Asthma management by New Zealand pharmacists: a pharmaceutical care demonstration project

Background: Pharmaceutical care services became recognized in New Zealand in the mid-1990s, albeit with limited evidence of the acceptability and effectiveness of the model. An asthma-specific pharmaceutical care service was trialled in southern New Zealand, based on a 'problem-action-outcome' method, with pharmacists adopting a patient-centred, outcome-focused approach with multidisciplinary consultation. Objective: To report on the implementation and outcomes of a specialist asthma service offered by community pharmacists. Design: Pharmacists in five pharmacies, servicing predominantly rural, established clientele, received training in the asthma service and research documentation. Ten patients per pharmacy were recruited in each year (years 1 and 2) of the study. The patients were entered into the study in cohorts of five per pharmacy twice yearly, with year 2 mirroring year 1. The phase-in design minimized the impact on the pharmacists. The patients acted as their own controls. All patients received individualized care and had approximately monthly consultations with the pharmacist, with clinical and quality of life (QoL) monitoring. Results: A total of 100 patients were recruited. On average, 4.3 medication-related problems were identified per patient; two-thirds of them were compliance-related. The most common interventions were revision of patients' asthma action plans, referral and medication counselling. Clinical outcomes included reduced bronchodilator use and improved symptom control in around two-thirds of patients. Asthma-specific QoL changes were more positive and correlated well with clinical indicators. Conclusion: Further research is warranted to integrate this service into daily practice. Clinical outcomes were generally positive and supported by QoL indicators. Characteristics of New Zealand practice and this sample of pharmacies may limit the generalizability of these findings.

Population effects on individual systolic blood pressure: A multilevel analysis of the World Health Organization MONICA project

Individuals from the same population share a number of contextual circumstances that may condition a common level of blood pressure over and above individual characteristics. Understanding this population effect is relevant for both etiologic research and prevention strategies. Using multilevel regression analyses, the authors quantified the extent to which individual differences in systolic blood pressure (SBP) could be attributed to the population level. They also investigated possible cross-level interactions between the population in which a person lived and pharmacological (antihypertensive medication) and nonpharmacological (body mass index) effects on individual SBP. They analyzed data on 23,796 men and 24,986 women aged 35-64 years from 39 worldwide Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) study populations participating in the final survey of this World Health Organization project (1989-1997). SBP was positively associated with low educational achievement, high body mass index, and use of antihypertensive medication and, for women, was negatively associated with smoking. About 7-8% of all SBP differences between subjects were attributed to the population level. However, this population effect was particularly strong (i.e., 20%) in antihypertensive medication users and overweight women. This empirical evidence of a population effect on individual SBP emphasizes the importance of developing population-wide strategies to reduce individual risk of hypertension.

Effect on trend estimates of the difference between survey respondents and non-respondents: Results from 27 populations in the WHO MONIICA Project

Introduction: In the World Health Organization (WHO) MONICA (multinational MONItoring of trends and determinants in CArdiovascular disease) Project considerable effort was made to obtain basic data on non-respondents to community based surveys of cardiovascular risk factors. The first purpose of this paper is to examine differences in socio-economic and health profiles among respondents and non-respondents. The second purpose is to investigate the effect of non-response on estimates of trends. Methods:Socio-economic and health profile between respondents and non-respondents in the WHO MONICA Project final survey were compared. The potential effect of non-response on the trend estimates between the initial survey and final survey approximately ten years later was investigated using both MONICA data and hypothetical data. Results: In most of the populations, non-respondents were more likely to be single, less well educated, and had poorer lifestyles and health profiles than respondents. As an example of the consequences, temporal trends in prevalence of daily smokers are shown to be overestimated in most populations if they were based only on data from respondents. Conclusions: The socio-economic and health profiles of respondents and non-respondents differed fairly consistently across 27 populations. Hence, the estimators of population trends based on respondent data are likely to be biased. Declining response rates therefore pose a threat to the accuracy of estimates of risk factor trends in many countries.

Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project

Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.