A comparison of methods for calculating population exposure estimates of daily weather for health research

BACKGROUND: To explain the possible effects of exposure to weather conditions on population health outcomes, weather data need to be calculated at a level in space and time that is appropriate for the health data. There are various ways of estimating exposure values from raw data collected at weather stations but the rationale for using one technique rather than another; the significance of the difference in the values obtained; and the effect these have on a research question are factors often not explicitly considered. In this study we compare different techniques for allocating weather data observations to small geographical areas and different options for weighting averages of these observations when calculating estimates of daily precipitation and temperature for Australian Postal Areas. Options that weight observations based on distance from population centroids and population size are more computationally intensive but give estimates that conceptually are more closely related to the experience of the population. RESULTS: Options based on values derived from sites internal to postal areas, or from nearest neighbour sites – that is, using proximity polygons around weather stations intersected with postal areas – tended to include fewer stations' observations in their estimates, and missing values were common. Options based on observations from stations within 50 kilometres radius of centroids and weighting of data by distance from centroids gave more complete estimates. Using the geographic centroid of the postal area gave estimates that differed slightly from the population weighted centroids and the population weighted average of sub-unit estimates. CONCLUSION: To calculate daily weather exposure values for analysis of health outcome data for small areas, the use of data from weather stations internal to the area only, or from neighbouring weather stations (allocated by the use of proximity polygons), is too limited. The most appropriate method conceptually is the use of weather data from sites within 50 kilometres radius of the area weighted to population centres, but a simpler acceptable option is to weight to the geographic centroid

Mental health first aid training of the public in a rural area: a cluster randomized trial [ISRCTN53887541]

BACKGROUND: A Mental Health First Aid course has been developed which trains members of the public in how to give initial help in mental health crisis situations and to support people developing mental health problems. This course has previously been evaluated in a randomized controlled trial in a workplace setting and found to produce a number of positive effects. However, this was an efficacy trial under relatively ideal conditions. Here we report the results of an effectiveness trial in which the course is given under more typical conditions. METHODS: The course was taught to members of the public in a large rural area in Australia by staff of an area health service. The 16 Local Government Areas that made up the area were grouped into pairs matched for size, geography and socio-economic level. One of each Local Government Area pair was randomised to receive immediate training while one served as a wait-list control. There were 753 participants in the trial: 416 in the 8 trained areas and 337 in the 8 control areas. Outcomes measured before the course started and 4 months after it ended were knowledge of mental disorders, confidence in providing help, actual help provided, and social distance towards people with mental disorders. The data were analysed taking account of the clustered design and using an intention-to-treat approach. RESULTS: Training was found to produce significantly greater recognition of the disorders, increased agreement with health professionals about which interventions are likely to be helpful, decreased social distance, increased confidence in providing help to others, and an increase in help actually provided. There was no change in the number of people with mental health problems that trainees had contact with nor in the percentage advising someone to seek professional help. CONCLUSIONS: Mental Health First Aid training produces positive changes in knowledge, attitudes and behaviour when the course is given to members of the public by instructors from the local health service

Decomposing socioeconomic inequality for binary health outcomes: an improved estimation that does not vary by choice of reference group

BACKGROUND Decomposition of concentration indices yields useful information regarding the relative importance of various determinants of inequitable health outcomes. But the two estimation approaches to decomposition in current use are not suitable for binary outcomes. FINDINGS The paper compares three estimation approaches for decomposition of inequality concentration indices: Ordinary Least Squares (OLS), probit, and the Generalized Linear Model (GLM) binomial distribution and identity link. Data are from the Thai Health and Welfare Survey 2003. The OLS estimates do not take into account the binary nature of the outcome and the probit estimates depend on the choice of reference groups, whereas the GLM binomial identity approach has neither of these problems. CONCLUSIONS The GLM with binomial distribution and identity link allows the inequality decomposition model to hold, and produces valid estimates of determinants that do not vary according to choice of reference groups. This GLM approach is readily available in standard statistical packages.The study was conducted under the auspices of the overarching project "The Thai Health-Risk Transition: a National Cohort Study", funded by the Wellcome Trust UK (GR071587 MA) and the Australian National Health and Medical Research Council (268055)