Institution
Center for Disease Dynamics, Economics & Policy
Nonprofit•Washington D.C., District of Columbia, United States•
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.
Topics: Population, Antibiotic resistance, Drug resistance, Public health, Antimicrobial stewardship
Papers published on a yearly basis
Papers
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TL;DR: In the largest well‐controlled study of acute kidney injury following contrast administration in the ED to date, intravenous contrast was not associated with an increased frequency of acute Kidney Injury Network/Kidney Disease Improving Global Outcomes results.
201 citations
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TL;DR: Rapid increases in Watch antibiotic consumption, particularly in LMICs, reflect challenges in antibiotic stewardship and the WHO national-level target of at least 60% of total antibiotic consumption being in the Access category by 2023 will be difficult to achieve.
Abstract: Summary Background The WHO Access, Watch, and Reserve (AWaRe) antibiotic classification framework aims to balance appropriate access to antibiotics and stewardship. We aimed to identify how patterns of antibiotic consumption in each of the AWaRe categories changed across countries over 15 years. Methods Antibiotic consumption was classified into Access, Watch, and Reserve categories for 76 countries between 2000, and 2015, using quarterly national sample survey data obtained from IQVIA. We measured the proportion of antibiotic use in each category, and calculated the ratio of Access antibiotics to Watch antibiotics (access-to-watch index), for each country. Findings Between 2000, and 2015, global per-capita consumption of Watch antibiotics increased by 90·9% (from 3·3 to 6·3 defined daily doses per 1000 inhabitants per day [DIDs]) compared with an increase of 26·2% (from 8·4 to 10·6 DIDs) in Access antibiotics. The increase in Watch antibiotic consumption was greater in low-income and middle-income countries (LMICs; 165·0%; from 2·0 to 5·3 DIDs) than in high-income countries (HICs; 27·9%; from 6·1 to 7·8 DIDs). The access-to-watch index decreased by 38·5% over the study period globally (from 2·6 to 1·6); 46·7% decrease in LMICs (from 3·0 to 1·6) and 16·7% decrease in HICs (from 1·8 to 1·5), and 37 (90%) of 41 LMICs had a decrease in their relative access-to-watch consumption. The proportion of countries in which Access antibiotics represented at least 60% of their total antibiotic consumption (the WHO national-level target) decreased from 50 (76%) of 66 countries in 2000, to 42 (55%) of 76 countries in 2015. Interpretation Rapid increases in Watch antibiotic consumption, particularly in LMICs, reflect challenges in antibiotic stewardship. Without policy changes, the WHO national-level target of at least 60% of total antibiotic consumption being in the Access category by 2023, will be difficult to achieve. The AWaRe framework is an important measure of the effort to combat antimicrobial resistance and to ensure equal access to effective antibiotics between countries. Funding US Centers for Disease Control and Prevention.
198 citations
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TL;DR: The contributions and limitations of studies that estimates the disease burden attributable to antibiotic resistance and studies that estimate the economic burden of resistance are reviewed and discussed.
191 citations
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TL;DR: Comprehensive SARS-CoV-2 testing and contact-tracing data from the Indian states of Tamil Nadu and Andhra Pradesh reveal stark contrasts from epidemics affecting high-income countries, with 92% of cases and 59.7% of deaths occurring among individuals <65 years old.
Abstract: Although most COVID-19 cases have occurred in low-resource countries, there is scarce information on the epidemiology of the disease in such settings. Comprehensive SARS-CoV-2 testing and contact-tracing data from the Indian states of Tamil Nadu and Andhra Pradesh reveal stark contrasts from epidemics affecting high-income countries, with 92.1% of cases and 59.7% of deaths occurring among individuals
183 citations
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TL;DR: Attributable hospital length of stay, hospital costs, and crude in-hospital mortality were estimated from discharge records using a multivariate matching analysis and a supplementary regression analysis.
Abstract: Background Health care–associated infections affect 1.7 million hospitalizations each year, but the clinical and economic costs attributable to these infections are poorly understood. Reliable estimates of these costs are needed to efficiently target limited resources for the greatest public health benefit. Methods Hospital discharge records from the Nationwide Inpatient Sample database were used to identify sepsis and pneumonia cases among 69 million discharges from hospitals in 40 US states between 1998 and 2006. Community-acquired infections were excluded using criteria adapted from previous studies. Because these criteria may not exclude all community-acquired infections, outcomes were examined separately for cases associated with invasive procedures, which were unlikely to result from preexisting infections. Attributable hospital length of stay, hospital costs, and crude in-hospital mortality were estimated from discharge records using a multivariate matching analysis and a supplementary regression analysis. These models controlled for patient diagnoses, procedures, comorbidities, demographics, and length of stay before infection. Results In cases associated with invasive surgery, attributable mean length of stay was 10.9 days, costs were $32 900, and mortality was 19.5% for sepsis; corresponding values for pneumonia were 14.0 days, $46 400, and 11.4%, respectively ( P P Conclusion Health care–associated sepsis and pneumonia impose substantial clinical and economic costs.
178 citations
Authors
Showing all 83 results
Name | H-index | Papers | Citations |
---|---|---|---|
David L. Smith | 96 | 331 | 47666 |
Amit Verma | 70 | 497 | 16162 |
Ramanan Laxminarayan | 67 | 287 | 25009 |
Niranjan Kissoon | 63 | 512 | 36599 |
Eili Y. Klein | 35 | 136 | 5996 |
Daniel J. Morgan | 32 | 133 | 3950 |
Carlos A Guerra | 28 | 31 | 10649 |
Thomas P. Van Boeckel | 28 | 52 | 8106 |
Anup Malani | 27 | 117 | 2384 |
Daniel J. Morgan | 27 | 60 | 2156 |
Sumanth Gandra | 24 | 67 | 6229 |
Arnaud Le Menach | 22 | 32 | 2288 |
Arthorn Riewpaiboon | 21 | 91 | 1269 |
Elena Martinez | 17 | 39 | 1774 |
Susmita Chatterjee | 17 | 50 | 1693 |