Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen-drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. MethodsWe estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen-drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drugresistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. FindingsOn the basis of our predictive statistical models, there were an estimated 4•95 million (3•62-6•57) deaths associated with bacterial AMR in 2019, including 1•27 million (95% UI 0•911-1•71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27•3 deaths per 100 000 (20•9-35•3), and lowest in Australasia, at 6•5 deaths (4•3-9•4) per 100 000. Lower respiratory infections accounted for more than 1•5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000-1 270 000) deaths attributable to AMR and 3•57 million (2•62-4•78) deaths associated with AMR in 2019. One pathogen-drug combination, meticillinresistant S aureus, caused more than 100 000 deaths attributa...

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Low-and middle-income countries (LMICs) bear a disproportionately high burden of the global morbidity and mortality caused by chronic respiratory diseases (CRDs) including asthma, chronic obstructive pulmonary disease, bronchiectasis and post-tuberculous lung disease. CRDs are strongly associated with poverty, infectious diseases, and other non-communicable diseases, and contribute to complex multi-morbidity, with significant consequences for the lives and livelihoods of those affected.The relevance of CRDs to health and socioeconomic wellbeing is expected to increase over time, as life expectancies rise and the competing risks of early childhood mortality and infectious diseases plateau. As such, the World Health Organization has identified the prevention and control of CRDs as an urgent development issue and essential to the achievement of the Sustainable Development Goals.In this review we focus on CRDs in low-and middle-income settings (LMICs). We discuss the early life origins of CRDs, challenges in prevention, diagnosis and management in LMICs, and pathways to solutions to achieve true Universal Health Coverage.

BackgroundWhile diabetes mellitus (DM) is a known risk factor for tuberculosis, the prevalence among TB patients in India is unknown. Routine screening of TB patients for DM may be an opportunity for its early diagnosis and improved management and might improve TB treatment outcomes. We conducted a cross-sectional survey of TB patients registered from June–July 2011 in the state of Kerala, India, to determine the prevalence of DM.Methodology/Principal FindingsA state-wide representative sample of TB patients in Kerala was interviewed and screened for DM using glycosylated hemoglobin (HbA1c); patients self-reporting a history of DM or those with HbA1c ≥6.5% were defined as diabetic. Among 552 TB patients screened, 243(44%) had DM – 128(23%) had previously known DM and 115(21%) were newly diagnosed - with higher prevalence among males and those aged >50years. The number needed to screen(NNS) to find one newly diagnosed case of DM was just four. Of 128 TB patients with previously known DM, 107(84%) had HbA1c ≥7% indicating poor glycemic control.Conclusions/SignificanceNearly half of TB patients in Kerala have DM, and approximately half of these patients were newly-diagnosed during this survey. Routine screening of TB patients for DM using HbA1c yielded a large number of DM cases and offered earlier management opportunities which may improve TB and DM outcomes. However, the most cost-effective ways of DM screening need to be established by futher operational research.

Background: Isoniazid preventive therapy (IPT) has been shown to reduce the risk of tuberculosis (TB) among people living with HIV (PLHIV). In 2017, India began a nationwide roll-out of IPT, but there is a lack of evidence on the implementation and the challenges. Objectives: Among PLHIV newly initiated on antiretroviral therapy (ART) from January 2017 to June 2018, to: (i) assess the proportion who started and completed IPT and (ii) explore reasons for non-initiation and non-completion from health-care providers' and patients' perspectives. Methods: An explanatory mixed-methods study was conducted in two selected districts of Karnataka, South India. A quantitative phase (cohort analysis of routinely collected program data) was followed by a qualitative phase involving thematic analysis of in-depth interviews with providers (n = 22) and patients (n = 8). Results: Of the 4020 included PLHIV, 3780 (94%) were eligible for IPT, of whom, 1496 (40%, 95% CI: 38%-41%) were initiated on IPT. Among those initiated, 423 (28.3%) were still on IPT at the time of analysis. Among 1073 patients with declared IPT outcomes 870 (81%, 95% CI: 79%-83%) had completed the six-month course of IPT. The main reason for IPT non-initiation and non-completion was frequent drug stock-outs. This required health-care providers to restrict IPT initiation in selected patient subgroups and earmark six-monthly courses for each patient to ensure that, once started, treatment was not interrupted. The other reasons for non-completion were adverse drug effects and loss to follow-up. Conclusion:The combined picture of 'low IPT initiation and high completion' seen in our study mirrors findings from other countries. Drug stock-out was the key challenge, which obliged health-care providers to prioritize 'IPT completion' over 'IPT initiation'. There is an urgent need to improve the procurement and supply chain management of isoniazid.