The burden of bacterial antimicrobial resistance in the WHO African region in 2019: a cross-country systematic analysisBACKGROUND: A critical and persistent challenge to global health and modern health care is the threat of antimicrobial resistance (AMR). Previous studies have reported a disproportionate burden of AMR in low-income and middle-income countries, but there remains an urgent need for more in-depth analyses across Africa. This study presents one of the most comprehensive sets of regional and country-level estimates of bacterial AMR burden in the WHO African region to date. METHODS: We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen-drug combinations for countries in the WHO African region in 2019. Our methodological approach consisted of five broad components: the number of deaths in which infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antimicrobial drug of interest, and the excess risk of mortality (or duration of an infection) associated with this resistance. These components were then used to estimate the disease burden by using two counterfactual scenarios: deaths attributable to AMR (considering an alternative scenario where infections with resistant pathogens are replaced with susceptible ones) and deaths associated with AMR (considering an alternative scenario where drug-resistant infections would not occur at all). We obtained data from research hospitals, surveillance networks, and infection databases maintained by private laboratories and medical technology companies. 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. FINDINGS: In the WHO African region in 2019, there were an estimated 1·05 million deaths (95% UI 829 000-1 316 000) associated with bacterial AMR and 250 000 deaths (192 000-325 000) attributable to bacterial AMR. The largest fatal AMR burden was attributed to lower respiratory and thorax infections (119 000 deaths [92 000-151 000], or 48% of all estimated bacterial pathogen AMR deaths), bloodstream infections (56 000 deaths [37 000-82 000], or 22%), intra-abdominal infections (26 000 deaths [17 000-39 000], or 10%), and tuberculosis (18 000 deaths [3850-39 000], or 7%). Seven leading pathogens were collectively responsible for 821 000 deaths (636 000-1 051 000) associated with resistance in this region, with four pathogens exceeding 100 000 deaths each: Streptococcus pneumoniae, Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus. Third-generation cephalosporin-resistant K pneumoniae and meticillin-resistant S aureus were shown to be the leading pathogen-drug combinations in 25 and 16 countries, respectively (53% and 34% of the whole region, comprising 47 countries) for deaths attributable to AMR. INTERPRETATION: This study reveals a high level of AMR burden for several bacterial pathogens and pathogen-drug combinations in the WHO African region. The high mortality rates associated with these pathogens demonstrate an urgent need to address the burden of AMR in Africa. These estimates also show that quality and access to health care and safe water and sanitation are correlated with AMR mortality, with a higher fatal burden found in lower resource settings. Our cross-country analyses within this region can help local governments to leverage domestic and global funding to create stewardship policies that target the leading pathogen-drug combinations. FUNDING: Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
Global burden associated with 85 pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019Mohsen Naghavi, Tomislav Meštrović, Authia P Gray et al.|The Lancet Infectious Diseases|2024 BACKGROUND: Despite a global epidemiological transition towards increased burden of non-communicable diseases, communicable diseases continue to cause substantial morbidity and mortality worldwide. Understanding the burden of a wide range of infectious diseases, and its variation by geography and age, is pivotal to research priority setting and resource mobilisation globally. METHODS: We estimated disability-adjusted life-years (DALYs) associated with 85 pathogens in 2019, globally, regionally, and for 204 countries and territories. The term pathogen included causative agents, pathogen groups, infectious conditions, and aggregate categories. We applied a novel methodological approach to account for underlying, immediate, and intermediate causes of death, which counted every death for which a pathogen had a role in the pathway to death. We refer to this measure as the burden associated with infection, which was estimated by combining different sources of information. To compare the burden among all pathogens, we used pathogen-specific ratios to incorporate the burden of immediate and intermediate causes of death for pathogens modelled previously by the GBD. We created the ratios by using multiple cause of death data, hospital discharge data, linkage data, and minimally invasive tissue sampling data to estimate the fraction of deaths coming from the pathway to death chain. We multiplied the pathogen-specific ratios by age-specific years of life lost (YLLs), calculated with GBD 2019 methods, and then added the adjusted YLLs to age-specific years lived with disability (YLDs) from GBD 2019 to produce adjusted DALYs to account for deaths in the chain. We used standard GBD methods to calculate 95% uncertainty intervals (UIs) for final estimates of DALYs by taking the 2·5th and 97·5th percentiles across 1000 posterior draws for each quantity of interest. We provided burden estimates pertaining to all ages and specifically to the under 5 years age group. FINDINGS: Globally in 2019, an estimated 704 million (95% UI 610-820) DALYs were associated with 85 different pathogens, including 309 million (250-377; 43·9% of the burden) in children younger than 5 years. This burden accounted for 27·7% (and 65·5% in those younger than 5 years) of the previously reported total DALYs from all causes in 2019. Comparing super-regions, considerable differences were observed in the estimated pathogen-associated burdens in relation to DALYs from all causes, with the highest burden observed in sub-Saharan Africa (314 million [270-368] DALYs; 61·5% of total regional burden) and the lowest in the high-income super-region (31·8 million [25·4-40·1] DALYs; 9·8%). Three leading pathogens were responsible for more than 50 million DALYs each in 2019: tuberculosis (65·1 million [59·0-71·2]), malaria (53·6 million [27·0-91·3]), and HIV or AIDS (52·1 million [46·6-60·9]). Malaria was the leading pathogen for DALYs in children younger than 5 years (37·2 million [17·8-64·2]). We also observed substantial burden associated with previously less recognised pathogens, including Staphylococcus aureus and specific Gram-negative bacterial species (ie, Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, and Helicobacter pylori). Conversely, some pathogens had a burden that was smaller than anticipated. INTERPRETATION: Our detailed breakdown of DALYs associated with a comprehensive list of pathogens on a global, regional, and country level has revealed the magnitude of the problem and helps to indicate where research funding mismatch might exist. Given the disproportionate impact of infection on low-income and middle-income countries, an essential next step is for countries and relevant stakeholders to address these gaps by making targeted investments. FUNDING: Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
PermPress: Machine Learning-Based Pipeline to Evaluate Permissions in App Privacy PoliciesPrivacy laws and app stores (e.g., Google Play Store) require mobile apps to have transparent privacy policies to disclose sensitive actions and data collection, such as accessing the phonebook, camera, storage, GPS, and microphone. However, many mobile apps do not accurately disclose their sensitive data access that requires sensitive (’dangerous’) permissions. Thus, analyzing discrepancies between apps’ permissions and privacy policies facilitates the identification of compliance issues upon which privacy regulators and marketplace operators can act. This paper proposes <i>PermPress</i> – an automated machine-learning system to evaluate an Android app’s permission-completeness, i.e., whether its privacy policy matches its dangerous permissions. <i>PermPress</i> combines machine learning techniques with human annotation of privacy policies to establish whether app policies contain permission-relevant information. <i>PermPress</i> leverages MPP-270, an annotated policy corpus, for establishing a gold standard dataset of permission completeness. This corpus shows that only 31% of apps disclose all dangerous permissions in privacy policies. By leveraging the annotated dataset and machine learning techniques, <i>PermPress</i> achieves an AUC score of 0.92 in predicting the permission-completeness of apps. A large-scale evaluation of 164, 156 Android apps shows that, on average, 7% of apps do not disclose more than half of their declared dangerous permissions in privacy policies, whereas 60% of apps omit to disclose at least one dangerous permission-related data collection in privacy policies. This paper’s investigation uncovers the non-transparent state of app privacy policies and highlights the need to standardize app privacy policies’ compliance and completeness checking process.
You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving FrameworksAutonomous Vehicles (AVs) increasingly use LiDAR-based object detection systems to perceive other vehicles and pedestrians on the road. While existing attacks on LiDAR-based autonomous driving architectures focus on lowering the confidence score of AV object detection models to induce obstacle misdetection, our research discovers how to leverage laser-based spoofing techniques to selectively remove the LiDAR point cloud data of genuine obstacles at the sensor level before being used as input to the AV perception. The ablation of this critical LiDAR information causes autonomous driving obstacle detectors to fail to identify and locate obstacles and, consequently, induces AVs to make dangerous automatic driving decisions. In this paper, we present a method invisible to the human eye that hides objects and deceives autonomous vehicles' obstacle detectors by exploiting inherent automatic transformation and filtering processes of LiDAR sensor data integrated with autonomous driving frameworks. We call such attacks Physical Removal Attacks (PRA), and we demonstrate their effectiveness against three popular AV obstacle detectors (Apollo, Autoware, PointPillars), and we achieve 45° attack capability. We evaluate the attack impact on three fusion models (Frustum-ConvNet, AVOD, and Integrated-Semantic Level Fusion) and the consequences on the driving decision using LGSVL, an industry-grade simulator. In our moving vehicle scenarios, we achieve a 92.7% success rate removing 90\% of a target obstacle's cloud points. Finally, we demonstrate the attack's success against two popular defenses against spoofing and object hiding attacks and discuss two enhanced defense strategies to mitigate our attack.
Side Eye: Characterizing the Limits of POV Acoustic Eavesdropping from Smartphone Cameras with Rolling Shutters and Movable LensesOur research discovers how the rolling shutter and movable lens structures widely found in smartphone cameras modulate structure-borne sounds onto camera images, creating a point-of-view (POV) optical-acoustic side channel for acoustic eavesdropping. The movement of smartphone camera hardware leaks acoustic information because images unwittingly modulate ambient sound as imperceptible distortions. Our experiments find that the side channel is further amplified by intrinsic behaviors of Complementary Metal-oxide–Semiconductor (CMOS) rolling shutters and movable lenses such as in Optical Image Stabilization (OIS) and Auto Focus (AF). Our paper characterizes the limits of acoustic information leakage caused by structure-borne sound that perturbs the POV of smartphone cameras. In contrast with traditional optical-acoustic eavesdropping on vibrating objects, this side channel requires no line of sight and no object within the camera’s field of view (images of a ceiling suffice). Our experiments test the limits of this side channel with a novel signal processing pipeline that extracts and recognizes the leaked acoustic information. Our evaluation with 10 smartphones on a spoken digit dataset reports 80.66%, 91.28%, and 99.67% accuracies on recognizing 10 spoken digits, 20 speakers, and 2 genders respectively. We further systematically discuss the possible defense strategies and implementations. By modeling, measuring, and demonstrating the limits of acoustic eavesdropping from smartphone camera image streams, our contributions explain the physics-based causality and possible ways to reduce the threat on current and future devices.