Europe tracks these closely

HAIs are actively monitored across Europe through the ECDC

• In 2016 (based on 2012 data), 2,609,911 new HAIs are estimated to have occurred.

The data were obtained in a point prevalence survey on an enormous scale

• 273, 753 patients
• 1,149 hospitals

Point prevalence survey

A point prevalence survey counts the number of people with a condition on a given day

For the Australian PPS:

• Adults in 19 large acute care public hospitals were sampled
• All acute care wards were include, non-acute, paediatric, neonatal ICUs, rehab and emergency departments were excluded.

The hospitals sampled make up approximately 60% of all overnight separations in Australia

Point prevalence survey

• 2767 patients were sampled between 6 Aug and 29 Nov 2018
• Median age: 67 (range 18-104)
• 52.9% male, 46.6% female, 0.5% unknown/other
• 85.7% patients in major city hospitals

Estimation methodology

Step 5: Adjustment for life expectancy

Use the McCabe score, which gives the life expectancy according to severity of disease. Patients are categorised as:

• non-fatal
• fatal (life expectancy 3 years)
• rapidly fatal (average life expectancy of 0.5 years)

These scores, combined with disease outcome trees, give DALYs and deaths.

Disease outcome trees

Key results

Key results

Key results

Novelty

• First estimate of HAI burden in Australia using (relatively) robust survey data in an established framework

• Based on first point prevalence survey since 1984

• There is no routine surveillance of HAIs in Australia

• Point prevalence surveys remain the only way to understand the burden of these conditions

Summary

• 498 DALYs per 100,000 is a large amount
  • Motor vehicles: 180 DALYs
  • Infectious diseases: 370 DALYs
  • Respiratory diseases: 1380 DALYs

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

Incidence in Victoria, Australia

Notification system

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

Notification system

Hospital outbreaks

The immediate question:

Data summary

The standard network

The standard network on a map

The standard network on a map

But movement changes all the time

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}

Model 2: Improved Static Method

Choose a threshold value, \omega, to separate direct and indirect transfers

d_{uv}(s,t) = d_{uv} = \frac{\sum_{s,t: (t-s) < \omega} w_{uv}(s,t)}{T_\Sigma N_u} \eta_{uv}(s,t) = \eta_{uv} = \frac{\sum_{s,t: (t-s) \geq \omega} w_{uv}(s,t)}{T_\Sigma N_u} \rho_{uv}(s,t) = \rho_{uv} = \left[ \frac{\sum_{s,t: (t-s) \geq \omega} (t-s) w_{uv}(s,t)}{\sum_{s,t: (t-s) \geq \omega} w_{uv}(s,t)} \right]^{-1}

Model 3: Snapshot Model

Choose a snapshot duration \omega. This defines the threshold duration of an indirect transfer.

\begin{aligned} \lambda(u(s) \to {z'}_{uv}(t)) &= \eta_{uv}(s, t)\\ \lambda({z'}_{uv}(t) \to z_{uv}(t)) &= \delta(t \; \mathrm{ mod } \; \omega)\\ \end{aligned}

Model 4: Temporal Model

Effecitvely, choose a very small snapshot window, \omega. Individuals are not re-admitted to z', instead they are re-admitted to v uniformly between t and t+\omega. This gives,

d_{uv}(s, t) = \frac{\sum_{t: (t-s) < \omega} w_{uv}(s, t)}{\omega N_u} \eta_{uv}(s, t) = \frac{\sum_{t: (t-s) \geq \omega} w_{uv}(s, t)}{\omega N_u} \rho_{uv}(s,t) = \frac{1}{\lceil t \rceil_\omega - t}

Putting them together

Current state-of-the-art: Patients move instantly, no concept of home, using average return rates over the entire data period

What difference does it make?

What difference does it make?

Takeaway

• Still need to consider surveillance algorithms on these weighted, directed, temporal networks
• Patients going home slows down infection spread
• Most hospital transfer patterns aren’t stationary
• Using the wrong collapsing process means you will probably miss infections
• May declare a hospital “disease-free” when it truly hasn’t arrived yet

Hospital outbreaks

The immediate question:

Bonus project: CPE Isolation Guidelines

Patients colonised with CPE have historically required isolation on every hospital admission post-diagnosis.

This means:

• Contact precautions for staff and
• Single-patient room for patients

independent of colonisation or future testing status.

Changing the guidelines

These rules have put a large strain on single-patient rooms, and has negative patient experiences in hospital.

Recently, this has been changed where individuals can be released from contact precautions if they have not tested positive for CPE in the last 12 months

Testing the impact of this change

We wanted to determine how many extra beds may be available under the new guidelines.

We assume contact precautions start on the ‘onset date’ of CPE for a patient.

• Under the old guidelines, patients are isolated at all future admissions.
• Under the new guidelines, after 12 months, patients are no longer isolated.

Result