Constarium
← Search

Data · dataset · 2025

Point Prevalence Survey (PPS) based on ECDC PPS 6.1 methodology and Data Analysis with Microsoft Power BI

Listed in Riga Stradins University dataverse

The dataset includes Point Prevalence Survey (PPS) dataset, developed according to the European Centre for Disease Prevention and Control (ECDC) PPS protocol version 6.1, aims to provide standardized data of healthcare-associated infections (HAIs) and antimicrobial use in acute care hospitals.

Description

Its purpose is to estimate prevalence and inform antimicrobial stewardship and infection prevention strategies. The dataset is cross-sectional in nature, capturing patient-level and institutional-level variables at a single point in time.

Patient level data is not publicly available. Data collection follows the ECDC PPS methodology, which includes specific patient inclusion criteria, and standardized case definitions for HAIs and indications for antimicrobial use. Data were gathered through manual entry and subsequently entered into HelicsWin.Net software according to HelicsWin.Net user manual v4.8.1.

Read the rest (1 more)

The scope of the dataset encompasses demographic and clinical characteristics, presence and type of HAIs, microbiological findings, and detailed antimicrobial prescribing information (agent, route, indication and changes to prescribed antimicrobial). Data analysis file with data visualisations for Latvia's acute care hospitals in Microsoft Power BI (PBI) is also included (available only in Latvian). The target audience for conducting PPS includes healthcare professionals directly involved in infection prevention and antimicrobial stewardship, such as infectious disease specialists, infection control nurses, clinical pharmacists, clinical microbiologists, and epidemiologists.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Disease 75%
Provenance · 1 source records, 10 field assertions
SourceKeyLast seenRaw
Riga Stradins University dataversedoi:10.48510/FK2/YUSZA59 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[disease].local:disease:diseaseenrichment · dataverse rsu lvkeyword-concept-rules@1.0.0title+description (75%)
concepts[field].dataverse_subject:medicine-health-and-life-sciencessource · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0/subjects
concepts[field].local:field:life-sciencesmapping · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0/subjects
concepts[field].local:field:medicine-healthmapping · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0/subjects
created_datesource · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0
descriptionsource · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0/description
publication_datesource · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0
titlesource · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0/name
updated_datesource · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0
version_labelsource · dataverse rsu lvconnector:dataverse_rsu_lv@1.0.0