Applying analysis of interrupted time series to a "natural experiment": evaluating the impact of restriction of antimicrobial sales without medical prescription on the etiology and antimicrobial resistance in urinary infections in Botucatu (SP)

• Grant number: 18/17210-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: December 01, 2018
• End date: November 30, 2020
• Field of knowledge: Health Sciences - Collective Health - Epidemiology
• Principal Investigator: Carlos Magno Castelo Branco Fortaleza
• Grantee: Helena Ribeiro Aiello Amat
• Host Institution: Faculdade de Medicina (FMB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

"Natural experiments" are studies that evaluate the impact of a natural or artificial intervention, external to the control of the researchers. On October 26, 2010, in an effort to contain the escalation of bacterial resistance to antimicrobials, the National Agency for Sanitary Surveillance (ANVISA) issued the Resolution of the Collegiate Board of Directors (RDC) 44, which conditioned the sale of these drugs in pharmacy to the presentation of prescription. In view of this "natural experiment", the analysis of interrupted time series is proposed to evaluate the impact of RDC44 on the etiology and antimicrobial resistance in community-onset urinary tract infection agents, identified in outpatient services and / or the first 48 hours of hospitalization at the Hospital das Clínicas of Botucatu Medical School (HCFMB). Data for this study will be collected in the microbiology laboratory of HCFMB. Three periods are considered: pre-intervention (January / 2005 to October / 2010), implementation (November / 2010-April / 2011) and post-intervention (May/2011-June /2018). Outcomes (proportion of etiological agents and antimicrobial resistance rates most commonly used in clinical practice) will be addressed by usual statistical tests and time series analyzes (autoregressive models, eg ARIMA - Autoregressive integrated moving average, segmented regression analysis).