scholarly journals Can the Ceftriaxone Breakpoints Be Increased Without Compromising Patient Outcomes?

2018 ◽  
Vol 5 (6) ◽  
Author(s):  
Pranita D Tamma ◽  
Virginia M Pierce ◽  
Sara E Cosgrove ◽  
Ebbing Lautenbach ◽  
Anthony Harris ◽  
...  
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Patient Outcomes ◽  
Nearest Neighbor ◽  
Clinical Laboratory ◽  
Severity Of Illness ◽  
Bloodstream Infections ◽  
Source Control ◽  
Exact Matching ◽  
Extended Spectrum

Abstract Background In 2010, the Clinical Laboratory and Standards Institute recommended a 3-fold lowering of ceftriaxone breakpoints to 1 mcg/mL for Enterobacteriaceae. Supportive clinical data at the time were from fewer than 50 patients. We compared the clinical outcomes of adults with Enterobacteriaceae bloodstream infections treated with ceftriaxone compared with matched patients (with exact matching on ceftriaxone minimum inhibitory concentrations [MICs]) treated with extended-spectrum agents to determine if ceftriaxone breakpoints could be increased without negatively impacting patient outcomes. Methods A retrospective cohort study was conducted at 3 large academic medical centers and included patients with Enterobacteriaceae bacteremia with ceftriaxone MICs of 2 mcg/mL treated with ceftriaxone or extended-spectrum β-lactams (ie, cefepime, piperacillin/tazobactam, meropenem, or imipenem/cilastatin) between 2008 and 2014; 1:2 nearest neighbor propensity score matching was performed to estimate the odds of recurrent bacteremia and mortality within 30 days. Results Propensity score matching yielded 108 patients in the ceftriaxone group and 216 patients in the extended-spectrum β-lactam group, with both groups well-balanced on demographics, preexisting medical conditions, severity of illness, source of bacteremia, and source control interventions. No difference in recurrent bacteremia (odds ratio [OR], 1.16; 95% confidence interval [CI], 0.49–2.73) or mortality (OR, 1.27; 95% CI, 0.56–2.91) between the treatment groups was observed for patients with isolates with ceftriaxone MICs of 2 mcg/mL. Only 6 isolates (1.6%) with ceftriaxone MICs of 2 mcg/mL were extended-spectrum β-lactamase (ESBL)–producing. Conclusions Our findings suggest that patient outcomes are similar when receiving ceftriaxone vs extended-spectrum agents for the treatment of Enterobacteriaceae bloodstream infections with ceftriaxone MICs of 2 mcg/mL. This warrants consideration of adjusting the ceftriaxone susceptibility breakpoint from 1 to 2 mcg/mL, as a relatively small increase in the antibiotic breakpoint could have the potential to limit the use of large numbers of extended-spectrum antibiotic agents.

scholarly journals IMPACTO DEL PROGRAMA JUNTOS SOBRE EL GASTO EN ALIMENTOS EN LOS HOGARES RURALES, 2015

2016 ◽  
Vol 5 (2) ◽  
pp. 68-83
Author(s):  
Alfredo Pelayo Calatayud Mendoza ◽  
Edson Apaza Mamani
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Nearest Neighbor ◽  
Nearest Neighbor Matching ◽  
Per Capita

El objetivo del presente estudio es estimar el impacto del programa Juntos sobre el gasto per cápita en alimentos en los hogares rurales, como fuente de información se utiliza la base de datos de la Encuesta Nacional de Hogares – 2015, la metodología es la técnica de diseño cuasi-experimental Propensity Score Matching – PSM con la técnica de emparejamiento de vecino más cercano (Nearest Neighbor Matching), este método consiste en comparar el gasto per cápita en alimentos que obtiene cada beneficiario tratado con el grupo de control que tenga el propensity score más cercano, luego se calcula la diferencia entre cada par de hogares emparejadas en el gasto per cápita en alimentos y luego se promedian todas las diferencias para calcular el ATT. La unidad de análisis son los hogares rurales de la sierra y selva del Perú en condición de pobreza y extrema pobreza. Los resultados reportan que la probabilidad de participar en el programa Juntos depende de las características del hogar, de la vivienda y del jefe de hogar. Asimismo, los resultados sugieren que el programa Juntos si tiene un impacto positivo sobre el gasto per cápita en alimentos, para aquellos hogares rurales en pobreza y extrema pobreza el programa Juntos ha incrementado el gasto per cápita en 8.9% (ATT=0.089) a un nivel de significancia de 10%.

scholarly journals 212. Outcomes of Adults with Uncomplicated Staphylococcus aureus Bacteremia Receiving Short-Course Vs. Prolonged-Course Antibiotic Therapy in a Multicenter, Propensity Score–Matched Cohort

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S125-S126
Author(s):  
Louise Thorlacius-Ussing ◽  
Jette Nissen ◽  
Jon J Rasmussen ◽  
Robert Skov ◽  
Magnus Arpi ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Propensity Score ◽  
Antibiotic Therapy ◽  
Antibiotic Treatment ◽  
Nearest Neighbor ◽  
Bloodstream Infections ◽  
Staphylococcus Aureus Bacteremia ◽  
Short Course ◽  
Short Courses

Abstract Background The recommended duration of antibiotic treatment for uncomplicated Staphylococcus aureus bloodstream infections is 14 days. We compared the outcomes of patients receiving short-course (6–10 days) vs. prolonged-course (11–16 days) antibiotic therapy for S. aureus bacteremia (SAB). Methods 30-day outcome of patients with penicillin (PSSAB, n = 202)) or methicillin-susceptible SAB (MSSAB, n = 203) treated with in vitro active therapy in the range of 6–16 days was analyzed using pooled data from two previously published, observational studies. Individuals were matched 1:1 by nearest neighbor propensity score matching without replacement. Regression analysis was performed to estimate the risk of all-cause mortality within 30 days after the end of antibiotic treatment. Eligible individuals had to have >1 day of follow-up after discontinuation of antimicrobials. Individuals with a diagnosis of endocarditis, bone infection, meningitis or pneumonia were excluded. Results There were 107 well-balanced matched pairs; 58 in the PSSAB and 39 in the MSSAB cohort. For PSSAB, the median duration of therapy was 8 (interquartile range [IQR], 7–10) in the short-course group and 12 days (IQR, 10–13) in the prolonged-course group. For the MSSAB cohort, these numbers were 9 days (IQR, 7–10) and 14 days (IQR, 13–16 days), respectively. No difference in mortality between short-course and prolonged-course treatment was observed (adjusted hazard ratio [aHR], 0.74; 95% confidence interval [CI], .23–2.41) and 1.14; 95% CI, 0.31–4.20), respectively for PSSAB and MSSAB. Conclusion Short courses of antibiotic therapy yielded similar clinical outcomes as prolonged courses of antibiotic therapy for S. aureus bacteremia. The findings warrant a randomized clinical trial to study the safety and efficacy of shortened antimicrobial therapy for the treatment of uncomplicated SAB. Disclosures All authors: No reported disclosures.

scholarly journals EFFECT OF MICROFINANCE ON SMALLHOLDER FARMERS’ LIVELIHOOD IN RWANDA: A CASE STUDY OF NYAMAGABE DISTRICT

AGROFOR ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Eularie MUTAMULIZA ◽  
Edouard MUSABANGANJI
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Rural Areas ◽  
Nearest Neighbor ◽  
Smallholder Farmers ◽  
Descriptive Statistics ◽  
Primary Data ◽  
Microfinance Institutions ◽  
Nearest Neighbor Matching ◽  
The Government

Microfinance in Rwanda is considered as one of the most crucial mechanisms in the implementation of the Government program to reduce poverty and to increase economic growth. However, despite the effort made by the Government of Rwanda to put in place microfinance institutions in rural areas, little is known about the effects of microfinance on smallholder farmers’ income in Nyamagabe District of Rwanda. This study aimed at examining the contribution of microfinance services to the income of smallholder farmers in Nyamagabe District. Primary data were collected from 240 respondents randomly selected in 3 sectors of Nyamagabe District using structured questionnaires. Data were analyzed using descriptive statistics to describe the socio-economic characteristics of the respondents and Propensity Score Matching was used to assess the effect of microfinance on smallholder farmers’ livelihood. The results from descriptive statistics showing that 117 respondents were participants in microfinance services and 123 were nonparticipants and more men were committed to participate and to access microfinance services than women. Results from Propensity Score Matching Model using both Kernel Based Matching and Nearest Neighbor Matching showed that the households participating in microfinance services increased their total annual income by 256,674 Rwandan francs and 228,246 Rwandan francs more than non-participants, respectively. The study recommended that smallholder farmers should be encouraged to participate in microfinance services to increase their income and agricultural productivity. The use of SACCOs and microfinance services needs to be promoted in order to provide an instrument for mobilizing savings and extending credit.

scholarly journals Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects

2019 ◽  
Vol 188 (7) ◽  
pp. 1345-1354 ◽  
Author(s):  
Anusha M Vable ◽  
Mathew V Kiang ◽  
M Maria Glymour ◽  
Joseph Rigdon ◽  
Emmanuel F Drabo ◽  
...  
Keyword(s):  
Propensity Score ◽  
Least Squares ◽  
Propensity Score Matching ◽  
Confidence Intervals ◽  
Error Rate ◽  
Type I Error ◽  
Ordinary Least Squares ◽  
Type I ◽  
Exact Matching ◽  
Common Support

AbstractMatching methods are assumed to reduce the likelihood of a biased inference compared with ordinary least squares (OLS) regression. Using simulations, we compared inferences from propensity score matching, coarsened exact matching, and unmatched covariate-adjusted OLS regression to identify which methods, in which scenarios, produced unbiased inferences at the expected type I error rate of 5%. We simulated multiple data sets and systematically varied common support, discontinuities in the exposure and/or outcome, exposure prevalence, and analytical model misspecification. Matching inferences were often biased in comparison with OLS, particularly when common support was poor; when analysis models were correctly specified and common support was poor, the type I error rate was 1.6% for propensity score matching (statistically inefficient), 18.2% for coarsened exact matching (high), and 4.8% for OLS (expected). Our results suggest that when estimates from matching and OLS are similar (i.e., confidence intervals overlap), OLS inferences are unbiased more often than matching inferences; however, when estimates from matching and OLS are dissimilar (i.e., confidence intervals do not overlap), matching inferences are unbiased more often than OLS inferences. This empirical “rule of thumb” may help applied researchers identify situations in which OLS inferences may be unbiased as compared with matching inferences.

scholarly journals IMPACTO DEL PROGRAMA JUNTOS SOBRE LA VIOLENCIA DOMÉSTICA

2016 ◽  
Vol 5 (1) ◽  
pp. 108-126
Author(s):  
Alfredo Pelayo Calatayud Mendoza ◽  
María Del Pilar Blanco Espesua
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Nearest Neighbor ◽  
Nearest Neighbor Matching ◽  
Salud Mental ◽  
Violencia Domestica

El objetivo del presente estudio, es estimar el impacto del programa Juntos sobre los niveles de violencia doméstica contra la mujer. Ciertamente, la violencia es un fenómeno complejo y está determinado por múltiples causas;  si bien es cierto, dentro de los objetivos del programa Juntos no está prevista directamente la disminución de la violencia doméstica. Sin embargo, un grupo de estudios advierten que una de las causas de la violencia contra la mujer es por razones de carácter económico y existen tres tipos de violencia doméstica: física, psicológica y sexual. Específicamente, el programa Juntos, es un programa social dirigido a la población de mayor vulnerabilidad, en situación de extrema pobreza, riesgo y exclusión; el subsidio consiste en la entrega mensual de dinero de 100 soles. El estudio sigue una metodología cuantitativa por la necesidad de evidenciar empíricamente el impacto del programa, concretamente es la técnica de Propensity Score Matching : Nearest Neighbor Matching y la fuente de datos utilizada para el análisis es la Encuesta Nacional de Demografía y de Salud Familiar - ENDES de los años 2014 y 2015. Los resultados señalan que el programa Juntos no ha logrado reducir los niveles de violencia doméstica a un nivel de significancia de 5%, no se reporta impacto favorable del programa en la reducción de la violencia física, psicológica y sexual ya sea en forma severa o algunas veces; no obstante que el número de hogares beneficiarios se ha incrementado exponencialmente, durante los 10 años de funcionamiento. Los resultados sugieren incorporar en el componente de salud del programa Juntos la salud mental de los hogares beneficiarios para ello es importante la participación de la mujer y de su pareja, la condición o el compromiso es modificar el comportamiento de la pareja en actos o actitudes de violencia.

scholarly journals Comparison of Nearest Neighbor and Caliper Algorithms in Outcome Propensity Score Matching to Study the Relationship between Type 2 Diabetes and Coronary Artery Disease

2021 ◽  
Author(s):  
Sara Sabbaghian Tousi ◽  
Hamed Tabesh ◽  
Azadeh Saki ◽  
Ali Tagipour ◽  
Mohammad Tajfard
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Artery Disease ◽  
The Relationship

Introduction: Propensity score matching (PSM) is a method to reduce the impact of essential and confounders. When the number of confounders is high, there may be a problem of matching, in which, finding matched pairs for the case group is difficult, or impossible. The propensity score (PS) minimizes the effect of the confounders, and it is reduced to one dimension. There are various algorithms in the field of PSM. This study aimed to compared the nearest neighbor and caliper algorithms. Methods: Data obtained in this study were from patients undergoing angiography at Ghaem Hospital in Mashhad, between 2011-12. The study was a retrospective case-control using PSM. In total, 604 patients were included in the case and control groups. A logistic regression model was used to calculate the propensity score and adjust the variables, such as age, gender, Body Mass Index (BMI), systolic blood pressure, smoking status, and triglyceride. Then, the Odds Ratios (ORs) with 95% Confidence Intervals (CIs) for the raw data and two matching algorithms were determined to examine the relationship between type 2 diabetes and coronary artery disease (CAD). Results: Propensity score in the nearest neighbor and caliper algorithms matched the total number of 604 samples, 200 and 178 pairs, respectively. All variables were significantly different between the two groups before matching (P<0.05). The gender was significantly different between the two groups after matching using the nearest neighbor algorithm (P=0.002). No variables created a significant difference between the two groups after matching with the caliper algorithm. Conclusion: Bias reduction in the caliper algorithm was greater than for the nearest neighbor algorithm for all variables except the triglyceride variable.

Propensity score matching versus coarsened exact matching in observational comparative effectiveness research

2021 ◽  
Author(s):  
David Guy ◽  
Igor Karp ◽  
Piotr Wilk ◽  
Joseph Chin ◽  
George Rodrigues
Keyword(s):  
Clinical Trials ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Data ◽  
Randomized Clinical Trials ◽  
Original Data ◽  
Considerable Proportion ◽  
Exact Matching ◽  
Coarsened Exact Matching ◽  
Comparison Groups

Aim & methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from randomized clinical trials to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in randomized clinical trials. Conclusion: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.

Clinical Risk Score for Prediction of Extended-Spectrum β-Lactamase–Producing Enterobacteriaceae in Bloodstream Isolates

2016 ◽  
Vol 38 (3) ◽  
pp. 266-272 ◽  
Author(s):  
Matthew R. Augustine ◽  
Traci L. Testerman ◽  
Julie Ann Justo ◽  
P. Brandon Bookstaver ◽  
Joseph Kohn ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Characteristic Curve ◽  
Severity Of Illness ◽  
Bloodstream Infections ◽  
Patient Specific ◽  
Retrospective Case ◽  
Extended Spectrum ◽  
High Discrimination ◽  
Independent Risk Factors

OBJECTIVETo develop a risk score to predict probability of bloodstream infections (BSIs) due to extended-spectrum β-lactamase–producing Enterobacteriaceae (ESBLE).DESIGNRetrospective case-control study.SETTINGTwo large community hospitals.PATIENTSHospitalized adults with Enterobacteriaceae BSI between January 1, 2010, and June 30, 2015.METHODSMultivariate logistic regression was used to identify independent risk factors for ESBLE BSI. Point allocation in extended-spectrum β-lactamase prediction score (ESBL-PS) was based on regression coefficients.RESULTSAmong 910 patients with Enterobacteriaceae BSI, 42 (4.6%) had ESBLE bloodstream isolates. Most ESBLE BSIs were community onset (33 of 42; 79%), and 25 (60%) were due to Escherichia coli. Independent risk factors for ESBLE BSI and point allocation in ESBL-PS included outpatient procedures within 1 month (adjusted odds ratio [aOR], 8.7; 95% confidence interval [CI], 3.1–22.9; 1 point), prior infections or colonization with ESBLE within 12 months (aOR, 26.8; 95% CI, 7.0–108.2; 4 points), and number of prior courses of β-lactams and/or fluoroquinolones used within 3 months of BSI: 1 course (aOR, 6.3; 95% CI, 2.7–14.7; 1 point), ≥2 courses (aOR, 22.0; 95% CI, 8.6–57.1; 3 points). The area under the receiver operating characteristic curve for the ESBL-PS model was 0.86. Patients with ESBL-PSs of 0, 1, 3, and 4 had estimated probabilities of ESBLE BSI of 0.7%, 5%, 24%, and 44%, respectively. Using ESBL-PS ≥3 to indicate high risk provided a negative predictive value of 97%.CONCLUSIONSESBL-PS estimated patient-specific risk of ESBLE BSI with high discrimination. Incorporation of ESBL-PS with acute severity of illness may improve adequacy of empirical antimicrobial therapy and reduce carbapenem utilization.Infect Control Hosp Epidemiol 2017;38:266–272

Causal Inference with Complex Survey Designs

2016 ◽  
Author(s):  
Ines Levin ◽  
Betsy Sinclair
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Propensity Score Matching ◽  
Nearest Neighbor ◽  
Target Population ◽  
Population Based ◽  
Real World Data ◽  
Monte Carlo Simulation Study ◽  
Inference Techniques ◽  
The Impact

This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of incorporating survey weights into propensity score matching procedures, as well as the problems that arise when survey weights are ignored. Finally, it explores the differences between population-based inferences and sample-based inferences using real-world data from the 2012 panel of The American Panel Survey (TAPS). The article highlights the impact of social media usage on political participation, when such impact is not actually apparent in the target population.

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