Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy

Background: System science approaches are increasingly used to explore complex public health problems. Quantitative methods, such as participatory dynamic simulation modelling, can mobilise knowledge to inform health policy decisions. However, the analytic and practical steps required to turn collaboratively developed, qualitative system maps into rigorous and policy relevant quantified dynamic simulation models are not well described. This paper reports on the processes, interactions and decisions that occurred at the interface between modellers and end-user participants in an applied health sector case study focusing on diabetes in pregnancy. Methods: An analysis was conducted using qualitative data from a participatory dynamic simulation modelling case study in an Australian health policy setting. Recordings of participatory model development workshops and subsequent meetings were analysed and triangulated with field notes and other written records of discussions and decisions. Case study vignettes were collated to illustrate the deliberations and decisions made throughout the model development process. Results: The key analytic objectives and decision-making processes included: defining the model scope; analysing and refining the model structure to maximise local relevance and utility; reviewing and incorporating evidence to inform model parameters and assumptions; focusing the model on priority policy questions; communicating results and applying the models to policy processes. These stages did not occur sequentially; the model development was cyclical and iterative with decisions being re-visited and refined throughout the process. Storytelling was an effective strategy to both communicate and resolve concerns about the model logic and structure, and to communicate the outputs of the model to a broader audience. Conclusion: The in-depth analysis reported here examined the application of participatory modelling methods to move beyond qualitative conceptual mapping to the development of a rigorously quantified and policy relevant, complex dynamic simulation model. The analytic objectives and decision-making themes identified provide guidance for interpreting, understanding and reporting future participatory modelling projects and methods

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Effectiveness of hospital-wide methicillin-resistant Staphylococcus aureus (MRSA) infection control policies differs by ward specialty.

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of preventable nosocomial infections and is endemic in hospitals worldwide. The effectiveness of infection control policies varies significantly across hospital settings. The impact of the hospital context towards the rate of nosocomial MRSA infections and the success of infection control is understudied. We conducted a modelling study to evaluate several infection control policies in surgical, intensive care, and medical ward specialties, each with distinct ward conditions and policies, of a tertiary public hospital in Sydney, Australia. We reconfirm hand hygiene as the most successful policy and find it to be necessary for the success of other policies. Active screening for MRSA, patient isolation in single-bed rooms, and additional staffing were found to be less effective. Across these ward specialties, MRSA transmission risk varied by 13% and reductions in the prevalence and nosocomial incidence rate of MRSA due to infection control policies varied by up to 45%. Different levels of infection control were required to reduce and control nosocomial MRSA infections for each ward specialty. Infection control policies and policy targets should be specific for the ward and context of the hospital. The model we developed is generic and can be calibrated to represent different ward settings and pathogens transmitted between patients indirectly through health care workers. This can aid the timely and cost effective design of synergistic and context specific infection control policies

A model of health care worker (HCW) and patient flow dynamics through hospital ward specialties and rooms.

<p>Solid arrows represent HCW and patient movement into, out of, and between ward specialties and rooms, and between MRSA susceptible and colonised states. Dashed arrows represent MRSA transmission occurring upon contact between HCWs and patients. When beds in single-bed rooms (SBR) or multiple-bed rooms (MBR) are not available, hospital patient admissions may be delayed and inpatients that are being moved from a ward or room remain in their current bed until the requested beds are made available.</p

The incidence rate of MRSA per 10,000 overnight bed days (OBD), averaged over 1000 simulations, in response to changes in both staff hand hygiene (HH) compliance and hand hygiene efficacy in (A) surgical, (B) intensive care, (C) short-stay medical, and (D) long-stay medical wards.

<p>The colours indicate the MRSA incidence rate per 10,000 OBD. The dark blue area represents the combination of hand hygiene compliance and efficacy ranges that achieved incidence rates of less than 10 cases per 10,000 OBD.</p

The effect of active surveillance (A and F), ward staffing levels or health care worker (HCW) to patient ratios (B and G), staff contact cohorting (C and H), patient isolation (D and I), and staff hand hygiene compliance (E and J) on the average daily prevalence and incidence rate per 10,000 overnight bed days (OBD) of MRSA, in surgical (blue), intensive care (red), short-stay medical (green), and long-stay medical (black) wards, in 2008 and 2009, averaged over 1000 simulations.

<p>The effect of active surveillance (A and F), ward staffing levels or health care worker (HCW) to patient ratios (B and G), staff contact cohorting (C and H), patient isolation (D and I), and staff hand hygiene compliance (E and J) on the average daily prevalence and incidence rate per 10,000 overnight bed days (OBD) of MRSA, in surgical (blue), intensive care (red), short-stay medical (green), and long-stay medical (black) wards, in 2008 and 2009, averaged over 1000 simulations.</p

The daily prevalence of MRSA in surgical wards (blue), intensive care wards (red), short-stay medical wards (green), and long-stay medical wards (black) of one hospital, from April 2008 to December 2009, averaged over 1000 simulations.

<p>The daily prevalence of MRSA in surgical wards (blue), intensive care wards (red), short-stay medical wards (green), and long-stay medical wards (black) of one hospital, from April 2008 to December 2009, averaged over 1000 simulations.</p

The average daily MRSA prevalence and MRSA incidence rate per 10,000 overnight bed days (OBD) in surgical, intensive care (ICU), and medical wards of one hospital, over 2008 and 2009, averaged over 1000 simulation runs.

<p>The average daily MRSA prevalence and MRSA incidence rate per 10,000 overnight bed days (OBD) in surgical, intensive care (ICU), and medical wards of one hospital, over 2008 and 2009, averaged over 1000 simulation runs.</p

Parameters used in the model.

<p> A, parameters based on assumptions; ALOS, average patient length of stay; C, MRSA colonised or infected patients; E, parameters estimated from data collected from a tertiary public hospital in Sydney, Australia and subject to sensitivity analysis; H, data collected from a tertiary public hospital in Sydney, Australia; ICU, intensive care units; MBR, multiple-bed rooms; PCR, polymerase chain reaction; S, patients not colonised with MRSA; SBR, single-bed rooms.</p