Evaluating Australia's National Medicines Policy using geographical mapping

Background: There has been a proliferation of quality use of medicines activities in Australia since the 1990s. However, knowledge of the nature and extent of these activities was lacking. A mechanism was required to map the activities to enable their coordination. Aims: To develop a geographical mapping facility as an evaluative tool to assist the planning and implementation of Australia's policy on the quality use of medicines. Methods: A web-based database incorporating geographical mapping software was developed. Quality use of medicines projects implemented across the country was identified from project listings funded by the Quality Use of Medicines Evaluation Program, the National Health and Medical Research Council, Mental Health Strategy, Rural Health Support, Education and Training Program, the Healthy Seniors Initiative, the General Practice Evaluation Program and the Drug Utilisation Evaluation Network. In addition, projects were identified through direct mail to persons working in the field. Results: The Quality Use of Medicines Mapping Project (QUMMP) was developed, providing a Web-based database that can be continuously updated. This database showed the distribution of quality use of medicines activities by: (i) geographical region, (ii) project type, (iii) target group, (iv) stakeholder involvement, (v) funding body and (vi) evaluation method. At September 2001, the database included 901 projects. Sixty-two per cent of projects had been conducted in Australian capital cities, where approximately 63% of the population reside. Distribution of projects varied between States. In Western Australia and Queensland, 36 and 73 projects had been conducted, respectively, representing approximately two projects per 100 000 people. By comparison, in South Australia and Tasmania approximately seven projects per 100 000 people were recorded, with six per 100 000 people in Victoria and three per 100 000 people in New South Wales. Rural and remote areas of the country had more limited project activity. Conclusions: The mapping of projects by geographical location enabled easy identification of high and low activity areas. Analysis of the types of projects undertaken in each region enabled identification of target groups that had not been involved or services that had not yet been developed. This served as a powerful tool for policy planning and implementation and will be used to support the continued implementation of Australia's policy on the quality use of medicines.

Estimation of body fatness from body mass index and bioelectrical impedance: comparison of New Zealand European, Maori and Pacific Island children

Objective: To compare percentage body fat (%BF) for a given body mass index (BMI) among New Zealand European, Maori and Pacific Island children. To develop prediction equations based on bioimpedance measurements for the estimation of fat-free mass (FFM) appropriate to children in these three ethnic groups. Design: Cross-sectional study. Purposive sampling of schoolchildren aimed at recruiting three children of each sex and ethnicity for each year of age. Double cross-validation of FFM prediction equations developed by multiple regression. Setting: Local schools in Auckland. Subjects: Healthy European, Maori and Pacific Island children (n = 172, 83 M, 89 F, mean age 9.4 +/- 2.8(s. d.), range 5 - 14 y). Measurements: Height, weight, age, sex and ethnicity were recorded. FFM was derived from measurements of total body water by deuterium dilution and resistance and reactance were measured by bioimpedance analysis. Results: For fixed BMI, the Maori and Pacific Island girls averaged 3.7% lower % BF than European girls. For boys a similar relation was not found since BMI did not significantly influence % BF of European boys ( P = 0.18). Based on bioimpedance measurements a single prediction equation was developed for all children: FFM (kg) = 0.622 height (cm)(2)/ resistance +0.234 weight (kg)+1.166, R-2 = 0.96, s. e. e. = 2.44 kg. Ethnicity, age and sex were not significant predictors. Conclusions: A robust equation for estimation of FFM in New Zealand European, Maori and Pacific Island children in the 5 - 14 y age range that is more suitable than BMI for the determination of body fatness in field studies has been developed. Sponsorship: Maurice and Phyllis Paykel Trust, Auckland University of Technology Contestable Grants Fund and the Ministry of Health.

Development of a quality use of medicines coding system to rate clinical pharmacists' medication review recommendations

Objective: To develop a 'quality use of medicines' coding system for the assessment of pharmacists' medication reviews and to apply it to an appropriate cohort. Method: A 'quality use of medicines' coding system was developed based on findings in the literature. These codes were then applied to 216 (111 intervention, 105 control) veterans' medication profiles by an independent clinical pharmacist who was supported by a clinical pharmacologist with the aim to assess the appropriateness of pharmacy interventions. The profiles were provided for veterans participating in a randomised, controlled trial in private hospitals evaluating the effect of medication review and discharge counselling. The reliability of the coding was tested by two independent clinical pharmacists in a random sample of 23 veterans from the study population. Main outcome measure: Interrater reliability was assessed by applying Cohen's kappa score on aggregated codes. Results: The coding system based on the literature consisted of 19 codes. The results from the three clinical pharmacists suggested that the original coding system had two major problems: (a) a lack of discrimination for certain recommendations e. g. adverse drug reactions, toxicity and mortality may be seen as variations in degree of a single effect and (b) certain codes e. g. essential therapy were in low prevalence. The interrater reliability for an aggregation of all codes into positive, negative and clinically non-significant codes ranged from 0.49-0.58 (good to fair). The interrater reliability increased to 0.72-0.79 (excellent) when all negative codes were excluded. Analysis of the sample of 216 profiles showed that the most prevalent recommendations from the clinical pharmacists were a positive impact in reducing adverse responses (31.9%), an improvement in good clinical pharmacy practice (25.5%) and a positive impact in reducing drug toxicity (11.1%). Most medications were assigned the clinically non-significant code (96.6%). In fact, the interventions led to a statistically significant difference in pharmacist recommendations in the categories; adverse response, toxicity and good clinical pharmacy practice measured by the quality use of medicine coding system. Conclusion: It was possible to use the quality use of medicine coding system to rate the quality and potential health impact of pharmacists' medication reviews, and the system did pick up differences between intervention and control patients. The interrater reliability for the summarised coding system was fair, but a larger sample of medication regimens is needed to assess the non-summarised quality use of medicines coding system.