Predicting the need for aged care services at the small area level: the CAREMOD spatial microsimulation model

Most industrialised societies face rapid population ageing over the next two decades, including sharp increases in the number of people aged 85 years and over. As a result, the supply of and demand for aged care services has assumed increasing policy prominence. The likely spatial distribution of the need for aged care services is critical for planners and policy makers. This article describes the development of a regional microsimulation model of the need for aged care in New South Wales, a state of Australia. It details the methods involved in reweighting the 1998 Survey of Disability, Ageing and Carers, a national level dataset, against the 2001 Census to produce synthetic small area estimates at the statistical local area level. Validation shows that survey variables not constrained in the weighting process can provide unreliable local estimates. A proposed solution to this problem is outlined, involving record cloning, value imputation and alignment. Indicative disability estimates arising from this process are then discussed.Disability, ageing, spatial analysis, aged care, cloning; imputation; alignment; NATSEM

Challenges and Solutions in Constructing a Microsimulation Model of the Use and Costs of Medical Services in Australia

This paper describes the development of a microsimulation model =HealthMod‘ which simulates the use and costs of medical and related services by Australian families. Australia has a universal social insurance scheme known as =Medicare‘ which provides all Australians with access to free or low-cost essential medical services. These services are provided primarily by general practitioners as well as specialist doctors but also include diagnostic and imaging services. Individuals may pay a direct out-of pocket contribution if fees charged for services are higher than the reimbursement schedule set by government. HealthMod is based on the Australian 2001 National Health Survey. This survey had a number of deficiencies in terms of modelling the national medical benefits scheme. The article outlines three major methodological steps that had to be taken in the model construction: the imputation of synthetic families, the imputation of short-term health conditions, and the annualisation of doctor visits and costs. Some preliminary results on the use of doctor services subsidised through Australia‘s Medicare are presented.Economic microsimulation modelling, medical services, use and costs, Australia

Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost

While static microsimulation models of the tax-transfer system are now available throughout the developed world, health microsimulation models are much rarer. This is, at least in part, due to the difficulties in creating adequate base micro-datasets upon which the microsimulation models can be constructed. In sharp contrast to tax-transfer modelling, no readily available microdata set typically contains all the health status, health service usage and socio-demographic information required for a sophisticated health microsimulation model. This paper describes three new techniques developed to overcome survey data limitations when constructing \'MediSim\', a microsimulation model of the Australian Pharmaceutical Benefits Scheme. Comparable statistical matching and data imputation techniques may be of relevance to other modellers, as they attempt to overcome similar data deficiencies. The 2001 national health survey (NHS) was the main data source for MediSim. However, the NHS has a number of limitations for use in a microsimulation model. To compensate for this, we statistically matched the NHS with another national survey to create synthetic families and get a complete record for every individual within each family. Further, we used complementary datasets to impute short term health conditions and prescribed drug usage for both short- and long-term health conditions. The application of statistical matching methods and use of complementary data sets significantly improved the usefulness of the NHS as a base dataset for MediSim.Base Data, Drug Usage, Microsimulation, Pharmaceutical Benefits, Scripts, Statistical Matching

Obesity prevention in infants using m-Health: the Growing Healthy program

About one quarter of Australian pre-school children are overweight. Early childhood is an important period for establishing behaviours that will affect weight gain and health across the life course. Early feeding choices, including breast and/or formula, timing of introduction of solids, physical activity and electronic media use among infants and young children are considered likely determinants of childhood obesity. Parents play a primary role in shaping these behaviours through parental modelling, feeding styles and the food and physical activity environments provided. Children from low socio-economic backgrounds have higher rates of obesity making early intervention particularly important. However, such families are often more difficult to reach and may be less likely to participate in traditional programs that support healthy behaviours. Parents across all socio-demographic groups frequently access primary health care (PHC) services including nurses in community health services and general practices, providing unparalleled opportunity for engagement to influence family behaviours. One emerging and promising area that might maximise engagement at a low cost is the provision of support for healthy parenting through electronic media such as the Internet or smart phones. This is referred to as mobile or m-health. The Growing Healthy study aimed to explore the feasibility of providing information and support for healthy parenting through electronic media in the form of an application for smart phones (app) and a website. Our background research suggested this as an emerging and promising area for engagement with families with young children and may provide a referral option for primary health care providers. It is also an intervention with a relatively low cost and potential for high reach. As families with young children have high levels of engagement with PHC services, these could be leveraged to recruit study participants via referral to the app. Complementing and not replacing the information and support provided by these existing primary health care services was an important objective as was ensuring the online information and support aligned with that provided by primary health care services and national guidelines. The aim was to make the app a ‘trusted source’ of information and support for families with children from birth to nine months of age.The research reported in this paper is a project of the Australian Primary Health Care Research Institute which is supported by a grant from the Australian Government Department of Health and Ageing under the Primary Health Care Research Evaluation and Development Strategy

Weight management for patients in general practice tailored to health literacy

Our aim was to develop and evaluate the feasibility and impact of a PHC approach to weight management tailored to the level of health literacy of obese patients. There were three key activities undertaken in this regard: 1) a literature review; 2) a pilot study; and 3) a weight management trial called “Better Management of Weight in General Practice” (BMWGP). In this report we describe the three activities and use the BMWGP baseline data to explore three issues. First, we look at the effectiveness of a screening tool to identify patients with low health literacy in general practice. Second, we describe the association between health literacy and a range of factors, behavioural intentions, lifestyle behaviours and quality of life to better understand the link between health literacy and health in a population of patients with obesity attending general practices. Third, we identify the groups most likely to experience weight stigma and how stigma relates to health literacy.The research reported in this paper is a project of the Australian Primary Health Care Research Institute which is supported by a grant from the Australian Government Department of Health and Ageing under the Primary Health Care Research Evaluation and Development Strategy

Assessing user engagement of an mHealth intervention: development and implementation of the growing healthy app engagement index

Background: Childhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics.Objective: The aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement.Methods: The Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant’s date of birth.Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models.Results: The overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including “app only” users or “both” app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005).Conclusions: The EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions

Preventing obesity in infants: the growing healthy feasibility trial protocol

INTRODUCTION: Early childhood is an important period for establishing behaviours that will affect weight gain and health across the life course. Early feeding choices, including breast and/or formula, timing of introduction of solids, physical activity and electronic media use among infants and young children are considered likely determinants of childhood obesity. Parents play a primary role in shaping these behaviours through parental modelling, feeding styles, and the food and physical activity environments provided. Children from low socio-economic backgrounds have higher rates of obesity, making early intervention particularly important. However, such families are often more difficult to reach and may be less likely to participate in traditional programs that support healthy behaviours. Parents across all socio-demographic groups frequently access primary health care (PHC) services, including nurses in community health services and general medical practices, providing unparalleled opportunity for engagement to influence family behaviours. One emerging and promising area that might maximise engagement at a low cost is the provision of support for healthy parenting through electronic media such as the Internet or smart phones. The Growing healthy study explores the feasibility of delivering such support via primary health care services. METHODS: This paper describes the Growing healthy study, a non-randomised quasi experimental study examining the feasibility of an intervention delivered via a smartphone app (or website) for parents living in socioeconomically disadvantaged areas, for promoting infant feeding and parenting behaviours that promote healthy rather than excessive weight gain. Participants will be recruited via their primary health care practitioner and followed until their infant is 9 months old. Data will be collected via web-based questionnaires and the data collected inherently by the app itself. ETHICS AND DISSEMINATION: This study received approval from the University of Technology Sydney Ethics committee and will be disseminated via peer-reviewed publications and conference presentations

Key lessons and impact of the growing healthy mHealth program on milk feeding, timing of introduction of solids, and infant growth: quasi-experimental study

BACKGROUND: The first year of life is an important window to initiate healthy infant feeding practices to promote healthy growth. Interventions delivered by mobile phone (mHealth) provide a novel approach for reaching parents; however, little is known about the effectiveness of mHealth for child obesity prevention. OBJECTIVE: The objective of this study was to determine the feasibility and effectiveness of an mHealth obesity prevention intervention in terms of reach, acceptability, and impact on key infant feeding outcomes. METHODS: A quasi-experimental study was conducted with an mHealth intervention group (Growing healthy) and a nonrandomized comparison group (Baby\u27s First Food). The intervention group received access to a free app and website containing information on infant feeding, sleep and settling, and general support for parents with infants aged 0 to 9 months. App-generated notifications directed parents to age-and feeding-specific content within the app. Both groups completed Web-based surveys when infants were less than 3 months old (T1), at 6 months of age (T2), and 9 months of age (T3). Survival analysis was used to examine the duration of any breastfeeding and formula introduction, and cox proportional hazard regression was performed to examine the hazard ratio for ceasing breast feeding between the two groups. Multivariate logistic regression with adjustment for a range of child and parental factors was used to compare the exclusive breastfeeding, formula feeding behaviors, and timing of solid introduction between the 2 groups. Mixed effect polynomial regression models were performed to examine the group differences in growth trajectory from birth to T3. RESULTS: A total of 909 parents initiated the enrollment process, and a final sample of 645 parents (Growing healthy=301, Baby\u27s First Food=344) met the eligibility criteria. Most mothers were Australian born and just under half had completed a university education. Retention of participants was high (80.3%, 518/645) in both groups. Most parents (226/260, 86.9%) downloaded and used the app; however, usage declined over time. There was a high level of satisfaction with the program, with 86.1% (143/166) reporting that they trusted the information in the app and 84.6% (170/201) claiming that they would recommend it to a friend. However, some technical problems were encountered with just over a quarter of parents reporting that the app failed to work at times. There were no significant differences between groups in any of the target behaviors. Growth trajectories also did not differ between the 2 groups. CONCLUSIONS: An mHealth intervention using a smartphone app to promote healthy infant feeding behaviors is a feasible and acceptable mode for delivering obesity prevention intervention to parents; however, app usage declined over time. Learnings from this study will be used to further enhance the program so as to improve its potential for changing infant feeding behaviors