Diagnosis and management of infectious disease in primary care during the COVID-19 lockdown: changes in antibacterial and antiviral use

Study type

• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology

  Drug utilisation

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Medical condition to be studied:
  Lower respiratory tract infection

  Upper respiratory tract infection

  Urinary tract infection

  Pyelonephritis

  Gastroenteritis

  Skin infection

  COVID-19

Population studied

• Age groups:
  • Paediatric Population (< 18 years)

    • Neonate

      • Preterm newborn infants (0 – 27 days)
      • Term newborn infants (0 – 27 days)
    • Infants and toddlers (28 days – 23 months)
    • Children (2 to < 12 years)
    • Adolescents (12 to < 18 years)
  • Adult and elderly population (≥18 years)

    • Adults (18 to < 65 years)

      • Adults (18 to < 46 years)
      • Adults (46 to < 65 years)
    • Elderly (≥ 65 years)

      • Adults (65 to < 75 years)
      • Adults (75 to < 85 years)
      • Adults (85 years and over)
• Estimated number of subjects:
  6000000

Study design details

• Main study objective:
  The study objective is to investigate changes in infectious disease diagnoses and antibacterial and antiviral prescribing in primary care, and associated hospital admissions, during the COVID-19 UK lockdown.
• Outcomes:
  • Changes in practice-level rates of specific infectious disease diagnoses, per 1,000 patients • Changes in practice-level rates of antibacterial and antiviral prescribing, per 1,000 patients • Change in practice-level rates of hospital attendance with specific infectious disease diagnoses, per 1,000 patients• Antibacterial or antiviral prescription (yes/no)
• Data analysis plan:
  Descriptive analysis will be used to explore variations in practice rates of diagnosis and prescribing, per 1000 patient population pre and during COVID-19 lockdown, and over time. Multivariable linear regression models will be used to investigate practice characteristics associated with changes in rates of relevant outcomes. Multivariable logistic regression will be used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to investigate whether antibacterial or antiviral medication were more or less likely to be prescribed during the COVID-19 lockdown compared to previously. Interaction terms will be used to investigate whether specific patient demographic and clinical factors moderate the relationshipMixed-effect models will be used to account for the structure of the data (i.e. patients clustered within practices, practices clusters within clinical commissioning groups).