Modelling the results of health promotion activities in Switzerland: development of the Swiss Model for Outcome Classification in Health Promotion and Prevention

This paper describes the Model for Outcome Classification in Health Promotion and Prevention adopted by Health Promotion Switzerland (SMOC, Swiss Model for Outcome Classification) and the process of its development. The context and method of model development, and the aim and objectives of the model are outlined. Preliminary experience with application of the model in evaluation planning and situation analysis is reported. On the basis of an extensive literature search, the model is situated within the wider international context of similar efforts to meet the challenge of developing tools to assess systematically the activities of health promotion and prevention.

Modelling the impact of deferring HCV treatment on liver-related complications in HIV coinfected men who have sex with men

BACKGROUND AND AIMS Hepatitis C (HCV) is a leading cause of morbidity and mortality in people who live with HIV. In many countries, access to direct acting antiviral agents to treat HCV is restricted to individuals with advanced liver disease (METAVIR stage F3 or F4). Our goal was to estimate the long term impact of deferring HCV treatment for men who have sex with men (MSM) who are coinfected with HIV and often have multiple risk factors for liver disease progression. METHODS We developed an individual-based model of liver disease progression in HIV/HCV coinfected men who have sex with men. We estimated liver-related morbidity and mortality as well as the median time spent with replicating HCV infection when individuals were treated in liver fibrosis stages F0, F1, F2, F3 or F4 on the METAVIR scale. RESULTS The percentage of individuals who died of liver-related complications was 2% if treatment was initiated in F0 or F1. It increased to 3% if treatment was deferred until F2, 7% if it was deferred until F3 and 22% if deferred until F4. The median time individuals spent with replicating HCV increased from 5 years if treatment was initiated in F2 to almost 15 years if it was deferred until F4. CONCLUSIONS Deferring HCV therapy until advanced liver fibrosis is established could increase liver-related morbidity and mortality in HIV/HCV coinfected individuals, and substantially prolong the time individuals spend with replicating HCV infection.

Structural dynamics research and applications

Research in structural dynamics has received considerable attention due to problems associated with emerging slender structures, increased vulnerability of structures to random loads and aging infrastructure. This paper briefly describes some such research carried out on i) dynamics of composite floor structure, ii) dynamics of cable supported footbridge, iii) seismic mitigation of frame-shear wall structure using passive dampers and iv) development of a damage assessment model for use in structural health modelling.

Measuring National Accessibility to Cardiac Services using Geographic Information Systems

The Cardiac Access-Remoteness Index of Australia (Cardiac ARIA) used geographic information systems (GIS) to model population level, road network accessibility to cardiac services before and after a cardiac event for all (20,387) population localities in Australia., The index ranged from 1A (access to all cardiac services within 1 h driving time) to 8E (limited or no access). The methodology derived an objective geographic measure of accessibility to required cardiac services across Australia. Approximately 71% of the 2006 Australian population had very good access to acute hospital services and services after hospital discharge. This GIS model could be applied to other regions or health conditions where spatially enabled data were available.

Predictive modelling : parents’ decision making to use online child health information to increase their understanding and/or diagnose or treat their child’s health

Abstract Background The quantum increases in home Internet access and available online health information with limited control over information quality highlight the necessity of exploring decision making processes in accessing and using online information, specifically in relation to children who do not make their health decisions. Objectives To understand the processes explaining parents’ decisions to use online health information for child health care. Methods Parents (N = 391) completed an initial questionnaire assessing the theory of planned behaviour constructs of attitude, subjective norm, and perceived behavioural control, as well as perceived risk, group norm, and additional demographic factors. Two months later, 187 parents completed a follow-up questionnaire assessing their decisions to use online information for their child’s health care, specifically to 1) diagnose and/or treat their child’s suspected medical condition/illness and 2) increase understanding about a diagnosis or treatment recommended by a health professional. Results Hierarchical multiple regression showed that, for both behaviours, attitude, subjective norm, perceived behavioural control, (less) perceived risk, group norm, and (non) medical background were the significant predictors of intention. For parents’ use of online child health information, for both behaviours, intention was the sole significant predictor of behaviour. The findings explain 77% of the variance in parents’ intention to treat/diagnose a child health problem and 74% of the variance in their intentions to increase their understanding about child health concerns. Conclusions Understanding parents’ socio-cognitive processes that guide their use of online information for child health care is important given the increase in Internet usage and the sometimes-questionable quality of health information provided online. Findings highlight parents’ thirst for information; there is an urgent need for health professionals to provide parents with evidence-based child health websites in addition to general population education on how to evaluate the quality of online health information.

Spatial modelling of population health screening : the role of accessibility in predicting expected patient flows

This paper describes a generalised linear mixed model (GLMM) approach for understanding spatial patterns of participation in population health screening, in the presence of multiple screening facilities. The models presented have dual focus, namely the prediction of expected patient flows from regions to services and relative rates of participation by region- service combination, with both outputs having meaningful implications for the monitoring of current service uptake and provision. The novelty of this paper lies with the former focus, and an approach for distributing expected participation by region based on proximity to services is proposed. The modelling of relative rates of participation is achieved through the combination of different random effects, as a means of assigning excess participation to different sources. The methodology is applied to participation data collected from a government-funded mammography program in Brisbane, Australia.

Predicting health programme participation: A gravity‐based, hierarchical modelling approach

Statistical analyses of health program participation seek to address a number of objectives compatible with the evaluation of demand for current resources. In this spirit, a spatial hierarchical model is developed for disentangling patterns in participation at the small area level, as a function of population-based demand and additional variation. For the former, a constrained gravity model is proposed to quantify factors associated with spatial choice and account for competition effects, for programs delivered by multiple clinics. The implications of gravity model misspecification within a mixed effects framework are also explored. The proposed model is applied to participation data from a no-fee mammography program in Brisbane, Australia. Attention is paid to the interpretation of various model outputs and their relevance for public health policy.

Development of modelling method selection tool for health services management: From problem structuring methods to modelling and simulation methods

Background: There is an increasing recognition that modelling and simulation can assist in the process of designing health care policies, strategies and operations. However, the current use is limited and answers to questions such as what methods to use and when remain somewhat underdeveloped. Aim. The aim of this study is to provide a mechanism for decision makers in health services planning and management to compare a broad range of modelling and simulation methods so that they can better select and use them or better commission relevant modelling and simulation work. Methods. This paper proposes a modelling and simulation method comparison and selection tool developed from a comprehensive literature review, the research team's extensive expertise and inputs from potential users. Twenty-eight different methods were identified, characterised by their relevance to different application areas, project life cycle stages, types of output and levels of insight, and four input resources required (time, money, knowledge and data). Results: The characterisation is presented in matrix forms to allow quick comparison and selection. This paper also highlights significant knowledge gaps in the existing literature when assessing the applicability of particular approaches to health services management, where modelling and simulation skills are scarce let alone money and time. Conclusions: A modelling and simulation method comparison and selection tool is developed to assist with the selection of methods appropriate to supporting specific decision making processes. In particular it addresses the issue of which method is most appropriate to which specific health services management problem, what the user might expect to be obtained from the method, and what is required to use the method. In summary, we believe the tool adds value to the scarce existing literature on methods comparison and selection. © 2011 Jun et al.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.