The problem of AIDS response has not only involved public health, but also had a great impact on the family burden.The objective of this study was to estimate the preventive and curative care expenditure(PCE)for AIDS of Hunan Province in 2017 based on System of Health Accounts 2011(SHA2011)by quantity,financing scheme,health provider,health function,and to analyses the factors affecting patients’ medical burden.Through stratified multi-stage sampling method, 1336 institutions were surveyed to obtain AIDS prevention and control data, and the official data collected from Health Statistical Yearbook, Health Financial Annual Reports and Government Input Monitoring System were used to estimate the AIDS PCE based on SHA2011. Univariate analyses and ordered logistic regression were used to evaluate the factors affecting the medical burden of AIDS patients.p < 0.05,OR > 1). Taking the age group under 30 as the reference, the partial regression coefficient of the age group over 60 was statistically significant  = 1.809, OR(OOPs) = 0.30).The AIDS PCE of Hunan Province in 2017 was 266.67 million, mainly flowed to hospitals and disease prevention and control institutions. The proportions of curative care expenditure(CCE) and prevention expenditure were 51.39 and 48.61% respectively. Prevention expenditure were mainly used for traditional prevention methods. All prevention expenditure and 88.52% of CCE were borne by public financing scheme. Family health expenditure accounted for 11.12% of CCE, but there were still some people with heavy burden of treatment. Non insurance, co-infection and length of stay are risk factors to the total hospitalization expensesand the out-of-pocket payments(OOPs)(all The financing structure of the PCE for AIDS in Hunan Province was relatively stable and the flow of institutions was reasonable. The functional flow of expenditure embodied the principle of “prevention first”. China should incorporate oral PrEP into the national guidelines as soon as possible to improve the allocation efficiency of AIDS prevention resources. Meantime, several measures should be taken to reduce the medical burden of AIDS patients, including expanding the scope of government assistance, adjusting insurance compensation measures, increasing the rate of patients participating in insurance,encouraging commercial insurance to join the AIDS insurance system,and controlling length of stay in hospital. AIDS is a malignant infectious disease caused by the human immunodeficiency virus (HIV). Up to now, about 37 million people are living with HIV all over the world . In ChinSome countries established National AIDS Account(NAA) by using the policy analysis framework of the National Health Accounts (NHA) . NHA modIn addition, the Joint United Nations Programme on HIV and AIDS(UNAIDS) exploited and initiated the National AIDS Spending Assessment(NASA) in 2006 to track actual expenditure on national or regional AIDS resources. A number of NASA-based studies have found that countries spend the most on prevention, with China, Nepal, and Sierra Leone accounting for 54.31, 46, and 61%, respectively , 12. AccWhether based on NHA or NASA, we can calculate the financing structure and flow direction of AIDS prevention and control fund, but we can not specify the individual burden of different groups of AIDS patients. Studies on various kinds of AIDS patients’ OOPs have been carried out through social survey. A research for OOPs incurred by individuals with HIV in Dominica showed that the OOPs exhibited regressivity, with lower-wage patients spending proportionally more to receive care . In CanaHealth Statistics Yearbook, Health Financial Yearbook, Government Input Monitoring Data of Hunan Province in 2017, etc. The other was procured from the sample institutions.The basic data used in this study were obtained from the current health expenditure research project in Hunan Province. Data were divided into two categories. A class of data were collected from official statistic data of Health Commission of Hunan Province, such as In this study, the multi-stage stratified sampling method was used to determine the sample institutions. In the first stage, Changsha, Yueyang, Zhuzhou, Yongzhou and Hengyang were selected as sample areas based on the comprehensive consideration of the economic development, demographic characteristics, medical and health conditions, and cultural differences. The second stage was to select one district and three counties from the monitoring cities. The third stage was to choose medical and public health institutions in the monitoring areas at all levels according to the type of institutions of administrations structure. The sample data were obtained from 76 hospitals, 28 public health institutions, 36 community health service centers, 102 township hospitals, 720 village clinics and 168 private clinics.The survey data included the income and expenditure of AIDS prevention and treatment services carried out by the sample institutions throughout 2017. Preventive data were obtained from the questionnaires completed by sample institutions, involving the information of human input, income, expenditure and special subsidy for AIDS prevention. The curative care data were derived from the information system of the sample institutions, involving age, gender, disease, length of stay, total and detailed costs of treatment, types of insurance coverage, insurance reimbursement, and OOPs of patients, but did not contain any information that may infer personal identity. After the invalid or false information was excluded in this study, there were 10,335,678 items involving the treatment and nursing expenditure, of which 1018 items of AIDS treatment were included. The sample of AIDS data were collected from 196 outpatients and 822 inpatients from 0 to 85 years old .Data were recorded and sorted with SPSS 17.0 and STATA 12.0 were used to calculate the PCE for AIDS.The PCE of AIDS based on SHA2011 framework refers to the monetary performance of residents’ AIDS prevention and curative service consumption consumed by a country (region) in a certain period , excluding capital investment such as fixed assets construction. The preventive care is limited to primary and secondary prevention in SHA 2011, so the expense of antiretroviral drugs for HIV-infected people is excluded in prevention expenditure, but it is included in the CCE in our study. The PEC formula for AIDS can be found in “A System of Health Accounts 2011 Edition” .Over 90% of CCE of AIDS was spent on inpatient services. Taking the hospitalization cost as the object, the total hospitalization expense and OOPs were studied on AIDS patients with different gender, age, length of stay, insurance plan and opportunistic infection. For that the inpatient data were distributed unnormally, Mann-Whitney U test was performed on the data among the two groups, and Kruskal-Wallis H test was carried out on the data between the multiple groups. Spearman correlation analysis was conducted to determine the relationship between length of stay and expenditure. Based on the results of univariate analysis, variables were selected for ordered logistic regression analysis to evaluate the factors affecting the Totalexp and OOPs(α = 0.05).In 2017, the PCE for AIDS in Hunan Province was 266.67 million RMB, accounting for 5.29% of the expenditure of prevention and treatment of infectious disease.From the functional distribution of PCE, 51.39% was used for prevention and 48.61% for treatment. As for the treatment expense, 45.24% (58.66 million RMB) expenditure was spent on free antiretroviral drugs for HIV-infected patients provided by the government, 54.76% was used to pay for other health care services (including treatment of opportunistic infections) for AIDS patients. The expenditure of preventive care was mainly applied to education of AIDS prevention, free distribution of condoms, the purchase of HIV testing reagents and HIV testing services, HIV mother-to-child transmission testing, free screening of pregnant women, etc.52.56% of the PCE for AIDS occurred in the general hospitals, and 26.47% went to disease prevention and control institutions. Maternal and child health care hospitals occupied 13.00%. Blood collection and supply institutions accounted for 6.52%. While the expenditure for other institutions was less than 2.00%. The expenditure of prevention mainly went to the institutions of disease control and prevention (51.52%). Almost all curative care expenditure went to general hospitals(99.64%)  of AIDS patients was 8415.22 RMB, which was higher than that of the whole population in this area in that year. From the perspective of the family treatment costs of patients in all age, the differences of Totalexp among groups of other factors had statistically significance(P < 0.001). There was no significant correlation between gender and OOPs(P = 0.288 > 0.05). The age, length of stay, insurance and opportunistic infection were correlated with OOPs, and the differences were statistically significant (P < 0.001).To seek out the factors about the family burden, the survey data of 822 inpatients were statistically analyzed. According to the results of nonparametric test Table and analyOR = 1.809, OR(OOPs) = 0.30. It was found that compared with the group under 30, the age group over 60 had higher hospitalization costs and lower OOPs. Taking self-paying patients as the control group, the partial regression coefficients of urban employee insurance and urban and rural residents groups were statistically significant, OR = 0.239/0.158, OR(OOPs)=0.0.01/0.13, indicating that urban employee insurance and urban and rural residents groups were lower than that of self-paying patients either on Totalexp or OOPs. Taking the group of none opportunistic infection as the reference, the partial regression coefficient of the group with opportunistic infection was statistically significant, OR = 11.056, OR(OOPs) = 75.49. The partial regression coefficient of length of stays was statistically significant, OR = 1.41, OR(OOPs) = 1.06.Table At present, there is no universal standard to measure the scale of the cost of AIDS prevention and treatment. In the international community, the PCE of AIDS is often discussed from the perspective of financing structure. In recent years, China’s government has continuously increased its investment in the prevention and control of AIDS. In 2017, China’s fiscal expenditure was almost 7 billion RMB on its AIDS response, an increase of more than 5.4% from 2016 . Taking From the flow of expenditure, the expenditure of AIDS was more spent on prevention than treatment, consistent with the research of Li based on NASA . It alsoPeople living with HIV may have to bear part of the cost of services other than antiretroviral treatment. When the funding structure of CCE by age group were compared, the proportion of the family health expenditure were found to be diverse between different age groups. Patients of 40–49 y had the highest proportion of family health expenditure, which may be affected by factors that patients were not insured or co-infected. Of the patients of aged 40–49 y we surveyed, 36% received treatment by self-paying and 48.23% had comorbidity. It is worth noting that the family health expenditure of patients aged 0–9 y exceeds 30%. Children infected with AIDS mainly through mother-to-child transmission. Catastrophic health spending is inevitable if their families have to pay for the treatment of two or more patients. In recent years, the Chinese government has set up special funds to support a series of measures to block mother-to-child transmission of AIDS. Through these measures, such as free AIDS testing and antiviral treatment for infected pregnant women and babies, the mother-to-child transmission rate of AIDS has dropped to 4.9% in 2017 . SimultaConcerning the treatment cost, the result of ordered logistic regression showed that non insurance, co-infection and length of stay were risk factors. Si Cunwu’s research found that by using different payment methods of medical insurance, medical insurance can play a role in controlling the increase of costs for the suppliers and needs of AIDS treatment. Patients who have not moved to medical insurance are not only lack of awareness of cost estimation, but also medical institutions are more likely to have excessive medical practices . It can AIDS patients are vulnerable to pathogens with weaker virulence than healthy people. The resulting infectious diseases are called opportunistic infections of AIDS . Many reWith regard to the influence of hospitalization days, Xie claimed that “the longer the length of hospitalization, the higher the cost of hospitalization” , which iRegarding the hospitalization expenses of different age groups, our statistical analysis results showed that the hospitalization expenses of group over 60 y were higher than those in the group under 30 y. There were several reasons to explain this cost gap. The elderly have relatively little awareness of AIDS or AIDS testing . A familTo our knowledge, this study is the first one to give a comprehensive report about expenditures of AIDS in China at the subnational level based on SHA2011. In this study, not only the scale and flow of PCE for AIDS were calculated from a macro point of view, but also the factors affecting the medical burden of AIDS patients were statistically analyzed from a micro point of view. However, it is undeniable that the study has two main limitations. Compared with the actual situation, the AIDS preventive expenditure based on the SHA2011 framework could be underestimated, because the condoms at patients’ own expense was not taken into account. In addition, in the calculation of CCE, only considered direct medical expenses were considered, excluding indirect expenses . Therefore, our calculation of OOPs could not fully reflect the actual economic burden of AIDS families.The PCE for AIDS based on SHA2011 can not only reveal the overall situation of AIDS PCE, but also analyze the financing burden of different groups. The PCE for AIDS in Hunan Province was 266.67 million in 2017, mainly used for prevention. The financing structure was stable and the flow of institutions was reasonable. Nevertheless, there were still some groups with the risk of poverty caused by AIDS treatment. Combined with the orderly logistic regression analyses, it was suggested that the burden of AIDS patients be reduced by optimizing the allocation of prevention costs to increase investment in efficient prevention methods, expanding the scope of government assistance, adjusting insurance compensation measures, increasing the rate of patients participating in social basic insurance, and controlling length of stay in hospital. Moreover, the commercial medical insurance should be incorporated into the AIDS medical insurance system to share the patients’ burden of medical treatment.


Supplemental Digital Content is available in the text. Discharge prescription errors from the pediatric emergency department (ED) are common. Despite the implementation of clinical pathways for common infections recommending specific antibiotic therapy and aids built into the electronic health record, errors in antibiotic prescriptions for patients discharged home from the ED persist.We developed and implemented ED antibiotic discharge order panels for urinary tract infection (UTI) and skin and soft tissue infections (SSTI) that modeled antibiotic therapy from our institutional clinical pathways. We aimed to reduce antibiotic prescription errors by 50% within 6 months of implementation.P < 0.001). Individually, the baseline number of prescriptions with errors for UTI and SSTI improved from 26.1% and 32.8%, respectively, to 13.8% and 12.5% within 6 months. Sustained improvement continued for 17 months after the implementation of the order panels.With the implementation of the ED discharge order panels, the overall error rate for prescriptions for UTI and SSTI improved from a baseline rate of 29.3% to 12.6% (Development and implementation of ED antibiotic discharge order panels decrease antibiotic prescription errors for UTI and SSTI by improving compliance with institutional clinical pathways. Additional order panels should be developed and implemented for other conditions to help reduce discharge prescription errors. The choice of correct liquid or tablet formulation depends on the child’s ability to take tablets and the conversion of doses into volume for liquid formulations. Dosing errors are the most common type of prescription error in pediatrics.6 In academic institutions, prescriptions are often written by rotating residents who may have varying experience with pediatric prescriptions leading to increased risk of errors. Strategies across pediatric disciplines that have reduced medication errors include the use of e-prescriptions, electronic ordering with dosing recommendations, and indication-specific prescribing.11 Kadmon et al10 reported that despite the initial reduction of inpatient prescription errors with the implementation of computer order entry, increasing inpatient prescription error rates can return over time, with new medications in the order system and new physicians. Updating and revising clinical support tools can decrease these error rates.12Antibiotics are frequently prescribed in the emergency department (ED) for common pediatric infections, including urinary tract infections (UTI) and skin and soft tissue infections (SSTI). Prescription errors are common both at our institution and in other centers.17 They can help guide both ED management and discharge care, including outpatient prescriptions. Clinical pathways for common pediatric infections often recommend the selection of appropriate antibiotics for outpatient care.18 Antibiotic selection to treat common pediatric infections, including UTI and SSTI, should be guided by local antibiotic resistance patterns and focus on the use of narrow-spectrum antibiotics. Poole et al18 demonstrated that a clinical pathway for UTI improved narrow-spectrum prescribing for UTIs discharged from the ED and urgent care settings.Clinical pathways help to standardize care for common ED diagnoses and improve overall timely care.Our ED uses a robust library of clinical pathways to guide the evaluation and management of common pediatric infections, including UTI, SSTI, community-acquired pneumonia, animal bite, and preseptal cellulitis. Each pathway has recommendations for outpatient therapy guided by the antibiotic stewardship program using local resistance patterns. The recommendations include appropriate antibiotic guidance , weight-based dosing, dosing interval, and duration of therapy. Despite having these pathways in place, deviation from prescription recommendations and prescription errors are still common.We developed a multidisciplinary quality improvement initiative to reduce antibiotic prescription errors in the ED through the implementation of diagnosis-specific standardized discharge prescription order panels. The team created and implemented order panels modeling antibiotic recommendations of our UTI and SSTI clinical pathways. The SMART  aim was to reduce ED antibiotic prescriptions with errors for UTI and SSTI by 50% within 6 months of implementation. The intervention was a high-reliability intervention anticipated to produce sustainable improvement.https://www.connecticutchildrens.org/clinical-pathways/). The hospital clinical effectiveness committee reviews and approves all clinical pathways before implementation. The monitoring of quality metrics for the pathways occurs monthly. Each pathway undergoes an annual quality review, a triennial review with a full pertinent medical literature review, and pathway revision as necessary. Our institutional antibiotic stewardship committee guides antibiotic selection and recommendations for dosing and length of the antibiotic course for the clinical pathways.We conducted the project at an academic urban tertiary children’s hospital with an annual ED volume of 60,000 patients. The ED is staffed by board-certified pediatric emergency medicine attending physicians, pediatric emergency medicine fellows, pediatric, emergency medicine, and family medicine residents, physician assistants, and nurse practitioners. The institution has a robust clinical pathway program with many pathways involving both ED and inpatient care  . After these changes, the team shared the order panels with a variety of end users, allowing them to use the order panels in the testing area of the EHR. Based on feedback from these sessions, there was no need for additional revisions. Education for end users on the use of the order panels occurred before final implementation. The improvement team created a document with step-by-step instructions for accessing and using the order panels. Vetting of the document by physician members of the improvement team and a small group of end users led to revisions of the document to clarify the process for access and use of the order panels. This document was disseminated by e-mail to all pediatric emergency medicine attending physicians, fellows, and ED advanced practice providers, as well as residents rotating through the ED. The pediatric emergency medicine attending and the fellow on the improvement team introduced the order panels to ED staff during the monthly ED meeting in the month before final implementation.We extracted data from an existing clinical pathway dataset to track pathway utilization and specific quality metrics. All patients with International Classification of Diseases, Tenth Revision (ICD 10) diagnostic codes for UTI and SSTI, who met the criteria for the respective pathway, were included in the quality dataset Table . We onlyBaseline percentage of prescriptions with errors were reviewed for 5 months from January 2018 through May 2018 before order panel implementation. We introduced the discharge antibiotic order panels for use in the ED on June 4, 2018. Data after implementation were reviewed monthly from the clinical pathway dataset in the same manner as the baseline data. Overall, we tracked 17 months of data after the implementation of the order panels until October 31, 2019.19Given the week-to-week and month-to-month variations in the number of cases of UTI and SSTI, which met clinical pathway criteria, we grouped cases in sets of 10 for data analysis. Data were plotted and tracked for both UTI and SSTI on statistical process control charts. Mean shifts were plotted after 8 consecutive data points above or below the mean with an identifiable cause, signaling special cause variation.This project was deemed to be a quality improvement project and not human subjects research. Therefore, the institutional review board did not require review and approval.P < 0.001) for both diagnoses combined (Table The baseline mean rates of discharge antibiotic prescriptions with errors for UTI and SSTI were 26.1% and 32.8%, respectively. After the implementation of discharge antibiotic order panels, the mean percentage of UTI prescriptions with errors initially decreased to 8.8% (Fig. By far, the most common type of error was the wrong antibiotic dosing interval, both pre- and postimplementation of the order panels Fig. . The wroThe development and implementation of antibiotic discharge order panels for UTI and SSTI decreased prescription errors by improving compliance with institutional clinical pathways. The overall reduction in prescriptions with errors 6 months after the implementation of the order panel for UTI and SSTI was 47.1% and 61.8%, respectively. These results nearly met our goal of a 50% reduction in errors for UTI and surpassed the goal for SSTI.22 Implementation of our discharge antibiotic order panels accomplished improvement both in the realm of standardizing care and promoting the use of narrow-spectrum antibiotics and appropriate duration of therapy.Clinical pathways provide standardized, evidence-based care. Any intervention that can increase compliance with such pathways eliminates unnecessary variation in the treatment of common conditions. Antibiotic therapy recommended based on local susceptibility patterns of common bacterial pathogens is a crucial tenant of antibiotic stewardship. Several past studies document the use of technology and the EHR to improve antibiotic stewardship.23 Despite these references, we considered this an error as it did not align with our clinical pathways and the most current evidence-based recommendations for the treatment of UTI and SSTI.25Although the implementation of order panels achieved a significant reduction in antibiotic prescription errors, the antibiotic dosing interval remained the most common error after implementation. There was also an increase in the percentage of wrong antibiotic selection errors based on the clinical pathway. These persistent errors in antibiotic dosing interval and the higher rate of incorrect antibiotic use after order panel implementation likely reflect a population of ED providers who did not use the order panels. The majority of the dosing interval errors were from twice-daily dosing prescriptions for cephalexin for both UTI and SSTI instead of the recommended dosing of 3 or 4 times a day. These errors likely resulted from ED providers using other dosing references that suggest a dosing interval of twice daily for cephalexin.The current configuration of our EHR made it impossible to track order panel use in the EHR. The final percentages of UTI and SSTI antibiotic prescriptions with errors, 13.8% and 12.5%, respectively, demonstrate less than 100% provider usage of the order panels 17 months after implementation. Given the level 1 reliability of the interventions of both the order panels themselves and the educational initiatives around them, one would expect an 80%−90% adoption of order panel use, which is reflected in the results of our interventions. A higher reliability intervention such as a hard stop in the EHR mandating the use of the order panel may help to increase panel use and decrease the error rate even further. Future improvement work should center on such an intervention.Our improvement was immediate after implementation and sustained over 17 months. Improvements seen initially with UTI prescription errors regressed somewhat after initial improvement but remained well above the preimplementation baseline for 11 months. Factors leading to immediate improvement included ease of use of the refined and pretested order panels, initial educational initiatives, and provider buy-in. The increase in UTI prescriptions with errors 7 months after implementation most likely occurred as the impact of the initial ED provider education waned. Sustained improvement continued because of ongoing monthly education to rotating residents and ongoing provider buy-in. Larger-scale reeducation initiatives may have led to an ongoing reduction in prescription errors.This project tracked the percentage of prescriptions with errors after the implementation of antibiotic order panels. However, as previously noted, it was unable to monitor for actual use of the order panels. There was a significant improvement in error rates, but we are unable to demonstrate that the implementation of the discharge order panels directly led to all of the improvement. With the project, there may have been an observer effect where provider awareness about the project leads to increased vigilance of following the institutional pathways without the use of the order panels. This possibility could be especially true given the immediate improvement after implementation but would be unlikely to explain the sustained improvement. Over time, with the introduction of more clinical pathways, providers may have been more aware of the institutional pathways and more apt to follow the pathway recommendations without explicitly using the discharge order panels. In the future, establishing a way to directly monitor the use of the ED order panels within the EHR could better quantify their effect.Another limitation of our project could have been patient-level factors that lead to deviation in therapy from our clinical pathways for UTI and SSTI. Though the improvement team conducted a chart review for every patient with a prescription error, there may have been factors not documented in the chart, which would have led to deviation from the clinical pathway, therefore, increasing the number of prescriptions with errors. This circumstance may explain some of the incorrect antibiotic selection and antibiotic duration but would be less likely to explain antibiotic dosing and frequency errors.A final limitation of this project is that we evaluated a process measure of prescription errors and not a specific outcome measure such as clinical improvement or treatment failure and subsequent need for hospitalization. We also only know what antibiotic was prescribed, and given the pathway recommendations for more frequent dosing of medications such as cephalexin, we do not know if the patient filled their prescription or if they completed the antibiotic as prescribed, including the appropriate dosing interval and duration of therapy.The next steps of this project should focus on further integrating the current order panels into the ED system and expanding the order panels to other domains. Further integration of the discharge order panels into the EHR, such as integrated prompts suggesting their use based on discharge diagnosis, would likely lead to increased provider use of the order panels and a further improvement in prescription errors.Expanding the discharge order panels to other clinical pathways that incorporate outpatient antibiotic management may lead to improved compliance with local pathways or consensus recommendations. Also, this work could be expanded from the ED to the inpatient arena for patients discharged home requiring additional oral antibiotic therapy. This work can also serve as a model for other pathways or clinical conditions where specific outpatient prescription therapy outside of antibiotics is required and would benefit from standardization.With the implementation of discharge antibiotic order panels, the percentage of prescriptions with errors for UTI and SSTI improved by 47.1% and 61.8%, respectively, by increasing compliance with institutional clinical pathways. The initial improvement was sustained for 17 months following the order panel implementation. Future work should concentrate on expanding these panels to other pathways and conditions. Also, higher reliability interventions, including EHR prompts to use the order panels, could sustain and increase the scope of the improvement initiative.The authors have no financial interest to declare in relation to the content of this article..Danny Vo assisted with the development of the order panels studied in this project.


Concrete exhibits a complex mechanical behavior, especially when approaching failure. Its behavior is governed by the interaction of the heterogeneous structures of the material at the first level of observation below the homogeneous continuum, i.e., at the mesoscale. Concrete is assumed to be a three-phase composite of coarse aggregates, mortar, and the interfacial transitional zone (ITZ) between them. Finite element modeling on a mesoscale requires appropriate meshes that discretize the three concrete components. As the weakest link in concrete, ITZ plays an important role. However, meshing ITZ is a challenging issue, due to its very reduced thickness. Representing ITZ with solid elements of such reduced size would produce very expensive finite element meshes. An alternative is to represent ITZ as zero-thickness interface elements. This work adopts interface elements for ITZ. Damage plasticity model is adopted to describe the softening behavior of mortar in compression, while cohesive fractures describe the cracking process. Numerical experiments are presented. First example deals with the estimation of concrete Young’s modulus. Experimental tests were performed to support the numerical test. A second experiment simulates a uniaxial compression test and last experiment simulates a uniaxial tensile test, where results are compared to data from the literature. Concrete is a very versatile material, applied in different types of constructions around the world. Some advantages of this material are its ability to mold complex shapes, its fire resistance, and resistance to atmospheric conditions. Economic factors also contribute to the wide use of concrete structures. However, its mechanical behavior is complex. One difficulty in modeling concrete structures is the definition of constitutive laws that are able to describe its nonlinear behavior and the process of cracking.Concrete exhibits a complex mechanical behavior, especially when approaching failure. Its behavior is mainly governed by the interaction of the heterogeneous structures of the material at the first level of observation below the homogeneous continuum, i.e., at the mesoscale at which concrete is assumed to be a three-phase composite composed of coarse aggregates, mortar, and the interfacial transitional zone (ITZ) between them .Macroscopic material models, where concrete is considered as continuous and homogeneous, are able to reproduce aspects of phenomenological behavior, but are not sufficient to establish cause-effect relationships between geometrical and physical properties of the heterogeneous components and its mechanical response. For this reason, with the increasing capacity of computers, mesoscale modelling has been developed in recent decades ,10,11,12Finite element modeling on a mesoscale requires appropriate meshes that discretize the three concrete components. Zhou et al.  investigThe concrete mechanical behavior is strongly influenced by the initiation and propagation of internal microcracks. The laws for crack initiation and evolution follow the concepts and principles of Fracture Mechanics. A well-recognized fracture approach for concrete is the cohesive crack implemented in the Fictitious Crack model (FCM) proposed by Hillerborg et al. . Zero-thThis work addresses the mechanical behavior of concrete in a 3D mesoscale finite element model. Softening behavior is represented differently in compression and tension. It proposes to combine damage plasticity to account for compressive softening behavior and interface cohesive fracture elements to account for tensile softening and cracking. The simulations are implemented using the public finite element library PZ. The mesoscale simulation requires the description of the three components: coarse aggregate, mortar, and the interfacial transitional zone (ITZ). Coarse aggregate is considered as an elastic material for simplicity because its resistance is much higher than that of mortar for the concretes analyzed in this work. Mortar is described as an elastoplastic material where damage controls the softening of the material in compression. In tension, mortar may crack. For that, cohesive fractures are adopted. The ITZ is described as a zero-thickness interface between aggregate and mortar elements. Thus, the mesh consists of 3D solid elements and 2D interfaces between mortar elements and between mortar and aggregate elements.The elasticity problem is given by the equilibrium equation:Coarse aggregate is considered as an elastic material for simplicity because its resistance is much higher than that of mortar for the concretes simulated in this work.Mortar is described as an elastoplastic material. The Mohr-Coulomb yielding criterion is adopted. Softening behavior is represented differently in compression and tension. For compression, a damage model is adopted. For tension, cohesive fractures are adopted.Mortar may exhibit some plastic deformation. Even when simulating a uniaxial test, the stress state of mortar elements is not uniaxial. Thus, an appropriate yielding criterion is necessary. The classic Mohr-Coulomb criterion  states tFor the Mohr–Coulomb criterion to fit a given uniaxial tensile strength The Mohr-Coulomb model is simulated, in the numerical tests of this work, without hardening. Thus, in a uniaxial test, the compressive stress would remain constant after yielding. Softening is described by means of a damage plasticity model where damage is function of plastic deformations.Damage can be defined as a collection of permanent microstructural changes concerning material mechanical properties, such as presence (and development) of microcracks and cavities. Not only is the withstanding capacity of the material affected, but also its stiffness. In this context, the elasticity law is given byThere are several possibilities for coupling plasticity and damage effects . In thisThis approach provides a simple way to separate the damage and plastic processes. Plastic effects, driven by the effective stresses, can be described independently from damage ones and vice versa .In this work, damage is split into tensile d Fenves . The MacIn the discrete crack modeling, cracks are modeled as displacement discontinuities between elements. The fictitious crack model ,20 or coequation :σcohesivi et al. .The interfacial transition zone (ITZ) is the weakest link between the aggregate and the mortar matrix. The properties of the ITZ are not fully understood yet, however it is well accepted that it has large heterogeneity and high porosity and its strength is lower than the mortar . The elag et al.  adopts YIn this work, ITZ is simulated as a zero-thickness interface element. The stress response of the interface element is given as Two crack models are implemented for ITZ: a cohesive model . PZ is a general finite element approximation software, which incorporates a variety of element geometries, variational formulations, and approximation spaces. It contains modules for a broad-classes of technologies such as system resolution, finite element geometric approximation, finite element approximation spaces , and others), and mesh adaptivity. The library allows the implementation of specific finite element formulations, such as those required for this work, namely, damage plasticity model and cohesive fractures.The simulations were implemented in C++ language using the object-oriented scientific computational environment PZ , 809 kgTests indicate an average concrete Young’s modulus of Mesh generation plays an important role in the mesoscale simulation. Different techniques to construct 2D and 3D mesoscale meshes can be found in the literature ,12,13,26The first step was to develop an aggregate shape generator, to create a variety of polyhedrons by randomly tweaking geometric parameters such as the number of vertices and their coordinates. This was an empirical stage, and the parameters range were set based on the visual resemblance between the resulting shapes and actual aggregates. The product of this step is shown in Fifty shapes were generated and scaled accordingly to match the actual concrete mix. The reference concrete was the one used in the laboratory tests. From the test mix, we obtained the aggregate-to-cement ratio and the particle size curve .A cube of side length of 46.4 mm was created, with the dimensions required to accommodate the 50 aggregates with the restriction of the aggregate-to-cement ratio. To determine the scaling factor of the aggregates, their minimum dimension was compared to the nominal aperture size of the desired sieve. For every sieve of the granulometric curve, new shapes were created until their correspondent aggregate volume was reached. After all the shapes were created, they were manually inserted inside the cube specimen and checked for noncolliding objects. It created the single block mesh, as shown in The single block mesh is used to construct a larger mesh. The set of aggregates was multiplied (8 blocks) and disposed into a cube twice the original side length, i.e., 92.8 mm, that ended up with 400 aggregates objects. At every insertion, the set of aggregates was rotated in a different way to eliminate symmetry as much as possible. The positions of the aggregates that were originally close to the boundary were tweaked once they were inserted into the bigger cube. This was necessary to avoid having sections planes with no aggregate in middle of the bigger cube. The obtained 8-blocks mesh is shown in In order to estimate the Young’s modulus of concrete, it is not necessary to consider all the non-linear features described in  Qingbin , and Con Qingbin . Mortar First simulations were carried out with Thus, predicted values are between The mesoscale model is applied to a uniaxial compression test and compared to the simulations of Huang et al. . The simIn this 3D mesoscale model, concrete is assumed to be a three-phase composite composed of coarse aggregates, mortar, and the interfacial transitional zone (ITZ) between them. The adopted data for each phase is described, based on Huang et al. .Coarse aggregate is considered as an elastic material. The elastic parameters are adopted with Young’s modulus of Mortar is described as an elastoplastic material. The nonassociative Mohr-Coulomb yielding criterion is adopted with cohesion The interfacial transition zone (ITZ) is simulated as a zero-thickness interface element with a brittle crack model. The stress response of the interface element is given as Vertical displacement is applied to all nodes on the upper face of the mesh and the opposite face is fixed vertically. The reaction force divided by the cross-section area provides an equivalent stress and stress-strain curves are obtained. The vertical load was applied in the three axes directions . The predicted strength values are The 3D mesoscale model is applied to a uniaxial tension test. Input data is the same as the previous compression test , except ITZ is modelled as a cohesive fracture following Hordijk  equationg et al. . The cohg et al. . It can g et al.  that areA 3D mesoscale finite element model of concrete was developed. Concrete is assumed to be a three-phase composite composed of coarse aggregates, mortar, and the interfacial transitional zone (ITZ) between them. The constitutive laws for each of the three phases are described. A damage plasticity model is adopted to describe the compressive behavior of mortar. In tension, mortar softening is described by a cohesive fracture model. Coarse aggregates are modelled as a linear elastic material, for simplicity, but the nonlinear framework developed for mortar could be applied to coarse aggregates. Special attention is devoted to ITZ. ITZ is the weakest link between the aggregate and the mortar matrix. The properties of the ITZ are not fully understood yet, however, it is well accepted that it has large heterogeneity, high porosity, and its strength is lower than the mortar. Modeling ITZ as 3D solid elements is a challenging issue, since ITZ is only 10–50 μm thick, what would produce very expensive finite element meshes. An alternative is to represent ITZ by zero-thickness interface elements, which is adopted in this work. The ITZ thickness and Young’s modulus are taken into account when defining the interface constitutive law. Two crack models are implemented for ITZ: a cohesive model (similar to mortar’s) and a brittle crack model.Numerical experiments are presented. First example deals with the estimation of concrete Young’s modulus. Experimental tests were executed to support the numerical test. The mesh generation process is described. The coarse aggregate size distribution is represented in the finite element mesh. Simulations were carried out varying ITZ properties and mesh refinement. Predicted values are between A second experiment simulates a uniaxial compression test from Huang et al. . The preLast experiment simulates a uniaxial tensile test, also from Huang et al. . The preg et al.  that areThe results confirm the numerical strategies and models proposed. The use of interface elements for ITZ led to good results in simulating both the Young’s modulus problem and uniaxial strength tests. Results also confirm the strategy of combining compressive damage plasticity and cohesive fractures.


Eighty percent of households with food animals owned chickens and 78% of those with chickens owned fewer than 10 birds. Among the households with small-scale food animals, 21% reported giving antibiotics to their food animals within the last six months. Simulated client visits indicated that veterinary sales agents frequently recommended inappropriate antibiotic use, as 66% of sales agents recommended growth promoting antibiotics, and 48% of sales agents recommended an antibiotic that was an inappropriate class for disease treatment. In contrast, few sales agents (3%) were willing to sell colistin, an antibiotic banned for veterinary use in Ecuador as of January 2020, which supports the effectiveness of government regulation in antibiotic stewardship. The cumulative evidence provided by this study indicates that veterinary sales agents play an active role in promoting indiscriminate and inappropriate use of antibiotics in small-scale food animal production.Small-scale food animal production has been celebrated as a means of economic mobility and improved food security but the use of veterinary antibiotics among these producers may be contributing to the spread of antibiotic resistance in animals and humans. In order to improve antibiotic stewardship in this sector, it is critical to identify the drivers of producers’ antibiotic use. This study assessed the determinants of antibiotic use in small-scale food animal production through simulated client visits to veterinary supply stores and surveys with households that owned food animals ( Small-scale food animal production is a vital source of food security and income for families and individuals in low- and middle-income countries (LMICs) and provides opportunities for poverty reduction and improvements in gender equity . HoweverThe spread of clinically-important drug resistant bacteria between small-scale food animals and humans is well documented . AntibioWhile the use of antibiotics in industrial food animal production is gaining attention, the impact of antibiotic use by small-scale producers is of growing concern. These producers often use antibiotics indiscriminately as a feed or water additive in order to promote growth, despite a lack of knowledge regarding the purpose, appropriate application, or dose of antibiotics ,20. All In response to increasing ABR, certain classes of antibiotics have been identified as “last-line” drugs, as bacterial infections that are resistant to these antibiotics would have few alternative treatments . AdditioIn a previous study, the authors conducted in-depth interviews with small-scale producers in highland Ecuador, where 84.5% of rural households and 28.5% of semi-urban households raised livestock or poultry . Nearly In addition to understanding the behaviors of small-scale producers, it is important to understand the attitudes, incentives, and behaviors of those who sell antibiotics. In Ecuador, as in many LMICs, antibiotics can be obtained over the counter without a prescription, which may lead to their misuse by small-scale livestock producers. Additionally, limited regulation and control of antibiotic use leads to a disparity between ideal and real-world antibiotic use practices . MoreovePrior studies have examined the contribution of antibiotic use by small-scale livestock producers to ABR, but few have examined upstream influences on producers’ decisions to use antibiotics for their livestock . In thisSurveys were conducted with 117 households who owned small-scale food animals. Over half (54%) of the households owned fewer than 10 animals and approximately one-fifth (21%) had between 11 to 20 animals. Of the households with food animals, chickens were the most common  species owned, and 92% of the households with chickens owned fewer than 20 birds. Nearly one-fifth (21%) of the households that owned food animals reported using antibiotics with their animals within the last six months, with poultry  being the most common  type of food animal to receive antibiotics. Most households received information for how to apply antibiotics either orally (42%) or in a written format 38%; see .Simulated client (SC) visits were made across the six neighborhoods to 38 veterinary supply stores in the growth promotion scenario and 40 stores using the disease treatment scenario. More than 80% of the stores carried antibiotic products. The most common professional role of the sales agent was employee, with other sales agent roles being store owner or veterinarian see . When presented with the growth promotion scenario, 37% of sales agents immediately recommended antibiotics to increase growth see . After tIn the growth promotion scenario, 66% of sales agents (25 of 38) recommended the use of antibiotics for growth promotion as their initial or final recommendation see , althoug2 = 1.138, p = 0.286), in the growth promotion scenario, there was a significant association between the presence of a veterinarian and the sales agents’ final recommendation  in this scenario  recommended the use of antibiotics to treat the sick chickens as their initial or final recommendation see . Of the In addition to the development of a novel methodology to assess veterinary healthcare provider behavior, the results of this study suggest that antibiotics are commonly used in small-scale food animal production. Veterinary shop sales agents frequently recommend antibiotic use for growth promotion, disease prevention, and disease treatment for small-scale food animal production. Furthermore, these sales agents frequently recommend inappropriate antibiotic classes for veterinary use. This evidence supports the hypothesis that sales agents play an active role in promoting the use of antibiotics in small-scale food animal production. Given the association between veterinary antibiotic use in food animal production and antibiotic resistance, this information provides crucial insights to inform antibiotic stewardship ,3,4,5,6.Multiple influences can inform a veterinary sales agents’ antibiotic recommendation practices. Deficits in resources, particularly inadequate training, can prevent sales agents from providing effective treatment and appropriate antibiotic recommendations. This was supported by the finding that the presence of a veterinarian was associated with final antibiotic recommendation in the growth promotion scenario, indicating that the additional support and resources provided by an on-site veterinarian may influence sales agent antibiotic recommendation practices . InadequThe proportion of sales agents who recommended antibiotics for growth promotion indicates that they may be encouraging inappropriate antibiotic use. Any recommendation of antibiotics for growth promotion is inappropriate, as this encourages indiscriminate use. In the growth promotion scenario, 37% of sales agents immediately recommended antibiotics for growth promotion, and the proportion of recommendations increased to 61% when the SC asked specifically if antibiotics would be appropriate for that use. As indiscriminate antibiotic use for growth promotion has been identified as a target for intervention, this finding highlights a possible driver of this phenomenon . While tIn addition to the large proportion of sales agents who recommended the use of antibiotics, the classes of these products that were recommended are also problematic. Given that the sales agent did not establish previous antibiotic treatment and did not have access to any additional diagnostic information, the only appropriate antibiotic classes would be low caution, as these antibiotics are an appropriate first-line of treatment . In the 2 = 4.316, p = 0.038), which suggests a relationship between the presence of a veterinarian and likelihood to concede to customer requests regarding antibiotics. This finding has potential implications on the relationship between the resources and training provided to sales agents and the likelihood that they would appease customer requests for inappropriate antibiotics. A 2019 study of veterinarians in Ecuador found that veterinarians frequently felt pressure to prescribe antibiotics, but it was not found that a significant relationship existed between veterinary antibiotic prescription practices and veterinary specialization or years of experience [In the growth promotion scenario, there was a significant relationship between veterinarian presence and recommendations for antibiotics after probing questions by the SC  in the disease treatment scenario offered to sell colistin to the SC. It is important to note that these data were collected in late 2019, during the regulatory grace period following Ecuador’s announcement of a colistin ban, when stores were discouraged from selling colistin but were legally permitted to sell their remaining colistin inventory before it would be banned in January 2020. This delayed implementation led to concerns that there may be a short-term increase in colistin sale and use, given sales agents’ desire to liquidate their remaining inventory, but this evidence contradicts that concern. This finding provides compelling evidence that regulatory restrictions can effectively curb inappropriate antibiotic use in an LMIC.The SCM described in this study is, to our knowledge, the first use of this methodology to determine veterinary recommendation practices. Observational and self-reported methods alone are insufficient to characterize sales agent behavior, as purely observational methods can be confounded by customer behavior, and self-reported methods can be vulnerable to various sources of bias . The metThese findings have several implications for antibiotic stewardship policy. Given the frequency of veterinary sales agents’ inappropriate recommendations of antibiotics for growth promotion as well as the recommendation of inappropriate antibiotic classes, these sales agents may be an effective target for an upstream intervention to reduce drivers of inappropriate antibiotic use. Substitution of preventive and non-pharmaceutical interventions for antibiotics has been identified as a primary strategy in reducing the spread of veterinary ABR . PotentiThere were several limitations to this study. From this information alone, one is unable to make causal statements regarding relationships between sales agent characteristics and recommendation practices or between antibiotic recommendation practices and actual antibiotic use. This study did not aim to compare antibiotic use or recommendation by scale of production. A mixed-methods study of antibiotic use in Ghana and Kenya found that medium- and large-scale poultry farmers’ antibiotic use practices were not correlated with animal service providers’ recommendations . FurtherSales agents may be less likely to recommend the use of antibiotics for certain species, and this study was not able to characterize sales agent behavior for different species of food animals. The results of the SCM scenarios, focused solely on chickens, may be different for different species of animals. Although, the survey respondents indicated that antibiotic use for small-scale poultry production was commonplace. Future studies could investigate this potential disparity in antibiotic recommendation practices by incorporating a range of food animals into SCM scenarios. Furthermore, future studies could assess the nuanced relationships between members of the supply chain to improve understanding of the influence of multiple stakeholders on antibiotic use in various types of food animal production.The study was conducted between 2019 and early 2020 in six peri-urban neighborhoods east of Quito, Ecuador in the Andean highlands: Quinche, Yaruqui, Checa, Pifo, Puembo, and Tumbaco. These neighborhoods include 53 geographically defined areas where small-scale livestock production is common and have a total population of approximately 204,000 inhabitants. This study included SC visits to veterinary supply stores and household surveys. All participants were at least 18 years old and fluent in Spanish. This study and all procedures were approved by the Bioethics Committee at the Universidad San Francisco de Quito (#2017-178M), the Ecuadorian Health Ministry (#MSPCURI000243-3), and the Committee for Protection of Human Subjects at the University of California Berkeley (#2019-02-11803).Through random sampling of veterinary supply shops stratified by neighborhood to ensure proportionate representation, 40 veterinary supply stores were selected from a sampling frame of 81 stores previously identified and mapped in the six neighborhoods. Stores were eligible for selection if they sold veterinary supplies for food animal production. Each store was visited twice by trained simulated clients, with one visit being a disease treatment scenario and one visit being a growth promotion scenario . Investigators ensured an interval of two months between visits to each store and that each SC only visited each store once, in order to avoid undermining the simulated client interaction. Sales agents included male and female cashiers, store owners, and veterinarians. The SCM enables investigators to characterize authentic sales behavior through realistic but standardized client interactions. The methodology requires that the simulated client accurately represents a typical customer, is comfortable with the data collector role, understands the subject material, and has strong recall abilities . This fuInteractions lasted between 10 and 20 minutes and included a scripted combination of background information, requests for advice, and probing questions. In the growth promotion scenario, the SC explained that he or she has 25 chickens, which appear to be growing slowly compared to a neighbor’s chickens, and then asked for recommendations see . FollowiIn the disease treatment scenario, the SC explained that he or she has 15 chickens that appear to be ill see , and thaThe SCs concluded each interaction in both scenarios by thanking the sales agent and explaining that they could not make a purchase at that time. Immediately following the interaction, the SC walked out of view of the storefront to a private location and immediately completed a standardized survey questionnaire that described the sales agents’ recommendations, statements, and behaviors of interest. At the end of each day, completed questionnaires were given to the field coordinator, who subsequently coded the data and entered them into a spreadsheet for analysis.Households were enrolled in this study if they met the following inclusion criteria: (1) there was a primary childcare provider present over the age of 18 years or legally emancipated; (2) there was a child present in the household between age 6 months to 5 years; (3) the household owned food animals; (4) consent was provided by a primary childcare provider to participate in the study. One hundred and seventeen households with small-scale food animals were recruited and enrolled into the study. The 2019 survey addressed a variety of topics related to food animal production and child health . While small-scale production lacks a formal cutoff point regarding the number of animals produced, this study considers small scale animal producers as individuals or households who own fewer than 500 food animals. Producers of any type of food animal were included in this study. In addition to demographic information, survey questions measured animal ownership and the use of antibiotics in food animal production.p-value less than 0.05 indicates that the two variables are associated with one another. The class of each antibiotic recommendation was analyzed to assess the proportion of antibiotic recommendations that belonged to each of the European Medicine Agency’s antibiotic categories for stewardship [Survey responses and records of simulated client interactions were converted to comma separated values (.csv) files and analyzed using R version 3.6.2 and R Studio version 1.1.383 . The average initial and final antibiotic recommendations were calculated and chi-square tests for independence were conducted to determine potential bivariate relationships between store characteristics and antibiotic recommendation practices. A chi-square test for independence tests whether two categorical variables are associated with one another, where a chi-square test statistic corresponding to a wardship . These cwardship . InvestiThe findings presented here suggest that households practicing small-scale food animal production are often encouraged to use antibiotics unnecessarily. Results from the novel simulated client method indicate that veterinary sales agents frequently recommend antibiotics for growth promotion and commonly recommend inappropriate classes of antibiotics for disease treatment. There was some evidence that antibiotic recommendation practices may be related to sales agent training. Few sales agents were willing to sell colistin, indicating the effectiveness of Ecuador’s recent ban despite its lack of enforcement. Given international efforts to promote small-scale food animal production in LMICs as a method of economic empowerment, proponents must consider traditional practices as well as new approaches to increase the prudent use of antibiotics and mitigate the public health impact of antibiotic use in small-scale food animal production systems. Finally, it is critical that supporters of small-scale production incorporate ABR considerations into their programs and policies in order to create alternatives to antibiotic use for small-scale food animal producers.


Angiosarcoma is an extremely rare and aggressive malignancy. Standard of care of localized tumors includes surgery ± radiation. Despite this multimodal treatment, >50% of the angiosarcoma patients develop local or distant recurrent disease. The role of neoadjuvant systemic therapy is still controversial and we therefore performed a systematic review of the literature to define the role of neoadjuvant systemic therapy based on available evidence. We focused on the effects of neoadjuvant systemic therapy on: 1. The success of surgical resection and 2. the long-term survival. All articles published before October 2019 on Ovid Medline, Ovid Embase, Cochrane library and Scopus were evaluated. Eighteen case reports and six retrospective cohort studies were included. There were no randomized controlled trials. This literature showed a beneficial role of neoadjuvant chemotherapy on downsizing of the tumor resulting in an improvement of the resection margins, especially in patients with cardiac or cutaneous angiosarcoma. However, no definitive conclusions on survival can be drawn based on the available literature lacking any prospective randomized studies in this setting. We advise that neoadjuvant chemotherapy should be considered, since this could lead to less mutilating resections and a higher rate of free resection margins. An international angiosarcoma registry could help to develop guidelines for this rare disease. BRCA1 or BRCA2 gene ..47].In this systematic review the terms complete response (CR), partial response (PR) and progressive disease (PD) refer to the terms as defined in the Response Evaluation Criteria in Solid Tumors (RECIST)  and wereUnfortunately, no definitive conclusions can be drawn regarding the outcome benefit of neoadjuvant chemotherapy in patients with angiosarcoma based on the current literature. All available studies were retrospective with heterogeneous, small patient groups and diverse treatment regimens with the inherent limitations. Keeping these limitations in mind, however, the retrospective cohort studies and case reports suggest that angiosarcoma is relatively sensitive to chemotherapy . Neoadjuvant chemotherapy could therefore probably be used to downsize the tumor. This downsizing could result in more resections with curative intend, less mutilating resections and a higher R0 resection rate . The studies show no clear survival benefit. Nevertheless, there is an urgent need for more studies addressing the role of neoadjuvant systemic therapy in angiosarcoma and an international angiosarcoma registry could help to develop guidelines.Neoadjuvant chemotherapy could be considered to downsize the tumor, since this could lead to less mutilating resections and a higher R0 resection rate.There is no survival benefit, but also no evidence of detriment of neoadjuvant chemotherapy.There is currently no evidence of the best possible chemotherapy regimen and apart from age of the patient, also the subtype may help define the treatment choice. In particularly for UV-exposed scalp angiosarcoma in elderly, paclitaxel is generally well tolerated and more recently also checkpoint inhibitors are showing interesting responses.An international angiosarcoma registry should be set up to collect all available data on angiosarcoma patients and help to develop guidelines.


Exposure to antibiotics in the first days of life is thought to affect various physiological aspects of neonatal development. Here, we investigate the long-term impact of antibiotic treatment in the neonatal period and early childhood on child growth in an unselected birth cohort of 12,422 children born at full term. We find significant attenuation of weight and height gain during the first 6 years of life after neonatal antibiotic exposure in boys, but not in girls, after adjusting for potential confounders. In contrast, antibiotic use after the neonatal period but during the first 6 years of life is associated with significantly higher body mass index throughout the study period in both boys and girls. Neonatal antibiotic exposure is associated with significant differences in the gut microbiome, particularly in decreased abundance and diversity of fecal Bifidobacteria until 2 years of age. Finally, we demonstrate that fecal microbiota transplant from antibiotic-exposed children to germ-free male, but not female, mice results in significant growth impairment. Thus, we conclude that neonatal antibiotic exposure is associated with a long-term gut microbiome perturbation and may result in reduced growth in boys during the first six years of life while antibiotic use later in childhood is associated with increased body mass index. In this study, Omry Koren, Samuli Rautava and colleagues report a sex-specific association between neonatal antibiotic exposure and weight and height gain during the first six years of life and showing that boys but not girls exposed to neonatal antibiotics exhibit impaired weight and height development. According to current recommendations1, all neonates with clinical signs suggesting bacterial infection, in addition to selected high-risk infants, receive empirical antimicrobial therapy. Consequently, a considerable number of neonates are treated with antibiotics during the first days of life4.Newborn infants are highly susceptible to invasive bacterial infections, and suspected infection is one of the most common reasons for admission to a neonatal unit. The incidence of blood culture-confirmed early-onset neonatal sepsis ranges between 0.5 and 0.8 in 1000 newborns5, but the clinical or microbiological long-term consequences of this exposure remain unknown. Given the causal links between the intestinal microbiome and growth, obesity, and metabolic disease7, together with data from epidemiological9 and experimental11 studies, it has been suggested that neonatal antibiotic exposure might have an impact on growth in early life12. Recently, antibiotic therapy in the first week of life was reported to be associated with decreased growth during the first year of life13, but longer-term outcomes remain unknown. Later in infancy and childhood, antibiotic use has been linked to increased risk of becoming overweight and obese12. We hypothesized that antibiotic treatment during the first days of life may exert a long-lasting effect on childhood growth by disrupting the natural gut microbial colonization process.Neonates subjected to antibiotic therapy reportedly exhibit altered gut microbiome composition during the first weeks of lifeIn this work, we investigated the association between antibiotic exposure during and after the neonatal period and child growth until the age of six years in a large unselected cohort and validated our findings in an independent cohort. We show that neonatal antibiotic exposure is associated with reduced weight and height gain during the first six years of life in boys but not in girls. After the neonatal period, antibiotic use is associated with increased body mass index (BMI) in both boys and girls. Experimental studies using germ-free mice demonstrate that fecal microbiota transplant (FMT) with fecal samples from infants exposed to antibiotics in the neonatal period results in growth failure in male but not in female mice, which suggests a causal role for the antibiotic-induced gut microbiome perturbations in the pathogenesis of the growth impairment in antibiotic-exposed boys.6/7 weeks of gestation) who did not have genetic abnormalities or significant chronic disorders affecting growth and who did not need long-term prophylactic antibiotic treatment were included in this study. Antibiotics had been administered within the first 14 days of life to 1,151 (9.3%) of the neonates in the study. A vast majority of the subjects were treated with a combination of intravenous benzylpenicillin and gentamicin as per the hospital guidelines. To differentiate between the impact of antibiotic exposure and the underlying infection, antibiotic treatment was categorized to brief empirical antibiotic treatment, which was discontinued after infection had been ruled out  and to antibiotics administered for confirmed or clinical infection (638 neonates or 5.1% of the study population). The background and clinical characteristics of the children are presented in detail in Table 14 and experimental10 studies suggest sexual dimorphism in the susceptibility to growth disturbances after early-life antibiotic exposure. Furthermore, boys were significantly more often exposed to neonatal antibiotics  consisting of all 14,969 children born at the Turku University Hospital in Turku, Finland during the years 2008–2010. Altogether 12,422 children born from singleton pregnancies at full term . Boys exposed to antibiotic treatment for infection also exhibited significantly lower height , 6.4%  of the neonates had an infection and antibiotic therapy. Relevant clinical characteristics, as mean with standard deviation (SD) or %, included birth weight Z-score , maternal pre-pregnancy BMI in kg/m2 , vaginal delivery  and gestational age in weeks . No differences in breastfeeding rates were detected between infants with neonatal infection and antibiotic treatment and non-infected infants during the first 6 months of life (Supplementary Table We next proceeded to confirm this finding in the independent German mother-child cohort Programming of Enhanced Adiposity Risk in Childhood–Early Screening (PEACHES). The PEACHES cohort consists of 1,707 offspring and their mothers recruited during pregnancy between 2010 and 201514. We, therefore, wanted to investigate whether the association between antibiotic use and child growth was dependent on the age at which the exposure occurs. Data regarding antibiotic purchases for the children in the SFBC were obtained from the Drug Prescription Register maintained by the Social Insurance Institution of Finland. The cumulative number of antibiotic courses after the neonatal period but during the first six years of life was associated with significantly higher BMI Z-scores throughout the study period in both boys  and girls  in a hierarchical linear mixed model for repeated measurements adjusted for gestational age, birth weight Z-score, mode of delivery, maternal prepregnancy BMI and neonatal antibiotic exposure. The number of antibiotic purchases was also associated with significantly higher weight Z-scores during the first six years of life in boys  but not in girls . Height development during the first six years of life was not associated with antibiotic use after the neonatal period in either boys , and 6 months (p < 0.003), demonstrating the persistence of the effect of antibiotic exposure on the microbiome . The features termed unclassified2 and unclassified3 were present mostly in the control infants , B. bifidum (11), B. breve (10), B. dentium (6), B. pseudocatenulatum (4), and B. adolescentis (3) . The differences in presence were most notable for B. bifidum  and B. longum . These species, along with E. coli and V. parvula, exhibited the most notable differences between the control and antibiotics groups of all 88 bacterial species detected with abundance >0.1% in the one-month samples ; however, difference in B. bifidum presence was notable  from antibiotic-exposed and non-exposed infants  from singleton pregnancies were included in the present study. If a woman had more than one pregnancy and delivery during the study period, only the first child was included in this study to ensure independence of the study subjects. Furthermore, chronic disease affecting growth such as genetic syndromes, significant congenital heart disease, malignancies or endocrine, and growth disturbances requiring growth hormone therapy served as criteria for exclusion from the study. Altogether 12,422 children were included in the present study.The epidemiological study is based on the Southwest Finland Birth Cohort (SFBC), which consists of all 14,946 children born in the hospital district of Southwest Finland during the years 2008–2010. Subjects born at full term  empirical antibiotic therapy, which was discontinued after infection had been ruled out, (2) antibiotic therapy for confirmed or clinical infection or (3) no neonatal antibiotic exposure. Of the 638 infants diagnosed with neonatal bacterial infection, 7 (1.1 %) had blood culture positive sepsis, 396 (62%) were deemed to suffer from clinical sepsis, 204 (32%) had neonatal pneumonia and 31 (4.9%) had other bacterial infections. Data regarding pre- and perinatal characteristics of the study subjects and their mothers including gestational age, birth weight, mode of delivery, maternal prepregnancy body mass index, intrapartum antibiotic treatment, time of commencement and duration of antibiotic therapy and antibiotic prescriptions during the first six years of life were extracted from the hospital records and national registers. Birth weight Z-scores were calculated using the recently published references specific to the Finnish population40 were used to obtain population-specific Z-scores for height, weight, and BMI. The association between neonatal antibiotic exposure and weight and height Z-scores at 6 months and 1 year and weight, height and BMI Z-scores at 2, 3, 4, 5, and 6 years of age were analyzed using hierarchical linear mixed model for repeated measurements using the “MIXED” procedure of SAS, version 9.4 . Neonatal antibiotic exposure, duration of pregnancy, birth weight Z-score, mode of delivery , time, maternal prepregnancy body mass index, and intrapartum antibiotic treatment (yes or no) were included in the model as explanatory variables. Interaction between neonatal antibiotic exposure and time was included in the model to examine whether mean change over time was different between antibiotic exposure groups. An unstructured covariance pattern was used for repeated measures. Normal distribution assumption was checked from studentized residuals. The data were analyzed separately for girls and boys. The same approach was used to analyze the association between the number of antibiotic purchases after the neonatal period and child growth during the first six years of life. The models contain the same responses. The number of antibiotic purchases, neonatal antibiotic exposure, gestational age, birth weight Z-score, mode of delivery , and maternal prepregnancy body mass index were included in the model as explanatory variables.The anthropometric data closest to the time points of 6 months and 1, 2, 3, 4, 5, and 6 years of age were used in the analyses. The Finnish growth charts16. The study investigates the long-term effect of pre-conception maternal obesity on the development of overweight and associated metabolic diseases in both mothers and their offspring. The inclusion criteria were maternal age ≥18 years, singleton pregnancy, gestational age at birth of ≥37 weeks, and maternal prepregnancy BMI ≥ 30 kg/m2 or 18.5–24.9 kg/m2 (control group). Women with a preexisting diagnosis of type 1 or type 2 diabetes or other chronic diseases before pregnancy and giving birth to offspring with malformations and/or genetic defects were excluded. For confirmation within the PEACHES cohort, we used R, version 3.6.0, (https://cran.r-project.org/) and the package nlme to perform a linear mixed-effects analysis of the relationship between neonatal infection treated with antibiotics and weight/height/BMI Z-scores together with the covariates child’s age, gestational age, mode of delivery , maternal prepregnancy BMI, intrapartum antibiotic treatment (yes/no), birth weight Z-score and any breastfeeding  during the first 6 months of life. These variables are included as fixed effects in the model, together with the interaction term between neonatal infection treated with antibiotics and child’s age. As random effects, we modeled the intercept for subjects as well as by-subject slope for the effect of child’s age. We assumed an unstructured covariance pattern for the repeated measures. Weight and height Z-scores were analyzed at 6 months and 1 year of age; weight, height, and BMI Z-scores at ages 2, 3, 4, 5 years. The PEACHES cohort study was approved by the ethics committee of the Ludwig-Maximilians-Universität München, Germany (protocol no. 165–10). Written informed consent was obtained from the participants.The PEACHES cohort consists of 1707 offspring and their mothers who were recruited during pregnancy in 23 obstetrics and gynecology departments in Bavaria (southern Germany), the University Hospital of Düsseldorf (western Germany), and parts of northern Germany at 4–6 weeks before their due date17. The clinical trial was a part of a series of prospective studies investigating the health implications of early host-microbe interaction, which included prospective gut microbiota sampling for the assessment of the impact of early exposures on gut microbiota composition and function. During the course of the clinical study, fecal samples were obtained from all participants at the ages of 1, 3, 6, 12, and 24 months for gut microbiota analyses. Altogether 13 infants exposed to neonatal antibiotics and 20 non-exposed infants were selected for this study based on sample availability. The samples were maintained at −80 °C and shipped on dry ice to the Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel. Antibiotic-free subjects were selected as controls. The study was approved by the ethics committee of the Intermunicipal Hospital District of Southwest Finland. Oral and written informed consent was obtained from all the families upon enrollment in the clinical study.The fecal samples for microbiota analyses were collected during a randomized controlled trial assessing the efficacy of maternal probiotic supplementation in reducing the risk of atopic eczema in the infant (NCT00167700) conducted at the Turku University Hospital in Turku, FinlandGerm-free (GF) Swiss Webster mice were obtained from Taconic Farms Inc.,  and maintained at the animal facility at the Azrieli Faculty of Medicine. GF mice that underwent fecal transplantat were housed in the conventional animal facility in regular cages and under standard housing protocols. All animals were housed under 12 h light–12 h dark regime and had free access to food and water; all mice were fed from the same food batch (Harlan-Teklad). The experiment was performed using protocols approved by the Bar Ilan University Animal Studies Committee.Fecal samples from each infant were transplanted to GF mice by oral gavage at 5 weeks of age. Each sample was suspended in 800 μl of sterile phosphate-buffered saline (PBS) and dissolved by vortex for 1-min. A total of 200 µl of the fecal suspension was administered by oral gavage to one to three GF mice. The process took place once, immediately after the mice were taken out of the isolator. To minimize cage effects, each treatment group was housed in at least two cages, while mice transplanted with the same sample were not housed in the same cage. Mice were followed for 43 days, and day 0 refers to the day of the fecal transplantation. Stool microbiome and weight was examined at seven-time points . Stool samples were stored at −80 °C.41. PCR reactions were carried out with the Primestar taq polymerase (Takara) for 30 cycles of denaturation (95 °C), annealing (55 °C) and extension (72 °C), and a final elongation at 72 °C. Products were purified using AMPure magnetic beads (Beckman Coulter), and quantified using Pico-green dsDNA quantitation kit (Invitrogen). Samples were pooled at equal concentrations (50 ng/µl), loaded on 2% E-Gel (Thermo Fisher), and purified using NucleoSpin Gel and PCR Clean-up (Macherey-Nagel). Purified products were sequenced using the Illumina MiSeq platform . For the shotgun samples, libraries were prepared with the NexteraXT DNA Library Preparation Kit (Illumina) and sequenced on the HiSeq2500 and NovaSeq machines (Illumina). We sequenced the 27 samples for a total of 76.9 Gb . The raw reads were submitted to the NCBI-SRA archive and are available under BioProject PRJNA606271.DNA was extracted from infant and stool samples using the Power Soil DNA Isolation Kit (MoBio) according to the manufacturer’s instructions, using a Beadbeater (BioSpec) for 2 min. Following DNA extraction, the V4 variable region of the bacterial 16 S rRNA gene was amplified by polymerase chain reaction (PCR) using the 515 F and 806 R primers, and each sample received a unique 515 F barcoded primer, in order to identify each sample during data analysis. Primer sequences used were: 515F- (barcode) 5′-AATGATACGGCGACCACCGAGATCTACACGCTAGCCTTCGTCGCTATGGTAATTGTG TGYCAGCMGCCGCGGTAA-3′ and 806 R 5′- CAAGCAGAAGACGGCATACGAGATAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT -3′Given the large variation in feature values, we transformed these values to Z-scores by adding a minimal value to each feature level (0.01) and calculating the log10 of each value. Statistical whitening was then performed on the table, by subtracting the average and dividing by the standard deviation of each feature. We then repeated the process for each patient.42. Single-end sequences were first demultiplexed using the barcode sequences. In order to improve taxonomic resolution, reads were denoised and clustered using DADA243, primers were trimmed off and single-end read were truncated to ≥150 base pairs. For taxonomy classification, final feature sequences were aligned against Greengenes database44 with 99% confidence. In order to avoid any possible contamination, the infant feature table was generated by filtering features which were not found in 40% of each sample group, and for the mouse feature table, features with <0.001% frequencies in total were removed.FASTQ data was processed and analyzed using Quantitative Insights into Microbial Ecology 2 (QIIME2) pipeline version 2018.245 (PD Whole tree) measure, referring to bacterial richness within the sample, while significant differences in bacterial richness between the groups were generated using the Kruskal–Wallis test. Beta diversity was analyzed according to weighted (quantitative) and unweighted  UniFrac46 distances in order to compare differences in gut bacterial communities between the sample groups. To evaluate the level of significance, a permutational multivariate analysis of variance (PERMANOVA) test was performed, as implemented in QIIME2 with the default of 999 permutations for both weighted and unweighted UniFrac.The analysis, for both infants and mice, was performed on a rarefied table of >10,000 reads per sample after filtration of mitochondria and chloroplast sequences. Alpha diversity was calculated using the Faith’s Phylogenetic Diversity47. The analysis and Heatmap visualization was created using Calour48.Regarding the mouse experiment, evaluation of significant differences in bacterial abundance between Abx and control transplanted groups, at all taxonomic levels, were done using the discrete false-discovery rate (DS-FDR) test according to the last day of the experiment (Day 43). dsFDR was calculated using the mean-rank test statistics with an FDR threshold of 0.05p-value of less than 0.05 was taken to indicate statistical significance. Asterisks indicate significance .Mouse body weight was normalized according to day 0, the first day of the experiment, in order to calculate changes between Abx and control transplanted mouse groups over time. Differences in weight gain fold change were assessed using unpaired two-tailed t-test. All weight data are mean ± SEM. Unsupervised Learning was performed on the rarefied and subsampled version of the 16 S rRNA feature table in order to recognize patterns in the data. Principal Component and Principal Coordinates Analysis were performed using Python version 3.5, and its sklearn package. A 2-tailed 49 v0.39 with parameters ILLUMINACLIP:NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. Each sample was assembled separately using megahit v1.1.39, with parameter -presets meta-sensitive with only scaffolds longer than 500 bp used for later analysis. To evaluate the fraction of reads that went into the assemblies and to calculate coverage for scaffolds we used bowtie2 v2.3.210 with parameters -sensitive –X 1000 -reorder. Total assembly size for the different samples ranges from 11–75 Mbp in scaffolds >500 bp long, with 93–99% of the reads mapped to the assembly. The high mapping rate suggests that our assemblies cover most of the genetic material in the samples.Twenty-seven metagenomics samples collected from 20 infants at the ages of 1 and 6 months were analyzed Table . SamplesBifidobacterium species we applied taxonomy- and differential-coverage based binning approaches50. For each sample, we used a semi-automatic procedure for genome recovery as follows. First, all genes in scaffolds longer than 4000 bps from each sample were aligned  against a database of Bifidobacterium genes collected from 456 Bifidobacterium strains taken from NCBI’s RefSeq database. Next, all scaffolds with a significant number of Bifidobacterium hits were assigned taxonomy based on the species with the highest number of hits. These scaffolds were further manually binned by plotting their assigned taxonomy, coverage, and %G + C using R. This approach allowed us to identify scaffolds that belong to the same genome but received different taxonomic assignments from closely related species. It also enabled us to identify different strains, with different coverages, for the same species. Taxonomy was assigned to the bins based on the most frequent hit to their genes. Completeness and contamination were evaluated using CheckM13. Overall, we reconstructed 54 Bifidobacterium genomes of which 36 are estimated to be >90% complete and <5% contaminated. Table To reconstruct bins for 14 to evaluate community composition. 5.5 million reads were used from each sample; species whose abundance was >0.1% were considered. Figures were created using R.We used metaphlan2 v2.6.0Supplementary InformationPeer Review File


Antibiotic‐resistant bacteria, especially multidrug‐resistant strains, play a key role in impeding critical patients from survival and recovery. The effectiveness of the empiric use of antibiotics in the circling manner in intensive care units (ICUs) has not been analyzed in detail and remains controversial. Therefore, this systematic review and meta‐analysis were conducted to evaluate antibiotic‐cycling effect on the incidence of antibiotic‐resistant bacteria.We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Web of Science for studies focusing on whether a cycling strategy of empiric use of antibiotics could curb the prevalence of antibiotic‐resistant bacteria in ICUs. The major outcomes were risk ratios (RRs) of antibiotic‐resistant infections or colonization per 1,000 patient days before and after the implementation of antibiotic cycling. A random‐effects model was adopted to estimate results in consideration of clinical heterogeneity among studies. The registration number of the meta‐analysis is CRD42018094464.p = .095). In subgroup analyses, the cycling strategy cut down the incidence of resistant bacteria more significantly than baseline period (p = .028) but showed no difference in comparison with mixing strategy (p = .758).Twelve studies, involving 2,261 episodes of resistant infections or colonization and 160,129 patient days, were included in the final analysis. Based on the available evidence, the antibiotic‐cycling strategy did not reduce the overall incidence of infections or colonization with resistant bacteria (RR = 0.823, 95% CI 0.655–1.035, Although the cycling strategy performed better than relatively free usage of antibiotics in the baseline period on reducing resistant bacteria, the cycling strategy did not show advantage when compared with the mixing strategy in subgroup analyses. In addition, these viewpoints still need more evidence to confirm. Escherichia coli, Klebsiella pneumonia, and Staphylococcus aureus presented significant resistant to antibiotics in at least 50% of isolates  claimed the primary species of bacteria such as tes WHO, . There htes WHO, . The lactes WHO, .Many strategies have emerged to control the emergence or dissemination of antibiotic‐resistant bacteria, such as hand hygiene, chlorhexidine bath, restriction of ineffective antibiotics, reducing treatment duration, antibiotic‐cycling strategy, and antibiotic mixing strategy  critically ill patients as research subjects; (2) receiving antibiotic‐cycling strategy; (3) having control period or control group; (4) providing data of resistant bacteria infections or colonization; and (5) with before–after or cluster‐randomized study designs.Studies were excluded for the following reasons: (1) did not provide enough data; (2) was not conducted in an intensive care unit; (3) used an unrepresentative cycling strategy; (4) included patients with hematological diseases as study subjects; (5) included patients less than 18 years old; and (6) did not include a control period or control group. Two authors independently screened the literature and extracted data from eligible studies according to a predesigned Excel form.The data relevant to results and characteristics of the studies were extracted from eligible articles, such as publication year, country, study design, medical setting, mixing or baseline as control period, infection or colonization, total cycling duration, each cycling period length, research quality, types of antibiotics, and incidence of antibiotic‐resistant bacteria.Disagreements between the authors were resolved by consulting a third author. Some studies provided both resistant‐infection and resistant‐colonization data, and we merged risk ratios (RRs) using the incidence of antibiotic‐resistant bacteria colonization per 1,000 patient days, considering the larger sample size for colonization than infection. If the total patient days for an ICU stay were not available, total patient days were calculated by the number of patients multiplied by the average duration of ICU stay. We chose the mixing period as the control period if a study contained both a baseline period and mixing period concerning the empirical use of antibiotics. To explore heterogeneity sources, the RRs of acquisition of gram‐positive resistant bacteria (RG+) and gram‐negative resistant bacteria (RG−) were treated as independent data in the meta‐regression analysis.The main purpose of the study is to evaluate whether the cycling strategy is a more valid measure to curb the incidence of resistant bacteria than control period, the latter includes baseline period (using antibiotic without a schedule) and the mixing strategy period. Considering before–after design with a washout period being adopted relevant to antibiotic stewardship researches, we think it is reasonable to use RRs to compare data between groups. The major outcomes were RRs of incidences of antibiotic‐resistant infections or colonization per 1,000 patient days before and after implementation of antibiotic cycling, including infections caused by RG‐ and RG + resistant bacteria. Secondary outcomes included RRs of ICU mortality, hospital mortality, ventilator‐associated pneumonia, and nosocomial infection.The quality of research was assessed independently by two authors (YL and XJL) using the National Institutes of Health’s Quality Assessment Tool for Before‐After (Pre–Post) Studies With No Control Group  of studies. Standard errors of the effect are smaller along with increasing sample size, and the points will locate the upper area of the funnel plot. Thus, data from included studies are expected to be symmetrically distributed in a funnel shape if there is no significant publication bias  guidelines . The overall pooled result after merging the RG‐ and RG + data did not present a statistically significant difference . We also collected data on the changes in common resistant bacteria affected by a cycling strategy. Pseudomonas aeruginosa and MRSA showed significant downward trends, but there were no differences among the resistant bacteria examined, including Acinetobacter baumannii, Clostridium difficile, Escherichia coli, ESBL, Klebsiella pneumoniae, Stenotrophomonas maltophilia, and VRE , and we also did not find a statistically significant difference in hospital mortality , as did ventilator‐associated infections (VAP) assessed in 6 studies . The acquisition of resistant bacteria showed a noteworthy decrease in the medical ICU subgroup  but not in the mixed and surgical ICU subgroups . In contrast to the studies using the baseline period as a control, the findings from studies using antibiotic mixing (a different class of antibiotics was prescribed to each consecutive patient) as a control did not show the effectiveness of the cycling strategy .Subgroup analyses were conducted based on the different characteristics of the included studies Table . When grp = .008), type of ICU (p = .019) and the mixing strategy as a control (p = .030). Publication bias, including an evaluation of the effect from studies with small sample sizes, was not identified by Egger’s test (p = .952) or funnel plot ]} × ∑|iA − iB|, where n is the number of considered antibiotics in this equation, iA is the proportion when all antibiotics in a given period were used in the same proportion, and Bi is the actual proportion of antimicrobials in the study  , also called the Peterson index, is calculated as following formula: 1 − {p = .632; Martínez et al., p = .013). Only one study . It means that the antibiotic stewardship measures conducted in the latter period presented more effective approaches than those performed in the previous period from available studies so far. Unfortunately, the measures for controlling nosocomial infections might be strengthened along with the progress of a study. For instance, an educational program that included controlling catheter‐related infections was implemented in the middle of the study  periods by an analysis of the combined data  Studies With No Control GroupTable S3. The PRISMA Checklist for Antibiotic‐Cycling StrategyTable S4. Characteristics of the Studies Included in the Meta‐analysis.Figure S1. Forest plot evaluating the effect of antibiotic cycling on the incidence of different types of antibiotic‐resistant bacteria.Figure S2. Forest plots evaluating the effect of cycling strategy on nosocomial infection and VAP.Figure S3. The funnel plot for publication bias.Click here for additional data file.