scholarly journals National Antibiotics Utilization Trends for Human Use in Tanzania from 2010 to 2016 Inferred from Tanzania Medicines and Medical Devices Authority Importation Data

2021 ◽  
Author(s):  
Raphael Zozimus Sangeda ◽  
Habibu A Saburi ◽  
Cassian Faustine ◽  
Beatrice Godwin Aiko ◽  
Erick Alexander ◽  
...  
Keyword(s):  
Time Series ◽  
Medical Devices ◽  
Fold Increase ◽  
P Value ◽  
Antimicrobial Use ◽  
Antibiotic Utilization ◽  
Antibiotics Use ◽  
Time Series Analyses ◽  
Human Use ◽  
Using Data

Antimicrobial use (AMU) is one of the major drivers of emerging antimicrobial resistance (AMR). Surveillance of AMU, a pillar of AMR stewardship (AMS), helps devise strategies to mitigate AMR. This descriptive, longitudinal retrospective study quantified the trends in human antibiotic utilization between 2010 and 2016 using data on all antibiotics imported for systemic human use into Tanzania's mainland. Regression and time series analyses were used to establish trends in antibiotics use. A total of 12,073 records for antibiotics were retrieved, totaling 154.51Daily Defined Doses per 1,000 inhabitants per day (DID) with a mean (± standard deviation) of 22.07 (±48.85) DID. The private sector contributed 93.76%% of utilized antibiotics. The top-ranking antibiotics were amoxicillin, metronidazole, tetracycline, ciprofloxacin and cefalexin. The DDIs and percentage contribution of these antibiotics were 53.78 (34.81%), 23.86 (15.44), 20.53 (13.29), 9.27 (6.0) and 6.94 (4.49), respectively. The time series model predicted significant increase in utilization(p-value =0.002). The model forecasted that by 2022, the total antibiotics consumed would reach 89.6 DIDs, corresponding to a 13-fold increase compared to 2010. Government intervention to curb inappropriate antibiotic utilization to mitigate the rising threat of antibiotic resistance should focus on implementing AMS programs in pharmacies and hospitals in Tanzania.

Time-Series Analyses of the Impact of Sex Offender Registration and Notification Law Implementation and Subsequent Modifications on Rates of Sexual Offenses

2017 ◽  
Vol 65 (11) ◽  
pp. 1483-1512 ◽  
Author(s):  
Jeff A. Bouffard ◽  
LaQuana N. Askew
Keyword(s):  
Time Series ◽  
Sex Offender ◽  
Sexual Offending ◽  
Public Awareness ◽  
Future Research ◽  
Sex Offender Registration ◽  
Sexual Offenses ◽  
Time Series Analyses ◽  
Using Data ◽  
The Impact

Sex offender registration and notification (SORN) laws were implemented to protect communities by increasing public awareness, and these laws have expanded over time to include registration by more types of offenders. Despite widespread implementation, research provides only inconsistent support for the impact of SORN laws on incidence of sexual offending. Using data from a large metropolitan area in Texas over the time period 1977 to 2012, and employing a number of time-series analyses, we examine the impact of the initial SORN implementation and two enhancements to the law. Results reveal no effect of SORN, or its subsequent modifications, on all sexual offenses or any of several specific offenses measures (e.g., crimes by repeat offenders). Implications for effective policy and future research are presented.

Effect of Using Age-Adjusted Suicide Rates on the Results of Time-Series Analyses of the Suicide Rate

1992 ◽  
Vol 75 (1) ◽  
pp. 310-310 ◽  
Author(s):  
David Lester ◽  
Yutaka Motohashi
Keyword(s):  
Time Series ◽  
Suicide Rate ◽  
Suicide Rates ◽  
Time Series Analyses ◽  
Using Data

Using data from Motohashi, differences in results of time-series analyses of crude and age-adjusted suicide rates in Japan were observed.

Violent Crime and Immigrant Removals: Reasons and Determinants of Immigrant Deportations, 1908-1986

2018 ◽  
Vol 15 (2) ◽  
pp. 239-254
Author(s):  
Ryan D. King ◽  
Denise Obinna
Keyword(s):  
United States ◽  
Time Series ◽  
Criminal Behavior ◽  
The United States ◽  
Homicide Rate ◽  
Immigration Law ◽  
Early 20Th Century ◽  
Time Series Analyses ◽  
Physical Defects ◽  
Using Data

This research describes and explains changes in non-citizen deportations from the United States between 1908 and 1986. Using data from historical immigration yearbooks, we first document and quantify the primary reasons given for removing immigrants from U.S. soil. A key finding is that perceived dispositional defects and threatening behavior (e.g., criminal behavior, mental or physical defects) accounted for a large proportion of deportations in the early 20th century, but these gave way to administrative rationales (e.g., improper documentation) as immigration law and the enforcement bureaucracy expanded. Results of time-series analyses further suggest that the homicide rate is correlated with deportations for administrative reasons and with deportations based on perceived dispositional defects and threatening behavior. Implications and relevance for understanding current immigration debates are discussed.

Improving Runoff Simulations using Satellite-observed Time-series of Snow Covered Area

2003 ◽  
Vol 34 (4) ◽  
pp. 281-294 ◽  
Author(s):  
R.V. Engeset ◽  
H-C. Udnæs ◽  
T. Guneriussen ◽  
H. Koren ◽  
E. Malnes ◽  
...  
Keyword(s):  
Time Series ◽  
Radar Sensors ◽  
Noaa Avhrr ◽  
Runoff Forecasting ◽  
Snow Covered Area ◽  
Covered Area ◽  
Satellite Sensors ◽  
Major Floods ◽  
Using Data ◽  
Southern Norway

Snowmelt can be a significant contributor to major floods, and hence updated snow information is very important to flood forecasting services. This study assesses whether operational runoff simulations could be improved by applying satellite-derived snow covered area (SCA) from both optical and radar sensors. Currently the HBV model is used for runoff forecasting in Norway, and satellite-observed SCA is used qualitatively but not directly in the model. Three catchments in southern Norway are studied using data from 1995 to 2002. The results show that satellite-observed SCA can be used to detect when the models do not simulate the snow reservoir correctly. Detecting errors early in the snowmelt season will help the forecasting services to update and correct the models before possible damaging floods. The method requires model calibration against SCA as well as runoff. Time-series from the satellite sensors NOAA AVHRR and ERS SAR are used. Of these, AVHRR shows good correlation with the simulated SCA, and SAR less so. Comparison of simultaneous data from AVHRR, SAR and Landsat ETM+ for May 2000 shows good inter-correlation. Of a total satellite-observed area of 1,088 km2, AVHRR observed a SCA of 823 km2 and SAR 720 km2, as compared to 889 km2 using ETM+.

Time‐series analyses of firearm‐related Google searches and U.S. suicide rates 2004–2016

2021 ◽  
Author(s):  
Daniel W. Capron ◽  
Rita Andel ◽  
Martin Voracek ◽  
Benedikt Till ◽  
Thomas Niederkrotenthaler ◽  
...  
Keyword(s):  
Time Series ◽  
Suicide Rates ◽  
Time Series Analyses

Reducing Broad Spectrum Antimicrobial Use in Extracorporeal Membrane Oxygenation (ECMO): Reduce AMMO Study

2021 ◽  
Author(s):  
Aditya Shah ◽  
Priya Sampathkumar ◽  
Ryan W Stevens ◽  
John K Bohman ◽  
Brian D Lahr ◽  
...  
Keyword(s):  
Time Series ◽  
Extracorporeal Membrane Oxygenation ◽  
Broad Spectrum ◽  
Antimicrobial Prophylaxis ◽  
Current Evidence ◽  
Antimicrobial Use ◽  
Infection Rates ◽  
Interrupted Time Series Analysis ◽  
Membrane Oxygenation ◽  
The Impact

Abstract Background The use of extracorporeal membrane oxygenation (ECMO) in critically ill adults is increasing. There are currently no guidelines for antimicrobial prophylaxis. We analyzed 7 years of prophylactic antimicrobial use across three time series for patients on ECMO at our institution in the development, improvement, and streamlining of our ECMO antimicrobial prophylaxis protocol. Study design and Methods In this quasi-experimental interrupted time series analysis, we evaluated the impact of an initial ECMO antimicrobial prophylaxis protocol, implemented in 2014, on antimicrobial use and NHSN reportable infection rates. Then, following a revision and streamlining of the protocol in November 2018, we re-evaluated the same metrics. Results Our study population included 338 ICU patients who received ECMO between July 2011 and November 2019. After implementation of the first version of the protocol we did not observe significant changes in antimicrobial use or infection rates in these patients; however, following revision and streamlining of the protocol, we demonstrated a significant reduction in broad spectrum antimicrobial use for prophylaxis in patients on ECMO without any evidence of a compensatory increase in infection rates. Conclusion Our final protocol significantly reduces broad spectrum antimicrobial use for prophylaxis in patients on ECMO. We propose a standard antimicrobial prophylaxis regimen for patients on ECMO based on current evidence and our experience.

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

Sign in / Sign up

Export Citation Format

Share Document