Antimicrobial resistance surveillance report, North Okkalapa General and Teaching Hospital, Myanmar, 02 Jan 2019 to 27 Dec 2019

Version 2 2021-01-12, 01:58

Version 1 2020-04-15, 08:21

dataset

posted on 2021-01-12, 01:58 authored by North Okkalapa General and Teaching Hospital, Yangon

Updated on 12^th January 2021

This update fixes inaccurate estimations of the proportion of non-susceptible isolates of E. coli and K. pneumoniae for ampicillin (page 9-10 and page 18-21 in the AMR surveillance report) in AMASS 1.0. The updated AMR surveillance report and output files were generated by AMASS 1.1 using the same data sets and data dictionary files. AMASS 1.1 (figshare link https://figshare.com/articles/dataset/AMASS_zip/7763330) fixes a typo in the R code (z_Rcode.R file) of the AMASS 1.0 that caused the inaccurate estimations.

AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) was developed as an offline, open-access and easy-to-use application that allows a hospital to perform data analysis independently and generate isolate-based and sample-based surveillance reports stratified by infection origin from routinely collected electronic databases. AMASS performs data analysis and generates reports automatically. The application can be downloaded from https://www.amass.website.

This is a repository of the files for North Okkalapa General and Teaching Hospital. The attached four files are:
1) The AMR surveillance report automatically generated from AMASS using hospital admission and microbiology data of North Okkalapa General and Teaching Hospital in Myanmar
2) Aggregated summary data in .csv format automatically generated from AMASS
3) Data dictionary for microbiology data file of North Okkalapa General and Teaching Hospital (configured by the hospital to be used with the AMASS application)
4) Data dictionary for hospital admission data of North Okkalapa General and Teaching Hospital (configured by the hospital to be used with the AMASS application)

Please note that the two data dictionary files can be re-used with the data files of the hospital in the future in cases when variables names and values in the raw data remained unchanged.