East North Central region has the highest prevalence of vancomycin-resistant Enterococcus faecalis in the United States

We read the article of Hayakawa et al with great interest. The report describes the growing prevalence of vancomycin-resistant Enterococcus faecalis in Michigan, a state that also has the most reports of vancomycin-resistant Staphylococcus aureus. Similar findings were reported in the tigecycline evaluation and surveillance trial (TEST). During the 2004–2009 period, 4.6% of 3,753 E. faecalis isolates were vancomycin resistant, with the highest rates of 7.6% in the East North Central region of United States. Here we report rates and trends of vancomycin-resistant E. faecalis in the East North Central region compared with national rates from 1999 to 2010

India\u27s ban on antimicrobial fixed-dose combinations: Winning the battle, losing the war?

BACKGROUND AND OBJECTIVES: India, the country with the largest market availability of antimicrobial fixed-dose combinations (FDCs), banned certain antimicrobial FDCs in September 2018. Our objective was to examine the impact of Government ban on the sales of antimicrobial FDCs. METHODS: The sales patterns of 14 of the 26 banned antimicrobial FDCs were analyzed using monthly private sector drug sales data from IQVIA (a comprehensive and nationally representative drug sales database) between January 2018 and December 2019. We carried out descriptive analyses to evaluate the trend in sales over time for banned and non-banned antimicrobial FDCs using cumulative sales volumes. RESULTS: Overall, the cumulative sales volume of banned antimicrobial FDCs declined by 75% between January and September 2018 and the same months of 2019, although some banned FDCs continued to be available in significant volumes. The effectiveness of the ban was offset by several pathways. First, the sales of combinations containing moieties belonging to the same drug-classes as the antimicrobials in the banned FDCs increased after the ban. Second, while certain formulations of particular combinations were banned, the sales of other non-banned formulation of these combinations increased. Third, in some cases, products containing new non-antimicrobial components added to the banned combinations remained available. INTERPRETATION AND CONCLUSIONS: While sales of the banned antimicrobial FDCs decreased in 2019, we identified several mechanisms that counterbalanced the ban, including implementation failure, rising sales of congeners, and products with additional non-antimicrobial components

Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis

Background Understanding of the factors driving global antimicrobial resistance is limited. We analysed antimicrobial resistance and antibiotic consumption worldwide versus many potential contributing factors. Methods Using three sources of data (ResistanceMap, the WHO 2014 report on antimicrobial resistance, and contemporary publications), we created two global indices of antimicrobial resistance for 103 countries using data from 2008 to 2014: Escherichia coli resistance—the global average prevalence of E coli bacteria that were resistant to third-generation cephalosporins and fluoroquinolones, and aggregate resistance—the combined average prevalence of E coli and Klebsiella spp resistant to third-generation cephalosporins, fluoroquinolones, and carbapenems, and meticillin-resistant Staphylococcus aureus. Antibiotic consumption data were obtained from the IQVIA MIDAS database. The World Bank DataBank was used to obtain data for governance, education, gross domestic product (GDP) per capita, health-care spending, and community infrastructure (eg, sanitation). A corruption index was derived using data from Transparency International. We examined associations between antimicrobial resistance and potential contributing factors using simple correlation for a univariate analysis and a logistic regression model for a multivariable analysis. Findings In the univariate analysis, GDP per capita, education, infrastructure, public health-care spending, and antibiotic consumption were all inversely correlated with the two antimicrobial resistance indices, whereas higher temperatures, poorer governance, and the ratio of private to public health expenditure were positively correlated. In the multivariable regression analysis (confined to the 73 countries for which antibiotic consumption data were available) considering the effect of changes in indices on E coli resistance (R2 0·54) and aggregate resistance (R2 0·75), better infrastructure (p=0·014 and p=0·0052) and better governance (p=0·025 and p<0·0001) were associated with lower antimicrobial resistance indices. Antibiotic consumption was not significantly associated with either antimicrobial resistance index in the multivariable analysis (p=0·64 and p=0·070). Interpretation Reduction of antibiotic consumption will not be sufficient to control antimicrobial resistance because contagion—the spread of resistant strains and resistance genes—seems to be the dominant contributing factor. Improving sanitation, increasing access to clean water, and ensuring good governance, as well as increasing public health-care expenditure and better regulating the private health sector are all necessary to reduce global antimicrobial resistance

Antibiotic overuse in the primary health care setting: A secondary data analysis of standardised patient studies from India, China and Kenya

INTRODUCTION: Determining whether antibiotic prescriptions are inappropriate requires knowledge of patients\u27 underlying conditions. In low-income and middle-income countries (LMICs), where misdiagnoses are frequent, this is challenging. Additionally, such details are often unavailable for prescription audits. Recent studies using standardised patients (SPs) offer a unique opportunity to generate unbiased prevalence estimates of antibiotic overuse, as the research design involves patients with predefined conditions. METHODS: Secondary analyses of data from nine SP studies were performed to estimate the proportion of SP-provider interactions resulting in inappropriate antibiotic prescribing across primary care settings in three LMICs (China, India and Kenya). In all studies, SPs portrayed conditions for which antibiotics are unnecessary (watery diarrhoea, presumptive tuberculosis (TB), angina and asthma). We conducted descriptive analyses reporting overall prevalence of antibiotic overprescribing by healthcare sector, location, provider qualification and case. The WHO Access-Watch-Reserve framework was used to categorise antibiotics based on their potential for selecting resistance. As richer data were available from India, we examined factors associated with antibiotic overuse in that country through hierarchical Poisson models. RESULTS: Across health facilities, antibiotics were given inappropriately in 2392/4798 (49.9%, 95% CI 40.8% to 54.5%) interactions in India, 83/166 (50.0%, 95% CI 42.2% to 57.8%) in Kenya and 259/899 (28.8%, 95% CI 17.8% to 50.8%) in China. Prevalence ratios of antibiotic overuse in India were significantly lower in urban versus rural areas (adjusted prevalence ratio (aPR) 0.70, 95% CI 0.52 to 0.96) and higher for qualified versus non-qualified providers (aPR 1.55, 95% CI 1.42 to 1.70), and for presumptive TB cases versus other conditions (aPR 1.19, 95% CI 1.07 to 1.33). Access antibiotics were predominantly used in Kenya (85%), but Watch antibiotics (mainly quinolones and cephalosporins) were highly prescribed in India (47.6%) and China (32.9%). CONCLUSION: Good-quality SP data indicate alarmingly high levels of antibiotic overprescription for key conditions across primary care settings in India, China and Kenya, with broad-spectrum agents being excessively used in India and China

Antimicrobial resistance surveillance in low- and middle-income countries: Progress and challenges in eight South Asian and Southeast Asian countries

SUMMARYAntimicrobial resistance (AMR) is a serious global health threat and is predicted to cause significant health and economic impacts, particularly in low- and middle-income countries (LMICs). AMR surveillance is critical in LMICs due to high burden of bacterial infections; however, conducting AMR surveillance in resource-limited settings is constrained by poorly functioning health systems, scarce financial resources, and lack of skilled personnel. In 2015, the United Nations World Health Assembly endorsed the World Health Organization\u27s Global Action Plan to tackle AMR; thus, several countries are striving to improve their AMR surveillance capacity, including making significant investments and establishing and expanding surveillance networks. Initial data generated from AMR surveillance networks in LMICs suggest the high prevalence of resistance, but these data exhibit several shortcomings, such as a lack of representativeness, lack of standardized laboratory practices, and underutilization of microbiology services. Despite significant progress, AMR surveillance networks in LMICs face several challenges in expansion and sustainability due to limited financial resources and technical capacity. This review summarizes the existing health infrastructure affecting the establishment of AMR surveillance programs, the burden of bacterial infections demonstrating the need for AMR surveillance, and current progress and challenges in AMR surveillance efforts in eight South and Southeast Asian countries

Antibiotic prescription practices in primary care in low- and middle-income countries: A systematic review and meta-analysis

BACKGROUND: The widespread use of antibiotics plays a major role in the development and spread of antimicrobial resistance. However, important knowledge gaps still exist regarding the extent of their use in low- and middle-income countries (LMICs), particularly at the primary care level. We performed a systematic review and meta-analysis of studies conducted in primary care in LMICs to estimate the prevalence of antibiotic prescriptions as well as the proportion of such prescriptions that are inappropriate. METHODS AND FINDINGS: We searched PubMed, Embase, Global Health, and CENTRAL for articles published between 1 January 2010 and 4 April 2019 without language restrictions. We subsequently updated our search on PubMed only to capture publications up to 11 March 2020. Studies conducted in LMICs (defined as per the World Bank criteria) reporting data on medicine use in primary care were included. Three reviewers independently screened citations by title and abstract, whereas the full-text evaluation of all selected records was performed by 2 reviewers, who also conducted data extraction and quality assessment. A modified version of a tool developed by Hoy and colleagues was utilized to evaluate the risk of bias of each included study. Meta-analyses using random-effects models were performed to identify the proportion of patients receiving antibiotics. The WHO Access, Watch, and Reserve (AWaRe) framework was used to classify prescribed antibiotics. We identified 48 studies from 27 LMICs, mostly conducted in the public sector and in urban areas, and predominantly based on medical records abstraction and/or drug prescription audits. The pooled prevalence proportion of antibiotic prescribing was 52% (95% CI: 51%-53%), with a prediction interval of 44%-60%. Individual studies\u27 estimates were consistent across settings. Only 9 studies assessed rationality, and the proportion of inappropriate prescription among patients with various conditions ranged from 8% to 100%. Among 16 studies in 15 countries that reported details on prescribed antibiotics, Access-group antibiotics accounted for more than 60% of the total in 12 countries. The interpretation of pooled estimates is limited by the considerable between-study heterogeneity. Also, most of the available studies suffer from methodological issues and report insufficient details to assess appropriateness of prescription. CONCLUSIONS: Antibiotics are highly prescribed in primary care across LMICs. Although a subset of studies reported a high proportion of inappropriate use, the true extent could not be assessed due to methodological limitations. Yet, our findings highlight the need for urgent action to improve prescription practices, starting from the integration of WHO treatment recommendations and the AWaRe classification into national guidelines. TRIAL REGISTRATION: PROSPERO registration number: CRD42019123269

Development and validation of the RCOS prognostic index: A bedside multivariable logistic regression model to predict hypoxaemia or death in patients with SARS-CoV-2 infection

Introduction: Previous COVID-19 prognostic models have been developed in hospital settings and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis. Methods: The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death. Results: 4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count, and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892-0.922), and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where the admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925-0.97) and the Brier score was 0.0188. Conclusions: The RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource-limited settings with poor healthcare infrastructure such as India. However, implementation in other settings is needed to cross-validate and verify our findings

Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis

Background Understanding of the factors driving global antimicrobial resistance is limited. We analysed antimicrobial resistance and antibiotic consumption worldwide versus many potential contributing factors. Methods Using three sources of data (ResistanceMap, the WHO 2014 report on antimicrobial resistance, and contemporary publications), we created two global indices of antimicrobial resistance for 103 countries using data from 2008 to 2014: Escherichia coli resistance—the global average prevalence of E coli bacteria that were resistant to third-generation cephalosporins and fluoroquinolones, and aggregate resistance—the combined average prevalence of E coli and Klebsiella spp resistant to third-generation cephalosporins, fluoroquinolones, and carbapenems, and meticillin-resistant Staphylococcus aureus. Antibiotic consumption data were obtained from the IQVIA MIDAS database. The World Bank DataBank was used to obtain data for governance, education, gross domestic product (GDP) per capita, health-care spending, and community infrastructure (eg, sanitation). A corruption index was derived using data from Transparency International. We examined associations between antimicrobial resistance and potential contributing factors using simple correlation for a univariate analysis and a logistic regression model for a multivariable analysis. Findings In the univariate analysis, GDP per capita, education, infrastructure, public health-care spending, and antibiotic consumption were all inversely correlated with the two antimicrobial resistance indices, whereas higher temperatures, poorer governance, and the ratio of private to public health expenditure were positively correlated. In the multivariable regression analysis (confined to the 73 countries for which antibiotic consumption data were available) considering the effect of changes in indices on E coli resistance (R2 0·54) and aggregate resistance (R2 0·75), better infrastructure (p=0·014 and p=0·0052) and better governance (p=0·025 and p<0·0001) were associated with lower antimicrobial resistance indices. Antibiotic consumption was not significantly associated with either antimicrobial resistance index in the multivariable analysis (p=0·64 and p=0·070). Interpretation Reduction of antibiotic consumption will not be sufficient to control antimicrobial resistance because contagion—the spread of resistant strains and resistance genes—seems to be the dominant contributing factor. Improving sanitation, increasing access to clean water, and ensuring good governance, as well as increasing public health-care expenditure and better regulating the private health sector are all necessary to reduce global antimicrobial resistance. Funding None