Adherence to Antiretroviral Therapy and Associated Factors among People Living with HIV/AIDS in Hara Town and Its Surroundings, North-Eastern Ethiopia: A Cross-Sectional Study

BACKGROUND: Adherence is the most important factor in determining Antiretroviral Therapy (ART) treatment success and long-term viral suppression. Nonadherence to ART led to the human Immunodeficiency Virus (HIV) related morbidity and mortality. Moreover, it intensifies the risk of the emerging drug resistant HIV strains. This study aimed to assess the level of ART adherence and to identify its predictive associated factors among people living with HIV/AIDS in Hara Town and its surroundings, North-Eastern Ethiopia.METHODS: An institutional facility based cross-sectional study was conducted from April-May 2017. A total of 454 individuals were on ART follow-up in the selected ART-clinic, and only 418 patients were recruited. Bivariate and multivariate logistic regression analyses were carried out to identify associated factors. Odds ratio and 95% Confidence Interval (CI) were calculated to determine the level of significance.RESULTS: The level of ART adherence in the study setting was 300 (71.8%). Participants who had not disclosed their HIV status to their families were 88% less likely to adhere to their ART medication than those who had disclosed their HIV status ((Odds ratio (OR): 0.12, 95%CI:0.05-0.58; p<0.001). On the other hand, participants who had not encountered drug side effects were 2.69 times more likely to adhere to their ART medication than those who had ever encountered drug side effects (OR: 2.69, 95%CI:1.27-5.05; p<0.001).CONCLUSION: A very low level of ART adherence was shown in the study population. It was below the recommended good adherence standard. Therefore, patients should get adequate and comprehensive ART adherence counselling before initiation ART treatment and during the follow-up time.

Global evolutionary epidemiology and resistome dynamics of Citrobacter species, Enterobacter hormaechei, Klebsiella variicola, and Proteeae clones

Citrobacter spp., Enterobacter hormaechei subsp., Klebsiella variicola and Proteae tribe members are rarely isolated Enterobacterales increasingly implicated in nosocomial infections. Herein, we show that these species contain multiple genes encoding resistance to important antibiotics and are widely and globally distributed, being isolated from human, animal, plant, and environmental sources in 67 countries. Certain clones and clades of these species were internationally disseminated, serving as reservoirs and mediums for the global dissemination of antibiotic resistance genes. As they can easily transmit these genes to more pathogenic species, additional molecular surveillance studies should be undertaken to identify and contain these antibiotic‐resistant species.Supplemental dataset 1. Raw metadata of downloaded genomes from PATRIC containing all the data associated with each genome.Supplemental dataset 2. Species by species tabulation and analyses of the resistomes, specimen sources, country of isolation, MLST, Biosample accession number, and strain name of all the genomes according to their order on the phylogeny trees.Supplemental dataset 3. Colour‐coded species by species tabulation of the resistomes, specimen sources, country of isolation, MLST, Biosample accession number, and strain name of all the genomes according to their order on the phylogeny trees.Supplemental dataset 4 Plasmid replicons and their associated resistomes and bacterial hosts. Selected genomes bearing carbapenemases, mcr, fluoroquinolones, aminoglycosides and other clinically important antibiotic resistance genes, were run through PlasmidFinder, pMLST, and BLASTn to identify their genetic environment. Most antibiotic resistance genes were found on plasmids, making them potentially mobile.Supplementary Figures: Figure S1A‐B Evolutionary epidemiology and resistome of global Citrobacter freundii, brakii, portucalensis, and amalonaticus isolates. S1A shows Citrobacter sp., particularly freundii, portucalensis and brakii clustering into clades A, B1, B2 and B3 whilst S1B shows C. amalonaticus strains clustering into clades A (red highlight), B1 (green highlight), B2 (blue highlight) and C (mauve highlight); clade B2 had very rich resistome repertoire and were all from France, but the other clades had very few resistance genes. Strains from humans (blue labels), animals (red labels), plants (purple/mauve labels) and the environment (green labels) were found in the same clade/cluster. Included in S1A are Pseudomonas, Klebsiella, and Escherichia coli species that were originally classified as C. freundii but later reclassified into their actual species using ANI; their clustering away from the Citrobacter species confirms the ANI results that they were initially misclassified. BlaSED and oqxAB were almost conserved in these genomes. Branches with bootstrap support values of ≥50 were defined as belonging to the same clade. The branch lengths also show the evolutionary distance between the isolates. Blue and red arrows show the direction of evolution as well as local and international dissemination of strains of the same clone/clade through different hosts. Fig. S1C. Evolutionary epidemiology and resistome of global Citrobacter koseri isolates. C. koseri strains clustered into clades A (grey highlight), B1 (light blue highlight), B2 (orange highlight) and B3 (mauve highlight). Strains from humans (blue labels) and animals (red labels) were found in the same clade/cluster. BlaCKO and blaMAL were almost conserved in these genomes. Included in S1C are Serratia marcescens, Klebsiella, Enterobacter and Escherichia coli species that were originally classified as C. koseri but later reclassified into their actual species using ANI; their clustering away from Citrobacter koseri confirms the ANI results that they were initially misclassified. Branches with bootstrap support values of ≥50 were defined as belonging to the same clade. The branch lengths also show the evolutionary distance between the isolates. Blue and red arrows show the direction of evolution as well as local and international dissemination of strains of the same clone/clade through different hosts. Fig. S1D. Evolutionary epidemiology and resistome of global Citrobacter spp, isolates, A and B. This tree shows information for additional Citrobacter freundii and Citrobacter sp. that were not featured figures 1, and S1A–C above. Included in S1D are Serratia marcescens, Klebsiella, Enterobacter and Escherichia coli species that were originally classified as C. freundii, but later reclassified into their actual species using ANI; their clustering away from C. freundii confirms the ANI results that they were initially misclassified. C. freundii clustered into four main clades (A, B1, B2 and B3), highlighted with distinct colours. Clade B3 had the most resistome abundance and diversity. Strains from humans (blue labels), animals (red labels), plants (purple/mauve labels) and the environment (green labels) were found in the same clade/cluster. BlaCMY was conserved in these genomes. Branches with bootstrap support values of ≥50 were defined as belonging to the same clade. The branch lengths also show the evolutionary distance between the isolates. Blue and red arrows show the direction of evolution as well as local and international dissemination of strains of the same clone/clade through different hosts. Figs S2A‐B. Evolutionary epidemiology and resistome of global Enterobacter hormaechei subsp. Hormaechei, Xiangfangensis, Oharae, and Steigerwaltii, isolates. S2A is strictly E. hormaechei subsp. Hormaechei and is an addition to Figure 4 whilst S2B is an addition to Figures 3‐4 above as additional genomes of E. hormaechei subsp. Xiangfangensis, Oharae, and Steigerwaltii; these could not be added to Figures 3‐4 and are shown here in Fig. S2B. The E. hormaechei isolates in S2A clustered into three main clades A, B and C (with distinct highlights) that contained strains distributed globally from humans (blue labels), and animals (red labels), plants (purple/mauve labels) and the environment (green labels). Clades B and C contained diverse and rich resistome repertoire. blaACT was conserved in these genomes. S2B contains E. hormaechei subsp.Xiangfangensis, Oharae, and Steigerwaltii isolates clustering into 6 branches (I‐VI); genomes of the same subsp. clustered closely together. Branches with bootstrap support values of ≥50 were defined as belonging to the same clade. The branch lengths also show the evolutionary distance between the isolates. Blue and red arrows show the direction of evolution as well as local and international dissemination of strains of the same clone/clade through different hosts. Fig. S3A‐B. Evolutionary epidemiology and resistome of global Klebsiella variicola isolates. S3A‐B are additional trees to Figure 5 and show additional K. variicola genomes that could not be added to Figure 5; in all, Figures 5 and S3A‐B show 600 K. variicola genomes. S3A and S3B trees are composed of different K. variicola genomes, which is reflected in the differences in the resistomes and tree topologies. Included in S3A and S3B are K. pneumoniae and K. pneumoniae and quasipneumoniae species respectively, that were originally classified as K. variicola, but later reclassified into their actual species using ANI; their clustering away from K. variicola confirms the ANI results that they were initially misclassified. The K. variicola strains clustered into eight (S3A) and seven (S3B) clades I‐VIII and I‐VII respectively, which were highlighted with distinct colours and were isolated from countries around the globe. The clades contained strains distributed globally from humans (blue labels), animals (red labels), plants (purple/mauve labels) and the environment (green labels). Besides a few strains in clade B2, the other strains contained very few resistance genes. blaLEN was conserved in these genomes. Branches with bootstrap support values of ≥50 were defined as belonging to the same clade. The branch lengths also show the evolutionary distance between the isolates. Blue and red arrows show the direction of evolution as well as local and international dissemination of strains of the same clone/clade through different hosts. Fig. S4. Evolutionary epidemiology and resistome of global Proteus mirabilis isolates. The P. mirabilis isolates clustered into 10 clades, A‐A3, B1‐B3, and C1‐C3 (shown with different highlights), which contained diverse and abundant resistomes with conserved catA and tet genes. The clades contained strains distributed globally from humans (blue labels), animals (red labels), plants (purple/mauve labels) and the environment (green labels). Branches with bootstrap support values of ≥50 were defined as belonging to the same clade. The branch lengths also show the evolutionary distance between the isolates. Blue and red arrows show the direction of evolution as well as local and international dissemination of strains of the same clone/clade through different hosts. Fig. S5 (A‐C). Count of antibiotic resistance genes (ARGs) in Citrobacter freundii (A), and Citrobacter species (B and C). The sum of each unique ARG and its variants are computed and shown as a bar chart to depict the most abundant ARGs. Fig. S6 (A‐B). Count of antibiotic resistance genes (ARGs) in Citrobacter amalonaticus (A), and Citrobacter koseri (B). The sum of each unique ARG and its variants are computed and shown as a bar chart to depict the most abundant ARGs. Fig. S7 (A‐B). Count of antibiotic resistance genes (ARGs) in Enterobacter steigerwaltii and oharae (A), and Enterobacter xiangfangensis (B). The sum of each unique ARG and its variants are computed and shown as a bar chart to depict the most abundant ARGs. Fig. S8 (A‐C). Count of antibiotic resistance genes (ARGs) in Enterobacter hormaechei (A, B and C). The sum of each unique ARG and its variants are computed and shown as a bar chart to depict the most abundant ARGs. Fig. S9 (A‐C). Count of antibiotic resistance genes (ARGs) in Klebsiella variicola (A, B and C). The sum of each unique ARG and its variants are computed and shown as a bar chart to depict the most abundant ARGs. Fig. S10 (A‐C). Count of antibiotic resistance genes (ARGs) in Morganella morganii (A), Proteus mirabilis (B) and Providencia species (C). The sum of each unique ARG and its variants are computed and shown as a bar chart to depict the most abundant ARGs.Table S4. Statistical analyses of resistome diversity, abundance, and relative richnesshttps://sfamjournals.onlinelibrary.wiley.com/journal/146229202022-01-07hj2021Medical Microbiolog

High enteric bacterial contamination of drinking water in Jigjiga city, Eastern Ethiopia

Background: The high prevalence of diarrheal disease among children and infants can be traced due to the use of unsafe water and unhygienic practices. The overall concept adopted for microbiological quality is that no water intended for human consumption shall contain Escherichia coli per 100 ml sample.Objective: The aim of this study was to assess household water handling and hygienic practices and to determine bacteriological quality of drinking water from different sources in Jigjiga city.Methods: A cross-sectional study was conducted to assess bacteriological quality of drinking water in Jigjiga city from May-August, 2013. Both simple random and convenient sampling techniques were applied to select 238 households to assess water handling and hygienic practices, and 125 water samples to assess bacteriological quality of drinking water respectively. The water samples were collected from household water container, pipeline, water reservoir, ‘Beyollie’, and main sources.Easily isolated bacteria called coliforms were used as indicator organisms of human and other animals’ fecal contamination status of drinking water. Data were summarized using descriptive and analytical statistics. Chi-square (χ2) and logistic regression tests were used and p<0.05 was considered as cut off value for statistical significance.Results: Overall, 71.2%(n=89) of water samples were contaminated by one or more bacterial species of E.coli, Shigella Sp, Salmonella Sp, and Vibrio sp. Particularly, 65(52%), 10(8%), 9(7.2%), and 8(6.4%) were contaminated by E.coli, Shigella sp, Salmonella sp, and Vibrio sp, respectively. On the other hand, 20% of the households and pipeline water samples had a fecal coliform count of 150 and above. Placement of water drinking utensils had a statistically significant association with illiterate education (p=0.01, AOR=5.47, 95% CI: (1.31, 22.78)) and male household head (p=0.02, AOR=2.11, 95% CI: (1.10, 4.05)).Conclusions: The majorities of drinking water sources were highly contaminated by Enterobacteriaceae. Regular bacteriological water quality control mechanisms need to be in place to ensure bacteriological safety of drinking water. [Ethiop. J. Health Dev. 2016;30(3):118-128]Keywords: Contamination, drinking water, households, enteric bacteria, Jigjig

Genomic and resistance epidemiology of gram-negative bacteria in Africa : a systematic review and phylogenomic analyses from a one health perspective

Antibiotic resistance (AR) remains a major threat to public and animal health globally. However, AR ramifications in developing countries are worsened by limited molecular diagnostics, expensive therapeutics, inadequate numbers of skilled clinicians and scientists, and unsanitary environments. The epidemiology of Gramnegative bacteria, their AR genes, and geographical distribution in Africa are described here. Data were extracted and analyzed from English-language articles published between 2015 and December 2019. The genomes and AR genes of the various species, obtained from the Pathosystems Resource Integration Center (PATRIC) and NCBI were analyzed phylogenetically using Randomized Axelerated Maximum Likelihood (RAxML) and annotated with Figtree. The geographic location of resistant clones/clades was mapped manually. Thirty species from 31 countries and 24 genera from 41 countries were analyzed from 146 articles and 3,028 genomes, respectively. Genes mediating resistance to -lactams (including blaTEM-1, blaCTX-M, blaNDM, blaIMP, blaVIM, and blaOXA-48/181), fluoroquinolones (oqxAB, qnrA/B/ D/S, gyrA/B, and parCE mutations, etc.), aminoglycosides (including armA and rmtC/ F), sulfonamides (sul1/2/3), trimethoprim (dfrA), tetracycline [tet(A/B/C/D/G/O/M/39)], colistin (mcr-1), phenicols (catA/B, cmlA), and fosfomycin (fosA) were mostly found in Enterobacter spp. and Klebsiella pneumoniae, and also in Serratia marcescens, Escherichia coli, Salmonella enterica, Pseudomonas, Acinetobacter baumannii, etc., on mostly IncF-type, IncX3/4, ColRNAI, and IncR plasmids, within IntI1 gene cassettes, insertion sequences, and transposons. Clonal and multiclonal outbreaks and dissemination of resistance genes across species and countries and between humans, animals, plants, and the environment were observed; Escherichia coli ST103, K. pneumoniae ST101, S. enterica ST1/2, and Vibrio cholerae ST69/515 were common strains. Most pathogens were of human origin, and zoonotic transmissions were relatively limited. IMPORTANCE Antibiotic resistance (AR) is one of the major public health threats and challenges to effective containment and treatment of infectious bacterial diseases worldwide. Here, we used different methods to map out the geographical hot spots, sources, and evolutionary epidemiology of AR. Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., Neisseria meningitis/gonorrhoeae, Vibrio cholerae, Campylobacter jejuni, etc., were common pathogens shuttling AR genes in Africa. Transmission of the same clones/strains across countries and between animals, humans, plants, and the environment was observed. We recommend Enterobacter spp. or K. pneumoniae as better sentinel species for AR surveillance.https://msystems.asm.orgam2021Medical Microbiolog

Microbiological Safety of Street Vended Foods in Jigjiga City, Eastern Ethiopia

Background: Food safety problems are particularly becoming an increasingly serious threat to public health in developing countries. This study was conducted to assess microbiological safety of street vended foods from May to November, 2014 in Jigjiga City.Methods: A cross-sectional design was used to answer questions concerning the current status of food hygiene and sanitation practire of street food vending sites. Interview and observational assessments were used to collect socio-demographic data about street food venders. One hundred thirty-two samples of street foods were aseptically collected from four ‘kebeles’ of Jigjiga City. Both descriptive and analytical statistical methods were applied.Results: The majority of the street food vendors were women, 120(90.9%), with the average age group of 23-49 years, (42.85%), and 99(66.7%) them were illiterate. The study revealed that 95(72%) of the food samples had pathogenic bacterial contaminations. Three different bacterial species were isolated: E. coli 68(51.5%), S. aureus 85(64.4%) and 26(19.7%) Salmonella species. The highest incidence of S. aureus 23/33(69%) was seen in ‘Sambusa’; the highest incidence of E. coli 24/33(73.5%) was observed in ‘Pasta’, while the highest Salmonella incidence was observed in ‘Ades’.Conclusion: This study revealed that there is a reasonable gap on food safety knowledge among street food venders. The microbial profile was also higher compared to standards set by the World Health Organization. Due attention should be given by the government to improve knowledge about food safety and the quality standard of street foods sold in the City.Keywords: Microbiological safety, Street vended foods, Isolation, Jigjig

Food handling practices and associated factors among food handlers working in public food and drink service establishments in Woldia town, Northeast Ethiopia

Introduction: foodborne disease (FBD) is a major public health problem globally. Inadequate food workers' knowledge, attitude, and low level of food handling practices (FHPs) may all contribute to the possibility of FBD outbreaks in public food service establishments. This study aimed to assess FHPs and associated factors among food handlers working in public food and drink service establishments in Woldia town, Northeast Ethiopia. Methods: an institutional-based cross-sectional study was conducted from 01st to 29th, January 2017. A total of 288 food handlers were recruited through a simple random selection method. A structured interviewer-administered questionnaire and observation checklists were used to collect the respondents' socio-demographic characteristics, knowledge status on FHPs, and food handling working practices data. Descriptive statistics, bivariate and multivariate logistic regression analysis were employed using SPSS version 20 software. Those variables with a p< 0.05 were considered statistically significant. Results: out of 288 participants, 91.7% were female, and 82.3% were single, while 69.8% were literate. One hundred eighty-four (63.9%) of them were under 15-25 years of age, with a median age of 23.3 years. The proportion of good FHP was (n=134, 46.5%) (95% CI:41.00-52.4%). Advanced age (adjusted odds ratio (AOR) =12.01, 95% CI:1.96-73.52), education (participants who attend grades 7-12 (AOR=2.33, 95% CI:1.14-4.79), and above secondary education (AOR=2.29, 95% CI:1.05-4.61), work experience above six years (AOR=2.43, 95% CI:2.08-3.17), received formal training (AOR=1.79, 95% CI:1.68-4.71), and inspection visits by a concerned body (AOR=2.24, 95% CI:1.05-3.09) were factors positively associated with handling practices. Conclusion: the study revealed that FHP in the study area was low. Age, education, service year, training received and sanitary inspection visits by the regulatory personnel were factors significantly associated with FHPs. This finding highlights the importance of employing regular sanitary inspection visits to public food service establishments by the concerned authority to ensure that all food handlers have the knowledge and the skill to provide safe food

Prevalence of drug resistance-conferring mutations associated with isoniazid- and rifampicin-resistant mycobacterium tuberculosis in Ethiopia : a systematic review and meta-analysis

OBJECTIVES : Globally, the incidence and mortality of tuberculosis (TB) are declining; however, low detection of drug-resistant disease threatens to reverse current progress toward global TB control. Multiple rapid molecular diagnostic tests have recently been developed to detect genetic mutations in Mycobacterium tuberculosis (Mtb) known to confer drug resistance. However, their utility depends on the frequency and distribution of resistance-associated mutations in the pathogen population. This review aimed to assess the prevalence of gene mutations associated with rifampicin (RIF)- and isoniazid (INH)-resistant Mtb in Ethiopia. METHODS : We searched the literature in PubMed/MEDLINE, Web of Science, Scopus and Cochrane Library. Data analysis was conducted in Stata 11. RESULTS : Totally, 909 (95.8%) of 949 INH-resistant Mtb isolates had detectable gene mutations: 95.8% in katG 315 and 5.9% in the inhA promoter region. Meta-analysis resulted in an estimated pooled prevalence of katG MUT1(S315T1) of 89.2% (95% CI 81.94–96.43%) and a pooled prevalence of inhA MUT1(C15T) of 77.5% (95% CI 57.84–97.13%). Moreover, 769 (90.8%) of 847 RIF-resistant strains had detectable rpoB gene mutations. Meta-analysis resulted in a pooled prevalence of rpoB MUT3(S531L) of 74.2% (95% CI 66.39–82.00%). CONCLUSION : RIF-resistant Mtb were widespread, particularly those harbouring rpoB (S531L) mutation. Sim- ilarly, INH-resistant Mtb with katG (S315T1) and inhA (C15T) mutations were common. Tracking S531L, S315T1 and C15T mutations among RIF- and INH-resistant isolates, respectively, would be diagnostically and epidemiologically valuable. Rapid diagnosis of RIF- and INH-resistant Mtb would expedite modifica- tion of TB treatment regimens, and proper timely infection control interventions could reduce the risk of development and transmission of multidrug-resistant TB.http://www.elsevier.com/locate/jgaram2022Medical Microbiolog

Antibiotic resistance of Mycobacterium tuberculosis complex in Africa : a systematic review of current reports of molecular epidemiology, mechanisms and diagnostics

BACKGROUND : Tuberculosis (TB) remains a main global public health problem. However, a systematic review of TB resistance epidemiology in Africa is wanting. METHODS : A comprehensive systematic search of PubMed, Web of Science and ScienceDirect for English research articles reporting on the molecular epidemiology of Mycobacterium tuberculosis complex resistance in Africa from January 2007 to December 2018 was undertaken. RESULTS AND CONCLUSION : Qualitative and quantitative synthesis were, respectively, undertaken with 232 and 186 included articles, representing 32 countries. TB monoresistance rate was highest for isoniazid (59%) and rifampicin (27%), particularly in Zimbabwe (100%), Swaziland (100%), and Sudan (67.9%) whilst multidrug resistance (MDR) rate was substantial in Zimbabwe (100%), Sudan (34.6%), Ivory Coast (24.5%) and Ethiopia (23.9%). Resistance-conferring mutations were commonly found in katG (n = 3694), rpoB (n = 3591), rrs (n = 1272), inhA (n = 1065), pncA (n = 1063) and embB (n = 705) in almost all included countries: S315G/I/N/R/T, V473D/F/G/I, Q471H/Q/R/Y, S303C/L etc. in katG; S531A/F/S/G, H526A/C/D/G, D516A/E/G etc. in rpoB; A1401G, A513C etc. in rrs; -15C→T, -17G→A/T, -16A→G etc. in inhA; Ins456C, Ins 172 G, L172P, C14R, Ins515G etc. in pncA. Commonest lineages and families such as T (n = 8139), LAM (n = 5243), Beijing (n = 5471), Cameroon (n = 3315), CAS (n = 2021), H (n = 1773) etc., with the exception of T, were not fairly distributed; Beijing, Cameroon and CAS were prevalent in South Africa (n = 4964), Ghana (n = 2306), and Ethiopia/Tanzania (n = 799/635), respectively. Resistance mutations were not lineage-specific and sputum (96.2%) were mainly used for diagnosing TB resistance using the LPA (38.5%), GeneXpert (17.2%), whole-genome sequencing (12.3%) and PCR/amplicon sequencing (9%/23%). Intercountry spread of strains was limited while intra-country dissemination was common. TB resistance and its diagnosis remain a major threat in Africa, necessitating urgent action to contain this global menace.Figure S1: Frequency of M. tuberculosis Lineages/sub-lineages in Africa, January 2007-December 2018: Frequency of Indo-oceanic lineage/sub-lineages (A); Beijing sublineage (B); CAS-sublineage (C), Euro-American lineage/sub-lineages (D); M. africanum West Africa I & II (E); M. tuberculosis Eth lineage 7 and sub-lineages (F); and proportion of each M. tuberculosis lineage in Africa (G).Table S1: Distribution of M. tuberculosis complex strains, specimen source/s, genotyping method/s, molecular anti-TB drug resistance rate and resistance mechanisms in M. tb across African countries, January 2007 to December 2018Table S2: Resistance mechanisms, molecular diagnosis method/s used, frequency and proportion of gene mutation per total resistant M. tuberculosis complex isolates across African countries, January 2007- December 2018Table S3: Molecular antibiotics resistance rates and resistance mechanisms in M. tuberculosis complex across African countries, January 2007-December 2018.Table S4: Frequency of gene mutation(s) and specific amino acid/nucleotide changes conferring antitubercular drug resistances across African countries January 2007- December 2018Table S5: Distribution of specimen source/s, phenotypic DST method/s used, total number of isolates, and phenotypic antibiotics monoresistance rate, MDR and XDR rate of M. tb complex across African countries, January 2007- December 2018Table S6: Distribution of genotypes/lineages/sub-lineages, frequency and patterns of antibiotics resistance-conferring mutations across African countries, January 2007- December 2018Supplementary dataset 1: Metadata of M. tuberculosis isolates included in phylogenomic analyses.Supplementary dataset 2: Country-by-country frequency of lineage and sub-lineage of M. tuberculosis in Africa: January 2007-December 2018http://www.elsevierhealth.com/journals/jinf2020-12-01hj2020Medical Microbiolog

Mycobacterium tuberculosis drug resistance in Ethiopia : an updated systematic review and meta-analysis

SUPPLEMENTARY MATERIALS : FIGURES S1A–E; FIGURES S2A–D; FIGURES S3A–D; FIGURES S4A–D; FIGURES S5A–D; FIGURES S6A–E; FIGURES S7A–D; FIGURES S8A–D. TABLE S1: PRISMA- 2020-checklist; TABLE S2: Search Strategy Medline/PubMed; TABLE S3: Quality assessments.DATA AVAILABILITY STATEMENT : The datasets analyzed during this review can be accessed from the corresponding author upon reasonable request.BACKGROUND : Tuberculosis (TB) remains a significant global public health issue, despite advances in diagnostic technologies, substantial global efforts, and the availability of effective chemotherapies. Mycobacterium tuberculosis, a species of pathogenic bacteria resistant to currently available anti-TB drugs, is on the rise, threatening national and international TB-control efforts. This systematic review and meta-analysis aims to estimate the pooled prevalence of drug-resistant TB (DR-TB) in Ethiopia. MATERIALS AND METHODS : A systematic literature search was undertaken using PubMed/MEDLINE, HINARI, theWeb of Science, ScienceDirect electronic databases, and Google Scholar (1 January 2011 to 30 November 2020). After cleaning and sorting the records, the data were analyzed using STATA 11. The study outcomes revealed the weighted pooled prevalence of any anti-tuberculosis drug resistance, any isoniazid (INH) and rifampicin (RIF) resistance, monoresistance to INH and RIF, and multidrug-resistant TB (MDR-TB) in newly diagnosed and previously treated patients with TB. RESULTS : A total of 24 studies with 18,908 patients with TB were included in the final analysis. The weighted pooled prevalence of any anti-TB drug resistance was 14.25% (95% confidence interval (CI): 7.05–21.44%)), whereas the pooled prevalence of any INH and RIF resistance was found in 15.62% (95%CI: 6.77–24.47%) and 9.75% (95%CI: 4.69–14.82%) of patients with TB, respectively. The pooled prevalence for INH and RIF-monoresistance was 6.23% (95%CI: 4.44–8.02%) and 2.33% (95%CI: 1.00–3.66%), respectively. MDR-TB was detected in 2.64% (95%CI: 1.46–3.82%) of newly diagnosed cases and 11.54% (95%CI: 2.12–20.96%) of retreated patients with TB, while the overall pooled prevalence of MDR-TB was 10.78% (95%CI: 4.74–16.83%). CONCLUSIONS : In Ethiopia, anti-tuberculosis drug resistance is widespread. The estimated pooled prevalence of INH and RIF-monoresistance rates were significantly higher in this review than in previous reports. Moreover, MDR-TB in newly diagnosed cases remained strong. Thus, early detection of TB cases, drug-resistance testing, proper and timely treatment, and diligent follow-up of TB patients all contribute to the improvement of DR-TB management and prevention. Besides this, we urge that a robust, routine laboratory-based drug-resistance surveillance system be implemented in the country.https://www.mdpi.com/journal/tropicalmedam2023Medical Microbiolog

Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000–17 : analysis for the Global Burden of Disease Study 2017

Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood diarrhoea. Methods We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor estimates. Findings The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1–65·8), 17·4% (7·7–28·4), and 59·5% (34·2–86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy coverage. Interpretation By co-analysing geospatial trends in diarrhoeal burden and its key risk factors, we could assess candidate drivers of subnational death reduction. Further, by doing a counterfactual analysis of the remaining disease burden using key risk factors, we identified potential intervention strategies for vulnerable populations. In view of the demands for limited resources in LMICs, accurately quantifying the burden of diarrhoea and its drivers is important for precision public health