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      Distribution patterns of drug resistance Mycobacterium tuberculosis among HIV negative and positive tuberculosis patients in Western Kenya


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          Globally anti-tuberculosis drug resistance is one of the major challenges affecting control and prevention of tuberculosis. Kenya is ranked among 30 high burden TB countries globally. However, there is scanty information on second line antituberculosis drug resistance among tuberculosis patients. Therefore, this study aimed at determining Mycobacterium tuberculosis drug resistant strain distribution pattern in 10 counties of Western Kenya among HIV positive and negative patients.


          A cross-sectional study was conducted in Western Kenya, which comprises 10 counties. A multistage sampling method was used where a single sub-county was randomly selected followed by sampling one high volume health facility from each sub-county. Consenting study subjects with at least two smear positive sputum at the time of enrolment were randomly selected. The collected sputum was decontaminated with N-acetyl- l-cysteine-sodium hydroxide (NALC-NaOH) and then stained with Ziehl Neelsen Stain before visualizing the presence of bacilli under microscope at ×100 magnification with oil immersion. Further, the identified bacilli were cultured and susceptibility test carried out using known first and second line antimycobacterial tuberculosis. HIV testing was carried out using Determine ® HIV-1/2 rapid test (Abbot Diagnostics, Maidenhead, United Kingdom). Those who had smear converted were dropped from the study. Finally, drug susceptibility pattern across the 10 counties of Western Kenya was evaluated.


          Our study showed that Mycobacterium tuberculosis drug resistance among HIV negative and positive cases in Western Kenya was prevalent in all the 10 counties surveyed. Based on the drug susceptibility tests, 53.2% and 42.7% of the study samples were resistant to at least one antituberculosis drug among HIV negative and HIV positive patients respectively. The data analysis revealed that among the HIV-positive and HIV-negative patients, resistance to INH was predominant (28.5%, and 23.6%, respectively), followed by RIF (16.4% and 14.6% respectively). Second-line drug resistant strains identified among HIV negative patients included Ethionamide (0.3%), Gatifloxacin (0.3%), Amikacin (0.3%) and Capreomycin (0.3%). There was no second line drug monoresistance among HIV positive TB patients. Multi/poly drug resistance were noted among HIV-negative patients in, INH + AMK (0.7%), INH + PZA (1%), INH + GFX (0.7%, INH + ETO (0.7%, STY + ETO (1%), ETH + ETO (1.0%), INH + KAN (0.7%) and INH + CAP (0.7%) strains/cases at 95% confidence interval. Among HIV positive patients INH + GFX (1.1%), INH + ETO (0.4%) and INH + KAN (0.4%) strains of M. tuberculosis were identified with a confidence interval of 95%. Geographical distribution patterns analysis of M. tuberculosis drug polyresistant strains across the 10 counties were recorded. Among HIV TB patients, resistant strains were identified in Nyamira (INH + GFX, INH + KAN), Bungoma ((ETO + STY), Busia (ETH + ETO and STY + ETO) Homabay (RIF + AMK. ETO + ETH and ETO + STY), Kisumu (ETH + ETO and PZA + ETO) and in Kakamega, Kisii and Vihiga (INH + KAN and RIF + AMK). There was no M. tuberculosis polyresistant strain identified in Migori and Siaya counties. Among HIV positive TB patients, M. tuberculosis resistant strains were identified in three counties, Nyamira (INH + KAN) Homabay (INH + GFX and INH + AMK) and Kakamega (INH + GFX). There was no polyresistant M. tuberculosis strain identified in Migori, Bungoma, Kisii, Vihiga, Busia, Siaya and Kisumu Counties.


          The distribution patterns of M. tuberculosis drug resistance among HIV negative and positive TB patients could be as a result of reported high prevalence of HIV in Western Kenya counties especially the area under study. Tuberculosis is one of the opportunistic diseases that have been shown to be the major cause of AIDS among HIV infected patients. Resent reports by National AIDS Control Council shows that Kisumu, Siaya, Homabay, Migori, Busia have the overall leading in HIV prevalence in Kenya. The low prevalence of drug resistant strains among HIV tuberculosis patients could be as a result of drug adherence attitude adopted by HIV patients, availability of continuous counselling and close follow up and notification by healthcare workers and community health volunteers.


          Drug resistant M. tuberculosis strains prevalence is still high among HIV negative and positive patients in Western Kenya with the most affected being HIV negative TB patients. It is therefore probable that the existing control measures are not adequate to control transmission of drug resistant strains. Further, miss diagnosis or delayed diagnosis of TB patients could be contributing to the emergence of M. tuberculosis drug polyresistant strains.


          Based on the result of this study, regular TB drug resistance surveillance should be conducted to ensure targeted interventions aimed at controlling increased transmission of the tuberculosis drug resistant strains among HIV/AIDS and HIV negative patients. There is also need for improved drug resistant infection control measures, timely and rapid diagnosis and enhanced and active screening strategies of tuberculosis among suspected TB patients need to be put in place. Further, studies using a larger patient cohort and from counties across the country would shed much needed insights on the true national prevalence of different variants of M. tuberculosis drug resistance.

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          The influence of gender and other patient characteristics on health care-seeking behaviour: a QUALICOPC study

          Background Canadians’ health care-seeking behaviour for physical and mental health issues was examined using the international Quality and Cost of Primary Care (QUALICOPC) survey that was conducted in 2013 in Canada. Method This study used the cross-sectional Patient Experiences Survey collected from 7260 patients in 759 practices across 10 Canadian provinces as part of the QUALICOPC study. A Responsive Care Scale (RCS) was constructed to reflect the degree of health care-seeking behaviour across 11 health conditions. Using several patient characteristics as independent variables, four multiple regression analyses were conducted. Results Patients’ self-reports indicated that there were gender differences in health care-seeking behaviour, with women reporting they visited their primary care provider to a greater extent than did men for both physical and mental health concerns. Overall, patients were less likely to seek care for mental health concerns in comparison to physical health concerns. For both women and men, the results of the regressions indicated that age, illness prevention, trust in physicians and chronic conditions were important factors when explaining health care-seeking behaviours for mental health concerns. Conclusion This study confirms the gender differences in health care-seeking behaviour advances previous research by exploring in detail the variables predicting differences in health care-seeking behaviour for men and women. The variables were better predictors of health care-seeking behaviour in response to mental health concerns than physical health concerns, likely reflecting greater variation among those seeking mental health care. This study has implications for those working to improve barriers to health care access by identifying those more likely to engage in health care-seeking behaviours and the variables predicting health care-seeking. Consequently, those who are not accessing primary care can be targeted and policies can be developed and put in place to promote their health care-seeking behavior.
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            Drug Resistance Mechanisms in Mycobacterium tuberculosis

            Tuberculosis (TB) is a serious public health problem worldwide. Its situation is worsened by the presence of multidrug resistant (MDR) strains of Mycobacterium tuberculosis, the causative agent of the disease. In recent years, even more serious forms of drug resistance have been reported. A better knowledge of the mechanisms of drug resistance of M. tuberculosis and the relevant molecular mechanisms involved will improve the available techniques for rapid drug resistance detection and will help to explore new targets for drug activity and development. This review article discusses the mechanisms of action of anti-tuberculosis drugs and the molecular basis of drug resistance in M. tuberculosis.
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              Multidrug-Resistant Tuberculosis and Extensively Drug-Resistant Tuberculosis.

              The continuing spread of drug-resistant tuberculosis (TB) is one of the most urgent and difficult challenges facing global TB control. Patients who are infected with strains resistant to isoniazid and rifampicin, called multidrug-resistant (MDR) TB, are practically incurable by standard first-line treatment. In 2012, there were approximately 450,000 new cases and 170,000 deaths because of MDR-TB. Extensively drug-resistant (XDR) TB refers to MDR-TB strains that are resistant to fluoroquinolones and second-line injectable drugs. The main causes of the spread of resistant TB are weak medical systems, amplification of resistance patterns through incorrect treatment, and transmission in communities and facilities. Although patients harboring MDR and XDR strains present a formidable challenge for treatment, cure is often possible with early identification of resistance and use of a properly designed regimen. Community-based programs can improve treatment outcomes by allowing patients to be treated in their homes and addressing socioeconomic barriers to adherence.

                Author and article information

                BMC Infect Dis
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central (London )
                22 November 2021
                22 November 2021
                : 21
                [1 ]GRID grid.411943.a, ISNI 0000 0000 9146 7108, School of Public Health, , Jomo Kenyatta University of Agriculture and Technology, ; Nairobi, Kenya
                [2 ]GRID grid.411943.a, ISNI 0000 0000 9146 7108, School of Biomedical Sciences, , Jomo Kenyatta University of Agriculture and Technology, ; Nairobi, Kenya
                [3 ]GRID grid.79730.3a, ISNI 0000 0001 0495 4256, Department of Medicine, , Moi University School of Medicine, ; Eldoret, Kenya
                [4 ]GRID grid.34429.38, ISNI 0000 0004 1936 8198, Department of Cellular and Molecular Biology, , University of Guelph, ; Guelph, ON Canada
                © The Author(s) 2021

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                Funded by: Africa-ai-Japan
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                Infectious disease & Microbiology


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