Center for Disease Dynamics, Economics & Policy

About: Center for Disease Dynamics, Economics & Policy is a nonprofit organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Antibiotic resistance. The organization has 76 authors who have published 320 publications receiving 21403 citations.

Antibiotic prescribing across age groups in the Kaiser Permanente Northern California population in association with different diagnoses, and with influenza incidence, 2010–2018

Abstract: There is limited information on the volume of antibiotic prescribing that is influenza-associated, resulting from influenza infections and their complications (such as streptococcal pharyngitis and otitis media). Here, we estimated age/diagnosis-specific proportions of antibiotic prescriptions (fills) for the Kaiser Permanente Northern California population during 2010-2018 that were influenza-associated. The proportion of influenza-associated antibiotic prescribing among all antibiotic prescribing was higher in children aged 5-17 years compared to children aged under 5 years, ranging from 1.4% [95% CI (0.7-2.1)] in aged <1 year to 2.7% (1.9-3.4) in aged 15-17 years. For adults aged over 20 years, the proportion of influenza-associated antibiotic prescribing among all antibiotic prescribing was lower, ranging from 0.7% (0.5-1) for aged 25-29 years to 1.6% (1.2-1.9) for aged 60-64 years. Most of the influenza-associated antibiotic prescribing in children aged under 10 years was for ear infections, while for age groups over 25 years, 45-84% of influenza-associated antibiotic prescribing was for respiratory diagnoses without a bacterial indication. This suggests a modest benefit of increasing influenza vaccination coverage for reducing antibiotic prescribing, as well as the potential benefit of other measures to reduce unnecessary antibiotic prescribing for respiratory diagnoses with no bacterial indication in persons aged over 25 years, both of which may further contribute to the mitigation of antimicrobial resistance.

Incentivizing responsible antibiotic use

Abstract: Superbugs is an immensely readable description of the challenges that encourage overuse of antibiotics and discourage new drug development.

Mesures essentielles aux programmes de bon usage des antibiotiques au niveau international

Abstract: Introduction Mettre en place des programmes de bon usage des antibiotiques dans tous les hopitaux est essentiel pour lutter contre l’antibioresistance. Une liste des mesures devant imperativement faire partie de ces programmes n’a ete etablie qu’en Europe, aux Etats-Unis et en Australie. L’objectif de ce travail etait d’identifier et valider avec une methode Delphi les mesures essentielles aux programmes de bon usage des antibiotiques qui peuvent etre applicables dans tous les hopitaux au niveau mondial. Materiels et methodes Une revue de la litterature a ete effectuee a partir d’une recherche Medline et sur les sites internet des principales organisations nationales et internationales afin d’identifier toutes les mesures decrites (core elements et checklist items). Un groupe d’experts a evalue chaque mesure identifiee en repondant a deux reprises a un questionnaire en ligne. Les mesures selectionnees par ≥ 80 % des experts etaient retenues des le premier questionnaire, celles selectionnees par 70–79 % des experts etaient reevaluees lors du deuxieme questionnaire et les mesures selectionnees par Resultats La revue de la litterature a identifie 48 references, a partir desquelles 7 core elements (type de strategie, e.g. Formation) et 29 checklist items (mesure precise au sein de la strategie, e.g. Formation des prescripteurs) ont ete recueillis. Quinze experts (Amerique du Nord = 2, Amerique latine et du Sud = 2, Europe = 5, Afrique = 2, Asie = 3, Australie = 1) ont participe a la procedure de consensus formalise. Au final, tous les 7 core elements ont ete retenus et 29 checklist items ont ete selectionnes (28 des 29 identifies initialement, + un item suggere par les experts). Conclusion Nous avons valide des standards minimaux qui peuvent etre applicables au niveau international pour les programmes de bon usage des antibiotiques hospitaliers. Ce travail peut aider chaque pays, y compris les pays aux ressources limitees, a developper des recommandations nationales.

Confidence interval methods for antimicrobial resistance surveillance data

Abstract: 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. 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. 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. 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.