The use of modelling studies to inform planning of health services: case study of rapidly increasing endoscopy services in Australia.

BACKGROUND: Demand for gastrointestinal endoscopy in Australia is increasing as a result of the expanding national bowel cancer screening program and a growing, ageing population. More services are required to meet demand and ensure patients are seen within clinically recommended timeframes. METHODS: A discrete event simulation model was developed to project endoscopy waiting list outcomes for two large metropolitan health services encompassing 8 public hospitals in Australia. The model applied routinely collected health service data to forecast the impacts of future endoscopic demand over 5 years and to identify the level of service activity required to address patient waiting times and meet key policy targets. The approach incorporated evidence from the literature to produce estimates of cost-effectiveness by showing longer term costs and Quality Adjusted Life Years (QALYs) associated with service expansion. RESULTS: The modelling revealed that doing nothing would lead to the number of patients waiting longer than clinically recommended doubling across each health service within 5 years. A 38% overall increase in the number of monthly procedures available was required to meet and maintain a target of 95-98% of patients being seen within clinically recommended timeframes to the year 2021. This was projected to cost the funder approximately $140 million in additional activity over a 5 year period. Due to improved patient outcomes associated with timely intervention, it was estimated that the increased activity would generate over 22,000 additional QALYs across the two health services. This translated to an incremental cost-effectiveness ratio of $6467 and $5974 per QALY for each health service respectively. CONCLUSIONS: Discrete event simulation modelling provided a rational, data based approach that allowed decision makers to quantify the future demand for endoscopy services and identify cost-effective strategies to meet community needs.

Impacts of licensed premises trading hour policies on alcohol-related harms.

BACKGROUND AND AIM: Evaluations of alcohol policy changes demonstrate that restriction of trading hours of both 'on'- and 'off'-licence venues can be an effective means of reducing rates of alcohol-related harm. Despite this, the effects of different trading hour policy options over time, accounting for different contexts and demographic characteristics, and the common co-occurrence of other harm reduction strategies in trading hour policy initiatives, are difficult to estimate. The aim of this study was to use dynamic simulation modelling to compare estimated impacts over time of a range of trading hour policy options on various indicators of acute alcohol-related harm. METHODS: An agent-based model of alcohol consumption in New South Wales, Australia was developed using existing research evidence, analysis of available data and a structured approach to incorporating expert opinion. Five policy scenarios were simulated, including restrictions to trading hours of on-licence venues and extensions to trading hours of bottle shops. The impact of the scenarios on four measures of alcohol-related harm were considered: total acute harms, alcohol-related violence, emergency department (ED) presentations and hospitalizations. RESULTS: Simulation of a 3 a.m. (rather than 5 a.m.) closing time resulted in an estimated 12.3 ± 2.4% reduction in total acute alcohol-related harms, a 7.9 ± 0.8% reduction in violence, an 11.9 ± 2.1% reduction in ED presentations and a 9.5 ± 1.8% reduction in hospitalizations. Further reductions were achieved simulating a 1 a.m. closing time, including a 17.5 ± 1.1% reduction in alcohol-related violence. Simulated extensions to bottle shop trading hours resulted in increases in rates of all four measures of harm, although most of the effects came from increasing operating hours from 10 p.m. to 11 p.m. CONCLUSIONS: An agent-based simulation model suggests that restricting trading hours of licensed venues reduces rates of alcohol-related harm and extending trading hours of bottle shops increases rates of alcohol-related harm. The model can estimate the effects of a range of policy options.

Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms.

OBJECTIVES: Alcohol misuse is a complex systemic problem. The aim of this study was to explore the feasibility of using a transparent and participatory agent-based modelling approach to develop a robust decision support tool to test alcohol policy scenarios before they are implemented in the real world. METHODS: A consortium of Australia's leading alcohol experts was engaged to collaboratively develop an agent-based model of alcohol consumption behaviour and related harms. As a case study, four policy scenarios were examined. RESULTS: A 19.5 ± 2.5% reduction in acute alcohol-related harms was estimated with the implementation of a 3 a.m. licensed venue closing time plus 1 a.m. lockout; and a 9 ± 2.6% reduction in incidence was estimated with expansion of treatment services to reach 20% of heavy drinkers. Combining the two scenarios produced a 33.3 ± 2.7% reduction in the incidence of acute alcohol-related harms, suggesting a synergistic effect. CONCLUSIONS: This study demonstrates the feasibility of participatory development of a contextually relevant computer simulation model of alcohol-related harms and highlights the value of the approach in identifying potential policy responses that best leverage limited resources.

Building Research Capacity: Results of a Feasibility Study Using a Novel mHealth Epidemiological Data Collection System Within a Gestational Diabetes Population.

Public health researchers have traditionally relied on individual self-reporting when collecting much epidemiological surveillance data. Data acquisition can be costly, difficult to acquire, and the data often notoriously unreliable. An interesting option for the collection of individual health (or indicators of individual health) data is the personal smartphone. Smartphones are ubiquitous, and the required infrastructure is well-developed across Canada, including many remote areas. Researchers and health professionals are asking themselves how they might exploit increasing smartphone uptake for the purposes of data collection, hopefully leading to improved individual and public health. A novel smartphone-based epidemiological data collection and analysis system has been developed by faculty and students from the CEPHIL (Computational Epidemiology and Public Health Informatics) Lab in the Department of Computer Science at the University of Saskatchewan. A pilot feasibility study was then designed to examine possible relationships between smartphone sensor data, surveys and individual clinical data within a population of pregnant women. The study focused on the development of Gestational Diabetes (GDM), a transient condition during pregnancy, but with serious potential post-birth complications for both mother and child. The researchers questioned whether real-time smartphone data could improve the clinical management and outcomes of women at risk for developing GDM, enabling earlier treatment. The initial results from this small study did not show improved prediction of GDM, but did demonstrate that real-time individual health and sensor data may be readily collected and analyzed efficiently while maintaining confidentiality. Because the original version of the data collection software could only run on Android phones, this often meant the study participants were required to carry two phones, and this often meant the study phone was not carried, and therefore data not collected. The lessons learned will greatly inform future research.

Design and methods of a social network isolation study for reducing respiratory infection transmission: The eX-FLU cluster randomized trial.

BACKGROUND: Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period. METHODOLOGY/PRINCIPAL FINDINGS: 590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012-January 2013. Of these, 262 joined as "seed" participants, who nominated their social contacts to join the study, of which 328 "nominees" enrolled. Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (N=103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall, iEpi participation, age, gender, race, and alcohol use (all P<0.002). CONCLUSIONS/SIGNIFICANCE: We identified a feasible study design for testing the impact of isolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the course of this project, including feasible isolation durations, the need for extensive organizational efforts, as well as the need for specialized programmers and server space for managing survey and smartphone data.