Spatial Patterns in Hospital-Acquired Infections in Portugal (2014-2017)

Autores da FMUP

• José Alberto Da Silva Freitas

  Autor

• António Carlos Megre Eugénio Sarmento

  Autor

Participantes de fora da FMUP

• Teixeira, H
• Nossa, P
• Gon?alves, H.
• Pina, MD

Unidades de investigação

• Laboratório Associado RISE- Rede de Investigação em Saúde

  

  Investigador

  Fernando Carlos De Landér Schmitt

• Medicina

• Medicina da Comunidade, Informação e Decisão em Saúde

• RISE-Health

  

  Investigador

  Fernando Carlos De Landér Schmitt

Abstract

Background: Hospital-Acquired Infections (HAIs) represent the most frequent adverse event associated with healthcare delivery and result in prolonged hospital stays and deaths worldwide. Aim: To analyze the spatial patterns of HAI incidence from 2014 to 2017 in Portugal. Methods: Data from the Portuguese Discharge Hospital Register were used. We selected episodes of patients with no infection on admission and with any of the following HAI diagnoses: catheter-related bloodstream infections, intestinal infections by Clostridium difficile, nosocomial pneumonia, surgical site infections, and urinary tract infections. We calculated age-standardized hospitalization rates (ASHR) by place of patient residence. We used empirical Bayes estimators to smooth the ASHR. The Moran Index and Local Index of Spatial Autocorrelation (LISA) were calculated to identify spatial clusters. Results: A total of 318,218 HAIs were registered, with men accounting for 49.8% cases. The median length of stay (LOS) was 9.0 days, and 15.7% of patients died during the hospitalization. The peak of HAIs (n = 81,690) occurred in 2015, representing 9.4% of the total hospital admissions. Substantial spatial inequalities were observed, with the center region presenting three times the ASHR of the north. A slight decrease in ASHR was observed after 2015. Pneumonia was the most frequent HAI in all age groups. Conclusion: The incidence of HAI is not randomly distributed in the space; clusters of high risk in the central region were seen over the entire study period. These findings may be useful to support healthcare policymakers and to promote a revision of infection control policies, providing insights for improved implementation.

Keywords

• Bayes Theorem; Cross Infection; Hospitals; Humans; Incidence; Male; Portugal; Urinary Tract Infections; Portugal; Clostridium difficile; autocorrelation; Bayesian analysis; disease control; epidemiology; health care; health policy; hospital sector; infectious disease; policy making; spatial analysis; adult; aged; Article; catheter infection; child; Clostridioides difficile; controlled study; disease registry; female; groups by age; health care policy; health promotion; high risk population; hospital admission; hospital infection; hospitalization; human; incidence; infection control; intestine infection; length of stay; major clinical study; male; middle aged; observational study; patient selection; pneumonia; Portugal; retrospective study; school child; surgical infection; urinary tract infection; very elderly; Bayes theorem; cross infection; hospital; urinary tract infection