Molecular epidemiology of tuberculosis.
        N Engl J Med. 2003; 349: 1149-1156
        • Blouin Y.
        • Hauck Y.
        • Soler C.
        • Fabre M.
        • Vong R.
        • Dehan C.
        • et al.
        Significance of the identification in the Horn of Africa of an exceptionally deep branching Mycobacterium tuberculosis clade.
        PLoS One. 2012; 7e52841
        • Danso E.
        • Addo I.Y.
        • Ampomah I.G.
        Patients’ compliance with tuberculosis medication in Ghana: evidence from a Periurban community.
        Adv Public Health. 2015; 2015: 6
        • de Jong B.C.
        • Antonio M.
        • Gagneux S.
        Mycobacterium africanum–review of an important cause of human tuberculosis in West Africa.
        PLoS Negl Trop Dis. 2010; 4: e744
        • de Jong B.C.
        • Hill P.C.
        • Aiken A.
        • Awine T.
        • Antonio M.
        • Adetifa I.M.
        • et al.
        Progression to active tuberculosis, but not transmission, varies by Mycobacterium tuberculosis lineage in The Gambia.
        J Infect Dis. 2008; 198: 1037-1043
        • Field N.
        • Cohen T.
        • Struelens M.J.
        • Palm D.
        • Cookson B.
        • Glynn J.R.
        • et al.
        Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID): an extension of the STROBE statement.
        Lancet Infect Dis. 2016; 14: 341-352
        • Gagneux S.
        • Small P.M.
        Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development.
        Lancet Infect Dis. 2007; 7: 328-337
        • Glynn J.R.
        • Alghamdi S.
        • Mallard K.
        • McNerney R.
        • Ndlovu R.
        • Munthali L.
        • et al.
        Changes in Mycobacterium tuberculosis genotype families over 20 years in a population-based study in Northern Malawi.
        PLoS One. 2010; 5e12259
        • Glynn J.R.
        • Vynnycky E.
        • Fine P.E.
        Influence of sampling on estimates of clustering and recent transmission of Mycobacterium tuberculosis derived from DNA fingerprinting techniques.
        Am J Epidemiol. 1999; 149: 366-371
        • Hall A.
        What is molecular epidemiology?.
        Trop Med Int Health. 1996; 1: 407-408
        • Hamblion E.L.
        • Le Menach A.
        • Anderson L.F.
        • Lalor M.K.
        • Brown T.
        • Abubakar I.
        • et al.
        Recent TB transmission, clustering and predictors of large clusters in London, 2010-2012: results from first 3 years of universal MIRU-VNTR strain typing.
        Thorax. 2016; 71: 749-756
        • Kallenius G.
        • Koivula T.
        • Ghebremichael S.
        • Hoffner S.E.
        • Norberg R.
        • Svensson E.
        • et al.
        Evolution and clonal traits of Mycobacterium tuberculosis complex in Guinea-Bissau.
        J Clin Microbiol. 1999; 37: 3872-3878
        • Kamerbeek J.
        • Schouls L.
        • Kolk A.
        • van Agterveld M.
        • van Soolingen D.
        • Kuijper S.
        • et al.
        Simultaneous detection and strain differentiation of Mycobacterium tuberculosis for diagnosis and epidemiology.
        J Clin Microbiol. 1997; 35: 907-914
        • Maguire H.
        • Dale J.W.
        • McHugh T.D.
        • Butcher P.D.
        • Gillespie S.H.
        • Costetsos A.
        • et al.
        Molecular epidemiology of tuberculosis in London 1995-7 showing low rate of active transmission.
        Thorax. 2002; 57: 617-622
        • Malm S.
        • Linguissi L.S.
        • Tekwu E.M.
        • Vouvoungui J.C.
        • Kohl T.A.
        • Beckert P.
        • et al.
        New Mycobacterium tuberculosis complex sublineage, Brazzaville, Congo.
        Emerg Infect Dis. 2017; 23: 423-429
        • Mears J.
        • Abubakar I.
        • Cohen T.
        • McHugh T.D.
        • Sonnenberg P.
        Effect of study design and setting on tuberculosis clustering estimates using Mycobacterial Interspersed Repetitive Units-Variable Number Tandem Repeats (MIRU-VNTR): a systematic review.
        BMJ Open. 2015; 5e005636
        • Mulenga C.
        • Shamputa I.C.
        • Mwakazanga D.
        • Kapata N.
        • Portaels F.
        • Rigouts L.
        Diversity of Mycobacterium tuberculosis genotypes circulating in Ndola, Zambia.
        BMC Infect Dis. 2010; 10: 177
        • Otchere I.D.
        • Asante-Poku A.
        • Osei-Wusu S.
        • Baddoo A.
        • Sarpong E.
        • Ganiyu A.H.
        • et al.
        Detection and characterization of drug-resistant conferring genes in Mycobacterium tuberculosis complex strains: a prospective study in two distant regions of Ghana.
        Tuberculosis. 2016; 99: 147-154
        • Seto J.
        • Wada T.
        • Suzuki Y.
        • Ikeda T.
        • Mizuta K.
        • Yamamoto T.
        • et al.
        Mycobacterium tuberculosis transmission among elderly persons, Yamagata Prefecture, Japan, 2009-2015.
        Emerg Infect Dis. 2017; 23: 448-455
        • Shabbeer A.
        • Cowan L.S.
        • Ozcaglar C.
        • Rastogi N.
        • Vandenberg S.L.
        • Yener B.
        • et al.
        TB-Lineage: an online tool for classification and analysis of strains of Mycobacterium tuberculosis complex.
        Infect Genet Evol. 2012; 12: 789-797
        • Supply P.
        • Allix C.
        • Lesjean S.
        • Cardoso-Oelemann M.
        • Rusch-Gerdes S.
        • Willery E.
        • et al.
        Proposal for standardization of optimized mycobacterial interspersed repetitive unit-variable-number tandem repeat typing of Mycobacterium tuberculosis.
        J Clin Microbiol. 2006; 44: 4498-4510
        • Surie D.
        • Fane O.
        • Finlay A.
        • Ogopotse M.
        • Tobias J.L.
        • Click E.S.
        • et al.
        Molecular, spatial, and field epidemiology suggesting TB transmission in community, not hospital, Gaborone, Botswana.
        Emerg Infect Dis. 2017; 23: 487-490
        • Varghese B.
        • Al-Omari R.
        • Grimshaw C.
        • Al-Hajoj S.
        Endogenous reactivation followed by exogenous re-infection with drug resistant strains, a new challenge for tuberculosis control in Saudi Arabia.
        Tuberculosis (Edinb). 2013; 93: 246-249
        • Vluggen C.
        • Soetaert K.
        • Groenen G.
        • Wanlin M.
        • Spitaels M.
        • Arrazola de Onate W.
        • et al.
        Molecular epidemiology of Mycobacterium tuberculosis complex in Brussels, 2010-2013.
        PLoS One. 2017; 12e0172554
        • Walker T.M.
        • Lalor M.K.
        • Broda A.
        • Saldana Ortega L.
        • Morgan M.
        • Parker L.
        • 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: 285-292
        • WHO
        Global tuberculosis report 2015.
        20 ed. World Health Organization, Geneva2015
        • WHO
        Global tuberculosis report.
        World Health Organization, Geneva2017
        • Yang C.
        • Luo T.
        • Shen X.
        • Wu J.
        • Gan M.
        • Xu P.
        • et al.
        Transmission of multidrug-resistant Mycobacterium tuberculosis in Shanghai, China: a retrospective observational study using whole-genome sequencing and epidemiological investigation.
        Lancet Infect Dis. 2016; 17: 275-284
        • Yeboah-Manu D.
        • Asare P.
        • Asante-Poku A.
        • Otchere I.D.
        • Osei-Wusu S.
        • Danso E.
        • et al.
        Spatio-temporal distribution of Mycobacterium tuberculosis complex strains in Ghana.
        PLoS One. 2016; 11e0161892
        • Yeboah-Manu D.
        • Yates M.D.
        • Wilson S.M.
        Application of a simple multiplex PCR to aid in routine work of the mycobacterium reference laboratory.
        J Clin Microbiol. 2001; 39: 4166-4168