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Rock burst disasters severely restrict the safe and efficient mining of coal. The fundamental cause of their occurrence is the concentration of stress within the coal mass. Stress monitoring in coal seam drilling is widely used as an effective method for rock burst monitoring. However, how to scientifically and reasonably set the critical values of early warning indicators that match the conditions of each mine has always been a key issue restricting the accurate prediction of rock burst by the drilling stress method. This paper adopts a method combining theoretical analysis and field practice to conduct research on the critical values of drilling stress early warning indicators. Based on perturbation response instability theory, a mechanical model for the occurrence of impact ground pressure has been established. Based on the instability theory of disturbance response, a mechanical model for the occurrence of impact ground pressure has been established, leading to the derivation of the expression for the near-field critical stress of impact ground pressure events. The theoretical formula for the critical value of drilling stress early warning indicators was obtained based on the difference between the critical stress of rock burst occurrence and the actual stress in the roadway. This formula includes the mechanical parameters of the coal mass and its propensity for rock burst, roadway support stress, mining depth, stress concentration coefficient, and the initial installation pressure of the stress gauge. They can be determined by the geological and mining technical conditions of each mine. This theoretical formula breaks the uniformity of the critical values for stress warning indicators in various mine drill holes, allowing each mine to scientifically determine its critical value based on its own conditions. This theoretical method has been applied to a high-stress mine in Shanxi, China, and the critical values of drilling stress early warning indicators were obtained. When the monitored stress exceeded the critical value, dynamic phenomena of anchor rod and cable fractures occurred in the roadway roof. The distribution of microseismic events also shifted towards the warning area, and the microseismic monitoring indicators reached the warning values. This confirmed the engineering feasibility of the critical values for drilling stress early warning indicators determined by the theoretical method.

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Complex spatio-temporal systems like lakes, forests and climate systems exhibit alternative stable states. In such systems, as the threshold value of the driver is crossed, the system may experience a sudden (discontinuous) transition or smooth (continuous) transition to an undesired steady state. Theories predict that changes in the structure of the underlying spatial patterns precede such transitions. While there has been a large body of research on identifying early warning signals of critical transitions, the problem of forecasting the type of transitions (sudden versus smooth) remains an open challenge. We address this gap by developing an advanced machine learning (ML) toolkit that serves as an early warning indicator of spatio-temporal critical transitions, Spatial Early Warning Signal Network (S-EWSNet). ML models typically resemble a black box and do not allow envisioning what the model learns in discerning the labels. Here, instead of naively relying upon the deep learning model, we let the deep neural network learn the latent features characteristic of transitions via an optimal sampling strategy (OSS) of spatial patterns. The S-EWSNet is trained on data from a stochastic cellular automata model deploying the OSS, providing an early warning indicator of transitions while detecting its type in simulated and empirical samples.

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Coral reef resilience is eroding at multiple spatial scales globally, with broad implications for coastal communities, and is thus a critical challenge for managing marine social-ecological systems (SESs). Many researchers believe that external stressors will cause key coral reefs to die by the end of the 21st century, virtually eliminating essential ecological and societal benefits. Here, we propose the use of resilience-based approaches to understand the dynamics of coral reef SESs and subsequent drivers of coral reef decline. Previous research has demonstrated the effectiveness of these methods, not only for tracking environmental change, but also for providing warning in advance of transitions, possibly allowing time for management interventions. The flexibility and utility of these methods make them ideal for assessing complex systems; however, they have not been used to study aquatic ecosystem dynamics at the global scale. Here, we evaluate these methods for examining spatiotemporal change in coral reef SESs across the global seascape and assess the subsequent impacts on coral reef resilience. We found that while univariate indicators failed to provide clear signals, multivariate resilience-based approaches effectively captured coral reef SES dynamics, unveiling distinctive patterns of variation throughout the global coral reef seascape. Additionally, our findings highlight global spatiotemporal variation, indicating patterns of degraded resilience. This degradation was reflected regionally, particularly in the Pacific Ocean and Indian Ocean SESs. These results underscore the utility of resilience-based approaches in assessing environmental change in SESs, detecting spatiotemporal variation at the global and regional scales, and facilitating more effective monitoring and management of coral reef SESs.

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The wide application of anaerobic digestion (AD) technology is limited by process fluctuations. Thus, process monitoring based on screening state parameters as early warning indicators (EWI) is a top priority for AD facilities. However, predicting anaerobic digester stability based on such indicators is difficult, and their threshold values are uncertain, case-specific, and sometimes produce conflicting results. Thus, new EWI should be proposed to integrate microbial and metabolic information. These microbial volatile organic compounds (mVOCs) including alkanes, alkenes, alkynes, aromatic compounds are produced by microorganisms (bacteria, archaea and fungi), which might serve as a promising diagnostic tool for environmental monitoring. Moreover, mVOCs diffuse in both gas and liquid phases and are considered the language of intra kingdom microbial interactions. Herein, we highlight the potential of mVOCs as EWI for AD process instability, including discussions regarding characteristics and sources of mVOCs as well as sampling and determination methods. Furthermore, existing challenges must be addressed, before mVOCs profiling can be used as an early warning system for diagnosing AD process instability, such as mVOCs sampling, analysis and identification. Finally, we discuss the potential biotechnology applications of mVOCs and approaches to overcome the challenges regarding their application.

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The continuous worldwide seagrasses decline calls for immediate actions in order to preserve this precious marine ecosystem. The main stressors that have been linked with decline in seagrasses are 1) the increasing ocean temperature due to climate change and 2) the continuous inputs of nutrients (eutrophication) associated with coastal human activities. To avoid the loss of seagrass populations, an "early warning" system is needed. We used Weighed Gene Co-expression Network Analysis (WGCNA), a systems biology approach, to identify potential candidate genes that can provide an early warning signal of stress in the Mediterranean iconic seagrass Posidonia oceanica, anticipating plant mortality. Plants were collected from both eutrophic (EU) and oligotrophic (OL) environments and were exposed to thermal and nutrient stress in a dedicated mesocosm. By correlating the whole-genome gene expression after 2-weeks exposure with the shoot survival percentage after 5-weeks exposure to stressors, we were able to identify several transcripts that indicated an early activation of several biological processes (BP) including: protein metabolic process, RNA metabolic process, organonitrogen compound biosynthetic process, catabolic process and response to stimulus, which were shared among OL and EU plants and among leaf and shoot apical meristem (SAM), in response to excessive heat and nutrients. Our results suggest a more dynamic and specific response of the SAM compared to the leaf, especially the SAM from plants coming from a stressful environment appeared more dynamic than the SAM from a pristine environment. A vast list of potential molecular markers is also provided that can be used as targets to assess field samples.

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Résumé:l'utilisation des antirétroviraux (ARV) à grande échelle aux personnes vivant avec le VIH (PVVIH) s'accompagne d'un développement de résistance au VIH (R-ARV). Nous avons déterminé la performance de trois indicateurs d'alerte précoce (IAP) associés à un risque élevé d'émergence de R-ARV au Burkina Faso à travers une étude transversale répétée entre 2014 et 2017. Nous avons analysé l'évolution des IAP de l'émergence de R-ARVau cours de cette période. Il s'agissait de l'IAP1 (Retrait des ARV dans les délais),l'IAP2 (Rétention sous traitement ARV «TARV¼ à 12 mois) et de l'IAP4 (pratiques de prescription des ARV).Au total, 27 sites ont été évalués. La médiane des patients qui ont retiré le TARV dans les délais était respectivement de 61% en 2015, 60% en 2016 et à 58% en 2017. Aucun site n ́a atteint le seuil d'au moins 95% dans les cohortes de 2014 et 2015. Les médianes de la rétention sous TARV à 12 mois pour les cohortes de 2014, 2015 et 2016 ont été de 70%, 69% et 65% respectivement. Le seuil de rétention dans le TARV à 12 mois d'au moins 85% n'a été atteint que dans deux sites. Même si la prescription des ARV est conforme aux directives nationales, la qualité des soins offerts aux PVVIH a régressé de 2014 à 2017 avec un risque élevé d'émergence de R-ARV. Pour minimiser ce risque, l'éducation thérapeutique des patients et le système de recherche des perdus de vue doivent être renforcés.

The widespread use of antiretrovirals (ARVs) to people living with HIV (PLHIV) is accompanied by the development of HIV resistance (R-ARV). We determined the performance of three early warning indicators (EWIs) associated with a high risk of R-ARV emergence in Burkina Faso through a repeated cross-sectional study between 2014 and 2017. We analyzed the evolution of the DPIs of R-ARV emergence over this period. These were DPI1 (Timely withdrawal of ARVs), DPI2 (Retention on ARV treatment "TARV" at 12 months) and DPI4 (ARV prescription practices). A total of 27 sites were evaluated. The median number of patients who withdrew ART on time changed from 61% in 2015, 60% in 2016 and 58% in 2017, respectively. No site reached the threshold of at least 95% in the 2014and 2015 cohorts. The medians of retention on ART at 12 months for the 2014, 2015 and 2016 cohorts were 70%, 69% and 65% respectively. The 12-month ART retention threshold of at least 85% was only reached at two sites. Although ARV prescribing is in line with national guidelines, the quality of care offered to PLHIV regressed from 2014 to 2017 with a high risk of R-ARV emergence. To minimize this risk, therapeutic patient education and the system for tracking lost patients need to be strengthened.

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Process monitoring is an essential measure to achieve efficient and stable performance in anaerobic digestion, thus requiring identification of effective early warning indicators. However, the application of early warning indicators to full-scale dry anaerobic engineering biogas plant still remains elusive. This study evaluated the effectiveness of common early warning indicators (including CH4, CO2, H2S, volatile fatty acids (VFAs), alkalinity (ALK), total ammonia concentration (TAN) and free ammonia concentration (FAN)) in monitoring the instability of anaerobic digestion process at a practical engineering plant. The results showed that the individual indicators could not provide a sufficient early warning time before the digester fell into failure collapse. In comparison, the coupling indicators (the ratio of CH4/CO2, CH4/pH, and CH4/H2S) had sensitive response to perturbation, which could regard as a potential early warning indicator, with the early warning time of 6, 7 and 10 days, respectively. Moreover, the VFA/ALK could be used as auxiliary indicators due to the limitation of complex detection methods. In addition, the result also indicated that the application of some warning indicators needs to be further verified, when transferring the result of laboratory scale to the practice application scenarios. This study provides insight into the stable operation of dry anaerobic engineering.

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Lakes are dynamic ecosystems that can transition among stable states. Since ecosystem-scale transitions can be detrimental and difficult to reverse, being able to predict impending critical transitions in state variables has become a major area of research. However, not all transitions are detrimental, and there is considerable interest in better evaluating the success of management interventions to support adaptive management strategies. Here, we retrospectively evaluated the agreement between time series statistics (i.e., standard deviation, autocorrelation, skewness, and kurtosis-also known as early warning indicators) and breakpoints in state variables in a lake (Lake Simcoe, Ontario, Canada) that has improved from a state of eutrophication. Long-term (1980 to 2019) monitoring data collected fortnightly throughout the ice-free season were used to evaluate historical changes in 15 state variables (e.g., dissolved organic carbon, phosphorus, chlorophyll a) and multivariate-derived time series at three monitoring stations (shallow, middepth, deep) in Lake Simcoe. Time series results from the two deep-water stations indicate that over this period Lake Simcoe transitioned from an algal-dominated state toward a state with increased water clarity (i.e., Secchi disk depth) and silica and lower nutrient and chlorophyll a concentrations, which coincided with both substantial management intervention and the establishment of invasive species (e.g., Dreissenid mussels). Consistent with improvement, Secchi depth at the deep-water stations demonstrated expected trends in statistical indicators prior to identified breakpoints, whereas total phosphorus and chlorophyll a revealed more nuanced patterns. Overall, state variables were largely found to yield inconsistent trends in statistical indicators, so many breakpoints were likely not reflective of traditional bifurcation critical transitions. Nevertheless, statistical indicators of state variable time series may be a valuable tool for the adaptive management and long-term monitoring of lake ecosystems, but we call for more research within the domain of early warning indicators to establish a better understanding of state variable behavior prior to lake changes.

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Nutrient enrichment caused by black carbon (BC) is a major ecological crisis in glacial ecosystems. The microbiological effects of BC were assessed in this study by using fluorescent fingerprinting assay based on flow cytometry (FCM) of bacterial communities with low (LNA) and high (HNA) nucleic acid-content bacteria. Here, we investigated a high-resolution temporal variation of bacterial abundance and LNA/HNA ratio in Tibetan ice cores. Our results revealed that bacterial abundance was proportional to the atmospheric BC on the glaciers. The shift of LNA functional groups to HNA functional groups in glaciers suggested BC emissions increased the proportion of highly active cells. In addition, distinct number of LNA and HNA functional groups was identified between the monsoon and non-monsoon seasons. Westerly winds with high amounts of BC accounted for high ratio of HNA functional groups during the non-monsoon season. In comparison, high moisture during the monsoon season decreased atmospheric BC loading, which increases the ratio of LNA functional groups. Correlations between BC and functional groups were very strong, showing that two functional groups may serve as early-warning indicators of microbiological effects of BC at low trophic level. Our approach provides a potential early-warning framework to study the influences of atmospheric BC on the glaciological community.

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Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical models to train a deep neural network to detect critical transitions-the Early Warning Signal Network (EWSNet). We then demonstrate that this network, trained on simulated data, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.

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Background: Patients with severe novel coronavirus disease (COVID-19) can likely develop comorbidities, which can lead to irreversible organ damage and, eventually, death. However, early indicators of disease progression remain unclear. This study aimed to identify early indicators of disease progression to provide a basis for improved prognostic prediction and disease management. Methods: We examined 53 recovered adult COVID-19 patients who were treated at Shanghai Public Health Clinical Center between January 20, 2020, and February 20, 2020. The patients were categorized into the following four groups according to their condition at admission: mild condition (n = 3), moderate (n = 41), severe (n = 7), and critical (n = 2). They were also categorized according to disease progression as mild or moderate conditions that remained stable (n = 26), moderate disease that progressed to severe condition (n = 18), and continuously severe or critical (n = 9). We then focused on investigating the differences in the epidemiological and laboratory indicators between remained stable cases and progressed to severe condition cases. Results: Mild or moderate patients were younger than severe or critical patients. The number of patients with shortness of breath and underlying diabetes and heart disease at admission was higher in the severe or critical group. This group also showed considerably lower or higher values in 28 laboratory indicators. In addition, mild and moderate patients who remained stable were younger than moderate patients progressing to severe disease. Men had a higher risk of disease progression. Patients who progressed had either higher or lower values in 11 laboratory indicators. Survival curve analysis showed that age, procalcitonin, D-dimer, serum C-reactive protein, lactate dehydrogenase, lymphocytes, neutrophils, CD4%, and CD4/CD8 ratio were significant predictors of progression to severe disease. Conclusions: Lactate dehydrogenase, procalcitonin, etc. are early warning indicators of severe COVID-19. Age (>64 years), shortness of breath, past histories of diabetes and heart disease, and abnormality in 28 other indicators at admission are indicative of severe or progression toward severe COVID-19. Meanwhile, abnormalities in 11 indicators and an abnormal coagulation function index at admission are risk factors for progression to severe disease.

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OBJECTIVE: The aim of this study was to assess status of early warning indicators (EWIs) for HIV drug resistance in Ethiopia. METHODS: A retrospective cohort study was conducted among 90 health facilities (HFs) in 2015. Data were abstracted for 'on time pill pickup' (EWI-I), 'Retention in care' (EWI-II), 'drug supply continuity'(EWI-III) and 'dispensing practices' (EWI-IV). Data analysis was conducted using WHO Excel tool and SPSS V20. RESULTS: EWI-IV was excellent across all of the six rounds of EWI surveys conducted between 2008 and 2015. There were improvements in EWI-II over time from 55.6% to 81%. However, EWI-I and EWI-III declined from 86.7% to 31% and 100% to 41%, respectively. During 2015, half of the HFs in Gambella, Amhara and Southern Nation, Nationalities and people regional (SNNPR) states achieved excellent performance for EWI-I. Similarly, all HFs in Afar, Amhara, Dire Dawa, Harari and Tigray regions achieved excellent performance for EWI-II. There were also differences by level of HFs for EWI-III; 62% of hospitals and 28% of health centers were out of stock of one or more ARV drugs by 2015. CONCLUSION: Excellent performance of EWI-IV and improvement of EWI-II over time shall be maintained. The program shall further work to ensure medication adherence and supply continuity.

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INTRODUCTION: since the launch of the "Treatment for All in Cameroon" strategy in 2015, an acceleration plan for antiretroviral (ARV) therapy in Cameroon was implemented, with remarkable progresses. These efforts were accompanied by the risk of developing HIV drug resistance. Then, the World Health Organization (WHO) proposed surveillance of early warning indicators (IAP) for HIV drug resistance. The purpose of this study was to assess the national HIV Drug Resistance Early Warning Indicators (EWI) in Cameroon. METHODS: we conducted a retrospective study in the ten regions of Cameroon in December 2017 to evaluate the six EWIs recommended by the WHO in 68 randomly selected HIV care sites. The reporting period ranged from July 2016 to June 2017. RESULTS: national scores were: drug withdrawal within the estimated time frame (EWI1): 66%; retention on antiretroviral therapy 12 months after treatment initiation (EWI2): 66%; stock-out of antiretroviral drugs over a 12-month period (EWI3): 53%; viral load testing coverage (CV) (EWI4): 10%; coverage suppression after 12 months of antiretroviral therapy (EWI5): 73% and practices for ARV drug delivery (EWI6): (100%). Regional scores were similar. CONCLUSION: the evaluation of EWI in Cameroon is limited and requires urgent interventions, primarily viral load testing coverage, optimal ARVs management and patient´s adherence.

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Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. Methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. Results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems.

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More than 1.1 million people currently receive lifelong antiretroviral treatment in Zimbabwe following the adoption of the test and treat strategy in 2017. The huge numbers of people on antiretroviral therapy (ART), combined with HIV's error-prone replication, increases the probability of HIV drug resistance (HIVDR) developing. HIVDR in resource-limited settings like Zimbabwe has significant human and financial implications. The early warning indicators of HIV drug resistance surveillance system was set up to monitor the ART programme and to identify factors that could raise the risk of HIVDR. Fifty-one health workers at 12 health facilities were interviewed in a cross-sectional study that sought to describe how the system operates and also to identify gaps (knowledge, perceived system usefulness, sensitivity) within the system. The system was seen to have multiple weaknesses including inadequate training, difficulties navigating the system, long duration of data abstraction, and poor feedback mechanisms. Opportunities observed during the evaluation centered on integration and incorporation of indicators into the electronic patient monitoring system and strengthening ownership of the programme.

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Increasing numbers of ecosystems globally are at risk of collapse. However, most descriptions of terrestrial ecosystem collapse are post hoc with few empirically based examples of ecosystems in the process of collapse. This limits learning about collapse and impedes development of effective early-warning indicators. Based on multidecadal and multifaceted monitoring, we present evidence that the Australian mainland Mountain Ash ecosystem is collapsing. Collapse is indicated by marked changes in ecosystem condition, particularly the rapid decline in populations of keystone ecosystem structures. There also has been significant decline in biodiversity strongly associated with these structures and disruptions of key ecosystem processes. In documenting the decline of the Mountain Ash ecosystem, we uncovered evidence of hidden collapse. This is where an ecosystem superficially appears to be relatively intact, but a prolonged period of decline coupled with long lag times for recovery of dominant ecosystem components mean that collapse is almost inevitable. In ecosystems susceptible to hidden collapse, management interventions will be required decades earlier than currently perceived by policy makers. Responding to hidden collapse is further complicated by our finding that different drivers produce different pathways to collapse, but these drivers can interact in ways that exacerbate and perpetuate collapse. Management must focus not only on reducing the number of critical stressors influencing an ecosystem but also on breaking feedbacks between stressors. We demonstrate the importance of multidecadal monitoring programs in measuring state variables that can inform quantitative predictions of collapse as well as help identify management responses that can avert system-wide collapse.

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This article provides an in-depth assessment of lone actor terrorists' attack planning and preparation. A codebook of 198 variables related to different aspects of pre-attack behavior is applied to a sample of 55 lone actor terrorists. Data were drawn from open-source materials and complemented where possible with primary sources. Most lone actors are not highly lethal or surreptitious attackers. They are generally poor at maintaining operational security, leak their motivations and capabilities in numerous ways, and generally do so months and even years before an attack. Moreover, the "loneness" thought to define this type of terrorism is generally absent; most lone actors uphold social ties that are crucial to their adoption and maintenance of the motivation and capability to commit terrorist violence. The results offer concrete input for those working to detect and prevent this form of terrorism and argue for a re-evaluation of the "lone actor" concept.

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INTRODUCTION: All 26 antiretroviral treatment (ART) centers of Gujarat were monitored by Gujarat State AIDS Control Society under the National AIDS Control Program. A comprehensive tool is needed to identify gap in service delivery and to prioritize monitoring visits. OBJECTIVES: To supplement the existing monitoring system, identify strengths/weakness of ART centers, and give recommendations. METHODOLOGY: Scorecard was developed in spreadsheet format with 17 scoring indicators on monthly base from March 2014 onward. The centers were classified in three color zones: green (score ≥80%), yellow (score <80% and ≥50%), and red (score <50%). Visits were prioritized at centers with more indicators in yellow/red zone. The performance of centers was compared for March 2014 and March 2015. RESULTS: The statistically significant improvement was observed in indicator "ART initiation within 2 months of eligibility," while after removing red zone from analysis, four more indicators named "eligible patients transferred out before ART initiation, general clients started on ART, antenatal women started on ART, and pre-ART follow-up CD4 done" reflect statistically significant improvement. Quadrant analysis was done for some indicators, which provide insight that less number of eligible patients may be a reason for low initiation of ART at one center, and at four other centers, the possible reasons for low retention are high death rate and high lost to follow-up rate. Based on these findings, the recommendations were made to regular mentoring centers, improve coordination between ART center and care and support centers (CSCs), and conduct verbal autopsy. CONCLUSION: Scorecard is a simple and cost-effective tool for monitoring, and by highlighting low-performing indicators, it helps in improving quality of services provided at ART centers.

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OBJECTIVES: The World Health Organization (WHO)'s HIV drug resistance (HIVDR) early warning indicators (EWIs) measure antiretroviral therapy (ART)-site factors associated with HIVDR prevention, without HIVDR laboratory testing. We assessed the relationship between EWIs and HIVDR acquisition using data from British Columbia, Canada. METHODS: Eligible patients were ART-naïve, were ≥ 19 years old, had initiated ART between 1 January 2000 and 31 December 2012, had ≥ 15 months of follow-up, and were without transmitted HIVDR. Patients were followed for acquired HIVDR until 31 March 2014, the last contact date, or death. We built logistic regression models to assess the associations and predictive ability of individual indicators and of the EWI Score (the number of indicators for which a patient did not meet the criteria) on HIVDR acquisition (to any class of HIVDR, lamivudine (3TC)/emtricitabine (FTC), nonnucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside reverse transcriptase inhibitors (NRTIs) or protease inhibitors (PIs)]). RESULTS: All explored EWIs were associated with at least one class of HIVDR, with the exception of 'ART prescribing practices'. We observed a dose-response relationship between acquiring HIVDR to any antiretroviral class and an increasing EWI score in our predictive logistic regression model. The area under the curve was 0.848 (excellent discrimination). The adjusted odds ratios for acquiring any class of HIVDR for an EWI score of 1, 2 and ≥ 3 versus 0 were 2.30 [95% confidence Interval (CI) 1.21-4.38], 3.35 (95% CI: 1.86-6.03) and 7.26 (95% CI: 4.18-12.61), respectively. CONCLUSIONS: Several EWIs were associated with and predictive of HIVDR, supporting the WHO EWIs as a component of the HIVDR prevention method in settings where HIVDR testing is not routinely or widely available.

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Background: The emergence of HIV drug resistance (HIVDR) is an unavoidable consequence of antiretroviral therapy (ART), and HIVDR early warning indicators (EWIs) could specifically assess factors at individual clinics associated with HIVDR. Thus, the present study aimed to collect data on EWIs for HIV/AIDS at 42 outpatient clinics (OPCs) in 25 northern provinces and cities of Vietnam in 2012. Methods: A retrospective cohort study was conducted. Data was collected from 42 OPCs in 25 northern provinces between October and December 2012. The information was collected retrospectively from outpatient records from 2008 to 2011. Results: In total, 99.8% ART patients were prescribed the correct regimen when starting ART treatment. All facilities met the target of under 20% patients lost to follow-up at 12 months. A total of 31/42 facilities reached the goal for on-time appointment keeping and 37/42 facilities achieved the target of first-line retention after 12-month ARV treatment.  Conclusions: EWIs should be performed routinely in HIV/AIDS facilities. The data collected will contribute to monitoring, supervision, periodic assessment, and future plans for HIV/AIDS care and treatment programs in the area.