scholarly journals Confidence interval methods for antimicrobial resistance surveillance data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Erta Kalanxhi ◽  
Gilbert Osena ◽  
Geetanjali Kapoor ◽  
Eili Klein
Keyword(s):  
Antimicrobial Resistance ◽  
Confidence Intervals ◽  
The United States ◽  
Interval Methods ◽  
Surveillance Network ◽  
Sample Mean ◽  
Standard Methods ◽  
Prevalence Estimates ◽  
Data Independence ◽  
Jeffreys Interval

Abstract Background Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents. Methods We used AMR data from up to 381 laboratories in the United States from The Surveillance Network to evaluate methods for estimating uncertainty of AMR prevalence estimates. We constructed confidence intervals for the proportion of resistant isolates using (1) methods that account for the clustered structure of the data, and (2) standard methods that assume data independence. Using samples of the full dataset with increasing facility coverage levels, we examined how likely the estimated confidence intervals were to include the population mean. Results Methods constructing 95% confidence intervals while accounting for possible within-cluster correlations (Survey and standard methods adjusted to employ cluster-robust errors), were more likely to include the sample mean than standard methods (Logit, Wilson score and Jeffreys interval) operating under the assumption of independence. While increased geographical coverage improved the probability of encompassing the mean for all methods, large samples still did not compensate for the bias introduced from the violation of the data independence assumption. Conclusion General methods for estimating the confidence intervals of AMR rates that assume data are independent, are likely to produce biased results. When feasible, the clustered structure of the data and any possible intra-cluster variation should be accounted for when calculating confidence intervals around AMR estimates, in order to better capture the uncertainty of prevalence estimates.

scholarly journals Prevalence of Salmonella in 11 Spices Offered for Sale from Retail Establishments and in Imported Shipments Offered for Entry to the United States

2017 ◽  
Vol 80 (11) ◽  
pp. 1791-1805 ◽  
Author(s):  
Guodong Zhang ◽  
Lijun Hu ◽  
Régis Pouillot ◽  
Aparna Tatavarthy ◽  
Jane M. Van Doren ◽  
...  
Keyword(s):  
United States ◽  
Confidence Intervals ◽  
Sesame Seed ◽  
The United States ◽  
Black Pepper ◽  
Red Pepper ◽  
Black And White ◽  
Prevalence Estimates ◽  
Plate Counts ◽  
Retail Establishments

ABSTRACT The U.S. Food and Drug Administration conducted a survey to evaluate Salmonella prevalence and aerobic plate counts in packaged (dried) spices offered for sale at retail establishments in the United States. The study included 7,250 retail samples of 11 spice types that were collected during November 2013 to September 2014 and October 2014 to March 2015. No Salmonella-positive samples (based on analysis of 125 g) were found among retail samples of cumin seed (whole or ground), sesame seed (whole, not roasted or toasted, and not black), and white pepper (ground or cracked), for prevalence estimates of 0.00% with 95% Clopper and Pearson's confidence intervals of 0.00 to 0.67%, 0.00 to 0.70%, and 0.00 to 0.63%, respectively. Salmonella prevalence estimates (confidence intervals) for the other eight spice types were 0.19% (0.0048 to 1.1%) for basil leaf (whole, ground, crushed, or flakes), 0.24% (0.049 to 0.69%) for black pepper (whole, ground, or cracked), 0.56% (0.11 to 1.6%) for coriander seed (ground), 0.19% (0.0049 to 1.1%) for curry powder (ground mixture of spices), 0.49% (0.10 to 1.4%) for dehydrated garlic (powder, granules, or flakes), 0.15% (0.0038 to 0.83%) for oregano leaf (whole, ground, crushed, or flakes), 0.25% (0.03 to 0.88%) for paprika (ground or cracked), and 0.64% (0.17 to 1.6%) for red pepper (hot red pepper, e.g., chili, cayenne; ground, cracked, crushed, or flakes). Salmonella isolates were serotyped, and genomes were sequenced. Samples of these same 11 spice types were also examined from shipments of imported spices offered for entry to the United States from 1 October 2011 to 30 September 2015. Salmonella prevalence estimates (based on analysis of two 375-g composite samples) for shipments of imported spices were 1.7 to 18%. The Salmonella prevalence estimates for spices offered for sale at retail establishments for all of the spice types except dehydrated garlic and basil were significantly lower than estimates for shipments of imported spice offered for entry.

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

scholarly journals THU0442 GOUT AND HEART FAILURE IN THE US: A NATIONAL PERSPECTIVE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 458.2-458
Author(s):  
G. Singh ◽  
M. Sehgal ◽  
A. Mithal
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Confidence Intervals ◽  
Burden Of Illness ◽  
Average Length ◽  
Private Insurance ◽  
Significant Risk ◽  
Case Fatality ◽  
The United States ◽  
The Us

Background:Heart failure (HF) is the eighth leading cause of death in the US, with a 38% increase in the number of deaths due to HF from 2011 to 2017 (1). Gout and hyperuricemia have previously been recognized as significant risk factors for heart failure (2), but there is little nationwide data on the clinical and economic consequences of these comorbidities.Objectives:To study heart failure hospitalizations in patients with gout in the United States (US) and estimate their clinical and economic impact.Methods:The Nationwide Inpatient Sample (NIS) is a stratified random sample of all US community hospitals. It is the only US national hospital database with information on all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. We examined all inpatient hospitalizations in the NIS in 2017, the most recent year of available data, with a primary or secondary diagnosis of gout and heart failure. Over 69,800 ICD 10 diagnoses were collapsed into a smaller number of clinically meaningful categories, consistent with the CDC Clinical Classification Software.Results:There were 35.8 million all-cause hospitalizations in patients in the US in 2017. Of these, 351,735 hospitalizations occurred for acute and/or chronic heart failure in patients with gout. These patients had a mean age of 73.3 years (95% confidence intervals 73.1 – 73.5 years) and were more likely to be male (63.4%). The average length of hospitalization was 6.1 days (95% confidence intervals 6.0 to 6.2 days) with a case fatality rate of 3.5% (95% confidence intervals 3.4% – 3.7%). The average cost of each hospitalization was $63,992 (95% confidence intervals $61,908 - $66,075), with a total annual national cost estimate of $22.8 billion (95% confidence intervals $21.7 billion - $24.0 billion).Conclusion:While gout and hyperuricemia have long been recognized as potential risk factors for heart failure, the aging of the US population is projected to significantly increase the burden of illness and costs of care of these comorbidities (1). This calls for an increased awareness and management of serious co-morbid conditions in patients with gout.References:[1]Sidney, S., Go, A. S., Jaffe, M. G., Solomon, M. D., Ambrosy, A. P., & Rana, J. S. (2019). Association Between Aging of the US Population and Heart Disease Mortality From 2011 to 2017. JAMA Cardiology. doi:10.1001/jamacardio.2019.4187[2]Krishnan E. Gout and the risk for incident heart failure and systolic dysfunction. BMJ Open 2012;2:e000282.doi:10.1136/bmjopen-2011-000282Disclosure of Interests: :Gurkirpal Singh Grant/research support from: Horizon Therapeutics, Maanek Sehgal: None declared, Alka Mithal: None declared

scholarly journals Pediatric Antimicrobial Susceptibility Trends across the United States

2013 ◽  
Vol 34 (12) ◽  
pp. 1244-1251 ◽  
Author(s):  
Pranita D. Tamma ◽  
Gwen L. Robinson ◽  
Jeffrey S. Gerber ◽  
Jason G. Newland ◽  
Chloe M. DeLisle ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Antimicrobial Susceptibility ◽  
The United States ◽  
Methicillin Resistant ◽  
Resistance Patterns ◽  
Gram Negative Organisms ◽  
Pediatric Healthcare ◽  
Regional Comparisons ◽  
Susceptibility Patterns ◽  
Over Time

Objective.Antimicrobial susceptibility patterns across US pediatric healthcare institutions are unknown. A national pooled pediatric antibiogram (1) identifies nationwide trends in antimicrobial resistance, (2) allows across-hospital benchmarking, and (3) provides guidance for empirical antimicrobial regimens for institutions unable to generate pediatric antibiograms.Methods.In January 2012, a request for submission of pediatric antibiograms between 2005 and 2011 was sent to 233 US hospitals. A summary antibiogram was compiled from participating institutions to generate proportions of antimicrobial susceptibility. Temporal and regional comparisons were evaluated using χ² tests and logistic regression, respectively.Results.Of 200 institutions (85%) responding to our survey, 78 (39%) reported generating pediatric antibiograms, and 55 (71%) submitted antibiograms. Carbapenems had the highest activity against the majority of gram-negative organisms tested, but no antibiotic had more than 90% activity against Pseudomonas aeruginosa. Approximately 50% of all Staphylococcus aureus isolates were methicillin resistant. Western hospitals had significantly lower proportions of S. aureus that were methicillin resistant compared with all other regions tested. Overall, 21% of S. aureus isolates had resistance to clindamycin. Among Enterococcus faecium isolates, the prevalence of susceptibility to ampicillin (25%) and vancomycin (45%) was low but improved over time (P < .01), and 8% of E. faecium isolates were resistant to linezolid. Southern hospitals reported significantly higher prevalence of E. faecium with susceptibilities to ampicillin, vancomycin, and linezolid compared with the other 3 regions (P < .01).Conclusions.A pooled, pediatric antibiogram can identify nationwide antimicrobial resistance patterns for common pathogens and might serve as a useful tool for benchmarking resistance and informing national prescribing guidelines for children.

scholarly journals Need for Annual Surveillance of Antimicrobial Resistance in Streptococcus pneumoniae in the United States: 2-Year Longitudinal Analysis

2001 ◽  
Vol 45 (4) ◽  
pp. 1037-1042 ◽  
Author(s):  
Daniel F. Sahm ◽  
James A. Karlowsky ◽  
Laurie J. Kelly ◽  
Ian A. Critchley ◽  
Mark E. Jones ◽  
...  
Keyword(s):  
United States ◽  
Streptococcus Pneumoniae ◽  
Antimicrobial Resistance ◽  
Antimicrobial Agents ◽  
The United States ◽  
Multidrug Resistant ◽  
Fluoroquinolone Resistance ◽  
Resistance Patterns ◽  
Changing Patterns ◽  
Resistance To Penicillin

ABSTRACT Although changing patterns in antimicrobial resistance inStreptococcus pneumoniae have prompted several surveillance initiatives in recent years, the frequency with which these studies are needed has not been addressed. To approach this issue, the extent to which resistance patterns change over a 1-year period was examined. In this study we analyzed S. pneumoniaeantimicrobial susceptibility results produced in our laboratory with isolates obtained over 2 consecutive years (1997–1998 and 1998–1999) from the same 96 institutions distributed throughout the United States. Comparison of results revealed increases in resistant percentages for all antimicrobial agents studied except vancomycin. For four of the agents tested (penicillin, cefuroxime, trimethoprim-sulfamethoxazole, and levofloxacin), the increases were statistically significant (P < 0.05). Resistance to the fluoroquinolone remained low in both years (0.1 and 0.6%, respectively); in contrast, resistance to macrolides was consistently greater than 20%, and resistance to trimethoprim-sulfamethoxazole increased from 13.3 to 27.3%. Multidrug resistance, concurrent resistance to three or more antimicrobials of different chemical classes, also increased significantly between years, from 5.9 to 11%. The most prevalent phenotype was resistance to penicillin, azithromycin (representative macrolide), and trimethoprim-sulfamethoxazole. Multidrug-resistant phenotypes that included fluoroquinolone resistance were uncommon; however, two phenotypes that included fluoroquinolone resistance not found in 1997–1998 were encountered in 1998–1999. This longitudinal surveillance study of resistance inS. pneumoniae revealed that significant changes do occur in just a single year and supports the need for surveillance at least on an annual basis, if not continuously.

CONCEPTUAL KNOWLEDGE OF CONFIDENCE INTERVALS IN PSYCHOLOGY UNDERGRADUATE AND GRADUATE STUDENTS

2019 ◽  
Vol 18 (1) ◽  
pp. 46-62
Author(s):  
NOELLE M. CROOKS ◽  
ANNA N. BARTEL ◽  
MARTHA W. ALIBALI
Keyword(s):  
Graduate Students ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Conceptual Knowledge ◽  
Statistics Education ◽  
Psychology Students ◽  
Sample Mean ◽  
P Values ◽  
Research Journal ◽  
Graduate Psychology Students

In recent years, there have been calls for researchers to report and interpret confidence intervals (CIs) rather than relying solely on p-values. Such reforms, however, may be hindered by a general lack of understanding of CIs and how to interpret them. In this study, we assessed conceptual knowledge of CIs in undergraduate and graduate psychology students. CIs were difficult and prone to misconceptions for both groups. Connecting CIs to estimation and sample mean concepts was associated with greater conceptual knowledge of CIs. Connecting CIs to null hypothesis  significance testing, however, was not associated with conceptual knowledge of CIs. It may therefore be beneficial to focus on estimation and sample mean concepts in instruction about CIs. First published May 2019 at Statistics Education Research Journal Archives

scholarly journals Confidence Intervals for the Mixture Transition Distribution (MTD) Model and Other Markovian Models

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 351
Author(s):  
André Berchtold
Keyword(s):  
Markov Chains ◽  
Markov Model ◽  
Em Algorithm ◽  
Hidden Markov Model ◽  
Confidence Intervals ◽  
Hidden Markov ◽  
Simple Calculation ◽  
Standard Methods ◽  
Transition Distribution ◽  
Simulation Results

The Mixture Transition Distribution (MTD) model used for the approximation of high-order Markov chains does not allow a simple calculation of confidence intervals, and computationnally intensive methods based on bootstrap are generally used. We show here how standard methods can be extended to the MTD model as well as other models such as the Hidden Markov Model. Starting from existing methods used for multinomial distributions, we describe how the quantities required for their application can be obtained directly from the data or from one run of the E-step of an EM algorithm. Simulation results indicate that when the MTD model is estimated reliably, the resulting confidence intervals are comparable to those obtained from more demanding methods.

scholarly journals Molecular Analysis ofEscherichia colifrom Retail Meats (2002–2004) from the United States National Antimicrobial Resistance Monitoring System

2009 ◽  
Vol 49 (2) ◽  
pp. 195-201 ◽  
Author(s):  
James R. Johnson ◽  
James S. McCabe ◽  
David G. White ◽  
Brian Johnston ◽  
Michael A. Kuskowski ◽  
...  
Keyword(s):  
United States ◽  
Antimicrobial Resistance ◽  
Monitoring System ◽  
Molecular Analysis ◽  
The United States ◽  
Resistance Monitoring ◽  
Retail Meats

scholarly journals Emergence of high rates of antimicrobial resistance among viridans group streptococci in the United States.

1996 ◽  
Vol 40 (4) ◽  
pp. 891-894 ◽  
Author(s):  
G V Doern ◽  
M J Ferraro ◽  
A B Brueggemann ◽  
K L Ruoff
Keyword(s):  
United States ◽  
Antimicrobial Resistance ◽  
Blood Culture ◽  
Rank Order ◽  
The United States ◽  
Medical Centers ◽  
Streptococcus Milleri ◽  
Intermediate Resistance ◽  
High Level ◽  
Viridans Group Streptococci

Three hundred fifty-two blood culture isolates of viridans group streptococci obtained from 43 U.S. medical centers during 1993 and 1994 were characterized. Included were 48 isolates of "Streptococcus milleri," 219 S. mitis isolates, 29 S. salivarius isolates, and 56 S. sanguis isolates. High-level penicillin resistance (MIC, > or = 4.0 micrograms/ml) was noted among 13.4% of the strains; for 42.9% of the strains, penicillin MICs were 0.25 to 2.0 micrograms/ml (i.e., intermediate resistance). In general, amoxicillin was slightly more active than penicillin. The rank order of activity for five cephalosporins versus viridans group streptococci was cefpodoxime = ceftriaxone > cefprozil = cefuroxime > cephalexin. The percentages of isolates resistant (MIC, > or = 2 micrograms/ml) to these agents were 15, 17, 18, 20, and 96, respectively. The rates of resistance to erythromycin, tetracycline, and trimethoprim-sulfamethoxazole were 12 to 38%. Resistance to either chloramphenicol or ofloxacin was uncommon (i.e., < 1%). In general, among the four species, S. mitis was the most resistant and "S. milleri" was the most susceptible.

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