'Turning the tide' on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling

INTRODUCTION

Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP.

METHODS

A consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact.

RESULTS

Population interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline ('business as usual' scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term.

DISCUSSION

Population-level weight reduction interventions will be necessary to 'turn the tide' on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated.

Gaming National Emergency Access Target performance using Emergency Treatment Performance definitions and emergency department short stay units

OBJECTIVE

To evaluate potential gaming of the 4 h ED length of stay metric known as the National Emergency Access Target (NEAT) in Australia and Emergency Treatment Performance (ETP) in New South Wales (NSW).

METHODS

Descriptive statistical analysis was used to recalculate and compare the scores for NEAT and the NSW ETP using variations in the definitions of their measurement on 32 184 presentations during 2016. A computer simulation using a discrete event model illustrated the effect of the use of ED short stay beds on the ETP scores.

RESULTS

Using the timestamp of the intent to discharge a patient, called, 'ready for departure' instead of the time of a patient physically leaving the department, resulted in an apparent 6% performance improvement. A local interpretation of the NSW state definition of the 'transferred' patient resulted in the ETP for 'admitted' patients improving by 16%. The discrete event model demonstrated that without changing patient length of stay, ETP scores can be improved by optimising the time of the admit decision or increasing the number of ED short stay beds.

CONCLUSIONS

The opportunity of NEAT may be squandered unless gaming of the definitions and use of ED short stay beds is addressed. We argue that the longstanding issue of 'departure time' should be defined as 'physically leaving' the department, in accordance with the Australasian College for Emergency Medicine (ACEM) definition. Patient occupancy is a real measure of ED resource use and NSW and national recommendations should be adjusted. ACEM accreditation of EDs should include review of their application of NEAT definitions to ensure they truly reflect patient flow processes.