"Social Medication" and the control of children: A qualitative study of over-the-counter medication among Australian children

Objective. The aim of the study was to identify the patterns of use of over-the-counter (OTC) medications among children. Methods. The study used a qualitative design, with in-depth interviews of 40 parents with children <5 years of age. Results. There were 3 striking and readily apparent themes in the use of OTC medications among children. One was the administration of OTC medications as a form of “social medication,” to give parents control over children’s behavior that they perceived as fractious and irritating. A related theme was the use of OTC medications to reduce the inconvenience to the parents of having a sick child, again giving parents greater control and better time-management abilities. Finally, acetaminophen was considered by many parents to have almost miraculous properties in calming, sedating, and lifting the mood of children. Conclusions. The use of OTC medications for the treatment of minor ailments among children is widespread, despite the lack of evidence of efficacy of the most commonly used medications and the potential for toxicity. With the increasing propensity to look to medication as a means of supporting changing lifestyles, there is an urgent need to review the prevalence and effects of social medication. Pediatrics 2004; 114:e378 –e383. URL: http://www.pediatrics.org/cgi/ content/full/114/3/e378; parental stress, parental perception, child behavior, drug safety, over-the-counter

Behaviour Change in Public Health: Evidence and Implications

Article ID 598672The evidence on the role of particular lifestyles, smoking, binge drinking, lack of physical activity, and poor health care seeking, in increased risks for mortality and morbidity is compelling [1]. Understanding the pathways through which these various “unhealthy” behaviours affect health is complicated by the broader ecological context in which they occur. The complexity is further enhanced because behaviours do not occur in isolation and there is often a convergence of associations. Interventions to achieve changes in either single or multiple behaviours have therefore often been limited in their effectiveness and longer term sustainability. In order to develop and implement a meaningful behaviour change agenda we need to establish innovative ways of operationalizing and understanding the complexity of behavioural factors and their dynamic interrelationships and how these collectively affect health. The Behaviour Change Research Cycle (BCRC) (Figure 1) provides a simple illustration of the life cycle of evidence required

Psychology ethics down under: A survey of student subject pools in Australia

A survey of the 37 psychology departments offering courses accredited by the Australian Psychological Society yielded a 92% response rate. Sixty-eight percent of departments employed students as research subjects, with larger departments being more likely to do so. Most of these departments drew their student subject pools from introductory courses. Student research participation was strictly voluntary in 57% of these departments, whereas 43% of the departments have failed to comply with normally accepted ethical standards. It is of great concern that institutional ethics committees apparently continue to condone, or fail to act against, unethical research practices. Although these committees have a duty of care to all subjects, the final responsibility for conducting research in an ethical manner lies with the individual researcher

The burden is great and the money little: Changing chronic disease management in low– and middle–income countries

Many health conditions are chronic, and only some of those chronic health conditions are NCDs. If the interest is on cause and prevention, then NCDs should be treated separately from other chronic diseases. If the interest is on health systems and management, then NCDs should be joined with other chronic diseases

Evolving forecasting classifications and applications in health forecasting

Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation

The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths.Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/ temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1).The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2)This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept. © 2013 Soyiri, Reidpath

Humans as Animal Sentinels for Forecasting Asthma Events: Helping Health Services Become More Responsive

The concept of forecasting asthma using humans as animal sentinels is uncommon. This study explores the plausibility of predicting future asthma daily admissions using retrospective data in London (2005-2006). Negative binomial regressions were used in modeling; allowing the non-contiguous autoregressive components. Selected lags were based on partial autocorrelation function (PACF) plot with a maximum lag of 7 days. The model was contrasted with naïve historical and seasonal models. All models were cross validated. Mean daily asthma admission in 2005 was 27.9 and in 2006 it was 28.9. The lags 1, 2, 3, 6 and 7 were independently associated with daily asthma admissions based on their PACF plots. The lag model prediction of peak admissions were often slightly out of synchronization with the actual data, but the days of greater admissions were better matched than the days of lower admissions. A further investigation across various populations is necessary. © 2012 Soyiri, Reidpath

HIV, Stigma, and Rates of Infection: A Rumour without Evidence: Authors' Reply

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub

Semistructured black-box prediction: proposed approach for asthma admissions in London

Asthma is a global public health problem and the most common chronic disease among children. The factors associated with the condition are diverse, and environmental factors appear to be the leading cause of asthma exacerbation and its worsening disease burden. However, it remains unknown how changes in the environment affect asthma over time, and how temporal or environmental factors predict asthma events. The methodologies for forecasting asthma and other similar chronic conditions are not comprehensively documented anywhere to account for semistructured noncausal forecasting approaches. This paper highlights and discusses practical issues associated with asthma and the environment, and suggests possible approaches for developing decision-making tools in the form of semistructured black-box models, which is relatively new for asthma. Two statistical methods which can potentially be used in predictive modeling and health forecasting for both anticipated and peak events are suggested. Importantly, this paper attempts to bridge the areas of epidemiology, environmental medicine and exposure risks, and health services provision. The ideas discussed herein will support the development and implementation of early warning systems for chronic respiratory conditions in large populations, and ultimately lead to better decision-making tools for improving health service delivery. © 2012 Soyiri and Reidpath, publisher and licensee Dove Medical Press Ltd