Janet Pearson

Promoting Independence in Residential Care: Successful Recruitment for a Randomized Controlled Trial

OBJECTIVES To describe the recruitment strategy and association between facility and staff characteristics and success of resident recruitment for the Promoting Independence in Residential Care (PIRC) trial. DESIGN Cross-sectional study of staff and facility characteristics and recruitment rates within facilities with calculation of cluster effects of multiple measures. SETTING AND PARTICIPANTS Staff of low-level dependency residential care facilities and residents able to engage in a physical activity program in 2 cities in New Zealand. MEASURES A global impression of staff willingness to facilitate research was gauged by research nurses, facility characteristics were measured by staff interview. Relevant outcomes were measured by resident interview and included the following: (1) Function: Late Life FDI scale, timed-up-and-go, FICSIT balance scale and the Elderly Mobility Scale; (2) Quality of Life: EuroQol quality of life scale, Life Satisfaction Index; and (3) falls were assessed by audit of the medical record. Correlation between recruitment rates, facility characteristics and global impression of staff willingness to participate were investigated. Design effects were calculated on outcomes. RESULTS Forty-one (85%) facilities and 682 (83%) residents participated, median age was 85 years (range 65-101), and 74% were women. Participants had complex health problems. Recruitment rates were associated (but did not increase linearly) with the perceived willingness of staff, and were not associated with facility size. Design effects from the cluster recruitment differed according to outcome. CONCLUSIONS The recruitment strategy was successful in recruiting a large sample of people with complex comorbidities and high levels of functional disability despite perceptions of staff reluctance. Staff willingness was related to recruitment success.

Using micro-simulation to create a synthesised data set and test policy options: The case of health service effects under demographic ageing

OBJECTIVES To assess micro-simulation for testing policy options under demographic ageing. METHODS Individual-level data were drawn from the New Zealand Health Survey (1996/7 and 2002/3), a national survey of ambulatory care in New Zealand (2001/2), and the Australian National Health Survey (1995). Health service effects assessed were visits to the family doctor, and rates of prescribing and referral. We created a representative set of synthetic health histories by imputation and tested the health service effects of different policy scenarios. These were created by varying ageing and morbidity trajectories, degree of social support available, and intensity of practitioner behaviour. RESULTS The set of synthetic health histories created by combining the data sources generated outcomes reasonably close to external benchmarks. Altering the age distribution of 2002 to approximate settings for 2021 produced no change in rates of visiting, prescribing, or referral for the 65-and-over population. Quantifying the health service effects of different scenarios showed no impact on visit rates by varying social support, but substantial differences for visits between high and low morbidity scenarios and for prescribing and referral rates according to practitioner behaviour. CONCLUSIONS There is potential for micro-simulation to assist in the synthesis of data and to help quantify scenario options for policy development.

Determinants and disparities: A simulation approach to the case of child health care

Though there is much agreement on the importance of the social determinants of health, debate continues on suitable empirically-based models to underpin efforts to tackle health and health care disparities. We demonstrate an approach that uses a dynamic micro-simulation model of the early life course, based on longitudinal data from a New Zealand cohort of children born in 1977, and counterfactual reasoning applied to a range of outcomes. The focus is on health service use with a comparison to outcomes in non-health domains, namely educational attainment and antisocial behaviour. We show an application of the model to test scenarios based on modifying key determinants and assessing the impact on putative outcomes. We found that appreciable improvement was only effected by modifying multiple determinants; structural determinants were relatively more important than intermediary ones as potential policy levers; there was a social gradient of effect; and interventions bestowed the greatest benefit to the most disadvantaged groups with a corresponding reduction in disparities between the worst-off and the best-off. Our findings provide evidence on how public policy initiatives might be more effective acting broadly across sectors and across social groups, and thus make a real difference to the most disadvantaged.

Primary Care in an Aging Society: Building and Testing a Microsimulation Model for Policy Purposes

The authors describe the development of a microsimulation model of primary medical care in New Zealand for 2002 and demonstrate its ability to test the impact of demographic ageing, community support, and practitioner repertoire. Micro-level data were drawn from four sources: two iterations of the New Zealand Health Survey (NZHS 1996/1997 and 2002/2003); a national survey of ambulatory care in New Zealand (New Zealand National Primary Medical Care Survey [NPMCS] 2001/ 2002); and the Australian National Health Survey (ANHS). Data from the New Zealand surveys were statistically matched to create a representative synthetic base file of over 13,000 individuals. Probabilities of health experiences and general practitioner (GP) use derived from the ANHS, and of GP activity derived from the NPMCS were applied via a Monte Carlo process to create health histories for the individuals in the base file. Final health care outcomes simulated—the number of visits in a year, the distribution of health conditions, and GP activity levels—were validated against external benchmarks. Policy-relevant scenarios were demonstrated by a forward projection to 2021 and by implementing counterfactuals on key attributes of the synthetic population. The results showed little change in model-predicted health care outcomes. There is potential for this approach to address policy purposes.

Data Matching to Allocate Doctors to Patients in a Microsimulation Model of the Primary Care Process in New Zealand

The authors aimed to use existing data to create a microsimulation model of the primary care process in New Zealand, including realistically simulating the allocation of general practitioners (GPs) to a population sample. This is important because GP behavior is likely to be a major determinant of future cost and service outcomes. Two nationally representative data sets were matched: a sample of GPs and their patients from the National Primary Medical Care Survey (NPMCS) and a population sample from the New Zealand Health Survey (NZHS). Matching involved first dividing the data sets into cells based on common variables. Further variables were then included in a distance function to guide matching within cells. A transportation optimization algorithm allocated GPs based on these—on similarities in patients’ attributes. Statistical matching performed well with high correlations for patient attributes and reduced average absolute rank differences on proportions of patients among GPs compared to random matching. Low Kullback–Leibler (K–L) divergences confirmed that our method of statistical matching had allocated GPs realistically. Models of primary care too frequently omit the role of the practitioner in driving health service outcomes. The authors developed a method to impute characteristics of GPs to a population-based microsimulation model of primary care.

Family violence in gambling help-seeking populations

ABSTRACT Elevated rates of family violence among treatment-seeking problem gamblers compared to general population estimates have been reported in Spain, Canada and Australia. This study examined the occurrence of family violence among 454 problem gambling help-seeking clients (370 gamblers, 84 affected others) recruited through 3 national gambling treatment services in New Zealand. Measures used were the Problem Gambling Severity Index, and a modified version of the HITS Scale which assessed physical, emotional, verbal and sexual abuse. Past-year family violence among gamblers in this sample was 46.8% for victimization, 41.2% for perpetration and 55.0% for any form of family violence. Among affected others the occurrence was 65.5% for victimization, 57.1% for perpetration and 71.4% for any form of violence. The most common type of violence was verbal intimate partner violence. Affected others and women gamblers reported higher rates of violence victimization and perpetration. These findings underscore the importance of screening gambling help-seeking clients for family violence, and the development of prevention and treatment programmes to address violence in this population, with particular attention to affected others and women gamblers. Future research should assess coercive control and the gendered nature of family violence among problem gambling help-seekers.

Analysis of data collected from right and left limbs: Accounting for dependence and improving statistical efficiency in musculoskeletal research

OBJECTIVES Statistical techniques currently used in musculoskeletal research often inefficiently account for paired-limb measurements or the relationship between measurements taken from multiple regions within limbs. This study compared three commonly used analysis methods with a mixed-models approach that appropriately accounted for the association between limbs, regions, and trials and that utilised all information available from repeated trials. METHOD Four analysis were applied to an existing data set containing plantar pressure data, which was collected for seven masked regions on right and left feet, over three trials, across three participant groups. Methods 1-3 averaged data over trials and analysed right foot data (Method 1), data from a randomly selected foot (Method 2), and averaged right and left foot data (Method 3). Method 4 used all available data in a mixed-effects regression that accounted for repeated measures taken for each foot, foot region and trial. Confidence interval widths for the mean differences between groups for each foot region were used as a criterion for comparison of statistical efficiency. RESULTS Mean differences in pressure between groups were similar across methods for each foot region, while the confidence interval widths were consistently smaller for Method 4. Method 4 also revealed significant between-group differences that were not detected by Methods 1-3. CONCLUSION A mixed effects linear model approach generates improved efficiency and power by producing more precise estimates compared to alternative approaches that discard information in the process of accounting for paired-limb measurements. This approach is recommended in generating more clinically sound and statistically efficient research outputs.

Cluster-randomised controlled trial of an occupational therapy intervention for children aged 11-13 years, designed to increase participation in order to prevent symptoms of mental illness

BACKGROUND The impact of occupational therapy on mental health outcomes for children is largely unexplored. The aim of this study was to investigate an evidence-based occupational therapy intervention designed to increase participation in daily occupations to prevent symptoms of mental illness for children and run in schools. METHODS The study used a pragmatic, cluster-randomised controlled trial design with two arms. Fourteen clusters (schools), equating to 151 child participants, were stratified by school decile-rank category and block randomised. Blinding of participants post-randomisation was not feasible; however, outcomes assessors were blinded. Outcomes were measured at baseline, after the parallel and crossover phases, and at follow-up; and were anxiety symptoms (primary), depression symptoms, self-esteem, participation and wellbeing. Intention-to-treat analysis was applied and mixed linear modelling was used to account for clusters and repeated measures, and to adjust for covariates identified. RESULTS This trial found significant positive effects of the intervention on child-rated satisfaction with their occupational performance and teacher-rated child anxiety. No evidence was found to support the effect of the intervention on anxiety and depression symptoms, self-esteem and wellbeing. CONCLUSIONS This was the first known cluster-randomised controlled trial to investigate an occupational therapy intervention promoting emotional wellbeing in a non-clinical sample of children. No compelling evidence was found to support the use of the intervention in schools in its current format, however, results were promising that the focus on occupations influenced participation. Recommendations are made to redesign the intervention as an embedded intervention in the classroom, cotaught by teachers and including parental involvement.