Joint developmental trajectories of internalizing and externalizing problems from mid-childhood to late adolescence and childhood risk factors: Findings from a prospective pre-birth cohort.

There is limited evidence on heterogenous co-developmental trajectories of internalizing (INT) and externalizing (EXT) problems from childhood to adolescence and predictors of these joint trajectories. We utilized longitudinal data from Raine Study participants (n = 2393) to identify these joint trajectories from 5 to 17 years using parallel-process latent class growth analysis and analyze childhood individual and family risk factors predicting these joint trajectories using multinomial logistic regression. Five trajectory classes were identified: Low-problems (Low-INT/Low-EXT, 29%), Moderate Externalizing (Moderate-EXT/Low-INT, 26.5%), Primary Internalizing (Moderate High-INT/Low-EXT, 17.5%), Co-occurring (High-INT/High-EXT, 17%), High Co-occurring (Very High-EXT/High-INT, 10%). Children classified in Co-occurring and High Co-occurring trajectories (27% of the sample) exhibited clinically meaningful co-occurring problem behaviors and experienced more adverse childhood risk-factors than other three trajectories. Compared with Low-problems: parental marital problems, low family income, and absent father predicted Co-occurring and High Co-occurring trajectories; maternal mental health problems commonly predicted Primary Internalizing, Co-occurring, and High Co-occurring trajectories; male sex and parental tobacco-smoking uniquely predicted High Co-occurring membership; other substance smoking uniquely predicted Co-occurring membership; speech difficulty uniquely predicted Primary Internalizing membership; child's temper-tantrums predicted all four trajectories, with increased odds ratios for High Co-occurring (OR = 8.95) and Co-occurring (OR = 6.07). Finding two co-occurring trajectories emphasizes the importance of early childhood interventions addressing comorbidity.

A randomised controlled trial of clinician supported vs self-help delivery of online cognitive behaviour therapy for Bulimia Nervosa.

High dropout rates and poor adherence associated with digital interventions have prompted research into modifications of these treatments to improve engagement and completion rates. This trial aimed to investigate the added benefit of clinician support when paired alongside a ten-session, online cognitive behaviour therapy (CBT) self-help intervention for bulimia nervosa (BN). As part of a three-arm, phase II randomised controlled trial, 114 participants (16 years or over) with full or subthreshold BN were randomly assigned to complete the intervention in a self-help mode (with administrative researcher contact; n = 38), with adjunct clinician support (weekly 30-minute videoconferencing sessions; n = 37), or a no-treatment waitlist control (WLC; n = 39). Baseline to post-treatment (12-weeks) decreases in objective binge episode frequency were significantly greater for clinician-supported participants as compared to WLC, but not for self-help when compared to WLC. However, due to continued improvements for self-help across follow-up (24-weeks), both arms outperformed WLC when analysed as an overall rate of change across three timepoints. Clinician-supported participants outperformed self-help in regards to laxative use and dietary restraint. Our results demonstrate that good clinical outcomes can be achieved with a relatively brief online CBT-based program even in the absence of structured clinical support, indicating a possible overreliance upon clinician support as a primary adherence-facilitating mechanism.

Health, social and economic implications of adolescent risk behaviours/states: protocol for Raine Study Gen2 cohort data linkage study.

Background: Risk-taking behaviours are a major contributor to youth morbidity and mortality. Vulnerability to these negative outcomes is constructed from individual behaviour including risk-taking, and from social context, ecological determinants, early life experience, developmental capacity and mental health, contributing to a state of higher risk. However, although risk-taking is part of normal adolescent development, there is no systematic way to distinguish young people with a high probability of serious adverse outcomes, hindering the capacity to screen and intervene. This study aims to explore the association between risk behaviours/states in adolescence and negative health, social and economic outcomes through young adulthood. Methods: The Raine Study is a prospective cohort study which recruited pregnant women in 1989-91, in Perth, Western Australia. The offspring cohort (N = 2,868) was followed up at regular intervals from 1 to 27 years of age. These data will be linked to State government health and welfare administrative data. We will empirically examine relationships across multiple domains of risk (for example, substance use, sexual behaviour, driving) with health and social outcomes (for instance, road-crash injury, educational underachievement). Microsimulation models will measure the impact of risk-taking on educational attainment and labour force outcomes. Discussion: Comprehensive preventive child health programmes and policy prioritise a healthy start to life. This is the first linkage study focusing on adolescence to adopt a multi-domain approach, and to integrate health economic modelling. This approach captures a more complete picture of health and social impacts of risk behaviour/​states in adolescence and young adulthood.

Supported online cognitive behavioural therapy for bulimia nervosa: a study protocol of a randomised controlled trial.

BACKGROUND: Despite the availability of effective treatments for bulimia nervosa (BN), a number of barriers to accessibility exist. Examples include access to trained clinicians, the expense of treatment, geographical limitations, and personal limitations such as stigma regarding help seeking. Self-help interventions, delivered via a digital platform, have the potential to overcome treatment gaps by providing patients with standardised, evidence-based treatments that are easily accessible, cost-effective, and require minimal clinician support. Equally, it is important to examine the shortcomings of digital interventions when compared to traditional to face-to-face delivery (e.g., high dropout rates) in order to maximise the therapeutic effectiveness of online, self-help interventions. METHODS: A three-arm, multisite randomised controlled trial will be conducted in Australia examining the effectiveness and cost-effectiveness of a newly developed online self-help intervention, Binge Eating eTherapy (BEeT), in a sample of patients with full or sub-threshold BN. The BEeT program consists of 10, multimedia sessions delivering the core components of cognitive behaviour therapy. Eligible participants will be randomised to one of three groups: independent completion of BEeT as a purely self-help program, completion of BEeT alongside clinician support (in the form of weekly telemedicine sessions), or waitlist control. Assessments will take place at baseline, weekly, post-intervention, and three-month follow up. The primary outcome is frequency of objective binge episodes. Secondary outcomes include frequency of other core eating disorder behavioural symptoms and beliefs, psychological distress, and quality of life. Statistical analyses will examine treatment effectiveness, feasibility, acceptability and cost effectiveness. DISCUSSION: There is limited capacity within the mental health workforce in Australia to meet the demand of people seeking treatment for eating disorders. This imbalance has only worsened following outbreak of the COVID-19 pandemic. Further research is required into innovative digital modes of treatment delivery with the capacity to service mental health needs in an accessible and affordable manner. Self-help programs may also appeal to individuals who are more reluctant to engage in traditional face-to-face treatment formats. This study will provide rigorous evidence on how to diversify treatment options for individuals with BN, ensuring more people with the illness can access evidence-based treatment. The study has been registered with the Australia New Zealand Clinical Trials Registry (ANZCTR Registration Number: ACTRN12619000123145p). Registered 22 January 2019, https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12619000123145 .

Effect of a smartphone application (Perx) on medication adherence and clinical outcomes: a 12-month randomised controlled trial.

OBJECTIVE: To determine whether the Perx app improves medication adherence and clinical outcomes over 12 months compared with standard care in patients requiring polypharmacy. DESIGN: Randomised controlled trial with 12-month follow-up. SETTING: Outpatient clinics in three tertiary hospitals in Sydney, Australia. PARTICIPANTS: Eligible participants were aged 18-75 years, with at least one chronic condition, taking ≥3 different medications (oral medications or injections), with smartphone accessibility. Participants were randomised in a 1:1 ratio. INTERVENTIONS: The intervention group used the Perx app that contained customised reminders and gamified interactions to reward verified medication adherence. MAIN OUTCOME MEASURES: The primary outcome was medication adherence over 12 months measured using pill counts. Secondary outcomes included clinical outcomes (haemoglobin A1c (HbA1c), cholesterol, blood glucose, triglycerides, creatinine, thyroid function, blood pressure and weight). RESULTS: Of 1412 participants screened for eligibility, 124 participants were randomised; 45 in the Perx arm and 40 in the control arm completed the study. The average age was 59.5, 58.9% were women, chronic conditions were cardiovascular disease (78%), type 2 diabetes (75%), obesity (65%) or other endocrine disorders (18%). On average, participants were taking six medications daily. The Perx group had greater improvements in adherence at month 2 (Coef. 8%; 95% CI 0.01 to 0.15), month 3 (Coef. 7%; 95% CI 0.00 to 0.14) and month 12 (Coef. 7%; 95% CI 0.00 to 0.13). The probability of HbA1c ≤6.5% was greater in the Perx group at months 9 and 12 and cholesterol (total and low-density lipoprotein cholesterol) was lower in the Perx group at month 3. The intervention was particularly effective for those with obesity, taking medications for diabetes and taking ≤4 medications. CONCLUSIONS: This study provides evidence that app-based behavioural change interventions can increase medication adherence and produce longer-term improvements in some clinical outcomes in adults managing multimorbidity. More trials are needed to build the evidence base. TRIAL REGISTRATION NUMBER: ACTRN12617001285347.

A Mixed Methods Study to Explore the Effects of Program Design Elements and Participant Characteristics on Parents' Engagement With an mHealth Program to Promote Healthy Infant Feeding: The Growing Healthy Program.

Purpose: Mobile health (mHealth) interventions have great potential to promote health. To increase consumer engagement in mHealth interventions it is necessary to address factors that influence the target demographic. The Growing healthy (GH) program is the first obesity prevention program delivered via a smartphone app and website offering evidence-based information on infant feeding from birth until 9 months of age. This sub-study aimed to explore how the design features, quality of the app and participant characteristics influenced parents' engagement with the GH app. Methods: A sequential mixed methods design was used. The GH app participants (225/301) were considered for this sub-study. Participant app engagement was measured through a purpose-built Engagement Index (EI) using app metrics. Participants were categorized as low, moderately or highly engaged based on their EI score upon completing the 9 months program and were then invited to participate in semi-structured telephone interviews. Participants who used the app program, given an EI score and expressed interest to participate in these interviews were eligible. The interviews explored factors that influenced app engagement including delivery features and quality. Thematic analysis networks was used for analysis. Results: 108/225 expressed interest and 18 interviews were conducted from low (n = 3), moderately (n = 7), or highly (n = 8) engaged participants based on purposeful sampling. Participants defined as highly engaged were likely to be a first-time parent, felt the app content to be trustworthy and the app design facilitated easy navigation and regularly opened the push notifications. Participants defined as having low or moderate engagement were likely to have experience from previous children, felt they had sufficient knowledge on infant feeding and the app did not provide further information, or experienced technological issues including app dysfunction due to system upgrades. Conclusions/Implications: This study demonstrated a novel approach to comprehensively analyse engagement in an mHealth intervention through quantitative (Engagement Index) and qualitative (interviews) methods. It provides an insight on maximizing data collected from these programs for measuring effectiveness and to understand users of various engagement levels interaction with program features. Measuring this can determine efficacy and refine programs to meet user requirements.

The Population Cost-Effectiveness of Weight Watchers with General Practitioner Referral Compared with Standard Care.

OBJECTIVE: This study aimed to assess population-level cost-effectiveness of the Weight Watchers (WW) program with doctor referral compared with standard care (SC) for Australian adults with overweight and obesity. METHODS: The target population was Australian adults ≥ 20 years old with BMI ≥ 27 kg/m2 , whose obesity status was subsequently modeled for 2015 to 2025. A microsimulation model (noncommunicable disease model [NCDMod]) was used to assess the incremental cost-effectiveness of WW compared with SC. A health system perspective was taken, and outcomes were measured by obesity cases averted in 2025, BMI units averted for 2015 to 2025, and quality-adjusted life years for 2015 to 2025. Univariate sensitivity testing was used to measure variations in the model parameters. RESULTS: The WW intervention resulted in 60,445 averted cases of obesity in 2025 (2,311 more cases than for SC), extra intervention costs of A$219 million, and cost savings within the health system of A$17,248 million (A$82 million more than for SC) for 2015 to 2025 compared with doing nothing. The modeled WW had an incremental cost-effectiveness ratio of A$35,195 in savings per case of obesity averted in 2025. WW remained dominant over SC for the different scenarios in the sensitivity analysis. CONCLUSIONS: The WW intervention represents good value for money. The WW intervention needs serious consideration in a national package of obesity health services.

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's 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's 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.

Impact of the Growing Healthy mHealth Program on Maternal Feeding Practices, Infant Food Preferences, and Satiety Responsiveness: Quasi-Experimental Study.

BACKGROUND: Infancy is an important life stage for obesity prevention efforts. Parents' infant feeding practices influence the development of infants' food preferences and eating behaviors and subsequently diet and weight. Mobile health (mHealth) may provide a feasible medium through which to deliver programs to promote healthy infant feeding as it allows low cost and easy access to tailored content. OBJECTIVE: The objective of this study was to describe the effects of an mHealth intervention on parental feeding practices, infant food preferences, and infant satiety responsiveness. METHODS: A quasi-experimental study was conducted with an mHealth intervention group (Growing Healthy) and a nonrandomized comparison group ("Baby's First Food"). The intervention group received access to a free app with age-appropriate push notifications, a website, and an online forum that provided them with evidence-based advice on infant feeding for healthy growth from birth until 9 months of age. Behavior change techniques were selected using the Behaviour Change Wheel framework. Participants in both groups completed three Web-based surveys, first when their infants were less than 3 months old (baseline, T1), then at 6 months (time 2, T2), and 9 months of age (time 3, T3). Surveys included questions on infant feeding practices and beliefs (Infant Feeding Questionnaire, IFQ), satiety responsiveness (Baby Eating Behaviour Questionnaire), and infant's food exposure and liking. Multivariate linear regression models, estimated using maximum likelihood with bootstrapped standard errors, were fitted to compare continuous outcomes between the intervention groups, with adjustment for relevant covariates. Multivariate logistic regression adjusting for the same covariates was performed for categorical outcomes. RESULTS: A total of 645 parents (Growing Healthy: n=301, Baby's First Food: n=344) met the eligibility criteria and were included in the study, reducing to a sample size of 546 (Growing Healthy: n=234, Baby's First Food: n=312) at T2 and a sample size of 518 (Growing Healthy: n=225, Baby's First Food: n=293) at T3. There were approximately equal numbers of boy and girl infants, and infants were aged less than 3 months at baseline (Growing Healthy: mean 7.0, SD 3.7 weeks; Baby's First Food: mean 7.9, SD 3.8 weeks), with Growing Healthy infants being slightly younger than Baby's First Food infants (P=.001). All but one (IFQ subscale "concerns about infant overeating or becoming overweight" at T2) of the measured outcomes did not differ between Growing Healthy and Baby's First Food. CONCLUSIONS: Although mHealth can be effective in promoting some health behaviors and offers many advantages in health promotion, the results of this study suggest that design and delivery characteristics needed to maximize the impact of mHealth interventions on infant feeding are uncertain. The sensitivity of available measurement tools and differences in baseline characteristics of participants may have also affected the results.

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.

A Comparison of Recruitment Methods for an mHealth Intervention Targeting Mothers: Lessons from the Growing Healthy Program.

BACKGROUND: Mobile health (mHealth) programs hold great promise for increasing the reach of public health interventions. However, mHealth is a relatively new field of research, presenting unique challenges for researchers. A key challenge is understanding the relative effectiveness and cost of various methods of recruitment to mHealth programs. OBJECTIVE: The objectives of this study were to (1) compare the effectiveness of various methods of recruitment to an mHealth intervention targeting healthy infant feeding practices, and (2) explore factors influencing practitioner referral to the intervention. METHODS: The Growing healthy study used a quasi-experimental design with an mHealth intervention group and a concurrent nonrandomized comparison group. Eligibility criteria included: expectant parents (>30 weeks of gestation) or parents with an infant <3 months old, ability to read and understand English, own a mobile phone, ≥18 years old, and living in Australia. Recruitment to the mHealth program consisted of: (1) practitioner-led recruitment through Maternal and Child Health nurses, midwives, and nurses in general practice; (2) face-to-face recruitment by researchers; and (3) online recruitment. Participants' baseline surveys provided information regarding how participants heard about the study, and their sociodemographic details. Costs per participant recruited were calculated by taking into account direct advertising costs and researcher time/travel costs. Practitioner feedback relating to the recruitment process was obtained through a follow-up survey and qualitative interviews. RESULTS: A total of 300 participants were recruited to the mHealth intervention. The cost per participant recruited was lowest for online recruitment (AUD $14) and highest for practice nurse recruitment (AUD $586). Just over half of the intervention group (50.3%, 151/300) were recruited online over a 22-week period compared to practitioner recruitment (29.3%, 88/300 over 46 weeks) and face-to-face recruitment by researchers (7.3%, 22/300 over 18 weeks). No significant differences were observed in participant sociodemographic characteristics between recruitment methods, with the exception that practitioner/face-to-face recruitment resulted in a higher proportion of first-time parents (68% versus 48%, P=.002). Less than half of the practitioners surveyed reported referring to the program often or most of the time. Key barriers to practitioner referral included lack of time, difficulty remembering to refer, staff changes, lack of parental engagement, and practitioner difficulty in accessing the app. CONCLUSIONS: Online recruitment using parenting-related Facebook pages was the most cost effective and timely method of recruitment to an mHealth intervention targeting parents of young infants. Consideration needs to be given to addressing practitioner barriers to referral, to further explore if this can be a viable method of recruitment.

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.

Lessons learned from the introduction of an electronic safety net to enhance test result management in an Australian mothers' hospital.

This study describes the implementation and impact of an electronic test result acknowledgement (RA) system in the Mater Mothers' Hospital in Brisbane, Australia. The Verdi application electronically records clinicians' acknowledgement of the review of results. Hospital data (August 2011-August 2012) were extracted to measure clinicians' acknowledgement practices. There were 27,354 inpatient test results for 6855 patients. All test results were acknowledged. 60% (95% CI 59% to 61%) of laboratory and 44% (95% CI 40% to 48%) of imaging results were acknowledged within 24 h. The median time between report availability and acknowledgement was 18.1 h for laboratory and 1 day 18 h for imaging results. The median time from when a result was first viewed to its acknowledgement was 7 min for laboratory and 1 min for imaging results. The longest recorded time to acknowledgement was 38 days. Electronic RA provides a safety net to enhance test result management.

Is the self-reported private health insurance status in the National Health Survey representative of private health insurance coverage in Australia?

OBJECTIVE: This study determines whether the distribution of self-reported private health insurance (PHI) status in the 2004/05 National Health Survey (NHS) is representative of PHI coverage in Australia. METHODS: Weighted estimates from the NHS 2004/05 are compared with PHI status reported for 2004/05 by the Private Health Insurance Administration Council (PHIAC, the independent regulator of the private health insurance industry). PHI status was imputed to children in the NHS based on PHI status of the adult in the household. The two data sources were deemed to be different if the PHIAC results were not within the 95% CI range for the NHS estimate. RESULTS: PHI status reported in the NHS and PHIAC are generally comparable except for some categories such as hospital cover of males aged 5-9 years and females aged 85 years and older where the NHS estimates are below PHIAC numbers; and males aged 25-29, 35-39, and 50-54 years where the NHS estimates are higher. CONCLUSIONS: The findings suggest that while the NHS 2004/05 estimates may accurately represent coverage in Australia particularly when examined at an aggregated level, there is some variation in the NHS estimates when examined by sex and age group. IMPLICATIONS: Researchers need to be aware of the potential for sampling and reporting bias to contribute to some misrepresentation of PHI status when using the NHS to generalise to the Australian population. Exploring corrective measures will ensure that the NHS continues to be a valuable data resource for health researchers in Australia.

Hospital admissions in the National Health Survey and hospital separations in the National Hospital Morbidity Dataset: what is the difference?

OBJECTIVE: To compare the National Health Survey (NHS) derived estimates of hospital admissions with the number of hospital separations registered in the National Hospital Morbidity Dataset (NHMD). METHODS: Using the person weights in the NHS, the Expanded Confidential Unit Record File of the 2004-05 NHS was used to derive a population estimate of the number of hospital admissions in the 12 months preceding the conduct of the survey. These estimates, by age and sex categories and whether or not the admission involved an overnight stay, were compared with the number of hospital separations registered in the NHMD. RESULTS: The number of hospital admissions estimated from the NHS was approximately two thirds the number of hospital separations registered in the NHMD. The discrepancy between the two data sources was greater when hospital episodes did not involve an overnight stay in hospital. CONCLUSION: There are systematic differences between the number of admissions/separations derived by the NHS and the NHMD for reasons including the technical difference between a hospital admission and a separation, and the sampling frame and scope of the NHS. Researchers looking for individual level data on hospital utilisation must take note of the differences between NHS and the NHMD, and recognise that there are methods to simulate a representative population by enhancing an existing dataset with information from multiple data sources, thus providing researchers a cost-effective data resource.

Effect of Australia's Walk to Work Day campaign on adults' active commuting and physical activity behavior.

PURPOSE: To determine whether Australia's Walk to Work Day media campaign resulted in behavioral change among targeted groups. METHODS: Pre- and postcampaign telephone surveys of a cohort of adults aged 18 to 65 years (n = 1100, 55% response rate) were randomly sampled from Australian major metropolitan areas. Tests for dependent samples were applied (McNemar chi2 or paired t-test). RESULTS: Among participants who did not usually actively commute to work was a significant decrease in "car only" use and an increase in walking combined with public transport. Among those who were employed was a significant increase in total time walking (+ 16 min/wk; t [780] = 2.04, p < .05) and in other moderate physical activity (+120 min/wk; t [1087] = 4.76, p < .005), resulting in a significant decrease in the proportion who were "inactive" (chi2 (1) = 6.1, p <.05). CONCLUSION: Although nonexperimental, the Walk to Work Day initiative elicited shortterm changes in targeted behaviors among target groups. Reinforcement by integrating worksite health promotion strategies may be required for sustained effects.