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Background The threat posed by antibiotic resistance is of increasing concern in low- and middle-income countries (LMICs) as their rates of antibiotic use increase. However, an understanding of the burden of resistance is lacking in LMICs, particularly for multidrug-resistant (MDR) pathogens. Methods We conducted a retrospective, 10-hospital study of the relationship between MDR pathogens and mortality in India. Patient-level antimicrobial susceptibility test (AST) results for Enterococcus spp., Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. were analyzed for their association with patient mortality outcomes. Results We analyzed data on 5103 AST results from 10 hospitals. The overall mortality rate of patients was 13.1% (n = 581), and there was a significant relationship between MDR and mortality. Infections with MDR and extensively drug resistant (XDR) E. coli, XDR K. pneumoniae, and MDR A. baumannii were associated with 2–3 times higher mortality. Mortality due to methicillin-resistant S. aureus (MRSA) was significantly higher than susceptible strains when the MRSA isolate was resistant to aminoglycosides. Conclusions This is one of the largest studies undertaken in an LMIC to measure the burden of antibiotic resistance. We found that MDR bacterial infections pose a significant risk to patients. While consistent with prior studies, the variations in drug resistance and associated mortality outcomes by pathogen are different from those observed in high-income countries and provide a baseline for studies in other LMICs. Future research should aim to elucidate the burden of resistance and the differential transmission mechanisms that drive this public health crisis., There is a lack of understanding of the burden of antibiotic resistance in low- and middle-income countries. Using data from 10 hospitals across India, we found that the risk of mortality was significantly higher for patients with multidrug-resistant infections.

Reducing inappropriate prescribing is key to mitigating antibiotic resistance, particularly in acute care settings. Clinicians' prescribing decisions are influenced by their judgments and actual or perceived patient expectations. Fuzzy trace theory predicts that patients and clinicians base such decisions on categorical gist representations that reflect the bottom-line understanding of information about antibiotics. However, due to clinicians' specialized training, the categorical gists driving clinicians' and patients' decisions might differ, which could result in mismatched expectations and inefficiencies in targeting interventions. We surveyed clinicians and patients from 2 large urban academic hospital emergency departments (EDs) and a sample of nonpatient subjects regarding their gist representations of antibiotic decisions, as well as relevant knowledge and expectations. Results were analyzed using exploratory factor analysis (EFA) and multifactor regression. In total, 149 clinicians (47% female; 74% white), 519 online subjects (45% female; 78% white), and 225 ED patients (61% female; 56% black) completed the survey. While clinicians demonstrated greater knowledge of antibiotics and concern about side effects than patients, the predominant categorical gist for both patients and clinicians was "why not take a risk," which compares the status quo of remaining sick to the possibility of benefit from antibiotics. This gist also predicted expectations and prior prescribing in the nonpatient sample. Other representations reflected the gist that "germs are germs" conflating bacteria and viruses, as well as perceptions of side effects and efficacy. Although individually rational, reliance on the "why not take a risk" representation can lead to socially suboptimal results, including antibiotic resistance and individual patient harm due to adverse events. Changing this representation could alter clinicians' and patients' expectations, suggesting opportunities to reduce overprescribing.

Supplemental material, Supplemental_Material_03-23-2018_online_supp for Patients’ and Clinicians’ Perceptions of Antibiotic Prescribing for Upper Respiratory Infections in the Acute Care Setting by David A. Broniatowski, Eili Y. Klein, Larissa May, Elena M. Martinez, Chelsea Ware and Valerie F. Reyna in Medical Decision Making

BackgroundAdherence to evidence-based antibiotic therapy guidelines for treatment of upper respiratory tract infections (URIs) varies widely among clinicians. Understanding this variability is key for reducing inappropriate prescribing.ObjectiveTo measure how emergency department (ED) clinicians' perceptions of antibiotic prescribing risks affect their decision-making.DesignClinician survey based on fuzzy-trace theory, a theory of medical decision-making, combined with retrospective data on prescribing outcomes for URI/pneumonia visits in two EDs. The survey predicts the categorical meanings, or gists, that individuals derive from given information.ParticipantsED physicians, residents, and physician assistants (PAs) who completed surveys and treated patients with URI/pneumonia diagnoses between August 2014 and December 2015.Main measuresGists derived from survey responses and their association with rates of antibiotic prescribing per visit.Key resultsOf 4474 URI/pneumonia visits, 2874 (64.2%) had an antibiotic prescription. However, prescribing rates varied from 7% to 91% for the 69 clinicians surveyed (65.2% response rate). Clinicians who framed therapy-prescribing decisions as a categorical choice between continued illness and possibly beneficial treatment ("why not take a risk?" gist, which assumes antibiotic therapy is essentially harmless) had higher rates of prescribing (OR 1.28 [95% CI, 1.06-1.54]). Greater agreement with the "antibiotics may be harmful" gist was associated with lower prescribing rates (OR 0.81 [95% CI, 0.67-0.98]).ConclusionsOur results indicate that clinicians who perceive prescribing as a categorical choice between patients remaining ill or possibly improving from therapy are more likely to prescribe antibiotics. However, this strategy assumes that antibiotics are essentially harmless. Clinicians who framed decision-making as a choice between potential harms from therapy and continued patient illness (e.g., increased appreciation of potential harms) had lower prescribing rates. These results suggest that interventions to reduce inappropriate prescribing should emphasize the non-negligible possibility of serious side effects.

AimCoinfecting bacterial pathogens are a major cause of morbidity and mortality in influenza. However, there remains a paucity of literature on the magnitude of coinfection in influenza patients.MethodA systematic search of MeSH, Cochrane Library, Web of Science, SCOPUS, EMBASE, and PubMed was performed. Studies of humans in which all individuals had laboratory confirmed influenza, and all individuals were tested for an array of common bacterial species, met inclusion criteria.ResultsTwenty-seven studies including 3215 participants metall inclusion criteria. Common etiologies were defined from a subset of eight articles. There was high heterogeneity in the results (I(2) =95%), with reported coinfection rates ranging from 2% to 65%. Although only a subset of papers were responsible for observed heterogeneity, subanalyses and meta-regression analysis found no study characteristic that was significantly associated with coinfection. The most common coinfecting species were Streptococcus pneumoniae and Staphylococcus aureus, which accounted for 35% (95% CI, 14%-56%) and 28% (95% CI, 16%-40%) of infections, respectively; a wide range of other pathogens caused the remaining infections. An assessment of bias suggested that lack of small-study publications may have biased the results.ConclusionsThe frequency of coinfection in the published studies included in this review suggests that although providers should consider possible bacterial coinfection in all patients hospitalized with influenza, they should not assume all patients are coinfected and be sure to properly treat underlying viral processes. Further, high heterogeneity suggests additional large-scale studies are needed to better understand the etiology of influenza bacterial coinfection.

Mosquito-borne diseases pose some of the greatest challenges in public health, especially in tropical and sub-tropical regions of theworld. Efforts to control these diseases have been underpinned by a theoretical framework developed for malaria by Ross and Macdonald, including models, metrics for measuring transmission, and theory of control that identifies key vulnerabilities in the transmission cycle. That framework, especially Macdonald's formula for R0 and its entomological derivative, vectorial capacity, are nowused to study dynamics and design interventions for many mosquito-borne diseases. A systematic review of 388 models published between 1970 and 2010 found that the vast majority adopted the Ross-Macdonald assumption of homogeneous transmission in a well-mixed population. Studies comparing models and data question these assumptions and point to the capacity to model heterogeneous, focal transmission as the most important but relatively unexplored component in current theory. Fine-scale heterogeneity causes transmission dynamics to be nonlinear, and poses problems for modeling, epidemiology and measurement. Novel mathematical approaches show how heterogeneity arises from the biology and the landscape on which the processes of mosquito biting and pathogen transmission unfold. Emerging theory focuses attention on the ecological and social context formosquito blood feeding, themovement of both hosts and mosquitoes, and the relevant spatial scales for measuring transmission and for modeling dynamics and control., Transactions of the Royal Society of Tropical Medicine and Hygiene, 108(4), pp.185-197; 2014