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BACKGROUND: Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. METHODS: For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. FINDINGS: Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberc

BACKGROUND: Australia has a low incidence of TB and has committed to eliminating the disease. Identification of risk factors associated with TB is critical to achieving this goal.METHODS: We undertook a prospective cohort study involving persons receiving TB treatment in four Australian jurisdictions. Risk factors and their association with delayed treatment completion (treatment delayed by at least 1 month) were analysed using univariate analyses and multivariate logistic regression.RESULTS: Baseline surveys were completed for 402 persons with TB. Most (86.1%) were born overseas. Exposure to a person with TB was reported by 19.4%. Diabetes mellitus (10.2%), homelessness (9.2%), cigarette smoking (8.7%), excess alcohol consumption (6.0%) and mental illness (6.2%) were other common risk factors. At follow-up, 24.8% of patients had delayed treatment completion, which was associated with adverse events (34.1%, aOR 6.67, 95% CI 3.36-13.27), excess alcohol consumption (6.0%, aOR 21.94, 95% CI 6.03-79.85) and HIV co-infection (2.7%, aOR 8.10, 95% CI 1.16-56.60).CONCLUSIONS: We identified risk factors for TB and their association with delayed treatment completion, not all of which are routinely collected for surveillance purposes. Recognition of these risk factors should facilitate patient-centred care and assist Australia in reaching TB elimination.

BACKGROUND: The sustainable development goals aim to improve health for all by 2030. They incorporate ambitious goals regarding tuberculosis (TB), which may be a significant cause of disability, yet to be quantified. Therefore, we aimed to quantify the prevalence and types of TB-related disabilities. METHODS: We performed a systematic review of TB-related disabilities. The pooled prevalence of disabilities was calculated using the inverse variance heterogeneity model. The maps of the proportions of common types of disabilities by country income level were created. RESULTS: We included a total of 131 studies (217,475 patients) that were conducted in 49 countries. The most common type of disabilities were mental health disorders (23.1%), respiratory impairment (20.7%), musculoskeletal impairment (17.1%), hearing impairment (14.5%), visual impairment (9.8%), renal impairment (5.7%), and neurological impairment (1.6%). The prevalence of respiratory impairment (61.2%) and mental health disorders (42.0%) was highest in low-income countries while neurological impairment was highest in lower middle-income countries (25.6%). Drug-resistant TB was associated with respiratory (58.7%), neurological (37.2%), and hearing impairments (25.0%) and mental health disorders (26.0%), respectively. CONCLUSIONS: TB-related disabilities were frequently reported. More uniform reporting tools for TB-related disability and further research to better quantify and mitigate it are urgently needed. PROSPERO REGISTRATION NUMBER: CRD42019147488.

Objective: This study aimed to investigate the spatial distribution of drug-resistant tuberculosis (DR-TB) in Hunan province, China. Methods: An ecological study was conducted using DR-TB data collected from the Tuberculosis Control Institute of Hunan Province between 2012 and 2018. Spatial clustering of DR-TB was explored using the Getis-Ord statistic. A Poisson regression model was fitted with a conditional autoregressive prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo simulation, to quantify associations with possible risk factors and identify clusters of high DR-TB risk. Results: A total of 2649 DR-TB patients were reported to Hunan TB Control Institute between 2012 and 2018. The majority of the patients were male (74.8%, n=1983) and had a history of TB treatment (88.53%, n=2345). The proportion of extensively DR-TB among all DR-TB was 3.3% (95% CI 2.7% to 4.1%), which increased from 2.8% in 2012 to 4.4% in 2018. Of 1287 DR-TB patients with registered treatment outcomes, 434 (33.8%) were cured, 198 (15.3%) completed treatment, 92 (7.1%) died, 108 (8.3%) had treatment failure and 455 (35.3%) were lost to follow-up. Half (50.9%, n=655) had poor treatment outcomes. The annual cumulative incidence rate of notified DR-TB increased over time from 0.25 per 100 000 people in 2012 to 0.83 per 100 000 people in 2018. Substantial spatial heterogeneity was observed, and hotspots were detected in counties located in the North and East parts of Hunan province. The cumulative incidence of notified DR-TB was significantly associated with urban communities. Conclusion: The annual incidence of notified DR-TB increased over time in Hunan province. Spatial clustering of DR-TB was detected and significantly associated with urbanisation. This finding suggests that targeting interventions to the highest risk areas and population groups would be effective in reducing the burden and ongoing transmission of DR-TB.

Tuberculosis (TB) is the leading cause of death from a bacterial pathogen worldwide. China has the third highest TB burden in the world, with a high reported burden in Hunan Province (amongst others). This study aimed to investigate the spatial distribution of TB and identify socioeconomic, demographic, and environmental drivers in Hunan Province, China. Numbers of reported cases of TB were obtained from the Tuberculosis Control Institute of Hunan Province, China. A wide range of covariates were collected from different sources, including from the Worldclim database, and the Hunan Bureau of Statistics. These variables were summarized at the county level and linked with TB notification data. Spatial clustering of TB was explored using Moran’s I statistic and the Getis–Ord statistic. Poisson regression models were developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian approach with Markov chain Monte Carlo (MCMC) simulation. A total of 323,340 TB cases were reported to the Hunan TB Control Institute from 2013 to 2018. The mean age of patients was 51.7 years (SD + 17.6 years). The majority of the patients were male (72.6%, n = 234,682) and had pulmonary TB (97.5%, n = 315,350). Of 319,825 TB patients with registered treatment outcomes, 306,107 (95.7%) patients had a successful treatment outcome. The annual incidence of TB decreased over time from 85.5 per 100,000 population in 2013 to 76.9 per 100,000 population in 2018. TB case numbers have shown seasonal variation, with the highest number of cases reported during the end of spring and the beginning of summer. Spatial clustering of TB incidence was observed at the county level, with hotspot areas detected in the west part of Hunan Province. The spatial clustering of TB incidence was significantly associated with low sunshine exposure (RR: 0.86; 95% CrI: 0.74, 0.96) and a low prevalence of contraceptive use (RR: 0.88; 95% CrI: 0.79, 0.98). Substantial

Background: The sustainable development goals aim to improve health for all by 2030. They incorporate ambitious goals regarding tuberculosis (TB), which may be a significant cause of disability, yet to be quantified. Therefore, we aimed to quantify the prevalence and types of TB-related disabilities. Methods: We performed a systematic review of TB-related disabilities. The pooled prevalence of disabilities was calculated using the inverse variance heterogeneity model. The maps of the proportions of common types of disabilities by country income level were created. Results: We included a total of 131 studies (217,475 patients) that were conducted in 49 countries. The most common type of disabilities were mental health disorders (23.1%), respiratory impairment (20.7%), musculoskeletal impairment (17.1%), hearing impairment (14.5%), visual impairment (9.8%), renal impairment (5.7%), and neurological impairment (1.6%). The prevalence of respiratory impairment (61.2%) and mental health disorders (42.0%) was highest in low-income countries while neurological impairment was highest in lower middle-income countries (25.6%). Drug-resistant TB was associated with respiratory (58.7%), neurological (37.2%), and hearing impairments (25.0%) and mental health disorders (26.0%), respectively. Conclusions: TB-related disabilities were frequently reported. More uniform reporting tools for TB-related disability and further research to better quantify and mitigate it are urgently needed. Prospero registration number: CRD42019147488

BACKGROUND: Treatment outcomes among patients treated for multidrug-resistant tuberculosis (MDR-TB) are often sub-optimal. Therefore, the early prediction of poor treatment outcomes may be useful in patient care, especially for clinicians when they have the ability to make treatment decisions or offer counselling or additional support to patients. The aim of this study was to develop a simple clinical risk score to predict poor treatment outcomes in patients with MDR-TB, using routinely collected data from two large countries in geographically distinct regions. METHODS: We used MDR-TB data collected from Hunan Chest Hospital, China and Gondar University Hospital, Ethiopia. The data were divided into derivation (n = 343; 60%) and validation groups (n = 227; 40%). A poor treatment outcome was defined as treatment failure, lost to follow up or death. A risk score for poor treatment outcomes was derived using a Cox proportional hazard model in the derivation group. The model was then validated in the validation group. RESULTS: The overall rate of poor treatment outcome was 39.5% (n = 225); 37.9% (n = 86) in the derivation group and 40.5% (n = 139) in the validation group. Three variables were identified as predictors of poor treatment outcomes, and each was assigned a number of points proportional to its regression coefficient. These predictors and their points were: 1) history of taking second-line TB treatment (2 points), 2) resistance to any fluoroquinolones (3 points), and 3) smear did not convert from positive to negative at two months (4 points). We summed these points to calculate the risk score for each patient; three risk groups were defined: low risk (0 to 2 points), medium risk (3 to 5 points), and high risk (6 to 9 points). In the derivation group, poor treatment outcomes were reported for these three groups as 14%, 27%, and 71%, respectively. The area under the receiver operating characteristic curve for the point system in the derivation group was 0.69 (95

BACKGROUND: Tuberculosis (TB) is the leading cause of death from an infectious disease in Ethiopia, killing more than 30 thousand people every year. This study aimed to determine whether the rates of poor TB treatment outcome varied geographically across Ethiopia at district and zone levels and whether such variability was associated with socioeconomic, behavioural, health care access, or climatic conditions. METHODS: A geospatial analysis was conducted using national TB data reported to the health management information system (HMIS), for the period 2015-2017. The prevalence of poor TB treatment outcomes was calculated by dividing the sum of treatment failure, death and loss to follow-up by the total number of TB patients. Binomial logistic regression models were computed and a spatial analysis was performed using a Bayesian framework. Estimates of parameters were generated using Markov chain Monte Carlo (MCMC) simulation. Geographic clustering was assessed using the Getis-Ord Gi* statistic, and global and local Moran's I statistics. RESULTS: A total of 223,244 TB patients were reported from 722 districts in Ethiopia during the study period. Of these, 63,556 (28.5%) were cured, 139,633 (62.4%) completed treatment, 6716 (3.0%) died, 1459 (0.7%) had treatment failure, and 12,200 (5.5%) were lost to follow-up. The overall prevalence of a poor TB treatment outcome was 9.0% (range, 1-58%). Hot-spots and clustering of poor TB treatment outcomes were detected in districts near the international borders in Afar, Gambelia, and Somali regions and cold spots were detected in Oromia and Amhara regions. Spatial clustering of poor TB treatment outcomes was positively associated with the proportion of the population with low wealth index (OR: 1.01; 95%CI: 1.0, 1.01), the proportion of the population with poor knowledge about TB (OR: 1.02; 95%CI: 1.01, 1.03), and higher annual mean temperature per degree Celsius (OR: 1.15; 95% CI: 1.08, 1.21). CONCLUSIONS: This study showed signif

Ethiopia is one of 30-high burden multidrug-resistant tuberculosis (MDR-TB) countries globally. The aim of this study was to describe the characteristics of patients with MDR-TB and to investigate risk factors for MDR-TB relative to having drug-susceptible tuberculosis (TB), in northwest Ethiopia. A hospital-based, unmatched case-control study was conducted. Cases were all MDR-TB patients (i.e., resistant to at least rifampicin and isoniazid) who were confirmed by culture and drug-susceptibility testing whilst enrolled on treatment at Gondar University Hospital. Controls were all drug-susceptible tuberculosis (DS-TB) patients who were confirmed by Gene Xpert MTB/RIF at Gondar University Hospital. Univariable and multivariable logistic regression models were used for comparisons, and odds ratios with 95% confidence intervals (CI) were computed to measure the strength of association between the dependent and independent variables. A total of 452 patients (242 MDR-TB and 210 DS-TB) were included in this study. The mean age of the study participants was 33 years (SD ± 14 years). Approximately one-fifth (78, 17%) of all study participants were human immunodeficiency virus (HIV) positive; 21% (51) of cases and 13% (27) of controls. Risk factors associated with MDR-TB were a history of previous TB treatment (Adjusted Odds Ratio (AOR): 83.8; 95% CI: 40.7, 172.5), low educational status (AOR: 5.32; 95% CI: 1.43, 19.81); and ages less than 20 years (AOR: 9.01; 95% CI: 2.30, 35.25) and 21-30 years (AOR: 2.61; 95% CI: 1.02, 6.64). HIV infection was also significantly associated with MDR-TB among new TB patients (AOR: 5.55; 95% CI: 1.17, 26.20). This study shows that clinical and demographic features can be used to indicate higher risks of drug resistance in this setting.

INTRODUCTION: The sequelae of multidrug-resistant tuberculosis (MDR-TB) are poorly understood and inconsistently reported. We will aim to assess the existing evidence for the clinical, psychological, social and economic sequelae of MDR-TB and to assess the health-related quality of life in patients with MDR-TB. METHODS AND ANALYSIS: We will perform a systematic review and meta-analysis of published studies reporting sequelae of MDR-TB. We will search PubMed, SCOPUS, ProQuest, Web of Science and PsychINFO databases up to 5 September 2017. MDR-TB sequelae will include any clinical, psychological, social and economic effects as well as health-related quality of life that occur after MDR-TB treatment or illness. Two researchers will screen the titles and abstracts of all citations identified in our search, extract data, and assess the scientific quality using standardised formats. Providing there is appropriate comparability in the studies, we will use a random-effects meta-analysis model to produce pooled estimates of MDR-TB sequelae from the included studies. We will stratify the analyses based on treatment regimen, comorbidities (such as HIV status and diabetes mellitus), previous TB treatment history and study setting. ETHICS AND DISSEMINATION: As this study will be based on published data, ethical approval is not required. The final report will be disseminated through publication in a peer-reviewed scientific journal and will also be presented at relevant conferences. PROSPERO REGISTRATION NUMBER: CRD42017073182.

BACKGROUND: The World Health Organization (WHO) has conditionally recommended the use of sputum smear microscopy and culture examination for the monitoring of multidrug-resistant tuberculosis (MDR-TB) treatment. We aimed to assess and compare the validity of smear and culture conversion at different time points during treatment for MDR-TB, as a prognostic marker for end-of-treatment outcomes. METHODS: We undertook a retrospective observational cohort study using data obtained from Hunan Chest Hospital, China and Gondar University Hospital, Ethiopia. The sensitivity and specificity of culture and sputum smear conversion for predicting treatment outcomes were analysed using a random-effects generalized linear mixed model. RESULTS: A total of 429 bacteriologically confirmed MDR-TB patients with a culture and smear positive result were included. Overall, 345 (80%) patients had a successful treatment outcome, and 84 (20%) patients had poor treatment outcomes. The sensitivity of smear and culture conversion to predict a successful treatment outcome were: 77.9% and 68.9% at 2 months after starting treatment (difference between tests, p = 0.007); 95.9% and 92.7% at 4 months (p = 0.06); 97.4% and 96.2% at 6 months (p = 0.386); and 99.4% and 98.9% at 12 months (p = 0.412), respectively. The specificity of smear and culture non-conversion to predict a poor treatment outcome were: 41.6% and 60.7% at 2 months (p = 0.012); 23.8% and 48.8% at 4 months (p<0.001); and 20.2% and 42.8% at 6 months (p<0.001); and 15.4% and 32.1% (p<0.001) at 12 months, respectively. The sensitivity of culture and smear conversion increased as the month of conversion increased but at the cost of decreased specificity. The optimum time points after conversion to provide the best prognostic marker of a successful treatment outcome were between two and four months after treatment commencement for smear, and between four and six months for culture. The common optimum time point for smear and culture convers

OBJECTIVE: Multidrug-resistant tuberculosis (MDR-TB) is an emerging public health problem in Ethiopia. The aim of this study was to assess MDR-TB treatment outcomes and determine predictors of poor treatment outcomes in north-west Ethiopia. METHODS: A retrospective cohort study was conducted using all MDR-TB patients who were enrolled at Gondar University Hospital since the establishment of the MDR-TB programme in 2010. A Cox proportional hazard model was used to identify the predictors of time to poor treatment outcomes, which were defined as death or treatment failure. RESULTS: Of the 242 patients who had complete records, 131 (54%) were cured, 23 (9%) completed treatment, 31 (13%) died, four (2%) experienced treatment failure, 27 (11%) were lost to follow-up, six (2%) transferred out, and 20 (8%) were still on treatment at the time of analysis. The overall cumulative probability survival of the patients at the end of treatment (which was 24 months in duration) was 80% (95% CI: 70%, 87%). The proportion of patients with poor treatment outcomes increased over time from 6% per person-year (PY) during 2010-2012, to 12% per PY during 2013-2015. The independent predictors of time to poor treatment outcome were being anaemic [AHR = 4.2; 95% CI: 1.1, 15.9] and being a farmer [AHR = 2.2; 95% CI: 1.0, 4.9]. CONCLUSIONS: Overall, in north-west Ethiopia, the MDR-TB treatment success rate was high. However, poor treatment outcomes have gradually increased since 2012. Being a farmer and being anaemic were associated with poor treatment outcomes. It would be beneficial to assess other risk factors that might affect treatment outcomes such as co-infection with malaria, poverty and other socio-economic and biological risk factors.

In November 2014, New South Wales Health was notified of a cluster of respiratory illness in a veterinary school. Active case finding identified another case at a local equine stud. All cases had exposure to the equine fetal membranes of Mare A. This tissue subsequently tested positive for Chlamydia psittaci using quantitative real-time polymerase chain reaction. We conducted a cohort study of the university and stud farm staff to determine risk factors for disease. Nine people were exposed to the fetal membranes of Mare A. Of these, five cases of psittacosis were identified. Two required hospital admission. Contact with birds was not associated with illness (RR = 0.5, 95% CI = 0.09-2.73). People who had direct contact with the abnormal fetal membranes were more likely to develop disease (RR = 11.77, 95% CI = 1.02-∞). The emergence of an association between horse exposure and C. psittaci infection has important implications for the prevention and control of psittacosis. Article summary line: Investigation of an outbreak of psittacosis in a rural veterinary school demonstrates novel source of infection for psittacosis through exposure to abnormal equine fetal membranes.

BACKGROUND: Understanding the geographical distribution of multidrug-resistant tuberculosis (MDR-TB) in high TB burden countries such as Ethiopia is crucial for effective control of TB epidemics in these countries, and thus globally. We present the first spatial analysis of multidrug resistant tuberculosis, and its relationship to socio-economic, demographic and household factors in northwest Ethiopia. METHODS: An ecological study was conducted using data on patients diagnosed with MDR-TB at the University of Gondar Hospital MDR-TB treatment centre, for the period 2010 to 2015. District level population data were extracted from the Ethiopia National and Regional Census Report. Spatial autocorrelation was explored using Moran's I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate Poisson regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo (MCMC) simulation approach with Gibbs sampling, in WinBUGS. RESULTS: A total of 264 MDR-TB patients were included in the analysis. The overall crude incidence rate of MDR-TB for the six-year period was 3.0 cases per 100,000 population. The highest incidence rate was observed in Metema (21 cases per 100,000 population) and Humera (18 cases per 100,000 population) districts; whereas nine districts had zero cases. Spatial clustering of MDR-TB was observed in districts located in the Ethiopia-Sudan and Ethiopia-Eritrea border regions, where large numbers of seasonal migrants live. Spatial clustering of MDR-TB was positively associated with urbanization (RR: 1.02; 95%CI: 1.01, 1.04) and the percentage of men (RR: 1.58; 95% CI: 1.26, 1.99) in the districts; after accounting for these factors there was no residual spatial clustering. CONCLUSION: Spatial clustering of MDR-TB, fully explained by demographic factors (urbanization and percent male), was detected in the border

BACKGROUND: The worldwide emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) has posed additional challenges for global tuberculosis (TB) control efforts, as limited treatment options are available and treatment outcomes are often sub-optimal. This study determined treatment outcomes among a cohort of MDR-TB and XDR-TB patients in Hunan Province, China, and identified factors associated with poor treatment outcomes. METHODS: We conducted a retrospective study using data obtained from medical records of TB patients in Hunan Chest Hospital, and from the internet-based TB management information system managed by the Tuberculosis Control Institute of Hunan Province, for the period 2011 to 2014. Treatment outcomes were assessed for patients diagnosed with MDR-TB (TB resistant to at least isoniazid and rifampicin) and XDR-TB (MDR-TB plus resistance to any fluoroquinolone and at least 1 second-line injectable drug). Cumulative incidence functions were used to estimate time to events (i.e. poor treatment outcomes, loss to follow-up, and unfavourable treatment outcomes); and a competing-risks survival regression model was used to identify predictors of treatment outcomes. RESULT: Of 481 bacteriologically-confirmed patients, with a mean age of 40 years (standard deviation SD ± 13 years), 10 (2%) had XDR-TB and the remainder (471; 98%) had MDR-TB. For the entire cohort, treatment success was 57% (n = 275); 58% (n = 272) for MDR-TB and 30% (n = 3) for XDR-TB. Overall, 27% were lost to follow-up (n = 130), 27% (n = 126) for MDR-TB and 40% (n = 4) for XDR-TB; and 16% had a poor treatment outcome (n = 76), 15% for MDR-TB and 30% (n = 3) for XDR-TB. Of the 10 XDR-TB patients, 3 (30%) completed treatment, 3 (30%) died and 4 (40%) were lost to follow-up. Of the 471 MDR-TB patients, 258 (57%) were cured, 16 (3%) completed treatment, 13 (3%) died, 60 (13%) experienced treatment failure, and 126 (27%) were lost to follow-up. Resis

The Global Burden of Disease 2010 Study is the largest study of its kind. It provides a large volume of information about the global burden of disease and associated risk factors. It estimates that lower respiratory infections, diabetes, diarrhea, and tuberculosis cause the greatest burden in the Pacific, and noncommunicable diseases caused a substantially greater burden in 2010 compared with 1990. Although the Pacific is considered to be a region rich in data, very little of these data has been analyzed, synthesized, and made publically available. Consequently, burden estimates for the Pacific are derived from models built with very limited data, and it is difficult to know how accurate they are. Health information in the Pacific needs strengthening, particularly in relation to data collection, analysis, use, and sharing. This will improve the reliability and comparability of burden of disease estimates.