Risk factors associated with multidrug resistant tuberculosis among patients referred to Kibong’oto Infectious Disease Hospital in northern Tanzania

Background: Multidrug resistant tuberculosis (MDR-TB) remains is an important public health problem in developing world. We conducted this study to determine risk factors associated with MDR-TB and drug susceptibility pattern to second line drug among MDR TB patients in Tanzania.Methods: Unmatched case control study was conducted at Kibong’oto Infectious Diseases Hospital in Tanzania in 2014. A case was defined as any patient whose sputum yielded Mycobacterium tuberculosis that were resistance to at least rifampin (RFP) and isoniazid (INH) whereas control was defined as those sensitive to rifampin (RFP) + isoniazid (INH).  One morning sputum sample was collected from each study subject and cultured on Löwenstein-Jensen (LJ) media for M. tuberculosis. Drug susceptibility testing of isolated M. tuberculosis was done for rifampicin, isoniazid, kanamycin and ofloxacin. A semi-structured questionnaire was used to collect socio-demographic and risk factors information for MDR-TB. Results: A total of 102 cases and 102 controls were enrolled. The predominant age group was 31- 40 years, of whom cases and controls accounted for 38 (37.3%) and 35 (34.3%) of the study subjects, respectively. Majority of participants (69% cases and 71% control) were males and self-employed (62.7% cases and 84.4% controls). More than half (52%) and approximately a quarter (24.5%) of cases and control had HIV infection, respectively. About two-thirds of cases (62.7%) were cigarette smokers compared to controls (42.2%). Previous history of TB treatment accounted for approximately three folds in cases (72.5%) and only 24.5% in controls. Risk factors independently associated with MDR-TB were previous history of treatment with first line anti-TB (OR= 3.3, 95% CI 1.7-6.3), smoking (OR=1.9, 95% CI 1.0-3.5), contact with TB case (OR=2.7, 95% CI 1.4-5.1) and history of TB. All MDR TB isolates were sensitive to kanamycin and ofloxacin.Conclusion: MDR-TB among patients referred to Kibong’oto Infectious Diseases Hospital is associated with previous history of TB contact, smoking habit, and contact with TB case. All MDR TB isolates were sensitive to the tested second line drugs, Kanamycin and Ofloxacin

Whole genome sequencing of Mycobacterium tuberculosis isolates and clinical outcomes of patients treated for multidrug-resistant tuberculosis in Tanzania.

BACKGROUND: Tuberculosis (TB), particularly multi- and or extensive drug resistant TB, is still a global medical emergency. Whole genome sequencing (WGS) is a current alternative to the WHO-approved probe-based methods for TB diagnosis and detection of drug resistance, genetic diversity and transmission dynamics of Mycobacterium tuberculosis complex (MTBC). This study compared WGS and clinical data in participants with TB. RESULTS: This cohort study performed WGS on 87 from MTBC DNA isolates, 57 (66%) and 30 (34%) patients with drug resistant and susceptible TB, respectively. Drug resistance was determined by Xpert® MTB/RIF assay and phenotypic culture-based drug-susceptibility-testing (DST). WGS and bioinformatics data that predict phenotypic resistance to anti-TB drugs were compared with participant's clinical outcomes. They were 47 female participants (54%) and the median age was 35 years (IQR): 29-44). Twenty (23%) and 26 (30%) of participants had TB/HIV co-infection BMI < 18 kg/m2 respectively. MDR-TB participants had MTBC with multiple mutant genes, compared to those with mono or polyresistant TB, and the majority belonged to lineage 3 Central Asian Strain (CAS). Also, MDR-TB was associated with delayed culture-conversion (median: IQR (83: 60-180 vs. 51:30-66) days). WGS had high concordance with both culture-based DST and Xpert® MTB/RIF assay in detecting drug resistance (kappa = 1.00). CONCLUSION: This study offers comparison of mutations detected by Xpert and WGS with phenotypic DST of M. tuberculosis isolates in Tanzania. The high concordance between the different methods and further insights provided by WGS such as PZA-DST, which is not routinely performed in most resource-limited-settings, provides an avenue for inclusion of WGS into diagnostic matrix of TB including drug-resistant TB

List of DENV-3 Tanzania sequences names, 2017–2019.

List of DENV-3 Tanzania sequences names, 2017–2019.</p

List of DENV-1 Tanzania sequences names, 2017–2019.

List of DENV-1 Tanzania sequences names, 2017–2019.</p

Distribution of dengue samples per region from 2017–2019.

Distribution of dengue samples per region from 2017–2019.</p

Distribution of dengue fever cases in Tanzania from 2017 to 2019.

The map shows 26 Tanzania mainland administrative regions. The five color-coded regions show dengue fever cases distribution between 2017–2019. Map created with QGIS 3.24.1 All shape files are openly available sources (https://www.nbs.go.tz/index.php/en/census-surveys/gis/385-2012-phc-shapefiles-level-one-and-two). The shapefiles were made based on the 2012 population and housing census, but in this study, the shapefile has been modified to capture all the regions and district information.</p

Genotyping of DENV-1 circulating in Tanzania in 2019.

Maximum likelihood phylogenetic tree reconstructed with the 341 sequences generated by this study and 69 additional sequences from GenBank to provide genotype reference and geographic-temporal context. The tree was rooted at midpoint. Genotype I was only detected from one sample in 2019, while genotype V was found widely circulated in the 2019 epidemic. The Tanzanian sequences (OM920075—OM920415) in red. Genotypes are presented with colored highlighted branches; Genotypes IV, III, V, II, and I are highlighted in pink, blue, green, gold, and purple, respectively. Contextual sequences are labeled with GenBank accession number, country of origin, and year of isolation.</p

Genotyping of DENV-3 circulating in Tanzania 2017–2018.

Maximum likelihood phylogenetic tree reconstructed with the 32 DENV-3 sequence generated by this study and 40 additional sequences from GenBank to provide genotype reference and geographic-temporal context. The tree was rooted at midpoint. pink, blue, green, gold and purple represents genotypes V, II, III, I, and IV, respectively. Tanzanian sequences (OM920035—OM920066) are in red. Contextual sequences are labeled with GenBank accession number, country of origin, and year of isolation.</p

Socio-demographic characteristics of patients with dengue infection in Tanzania from 2017 to 2019.

Socio-demographic characteristics of patients with dengue infection in Tanzania from 2017 to 2019.</p