Hospital outbreaks

In 2018, a multi-institutional outbreak of CPE highlighted the interlinked nature of healthcare in Victoria.

Since then, CPE is now notifiable, but there is not a centralised surveillance or outbreak response system.

Carbanememase-producing Enterobacterales

  • Acquired primarily in hospital
  • Resistant to last-line antibiotics
  • Mortality, given clinical symptoms, in the order of 30%
  • Carriage for >= 12 months
  • Testing via rectal swab
  • Can carry with no symptoms
  • Unknown if asymptomatic people can transmit

Carbanememase-producing Enterobacterales incidence

Notification system

Notifications are required to be sent to the Department of Health within 24 hours of a positive result.

However, this is a one-way system. Hospitals can not look up a patient to see if they have tested positive previously at a different health service.

The current surveillance system

Surveillance and outbreak control

End goal is to model outbreak spread, and then surveillance/control methods.

To do this, we need to model how patients move around in hospitals

Data

We have line-listed hospital admissions data for all hospitals in Victoria, Australia, from 2016-2021.

The dataset is linked, allowing us to follow individuals through time. Each observation has:

  • Admission time,
  • Discharge (separation time),
  • Hospital of admission,
  • Diagnosis codes,
  • Handful of demographics

Current state of the field

We need to convert this sort of data into a structure appropriate for modelling.

patient_id admission_date separation_date location diagnosis_codes
1 2020-01-01 2020-01-02 H1 A B C
2 2020-01-01 2020-01-02 H2 A B C
1 2020-01-03 2020-01-04 H2 A B C

This is interpreted as one transfer from H1 to H2, with a one-day gap.

Different ways to collapse the data

Some notation:

  • u, v are locations
  • s, t are times, t \geq s
  • Hazard for patients returning home, never readmitted: \zeta_u(t)
  • w_{uv}(s,t) patients discharged from u at s, then readmitted to v at t

Different ways to collapse the data

Different ways to collapse the data

Then, we have the following set of governing equations:

  • Hospital to home: \lambda(u(s) \to \emptyset) = \zeta_u(s)
  • Direct transfers: \lambda(u(s) \to v(t)) = d_{uv}(s, t)
  • Indirect transfers:
    • Hospital to home: \lambda(u(s) \to z_{uv}(t)) = \eta_{uv}(s, t)
    • Home to hospital: \lambda(z_{uv}(t) \to v(t)) = \rho_{uv}(s, t)

Model 1: Naive Static Method

d_{uv}(s,t) = \frac{\sum_{s,t} w_{uv}(s,t)}{T_\Sigma N_u}

Tip

All movements are instanteous, and the rate of movement is the mean over the entire observation period.

Model 1: Naive Static Method